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Deployed eca0e7c with MkDocs version: 1.0.4
2026-09-12 12:40:20 +08:00 · 2019-11-06 15:08:12 -05:00 · 2019-10-18 23:57:14 +01:00 · 2019-10-05 16:34:18 -05:00 · 2019-10-05 16:32:22 -05:00 · 2019-10-05 16:18:33 -05:00
173 changed files with 15140 additions and 13556 deletions
@@ -1,90 +0,0 @@
-# Python CircleCI 2.0 configuration file
-#
-# Check https://circleci.com/docs/2.0/language-python/ for more details
-#
-version: 2.0
-
-references:
-
-  install_deps: &install_deps
-    run:
-      name: Install Dependences
-      command: |
-        pip install "$TORCH_VERSION" --user
-        # this is temporal fix til test-tube is not merged and released
-        pip install -r requirements.txt --user
-        sudo pip install pytest pytest-cov pytest-flake8
-        pip install -r ./tests/requirements.txt --user
-
-  tests_format: &tests_format
-   run:
-     name: Tests and formating
-     command: |
-       python --version ; pip --version ; pip list
-       py.test pytorch_lightning tests pl_examples -v --doctest-modules --junitxml=test-reports/pytest_junit.xml --flake8
-     no_output_timeout: 15m
-
-  make_docs: &make_docs
-   run:
-     name: Make Documentation
-     command: |
-       # sudo apt-get install pandoc
-       pip install -r requirements.txt --user
-       sudo pip install -r docs/requirements.txt
-       # sphinx-apidoc -o ./docs/source ./pytorch_lightning **/test_* --force --follow-links
-       cd docs; make clean ; make html
-
-jobs:
-
-  Build-Docs:
-    docker:
-      - image: circleci/python:3.7
-    steps:
-      - checkout
-      - *make_docs
-
-  PyTorch:
-    docker:
-      - image: circleci/python:3.7
-    environment:
-      - TORCH_VERSION: "torch"
-    steps: &steps
-      - checkout
-
-      - *install_deps
-      - *tests_format
-
-      - store_test_results:
-          path: test-reports
-      - store_artifacts:
-          path: test-reports
-
-  PyTorch-v1.1:
-    docker:
-      - image: circleci/python:3.6
-    environment:
-      - TORCH_VERSION: "torch>=1.1, <1.2"
-    steps: *steps
-
-  PyTorch-v1.2:
-    docker:
-      - image: circleci/python:3.6
-    environment:
-      - TORCH_VERSION: "torch>=1.2, <1.3"
-    steps: *steps
-
-  PyTorch-v1.3:
-    docker:
-      - image: circleci/python:3.6
-    environment:
-      - TORCH_VERSION: "torch>=1.3, <1.4"
-    steps: *steps
-
-workflows:
-  version: 2
-  build:
-    jobs:
-      - Build-Docs
-      - PyTorch-v1.1
-      - PyTorch-v1.2
-      - PyTorch-v1.3
@@ -1,46 +0,0 @@
-# see https://docs.codecov.io/docs/codecov-yaml
-# Validation check:
-# $ curl --data-binary @.codecov.yml https://codecov.io/validate
-
-codecov:
-  notify:
-    require_ci_to_pass: yes
-
-coverage:
-  precision: 0  # 2 = xx.xx%, 0 = xx%
-  round: nearest # how coverage is rounded: down/up/nearest
-  range: 40...100 # custom range of coverage colors from red -> yellow -> green
-  status:
-    # https://codecov.readme.io/v1.0/docs/commit-status
-    project:
-      default:
-        against: auto
-        target: 99% # specify the target coverage for each commit status
-        threshold: 20% # allow this little decrease on project
-        # https://github.com/codecov/support/wiki/Filtering-Branches
-        # branches: master
-        if_ci_failed: error
-    # https://github.com/codecov/support/wiki/Patch-Status
-    patch:
-      default:
-        against: auto
-        target: 40% # specify the target "X%" coverage to hit
-        # threshold: 50% # allow this much decrease on patch
-    changes: false
-
-parsers:
-  gcov:
-    branch_detection:
-      conditional: true
-      loop: true
-      macro: false
-      method: false
-  javascript:
-    enable_partials: false
-
-comment:
-  layout: header, diff
-  require_changes: false
-  behavior: default  # update if exists else create new
-  # branches: *
-
@@ -1,59 +0,0 @@
-# How to become a core contributor
-
-Thanks for your interest in joining the Lightning team! We’re a rapidly growing project which is poised to become the go-to framework for DL researchers! 
-We're currently recruiting for a team of 5 core maintainers. 
-
-As a core maintainer you will have a strong say in the direction of the project. Big changes will require a majority of maintainers to agree.
-
-### Code of conduct  
-First and foremost, you'll be evaluated against [these core values](https://github.com/williamFalcon/pytorch-lightning/blob/master/.github/CONTRIBUTING.md). Any code we commit or feature we add needs to align with those core values.
-
-### The bar for joining the team   
-Lightning is being used to solve really hard problems at the top AI labs in the world. As such, the bar for adding team members is extremely high. Candidates must have solid engineering skills, have a good eye for user experience, and must be a power user of Lightning and PyTorch.
-
-With that said, the Lightning team will be diverse and a reflection of an inclusive AI community. You don't have to be an engineer to conntribute! Scientists with great usability intuition and PyTorch ninja skills are welcomed!
-
-### Responsibilities:
-The responsibilities mainly revolve around 3 things.
-
-#### Github issues
- Here we want to help users have an amazing experience. These range from questions from new people getting into DL to questions from researchers about doing something esoteric with Lightning
-Often, these issues require some sort of bug fix, document clarification or new functionality to be scoped out.
-
- To become a core member you must resolve at least 10 Github issues which align with the API design goals for Lightning. By the end of these 10 issues I should feel comfortable in the way you answer user questions
-Pleasant/helpful tone.
-
- Can abstract from that issue or bug into functionality that might solve other related issues or makes the platform more flexible.
-
- Don’t make users feel like they don’t know what they’re doing. We’re here to help and to make everyone’s experience delightful.
-
-#### Pull requests
-
- Here we need to ensure the code that enters Lightning is high quality. For each PR we need to:
- Make sure code coverage does not decrease
- Documents are updated
- Code is elegant and simple
- Code is NOT overly engineered or hard to read
- Ask yourself, could a non-engineer understand what’s happening here?
- Make sure new tests are written
- Is this NECESSARY for Lightning? There are some PRs which are just purely about adding engineering complexity which have no place in Lightning. 
-Guidance
- Some other PRs are for people who are wanting to get involved and add something unnecessary. We do want their help though! So don’t approve the PR, but direct them to a Github issue that they might be interested in helping with instead!
- To be considered for core contributor, please review 10 PRs and help the authors land it on master. Once you've finished the review, ping me 
-for a sanity check. At the end of 10 PRs if your PR reviews are inline with expectations described above, then you can merge PRs on your own going forward,
-otherwise we'll do a few more until we're both comfortable :)
-
-#### Project directions
-There are some big decisions which the project must make. For these I expect core contributors to have something meaningful to add if it’s their area of expertise.
-
-#### Diversity
-Lightning should reflect the broader community it serves. As such we should have scientists/researchers from 
-different fields contributing!   
-
-The first 5 core contributors will fit this profile. Thus if you overlap strongly with experiences and expertise as someone else on the team, you might have to wait until the next set of contributors are added.
-
-#### Summary: Requirements to apply
- Solve 10 Github issues. The goal is to be inline with expectations for solving issues by the last one so you can do them on your own. If not, I might ask you to solve a few more specific ones.
- Do 10 PR reviews. The goal is to be inline with expectations for solving issues by the last one so you can do them on your own. If not, I might ask you to solve a few more specific ones.
-
-If you want to be considered, ping me on gitter and start [tracking your progress here](https://docs.google.com/spreadsheets/d/15D58gp8DvI0Z6qbbYVRuaWioiwzafcP58-UlbuO_CMU/edit?usp=sharing).
@@ -1,76 +0,0 @@
-# Contributor Covenant Code of Conduct
-
-## Our Pledge
-
-In the interest of fostering an open and welcoming environment, we as
-contributors and maintainers pledge to making participation in our project and
-our community a harassment-free experience for everyone, regardless of age, body
-size, disability, ethnicity, sex characteristics, gender identity and expression,
-level of experience, education, socio-economic status, nationality, personal
-appearance, race, religion, or sexual identity and orientation.
-
-## Our Standards
-
-Examples of behavior that contributes to creating a positive environment
-include:
-
-* Using welcoming and inclusive language
-* Being respectful of differing viewpoints and experiences
-* Gracefully accepting constructive criticism
-* Focusing on what is best for the community
-* Showing empathy towards other community members
-
-Examples of unacceptable behavior by participants include:
-
-* The use of sexualized language or imagery and unwelcome sexual attention or
- advances
-* Trolling, insulting/derogatory comments, and personal or political attacks
-* Public or private harassment
-* Publishing others' private information, such as a physical or electronic
- address, without explicit permission
-* Other conduct which could reasonably be considered inappropriate in a
- professional setting
-
-## Our Responsibilities
-
-Project maintainers are responsible for clarifying the standards of acceptable
-behavior and are expected to take appropriate and fair corrective action in
-response to any instances of unacceptable behavior.
-
-Project maintainers have the right and responsibility to remove, edit, or
-reject comments, commits, code, wiki edits, issues, and other contributions
-that are not aligned to this Code of Conduct, or to ban temporarily or
-permanently any contributor for other behaviors that they deem inappropriate,
-threatening, offensive, or harmful.
-
-## Scope
-
-This Code of Conduct applies both within project spaces and in public spaces
-when an individual is representing the project or its community. Examples of
-representing a project or community include using an official project e-mail
-address, posting via an official social media account, or acting as an appointed
-representative at an online or offline event. Representation of a project may be
-further defined and clarified by project maintainers.
-
-## Enforcement
-
-Instances of abusive, harassing, or otherwise unacceptable behavior may be
-reported by contacting the project team at waf2107@columbia.edu. All
-complaints will be reviewed and investigated and will result in a response that
-is deemed necessary and appropriate to the circumstances. The project team is
-obligated to maintain confidentiality with regard to the reporter of an incident.
-Further details of specific enforcement policies may be posted separately.
-
-Project maintainers who do not follow or enforce the Code of Conduct in good
-faith may face temporary or permanent repercussions as determined by other
-members of the project's leadership.
-
-## Attribution
-
-This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4,
-available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html
-
-[homepage]: https://www.contributor-covenant.org
-
-For answers to common questions about this code of conduct, see
-https://www.contributor-covenant.org/faq
@@ -1,53 +0,0 @@
-# Contributing    
-Welcome to the PyTorch Lightning community! We're building the most advanced research platform on the planet to implement the latest, best practices that the amazing PyTorch team rolls out!   
-
-## Main Core Value: One less thing to remember    
-Simplify the API as much as possible from the user perspective. Any additions or improvements should minimize things the user needs to remember.   
-
-For example: One benefit of the validation_step is that the user doesn't have to remember to set the model to .eval(). This avoids all sorts of subtle errors the user could make.  
-
-## Lightning Design Principles   
-We encourage all sorts of contributions you're interested in adding! When coding for lightning, please follow these principles.    
-#### No PyTorch Interference   
-We don't want to add any abstractions on top of pure PyTorch. This gives researchers all the control they need without having to learn yet another framework.    
-
-#### Simple Internal Code    
-It's useful for users to look at the code and understand very quickly what's happening. Many users won't be engineers. Thus we need to value clear, simple code over condensed ninja moves. While that's super cool, this isn't the project for that :)      
-
-#### Force User Decisions To Best Practices    
-There are 1,000 ways to do something. However, something eventually becomes standard practice that everyone does. Thus we pick one way of doing it and force everyone to do it this way. A good example is accumulated gradients. There are many ways to implement, we just pick one and force users to use that one. A bad forced decision would be to make users use a specific library to do something.    
-
-When something becomes a best practice, we add it to the framework. This likely looks like code in utils or in the model file that everyone keeps adding over and over again across projects. When this happens, bring that code inside the trainer and add a flag for it.
-
-#### Simple External API    
-What makes sense to you may not make sense to others. Create an issue with an API change suggestion and validate that it makes sense for others. Treat code changes how you treat a startup: validate that it's a needed feature, then add if it makes sense for many people. 
-
-#### Backward-compatible API   
-We all hate updating our deep learning packages because we don't want to refactor a bunch of stuff. In Lightning, we make sure every change we make which could break an API is backwards compatible with good deprecation warnings.
-
-You shouldn't be afraid to upgrade Lightning :)
-
-#### Gain User Trust    
-As a researcher you can't have any part of your code going wrong. So, make thorough tests that ensure an implementation of a new trick or subbtle change is correct.    
-
-#### Interoperability  
-Have a favorite feature from other libraries like fast.ai or transformers? Those should just work with lightning as well. Grab your favorite model or learning rate scheduler from your favorite library and run it in Lightning.
-
-## Contribution Types    
-Currently looking for help implementing new features or adding bug fixes. 
-
-A lot of good work has already been done in project mechanics (requirements.txt, setup.py, pep8, badges, ci, etc...) we're in a good state there thanks to all the early contributors (even pre-beta release)!   
-
-## Bug Fixes:  
-1. Submit a github issue.   
-2. Fix it.  
-3. Submit a PR! 
-
-## New Features:  
-1. Submit a github issue.   
-2. We'll agree on the feature scope.     
-3. Submit a PR! (with updated docs and tests 🙃).   
-
-## Coding Styleguide
-1. Test the code with flake8.
-2. Use f-strings.
@@ -1,62 +0,0 @@
---
-name: Bug report
-about: Create a report to help us improve
-title: ''
-labels: bug
-assignees: ''
-
---
-
-### Common bugs:
-1. Tensorboard not showing in Jupyter-notebook see [issue 79](https://github.com/williamFalcon/pytorch-lightning/issues/79).    
-2. PyTorch 1.1.0 vs 1.2.0 support [see FAQ](https://github.com/williamFalcon/pytorch-lightning#faq)    
-
-## 🐛 Bug
-
-<!-- A clear and concise description of what the bug is. -->
-
-### To Reproduce
-
-Steps to reproduce the behavior:
-
-1. Go to '...'
-2. Run '....'
-3. Scroll down to '....'
-4. See error
-
-<!-- If you have a code sample, error messages, stack traces, please provide it here as well -->
-
-
-#### Code sample
-<!-- Ideally attach a minimal code sample to reproduce the decried issue. 
-Minimal means having the shortest code but still preserving the bug. -->
-
-### Expected behavior
-
-<!-- A clear and concise description of what you expected to happen. -->
-
-### Environment
-
-Please copy and paste the output from our
-[environment collection script](https://raw.githubusercontent.com/pytorch/pytorch/master/torch/utils/collect_env.py)
-(or fill out the checklist below manually).
-
-You can get the script and run it with:
-```
-wget https://raw.githubusercontent.com/pytorch/pytorch/master/torch/utils/collect_env.py
-# For security purposes, please check the contents of collect_env.py before running it.
-python collect_env.py
-```
-
- - PyTorch Version (e.g., 1.0):
- - OS (e.g., Linux):
- - How you installed PyTorch (`conda`, `pip`, source):
- - Build command you used (if compiling from source):
- - Python version:
- - CUDA/cuDNN version:
- - GPU models and configuration:
- - Any other relevant information:
-
-### Additional context
-
-<!-- Add any other context about the problem here. -->
@@ -1,18 +0,0 @@
---
-name: Typos and doc fixes
-about: Typos and doc fixes
-title: ''
-labels: typo
-assignees: ''
-
---
-
-## 📚 Documentation
-
-For typos and doc fixes, please go ahead and:
-
-1. Create an issue.
-2. Fix the typo.   
-3. Submit a PR.
-
-Thanks!
@@ -1,27 +0,0 @@
---
-name: Feature request
-about: Suggest an idea for this project
-title: ''
-labels: enhancement, help wanted
-assignees: ''
-
---
-
-## 🚀 Feature
-<!-- A clear and concise description of the feature proposal -->
-
-### Motivation
-
-<!-- Please outline the motivation for the proposal. Is your feature request related to a problem? e.g., I'm always frustrated when [...]. If this is related to another GitHub issue, please link here too -->
-
-### Pitch
-
-<!-- A clear and concise description of what you want to happen. -->
-
-### Alternatives
-
-<!-- A clear and concise description of any alternative solutions or features you've considered, if any. -->
-
-### Additional context
-
-<!-- Add any other context or screenshots about the feature request here. -->
@@ -1,30 +0,0 @@
---
-name: How to question
-about: Asking how-to questions
-title: ''
-labels: question
-assignees: ''
-
---
-
-## ❓ Questions and Help
-
-### Before asking:   
-1. search the issues.   
-2. search the docs.    
-
-<!-- If you still can't find what you need: -->
-
-#### What is your question?
-
-#### Code
-
-<!-- Please paste a code snippet if your question requires it! -->   
-
-#### What have you tried?
-
-#### What's your environment?
-
- - OS: [e.g. iOS, Linux, Win]
- - Packaging [e.g. pip, conda]
- - Version [e.g. 0.5.2.1]
@@ -1,16 +0,0 @@
-# Before submitting
-
- [ ] Was this discussed/approved via a Github issue? (no need for typos, doc improvements)
- [ ] Did you read the [contributor guideline](https://github.com/williamFalcon/pytorch-lightning/blob/master/.github/CONTRIBUTING.md)?
- [ ] Did you make sure to update the docs?   
- [ ] Did you write any new necessary tests?  
-
-## What does this PR do?
-Fixes # (issue).
-
-## PR review    
-Anyone in the community is free to review the PR once the tests have passed.     
-If we didn't discuss your PR in Github issues there's a high chance it will not be merged.
-
-## Did you have fun?
-Make sure you had fun coding 🙃
@@ -1,127 +0,0 @@
-# project
-.DS_Store
-.data/
-run_configs/
-test_tube_logs/
-test_tube_data/
-datasets/
-model_weights/
-app/models/
-pip-wheel-metadata/
-test_tube_exp/
-tests/tests_tt_dir/
-tests/save_dir
-default/
-lightning_logs/
-tests/tests/
-
-# Byte-compiled / optimized / DLL files
-__pycache__/
-*.py[cod]
-*$py.class
-example.py
-timit_data/
-LJSpeech-1.1/
-
-# C extensions
-*.so
-
-.idea/
-
-# Distribution / packaging
-.Python
-env/
-ide_layouts/
-build/
-develop-eggs/
-dist/
-downloads/
-eggs/
-.eggs/
-lib/
-lib64/
-parts/
-sdist/
-var/
-wheels/
-*.egg-info/
-.installed.cfg
-*.egg
-
-# PyInstaller
-#  Usually these files are written by a python script from a template
-#  before PyInstaller builds the exe, so as to inject date/other infos into it.
-*.manifest
-*.spec
-
-# Installer logs
-pip-log.txt
-pip-delete-this-directory.txt
-
-# Unit test / coverage reports
-htmlcov/
-.tox/
-.coverage
-.coverage.*
-.cache
-nosetests.xml
-coverage.xml
-*.cover
-.hypothesis/
-
-# Translations
-*.mo
-*.pot
-
-# Django stuff:
-*.log
-local_settings.py
-
-# Flask stuff:
-instance/
-.webassets-cache
-
-# Scrapy stuff:
-.scrapy
-
-# Sphinx documentation
-docs/_build/
-
-# PyBuilder
-target/
-
-# Jupyter Notebook
-.ipynb_checkpoints
-
-# pyenv
-.python-version
-
-# celery beat schedule file
-celerybeat-schedule
-
-# SageMath parsed files
-*.sage.py
-
-# dotenv
-.env
-
-# virtualenv
-.venv
-venv/
-ENV/
-
-# Spyder project settings
-.spyderproject
-.spyproject
-
-# Rope project settings
-.ropeproject
-
-# mkdocs documentation
-/site
-
-# mypy
-.mypy_cache/
-
-# data
-mnist/
@@ -1,24 +0,0 @@
-# .readthedocs.yml
-# Read the Docs configuration file
-# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details
-
-# Required
-version: 2
-
-# Build documentation in the docs/ directory with Sphinx
-sphinx:
-  configuration: docs/source/conf.py
-
-# Build documentation with MkDocs
-#mkdocs:
-#  configuration: mkdocs.yml
-
-# Optionally build your docs in additional formats such as PDF and ePub
-formats: all
-
-# Optionally set the version of Python and requirements required to build your docs
-python:
-  version: 3.7
-  install:
-    #- requirements: requirements.txt
-    - requirements: docs/requirements.txt
@@ -1,9 +0,0 @@
-# use this to run tests
-rm -rf _ckpt_*
-rm -rf tests/save_dir*
-rm -rf tests/mlruns_*
-rm -rf tests/cometruns*
-rm -rf tests/tests/*
-rm -rf lightning_logs
-coverage run --source pytorch_lightning -m py.test pytorch_lightning tests pl_examples -v --doctest-modules
-coverage report -m
@@ -1,90 +0,0 @@
-# vim ft=yaml
-
-# After changing this file, check it on:
-#   http://yaml-online-parser.appspot.com/
-
-# See doc/travis_notes.txt for some guidelines
-
-# this file is *not* meant to cover or endorse the use of travis, but rather to
-# help confirm pull requests to this project.
-
-env:
-  global:
-    - DISPLAY=""
-
-language: python
-
-matrix:
-  include:
-    - dist: xenial  # Ubuntu 16.04
-      python: 3.6
-      env:
-        - TOXENV=py36
-        - MIN_REQUIREMENTS=1
-    - dist: xenial  # Ubuntu 16.04
-      python: 3.7
-      env:
-        - TOXENV=py37
-        - MIN_REQUIREMENTS=1
-    - dist: bionic  # Ubuntu 18.04
-      python: 3.6
-      env: TOXENV=py36
-    - dist: bionic  # Ubuntu 18.04
-      python: 3.7
-      env: TOXENV=py37
-    - os: osx
-      # https://blog.travis-ci.com/2019-08-07-extensive-python-testing-on-travis-ci
-      osx_image: xcode10.3
-      language: generic
-      env: TOXENV=py37
-      #addons:
-      #  homebrew:
-      #    # update: true
-      #    packages: python3.7
-      before_install:
-        - pip3 install virtualenv
-        - virtualenv -p python3 ~/venv
-        - source ~/venv/bin/activate
-#    - os: windows
-#      language: minimal
-#      before_install:
-#        - choco install python3
-#        - export PATH="/c/Python37:/c/Python37/Scripts:$PATH"
-#      env: TOXENV=py37
-
-# See http://docs.travis-ci.com/user/caching/#pip-cache
-cache: pip
-
-install:
-  - pip install future  # needed for `builtins`
-  - sudo pip install tox
-
-before_script:
-  # rewrite all minimal requirements as strict
-  - if [[ "${MIN_REQUIREMENTS}" == "1" ]]; then
-      python -c "req = open('requirements.txt').read().replace('>', '=') ; open('requirements-ci.txt', 'w').write(req)" ;
-    else
-      cp requirements.txt requirements-ci.txt ;
-    fi
-  - pip install -r requirements-ci.txt -U
-
-script:
-  # integration
-  - tox --sitepackages
-
-  #- python setup.py install --dry-run --user
-  - virtualenv vEnv ;
-    source vEnv/bin/activate
-  - pip install --editable . ;
-    cd .. & python -c "import pytorch_lightning ; print(pytorch_lightning.__version__)"
-  - deactivate ;
-    rm -rf vEnv
-
-after_success:
-  - coverage report
-  # disable auto coverage bc it isn't accurate since it misses gpu code.
-  # to get coverage, run local and push results
-  # - codecov
-
-notifications:
-  email: false
@@ -0,0 +1,510 @@
+
+
+
+
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+                
+                  <a href="https://github.com/williamFalcon/pytorch-lightning/edit/master/docs/LightningModule/methods.md" title="Edit this page" class="md-icon md-content__icon">&#xE3C9;</a>
+                
+                
+                  <h1>Methods</h1>
+                
+                <p>Lightning modules are strict superclasses of torch.nn.Module. A LightningModule offers the following in addition to that API.</p>
+<hr />
+<h3 id="freeze">freeze</h3>
+<p>Freeze all params for inference</p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1
+2</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">MyLightningModule</span><span class="p">(</span><span class="o">...</span><span class="p">)</span>
+<span class="n">model</span><span class="o">.</span><span class="n">freeze</span><span class="p">()</span>
+</pre></div>
+</td></tr></table>
+
+<hr />
+<h3 id="load_from_metrics">load_from_metrics</h3>
+<p>This is the easiest/fastest way which loads hyperparameters and weights from a checkpoint,
+such as the one saved by the <code>ModelCheckpoint</code> callback</p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1
+2
+3
+4
+5
+6
+7
+8</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="n">pretrained_model</span> <span class="o">=</span> <span class="n">MyLightningModule</span><span class="o">.</span><span class="n">load_from_checkpoint</span><span class="p">(</span>
+    <span class="n">checkpoint_path</span><span class="o">=</span><span class="s1">&#39;/path/to/pytorch_checkpoint.ckpt&#39;</span>
+<span class="p">)</span>
+
+<span class="c1"># predict</span>
+<span class="n">pretrained_model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
+<span class="n">pretrained_model</span><span class="o">.</span><span class="n">freeze</span><span class="p">()</span>
+<span class="n">y_hat</span> <span class="o">=</span> <span class="n">pretrained_model</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+</pre></div>
+</td></tr></table>
+
+<hr />
+<h3 id="load_from_metrics_1">load_from_metrics</h3>
+<p>If you're using test tube, there is an alternate method which uses the meta_tags.csv
+file from test-tube to rebuild the model. The meta_tags.csv file can be found in the
+test-tube experiment save_dir.       </p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre> 1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10
+11</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="n">pretrained_model</span> <span class="o">=</span> <span class="n">MyLightningModule</span><span class="o">.</span><span class="n">load_from_metrics</span><span class="p">(</span>
+    <span class="n">weights_path</span><span class="o">=</span><span class="s1">&#39;/path/to/pytorch_checkpoint.ckpt&#39;</span><span class="p">,</span>
+    <span class="n">tags_csv</span><span class="o">=</span><span class="s1">&#39;/path/to/test_tube/experiment/version/meta_tags.csv&#39;</span><span class="p">,</span>
+    <span class="n">on_gpu</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span>
+    <span class="n">map_location</span><span class="o">=</span><span class="bp">None</span>
+<span class="p">)</span>
+
+<span class="c1"># predict</span>
+<span class="n">pretrained_model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
+<span class="n">pretrained_model</span><span class="o">.</span><span class="n">freeze</span><span class="p">()</span>
+<span class="n">y_hat</span> <span class="o">=</span> <span class="n">pretrained_model</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+</pre></div>
+</td></tr></table>
+
+<p><strong>Params</strong>    </p>
+<table>
+<thead>
+<tr>
+<th>Param</th>
+<th>description</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td>weights_path</td>
+<td>Path to a PyTorch checkpoint</td>
+</tr>
+<tr>
+<td>tags_csv</td>
+<td>Path to meta_tags.csv file generated by the test-tube Experiment</td>
+</tr>
+<tr>
+<td>on_gpu</td>
+<td>if True, puts model on GPU. Make sure to use transforms option if model devices have changed</td>
+</tr>
+<tr>
+<td>map_location</td>
+<td>A dictionary mapping saved weight GPU devices to new GPU devices</td>
+</tr>
+</tbody>
+</table>
+<p><strong>Returns</strong>    </p>
+<p>LightningModule - The pretrained LightningModule</p>
+<hr />
+<h3 id="unfreeze">unfreeze</h3>
+<p>Unfreeze all params for inference</p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1
+2</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">MyLightningModule</span><span class="p">(</span><span class="o">...</span><span class="p">)</span>
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+                  <a href="https://github.com/williamFalcon/pytorch-lightning/edit/master/docs/LightningModule/properties.md" title="Edit this page" class="md-icon md-content__icon">&#xE3C9;</a>
+                
+                
+                  <h1>Properties</h1>
+                
+                <p>A LightningModule has the following properties which you can access at any time</p>
+<hr />
+<h4 id="current_epoch">current_epoch</h4>
+<p>The current epoch   </p>
+<hr />
+<h4 id="dtype">dtype</h4>
+<p>Current dtype    </p>
+<hr />
+<h4 id="logger">logger</h4>
+<p>A reference to the logger you passed into trainer. 
