Deployed b35229d with MkDocs version: 1.0.4

.circleci/config.yml

@@ -1,182 +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: |
-        sudo apt-get update && sudo apt-get install -y cmake
-        pip install "$TORCH_VERSION"
-        pip install -r requirements.txt -q
-        sudo pip install pytest -q
-        pip install -r ./tests/requirements-devel.txt -q
-
-  tests: &tests
-   run:
-     name: Testing
-     command: |
-       python --version ; pip --version ; pip list
-       py.test pytorch_lightning tests -v --doctest-modules --junitxml=test-reports/pytest_junit.xml
-     no_output_timeout: 15m
-
-  examples: &examples
-   run:
-     name: PL Examples
-     command: |
-       pip install -r ./pl_examples/requirements.txt --user
-       python --version ; pip --version ; pip list
-       py.test pl_examples -v --doctest-modules --junitxml=test-reports/pytest_junit.xml
-     no_output_timeout: 20m
-
-  install_pkg: &install_pkg
-    run:
-      name: Install package
-      command: |
-        virtualenv vEnv ; source vEnv/bin/activate
-        pip install --editable . ; cd .. & python -c "import pytorch_lightning ; print(pytorch_lightning.__version__)"
-        deactivate ; rm -rf vEnv
-
-  create_pkg: &create_pkg
-    run:
-      name: Create package
-      command: |
-        sudo pip install twine==1.13.0
-        python setup.py sdist
-        twine check dist/*
-        python setup.py clean
-
-  format: &format
-   run:
-     name: Formatting
-     command: |
-       python --version ; pip --version
-       sudo pip install flake8 -q
-       pip list
-       flake8 .
-
-  make_docs: &make_docs
-   run:
-     name: Make Documentation
-     command: |
-       # First run the same pipeline as Read-The-Docs
-       sudo apt-get update && sudo apt-get install -y cmake
-       sudo pip install -r docs/requirements.txt
-       cd docs; make clean; make html --debug --jobs 2 SPHINXOPTS="-W"
-
-  test_docs: &test_docs
-   run:
-     name: Testing Documentation
-     command: |
-       # Second run examples in docs
-       sudo apt-get update && sudo apt-get install -y cmake
-       sudo pip install -r docs/requirements.txt
-       cd docs; make doctest; make coverage
-
-jobs:
-
-  Build-Docs:
-    docker:
-      - image: circleci/python:3.7
-    steps:
-      - checkout
-      - *make_docs
-      - store_artifacts:
-          # allows us to preview the generated html pages
-          path: docs/build/html/
-          destination: html
-
-  Formatting:
-    docker:
-      - image: circleci/python:3.7
-    environment:
-        - TORCH_VERSION: "torch"
-    steps:
-      - checkout
-      - *format
-
-  PyTorch:
-    docker:
-      - image: circleci/python:3.6
-    environment:
-      - TORCH_VERSION: "torch"
-    steps: &steps
-      - checkout
-      #- restore_cache:
-      #    keys:
-      #      # when lock file changes, use increasingly general patterns to restore cache
-      #      - pip-packages--{{ .Environment.CIRCLE_JOB }}
-      #      - pip-packages--
-      - *install_deps
-      #- save_cache:
-      #    key: pip-packages--{{ .Environment.CIRCLE_JOB }}
-      #    paths:
-      #      # this path depends on where pipenv creates a virtualenv
-      #      - "~/.cache/pip"
-      #      - "/usr/local/lib/python3.6/site-packages"
-      #      - "/usr/local/lib/site-python"
-      - *tests
-      - store_test_results:
-          path: test-reports
-      - store_artifacts:
-          path: test-reports
-
-  PyTorch-v1_3:
-    docker:
-      - image: circleci/python:3.6
-    environment:
-      - TORCH_VERSION: "torch>=1.3, <1.4"
-    steps: *steps
-
-  PyTorch-v1_4:
-    docker:
-      - image: circleci/python:3.6
-    environment:
-      - TORCH_VERSION: "torch>=1.4, <1.5"
-    steps: *steps
-
-  PyTorch-v1_5:
-    docker:
-      - image: circleci/python:3.6
-    environment:
-      - TORCH_VERSION: "torch>=1.5, <1.6"
-    steps: *steps
-
-  Examples:
-    docker:
-      - image: circleci/python:3.7
-    environment:
-        - TORCH_VERSION: "torch"
-        - SPHINX_MOCK_REQUIREMENTS: 0
-    steps:
-      - checkout
-      - *install_deps
-      - *test_docs
-      - *examples
-
-  Install-pkg:
-    docker:
-      - image: circleci/python:3.7
-    steps:
-      - checkout
-      - *create_pkg
-      - *install_pkg
-
-#orbs:
-#  python: circleci/python@0.2.1
-
-workflows:
-  version: 2
-  build:
-    jobs:
-      - Formatting
-      - Build-Docs
-      - PyTorch-v1_3
-      - PyTorch-v1_4
-      - PyTorch-v1_5
-      - Install-pkg
-      - Examples

.codecov.yml

@@ -1,54 +0,0 @@
-# see https://docs.codecov.io/docs/codecov-yaml
-# Validation check:
-# $ curl --data-binary @.codecov.yml https://codecov.io/validate
-
-
-# https://docs.codecov.io/docs/codecovyml-reference
-codecov:
-  bot: "codecov-io"
-  strict_yaml_branch: "yaml-config"
-  require_ci_to_pass: yes
-  notify:
-    # after_n_builds: 2
-    wait_for_ci: yes
-  # https://docs.codecov.io/docs/codecov-yaml#section-expired-reports
-  max_report_age: off
-
-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: 30% # 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: 50% # 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: *
-

.drone.yml

@@ -1,58 +0,0 @@
-# https://docs.drone.io/pipeline/docker/examples/languages/python/#python-example
-
-kind: pipeline
-type: docker
-name: torch-GPU
-
-steps:
-- name: testing
-  image: pytorchlightning/pytorch_lightning:devel-pt_1_4
-
-  environment:
-    SLURM_LOCALID: 0
-    CODECOV_TOKEN:
-      from_secret: codecov_token
-    HOROVOD_GPU_ALLREDUCE: NCCL
-    HOROVOD_GPU_BROADCAST: NCCL
-    HOROVOD_WITH_PYTORCH: 1
-    HOROVOD_WITHOUT_TENSORFLOW: 1
-    HOROVOD_WITHOUT_MXNET: 1
-    HOROVOD_WITH_GLOO: 1
-    HOROVOD_WITHOUT_MPI: 1
-
-  #volumes:
-  