+Passing a logger is optional. If you don't pass one in, Lightning will create one for you automatically.
+This logger saves logs to '''/os.getcwd()/lightning_logs'''</p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="n">Trainer</span><span class="p">(</span><span class="n">logger</span><span class="o">=</span><span class="n">your_logger</span><span class="p">)</span>
+</pre></div>
+</td></tr></table>
+
+<p>Call it from anywhere in your LightningModule to add metrics, images, etc... whatever your logger supports.  </p>
+<p>Here is an example using the TestTubeLogger (which is a wrapper on <a href="https://pytorch.org/docs/stable/tensorboard.html">PyTorch SummaryWriter</a> with versioned folder structure).     </p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1
+2
+3
+4</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="c1"># if logger is a tensorboard logger or TestTubeLogger</span>
+<span class="bp">self</span><span class="o">.</span><span class="n">logger</span><span class="o">.</span><span class="n">experiment</span><span class="o">.</span><span class="n">add_embedding</span><span class="p">(</span><span class="o">...</span><span class="p">)</span>   
+<span class="bp">self</span><span class="o">.</span><span class="n">logger</span><span class="o">.</span><span class="n">experiment</span><span class="o">.</span><span class="n">log</span><span class="p">({</span><span class="s1">&#39;val_loss&#39;</span><span class="p">:</span> <span class="mf">0.9</span><span class="p">})</span>   
+<span class="bp">self</span><span class="o">.</span><span class="n">logger</span><span class="o">.</span><span class="n">experiment</span><span class="o">.</span><span class="n">add_scalars</span><span class="p">(</span><span class="o">...</span><span class="p">)</span>   
+</pre></div>
+</td></tr></table>
+
+<hr />
+<h4 id="global_step">global_step</h4>
+<p>Total training batches seen across all epochs   </p>
+<hr />
+<h4 id="gradient_clip_val">gradient_clip_val</h4>
+<p>The current gradient clip value    </p>
+<hr />
+<h4 id="on_gpu">on_gpu</h4>
+<p>True if your model is currently running on GPUs. Useful to set flags around the LightningModule for different CPU vs GPU behavior.    </p>
+<hr />
+<h4 id="trainer">trainer</h4>
+<p>Last resort access to any state the trainer has. Changing certain properties here could affect your training run.</p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1
+2
+3</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="bp">self</span><span class="o">.</span><span class="n">trainer</span><span class="o">.</span><span class="n">optimizers</span>
+<span class="bp">self</span><span class="o">.</span><span class="n">trainer</span><span class="o">.</span><span class="n">current_epoch</span>
+<span class="o">...</span>   
+</pre></div>
+</td></tr></table>
+
+<h2 id="debugging">Debugging</h2>
+<p>The LightningModule also offers these tricks to help debug.   </p>
+<hr />
+<h4 id="example_input_array">example_input_array</h4>
+<p>In the LightningModule init, you can set a dummy tensor for this property
+to get a print out of sizes coming into and out of every layer.   </p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1
+2
+3</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+    <span class="c1"># put the dimensions of the first input to your system</span>
+    <span class="bp">self</span><span class="o">.</span><span class="n">example_input_array</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">28</span> <span class="o">*</span> <span class="mi">28</span><span class="p">)</span>
+</pre></div>
+</td></tr></table>
+                
+                  
+                
+              
+              
+                
+
+
+              
+            </article>
+          </div>
+        </div>
+      </main>
+      
+        
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+  
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+</html>
@@ -1,43 +0,0 @@
-# Manifest syntax https://docs.python.org/2/distutils/sourcedist.html
-graft wheelhouse
-
-recursive-include birl *.py
-recursive-exclude __pycache__  *.py[cod] *.orig
-
-# Include the README
-include *.md
-
-# Include the license file
-include LICENSE
-
-exclude *.sh
-exclude *.toml
-exclude *.svg    
-recursive-include pytorch_lightning *.py
-
-# include examples
-recursive-include pl_examples *.py
-recursive-include pl_examples *.md
-recursive-include pl_examples *.sh
-
-# exclude tests from package
-recursive-exclude tests *
-recursive-exclude site *
-exclude tests
-
-# Exclude the documentation files
-recursive-exclude docs *
-exclude docs
-
-# Include the Requirements
-include requirements.txt
-
-# Exclude build configs
-exclude *.yml
-
-prune .git
-prune .github
-prune .circleci
-prune notebook*
-prune temp*
-prune test*
@@ -1,429 +0,0 @@
-<div align="center">
-
-![Logo](docs/source/_static/images/lightning_logo_small.png)
-
-# PyTorch Lightning
-
-**The lightweight PyTorch wrapper for ML researchers. Scale your models. Write less boilerplate.**
-
-
-[![PyPI Status](https://badge.fury.io/py/pytorch-lightning.svg)](https://badge.fury.io/py/pytorch-lightning)
-[![PyPI Status](https://pepy.tech/badge/pytorch-lightning)](https://pepy.tech/project/pytorch-lightning)
-[![Build Status](https://travis-ci.org/williamFalcon/pytorch-lightning.svg?branch=master)](https://travis-ci.org/williamFalcon/pytorch-lightning)
-[![Build status](https://ci.appveyor.com/api/projects/status/NEW-PROJECT-ID?svg=true)](https://ci.appveyor.com/project/williamFalcon/pytorch-lightning)
-[![Coverage](docs/source/_static/images/coverage.svg)](https://github.com/williamFalcon/pytorch-lightning/tree/master/tests#running-coverage)
-[![CodeFactor](https://www.codefactor.io/repository/github/borda/pytorch-lightning/badge)](https://www.codefactor.io/repository/github/borda/pytorch-lightning)    
-
-[![ReadTheDocs](https://readthedocs.org/projects/pytorch-lightning/badge/?version=latest)](https://pytorch-lightning.readthedocs.io/en/latest)
-[![Slack](https://img.shields.io/badge/slack-chat-green.svg?logo=slack)](https://join.slack.com/t/pytorch-lightning/shared_invite/enQtODU5ODIyNTUzODQwLTFkMDg5Mzc1MDBmNjEzMDgxOTVmYTdhYjA1MDdmODUyOTg2OGQ1ZWZkYTQzODhhNzdhZDA3YmNhMDhlMDY4YzQ)
-[![license](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://github.com/williamFalcon/pytorch-lightning/blob/master/LICENSE)
-[![Next Release](https://img.shields.io/badge/Next%20Release-Dec%206-<COLOR>.svg)](https://shields.io/)
-
-<!-- 
-removed until codecov badge isn't empy. likely a config error showing nothing on master.
-[![codecov](https://codecov.io/gh/Borda/pytorch-lightning/branch/master/graph/badge.svg)](https://codecov.io/gh/Borda/pytorch-lightning)
-->
-
-</div>
-
-Simple installation from PyPI
-```bash
-pip install pytorch-lightning  
-```
-
-## Docs   
-**[View the docs here](https://williamfalcon.github.io/pytorch-lightning/)**
-
-## Demo  
-[Copy and run this COLAB!](https://colab.research.google.com/drive/1F_RNcHzTfFuQf-LeKvSlud6x7jXYkG31#scrollTo=HOk9c4_35FKg)
-
-## What is it?  
-Lightning is a very lightweight wrapper on PyTorch that decouples the science code from the engineering code. It's more of a style-guide than a framework. By refactoring your code, we can automate most of the non-research code.  
-
-To use Lightning, simply refactor your research code into the [LightningModule](https://github.com/williamFalcon/pytorch-lightning#how-do-i-do-use-it) format (the science) and Lightning will automate the rest (the engineering). Lightning guarantees tested, correct, modern best practices for the automated parts.
-
- If you are a researcher, Lightning is infinitely flexible, you can modify everything down to the way .backward is called or distributed is set up. 
- If you are a scientist or production team, lightning is very simple to use with best practice defaults.
-
-## What does lightning control for me?   
-
-Everything in Blue!   
-This is how lightning separates the science (red) from the engineering (blue).
-
-![Overview](docs/source/_static/images/pl.gif)
-
-## How much effort is it to convert?
-You're probably tired of switching frameworks at this point. But it is a very quick process to refactor into the Lightning format (ie: hours). [Check out this tutorial](https://towardsdatascience.com/how-to-refactor-your-pytorch-code-to-get-these-42-benefits-of-pytorch-lighting-6fdd0dc97538)
-
-## Starting a new project?   
-[Use our seed-project aimed at reproducibility!](https://github.com/williamFalcon/pytorch-lightning-conference-seed)     
-
-## Why do I want to use lightning?
-Every research project starts the same, a model, a training loop, validation loop, etc. As your research advances, you're likely to need distributed training, 16-bit precision, checkpointing, gradient accumulation, etc.   
-
-Lightning sets up all the boilerplate state-of-the-art training for you so you can focus on the research.   
-
---
- 
-## README Table of Contents        
- [How do I use it](https://github.com/williamFalcon/pytorch-lightning#how-do-i-do-use-it)     
- [What lightning automates](https://github.com/williamFalcon/pytorch-lightning#what-does-lightning-control-for-me)    
- [Tensorboard integration](https://github.com/williamFalcon/pytorch-lightning#tensorboard)    
- [Lightning features](https://github.com/williamFalcon/pytorch-lightning#lightning-automates-all-of-the-following-each-is-also-configurable)    
- [Examples](https://github.com/williamFalcon/pytorch-lightning#examples)    
- [Tutorials](https://github.com/williamFalcon/pytorch-lightning#tutorials)
- [Contributing](https://github.com/williamFalcon/pytorch-lightning/blob/master/.github/CONTRIBUTING.md)
- [Bleeding edge install](https://github.com/williamFalcon/pytorch-lightning#bleeding-edge)   
- [Lightning Design Principles](https://github.com/williamFalcon/pytorch-lightning#lightning-design-principles)   
- [Asking for help](https://github.com/williamFalcon/pytorch-lightning#asking-for-help)
- [FAQ](https://github.com/williamFalcon/pytorch-lightning#faq)    
-
---
-
-## How do I do use it?   
-Think about Lightning as refactoring your research code instead of using a new framework. The research code goes into a [LightningModule](https://williamfalcon.github.io/pytorch-lightning/LightningModule/RequiredTrainerInterface/) which you fit using a Trainer.
-
-The LightningModule defines a *system* such as seq-2-seq, GAN, etc... It can ALSO define a simple classifier such as the example below.     
-
-To use lightning do 2 things:  
-1. [Define a LightningModule](https://williamfalcon.github.io/pytorch-lightning/LightningModule/RequiredTrainerInterface/)
-**WARNING:** This syntax is for version 0.5.0+ where abbreviations were removed.
-    ```python
-    import os
-    
-    import torch
-    from torch.nn import functional as F
-    from torch.utils.data import DataLoader
-    from torchvision.datasets import MNIST
-    from torchvision import transforms
-    
-    import pytorch_lightning as pl
-    
-    class CoolSystem(pl.LightningModule):
-    
-        def __init__(self):
-            super(CoolSystem, self).__init__()
-            # not the best model...
-            self.l1 = torch.nn.Linear(28 * 28, 10)
-    
-        def forward(self, x):
-            return torch.relu(self.l1(x.view(x.size(0), -1)))
-    
-        def training_step(self, batch, batch_idx):
-            # REQUIRED
-            x, y = batch
-            y_hat = self.forward(x)
-            loss = F.cross_entropy(y_hat, y)
-            tensorboard_logs = {'train_loss': loss}
-            return {'loss': loss, 'log': tensorboard_logs}
-    
-        def validation_step(self, batch, batch_idx):
-            # OPTIONAL
-            x, y = batch
-            y_hat = self.forward(x)
-            return {'val_loss': F.cross_entropy(y_hat, y)}
-    
-        def validation_end(self, outputs):
-            # OPTIONAL
-            avg_loss = torch.stack([x['val_loss'] for x in outputs]).mean()
-            tensorboard_logs = {'val_loss': avg_loss}
-            return {'avg_val_loss': avg_loss, 'log': tensorboard_logs}
-    
-        def configure_optimizers(self):
-            # REQUIRED
-            # can return multiple optimizers and learning_rate schedulers
-            # (LBFGS it is automatically supported, no need for closure function)
-            return torch.optim.Adam(self.parameters(), lr=0.02)
-    
-        @pl.data_loader
-        def train_dataloader(self):
-            # REQUIRED
-            return DataLoader(MNIST(os.getcwd(), train=True, download=True, transform=transforms.ToTensor()), batch_size=32)
-    
-        @pl.data_loader
-        def val_dataloader(self):
-            # OPTIONAL
-            return DataLoader(MNIST(os.getcwd(), train=True, download=True, transform=transforms.ToTensor()), batch_size=32)
-    
-        @pl.data_loader
-        def test_dataloader(self):
-            # OPTIONAL
-            return DataLoader(MNIST(os.getcwd(), train=False, download=True, transform=transforms.ToTensor()), batch_size=32)
-    ```
-2. Fit with a [trainer](https://williamfalcon.github.io/pytorch-lightning/Trainer/)    
-    ```python
-    from pytorch_lightning import Trainer
-    
-    model = CoolSystem()
-    
-    # most basic trainer, uses good defaults
-    trainer = Trainer()    
-    trainer.fit(model)   
-    ```
-
-Trainer sets up a tensorboard logger, early stopping and checkpointing by default (you can modify all of them or
-use something other than tensorboard).   
-
-Here are more advanced examples
-```python   
-# train on cpu using only 10% of the data (for demo purposes)
-trainer = Trainer(max_epochs=1, train_percent_check=0.1)
-
-# train on 4 gpus (lightning chooses GPUs for you)
-# trainer = Trainer(max_epochs=1, gpus=4, distributed_backend='ddp')  
-
-# train on 4 gpus (you choose GPUs)
-# trainer = Trainer(max_epochs=1, gpus=[0, 1, 3, 7], distributed_backend='ddp')   
-
-# train on 32 gpus across 4 nodes (make sure to submit appropriate SLURM job)
-# trainer = Trainer(max_epochs=1, gpus=8, num_gpu_nodes=4, distributed_backend='ddp')
-
-# train (1 epoch only here for demo)
-trainer.fit(model)
-
-# view tensorboard logs 
-logging.info(f'View tensorboard logs by running\ntensorboard --logdir {os.getcwd()}')
-logging.info('and going to http://localhost:6006 on your browser')
-```
-
-When you're all done you can even run the test set separately.   
-```python
-trainer.test()
-```
-
-**Could be as complex as seq-2-seq + attention**    
-
-```python
-# define what happens for training here
-def training_step(self, batch, batch_idx):
-    x, y = batch
-    
-    # define your own forward and loss calculation
-    hidden_states = self.encoder(x)
-     
-    # even as complex as a seq-2-seq + attn model
-    # (this is just a toy, non-working example to illustrate)
-    start_token = '<SOS>'
-    last_hidden = torch.zeros(...)
-    loss = 0
-    for step in range(max_seq_len):
-        attn_context = self.attention_nn(hidden_states, start_token)
-        pred = self.decoder(start_token, attn_context, last_hidden) 
-        last_hidden = pred
-        pred = self.predict_nn(pred)
-        loss += self.loss(last_hidden, y[step])
-        
-    #toy example as well
-    loss = loss / max_seq_len
-    return {'loss': loss} 
-```
-
-**Or as basic as CNN image classification**      
-
-```python
-# define what happens for validation here
-def validation_step(self, batch, batch_idx):    
-    x, y = batch
-    
-    # or as basic as a CNN classification
-    out = self.forward(x)
-    loss = my_loss(out, y)
-    return {'loss': loss} 
-```
-
-**And you also decide how to collate the output of all validation steps**    
-
-```python
-def validation_end(self, outputs):
-    """
-    Called at the end of validation to aggregate outputs
-    :param outputs: list of individual outputs of each validation step
-    :return:
-    """
-    val_loss_mean = 0
-    val_acc_mean = 0
-    for output in outputs:
-        val_loss_mean += output['val_loss']
-        val_acc_mean += output['val_acc']
-
-    val_loss_mean /= len(outputs)
-    val_acc_mean /= len(outputs)
-    logs = {'val_loss': val_loss_mean.item(), 'val_acc': val_acc_mean.item()}
-    result = {'log': logs}
-    return result
-```
-   
-## Tensorboard    
-Lightning is fully integrated with tensorboard, MLFlow and supports any logging module.   
-
-![tensorboard-support](docs/source/_static/images/tf_loss.png)
-
-Lightning also adds a text column with all the hyperparameters for this experiment.      
-
-![tensorboard-support](docs/source/_static/images/tf_tags.png)
-
-## Lightning automates all of the following ([each is also configurable](https://williamfalcon.github.io/pytorch-lightning/Trainer/)):
-
-#### Checkpointing    
-
- [Checkpoint callback](https://williamfalcon.github.io/pytorch-lightning/Trainer/Checkpointing/#model-saving)
- [Model saving](https://williamfalcon.github.io/pytorch-lightning/Trainer/Checkpointing/#model-saving)
- [Model loading](https://williamfalcon.github.io/pytorch-lightning/LightningModule/methods/#load-from-metrics) 
- [Restoring training session](https://williamfalcon.github.io/pytorch-lightning/Trainer/Checkpointing/#restoring-training-session)
-
-#### Computing cluster (SLURM)    
-
- [Running grid search on a cluster](https://williamfalcon.github.io/pytorch-lightning/Trainer/SLURM%20Managed%20Cluster#running-grid-search-on-a-cluster) 
- [Walltime auto-resubmit](https://williamfalcon.github.io/pytorch-lightning/Trainer/SLURM%20Managed%20Cluster#walltime-auto-resubmit)   
-
-#### Debugging  
-
- [Fast dev run](https://williamfalcon.github.io/pytorch-lightning/Trainer/debugging/#fast-dev-run)
- [Inspect gradient norms](https://williamfalcon.github.io/pytorch-lightning/Trainer/debugging/#inspect-gradient-norms)
- [Log GPU usage](https://williamfalcon.github.io/pytorch-lightning/Trainer/debugging/#Log-gpu-usage)
- [Make model overfit on subset of data](https://williamfalcon.github.io/pytorch-lightning/Trainer/debugging/#make-model-overfit-on-subset-of-data)
- [Print the parameter count by layer](https://williamfalcon.github.io/pytorch-lightning/Trainer/debugging/#print-the-parameter-count-by-layer)
- [Print which gradients are nan](https://williamfalcon.github.io/pytorch-lightning/Trainer/debugging/#print-which-gradients-are-nan)
- [Print input and output size of every module in system](https://williamfalcon.github.io/pytorch-lightning/LightningModule/properties/#example_input_array)
-
-
-#### Distributed training    
-
- [Implement Your Own Distributed (DDP) training](https://williamfalcon.github.io/pytorch-lightning/Trainer/hooks/#init_ddp_connection)
- [16-bit mixed precision](https://williamfalcon.github.io/pytorch-lightning/Trainer/Distributed%20training/#16-bit-mixed-precision)
- [Multi-GPU](https://williamfalcon.github.io/pytorch-lightning/Trainer/Distributed%20training/#Multi-GPU)
- [Multi-node](https://williamfalcon.github.io/pytorch-lightning/Trainer/Distributed%20training/#Multi-node)
- [Single GPU](https://williamfalcon.github.io/pytorch-lightning/Trainer/Distributed%20training/#single-gpu)
- [Self-balancing architecture](https://williamfalcon.github.io/pytorch-lightning/Trainer/Distributed%20training/#self-balancing-architecture)
-
-
-#### Experiment Logging   
-
- [Display metrics in progress bar](https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#display-metrics-in-progress-bar)
- [Log metric row every k batches](https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#log-metric-row-every-k-batches)
- [Process position](https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#process-position)
- [Tensorboard support](https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#tensorboard-support)
- [Save a snapshot of all hyperparameters](https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#save-a-snapshot-of-all-hyperparameters) 
- [Snapshot code for a training run](https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#snapshot-code-for-a-training-run) 
- [Write logs file to csv every k batches](https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#write-logs-file-to-csv-every-k-batches)
-
-#### Training loop    
-
- [Accumulate gradients](https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#accumulated-gradients)
- [Force training for min or max epochs](https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#force-training-for-min-or-max-epochs)
- [Early stopping callback](https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#early-stopping)
- [Force disable early stop](https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#force-disable-early-stop)
- [Gradient Clipping](https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#gradient-clipping)
- [Hooks](https://williamfalcon.github.io/pytorch-lightning/Trainer/hooks/)
- [Learning rate scheduling](https://williamfalcon.github.io/pytorch-lightning/LightningModule/RequiredTrainerInterface/#configure_optimizers)    
- [Use multiple optimizers (like GANs)](https://williamfalcon.github.io/pytorch-lightning/LightningModule/RequiredTrainerInterface/#configure_optimizers)
- [Set how much of the training set to check (1-100%)](https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#set-how-much-of-the-training-set-to-check)
- [Step optimizers at arbitrary intervals](https://williamfalcon.github.io/pytorch-lightning/Trainer/hooks/#optimizer_step)
-
-#### Validation loop    
-
- [Check validation every n epochs](https://williamfalcon.github.io/pytorch-lightning/Trainer/Validation%20loop/#check-validation-every-n-epochs)
- [Hooks](https://williamfalcon.github.io/pytorch-lightning/Trainer/hooks/)
- [Set how much of the validation set to check](https://williamfalcon.github.io/pytorch-lightning/Trainer/Validation%20loop/#set-how-much-of-the-validation-set-to-check)
- [Set how much of the test set to check](https://williamfalcon.github.io/pytorch-lightning/Trainer/Validation%20loop/#set-how-much-of-the-test-set-to-check)
- [Set validation check frequency within 1 training epoch](https://williamfalcon.github.io/pytorch-lightning/Trainer/Validation%20loop/#set-validation-check-frequency-within-1-training-epoch)
- [Set the number of validation sanity steps](https://williamfalcon.github.io/pytorch-lightning/Trainer/Validation%20loop/#set-the-number-of-validation-sanity-steps)
-
-#### Testing loop  
- [Run test set](https://williamfalcon.github.io/pytorch-lightning/Trainer/Testing%20loop/)  
-
-## Examples   
- [GAN](https://github.com/williamFalcon/pytorch-lightning/tree/master/pl_examples/domain_templates/gan.py)    
- [MNIST](https://github.com/williamFalcon/pytorch-lightning/tree/master/pl_examples/basic_examples)      
- [Other projects using Lightning](https://github.com/williamFalcon/pytorch-lightning/network/dependents?package_id=UGFja2FnZS0zNzE3NDU4OTM%3D)    
- [Multi-node](https://github.com/williamFalcon/pytorch-lightning/tree/master/pl_examples/multi_node_examples)   
-
-## Tutorials   
- [Basic Lightning use](https://towardsdatascience.com/supercharge-your-ai-research-with-pytorch-lightning-337948a99eec)    
- [9 key speed features in Pytorch-Lightning](https://towardsdatascience.com/9-tips-for-training-lightning-fast-neural-networks-in-pytorch-8e63a502f565)    
- [SLURM, multi-node training with Lightning](https://towardsdatascience.com/trivial-multi-node-training-with-pytorch-lightning-ff75dfb809bd)     
-
---
-
-## Asking for help    
-Welcome to the Lightning community!   
-
-If you have any questions, feel free to:   
-1. [read the docs](https://williamfalcon.github.io/pytorch-lightning/).     
-2. [Search through the issues](https://github.com/williamFalcon/pytorch-lightning/issues?utf8=%E2%9C%93&q=my++question).      
-3. [Ask on stackoverflow](https://stackoverflow.com/questions/ask?guided=false) with the tag pytorch-lightning.   
-
-If no one replies to you quickly enough, feel free to post the stackoverflow link to our Gitter chat!   
-
-To chat with the rest of us visit our [gitter channel](https://gitter.im/PyTorch-Lightning/community)!     
-
---   
-## FAQ    
-**How do I use Lightning for rapid research?**   
-[Here's a walk-through](https://williamfalcon.github.io/pytorch-lightning/)  
-
-**Why was Lightning created?**     
-Lightning has 3 goals in mind:
-1. Maximal flexibility while abstracting out the common boilerplate across research projects.   
-2. Reproducibility. If all projects use the LightningModule template, it will be much much easier to understand what's going on and where to look! It will also mean every implementation follows a standard format.   
-3. Democratizing PyTorch power user features. Distributed training? 16-bit? know you need them but don't want to take the time to implement? All good... these come built into Lightning.    
-
-**How does Lightning compare with Ignite and fast.ai?**     
-[Here's a thorough comparison](https://medium.com/@_willfalcon/pytorch-lightning-vs-pytorch-ignite-vs-fast-ai-61dc7480ad8a).    
-
-**Is this another library I have to learn?**    
-Nope! We use pure Pytorch everywhere and don't add unecessary abstractions!   
-
-**Are there plans to support Python 2?**          
-Nope.     
-
-**Are there plans to support virtualenv?**    
-Nope. Please use anaconda or miniconda.    
-
-**Which PyTorch versions do you support?**    
- **PyTorch 1.1.0**       
-    ```bash    
-    # install pytorch 1.1.0 using the official instructions   
-    
-    # install test-tube 0.6.7.6 which supports 1.1.0   
-    pip install test-tube==0.6.7.6   
-    
-    # install latest Lightning version without upgrading deps    
-    pip install -U --no-deps pytorch-lightning
-    ```     
- **PyTorch 1.2.0, 1.3.0,**
-    Install via pip as normal   
-
-## Custom installation
-
-### Bleeding edge
-
-If you can't wait for the next release, install the most up to date code with:
-* using GIT (locally clone whole repo with full history)
-    ```bash
-    pip install git+https://github.com/williamFalcon/pytorch-lightning.git@master --upgrade
-    ```
-* using instant zip (last state of the repo without git history)
-    ```bash
-    pip install https://github.com/williamFalcon/pytorch-lightning/archive/master.zip --upgrade
-    ```
-
-### Any release installation
-
-You can also install any past release from this repository:
-```bash
-pip install https://github.com/williamFalcon/pytorch-lightning/archive/0.4.4.zip --upgrade
-```
-
-## Bibtex
-If you want to cite the framework feel free to use this (but only if you loved it 😊):
-```
-@misc{Falcon2019,
-  author = {Falcon, W.A.},
-  title = {PyTorch Lightning},
-  year = {2019},
-  publisher = {GitHub},
-  journal = {GitHub repository},
-  howpublished = {\url{https://github.com/williamFalcon/pytorch-lightning}}
-}
-```
@@ -0,0 +1,756 @@
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+    Model saving
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#restoring-training-session" title="Restoring training session" class="md-nav__link">
+    Restoring training session
+  </a>
+  
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+                  <a href="https://github.com/williamFalcon/pytorch-lightning/edit/master/docs/Trainer/Checkpointing.md" title="Edit this page" class="md-icon md-content__icon">&#xE3C9;</a>
+                
+                
+                  <h1>Checkpointing</h1>
+                
+                <p>Lightning can automate saving and loading checkpoints.</p>
+<hr />
+<h3 id="model-saving">Model saving</h3>
+<p>Checkpointing is enabled by default to the current working directory.
+To change the checkpoint path pass in :</p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="n">Trainer</span><span class="p">(</span><span class="n">default_save_path</span><span class="o">=</span><span class="s1">&#39;/your/path/to/save/checkpoints&#39;</span><span class="p">)</span>
+</pre></div>
+</td></tr></table>
+
+<p>To modify the behavior of checkpointing pass in your own callback.</p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre> 1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10
+11
+12
+13</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="kn">from</span> <span class="nn">pytorch_lightning.callbacks</span> <span class="kn">import</span> <span class="n">ModelCheckpoint</span>
+
+<span class="c1"># DEFAULTS used by the Trainer</span>
+<span class="n">checkpoint_callback</span> <span class="o">=</span> <span class="n">ModelCheckpoint</span><span class="p">(</span>
+    <span class="n">filepath</span><span class="o">=</span><span class="n">os</span><span class="o">.</span><span class="n">getcwd</span><span class="p">(),</span>
+    <span class="n">save_best_only</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span>
+    <span class="n">verbose</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span>
+    <span class="n">monitor</span><span class="o">=</span><span class="s1">&#39;val_loss&#39;</span><span class="p">,</span>
+    <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;min&#39;</span><span class="p">,</span>
+    <span class="n">prefix</span><span class="o">=</span><span class="s1">&#39;&#39;</span>
+<span class="p">)</span>
+
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">checkpoint_callback</span><span class="o">=</span><span class="n">checkpoint_callback</span><span class="p">)</span>
+</pre></div>
+</td></tr></table>
+
+<hr />
+<h3 id="restoring-training-session">Restoring training session</h3>
+<p>You might want to not only load a model but also continue training it. Use this method to
+restore the trainer state as well. This will continue from the epoch and global step you last left off.<br />
+However, the dataloaders will start from the first batch again (if you shuffled it shouldn't matter).   </p>
+<p>Lightning will restore the session if you pass a logger with the same version and there's a saved checkpoint.   </p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre> 1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10
+11
+12
+13
+14
+15
+16</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="kn">from</span> <span class="nn">pytorch_lightning</span> <span class="kn">import</span> <span class="n">Trainer</span>
+<span class="kn">from</span> <span class="nn">pytorch_lightning.logging</span> <span class="kn">import</span> <span class="n">TestTubeLogger</span>
+
+<span class="n">logger</span> <span class="o">=</span> <span class="n">TestTubeLogger</span><span class="p">(</span>
+    <span class="n">save_dir</span><span class="o">=</span><span class="s1">&#39;./savepath&#39;</span><span class="p">,</span>
+    <span class="n">version</span><span class="o">=</span><span class="mi">1</span>  <span class="c1"># An existing version with a saved checkpoint</span>
+<span class="p">)</span>
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span>
+    <span class="n">logger</span><span class="o">=</span><span class="n">logger</span><span class="p">,</span>
+    <span class="n">default_save_path</span><span class="o">=</span><span class="s1">&#39;./savepath&#39;</span>
+<span class="p">)</span>
+
+<span class="c1"># this fit call loads model weights and trainer state</span>
+<span class="c1"># the trainer continues seamlessly from where you left off</span>
+<span class="c1"># without having to do anything else.</span>
+<span class="n">trainer</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
+</pre></div>
+</td></tr></table>
+
+<p>The trainer restores:  </p>
+<ul>
+<li>global_step    </li>
+<li>current_epoch    </li>
+<li>All optimizers    </li>
+<li>All lr_schedulers    </li>
+<li>Model weights</li>
+</ul>
+<p>You can even change the logic of your model as long as the weights and "architecture" of 
+the system isn't different. If you add a layer, for instance, it might not work.   </p>
+<p>At a rough level, here's <a href="https://github.com/williamFalcon/pytorch-lightning/blob/master/pytorch_lightning/root_module/model_saving.py#L63">what happens inside Trainer</a>:   </p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre> 1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10
+11
+12
+13
+14
+15</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="bp">self</span><span class="o">.</span><span class="n">global_step</span> <span class="o">=</span> <span class="n">checkpoint</span><span class="p">[</span><span class="s1">&#39;global_step&#39;</span><span class="p">]</span>
+<span class="bp">self</span><span class="o">.</span><span class="n">current_epoch</span> <span class="o">=</span> <span class="n">checkpoint</span><span class="p">[</span><span class="s1">&#39;epoch&#39;</span><span class="p">]</span>
+
+<span class="c1"># restore the optimizers</span>
+<span class="n">optimizer_states</span> <span class="o">=</span> <span class="n">checkpoint</span><span class="p">[</span><span class="s1">&#39;optimizer_states&#39;</span><span class="p">]</span>
+<span class="k">for</span> <span class="n">optimizer</span><span class="p">,</span> <span class="n">opt_state</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">optimizers</span><span class="p">,</span> <span class="n">optimizer_states</span><span class="p">):</span>
+    <span class="n">optimizer</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span><span class="n">opt_state</span><span class="p">)</span>
+
+<span class="c1"># restore the lr schedulers</span>
+<span class="n">lr_schedulers</span> <span class="o">=</span> <span class="n">checkpoint</span><span class="p">[</span><span class="s1">&#39;lr_schedulers&#39;</span><span class="p">]</span>
+<span class="k">for</span> <span class="n">scheduler</span><span class="p">,</span> <span class="n">lrs_state</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">lr_schedulers</span><span class="p">,</span> <span class="n">lr_schedulers</span><span class="p">):</span>
+    <span class="n">scheduler</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span><span class="n">lrs_state</span><span class="p">)</span>
+
+<span class="c1"># uses the model you passed into trainer        </span>
+<span class="n">model</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span><span class="n">checkpoint</span><span class="p">[</span><span class="s1">&#39;state_dict&#39;</span><span class="p">])</span>
+</pre></div>
+</td></tr></table>
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+                  <a href="https://github.com/williamFalcon/pytorch-lightning/edit/master/docs/Trainer/SLURM Managed Cluster.md" title="Edit this page" class="md-icon md-content__icon">&#xE3C9;</a>
+                
+                
+                  <h1>SLURM Managed Cluster</h1>
+                
+                <p>Lightning supports model training on a cluster managed by SLURM in the following cases:    </p>
+<ol>
+<li>Training on a single cpu or single GPU.</li>
+<li>Train on multiple GPUs on the same node using DataParallel or DistributedDataParallel</li>
+<li>Training across multiple GPUs on multiple different nodes via DistributedDataParallel.</li>
+</ol>
+<p><strong>Note: A node means a machine with multiple GPUs</strong></p>
+<hr />
+<h4 id="running-grid-search-on-a-cluster">Running grid search on a cluster</h4>
+<p>To use lightning to run a hyperparameter search (grid-search or random-search) on a cluster do 4 things:   </p>
+<p>(1). Define the parameters for the grid search    </p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre> 1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="kn">from</span> <span class="nn">test_tube</span> <span class="kn">import</span> <span class="n">HyperOptArgumentParser</span>
+
+<span class="c1"># subclass of argparse</span>
+<span class="n">parser</span> <span class="o">=</span> <span class="n">HyperOptArgumentParser</span><span class="p">(</span><span class="n">strategy</span><span class="o">=</span><span class="s1">&#39;random_search&#39;</span><span class="p">)</span>
+<span class="n">parser</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span><span class="s1">&#39;--learning_rate&#39;</span><span class="p">,</span> <span class="n">default</span><span class="o">=</span><span class="mf">0.002</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="nb">float</span><span class="p">,</span> <span class="n">help</span><span class="o">=</span><span class="s1">&#39;the learning rate&#39;</span><span class="p">)</span>
+
+<span class="c1"># let&#39;s enable optimizing over the number of layers in the network</span>
+<span class="n">parser</span><span class="o">.</span><span class="n">opt_list</span><span class="p">(</span><span class="s1">&#39;--nb_layers&#39;</span><span class="p">,</span> <span class="n">default</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="nb">int</span><span class="p">,</span> <span class="n">tunable</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span> <span class="n">options</span><span class="o">=</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">8</span><span class="p">])</span>
+
+<span class="n">hparams</span> <span class="o">=</span> <span class="n">parser</span><span class="o">.</span><span class="n">parse_args</span><span class="p">()</span>    
+</pre></div>
+</td></tr></table>
+
+<p><strong>NOTE</strong> You must set <code>Tunable=True</code> for that argument to be considered in the permutation set. Otherwise
+test-tube will use the default value. This flag is useful when you don't want to search over an argument and
+want to use the default instead.   </p>
+<p>(2). Define the cluster options in the <a href="https://williamfalcon.github.io/test-tube/hpc/SlurmCluster/">SlurmCluster object</a> (over 5 nodes and 8 gpus)    </p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre> 1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10
+11
+12
+13
+14
+15
+16
+17
+18
+19
+20
+21
+22
+23
+24
+25
+26</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="kn">from</span> <span class="nn">test_tube.hpc</span> <span class="kn">import</span> <span class="n">SlurmCluster</span>
+
+<span class="c1"># hyperparameters is a test-tube hyper params object</span>
+<span class="c1"># see https://williamfalcon.github.io/test-tube/hyperparameter_optimization/HyperOptArgumentParser/</span>
+<span class="n">hyperparams</span> <span class="o">=</span> <span class="n">args</span><span class="o">.</span><span class="n">parse</span><span class="p">()</span>
+
+<span class="c1"># init cluster</span>
+<span class="n">cluster</span> <span class="o">=</span> <span class="n">SlurmCluster</span><span class="p">(</span>
+    <span class="n">hyperparam_optimizer</span><span class="o">=</span><span class="n">hyperparams</span><span class="p">,</span>
+    <span class="n">log_path</span><span class="o">=</span><span class="s1">&#39;/path/to/log/results/to&#39;</span><span class="p">,</span>
+    <span class="n">python_cmd</span><span class="o">=</span><span class="s1">&#39;python3&#39;</span>
+<span class="p">)</span>
+
+<span class="c1"># let the cluster know where to email for a change in job status (ie: complete, fail, etc...)</span>
+<span class="n">cluster</span><span class="o">.</span><span class="n">notify_job_status</span><span class="p">(</span><span class="n">email</span><span class="o">=</span><span class="s1">&#39;some@email.com&#39;</span><span class="p">,</span> <span class="n">on_done</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span> <span class="n">on_fail</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
+
+<span class="c1"># set the job options. In this instance, we&#39;ll run 20 different models</span>
+<span class="c1"># each with its own set of hyperparameters giving each one 1 GPU (ie: taking up 20 GPUs)</span>
+<span class="n">cluster</span><span class="o">.</span><span class="n">per_experiment_nb_gpus</span> <span class="o">=</span> <span class="mi">8</span>
+<span class="n">cluster</span><span class="o">.</span><span class="n">per_experiment_nb_nodes</span> <span class="o">=</span> <span class="mi">5</span>
+
+<span class="c1"># we&#39;ll request 10GB of memory per node</span>
+<span class="n">cluster</span><span class="o">.</span><span class="n">memory_mb_per_node</span> <span class="o">=</span> <span class="mi">10000</span>
+
+<span class="c1"># set a walltime of 10 minues</span>
+<span class="n">cluster</span><span class="o">.</span><span class="n">job_time</span> <span class="o">=</span> <span class="s1">&#39;10:00&#39;</span>
+</pre></div>
+</td></tr></table>
+
+<p>(3). Make a main function with your model and trainer. Each job will call this function with a particular
+hparams configuration.    </p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre> 1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="kn">from</span> <span class="nn">pytorch_lightning</span> <span class="kn">import</span> <span class="n">Trainer</span>
+
+<span class="k">def</span> <span class="nf">train_fx</span><span class="p">(</span><span class="n">trial_hparams</span><span class="p">,</span> <span class="n">cluster_manager</span><span class="p">,</span> <span class="n">_</span><span class="p">):</span>
+    <span class="c1"># hparams has a specific set of hyperparams</span>
+
+    <span class="n">my_model</span> <span class="o">=</span> <span class="n">MyLightningModel</span><span class="p">()</span>
+
+    <span class="c1"># give the trainer the cluster object</span>
+    <span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">()</span>
+    <span class="n">trainer</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">my_model</span><span class="p">)</span>
+</pre></div>
+</td></tr></table>
+
+<p>(3). Start the grid/random search     </p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1
+2
+3
+4
+5
+6</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="c1"># run the models on the cluster</span>
+<span class="n">cluster</span><span class="o">.</span><span class="n">optimize_parallel_cluster_gpu</span><span class="p">(</span>
+    <span class="n">train_fx</span><span class="p">,</span> 
+    <span class="n">nb_trials</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> 
+    <span class="n">job_name</span><span class="o">=</span><span class="s1">&#39;my_grid_search_exp_name&#39;</span><span class="p">,</span> 
+    <span class="n">job_display_name</span><span class="o">=</span><span class="s1">&#39;my_exp&#39;</span><span class="p">)</span>
+</pre></div>
+</td></tr></table>
+
+<p><strong>NOTE</strong> nb_trials specifies how many of the possible permutations to use. If using <code>grid_search</code> it will use
+the depth first ordering. If using <code>random_search</code> it will use the first k shuffled options. FYI, random search
+has been shown to be just as good as any Bayesian optimization method when using a reasonable number of samples (60),
+<a href="http://www.jmlr.org/papers/volume13/bergstra12a/bergstra12a.pdf">see this paper for more information</a>.</p>
+<hr />
+<h4 id="walltime-auto-resubmit">Walltime auto-resubmit</h4>
+<p>Lightning automatically resubmits jobs when they reach the walltime. Make sure to set the SIGUSR1 signal in 
+your SLURM script.   </p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1
+2</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="c1"># 90 seconds before training ends</span>
+<span class="c1">#SBATCH --signal=SIGUSR1@90</span>
+</pre></div>
+</td></tr></table>
+
+<p>When lightning receives the SIGUSR1 signal it will:
+1. save a checkpoint with 'hpc_ckpt' in the name.
+2. resubmit the job using the SLURM_JOB_ID  </p>
+<p>When the script starts again, Lightning will:
+1. search for a 'hpc_ckpt' checkpoint. 
+2. restore the model, optimizers, schedulers, epoch, etc...   </p>
+                
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+                  <a href="https://github.com/williamFalcon/pytorch-lightning/edit/master/docs/Trainer/Testing loop.md" title="Edit this page" class="md-icon md-content__icon">&#xE3C9;</a>
+                
+                
+                  <h1>Testing loop</h1>
+                
+                <p>To ensure you don't accidentally use test data to guide training decisions Lightning makes running the test set deliberate.   </p>
+<hr />
+<h4 id="test">test</h4>
+<p>You have two options to run the test set. 
+First case is where you test right after a full training routine.</p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1
+2
+3
+4
+5</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="c1"># run full training</span>
+<span class="n">trainer</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
+
+<span class="c1"># run test set</span>
+<span class="n">trainer</span><span class="o">.</span><span class="n">test</span><span class="p">()</span>
+</pre></div>
+</td></tr></table>
+
+<p>Second case is where you load a model and run the test set   </p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre> 1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10
+11
+12</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">MyLightningModule</span><span class="o">.</span><span class="n">load_from_metrics</span><span class="p">(</span>
+    <span class="n">weights_path</span><span class="o">=</span><span class="s1">&#39;/path/to/pytorch_checkpoint.ckpt&#39;</span><span class="p">,</span>
+    <span class="n">tags_csv</span><span class="o">=</span><span class="s1">&#39;/path/to/test_tube/experiment/version/meta_tags.csv&#39;</span><span class="p">,</span>
+    <span class="n">on_gpu</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span>
+    <span class="n">map_location</span><span class="o">=</span><span class="bp">None</span>
+<span class="p">)</span>
+
+<span class="c1"># init trainer with whatever options</span>
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="o">...</span><span class="p">)</span>
+
+<span class="c1"># test (pass in the model)</span>
+<span class="n">trainer</span><span class="o">.</span><span class="n">test</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
+</pre></div>
+</td></tr></table>
+
+<p>In this second case, the options you pass to trainer will be used when running the test set (ie: 16-bit, dp, ddp, etc...)  </p>
+                
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+      
+        <li class="md-nav__item">
+  <a href="#accumulated-gradients" title="Accumulated gradients" class="md-nav__link">
+    Accumulated gradients
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#force-training-for-min-or-max-epochs" title="Force training for min or max epochs" class="md-nav__link">
+    Force training for min or max epochs
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#early-stopping" title="Early stopping" class="md-nav__link">
+    Early stopping
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#force-disable-early-stop" title="Force disable early stop" class="md-nav__link">
+    Force disable early stop
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#gradient-clipping" title="Gradient Clipping" class="md-nav__link">
+    Gradient Clipping
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#inspect-gradient-norms" title="Inspect gradient norms" class="md-nav__link">
+    Inspect gradient norms
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#set-how-much-of-the-training-set-to-check" title="Set how much of the training set to check" class="md-nav__link">
+    Set how much of the training set to check
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#packed-sequences-as-inputs" title="Packed sequences as inputs" class="md-nav__link">
+    Packed sequences as inputs
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#truncated-back-propagation-through-time" title="Truncated Back Propagation Through Time" class="md-nav__link">
+    Truncated Back Propagation Through Time
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+      
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+  <a href="#accumulated-gradients" title="Accumulated gradients" class="md-nav__link">
+    Accumulated gradients
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#force-training-for-min-or-max-epochs" title="Force training for min or max epochs" class="md-nav__link">
+    Force training for min or max epochs
+  </a>
+  
+</li>
+      
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+  <a href="#early-stopping" title="Early stopping" class="md-nav__link">
+    Early stopping
+  </a>
+  
+</li>
+      
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+  <a href="#force-disable-early-stop" title="Force disable early stop" class="md-nav__link">
+    Force disable early stop
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#gradient-clipping" title="Gradient Clipping" class="md-nav__link">
+    Gradient Clipping
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#inspect-gradient-norms" title="Inspect gradient norms" class="md-nav__link">
+    Inspect gradient norms
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#set-how-much-of-the-training-set-to-check" title="Set how much of the training set to check" class="md-nav__link">
+    Set how much of the training set to check
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#packed-sequences-as-inputs" title="Packed sequences as inputs" class="md-nav__link">
+    Packed sequences as inputs
+  </a>
+  
+</li>
+      
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+    Truncated Back Propagation Through Time
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+              
+                
+                  <a href="https://github.com/williamFalcon/pytorch-lightning/edit/master/docs/Trainer/Training Loop.md" title="Edit this page" class="md-icon md-content__icon">&#xE3C9;</a>
+                
+                
+                  <h1>Training Loop</h1>
+                
+                <p>The lightning training loop handles everything except the actual computations of your model. To decide what will happen in your training loop, define the <a href="https://williamfalcon.github.io/pytorch-lightning/LightningModule/RequiredTrainerInterface/#training_step">training_step function</a>.</p>
+<p>Below are all the things lightning automates for you in the training loop.</p>
+<hr />
+<h4 id="accumulated-gradients">Accumulated gradients</h4>
+<p>Accumulated gradients runs K small batches of size N before doing a backwards pass. The effect is a large effective batch size of size KxN.</p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1
+2</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="c1"># DEFAULT (ie: no accumulated grads)</span>
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">accumulate_grad_batches</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+</pre></div>
+</td></tr></table>
+
+<hr />
+<h4 id="force-training-for-min-or-max-epochs">Force training for min or max epochs</h4>
+<p>It can be useful to force training for a minimum number of epochs or limit to a max number</p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1
+2</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="c1"># DEFAULT</span>
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">min_nb_epochs</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">max_nb_epochs</span><span class="o">=</span><span class="mi">1000</span><span class="p">)</span>
+</pre></div>
+</td></tr></table>
+
+<hr />
+<h4 id="early-stopping">Early stopping</h4>
+<p>The trainer already sets up default early stopping for you.
+To modify this behavior, pass in your own EarlyStopping callback.</p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre> 1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10
+11
+12
+13
+14
+15
+16
+17
+18
+19</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="kn">from</span> <span class="nn">pytorch_lightning.callbacks</span> <span class="kn">import</span> <span class="n">EarlyStopping</span>
+
+<span class="c1"># DEFAULTS used by Trainer</span>
+<span class="n">early_stop_callback</span> <span class="o">=</span> <span class="n">EarlyStopping</span><span class="p">(</span>
+    <span class="n">monitor</span><span class="o">=</span><span class="s1">&#39;val_loss&#39;</span><span class="p">,</span>
+    <span class="n">min_delta</span><span class="o">=</span><span class="mf">0.00</span><span class="p">,</span>
+    <span class="n">patience</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span>
+    <span class="n">verbose</span><span class="o">=</span><span class="bp">False</span><span class="p">,</span>
+    <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;min&#39;</span>
+<span class="p">)</span>
+
+<span class="c1"># without passing anything in, uses the default callback above</span>
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">()</span>
+
+<span class="c1"># pass in your own to override the default callback</span>
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">early_stop_callback</span><span class="o">=</span><span class="n">early_stop_callback</span><span class="p">)</span>
+
+<span class="c1"># pass in None to disable it</span>
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">early_stop_callback</span><span class="o">=</span><span class="bp">None</span><span class="p">)</span>
+</pre></div>
+</td></tr></table>
+
+<hr />
+<h4 id="force-disable-early-stop">Force disable early stop</h4>
+<p>To disable early stopping pass None to the early_stop_callback</p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1
+2</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="c1"># DEFAULT</span>
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">early_stop_callback</span><span class="o">=</span><span class="bp">None</span><span class="p">)</span>
+</pre></div>
+</td></tr></table>
+
+<hr />
+<h4 id="gradient-clipping">Gradient Clipping</h4>
+<p>Gradient clipping may be enabled to avoid exploding gradients.
+Specifically, this will <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.utils.clip_grad_norm_">clip the gradient norm computed over all model parameters <em>together</em></a>.</p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1
+2
+3
+4
+5</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="c1"># DEFAULT (ie: don&#39;t clip)</span>
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">gradient_clip_val</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+
+<span class="c1"># clip gradients with norm above 0.5</span>
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">gradient_clip_val</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
+</pre></div>
+</td></tr></table>
+
+<hr />
+<h4 id="inspect-gradient-norms">Inspect gradient norms</h4>
+<p>Looking at grad norms can help you figure out where training might be going wrong.</p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1
+2
+3
+4
+5</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="c1"># DEFAULT (-1 doesn&#39;t track norms)</span>
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">track_grad_norm</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+
+<span class="c1"># track the LP norm (P=2 here)</span>
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">track_grad_norm</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+</pre></div>
+</td></tr></table>
+
+<hr />
+<h4 id="set-how-much-of-the-training-set-to-check">Set how much of the training set to check</h4>
+<p>If you don't want to check 100% of the training set (for debugging or if it's huge), set this flag.</p>
+<p>train_percent_check will be overwritten by overfit_pct if <code>overfit_pct &gt; 0</code></p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1
+2
+3
+4
+5</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="c1"># DEFAULT</span>
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">train_percent_check</span><span class="o">=</span><span class="mf">1.0</span><span class="p">)</span>
+
+<span class="c1"># check 10% only</span>
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">train_percent_check</span><span class="o">=</span><span class="mf">0.1</span><span class="p">)</span>
+</pre></div>
+</td></tr></table>
+
+<hr />
+<h4 id="packed-sequences-as-inputs">Packed sequences as inputs</h4>
+<p>When using PackedSequence, do 2 things:
+1. return either a padded tensor in dataset or a list of variable length tensors in the dataloader collate_fn (example above shows the list implementation).  <br />
+2. Pack the sequence in forward or training and validation steps depending on use case.</p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre> 1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="c1"># For use in dataloader</span>
+<span class="k">def</span> <span class="nf">collate_fn</span><span class="p">(</span><span class="n">batch</span><span class="p">):</span>
+    <span class="n">x</span> <span class="o">=</span> <span class="p">[</span><span class="n">item</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">item</span> <span class="ow">in</span> <span class="n">batch</span><span class="p">]</span>
+    <span class="n">y</span> <span class="o">=</span> <span class="p">[</span><span class="n">item</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">item</span> <span class="ow">in</span> <span class="n">batch</span><span class="p">]</span>
+    <span class="k">return</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span>
+
+<span class="c1"># In module</span>
+<span class="k">def</span> <span class="nf">training_step</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch</span><span class="p">,</span> <span class="n">batch_nb</span><span class="p">):</span>
+    <span class="n">x</span> <span class="o">=</span> <span class="n">rnn</span><span class="o">.</span><span class="n">pack_sequence</span><span class="p">(</span><span class="n">batch</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">enforce_sorted</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
+    <span class="n">y</span> <span class="o">=</span> <span class="n">rnn</span><span class="o">.</span><span class="n">pack_sequence</span><span class="p">(</span><span class="n">batch</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">enforce_sorted</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
+</pre></div>
+</td></tr></table>
+
+<hr />
+<h4 id="truncated-back-propagation-through-time">Truncated Back Propagation Through Time</h4>
+<p>There are times when multiple backwards passes are needed for each batch. For example, it may save memory to use Truncated Back Propagation Through Time when training RNNs.</p>
+<p>When this flag is enabled each batch is split into sequences of size truncated_bptt_steps and passed to training_step(...) separately. A default splitting function is provided, however, you can override it for more flexibility. See <a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/hooks#tbptt_split_batch">tbptt_split_batch</a>.</p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1
+2
+3
+4
+5</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="c1"># DEFAULT (single backwards pass per batch)</span>
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">truncated_bptt_steps</span><span class="o">=</span><span class="bp">None</span><span class="p">)</span>
+
+<span class="c1"># (split batch into sequences of size 2)</span>
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">truncated_bptt_steps</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+</pre></div>
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+      
+        <li class="md-nav__item">
+  <a href="#check-validation-every-n-epochs" title="Check validation every n epochs" class="md-nav__link">
+    Check validation every n epochs
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#set-how-much-of-the-validation-set-to-check" title="Set how much of the validation set to check" class="md-nav__link">
+    Set how much of the validation set to check
+  </a>
+  
+</li>
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+    Set how much of the test set to check
+  </a>
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+      
+        <li class="md-nav__item">
+  <a href="#set-validation-check-frequency-within-1-training-epoch" title="Set validation check frequency within 1 training epoch" class="md-nav__link">
+    Set validation check frequency within 1 training epoch
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#set-the-number-of-validation-sanity-steps" title="Set the number of validation sanity steps" class="md-nav__link">
+    Set the number of validation sanity steps
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+    Set how much of the test set to check
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#set-validation-check-frequency-within-1-training-epoch" title="Set validation check frequency within 1 training epoch" class="md-nav__link">
+    Set validation check frequency within 1 training epoch
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#set-the-number-of-validation-sanity-steps" title="Set the number of validation sanity steps" class="md-nav__link">
+    Set the number of validation sanity steps
+  </a>
+  
+</li>
+      
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+      
+      
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+                  <a href="https://github.com/williamFalcon/pytorch-lightning/edit/master/docs/Trainer/Validation loop.md" title="Edit this page" class="md-icon md-content__icon">&#xE3C9;</a>
+                
+                
+                  <h1>Validation loop</h1>
+                
+                <p>The lightning validation loop handles everything except the actual computations of your model. To decide what will happen in your validation loop, define the <a href="https://williamfalcon.github.io/pytorch-lightning/LightningModule/RequiredTrainerInterface/#validation_step">validation_step function</a>.
+Below are all the things lightning automates for you in the validation loop.</p>
+<p><strong>Note</strong> <br />
+Lightning will run 5 steps of validation in the beginning of training as a sanity check so you don't have to wait until a full epoch to catch possible validation issues.</p>
+<hr />
+<h4 id="check-validation-every-n-epochs">Check validation every n epochs</h4>
+<p>If you have a small dataset you might want to check validation every n epochs</p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1
+2</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="c1"># DEFAULT</span>
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">check_val_every_n_epoch</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+</pre></div>
+</td></tr></table>
+
+<hr />
+<h4 id="set-how-much-of-the-validation-set-to-check">Set how much of the validation set to check</h4>
+<p>If you don't want to check 100% of the validation set (for debugging or if it's huge), set this flag</p>
+<p>val_percent_check will be overwritten by overfit_pct if <code>overfit_pct &gt; 0</code></p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1
+2
+3
+4
+5</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="c1"># DEFAULT</span>
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">val_percent_check</span><span class="o">=</span><span class="mf">1.0</span><span class="p">)</span>
+
+<span class="c1"># check 10% only</span>
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">val_percent_check</span><span class="o">=</span><span class="mf">0.1</span><span class="p">)</span>
+</pre></div>
+</td></tr></table>
+
+<hr />
+<h4 id="set-how-much-of-the-test-set-to-check">Set how much of the test set to check</h4>
+<p>If you don't want to check 100% of the test set (for debugging or if it's huge), set this flag</p>
+<p>test_percent_check will be overwritten by overfit_pct if <code>overfit_pct &gt; 0</code></p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1
+2
+3
+4
+5</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="c1"># DEFAULT</span>
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">test_percent_check</span><span class="o">=</span><span class="mf">1.0</span><span class="p">)</span>
+
+<span class="c1"># check 10% only</span>
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">test_percent_check</span><span class="o">=</span><span class="mf">0.1</span><span class="p">)</span>
+</pre></div>
+</td></tr></table>
+
+<hr />
+<h4 id="set-validation-check-frequency-within-1-training-epoch">Set validation check frequency within 1 training epoch</h4>
+<p>For large datasets it's often desirable to check validation multiple times within a training loop.
+Pass in a float to check that often within 1 training epoch.
+Pass in an int k to check every k training batches. Must use an int if using 
+an IterableDataset.</p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1
+2
+3
+4
+5
+6
+7
+8</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="c1"># DEFAULT</span>
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">val_check_interval</span><span class="o">=</span><span class="mf">0.95</span><span class="p">)</span>
+
+<span class="c1"># check every .25 of an epoch </span>
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">val_check_interval</span><span class="o">=</span><span class="mf">0.25</span><span class="p">)</span>
+
+<span class="c1"># check every 100 train batches (ie: for IterableDatasets or fixed frequency)</span>
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">val_check_interval</span><span class="o">=</span><span class="mi">100</span><span class="p">)</span>
+</pre></div>
+</td></tr></table>
+
+<hr />
+<h4 id="set-the-number-of-validation-sanity-steps">Set the number of validation sanity steps</h4>
+<p>Lightning runs a few steps of validation in the beginning of training. This avoids crashing in the validation loop sometime deep into a lengthy training loop.</p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1
+2</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="c1"># DEFAULT</span>
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">nb_sanity_val_steps</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
+</pre></div>
+</td></tr></table>
+
+<p>You can use <code>Trainer(nb_sanity_val_steps=0)</code> to skip the sanity check.</p>
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+      
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+  <a href="#inspect-gradient-norms" title="Inspect gradient norms" class="md-nav__link">
+    Inspect gradient norms
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+  <a href="#make-model-overfit-on-subset-of-data" title="Make model overfit on subset of data" class="md-nav__link">
+    Make model overfit on subset of data
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+    Print which gradients are nan
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+    Fast dev run
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+    Inspect gradient norms
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+    Make model overfit on subset of data
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+</li>
+      
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+    Print the parameter count by layer
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+      
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+    Print which gradients are nan
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+              
+                
+                  <a href="https://github.com/williamFalcon/pytorch-lightning/edit/master/docs/Trainer/debugging.md" title="Edit this page" class="md-icon md-content__icon">&#xE3C9;</a>
+                
+                
+                  <h1>Debugging</h1>
+                
+                <p>These flags are useful to help debug a model.</p>
+<hr />
+<h4 id="fast-dev-run">Fast dev run</h4>
+<p>This flag is meant for debugging a full train/val/test loop. It'll activate callbacks, everything but only with 1 training and 1 validation batch.
+Use this to debug a full run of your program quickly</p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1
+2</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="c1"># DEFAULT</span>
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">fast_dev_run</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
+</pre></div>
+</td></tr></table>
+
+<hr />
+<h4 id="inspect-gradient-norms">Inspect gradient norms</h4>
+<p>Looking at grad norms can help you figure out where training might be going wrong.</p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1
+2
+3
+4
+5</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="c1"># DEFAULT (-1 doesn&#39;t track norms)</span>
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">track_grad_norm</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+
+<span class="c1"># track the LP norm (P=2 here)</span>
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">track_grad_norm</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+</pre></div>
+</td></tr></table>
+
+<hr />
+<h4 id="make-model-overfit-on-subset-of-data">Make model overfit on subset of data</h4>
+<p>A useful debugging trick is to make your model overfit a tiny fraction of the data.</p>
+<p>setting <code>overfit_pct &gt; 0</code> will overwrite train_percent_check, val_percent_check, test_percent_check</p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1
+2
+3
+4
+5</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="c1"># DEFAULT don&#39;t overfit (ie: normal training)</span>
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">overfit_pct</span><span class="o">=</span><span class="mf">0.0</span><span class="p">)</span>
+
+<span class="c1"># overfit on 1% of data </span>
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">overfit_pct</span><span class="o">=</span><span class="mf">0.01</span><span class="p">)</span>
+</pre></div>
+</td></tr></table>
+
+<hr />
+<h4 id="print-the-parameter-count-by-layer">Print the parameter count by layer</h4>
+<p>By default lightning prints a list of parameters <em>and submodules</em> when it starts training.</p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1
+2
+3
+4
+5</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="c1"># DEFAULT print a full list of all submodules and their parameters.</span>
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">weights_summary</span><span class="o">=</span><span class="s1">&#39;full&#39;</span><span class="p">)</span>
+
+<span class="c1"># only print the top-level modules (i.e. the children of LightningModule).</span>
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">weights_summary</span><span class="o">=</span><span class="s1">&#39;top&#39;</span><span class="p">)</span>
+</pre></div>
+</td></tr></table>
+
+<hr />
+<h4 id="print-which-gradients-are-nan">Print which gradients are nan</h4>
+<p>This option prints a list of tensors with nan gradients.</p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1
+2</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="c1"># DEFAULT</span>
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">print_nan_grads</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
+</pre></div>
+</td></tr></table>
+
+<hr />
+<h4 id="log-gpu-usage">Log GPU usage</h4>
+<p>Lightning automatically logs gpu usage to the test tube logs. It'll only do it at the metric logging interval, so it doesn't slow down training.</p>
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+    <a href="Checkpointing/" title="Checkpointing" class="md-nav__link">
+      Checkpointing
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+    <a href="Distributed training/" title="Distributed training" class="md-nav__link">
+      Distributed training
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+    <a href="Logging/" title="Logging" class="md-nav__link">
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+    <a href="SLURM Managed Cluster/" title="SLURM Managed Cluster" class="md-nav__link">
+      SLURM Managed Cluster
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+    <a href="Testing loop/" title="Testing loop" class="md-nav__link">
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+              
+                
+                  <a href="https://github.com/williamFalcon/pytorch-lightning/edit/master/docs/Trainer/index.md" title="Edit this page" class="md-icon md-content__icon">&#xE3C9;</a>
+                
+                
+                <h1 id="trainer">Trainer</h1>
+<p>[<a href="https://github.com/williamFalcon/pytorch-lightning/blob/master/pytorch_lightning/trainer/trainer.py">Github Code</a>]</p>
+<p>The lightning trainer abstracts best practices for running a training, val, test routine. It calls parts of your model when it wants to hand over full control and otherwise makes training assumptions which are now standard practice in AI research.</p>
+<p>This is the basic use of the trainer:</p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1
+2
+3
+4
+5
+6</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="kn">from</span> <span class="nn">pytorch_lightning</span> <span class="kn">import</span> <span class="n">Trainer</span>
+
+<span class="n">model</span> <span class="o">=</span> <span class="n">LightningTemplate</span><span class="p">()</span>
+
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">()</span>
+<span class="n">trainer</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
+</pre></div>
+</td></tr></table>
+
+<p>But of course the fun is in all the advanced things it can do:</p>
+<p><strong>Checkpointing</strong>    </p>
+<ul>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Checkpointing/#model-saving">Checkpoint callback</a>    </li>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Checkpointing/#model-saving">Model saving</a></li>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/LightningModule/methods/#load-from-metrics">Model loading</a> </li>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Checkpointing/#restoring-training-session">Restoring training session</a></li>
+</ul>
+<p><strong>Computing cluster (SLURM)</strong>    </p>
+<ul>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/SLURM%20Managed%20Cluster#running-grid-search-on-a-cluster">Running grid search on a cluster</a> </li>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/SLURM%20Managed%20Cluster#walltime-auto-resubmit">Walltime auto-resubmit</a>   </li>
+</ul>
+<p><strong>Debugging</strong>  </p>
+<ul>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/debugging/#fast-dev-run">Fast dev run</a></li>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/debugging/#inspect-gradient-norms">Inspect gradient norms</a></li>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/debugging/#Log-gpu-usage">Log GPU usage</a></li>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/debugging/#make-model-overfit-on-subset-of-data">Make model overfit on subset of data</a></li>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/debugging/#print-the-parameter-count-by-layer">Print the parameter count by layer</a></li>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/debugging/#print-which-gradients-are-nan">Print which gradients are nan</a></li>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/LightningModule/properties/#example_input_array">Print input and output size of every module in system</a></li>
+</ul>
+<p><strong>Distributed training</strong>    </p>
+<ul>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/hooks/#init_ddp_connection">Implement Your Own Distributed (DDP) training</a></li>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Distributed%20training/#16-bit-mixed-precision">16-bit mixed precision</a></li>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Distributed%20training/#Multi-GPU">Multi-GPU</a></li>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Distributed%20training/#Multi-node">Multi-node</a></li>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Distributed%20training/#single-gpu">Single GPU</a></li>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Distributed%20training/#self-balancing-architecture">Self-balancing architecture</a></li>
+</ul>
+<p><strong>Experiment Logging</strong>   </p>
+<ul>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#display-metrics-in-progress-bar">Display metrics in progress bar</a></li>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#log-metric-row-every-k-batches">Log metric row every k batches</a></li>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#process-position">Process position</a></li>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#tensorboard-support">Tensorboard support</a></li>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#save-a-snapshot-of-all-hyperparameters">Save a snapshot of all hyperparameters</a> </li>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#snapshot-code-for-a-training-run">Snapshot code for a training run</a> </li>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#write-logs-file-to-csv-every-k-batches">Write logs file to csv every k batches</a></li>
+</ul>
+<p><strong>Training loop</strong>    </p>
+<ul>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#accumulated-gradients">Accumulate gradients</a>    </li>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#force-training-for-min-or-max-epochs">Force training for min or max epochs</a></li>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#early-stopping">Early stopping callback</a>    </li>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#force-disable-early-stop">Force disable early stop</a></li>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#gradient-clipping">Gradient Clipping</a></li>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/hooks/">Hooks</a></li>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/LightningModule/RequiredTrainerInterface/#configure_optimizers">Learning rate scheduling</a></li>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/LightningModule/RequiredTrainerInterface/#configure_optimizers">Use multiple optimizers (like GANs)</a></li>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#set-how-much-of-the-training-set-to-check">Set how much of the training set to check (1-100%)</a></li>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/hooks/#optimizer_step">Step optimizers at arbitrary intervals</a></li>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#packed-sequences-as-inputs">Packed sequences</a></li>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning//Training%20Loop/#truncated-back-propation-through-time">Truncated Back Propagation Through Time</a></li>
+</ul>
+<p><strong>Validation loop</strong>    </p>
+<ul>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Validation%20loop/#check-validation-every-n-epochs">Check validation every n epochs</a></li>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/hooks/">Hooks</a></li>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Validation%20loop/#set-how-much-of-the-validation-set-to-check">Set how much of the validation set to check</a></li>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Validation%20loop/#set-how-much-of-the-test-set-to-check">Set how much of the test set to check</a></li>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Validation%20loop/#set-validation-check-frequency-within-1-training-epoch">Set validation check frequency within 1 training epoch</a></li>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Validation%20loop/#set-the-number-of-validation-sanity-steps">Set the number of validation sanity steps</a></li>
+</ul>
+<p><strong>Testing loop</strong>    </p>
+<ul>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Testing%20loop/">Run test set</a>  </li>
+</ul>
+                
+                  
+                
+              
+              
+                
+
+
+              
+            </article>
+          </div>
+        </div>
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@@ -1,68 +0,0 @@
-# https://www.appveyor.com/docs/appveyor-yml/
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-
-  # SDK v7.0 MSVC Express 2008's SetEnv.cmd script will fail if the
-  # /E:ON and /V:ON options are not enabled in the batch script interpreter
-  # See: http://stackoverflow.com/a/13751649/163740
-  CMD_IN_ENV: "cmd /E:ON /V:ON /C obvci_appveyor_python_build_env.cmd"
-
-  matrix:
-    # Pre-installed Python versions, which Appveyor may upgrade to
-    # a later point release.
-    # See: http://www.appveyor.com/docs/installed-software#python
-
-
-    # - PYTHON: "C:\\Python35-x64"
-    #   PYTHON_VERSION: "3.5.x"
-    #   PYTHON_ARCH: "64"
-    #  TOXENV: "py35"
-
-    - PYTHON: "C:\\Python36-x64"
-      PYTHON_VERSION: "3.6.x"
-      PYTHON_ARCH: "64"
-      TOXENV: "py36"
-      PIP_PYVER: "36"
-
-    - PYTHON: "C:\\Python37-x64"
-      PYTHON_VERSION: "3.7.x"
-      PYTHON_ARCH: "64"
-      TOXENV: "py37"
-      PIP_PYVER: "37"
-
-build: off
-
-# https://www.appveyor.com/docs/build-cache/
-cache:
-  - C:\ProgramData\chocolatey\bin -> appveyor.yml
-  - C:\ProgramData\chocolatey\lib -> appveyor.yml
-  - '%LOCALAPPDATA%\pip\Cache -> appveyor.yml'
-
-# scripts that run after cloning repository
-install:
-  # If there is a newer build queued for the same PR, cancel this one.
-  # The AppVeyor 'rollout builds' option is supposed to serve the same
-  # purpose but it is problematic because it tends to cancel builds pushed
-  # directly to master instead of just PR builds (or the converse).
-  - SET PATH=%PYTHON%;%PYTHON%\\Scripts;%path%
-  #- pip install -U --user "pip<19.3"
-  - python -m pip install -r requirements.txt -f https://download.pytorch.org/whl/torch_stable.html
-  - python -m pip install -r ./tests/requirements.txt
-  - python -m pip install pytest-flake8
-
-# scripts to run before tests (working directory and environment changes
-# are persisted from the previous steps such as "before_build")
-before_test:
-  - python --version
-  - pip --version
-  - pip list
-  - dir
-
-# to run your custom scripts instead of automatic tests
-test_script:
-  - coverage run --source pytorch_lightning -m py.test pytorch_lightning tests pl_examples -v --doctest-modules --flake8
-  #- python setup.py sdist
-  #- twine check dist/*
-
-on_success:
-  - coverage report
-  # - codecov
@@ -0,0 +1,13 @@
+/*!
+ * Licensed under the Apache License, Version 2.0 (the "License"); you may not
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+ *
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+!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");var r,m,i;e.da=function(){this.pipeline.reset(),this.pipeline.add(e.da.trimmer,e.da.stopWordFilter,e.da.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.da.stemmer))},e.da.wordCharacters="A-Za-zªºÀ-ÖØ-öø-ʸˠ-ˤᴀ-ᴥᴬ-ᵜᵢ-ᵥᵫ-ᵷᵹ-ᶾḀ-ỿⁱⁿₐ-ₜKÅℲⅎⅠ-ↈⱠ-ⱿꜢ-ꞇꞋ-ꞭꞰ-ꞷꟷ-ꟿꬰ-ꭚꭜ-ꭤﬀ-ﬆＡ-Ｚａ-ｚ",e.da.trimmer=e.trimmerSupport.generateTrimmer(e.da.wordCharacters),e.Pipeline.registerFunction(e.da.trimmer,"trimmer-da"),e.da.stemmer=(r=e.stemmerSupport.Among,m=e.stemmerSupport.SnowballProgram,i=new function(){var i,t,n,s=[new r("hed",-1,1),new r("ethed",0,1),new r("ered",-1,1),new r("e",-1,1),new r("erede",3,1),new r("ende",3,1),new r("erende",5,1),new r("ene",3,1),new r("erne",3,1),new r("ere",3,1),new r("en",-1,1),new r("heden",10,1),new r("eren",10,1),new r("er",-1,1),new r("heder",13,1),new r("erer",13,1),new r("s",-1,2),new r("heds",16,1),new r("es",16,1),new r("endes",18,1),new r("erendes",19,1),new r("enes",18,1),new r("ernes",18,1),new r("eres",18,1),new r("ens",16,1),new r("hedens",24,1),new r("erens",24,1),new r("ers",16,1),new r("ets",16,1),new r("erets",28,1),new r("et",-1,1),new r("eret",30,1)],o=[new r("gd",-1,-1),new r("dt",-1,-1),new r("gt",-1,-1),new r("kt",-1,-1)],a=[new r("ig",-1,1),new r("lig",0,1),new r("elig",1,1),new r("els",-1,1),new r("løst",-1,2)],d=[17,65,16,1,0,0,0,0,0,0,0,0,0,0,0,0,48,0,128],u=[239,254,42,3,0,0,0,0,0,0,0,0,0,0,0,0,16],c=new m;function l(){var e,r=c.limit-c.cursor;c.cursor>=t&&(e=c.limit_backward,c.limit_backward=t,c.ket=c.cursor,c.find_among_b(o,4)?(c.bra=c.cursor,c.limit_backward=e,c.cursor=c.limit-r,c.cursor>c.limit_backward&&(c.cursor--,c.bra=c.cursor,c.slice_del())):c.limit_backward=e)}this.setCurrent=function(e){c.setCurrent(e)},this.getCurrent=function(){return c.getCurrent()},this.stem=function(){var e,r=c.cursor;return function(){var e,r=c.cursor+3;if(t=c.limit,0<=r&&r<=c.limit){for(i=r;;){if(e=c.cursor,c.in_grouping(d,97,248)){c.cursor=e;break}if((c.cursor=e)>=c.limit)return;c.cursor++}for(;!c.out_grouping(d,97,248);){if(c.cursor>=c.limit)return;c.cursor++}(t=c.cursor)<i&&(t=i)}}(),c.limit_backward=r,c.cursor=c.limit,function(){var e,r;if(c.cursor>=t&&(r=c.limit_backward,c.limit_backward=t,c.ket=c.cursor,e=c.find_among_b(s,32),c.limit_backward=r,e))switch(c.bra=c.cursor,e){case 1:c.slice_del();break;case 2:c.in_grouping_b(u,97,229)&&c.slice_del()}}(),c.cursor=c.limit,l(),c.cursor=c.limit,function(){var e,r,i,n=c.limit-c.cursor;if(c.ket=c.cursor,c.eq_s_b(2,"st")&&(c.bra=c.cursor,c.eq_s_b(2,"ig")&&c.slice_del()),c.cursor=c.limit-n,c.cursor>=t&&(r=c.limit_backward,c.limit_backward=t,c.ket=c.cursor,e=c.find_among_b(a,5),c.limit_backward=r,e))switch(c.bra=c.cursor,e){case 1:c.slice_del(),i=c.limit-c.cursor,l(),c.cursor=c.limit-i;break;case 2:c.slice_from("løs")}}(),c.cursor=c.limit,c.cursor>=t&&(e=c.limit_backward,c.limit_backward=t,c.ket=c.cursor,c.out_grouping_b(d,97,248)?(c.bra=c.cursor,n=c.slice_to(n),c.limit_backward=e,c.eq_v_b(n)&&c.slice_del()):c.limit_backward=e),!0}},function(e){return"function"==typeof e.update?e.update(function(e){return i.setCurrent(e),i.stem(),i.getCurrent()}):(i.setCurrent(e),i.stem(),i.getCurrent())}),e.Pipeline.registerFunction(e.da.stemmer,"stemmer-da"),e.da.stopWordFilter=e.generateStopWordFilter("ad af alle alt anden at blev blive bliver da de dem den denne der deres det dette dig din disse dog du efter eller en end er et for fra ham han hans har havde have hende hendes her hos hun hvad hvis hvor i ikke ind jeg jer jo kunne man mange med meget men mig min mine mit mod ned noget nogle nu når og også om op os over på selv sig sin sine sit skal skulle som sådan thi til ud under var vi vil ville vor være været".split(" ")),e.Pipeline.registerFunction(e.da.stopWordFilter,"stopWordFilter-da")}});
@@ -0,0 +1 @@
+!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(m){if(void 0===m)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===m.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");var l="2"==m.version[0];m.ja=function(){this.pipeline.reset(),this.pipeline.add(m.ja.trimmer,m.ja.stopWordFilter,m.ja.stemmer),l?this.tokenizer=m.ja.tokenizer:(m.tokenizer&&(m.tokenizer=m.ja.tokenizer),this.tokenizerFn&&(this.tokenizerFn=m.ja.tokenizer))};var j=new m.TinySegmenter;m.ja.tokenizer=function(e){var r,t,i,n,o,s,p,a,u;if(!arguments.length||null==e||null==e)return[];if(Array.isArray(e))return e.map(function(e){return l?new m.Token(e.toLowerCase()):e.toLowerCase()});for(r=(t=e.toString().toLowerCase().replace(/^\s+/,"")).length-1;0<=r;r--)if(/\S/.test(t.charAt(r))){t=t.substring(0,r+1);break}for(o=[],i=t.length,p=a=0;a<=i;a++)if(s=a-p,t.charAt(a).match(/\s/)||a==i){if(0<s)for(n=j.segment(t.slice(p,a)).filter(function(e){return!!e}),u=p,r=0;r<n.length;r++)l?o.push(new m.Token(n[r],{position:[u,n[r].length],index:o.length})):o.push(n[r]),u+=n[r].length;p=a+1}return o},m.ja.stemmer=function(e){return e},m.Pipeline.registerFunction(m.ja.stemmer,"stemmer-ja"),m.ja.wordCharacters="一二三四五六七八九十百千万億兆一-龠々〆ヵヶぁ-んァ-ヴーｱ-ﾝﾞa-zA-Zａ-ｚＡ-Ｚ0-9０-９",m.ja.trimmer=m.trimmerSupport.generateTrimmer(m.ja.wordCharacters),m.Pipeline.registerFunction(m.ja.trimmer,"trimmer-ja"),m.ja.stopWordFilter=m.generateStopWordFilter("これ それ あれ この その あの ここ そこ あそこ こちら どこ だれ なに なん 何 私 貴方 貴方方 我々 私達 あの人 あのかた 彼女 彼 です あります おります います は が の に を で え から まで より も どの と し それで しかし".split(" ")),m.Pipeline.registerFunction(m.ja.stopWordFilter,"stopWordFilter-ja"),m.jp=m.ja,m.Pipeline.registerFunction(m.jp.stemmer,"stemmer-jp"),m.Pipeline.registerFunction(m.jp.trimmer,"trimmer-jp"),m.Pipeline.registerFunction(m.jp.stopWordFilter,"stopWordFilter-jp")}});
@@ -0,0 +1 @@
+module.exports=require("./lunr.ja");
@@ -0,0 +1 @@
+!function(e,i){"function"==typeof define&&define.amd?define(i):"object"==typeof exports?module.exports=i():i()(e.lunr)}(this,function(){return function(o){o.multiLanguage=function(){for(var e=Array.prototype.slice.call(arguments),i=e.join("-"),t="",r=[],n=[],s=0;s<e.length;++s)"en"==e[s]?(t+="\\w",r.unshift(o.stopWordFilter),r.push(o.stemmer),n.push(o.stemmer)):(t+=o[e[s]].wordCharacters,r.unshift(o[e[s]].stopWordFilter),r.push(o[e[s]].stemmer),n.push(o[e[s]].stemmer));var p=o.trimmerSupport.generateTrimmer(t);return o.Pipeline.registerFunction(p,"lunr-multi-trimmer-"+i),r.unshift(p),function(){this.pipeline.reset(),this.pipeline.add.apply(this.pipeline,r),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add.apply(this.searchPipeline,n))}}}});
@@ -0,0 +1 @@
+!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");var r,n,i;e.no=function(){this.pipeline.reset(),this.pipeline.add(e.no.trimmer,e.no.stopWordFilter,e.no.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.no.stemmer))},e.no.wordCharacters="A-Za-zªºÀ-ÖØ-öø-ʸˠ-ˤᴀ-ᴥᴬ-ᵜᵢ-ᵥᵫ-ᵷᵹ-ᶾḀ-ỿⁱⁿₐ-ₜKÅℲⅎⅠ-ↈⱠ-ⱿꜢ-ꞇꞋ-ꞭꞰ-ꞷꟷ-ꟿꬰ-ꭚꭜ-ꭤﬀ-ﬆＡ-Ｚａ-ｚ",e.no.trimmer=e.trimmerSupport.generateTrimmer(e.no.wordCharacters),e.Pipeline.registerFunction(e.no.trimmer,"trimmer-no"),e.no.stemmer=(r=e.stemmerSupport.Among,n=e.stemmerSupport.SnowballProgram,i=new function(){var o,s,a=[new r("a",-1,1),new r("e",-1,1),new r("ede",1,1),new r("ande",1,1),new r("ende",1,1),new r("ane",1,1),new r("ene",1,1),new r("hetene",6,1),new r("erte",1,3),new r("en",-1,1),new r("heten",9,1),new r("ar",-1,1),new r("er",-1,1),new r("heter",12,1),new r("s",-1,2),new r("as",14,1),new r("es",14,1),new r("edes",16,1),new r("endes",16,1),new r("enes",16,1),new r("hetenes",19,1),new r("ens",14,1),new r("hetens",21,1),new r("ers",14,1),new r("ets",14,1),new r("et",-1,1),new r("het",25,1),new r("ert",-1,3),new r("ast",-1,1)],m=[new r("dt",-1,-1),new r("vt",-1,-1)],l=[new r("leg",-1,1),new r("eleg",0,1),new r("ig",-1,1),new r("eig",2,1),new r("lig",2,1),new r("elig",4,1),new r("els",-1,1),new r("lov",-1,1),new r("elov",7,1),new r("slov",7,1),new r("hetslov",9,1)],u=[17,65,16,1,0,0,0,0,0,0,0,0,0,0,0,0,48,0,128],d=[119,125,149,1],c=new n;this.setCurrent=function(e){c.setCurrent(e)},this.getCurrent=function(){return c.getCurrent()},this.stem=function(){var e,r,n,i,t=c.cursor;return function(){var e,r=c.cursor+3;if(s=c.limit,0<=r||r<=c.limit){for(o=r;;){if(e=c.cursor,c.in_grouping(u,97,248)){c.cursor=e;break}if(e>=c.limit)return;c.cursor=e+1}for(;!c.out_grouping(u,97,248);){if(c.cursor>=c.limit)return;c.cursor++}(s=c.cursor)<o&&(s=o)}}(),c.limit_backward=t,c.cursor=c.limit,function(){var e,r,n;if(c.cursor>=s&&(r=c.limit_backward,c.limit_backward=s,c.ket=c.cursor,e=c.find_among_b(a,29),c.limit_backward=r,e))switch(c.bra=c.cursor,e){case 1:c.slice_del();break;case 2:n=c.limit-c.cursor,c.in_grouping_b(d,98,122)?c.slice_del():(c.cursor=c.limit-n,c.eq_s_b(1,"k")&&c.out_grouping_b(u,97,248)&&c.slice_del());break;case 3:c.slice_from("er")}}(),c.cursor=c.limit,r=c.limit-c.cursor,c.cursor>=s&&(e=c.limit_backward,c.limit_backward=s,c.ket=c.cursor,c.find_among_b(m,2)?(c.bra=c.cursor,c.limit_backward=e,c.cursor=c.limit-r,c.cursor>c.limit_backward&&(c.cursor--,c.bra=c.cursor,c.slice_del())):c.limit_backward=e),c.cursor=c.limit,c.cursor>=s&&(i=c.limit_backward,c.limit_backward=s,c.ket=c.cursor,(n=c.find_among_b(l,11))?(c.bra=c.cursor,c.limit_backward=i,1==n&&c.slice_del()):c.limit_backward=i),!0}},function(e){return"function"==typeof e.update?e.update(function(e){return i.setCurrent(e),i.stem(),i.getCurrent()}):(i.setCurrent(e),i.stem(),i.getCurrent())}),e.Pipeline.registerFunction(e.no.stemmer,"stemmer-no"),e.no.stopWordFilter=e.generateStopWordFilter("alle at av bare begge ble blei bli blir blitt både båe da de deg dei deim deira deires dem den denne der dere deres det dette di din disse ditt du dykk dykkar då eg ein eit eitt eller elles en enn er et ett etter for fordi fra før ha hadde han hans har hennar henne hennes her hjå ho hoe honom hoss hossen hun hva hvem hver hvilke hvilken hvis hvor hvordan hvorfor i ikke ikkje ikkje ingen ingi inkje inn inni ja jeg kan kom korleis korso kun kunne kva kvar kvarhelst kven kvi kvifor man mange me med medan meg meget mellom men mi min mine mitt mot mykje ned no noe noen noka noko nokon nokor nokre nå når og også om opp oss over på samme seg selv si si sia sidan siden sin sine sitt sjøl skal skulle slik so som som somme somt så sånn til um upp ut uten var vart varte ved vere verte vi vil ville vore vors vort vår være være vært å".split(" ")),e.Pipeline.registerFunction(e.no.stopWordFilter,"stopWordFilter-no")}});
@@ -0,0 +1 @@
+!function(r,t){"function"==typeof define&&define.amd?define(t):"object"==typeof exports?module.exports=t():t()(r.lunr)}(this,function(){return function(r){r.stemmerSupport={Among:function(r,t,i,s){if(this.toCharArray=function(r){for(var t=r.length,i=new Array(t),s=0;s<t;s++)i[s]=r.charCodeAt(s);return i},!r&&""!=r||!t&&0!=t||!i)throw"Bad Among initialisation: s:"+r+", substring_i: "+t+", result: "+i;this.s_size=r.length,this.s=this.toCharArray(r),this.substring_i=t,this.result=i,this.method=s},SnowballProgram:function(){var b;return{bra:0,ket:0,limit:0,cursor:0,limit_backward:0,setCurrent:function(r){b=r,this.cursor=0,this.limit=r.length,this.limit_backward=0,this.bra=this.cursor,this.ket=this.limit},getCurrent:function(){var r=b;return b=null,r},in_grouping:function(r,t,i){if(this.cursor<this.limit){var s=b.charCodeAt(this.cursor);if(s<=i&&t<=s&&r[(s-=t)>>3]&1<<(7&s))return this.cursor++,!0}return!1},in_grouping_b:function(r,t,i){if(this.cursor>this.limit_backward){var s=b.charCodeAt(this.cursor-1);if(s<=i&&t<=s&&r[(s-=t)>>3]&1<<(7&s))return this.cursor--,!0}return!1},out_grouping:function(r,t,i){if(this.cursor<this.limit){var s=b.charCodeAt(this.cursor);if(i<s||s<t)return this.cursor++,!0;if(!(r[(s-=t)>>3]&1<<(7&s)))return this.cursor++,!0}return!1},out_grouping_b:function(r,t,i){if(this.cursor>this.limit_backward){var s=b.charCodeAt(this.cursor-1);if(i<s||s<t)return this.cursor--,!0;if(!(r[(s-=t)>>3]&1<<(7&s)))return this.cursor--,!0}return!1},eq_s:function(r,t){if(this.limit-this.cursor<r)return!1;for(var i=0;i<r;i++)if(b.charCodeAt(this.cursor+i)!=t.charCodeAt(i))return!1;return this.cursor+=r,!0},eq_s_b:function(r,t){if(this.cursor-this.limit_backward<r)return!1;for(var i=0;i<r;i++)if(b.charCodeAt(this.cursor-r+i)!=t.charCodeAt(i))return!1;return this.cursor-=r,!0},find_among:function(r,t){for(var i=0,s=t,e=this.cursor,n=this.limit,u=0,o=0,h=!1;;){for(var c=i+(s-i>>1),a=0,f=u<o?u:o,l=r[c],_=f;_<l.s_size;_++){if(e+f==n){a=-1;break}if(a=b.charCodeAt(e+f)-l.s[_])break;f++}if(a<0?(s=c,o=f):(i=c,u=f),s-i<=1){if(0<i||s==i||h)break;h=!0}}for(;;){if(u>=(l=r[i]).s_size){if(this.cursor=e+l.s_size,!l.method)return l.result;var m=l.method();if(this.cursor=e+l.s_size,m)return l.result}if((i=l.substring_i)<0)return 0}},find_among_b:function(r,t){for(var i=0,s=t,e=this.cursor,n=this.limit_backward,u=0,o=0,h=!1;;){for(var c=i+(s-i>>1),a=0,f=u<o?u:o,l=(_=r[c]).s_size-1-f;0<=l;l--){if(e-f==n){a=-1;break}if(a=b.charCodeAt(e-1-f)-_.s[l])break;f++}if(a<0?(s=c,o=f):(i=c,u=f),s-i<=1){if(0<i||s==i||h)break;h=!0}}for(;;){var _;if(u>=(_=r[i]).s_size){if(this.cursor=e-_.s_size,!_.method)return _.result;var m=_.method();if(this.cursor=e-_.s_size,m)return _.result}if((i=_.substring_i)<0)return 0}},replace_s:function(r,t,i){var s=i.length-(t-r);return b=b.substring(0,r)+i+b.substring(t),this.limit+=s,this.cursor>=t?this.cursor+=s:this.cursor>r&&(this.cursor=r),s},slice_check:function(){if(this.bra<0||this.bra>this.ket||this.ket>this.limit||this.limit>b.length)throw"faulty slice operation"},slice_from:function(r){this.slice_check(),this.replace_s(this.bra,this.ket,r)},slice_del:function(){this.slice_from("")},insert:function(r,t,i){var s=this.replace_s(r,t,i);r<=this.bra&&(this.bra+=s),r<=this.ket&&(this.ket+=s)},slice_to:function(){return this.slice_check(),b.substring(this.bra,this.ket)},eq_v_b:function(r){return this.eq_s_b(r.length,r)}}}},r.trimmerSupport={generateTrimmer:function(r){var t=new RegExp("^[^"+r+"]+"),i=new RegExp("[^"+r+"]+$");return function(r){return"function"==typeof r.update?r.update(function(r){return r.replace(t,"").replace(i,"")}):r.replace(t,"").replace(i,"")}}}}});
@@ -0,0 +1 @@
+!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");var r,l,n;e.sv=function(){this.pipeline.reset(),this.pipeline.add(e.sv.trimmer,e.sv.stopWordFilter,e.sv.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.sv.stemmer))},e.sv.wordCharacters="A-Za-zªºÀ-ÖØ-öø-ʸˠ-ˤᴀ-ᴥᴬ-ᵜᵢ-ᵥᵫ-ᵷᵹ-ᶾḀ-ỿⁱⁿₐ-ₜKÅℲⅎⅠ-ↈⱠ-ⱿꜢ-ꞇꞋ-ꞭꞰ-ꞷꟷ-ꟿꬰ-ꭚꭜ-ꭤﬀ-ﬆＡ-Ｚａ-ｚ",e.sv.trimmer=e.trimmerSupport.generateTrimmer(e.sv.wordCharacters),e.Pipeline.registerFunction(e.sv.trimmer,"trimmer-sv"),e.sv.stemmer=(r=e.stemmerSupport.Among,l=e.stemmerSupport.SnowballProgram,n=new function(){var n,t,i=[new r("a",-1,1),new r("arna",0,1),new r("erna",0,1),new r("heterna",2,1),new r("orna",0,1),new r("ad",-1,1),new r("e",-1,1),new r("ade",6,1),new r("ande",6,1),new r("arne",6,1),new r("are",6,1),new r("aste",6,1),new r("en",-1,1),new r("anden",12,1),new r("aren",12,1),new r("heten",12,1),new r("ern",-1,1),new r("ar",-1,1),new r("er",-1,1),new r("heter",18,1),new r("or",-1,1),new r("s",-1,2),new r("as",21,1),new r("arnas",22,1),new r("ernas",22,1),new r("ornas",22,1),new r("es",21,1),new r("ades",26,1),new r("andes",26,1),new r("ens",21,1),new r("arens",29,1),new r("hetens",29,1),new r("erns",21,1),new r("at",-1,1),new r("andet",-1,1),new r("het",-1,1),new r("ast",-1,1)],s=[new r("dd",-1,-1),new r("gd",-1,-1),new r("nn",-1,-1),new r("dt",-1,-1),new r("gt",-1,-1),new r("kt",-1,-1),new r("tt",-1,-1)],a=[new r("ig",-1,1),new r("lig",0,1),new r("els",-1,1),new r("fullt",-1,3),new r("löst",-1,2)],o=[17,65,16,1,0,0,0,0,0,0,0,0,0,0,0,0,24,0,32],u=[119,127,149],m=new l;this.setCurrent=function(e){m.setCurrent(e)},this.getCurrent=function(){return m.getCurrent()},this.stem=function(){var e,r=m.cursor;return function(){var e,r=m.cursor+3;if(t=m.limit,0<=r||r<=m.limit){for(n=r;;){if(e=m.cursor,m.in_grouping(o,97,246)){m.cursor=e;break}if(m.cursor=e,m.cursor>=m.limit)return;m.cursor++}for(;!m.out_grouping(o,97,246);){if(m.cursor>=m.limit)return;m.cursor++}(t=m.cursor)<n&&(t=n)}}(),m.limit_backward=r,m.cursor=m.limit,function(){var e,r=m.limit_backward;if(m.cursor>=t&&(m.limit_backward=t,m.cursor=m.limit,m.ket=m.cursor,e=m.find_among_b(i,37),m.limit_backward=r,e))switch(m.bra=m.cursor,e){case 1:m.slice_del();break;case 2:m.in_grouping_b(u,98,121)&&m.slice_del()}}(),m.cursor=m.limit,e=m.limit_backward,m.cursor>=t&&(m.limit_backward=t,m.cursor=m.limit,m.find_among_b(s,7)&&(m.cursor=m.limit,m.ket=m.cursor,m.cursor>m.limit_backward&&(m.bra=--m.cursor,m.slice_del())),m.limit_backward=e),m.cursor=m.limit,function(){var e,r;if(m.cursor>=t){if(r=m.limit_backward,m.limit_backward=t,m.cursor=m.limit,m.ket=m.cursor,e=m.find_among_b(a,5))switch(m.bra=m.cursor,e){case 1:m.slice_del();break;case 2:m.slice_from("lös");break;case 3:m.slice_from("full")}m.limit_backward=r}}(),!0}},function(e){return"function"==typeof e.update?e.update(function(e){return n.setCurrent(e),n.stem(),n.getCurrent()}):(n.setCurrent(e),n.stem(),n.getCurrent())}),e.Pipeline.registerFunction(e.sv.stemmer,"stemmer-sv"),e.sv.stopWordFilter=e.generateStopWordFilter("alla allt att av blev bli blir blivit de dem den denna deras dess dessa det detta dig din dina ditt du där då efter ej eller en er era ert ett från för ha hade han hans har henne hennes hon honom hur här i icke ingen inom inte jag ju kan kunde man med mellan men mig min mina mitt mot mycket ni nu när någon något några och om oss på samma sedan sig sin sina sitta själv skulle som så sådan sådana sådant till under upp ut utan vad var vara varför varit varje vars vart vem vi vid vilka vilkas vilken vilket vår våra vårt än är åt över".split(" ")),e.Pipeline.registerFunction(e.sv.stopWordFilter,"stopWordFilter-sv")}});
@@ -0,0 +1 @@
+!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(t){if(void 0===t)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===t.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");var i="2"==t.version[0];t.th=function(){this.pipeline.reset(),this.pipeline.add(t.th.trimmer),i?this.tokenizer=t.th.tokenizer:(t.tokenizer&&(t.tokenizer=t.th.tokenizer),this.tokenizerFn&&(this.tokenizerFn=t.th.tokenizer))},t.th.wordCharacters="[฀-๿]",t.th.trimmer=t.trimmerSupport.generateTrimmer(t.th.wordCharacters),t.Pipeline.registerFunction(t.th.trimmer,"trimmer-th");var n=t.wordcut;n.init(),t.th.tokenizer=function(e){if(!arguments.length||null==e||null==e)return[];if(Array.isArray(e))return e.map(function(e){return i?new t.Token(e):e});var r=e.toString().replace(/^\s+/,"");return n.cut(r).split("|")}}});
@@ -1,19 +0,0 @@
-# Minimal makefile for Sphinx documentation
-#
-
-# You can set these variables from the command line.
-SPHINXOPTS    =
-SPHINXBUILD   = sphinx-build
-SOURCEDIR     = source
-BUILDDIR      = build
-
-# Put it first so that "make" without argument is like "make help".
-help:
-	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
-
-.PHONY: help Makefile
-
-# Catch-all target: route all unknown targets to Sphinx using the new
-# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
-%: Makefile
-	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
@@ -1,35 +0,0 @@
-@ECHO OFF
-
-pushd %~dp0
-
-REM Command file for Sphinx documentation
-
-if "%SPHINXBUILD%" == "" (
-	set SPHINXBUILD=sphinx-build
-)
-set SOURCEDIR=source
-set BUILDDIR=build
-
-if "%1" == "" goto help
-
-%SPHINXBUILD% >NUL 2>NUL
-if errorlevel 9009 (
-	echo.
-	echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
-	echo.installed, then set the SPHINXBUILD environment variable to point
-	echo.to the full path of the 'sphinx-build' executable. Alternatively you
-	echo.may add the Sphinx directory to PATH.
-	echo.
-	echo.If you don't have Sphinx installed, grab it from
-	echo.http://sphinx-doc.org/
-	exit /b 1
-)
-
-%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS%
-goto end
-
-:help
-%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS%
-
-:end
-popd
@@ -1,9 +0,0 @@
-sphinx>=1.8.3
-recommonmark  # fails with badges
-m2r  # fails with multi-line text
-nbsphinx
-pandoc
-docutils
-git+https://github.com/Borda/lightning_sphinx_theme.git
-sphinxcontrib-fulltoc
-sphinxcontrib-mockautodoc
@@ -1,17 +0,0 @@
-{%- set external_urls = {
-  'github': 'https://github.com/williamFalcon/pytorch-lightning',
-  'github_issues': 'https://github.com/williamFalcon/pytorch-lightning/issues',
-  'contributing': 'https://github.com/williamFalcon/pytorch-lightning/blob/master/CONTRIBUTING.md',
-  'docs': 'https://williamfalcon.github.io/pytorch-lightning',
-  'twitter': 'https://twitter.com/PyTorchLightnin',
-  'discuss': 'https://discuss.pytorch.org',
-  'tutorials': 'https://williamfalcon.github.io/pytorch-lightning/',
-  'previous_pytorch_versions': 'https://williamfalcon.github.io/pytorch-lightning/',
-  'home': 'https://williamfalcon.github.io/pytorch-lightning/',
-  'get_started': 'https://williamfalcon.github.io/pytorch-lightning/',
-  'features': 'https://williamfalcon.github.io/pytorch-lightning/',
-  'blog': 'https://williamfalcon.github.io/pytorch-lightning/',
-  'resources': 'https://williamfalcon.github.io/pytorch-lightning/',
-  'support': 'https://williamfalcon.github.io/pytorch-lightning/',
-}
-%}
@@ -1,357 +0,0 @@
-# -*- coding: utf-8 -*-
-#
-# Configuration file for the Sphinx documentation builder.
-#
-# This file does only contain a selection of the most common options. For a
-# full list see the documentation:
-# http://www.sphinx-doc.org/en/master/config
-
-# -- Path setup --------------------------------------------------------------
-
-# If extensions (or modules to document with autodoc) are in another directory,
-# add these directories to sys.path here. If the directory is relative to the
-# documentation root, use os.path.abspath to make it absolute, like shown here.
-
-import os
-import sys
-import glob
-import shutil
-import inspect
-
-# import m2r
-import builtins
-import pt_lightning_sphinx_theme
-
-PATH_HERE = os.path.abspath(os.path.dirname(__file__))
-PATH_ROOT = os.path.join(PATH_HERE, '..', '..')
-sys.path.insert(0, os.path.abspath(PATH_ROOT))
-
-builtins.__LIGHTNING_SETUP__ = True
-
-import pytorch_lightning  # noqa: E402
-
-# -- Project documents -------------------------------------------------------
-
-# # export the documentation
-# with open('intro.rst', 'w') as fp:
-#     intro = pytorch_lightning.__doc__.replace(os.linesep + ' ', '')
-#     fp.write(m2r.convert(intro))
-#     # fp.write(pytorch_lightning.__doc__)
-
-# # export the READme
-# with open(os.path.join(PATH_ROOT, 'README.md'), 'r') as fp:
-#     readme = fp.read()
-# # replace all paths to relative
-# for ndir in (os.path.basename(p) for p in glob.glob(os.path.join(PATH_ROOT, '*'))
-#              if os.path.isdir(p)):
-#     readme = readme.replace('](%s/' % ndir, '](%s/%s/' % (PATH_ROOT, ndir))
-# with open('readme.md', 'w') as fp:
-#     fp.write(readme)
-
-for md in glob.glob(os.path.join(PATH_ROOT, '.github', '*.md')):
-    shutil.copy(md, os.path.join(PATH_HERE, os.path.basename(md)))
-
-# -- Project information -----------------------------------------------------
-
-project = 'PyTorch-Lightning'
-copyright = pytorch_lightning.__copyright__
-author = pytorch_lightning.__author__
-
-# The short X.Y version
-version = pytorch_lightning.__version__
-# The full version, including alpha/beta/rc tags
-release = pytorch_lightning.__version__
-
-
-# -- General configuration ---------------------------------------------------
-
-# If your documentation needs a minimal Sphinx version, state it here.
-
-needs_sphinx = '1.4'
-
-# Add any Sphinx extension module names here, as strings. They can be
-# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
-# ones.
-extensions = [
-    'sphinx.ext.autodoc',
-    'sphinxcontrib.mockautodoc',
-    # 'sphinxcontrib.fulltoc',  # breaks pytorch-theme with unexpected kw argument 'titles_only'
-    'sphinx.ext.doctest',
-    'sphinx.ext.intersphinx',
-    'sphinx.ext.todo',
-    'sphinx.ext.coverage',
-    'sphinx.ext.linkcode',
-    'sphinx.ext.autosummary',
-    'sphinx.ext.napoleon',
-    'recommonmark',
-    # 'm2r',
-    'nbsphinx',
-]
-
-# Add any paths that contain templates here, relative to this directory.
-templates_path = ['_templates']
-
-# https://berkeley-stat159-f17.github.io/stat159-f17/lectures/14-sphinx..html#conf.py-(cont.)
-# https://stackoverflow.com/questions/38526888/embed-ipython-notebook-in-sphinx-document
-# I execute the notebooks manually in advance. If notebooks test the code,
-# they should be run at build time.
-nbsphinx_execute = 'never'
-nbsphinx_allow_errors = True
-
-# The suffix(es) of source filenames.
-# You can specify multiple suffix as a list of string:
-#
-# source_suffix = ['.rst', '.md']
-# source_suffix = ['.rst', '.md', '.ipynb']
-source_suffix = {
-    '.rst': 'restructuredtext',
-    '.txt': 'markdown',
-    '.md': 'markdown',
-    '.ipynb': 'nbsphinx',
-}
-
-# The master toctree document.
-master_doc = 'index'
-
-# The language for content autogenerated by Sphinx. Refer to documentation
-# for a list of supported languages.
-#
-# This is also used if you do content translation via gettext catalogs.
-# Usually you set "language" from the command line for these cases.
-language = None
-
-# List of patterns, relative to source directory, that match files and
-# directories to ignore when looking for source files.
-# This pattern also affects html_static_path and html_extra_path.
-exclude_patterns = ['*.test_*']
-
-# The name of the Pygments (syntax highlighting) style to use.
-pygments_style = None
-
-
-# -- Options for HTML output -------------------------------------------------
-
-# The theme to use for HTML and HTML Help pages.  See the documentation for
-# a list of builtin themes.
-# http://www.sphinx-doc.org/en/master/usage/theming.html#builtin-themes
-# html_theme = 'bizstyle'
-# https://sphinx-themes.org
-html_theme = 'pt_lightning_sphinx_theme'
-html_theme_path = [pt_lightning_sphinx_theme.get_html_theme_path()]
-
-# Theme options are theme-specific and customize the look and feel of a theme
-# further.  For a list of options available for each theme, see the
-# documentation.
-
-html_theme_options = {
-    'pytorch_project': pytorch_lightning.__homepage__,
-    'canonical_url': pytorch_lightning.__homepage__,
-    'collapse_navigation': False,
-    'display_version': True,
-    'logo_only': False,
-}
-
-html_logo = '_static/images/lightning_logo_small.png'
-
-# Add any paths that contain custom static files (such as style sheets) here,
-# relative to this directory. They are copied after the builtin static files,
-# so a file named "default.css" will overwrite the builtin "default.css".
-html_static_path = ['_static']
-
-# Custom sidebar templates, must be a dictionary that maps document names
-# to template names.
-#
-# The default sidebars (for documents that don't match any pattern) are
-# defined by theme itself.  Builtin themes are using these templates by
-# default: ``['localtoc.html', 'relations.html', 'sourcelink.html',
-# 'searchbox.html']``.
-#
-# html_sidebars = {}
-
-
-# -- Options for HTMLHelp output ---------------------------------------------
-
-# Output file base name for HTML help builder.
-htmlhelp_basename = project + '-doc'
-
-
-# -- Options for LaTeX output ------------------------------------------------
-
-latex_elements = {
-    # The paper size ('letterpaper' or 'a4paper').
-    # 'papersize': 'letterpaper',
-
-    # The font size ('10pt', '11pt' or '12pt').
-    # 'pointsize': '10pt',
-
-    # Additional stuff for the LaTeX preamble.
-    # 'preamble': '',
-
-    # Latex figure (float) alignment
-    'figure_align': 'htbp',
-}
-
-# Grouping the document tree into LaTeX files. List of tuples
-# (source start file, target name, title,
-#  author, documentclass [howto, manual, or own class]).
-latex_documents = [
-    (master_doc, project + '.tex', project + ' Documentation', author, 'manual'),
-]
-
-
-# -- Options for manual page output ------------------------------------------
-
-# One entry per manual page. List of tuples
-# (source start file, name, description, authors, manual section).
-man_pages = [
-    (master_doc, project, project + ' Documentation', [author], 1)
-]
-
-
-# -- Options for Texinfo output ----------------------------------------------
-
-# Grouping the document tree into Texinfo files. List of tuples
-# (source start file, target name, title, author,
-#  dir menu entry, description, category)
-texinfo_documents = [
-    (master_doc, project, project + ' Documentation', author, project,
-     'One line description of project.', 'Miscellaneous'),
-]
-
-
-# -- Options for Epub output -------------------------------------------------
-
-# Bibliographic Dublin Core info.
-epub_title = project
-
-# The unique identifier of the text. This can be a ISBN number
-# or the project homepage.
-#
-# epub_identifier = ''
-
-# A unique identification for the text.
-#
-# epub_uid = ''
-
-# A list of files that should not be packed into the epub file.
-epub_exclude_files = ['search.html']
-
-
-# -- Extension configuration -------------------------------------------------
-
-# -- Options for intersphinx extension ---------------------------------------
-
-# Example configuration for intersphinx: refer to the Python standard library.
-intersphinx_mapping = {'https://docs.python.org/': None}
-
-# -- Options for todo extension ----------------------------------------------
-
-# If true, `todo` and `todoList` produce output, else they produce nothing.
-todo_include_todos = True
-
-
-# https://github.com/rtfd/readthedocs.org/issues/1139
-# I use sphinx-apidoc to auto-generate API documentation for my project.
-# Right now I have to commit these auto-generated files to my repository
-# so that RTD can build them into HTML docs. It'd be cool if RTD could run
-# sphinx-apidoc for me, since it's easy to forget to regen API docs
-# and commit them to my repo after making changes to my code.
-
-PACKAGES = [
-    pytorch_lightning.__name__,
-    'pl_examples',
-]
-
-
-def run_apidoc(_):
-    for pkg in PACKAGES:
-        argv = ['-e', '-o', PATH_HERE, os.path.join(PATH_HERE, PATH_ROOT, pkg),
-                '**/test_*', '--force', '--private', '--module-first']
-        try:
-            # Sphinx 1.7+
-            from sphinx.ext import apidoc
-            apidoc.main(argv)
-        except ImportError:
-            # Sphinx 1.6 (and earlier)
-            from sphinx import apidoc
-            argv.insert(0, apidoc.__file__)
-            apidoc.main(argv)
-
-
-def setup(app):
-    app.connect('builder-inited', run_apidoc)
-
-
-# copy all notebooks to local folder
-path_nbs = os.path.join(PATH_HERE, 'notebooks')
-if not os.path.isdir(path_nbs):
-    os.mkdir(path_nbs)
-for path_ipynb in glob.glob(os.path.join(PATH_ROOT, 'notebooks', '*.ipynb')):
-    path_ipynb2 = os.path.join(path_nbs, os.path.basename(path_ipynb))
-    shutil.copy(path_ipynb, path_ipynb2)
-
-# Ignoring Third-party packages
-# https://stackoverflow.com/questions/15889621/sphinx-how-to-exclude-imports-in-automodule
-
-MOCK_REQUIRE_PACKAGES = []
-with open(os.path.join(PATH_ROOT, 'requirements.txt'), 'r') as fp:
-    for ln in fp.readlines():
-        found = [ln.index(ch) for ch in list(',=<>#') if ch in ln]
-        pkg = ln[:min(found)] if found else ln
-        if pkg.rstrip():
-            MOCK_REQUIRE_PACKAGES.append(pkg.rstrip())
-
-# TODO: better parse from package since the import name and package name may differ
-MOCK_MANUAL_PACKAGES = ['torch', 'torchvision', 'sklearn', 'test_tube', 'mlflow', 'comet_ml']
-autodoc_mock_imports = MOCK_REQUIRE_PACKAGES + MOCK_MANUAL_PACKAGES
-# for mod_name in MOCK_REQUIRE_PACKAGES:
-#     sys.modules[mod_name] = mock.Mock()
-
-
-# Options for the linkcode extension
-# ----------------------------------
-github_user = 'williamFalcon'
-github_repo = project
-
-
-# Resolve function
-# This function is used to populate the (source) links in the API
-def linkcode_resolve(domain, info):
-    def find_source():
-        # try to find the file and line number, based on code from numpy:
-        # https://github.com/numpy/numpy/blob/master/doc/source/conf.py#L286
-        obj = sys.modules[info['module']]
-        for part in info['fullname'].split('.'):
-            obj = getattr(obj, part)
-        fname = inspect.getsourcefile(obj)
-        # https://github.com/rtfd/readthedocs.org/issues/5735
-        if any([s in fname for s in ('readthedocs', 'checkouts')]):
-            # /home/docs/checkouts/readthedocs.org/user_builds/pytorch_lightning/checkouts/
-            #  devel/pytorch_lightning/utilities/cls_experiment.py#L26-L176
-            path_top = os.path.abspath(os.path.join('..', '..', '..'))
-            fname = os.path.relpath(fname, start=path_top)
-        else:
-            # Local build, imitate master
-            fname = 'master/' + os.path.relpath(fname, start=os.path.abspath('..'))
-        source, lineno = inspect.getsourcelines(obj)
-        return fname, lineno, lineno + len(source) - 1
-
-    if domain != 'py' or not info['module']:
-        return None
-    try:
-        filename = '%s#L%d-L%d' % find_source()
-    except Exception:
-        filename = info['module'].replace('.', '/') + '.py'
-    # import subprocess
-    # tag = subprocess.Popen(['git', 'rev-parse', 'HEAD'], stdout=subprocess.PIPE,
-    #                        universal_newlines=True).communicate()[0][:-1]
-    return "https://github.com/%s/%s/blob/%s" \
-           % (github_user, github_repo, filename)
-
-
-autodoc_member_order = 'groupwise'
-autoclass_content = 'both'
-autodoc_default_flags = [
-    'members', 'undoc-members', 'show-inheritance', 'private-members',
-    # 'special-members', 'inherited-members'
-]
@@ -1,8 +0,0 @@
-Documentation
-=============
-
-
-.. toctree::
-   :maxdepth: 4
-
-   pytorch_lightning
@@ -1,8 +0,0 @@
-Examples & Tutorials
-====================
-
-
-.. toctree::
-   :maxdepth: 3
-
-   pl_examples
@@ -1,40 +0,0 @@
-.. PyTorch-Lightning documentation master file, created by
-   sphinx-quickstart on Fri Nov 15 07:48:22 2019.
-   You can adapt this file completely to your liking, but it should at least
-   contain the root `toctree` directive.
-
-Welcome to PyTorch-Lightning!
-=============================
-
-.. toctree::
-   :maxdepth: 4
-   :name: start
-   :caption: Quick Start
-
-   new-project
-   examples
-
-.. toctree::
-   :maxdepth: 4
-   :name: docs
-   :caption: Docs
-
-   documentation
-
-.. toctree::
-   :maxdepth: 1
-   :name: community
-   :caption: Community
-
-   CODE_OF_CONDUCT.md
-   CONTRIBUTING.md
-   BECOMING_A_CORE_CONTRIBUTOR.md
-
-
-Indices and tables
------------------
-
-* :ref:`genindex`
-* :ref:`modindex`
-* :ref:`search`
-
@@ -1,71 +0,0 @@
-Quick Start
-===========
-To start a new project define two files, a LightningModule and a Trainer file.    
-To illustrate Lightning power and simplicity, here's an example of a typical research flow.    
-
-Case 1: BERT
------------
-
-Let's say you're working on something like BERT but want to try different ways of training or even different networks.  
-You would define a single LightningModule and use flags to switch between your different ideas.
-
-.. code-block:: python
-
-    class BERT(pl.LightningModule):
-        def __init__(self, model_name, task):
-            self.task = task
-
-            if model_name == 'transformer':
-                self.net = Transformer()
-            elif model_name == 'my_cool_version':
-                self.net = MyCoolVersion()
-
-        def training_step(self, batch, batch_idx):
-            if self.task == 'standard_bert':
-                # do standard bert training with self.net...
-                # return loss
-
-            if self.task == 'my_cool_task':
-                # do my own version with self.net
-                # return loss
-
-
-Case 2: COOLER NOT BERT
-----------------------
-
-But if you wanted to try something **completely** different, you'd define a new module for that.
-
-
-.. code-block:: python
-
-    class CoolerNotBERT(pl.LightningModule):
-        def __init__(self):
-            self.net = ...
-
-        def training_step(self, batch, batch_idx):
-            # do some other cool task
-            # return loss
-
-
-Rapid research flow
-------------------
-
-Then you could do rapid research by switching between these two and using the same trainer.
-
-
-.. code-block:: python
-
-    if use_bert:
-        model = BERT()
-    else:
-        model = CoolerNotBERT()
-
-    trainer = Trainer(gpus=4, use_amp=True)
-    trainer.fit(model)
-
-
-**Notice a few things about this flow:**
-
-1. You're writing pure PyTorch... no unnecessary abstractions or new libraries to learn.   
-2. You get free GPU and 16-bit support without writing any of that code in your model.   
-3. You also get all of the capabilities below (without coding or testing yourself).     
@@ -0,0 +1,871 @@
+
+
+
+
+<!doctype html>
+<html lang="en" class="no-js">
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+  <a href="#hyperparameter-search-on-a-single-or-multiple-gpus" title="Hyperparameter search on a single or multiple GPUs" class="md-nav__link">
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+  </a>
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+  <a href="#hyperparameter-search-on-a-single-or-multiple-gpus" title="Hyperparameter search on a single or multiple GPUs" class="md-nav__link">
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+  </a>
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+  </a>
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+              
+                
+                  <a href="https://github.com/williamFalcon/pytorch-lightning/edit/master/docs/examples/Examples.md" title="Edit this page" class="md-icon md-content__icon">&#xE3C9;</a>
+                
+                
+                  <h1>Examples</h1>
+                
+                <h3 id="template-model-definition">Template model definition</h3>
+<p>In 99% of cases you want to just copy <a href="https://github.com/williamFalcon/pytorch-lightning/tree/master/pl_examples">one of the examples</a> to start a new lightningModule and change the core of what your model is actually trying to do.</p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1
+2</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="c1"># get a copy of the module template</span>
+wget https://raw.githubusercontent.com/williamFalcon/pytorch-lightning/master/pl_examples/new_project_templates/lightning_module_template.py
+</pre></div>
+</td></tr></table>
+
+<hr />
+<h3 id="trainer-example">Trainer Example</h3>
+<p><strong> __main__ function</strong>    </p>
+<p>Normally, we want to let the __main__ function start the training.
+Inside the main we parse training arguments with whatever hyperparameters we want. Your LightningModule will have a 
+chance to add hyperparameters.   </p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre> 1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10
+11
+12
+13
+14
+15</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="kn">from</span> <span class="nn">test_tube</span> <span class="kn">import</span> <span class="n">HyperOptArgumentParser</span>
+
+<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;__main__&#39;</span><span class="p">:</span>
+
+    <span class="c1"># use default args given by lightning</span>
+    <span class="n">root_dir</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">dirname</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">modules</span><span class="p">[</span><span class="s1">&#39;__main__&#39;</span><span class="p">]</span><span class="o">.</span><span class="vm">__file__</span><span class="p">))[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="n">parent_parser</span> <span class="o">=</span> <span class="n">HyperOptArgumentParser</span><span class="p">(</span><span class="n">strategy</span><span class="o">=</span><span class="s1">&#39;random_search&#39;</span><span class="p">,</span> <span class="n">add_help</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
+    <span class="n">add_default_args</span><span class="p">(</span><span class="n">parent_parser</span><span class="p">,</span> <span class="n">root_dir</span><span class="p">)</span>
+
+    <span class="c1"># allow model to overwrite or extend args</span>
+    <span class="n">parser</span> <span class="o">=</span> <span class="n">ExampleModel</span><span class="o">.</span><span class="n">add_model_specific_args</span><span class="p">(</span><span class="n">parent_parser</span><span class="p">)</span>
+    <span class="n">hyperparams</span> <span class="o">=</span> <span class="n">parser</span><span class="o">.</span><span class="n">parse_args</span><span class="p">()</span>
+
+    <span class="c1"># train model</span>
+    <span class="n">main</span><span class="p">(</span><span class="n">hyperparams</span><span class="p">)</span>
+</pre></div>
+</td></tr></table>
+
+<p><strong>Main Function</strong>      </p>
+<p>The main function is your entry into the program. This is where you init your model, checkpoint directory, and launch the training.
+The main function should have 3 arguments: <br />
+- hparams: a configuration of hyperparameters.  <br />
+- slurm_manager: Slurm cluster manager object (can be None)
+- dict: for you to return any values you want (useful in meta-learning, otherwise set to _)    </p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre> 1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10
+11
+12
+13
+14</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="k">def</span> <span class="nf">main</span><span class="p">(</span><span class="n">hparams</span><span class="p">,</span> <span class="n">cluster</span><span class="p">,</span> <span class="n">results_dict</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Main training routine specific for this project</span>
+<span class="sd">    :param hparams:</span>
+<span class="sd">    :return:</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># build model</span>
+    <span class="n">model</span> <span class="o">=</span> <span class="n">MyLightningModule</span><span class="p">(</span><span class="n">hparams</span><span class="p">)</span>
+
+    <span class="c1"># configure trainer</span>
+    <span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">()</span>
+
+    <span class="c1"># train model</span>
+    <span class="n">trainer</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
+</pre></div>
+</td></tr></table>
+
+<p>The <strong>main</strong> function will start training on your <strong>main</strong> function. If you use the HyperParameterOptimizer
+in hyper parameter optimization mode, this main function will get one set of hyperparameters. If you use it as a simple
+argument parser you get the default arguments in the argument parser.</p>
+<p>So, calling main(hyperparams) runs the model with the default argparse arguments.       </p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="n">main</span><span class="p">(</span><span class="n">hyperparams</span><span class="p">)</span>
+</pre></div>
+</td></tr></table>
+
+<hr />
+<h4 id="cpu-hyperparameter-search">CPU hyperparameter search</h4>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1
+2
+3
+4
+5
+6</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="c1"># run a grid search over 20 hyperparameter combinations.</span>
+<span class="n">hyperparams</span><span class="o">.</span><span class="n">optimize_parallel_cpu</span><span class="p">(</span>
+    <span class="n">main_local</span><span class="p">,</span>
+    <span class="n">nb_trials</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span>
+    <span class="n">nb_workers</span><span class="o">=</span><span class="mi">1</span>
+<span class="p">)</span>
+</pre></div>
+</td></tr></table>
+
+<hr />
+<h4 id="hyperparameter-search-on-a-single-or-multiple-gpus">Hyperparameter search on a single or multiple GPUs</h4>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1
+2
+3
+4
+5
+6
+7</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="c1"># run a grid search over 20 hyperparameter combinations.</span>
+<span class="n">hyperparams</span><span class="o">.</span><span class="n">optimize_parallel_gpu</span><span class="p">(</span>
+    <span class="n">main_local</span><span class="p">,</span>
+    <span class="n">nb_trials</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span>
+    <span class="n">nb_workers</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+    <span class="n">gpus</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">]</span>
+<span class="p">)</span>
+</pre></div>
+</td></tr></table>
+
+<hr />
+<h4 id="hyperparameter-search-on-a-slurm-hpc-cluster">Hyperparameter search on a SLURM HPC cluster</h4>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre> 1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10
+11
+12
+13
+14
+15
+16
+17
+18
+19
+20
+21
+22
+23
+24
+25
+26
+27
+28
+29
+30
+31
+32
+33
+34</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="k">def</span> <span class="nf">optimize_on_cluster</span><span class="p">(</span><span class="n">hyperparams</span><span class="p">):</span>
+    <span class="c1"># enable cluster training</span>
+    <span class="n">cluster</span> <span class="o">=</span> <span class="n">SlurmCluster</span><span class="p">(</span>
+        <span class="n">hyperparam_optimizer</span><span class="o">=</span><span class="n">hyperparams</span><span class="p">,</span>
+        <span class="n">log_path</span><span class="o">=</span><span class="n">hyperparams</span><span class="o">.</span><span class="n">tt_save_path</span><span class="p">,</span>
+        <span class="n">test_tube_exp_name</span><span class="o">=</span><span class="n">hyperparams</span><span class="o">.</span><span class="n">tt_name</span>
+    <span class="p">)</span>
+
+    <span class="c1"># email for cluster coms</span>
+    <span class="n">cluster</span><span class="o">.</span><span class="n">notify_job_status</span><span class="p">(</span><span class="n">email</span><span class="o">=</span><span class="s1">&#39;add_email_here&#39;</span><span class="p">,</span> <span class="n">on_done</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span> <span class="n">on_fail</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
+
+    <span class="c1"># configure cluster</span>
+    <span class="n">cluster</span><span class="o">.</span><span class="n">per_experiment_nb_gpus</span> <span class="o">=</span> <span class="n">hyperparams</span><span class="o">.</span><span class="n">per_experiment_nb_gpus</span>
+    <span class="n">cluster</span><span class="o">.</span><span class="n">job_time</span> <span class="o">=</span> <span class="s1">&#39;48:00:00&#39;</span>
+    <span class="n">cluster</span><span class="o">.</span><span class="n">gpu_type</span> <span class="o">=</span> <span class="s1">&#39;1080ti&#39;</span>
+    <span class="n">cluster</span><span class="o">.</span><span class="n">memory_mb_per_node</span> <span class="o">=</span> <span class="mi">48000</span>
+
+    <span class="c1"># any modules for code to run in env</span>
+    <span class="n">cluster</span><span class="o">.</span><span class="n">add_command</span><span class="p">(</span><span class="s1">&#39;source activate pytorch_lightning&#39;</span><span class="p">)</span>
+
+    <span class="c1"># name of exp</span>
+    <span class="n">job_display_name</span> <span class="o">=</span> <span class="n">hyperparams</span><span class="o">.</span><span class="n">tt_name</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;_&#39;</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="n">job_display_name</span> <span class="o">=</span> <span class="n">job_display_name</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">3</span><span class="p">]</span>
+
+    <span class="c1"># run hopt</span>
+    <span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s1">&#39;submitting jobs...&#39;</span><span class="p">)</span>
+    <span class="n">cluster</span><span class="o">.</span><span class="n">optimize_parallel_cluster_gpu</span><span class="p">(</span>
+        <span class="n">main</span><span class="p">,</span>
+        <span class="n">nb_trials</span><span class="o">=</span><span class="n">hyperparams</span><span class="o">.</span><span class="n">nb_hopt_trials</span><span class="p">,</span>
+        <span class="n">job_name</span><span class="o">=</span><span class="n">job_display_name</span>
+    <span class="p">)</span>
+
+<span class="c1"># run cluster hyperparameter search    </span>
+<span class="n">optimize_on_cluster</span><span class="p">(</span><span class="n">hyperparams</span><span class="p">)</span>
+</pre></div>
+</td></tr></table>
+                
+                  
+                
+              
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+              
+            </article>
+          </div>
+        </div>
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+                  <a href="https://github.com/williamFalcon/pytorch-lightning/edit/master/docs/index.md" title="Edit this page" class="md-icon md-content__icon">&#xE3C9;</a>
+                
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+                  <h1>Home</h1>
+                
+                <h6 id="new-project-quick-start">New project Quick Start</h6>
+<p>To start a new project define two files, a LightningModule and a Trainer file.  <br />
+To illustrate Lightning power and simplicity, here's an example of a typical research flow.    </p>
+<h6 id="case-1-bert">Case 1: BERT</h6>
+<p>Let's say you're working on something like BERT but want to try different ways of training or even different networks.<br />
+You would define a single LightningModule and use flags to switch between your different ideas.   </p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre> 1
+ 2
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+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10
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+13
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+15
+16
+17</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="k">class</span> <span class="nc">BERT</span><span class="p">(</span><span class="n">pl</span><span class="o">.</span><span class="n">LightningModule</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model_name</span><span class="p">,</span> <span class="n">task</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">task</span> <span class="o">=</span> <span class="n">task</span>
+
+        <span class="k">if</span> <span class="n">model_name</span> <span class="o">==</span> <span class="s1">&#39;transformer&#39;</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">net</span> <span class="o">=</span> <span class="n">Transformer</span><span class="p">()</span>
+        <span class="k">elif</span> <span class="n">model_name</span> <span class="o">==</span> <span class="s1">&#39;my_cool_version&#39;</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">net</span> <span class="o">=</span> <span class="n">MyCoolVersion</span><span class="p">()</span>
+
+    <span class="k">def</span> <span class="nf">training_step</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch</span><span class="p">,</span> <span class="n">batch_nb</span><span class="p">):</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">task</span> <span class="o">==</span> <span class="s1">&#39;standard_bert&#39;</span><span class="p">:</span>
+            <span class="c1"># do standard bert training with self.net...</span>
+            <span class="c1"># return loss</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">task</span> <span class="o">==</span> <span class="s1">&#39;my_cool_task&#39;</span><span class="p">:</span>
+            <span class="c1"># do my own version with self.net</span>
+            <span class="c1"># return loss</span>
+</pre></div>
+</td></tr></table>
+
+<h6 id="case-2-cooler-not-bert">Case 2: COOLER NOT BERT</h6>
+<p>But if you wanted to try something <strong>completely</strong> different, you'd define a new module for that.    </p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1
+2
+3
+4
+5
+6
+7</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="k">class</span> <span class="nc">CoolerNotBERT</span><span class="p">(</span><span class="n">pl</span><span class="o">.</span><span class="n">LightningModule</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">net</span> <span class="o">=</span> <span class="o">...</span>
+
+    <span class="k">def</span> <span class="nf">training_step</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch</span><span class="p">,</span> <span class="n">batch_nb</span><span class="p">):</span>
+        <span class="c1"># do some other cool task</span>
+        <span class="c1"># return loss   </span>
+</pre></div>
+</td></tr></table>
+
+<h6 id="rapid-research-flow">Rapid research flow</h6>
+<p>Then you could do rapid research by switching between these two and using the same trainer.   </p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1
+2
+3
+4
+5
+6
+7</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="k">if</span> <span class="n">use_bert</span><span class="p">:</span>
+    <span class="n">model</span> <span class="o">=</span> <span class="n">BERT</span><span class="p">()</span>
+<span class="k">else</span><span class="p">:</span>
+    <span class="n">model</span> <span class="o">=</span> <span class="n">CoolerNotBERT</span><span class="p">()</span>
+
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">gpus</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">use_amp</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
+<span class="n">trainer</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
+</pre></div>
+</td></tr></table>
+
+<p>Notice a few things about this flow: <br />
+1. You're writing pure PyTorch... no unnecessary abstractions or new libraries to learn. <br />
+2. You get free GPU and 16-bit support without writing any of that code in your model. <br />
+3. You also get all of the capabilities below (without coding or testing yourself).     </p>
+<hr />
+<h6 id="templates">Templates</h6>
+<ol>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/LightningModule/RequiredTrainerInterface/#minimal-example">MNIST LightningModule</a> </li>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/">Trainer</a><ul>
+<li><a href="https://github.com/williamFalcon/pytorch-lightning/tree/master/pl_examples/basic_examples">Basic CPU, GPU Trainer Template</a></li>
+<li><a href="https://github.com/williamFalcon/pytorch-lightning/tree/master/pl_examples/multi_node_examples">GPU cluster Trainer Template</a></li>
+</ul>
+</li>
+</ol>
+<h6 id="docs-shortcuts">Docs shortcuts</h6>
+<ul>
+<li><a href="LightningModule/RequiredTrainerInterface/">LightningModule</a>  </li>
+<li><a href="Trainer/">Trainer</a>  </li>
+</ul>
+<h6 id="quick-start-examples">Quick start examples</h6>
+<ul>
+<li><a href="examples/Examples/#cpu-hyperparameter-search">CPU example</a>   </li>
+<li><a href="examples/Examples/#hyperparameter-search-on-a-single-or-multiple-gpus">Hyperparameter search on single GPU</a>    </li>
+<li><a href="examples/Examples/#hyperparameter-search-on-a-single-or-multiple-gpus">Hyperparameter search on multiple GPUs on same node</a>  </li>
+<li><a href="examples/Examples/#Hyperparameter search on a SLURM HPC cluster">Hyperparameter search on a SLURM HPC cluster</a>      </li>
+</ul>
+<h6 id="checkpointing">Checkpointing</h6>
+<ul>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Checkpointing/#model-saving">Checkpoint callback</a>    </li>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Checkpointing/#model-saving">Model saving</a></li>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/LightningModule/methods/#load-from-metrics">Model loading</a> </li>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Checkpointing/#restoring-training-session">Restoring training session</a></li>
+</ul>
+<h6 id="computing-cluster-slurm">Computing cluster (SLURM)</h6>
+<ul>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/SLURM%20Managed%20Cluster#running-grid-search-on-a-cluster">Running grid search on a cluster</a> </li>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/SLURM%20Managed%20Cluster#walltime-auto-resubmit">Walltime auto-resubmit</a>   </li>
+</ul>
+<h6 id="debugging">Debugging</h6>
+<ul>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/debugging/#fast-dev-run">Fast dev run</a></li>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/debugging/#inspect-gradient-norms">Inspect gradient norms</a></li>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/debugging/#Log-gpu-usage">Log GPU usage</a></li>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/debugging/#make-model-overfit-on-subset-of-data">Make model overfit on subset of data</a></li>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/debugging/#print-the-parameter-count-by-layer">Print the parameter count by layer</a></li>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/debugging/#print-which-gradients-are-nan">Pring which gradients are nan</a></li>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/LightningModule/properties/#example_input_array">Print input and output size of every module in system</a></li>
+</ul>
+<h6 id="distributed-training">Distributed training</h6>
+<ul>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/hooks/#init_ddp_connection">Implement Your Own Distributed (DDP) training</a></li>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Distributed%20training/#16-bit-mixed-precision">16-bit mixed precision</a></li>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Distributed%20training/#Multi-GPU">Multi-GPU</a></li>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Distributed%20training/#Multi-node">Multi-node</a></li>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Distributed%20training/#single-gpu">Single GPU</a></li>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Distributed%20training/#self-balancing-architecture">Self-balancing architecture</a></li>
+</ul>
+<h6 id="experiment-logging">Experiment Logging</h6>
+<ul>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#display-metrics-in-progress-bar">Display metrics in progress bar</a></li>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#log-metric-row-every-k-batches">Log metric row every k batches</a></li>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#process-position">Process position</a></li>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#tensorboard-support">Tensorboard support</a></li>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#save-a-snapshot-of-all-hyperparameters">Save a snapshot of all hyperparameters</a> </li>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#snapshot-code-for-a-training-run">Snapshot code for a training run</a> </li>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#write-logs-file-to-csv-every-k-batches">Write logs file to csv every k batches</a></li>
+</ul>
+<h6 id="training-loop">Training loop</h6>
+<ul>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#accumulated-gradients">Accumulate gradients</a></li>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#force-training-for-min-or-max-epochs">Force training for min or max epochs</a></li>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#early-stopping">Early stopping callback</a>    </li>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#force-disable-early-stop">Force disable early stop</a></li>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#gradient-clipping">Gradient Clipping</a></li>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/hooks/">Hooks</a></li>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/LightningModule/RequiredTrainerInterface/#configure_optimizers">Learning rate scheduling</a></li>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/LightningModule/RequiredTrainerInterface/#configure_optimizers">Use multiple optimizers (like GANs)</a></li>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#set-how-much-of-the-training-set-to-check">Set how much of the training set to check (1-100%)</a></li>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/hooks/#optimizer_step">Step optimizers at arbitrary intervals</a></li>
+</ul>
+<h6 id="validation-loop">Validation loop</h6>
+<ul>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Validation%20loop/#check-validation-every-n-epochs">Check validation every n epochs</a></li>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/hooks/">Hooks</a></li>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Validation%20loop/#set-how-much-of-the-validation-set-to-check">Set how much of the validation set to check</a></li>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Validation%20loop/#set-how-much-of-the-test-set-to-check">Set how much of the test set to check</a></li>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Validation%20loop/#set-validation-check-frequency-within-1-training-epoch">Set validation check frequency within 1 training epoch</a></li>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Validation%20loop/#set-the-number-of-validation-sanity-steps">Set the number of validation sanity steps</a></li>
+</ul>
+<h6 id="testing-loop">Testing loop</h6>
+<ul>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Testing%20loop/">Run test set</a>  </li>
+</ul>
+                
+                  
+                
+              
+              
+                
+
+
+              
+            </article>
+          </div>
+        </div>
+      </main>
+      
+        
+<footer class="md-footer">
+  
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+      <script>app.initialize({version:"1.0.4",url:{base:"."}})</script>
+      
+    
+  </body>
+</html>
@@ -1,11 +0,0 @@
-# Examples   
-This folder has 3 sections:   
-
-### Domain templates   
-These are templates to show common approaches such as GANs and RL.   
-
-### Basic examples   
-These show the most common use of Lightning for either CPU or GPU training.   
-
-### Multi-node examples   
-These show how to run jobs on a GPU cluster using lightning.
@@ -1,146 +0,0 @@
-"""
-Template model definition
-------------------------
-
-In 99% of cases you want to just copy `one of the examples
- <https://github.com/williamFalcon/pytorch-lightning/tree/master/pl_examples>`_
- to start a new lightningModule and change the core of what your model is actually trying to do.
-
-.. code-block:: bash
-
-    # get a copy of the module template
-    wget https://raw.githubusercontent.com/williamFalcon/pytorch-lightning/master/pl_examples/new_project_templates/lightning_module_template.py  # noqa: E501
-
-
-Trainer Example
---------------
-
-**`__main__` function**
-
-Normally, we want to let the `__main__` function start the training.
- Inside the main we parse training arguments with whatever hyperparameters we want.
- Your LightningModule will have a chance to add hyperparameters.
-
-.. code-block:: python
-
-    from test_tube import HyperOptArgumentParser
-
-    if __name__ == '__main__':
-
-        # use default args given by lightning
-        root_dir = os.path.split(os.path.dirname(sys.modules['__main__'].__file__))[0]
-        parent_parser = HyperOptArgumentParser(strategy='random_search', add_help=False)
-        add_default_args(parent_parser, root_dir)
-
-        # allow model to overwrite or extend args
-        parser = ExampleModel.add_model_specific_args(parent_parser)
-        hyperparams = parser.parse_args()
-
-        # train model
-        main(hyperparams)
-
-**Main Function**
-
-The main function is your entry into the program. This is where you init your model, checkpoint directory,
- and launch the training. The main function should have 3 arguments:
- hparams: a configuration of hyperparameters.
- slurm_manager: Slurm cluster manager object (can be None)
- dict: for you to return any values you want (useful in meta-learning, otherwise set to)
-
-.. code-block:: python
-
-    def main(hparams, cluster, results_dict):
-        # build model
-        model = MyLightningModule(hparams)
-
-        # configure trainer
-        trainer = Trainer()
-
-        # train model
-        trainer.fit(model)
-
-
-The `__main__` function will start training on your **main** function.
- If you use the HyperParameterOptimizer in hyper parameter optimization mode,
- this main function will get one set of hyperparameters. If you use it as a simple
- argument parser you get the default arguments in the argument parser.
-
-So, calling main(hyperparams) runs the model with the default argparse arguments.::
-
-    main(hyperparams)
-
-
-CPU hyperparameter search
-------------------------
-
-.. code-block:: python
-
-    # run a grid search over 20 hyperparameter combinations.
-    hyperparams.optimize_parallel_cpu(
-        main_local,
-        nb_trials=20,
-        nb_workers=1
-    )
-
-
-Hyperparameter search on a single or multiple GPUs
--------------------------------------------------
-
-.. code-block:: python
-
-    # run a grid search over 20 hyperparameter combinations.
-    hyperparams.optimize_parallel_gpu(
-        main_local,
-        nb_trials=20,
-        nb_workers=1,
-        gpus=[0,1,2,3]
-    )
-
-
-Hyperparameter search on a SLURM HPC cluster
--------------------------------------------
-
-.. code-block:: python
-
-    def optimize_on_cluster(hyperparams):
-        # enable cluster training
-        cluster = SlurmCluster(
-            hyperparam_optimizer=hyperparams,
-            log_path=hyperparams.tt_save_path,
-            test_tube_exp_name=hyperparams.tt_name
-        )
-
-        # email for cluster coms
-        cluster.notify_job_status(email='add_email_here', on_done=True, on_fail=True)
-
-        # configure cluster
-        cluster.per_experiment_nb_gpus = hyperparams.per_experiment_nb_gpus
-        cluster.job_time = '48:00:00'
-        cluster.gpu_type = '1080ti'
-        cluster.memory_mb_per_node = 48000
-
-        # any modules for code to run in env
-        cluster.add_command('source activate pytorch_lightning')
-
-        # name of exp
-        job_display_name = hyperparams.tt_name.split('_')[0]
-        job_display_name = job_display_name[0:3]
-
-        # run hopt
-        logging.info('submitting jobs...')
-        cluster.optimize_parallel_cluster_gpu(
-            main,
-            nb_trials=hyperparams.nb_hopt_trials,
-            job_name=job_display_name
-        )
-
-    # run cluster hyperparameter search
-    optimize_on_cluster(hyperparams)
-
-"""
-
-from .basic_examples.lightning_module_template import LightningTemplateModel
-
-__all__ = [
-    'LightningTemplateModel'
-]
@@ -1,39 +0,0 @@
-# Basic Examples   
-Use these examples to test how lightning works.   
-
-#### Test on CPU  
-```bash
-python cpu_template.py
-```
-
---   
-#### Train on a single GPU
-```bash
-python gpu_template.py --gpus 1
-```   
-
---    
-#### DataParallel (dp)   
-Train on multiple GPUs using DataParallel.
-
-```bash
-python gpu_template.py --gpus 2 --distributed_backend dp
-```   
-
---
-#### DistributedDataParallel (ddp)    
-
-Train on multiple GPUs using DistributedDataParallel   
-```bash
-python gpu_template.py --gpus 2 --distributed_backend ddp
-```
-
---
-#### DistributedDataParallel+DP (ddp2)    
-
-Train on multiple GPUs using DistributedDataParallel + dataparallel.
-On a single node, uses all GPUs for 1 model. Then shares gradient information
-across nodes.   
-```bash
-python gpu_template.py --gpus 2 --distributed_backend ddp2
-```
@@ -1,54 +0,0 @@
-"""
-Runs a model on a single node across N-gpus.
-"""
-import os
-from argparse import ArgumentParser
-
-import numpy as np
-import torch
-
-from pl_examples.basic_examples.lightning_module_template import LightningTemplateModel
-from pytorch_lightning import Trainer
-
-SEED = 2334
-torch.manual_seed(SEED)
-np.random.seed(SEED)
-
-
-def main(hparams):
-    """
-    Main training routine specific for this project
-    :param hparams:
-    """
-    # ------------------------
-    # 1 INIT LIGHTNING MODEL
-    # ------------------------
-    model = LightningTemplateModel(hparams)
-
-    # ------------------------
-    # 2 INIT TRAINER
-    # ------------------------
-    trainer = Trainer()
-
-    # ------------------------
-    # 3 START TRAINING
-    # ------------------------
-    trainer.fit(model)
-
-
-if __name__ == '__main__':
-    # ------------------------
-    # TRAINING ARGUMENTS
-    # ------------------------
-    # these are project-wide arguments
-    root_dir = os.path.dirname(os.path.realpath(__file__))
-    parent_parser = ArgumentParser(add_help=False)
-
-    # each LightningModule defines arguments relevant to it
-    parser = LightningTemplateModel.add_model_specific_args(parent_parser, root_dir)
-    hyperparams = parser.parse_args()
-
-    # ---------------------
-    # RUN TRAINING
-    # ---------------------
-    main(hyperparams)
@@ -1,79 +0,0 @@
-"""
-Runs a model on a single node across N-gpus.
-"""
-import os
-from argparse import ArgumentParser
-
-import numpy as np
-import torch
-
-from pl_examples.basic_examples.lightning_module_template import LightningTemplateModel
-from pytorch_lightning import Trainer
-
-SEED = 2334
-torch.manual_seed(SEED)
-np.random.seed(SEED)
-
-
-def main(hparams):
-    """
-    Main training routine specific for this project
-    :param hparams:
-    """
-    # ------------------------
-    # 1 INIT LIGHTNING MODEL
-    # ------------------------
-    model = LightningTemplateModel(hparams)
-
-    # ------------------------
-    # 2 INIT TRAINER
-    # ------------------------
-    trainer = Trainer(
-        gpus=hparams.gpus,
-        distributed_backend=hparams.distributed_backend,
-        use_amp=hparams.use_16bit
-    )
-
-    # ------------------------
-    # 3 START TRAINING
-    # ------------------------
-    trainer.fit(model)
-
-
-if __name__ == '__main__':
-    # ------------------------
-    # TRAINING ARGUMENTS
-    # ------------------------
-    # these are project-wide arguments
-
-    root_dir = os.path.dirname(os.path.realpath(__file__))
-    parent_parser = ArgumentParser(add_help=False)
-
-    # gpu args
-    parent_parser.add_argument(
-        '--gpus',
-        type=int,
-        default=2,
-        help='how many gpus'
-    )
-    parent_parser.add_argument(
-        '--distributed_backend',
-        type=str,
-        default='dp',
-        help='supports three options dp, ddp, ddp2'
-    )
-    parent_parser.add_argument(
-        '--use_16bit',
-        dest='use_16bit',
-        action='store_true',
-        help='if true uses 16 bit precision'
-    )
-
-    # each LightningModule defines arguments relevant to it
-    parser = LightningTemplateModel.add_model_specific_args(parent_parser, root_dir)
-    hyperparams = parser.parse_args()
-
-    # ---------------------
-    # RUN TRAINING
-    # ---------------------
-    main(hyperparams)
@@ -1,258 +0,0 @@
-"""
-Example template for defining a system
-"""
-import os
-import logging
-from argparse import ArgumentParser
-from collections import OrderedDict
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torchvision.transforms as transforms
-from torch import optim
-from torch.utils.data import DataLoader
-from torch.utils.data.distributed import DistributedSampler
-from torchvision.datasets import MNIST
-
-import pytorch_lightning as pl
-from pytorch_lightning.core.lightning import LightningModule
-
-
-class LightningTemplateModel(LightningModule):
-    """
-    Sample model to show how to define a template
-    """
-
-    def __init__(self, hparams):
-        """
-        Pass in parsed HyperOptArgumentParser to the model
-        :param hparams:
-        """
-        # init superclass
-        super(LightningTemplateModel, self).__init__()
-        self.hparams = hparams
-
-        self.batch_size = hparams.batch_size
-
-        # if you specify an example input, the summary will show input/output for each layer
-        self.example_input_array = torch.rand(5, 28 * 28)
-
-        # build model
-        self.__build_model()
-
-    # ---------------------
-    # MODEL SETUP
-    # ---------------------
-    def __build_model(self):
-        """
-        Layout model
-        :return:
-        """
-        self.c_d1 = nn.Linear(in_features=self.hparams.in_features,
-                              out_features=self.hparams.hidden_dim)
-        self.c_d1_bn = nn.BatchNorm1d(self.hparams.hidden_dim)
-        self.c_d1_drop = nn.Dropout(self.hparams.drop_prob)
-
-        self.c_d2 = nn.Linear(in_features=self.hparams.hidden_dim,
-                              out_features=self.hparams.out_features)
-
-    # ---------------------
-    # TRAINING
-    # ---------------------
-    def forward(self, x):
-        """
-        No special modification required for lightning, define as you normally would
-        :param x:
-        :return:
-        """
-
-        x = self.c_d1(x)
-        x = torch.tanh(x)
-        x = self.c_d1_bn(x)
-        x = self.c_d1_drop(x)
-
-        x = self.c_d2(x)
-        logits = F.log_softmax(x, dim=1)
-
-        return logits
-
-    def loss(self, labels, logits):
-        nll = F.nll_loss(logits, labels)
-        return nll
-
-    def training_step(self, batch, batch_idx):
-        """
-        Lightning calls this inside the training loop
-        :param batch:
-        :return:
-        """
-        # forward pass
-        x, y = batch
-        x = x.view(x.size(0), -1)
-
-        y_hat = self.forward(x)
-
-        # calculate loss
-        loss_val = self.loss(y, y_hat)
-
-        # in DP mode (default) make sure if result is scalar, there's another dim in the beginning
-        if self.trainer.use_dp or self.trainer.use_ddp2:
-            loss_val = loss_val.unsqueeze(0)
-
-        tqdm_dict = {'train_loss': loss_val}
-        output = OrderedDict({
-            'loss': loss_val,
-            'progress_bar': tqdm_dict,
-            'log': tqdm_dict
-        })
-
-        # can also return just a scalar instead of a dict (return loss_val)
-        return output
-
-    def validation_step(self, batch, batch_idx):
-        """
-        Lightning calls this inside the validation loop
-        :param batch:
-        :return:
-        """
-        x, y = batch
-        x = x.view(x.size(0), -1)
-        y_hat = self.forward(x)
-
-        loss_val = self.loss(y, y_hat)
-
-        # acc
-        labels_hat = torch.argmax(y_hat, dim=1)
-        val_acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0)
-        val_acc = torch.tensor(val_acc)
-
-        if self.on_gpu:
-            val_acc = val_acc.cuda(loss_val.device.index)
-
-        # in DP mode (default) make sure if result is scalar, there's another dim in the beginning
-        if self.trainer.use_dp or self.trainer.use_ddp2:
-            loss_val = loss_val.unsqueeze(0)
-            val_acc = val_acc.unsqueeze(0)
-
-        output = OrderedDict({
-            'val_loss': loss_val,
-            'val_acc': val_acc,
-        })
-
-        # can also return just a scalar instead of a dict (return loss_val)
-        return output
-
-    def validation_end(self, outputs):
-        """
-        Called at the end of validation to aggregate outputs
-        :param outputs: list of individual outputs of each validation step
-        :return:
-        """
-        # if returned a scalar from validation_step, outputs is a list of tensor scalars
-        # we return just the average in this case (if we want)
-        # return torch.stack(outputs).mean()
-
-        val_loss_mean = 0
-        val_acc_mean = 0
-        for output in outputs:
-            val_loss = output['val_loss']
-
-            # reduce manually when using dp
-            if self.trainer.use_dp or self.trainer.use_ddp2:
-                val_loss = torch.mean(val_loss)
-            val_loss_mean += val_loss
-
-            # reduce manually when using dp
-            val_acc = output['val_acc']
-            if self.trainer.use_dp or self.trainer.use_ddp2:
-                val_acc = torch.mean(val_acc)
-
-            val_acc_mean += val_acc
-
-        val_loss_mean /= len(outputs)
-        val_acc_mean /= len(outputs)
-        tqdm_dict = {'val_loss': val_loss_mean, 'val_acc': val_acc_mean}
-        result = {'progress_bar': tqdm_dict, 'log': tqdm_dict, 'val_loss': val_loss_mean}
-        return result
-
-    # ---------------------
-    # TRAINING SETUP
-    # ---------------------
-    def configure_optimizers(self):
-        """
-        return whatever optimizers we want here
-        :return: list of optimizers
-        """
-        optimizer = optim.Adam(self.parameters(), lr=self.hparams.learning_rate)
-        scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=10)
-        return [optimizer], [scheduler]
-
-    def __dataloader(self, train):
-        # init data generators
-        transform = transforms.Compose([transforms.ToTensor(),
-                                        transforms.Normalize((0.5,), (1.0,))])
-        dataset = MNIST(root=self.hparams.data_root, train=train,
-                        transform=transform, download=True)
-
-        # when using multi-node (ddp) we need to add the  datasampler
-        train_sampler = None
-        batch_size = self.hparams.batch_size
-
-        if self.use_ddp:
-            train_sampler = DistributedSampler(dataset)
-
-        should_shuffle = train_sampler is None
-        loader = DataLoader(
-            dataset=dataset,
-            batch_size=batch_size,
-            shuffle=should_shuffle,
-            sampler=train_sampler,
-            num_workers=0
-        )
-
-        return loader
-
-    @pl.data_loader
-    def train_dataloader(self):
-        logging.info('training data loader called')
-        return self.__dataloader(train=True)
-
-    @pl.data_loader
-    def val_dataloader(self):
-        logging.info('val data loader called')
-        return self.__dataloader(train=False)
-
-    @pl.data_loader
-    def test_dataloader(self):
-        logging.info('test data loader called')
-        return self.__dataloader(train=False)
-
-    @staticmethod
-    def add_model_specific_args(parent_parser, root_dir):  # pragma: no cover
-        """
-        Parameters you define here will be available to your model through self.hparams
-        :param parent_parser:
-        :param root_dir:
-        :return:
-        """
-        parser = ArgumentParser(parents=[parent_parser])
-
-        # param overwrites
-        # parser.set_defaults(gradient_clip_val=5.0)
-
-        # network params
-        parser.add_argument('--in_features', default=28 * 28, type=int)
-        parser.add_argument('--out_features', default=10, type=int)
-        # use 500 for CPU, 50000 for GPU to see speed difference
-        parser.add_argument('--hidden_dim', default=50000, type=int)
-        parser.add_argument('--drop_prob', default=0.2, type=float)
-        parser.add_argument('--learning_rate', default=0.001, type=float)
-
-        # data
-        parser.add_argument('--data_root', default=os.path.join(root_dir, 'mnist'), type=str)
-
-        # training params (opt)
-        parser.add_argument('--optimizer_name', default='adam', type=str)
-        parser.add_argument('--batch_size', default=64, type=int)
-        return parser
@@ -1,217 +0,0 @@
-"""
-To run this template just do:
-python gan.py
-
-After a few epochs, launch tensorboard to see the images being generated at every batch.
-
-tensorboard --logdir default
-"""
-import os
-from argparse import ArgumentParser
-from collections import OrderedDict
-
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torchvision
-import torchvision.transforms as transforms
-from torch.utils.data import DataLoader
-from torchvision.datasets import MNIST
-
-import pytorch_lightning as pl
-
-
-class Generator(nn.Module):
-    def __init__(self, latent_dim, img_shape):
-        super(Generator, self).__init__()
-        self.img_shape = img_shape
-
-        def block(in_feat, out_feat, normalize=True):
-            layers = [nn.Linear(in_feat, out_feat)]
-            if normalize:
-                layers.append(nn.BatchNorm1d(out_feat, 0.8))
-            layers.append(nn.LeakyReLU(0.2, inplace=True))
-            return layers
-
-        self.model = nn.Sequential(
-            *block(latent_dim, 128, normalize=False),
-            *block(128, 256),
-            *block(256, 512),
-            *block(512, 1024),
-            nn.Linear(1024, int(np.prod(img_shape))),
-            nn.Tanh()
-        )
-
-    def forward(self, z):
-        img = self.model(z)
-        img = img.view(img.size(0), *self.img_shape)
-        return img
-
-
-class Discriminator(nn.Module):
-    def __init__(self, img_shape):
-        super(Discriminator, self).__init__()
-
-        self.model = nn.Sequential(
-            nn.Linear(int(np.prod(img_shape)), 512),
-            nn.LeakyReLU(0.2, inplace=True),
-            nn.Linear(512, 256),
-            nn.LeakyReLU(0.2, inplace=True),
-            nn.Linear(256, 1),
-            nn.Sigmoid(),
-        )
-
-    def forward(self, img):
-        img_flat = img.view(img.size(0), -1)
-        validity = self.model(img_flat)
-
-        return validity
-
-
-class GAN(pl.LightningModule):
-
-    def __init__(self, hparams):
-        super(GAN, self).__init__()
-        self.hparams = hparams
-
-        # networks
-        mnist_shape = (1, 28, 28)
-        self.generator = Generator(latent_dim=hparams.latent_dim, img_shape=mnist_shape)
-        self.discriminator = Discriminator(img_shape=mnist_shape)
-
-        # cache for generated images
-        self.generated_imgs = None
-        self.last_imgs = None
-
-    def forward(self, z):
-        return self.generator(z)
-
-    def adversarial_loss(self, y_hat, y):
-        return F.binary_cross_entropy(y_hat, y)
-
-    def training_step(self, batch, batch_idx, optimizer_idx):
-        imgs, _ = batch
-        self.last_imgs = imgs
-
-        # train generator
-        if optimizer_idx == 0:
-            # sample noise
-            z = torch.randn(imgs.shape[0], self.hparams.latent_dim)
-
-            # match gpu device (or keep as cpu)
-            if self.on_gpu:
-                z = z.cuda(imgs.device.index)
-
-            # generate images
-            self.generated_imgs = self.forward(z)
-
-            # log sampled images
-            # sample_imgs = self.generated_imgs[:6]
-            # grid = torchvision.utils.make_grid(sample_imgs)
-            # self.logger.experiment.add_image('generated_images', grid, 0)
-
-            # ground truth result (ie: all fake)
-            # put on GPU because we created this tensor inside training_loop
-            valid = torch.ones(imgs.size(0), 1)
-            if self.on_gpu:
-                valid = valid.cuda(imgs.device.index)
-
-            # adversarial loss is binary cross-entropy
-            g_loss = self.adversarial_loss(self.discriminator(self.generated_imgs), valid)
-            tqdm_dict = {'g_loss': g_loss}
-            output = OrderedDict({
-                'loss': g_loss,
-                'progress_bar': tqdm_dict,
-                'log': tqdm_dict
-            })
-            return output
-
-        # train discriminator
-        if optimizer_idx == 1:
-            # Measure discriminator's ability to classify real from generated samples
-
-            # how well can it label as real?
-            valid = torch.ones(imgs.size(0), 1)
-            if self.on_gpu:
-                valid = valid.cuda(imgs.device.index)
-
-            real_loss = self.adversarial_loss(self.discriminator(imgs), valid)
-
-            # how well can it label as fake?
-            fake = torch.zeros(imgs.size(0), 1)
-            if self.on_gpu:
-                fake = fake.cuda(imgs.device.index)
-
-            fake_loss = self.adversarial_loss(
-                self.discriminator(self.generated_imgs.detach()), fake)
-
-            # discriminator loss is the average of these
-            d_loss = (real_loss + fake_loss) / 2
-            tqdm_dict = {'d_loss': d_loss}
-            output = OrderedDict({
-                'loss': d_loss,
-                'progress_bar': tqdm_dict,
-                'log': tqdm_dict
-            })
-            return output
-
-    def configure_optimizers(self):
-        lr = self.hparams.lr
-        b1 = self.hparams.b1
-        b2 = self.hparams.b2
-
-        opt_g = torch.optim.Adam(self.generator.parameters(), lr=lr, betas=(b1, b2))
-        opt_d = torch.optim.Adam(self.discriminator.parameters(), lr=lr, betas=(b1, b2))
-        return [opt_g, opt_d], []
-
-    @pl.data_loader
-    def train_dataloader(self):
-        transform = transforms.Compose([transforms.ToTensor(),
-                                        transforms.Normalize([0.5], [0.5])])
-        dataset = MNIST(os.getcwd(), train=True, download=True, transform=transform)
-        return DataLoader(dataset, batch_size=self.hparams.batch_size)
-
-    def on_epoch_end(self):
-        z = torch.randn(8, self.hparams.latent_dim)
-        # match gpu device (or keep as cpu)
-        if self.on_gpu:
-            z = z.cuda(self.last_imgs.device.index)
-
-        # log sampled images
-        sample_imgs = self.forward(z)
-        grid = torchvision.utils.make_grid(sample_imgs)
-        self.logger.experiment.add_image(f'generated_images', grid, self.current_epoch)
-
-
-def main(hparams):
-    # ------------------------
-    # 1 INIT LIGHTNING MODEL
-    # ------------------------
-    model = GAN(hparams)
-
-    # ------------------------
-    # 2 INIT TRAINER
-    # ------------------------
-    trainer = pl.Trainer()
-
-    # ------------------------
-    # 3 START TRAINING
-    # ------------------------
-    trainer.fit(model)
-
-
-if __name__ == '__main__':
-    parser = ArgumentParser()
-    parser.add_argument("--batch_size", type=int, default=64, help="size of the batches")
-    parser.add_argument("--lr", type=float, default=0.0002, help="adam: learning rate")
-    parser.add_argument("--b1", type=float, default=0.5,
-                        help="adam: decay of first order momentum of gradient")
-    parser.add_argument("--b2", type=float, default=0.999,
-                        help="adam: decay of first order momentum of gradient")
-    parser.add_argument("--latent_dim", type=int, default=100,
-                        help="dimensionality of the latent space")
-
-    hparams = parser.parse_args()
-
-    main(hparams)
@@ -1,248 +0,0 @@
-"""
-This example is largely adapted from https://github.com/pytorch/examples/blob/master/imagenet/main.py
-"""
-import argparse
-import os
-import random
-from collections import OrderedDict
-
-import torch
-import torch.backends.cudnn as cudnn
-import torch.nn.parallel
-import torch.nn.functional as F
-import torch.optim as optim
-import torch.optim.lr_scheduler as lr_scheduler
-import torch.utils.data
-import torch.utils.data.distributed
-
-import torchvision.transforms as transforms
-import torchvision.models as models
-import torchvision.datasets as datasets
-
-import pytorch_lightning as pl
-
-
-# pull out resnet names from torchvision models
-MODEL_NAMES = sorted(
-    name for name in models.__dict__
-    if name.islower() and not name.startswith("__") and callable(models.__dict__[name])
-)
-
-
-class ImageNetLightningModel(pl.LightningModule):
-
-    def __init__(self, hparams):
-        super(ImageNetLightningModel, self).__init__()
-        self.hparams = hparams
-        self.model = models.__dict__[self.hparams.arch](pretrained=self.hparams.pretrained)
-
-    def training_step(self, batch, batch_idx):
-        images, target = batch
-        output = self.model(images)
-        loss_val = F.cross_entropy(output, target)
-        acc1, acc5 = self.__accuracy(output, target, topk=(1, 5))
-
-        # in DP mode (default) make sure if result is scalar, there's another dim in the beginning
-        if self.trainer.use_dp or self.trainer.use_ddp2:
-            loss_val = loss_val.unsqueeze(0)
-            acc1 = acc1.unsqueeze(0)
-            acc5 = acc5.unsqueeze(0)
-
-        tqdm_dict = {'train_loss': loss_val}
-        output = OrderedDict({
-            'loss': loss_val,
-            'acc1': acc1,
-            'acc5': acc5,
-            'progress_bar': tqdm_dict,
-            'log': tqdm_dict
-        })
-
-        return output
-
-    def validation_step(self, batch, batch_idx):
-        images, target = batch
-        output = self.model(images)
-        loss_val = F.cross_entropy(output, target)
-        acc1, acc5 = self.__accuracy(output, target, topk=(1, 5))
-
-        # in DP mode (default) make sure if result is scalar, there's another dim in the beginning
-        if self.trainer.use_dp or self.trainer.use_ddp2:
-            loss_val = loss_val.unsqueeze(0)
-            acc1 = acc1.unsqueeze(0)
-            acc5 = acc5.unsqueeze(0)
-
-        output = OrderedDict({
-            'val_loss': loss_val,
-            'val_acc1': acc1,
-            'val_acc5': acc5,
-        })
-
-        return output
-
-    def validation_end(self, outputs):
-
-        tqdm_dict = {}
-
-        for metric_name in ["val_loss", "val_acc1", "val_acc5"]:
-            metric_total = 0
-
-            for output in outputs:
-                metric_value = output[metric_name]
-
-                # reduce manually when using dp
-                if self.trainer.use_dp or self.trainer.use_ddp2:
-                    metric_value = torch.mean(metric_value)
-
-                metric_total += metric_value
-
-            tqdm_dict[metric_name] = metric_total / len(outputs)
-
-        result = {'progress_bar': tqdm_dict, 'log': tqdm_dict, 'val_loss': tqdm_dict["val_loss"]}
-        return result
-
-    @classmethod
-    def __accuracy(cls, output, target, topk=(1,)):
-        """Computes the accuracy over the k top predictions for the specified values of k"""
-        with torch.no_grad():
-            maxk = max(topk)
-            batch_size = target.size(0)
-
-            _, pred = output.topk(maxk, 1, True, True)
-            pred = pred.t()
-            correct = pred.eq(target.view(1, -1).expand_as(pred))
-
-            res = []
-            for k in topk:
-                correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
-                res.append(correct_k.mul_(100.0 / batch_size))
-            return res
-
-    def configure_optimizers(self):
-        optimizer = optim.SGD(
-            self.parameters(),
-            lr=self.hparams.lr,
-            momentum=self.hparams.momentum,
-            weight_decay=self.hparams.weight_decay
-        )
-        scheduler = lr_scheduler.ExponentialLR(optimizer, gamma=0.1)
-        return [optimizer], [scheduler]
-
-    @pl.data_loader
-    def train_dataloader(self):
-        normalize = transforms.Normalize(
-            mean=[0.485, 0.456, 0.406],
-            std=[0.229, 0.224, 0.225],
-        )
-
-        train_dir = os.path.join(self.hparams.data, 'train')
-        train_dataset = datasets.ImageFolder(
-            train_dir,
-            transforms.Compose([
-                transforms.RandomResizedCrop(224),
-                transforms.RandomHorizontalFlip(),
-                transforms.ToTensor(),
-                normalize,
-            ]))
-
-        if self.use_ddp:
-            train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
-        else:
-            train_sampler = None
-
-        train_loader = torch.utils.data.DataLoader(
-            dataset=train_dataset,
-            batch_size=self.hparams.batch_size,
-            shuffle=(train_sampler is None),
-            num_workers=0,
-            sampler=train_sampler
-        )
-        return train_loader
-
-    @pl.data_loader
-    def val_dataloader(self):
-        normalize = transforms.Normalize(
-            mean=[0.485, 0.456, 0.406],
-            std=[0.229, 0.224, 0.225],
-        )
-        val_dir = os.path.join(self.hparams.data, 'val')
-        val_loader = torch.utils.data.DataLoader(
-            datasets.ImageFolder(val_dir, transforms.Compose([
-                transforms.Resize(256),
-                transforms.CenterCrop(224),
-                transforms.ToTensor(),
-                normalize,
-            ])),
-            batch_size=self.hparams.batch_size,
-            shuffle=False,
-            num_workers=0,
-        )
-        return val_loader
-
-    @staticmethod
-    def add_model_specific_args(parent_parser):  # pragma: no cover
-        parser = argparse.ArgumentParser(parents=[parent_parser])
-        parser.add_argument('-a', '--arch', metavar='ARCH', default='resnet18', choices=MODEL_NAMES,
-                            help='model architecture: ' +
-                                 ' | '.join(MODEL_NAMES) +
-                                 ' (default: resnet18)')
-        parser.add_argument('--epochs', default=90, type=int, metavar='N',
-                            help='number of total epochs to run')
-        parser.add_argument('--seed', type=int, default=None,
-                            help='seed for initializing training. ')
-        parser.add_argument('-b', '--batch-size', default=256, type=int,
-                            metavar='N',
-                            help='mini-batch size (default: 256), this is the total '
-                                 'batch size of all GPUs on the current node when '
-                                 'using Data Parallel or Distributed Data Parallel')
-        parser.add_argument('--lr', '--learning-rate', default=0.1, type=float,
-                            metavar='LR', help='initial learning rate', dest='lr')
-        parser.add_argument('--momentum', default=0.9, type=float, metavar='M',
-                            help='momentum')
-        parser.add_argument('--wd', '--weight-decay', default=1e-4, type=float,
-                            metavar='W', help='weight decay (default: 1e-4)',
-                            dest='weight_decay')
-        parser.add_argument('--pretrained', dest='pretrained', action='store_true',
-                            help='use pre-trained model')
-        return parser
-
-
-def get_args():
-    parent_parser = argparse.ArgumentParser(add_help=False)
-    parent_parser.add_argument('--data-path', metavar='DIR', type=str,
-                               help='path to dataset')
-    parent_parser.add_argument('--save-path', metavar='DIR', default=".", type=str,
-                               help='path to save output')
-    parent_parser.add_argument('--gpus', type=int, default=1,
-                               help='how many gpus')
-    parent_parser.add_argument('--distributed-backend', type=str, default='dp', choices=('dp', 'ddp', 'ddp2'),
-                               help='supports three options dp, ddp, ddp2')
-    parent_parser.add_argument('--use-16bit', dest='use-16bit', action='store_true',
-                               help='if true uses 16 bit precision')
-    parent_parser.add_argument('-e', '--evaluate', dest='evaluate', action='store_true',
-                               help='evaluate model on validation set')
-
-    parser = ImageNetLightningModel.add_model_specific_args(parent_parser)
-    return parser.parse_args()
-
-
-def main(hparams):
-    model = ImageNetLightningModel(hparams)
-    if hparams.seed is not None:
-        random.seed(hparams.seed)
-        torch.manual_seed(hparams.seed)
-        cudnn.deterministic = True
-    trainer = pl.Trainer(
-        default_save_path=hparams.save_path,
-        gpus=hparams.gpus,
-        max_epochs=hparams.epochs,
-        distributed_backend=hparams.distributed_backend,
-        use_amp=hparams.use_16bit
-    )
-    if hparams.evaluate:
-        trainer.run_evaluation()
-    else:
-        trainer.fit(model)
-
-
-if __name__ == '__main__':
-    main(get_args())
@@ -1,21 +0,0 @@
-# Multi-node example   
-
-This demo launches a job using 2 GPUs on 2 different nodes (4 GPUs total).
-To run this demo do the following:
-
-1. Log into the jumphost node of your SLURM-managed cluster.  
-2. Create a conda environment with Lightning and a GPU PyTorch version.   
-3. Choose a script to submit    
-
-#### DDP  
-Submit this job to run with distributedDataParallel (2 nodes, 2 gpus each)
-```bash
-sbatch ddp_job_submit.sh YourEnv
-```
-
-#### DDP2  
-Submit this job to run with a different implementation of distributedDataParallel.
-In this version, each node acts like DataParallel but syncs across nodes like DDP.
-```bash
-sbatch ddp2_job_submit.sh YourEnv
-```
@@ -1,27 +0,0 @@
-#!/bin/bash -l
-
-# SLURM SUBMIT SCRIPT
-#SBATCH --nodes=2
-#SBATCH --gres=gpu:2
-#SBATCH --ntasks-per-node=1
-#SBATCH --mem=0
-#SBATCH --time=0-02:00:00
-
-# activate conda env
-source activate $1
-
-# -------------------------
-# debugging flags (optional)
- export NCCL_DEBUG=INFO
- export PYTHONFAULTHANDLER=1
-
-# on your cluster you might need these:
-# set the network interface
-# export NCCL_SOCKET_IFNAME=^docker0,lo
-
-# might need the latest cuda
-# module load NCCL/2.4.7-1-cuda.10.0
-# -------------------------
-
-# run script from above
-srun python3 multi_node_ddp2_demo.py
@@ -1,27 +0,0 @@
-#!/bin/bash -l
-
-# SLURM SUBMIT SCRIPT
-#SBATCH --nodes=2
-#SBATCH --gres=gpu:2
-#SBATCH --ntasks-per-node=2
-#SBATCH --mem=0
-#SBATCH --time=0-02:00:00
-
-# activate conda env
-source activate $1
-
-# -------------------------
-# debugging flags (optional)
- export NCCL_DEBUG=INFO
- export PYTHONFAULTHANDLER=1
-
-# on your cluster you might need these:
-# set the network interface
-# export NCCL_SOCKET_IFNAME=^docker0,lo
-
-# might need the latest cuda
-# module load NCCL/2.4.7-1-cuda.10.0
-# -------------------------
-
-# run script from above
-srun python3 multi_node_ddp_demo.py
@@ -1,55 +0,0 @@
-"""
-Multi-node example (GPU)
-"""
-import os
-from argparse import ArgumentParser
-
-import numpy as np
-import torch
-
-from pl_examples.basic_examples.lightning_module_template import LightningTemplateModel
-from pytorch_lightning import Trainer
-
-SEED = 2334
-torch.manual_seed(SEED)
-np.random.seed(SEED)
-
-
-def main(hparams):
-    """
-    Main training routine specific for this project
-    :param hparams:
-    :return:
-    """
-    # ------------------------
-    # 1 INIT LIGHTNING MODEL
-    # ------------------------
-    model = LightningTemplateModel(hparams)
-
-    # ------------------------
-    # 2 INIT TRAINER
-    # ------------------------
-    trainer = Trainer(
-        gpus=2,
-        num_nodes=2,
-        distributed_backend='ddp2'
-    )
-
-    # ------------------------
-    # 3 START TRAINING
-    # ------------------------
-    trainer.fit(model)
-
-
-if __name__ == '__main__':
-    root_dir = os.path.dirname(os.path.realpath(__file__))
-    parent_parser = ArgumentParser(add_help=False)
-
-    # each LightningModule defines arguments relevant to it
-    parser = LightningTemplateModel.add_model_specific_args(parent_parser, root_dir)
-    hyperparams = parser.parse_args()
-
-    # ---------------------
-    # RUN TRAINING
-    # ---------------------
-    main(hyperparams)
@@ -1,55 +0,0 @@
-"""
-Multi-node example (GPU)
-"""
-import os
-from argparse import ArgumentParser
-
-import numpy as np
-import torch
-
-from pl_examples.basic_examples.lightning_module_template import LightningTemplateModel
-from pytorch_lightning import Trainer
-
-SEED = 2334
-torch.manual_seed(SEED)
-np.random.seed(SEED)
-
-
-def main(hparams):
-    """
-    Main training routine specific for this project
-    :param hparams:
-    :return:
-    """
-    # ------------------------
-    # 1 INIT LIGHTNING MODEL
-    # ------------------------
-    model = LightningTemplateModel(hparams)
-
-    # ------------------------
-    # 2 INIT TRAINER
-    # ------------------------
-    trainer = Trainer(
-        gpus=2,
-        num_nodes=2,
-        distributed_backend='ddp'
-    )
-
-    # ------------------------
-    # 3 START TRAINING
-    # ------------------------
-    trainer.fit(model)
-
-
-if __name__ == '__main__':
-    root_dir = os.path.dirname(os.path.realpath(__file__))
-    parent_parser = ArgumentParser(add_help=False)
-
-    # each LightningModule defines arguments relevant to it
-    parser = LightningTemplateModel.add_model_specific_args(parent_parser, root_dir)
-    hyperparams = parser.parse_args()
-
-    # ---------------------
-    # RUN TRAINING
-    # ---------------------
-    main(hyperparams)
@@ -1,5 +0,0 @@
-[build-system]
-requires = [
-    "setuptools",
-    "wheel",
-]
@@ -1,38 +0,0 @@
-"""Package info"""
-
-__version__ = '0.6.0'
-__author__ = 'William Falcon et al.'
-__author_email__ = 'waf2107@columbia.edu'
-__license__ = 'Apache-2.0'
-__copyright__ = 'Copyright (c) 2018-2019, %s.' % __author__
-__homepage__ = 'https://github.com/williamFalcon/pytorch-lightning'
-# this has to be simple string, see: https://github.com/pypa/twine/issues/522
-__docs__ = "PyTorch Lightning is the lightweight PyTorch wrapper for ML researchers." \
-           " Scale your models. Write less boilerplate."
-
-
-try:
-    # This variable is injected in the __builtins__ by the build
-    # process. It used to enable importing subpackages of skimage when
-    # the binaries are not built
-    __LIGHTNING_SETUP__
-except NameError:
-    __LIGHTNING_SETUP__ = False
-
-if __LIGHTNING_SETUP__:
-    import sys
-    sys.stderr.write('Partial import of skimage during the build process.\n')
-    # We are not importing the rest of the scikit during the build
-    # process, as it may not be compiled yet
-else:
-    from .trainer.trainer import Trainer
-    from .core.lightning import LightningModule
-    from .core.decorators import data_loader
-    import logging
-
-    __all__ = [
-        'Trainer',
-        'LightningModule',
-        'data_loader',
-    ]
-    logging.basicConfig(level=logging.INFO)
@@ -1,7 +0,0 @@
-from .pt_callbacks import EarlyStopping, ModelCheckpoint, GradientAccumulationScheduler
-
-__all__ = [
-    'EarlyStopping',
-    'ModelCheckpoint',
-    'GradientAccumulationScheduler',
-]
--- a/Show More
+++ b/Show More
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William Falcon	65095b6cac	Deployed `58d52c2` with MkDocs version: 1.0.4	2019-10-18 23:57:14 +01:00
William Falcon	0675db8074	Deployed `eca0e7c` with MkDocs version: 1.0.4	2019-10-05 16:34:18 -05:00
William Falcon	9b5ef41046	Deployed `49c7d54` with MkDocs version: 1.0.4	2019-10-05 16:32:22 -05:00
William Falcon	5f6a2c6d65	Deployed `49c7d54` with MkDocs version: 1.0.4	2019-10-05 16:18:33 -05:00
William Falcon	09017dfd67	Deployed `0eb6950` with MkDocs version: 1.0.4	2019-10-05 16:10:29 -05:00
William Falcon	c5d8042012	Deployed `222d7d2` with MkDocs version: 1.0.4	2019-10-02 09:30:25 -05:00
William Falcon	736c747c21	Deployed `133d6b3` with MkDocs version: 1.0.4	2019-10-01 05:38:20 -05:00
William Falcon	3c34e029ed	Deployed `fbc2cfd` with MkDocs version: 1.0.4	2019-10-01 05:34:27 -05:00
William Falcon	e77709ff8b	Deployed `1d7ffd1` with MkDocs version: 1.0.4	2019-09-26 16:54:08 -05:00
William Falcon	6d7eb70130	Deployed `059b2fa` with MkDocs version: 1.0.4	2019-09-26 14:58:07 -05:00
William Falcon	452c972d07	Deployed `acb4ebe` with MkDocs version: 1.0.4	2019-09-26 11:02:20 -05:00
William Falcon	a5cc9ec2dc	Deployed `5a9320d` with MkDocs version: 1.0.4	2019-09-26 10:28:41 -05:00
William Falcon	8f53e506cf	Deployed `2a1bc22` with MkDocs version: 1.0.4	2019-09-17 08:57:25 -05:00
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William Falcon	ba18f5f26a	Deployed `286625a` with MkDocs version: 1.0.4	2019-09-16 10:02:11 -05:00
William Falcon	9696342652	Deployed `b354988` with MkDocs version: 1.0.4	2019-09-16 09:59:41 -05:00
William Falcon	2a8ef263a7	Deployed `974afba` with MkDocs version: 1.0.4	2019-09-16 09:55:01 -05:00
William Falcon	f70f01b8a8	Deployed 5d49f1f with MkDocs version: 1.0.4	2019-08-31 02:20:46 -05:00
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William Falcon	971d59efe8	Deployed `1b7d66d` with MkDocs version: 1.0.4	2019-08-16 16:14:51 -05:00
William Falcon	3e511d129c	Deployed `bdd8608` with MkDocs version: 1.0.4	2019-08-16 09:08:04 -05:00
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William Falcon	9eae41f903	Deployed `0e92a9d` with MkDocs version: 1.0.4	2019-08-15 20:19:40 -05:00
William Falcon	8d6f8d8146	Deployed `8183722` with MkDocs version: 1.0.4	2019-08-15 13:00:00 -05:00
William Falcon	9b8a06bfdb	Deployed `f0af138` with MkDocs version: 1.0.4	2019-08-13 15:21:24 -05:00
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William Falcon	25bf79b9da	Deployed `699fbab` with MkDocs version: 1.0.4	2019-08-13 10:59:40 -05:00
William Falcon	6412446781	Deployed `fd845d4` with MkDocs version: 1.0.4	2019-08-13 10:57:09 -05:00
William Falcon	e4a503a016	Deployed `d7660d3` with MkDocs version: 1.0.4	2019-08-13 10:55:16 -05:00
William Falcon	5a6d112ed6	Deployed `7e38f1f` with MkDocs version: 1.0.4	2019-08-13 10:54:24 -05:00
William Falcon	cc924cd8eb	Deployed `7898d0c` with MkDocs version: 1.0.4	2019-08-13 10:51:37 -05:00
William Falcon	ef857d6f84	Deployed `53ec3bc` with MkDocs version: 1.0.4	2019-08-13 10:47:58 -05:00
William Falcon	589e5381be	Deployed `6f3152b` with MkDocs version: 1.0.4	2019-08-13 05:43:30 -05:00
William Falcon	2b89c3513b	Deployed `190a3a9` with MkDocs version: 1.0.4	2019-08-13 05:39:53 -05:00
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William Falcon	3007636435	Deployed `0ce180f` with MkDocs version: 1.0.4	2019-07-26 22:24:14 -05:00
William Falcon	0985c7a1ca	Deployed `d18f38c` with MkDocs version: 1.0.4	2019-07-25 11:40:24 -05:00
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William Falcon	92b8d2056d	Deployed `1cbe54f` with MkDocs version: 1.0.4	2019-07-25 11:35:36 -05:00
William Falcon	a19dd86e4e	Deployed `b914866` with MkDocs version: 1.0.4	2019-07-25 11:12:53 -05:00
William Falcon	4c60f2e8c3	Deployed `4562580` with MkDocs version: 1.0.4	2019-07-25 10:58:14 -05:00
William Falcon	f3a4149d50	Deployed `d272f29` with MkDocs version: 1.0.4	2019-07-25 10:53:03 -05:00
William Falcon	c94858db8e	Deployed `600c755` with MkDocs version: 1.0.4	2019-07-25 10:44:39 -05:00
William Falcon	f6f494585d	Deployed `0f79e9d` with MkDocs version: 1.0.4	2019-07-25 10:35:21 -05:00
William Falcon	88bae42294	Deployed `9fa8120` with MkDocs version: 1.0.4	2019-07-25 10:30:31 -05:00
William Falcon	3536c7c427	Deployed `7ac344e` with MkDocs version: 1.0.4	2019-07-21 07:35:40 -05:00
William Falcon	6eea1eac31	Deployed `f6b98fe` with MkDocs version: 1.0.4	2019-07-21 07:34:01 -05:00
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				!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");var r,m,i;e.da=function(){this.pipeline.reset(),this.pipeline.add(e.da.trimmer,e.da.stopWordFilter,e.da.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.da.stemmer))},e.da.wordCharacters="A-Za-zªºÀ-ÖØ-öø-ʸˠ-ˤᴀ-ᴥᴬ-ᵜᵢ-ᵥᵫ-ᵷᵹ-ᶾḀ-ỿⁱⁿₐ-ₜKÅℲⅎⅠ-ↈⱠ-ⱿꜢ-ꞇꞋ-ꞭꞰ-ꞷꟷ-ꟿꬰ-ꭚꭜ-ꭤﬀ-ﬆＡ-Ｚａ-ｚ",e.da.trimmer=e.trimmerSupport.generateTrimmer(e.da.wordCharacters),e.Pipeline.registerFunction(e.da.trimmer,"trimmer-da"),e.da.stemmer=(r=e.stemmerSupport.Among,m=e.stemmerSupport.SnowballProgram,i=new function(){var i,t,n,s=[new r("hed",-1,1),new r("ethed",0,1),new r("ered",-1,1),new r("e",-1,1),new r("erede",3,1),new r("ende",3,1),new r("erende",5,1),new r("ene",3,1),new r("erne",3,1),new r("ere",3,1),new r("en",-1,1),new r("heden",10,1),new r("eren",10,1),new r("er",-1,1),new r("heder",13,1),new r("erer",13,1),new r("s",-1,2),new r("heds",16,1),new r("es",16,1),new r("endes",18,1),new r("erendes",19,1),new r("enes",18,1),new r("ernes",18,1),new r("eres",18,1),new r("ens",16,1),new r("hedens",24,1),new r("erens",24,1),new r("ers",16,1),new r("ets",16,1),new r("erets",28,1),new r("et",-1,1),new r("eret",30,1)],o=[new r("gd",-1,-1),new r("dt",-1,-1),new r("gt",-1,-1),new r("kt",-1,-1)],a=[new r("ig",-1,1),new r("lig",0,1),new r("elig",1,1),new r("els",-1,1),new r("løst",-1,2)],d=[17,65,16,1,0,0,0,0,0,0,0,0,0,0,0,0,48,0,128],u=[239,254,42,3,0,0,0,0,0,0,0,0,0,0,0,0,16],c=new m;function l(){var e,r=c.limit-c.cursor;c.cursor>=t&&(e=c.limit_backward,c.limit_backward=t,c.ket=c.cursor,c.find_among_b(o,4)?(c.bra=c.cursor,c.limit_backward=e,c.cursor=c.limit-r,c.cursor>c.limit_backward&&(c.cursor--,c.bra=c.cursor,c.slice_del())):c.limit_backward=e)}this.setCurrent=function(e){c.setCurrent(e)},this.getCurrent=function(){return c.getCurrent()},this.stem=function(){var e,r=c.cursor;return function(){var e,r=c.cursor+3;if(t=c.limit,0<=r&&r<=c.limit){for(i=r;;){if(e=c.cursor,c.in_grouping(d,97,248)){c.cursor=e;break}if((c.cursor=e)>=c.limit)return;c.cursor++}for(;!c.out_grouping(d,97,248);){if(c.cursor>=c.limit)return;c.cursor++}(t=c.cursor)<i&&(t=i)}}(),c.limit_backward=r,c.cursor=c.limit,function(){var e,r;if(c.cursor>=t&&(r=c.limit_backward,c.limit_backward=t,c.ket=c.cursor,e=c.find_among_b(s,32),c.limit_backward=r,e))switch(c.bra=c.cursor,e){case 1:c.slice_del();break;case 2:c.in_grouping_b(u,97,229)&&c.slice_del()}}(),c.cursor=c.limit,l(),c.cursor=c.limit,function(){var e,r,i,n=c.limit-c.cursor;if(c.ket=c.cursor,c.eq_s_b(2,"st")&&(c.bra=c.cursor,c.eq_s_b(2,"ig")&&c.slice_del()),c.cursor=c.limit-n,c.cursor>=t&&(r=c.limit_backward,c.limit_backward=t,c.ket=c.cursor,e=c.find_among_b(a,5),c.limit_backward=r,e))switch(c.bra=c.cursor,e){case 1:c.slice_del(),i=c.limit-c.cursor,l(),c.cursor=c.limit-i;break;case 2:c.slice_from("løs")}}(),c.cursor=c.limit,c.cursor>=t&&(e=c.limit_backward,c.limit_backward=t,c.ket=c.cursor,c.out_grouping_b(d,97,248)?(c.bra=c.cursor,n=c.slice_to(n),c.limit_backward=e,c.eq_v_b(n)&&c.slice_del()):c.limit_backward=e),!0}},function(e){return"function"==typeof e.update?e.update(function(e){return i.setCurrent(e),i.stem(),i.getCurrent()}):(i.setCurrent(e),i.stem(),i.getCurrent())}),e.Pipeline.registerFunction(e.da.stemmer,"stemmer-da"),e.da.stopWordFilter=e.generateStopWordFilter("ad af alle alt anden at blev blive bliver da de dem den denne der deres det dette dig din disse dog du efter eller en end er et for fra ham han hans har havde have hende hendes her hos hun hvad hvis hvor i ikke ind jeg jer jo kunne man mange med meget men mig min mine mit mod ned noget nogle nu når og også om op os over på selv sig sin sine sit skal skulle som sådan thi til ud under var vi vil ville vor være været".split(" ")),e.Pipeline.registerFunction(e.da.stopWordFilter,"stopWordFilter-da")}});
				`@@ -0,0 +1 @@`
				!function(e,i){"function"==typeof define&&define.amd?define(i):"object"==typeof exports?module.exports=i():i()(e.lunr)}(this,function(){return function(o){o.multiLanguage=function(){for(var e=Array.prototype.slice.call(arguments),i=e.join("-"),t="",r=[],n=[],s=0;s<e.length;++s)"en"==e[s]?(t+="\\w",r.unshift(o.stopWordFilter),r.push(o.stemmer),n.push(o.stemmer)):(t+=o[e[s]].wordCharacters,r.unshift(o[e[s]].stopWordFilter),r.push(o[e[s]].stemmer),n.push(o[e[s]].stemmer));var p=o.trimmerSupport.generateTrimmer(t);return o.Pipeline.registerFunction(p,"lunr-multi-trimmer-"+i),r.unshift(p),function(){this.pipeline.reset(),this.pipeline.add.apply(this.pipeline,r),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add.apply(this.searchPipeline,n))}}}});
				`@@ -0,0 +1 @@`
				!function(r,t){"function"==typeof define&&define.amd?define(t):"object"==typeof exports?module.exports=t():t()(r.lunr)}(this,function(){return function(r){r.stemmerSupport={Among:function(r,t,i,s){if(this.toCharArray=function(r){for(var t=r.length,i=new Array(t),s=0;s<t;s++)i[s]=r.charCodeAt(s);return i},!r&&""!=r\|\|!t&&0!=t\|\|!i)throw"Bad Among initialisation: s:"+r+", substring_i: "+t+", result: "+i;this.s_size=r.length,this.s=this.toCharArray(r),this.substring_i=t,this.result=i,this.method=s},SnowballProgram:function(){var b;return{bra:0,ket:0,limit:0,cursor:0,limit_backward:0,setCurrent:function(r){b=r,this.cursor=0,this.limit=r.length,this.limit_backward=0,this.bra=this.cursor,this.ket=this.limit},getCurrent:function(){var r=b;return b=null,r},in_grouping:function(r,t,i){if(this.cursor<this.limit){var s=b.charCodeAt(this.cursor);if(s<=i&&t<=s&&r[(s-=t)>>3]&1<<(7&s))return this.cursor++,!0}return!1},in_grouping_b:function(r,t,i){if(this.cursor>this.limit_backward){var s=b.charCodeAt(this.cursor-1);if(s<=i&&t<=s&&r[(s-=t)>>3]&1<<(7&s))return this.cursor--,!0}return!1},out_grouping:function(r,t,i){if(this.cursor<this.limit){var s=b.charCodeAt(this.cursor);if(i<s\|\|s<t)return this.cursor++,!0;if(!(r[(s-=t)>>3]&1<<(7&s)))return this.cursor++,!0}return!1},out_grouping_b:function(r,t,i){if(this.cursor>this.limit_backward){var s=b.charCodeAt(this.cursor-1);if(i<s\|\|s<t)return this.cursor--,!0;if(!(r[(s-=t)>>3]&1<<(7&s)))return this.cursor--,!0}return!1},eq_s:function(r,t){if(this.limit-this.cursor<r)return!1;for(var i=0;i<r;i++)if(b.charCodeAt(this.cursor+i)!=t.charCodeAt(i))return!1;return this.cursor+=r,!0},eq_s_b:function(r,t){if(this.cursor-this.limit_backward<r)return!1;for(var i=0;i<r;i++)if(b.charCodeAt(this.cursor-r+i)!=t.charCodeAt(i))return!1;return this.cursor-=r,!0},find_among:function(r,t){for(var i=0,s=t,e=this.cursor,n=this.limit,u=0,o=0,h=!1;;){for(var c=i+(s-i>>1),a=0,f=u<o?u:o,l=r[c],_=f;_<l.s_size;_++){if(e+f==n){a=-1;break}if(a=b.charCodeAt(e+f)-l.s[_])break;f++}if(a<0?(s=c,o=f):(i=c,u=f),s-i<=1){if(0<i\|\|s==i\|\|h)break;h=!0}}for(;;){if(u>=(l=r[i]).s_size){if(this.cursor=e+l.s_size,!l.method)return l.result;var m=l.method();if(this.cursor=e+l.s_size,m)return l.result}if((i=l.substring_i)<0)return 0}},find_among_b:function(r,t){for(var i=0,s=t,e=this.cursor,n=this.limit_backward,u=0,o=0,h=!1;;){for(var c=i+(s-i>>1),a=0,f=u<o?u:o,l=(_=r[c]).s_size-1-f;0<=l;l--){if(e-f==n){a=-1;break}if(a=b.charCodeAt(e-1-f)-_.s[l])break;f++}if(a<0?(s=c,o=f):(i=c,u=f),s-i<=1){if(0<i\|\|s==i\|\|h)break;h=!0}}for(;;){var _;if(u>=(_=r[i]).s_size){if(this.cursor=e-_.s_size,!_.method)return _.result;var m=_.method();if(this.cursor=e-_.s_size,m)return _.result}if((i=_.substring_i)<0)return 0}},replace_s:function(r,t,i){var s=i.length-(t-r);return b=b.substring(0,r)+i+b.substring(t),this.limit+=s,this.cursor>=t?this.cursor+=s:this.cursor>r&&(this.cursor=r),s},slice_check:function(){if(this.bra<0\|\|this.bra>this.ket\|\|this.ket>this.limit\|\|this.limit>b.length)throw"faulty slice operation"},slice_from:function(r){this.slice_check(),this.replace_s(this.bra,this.ket,r)},slice_del:function(){this.slice_from("")},insert:function(r,t,i){var s=this.replace_s(r,t,i);r<=this.bra&&(this.bra+=s),r<=this.ket&&(this.ket+=s)},slice_to:function(){return this.slice_check(),b.substring(this.bra,this.ket)},eq_v_b:function(r){return this.eq_s_b(r.length,r)}}}},r.trimmerSupport={generateTrimmer:function(r){var t=new RegExp("^[^"+r+"]+"),i=new RegExp("[^"+r+"]+$");return function(r){return"function"==typeof r.update?r.update(function(r){return r.replace(t,"").replace(i,"")}):r.replace(t,"").replace(i,"")}}}}});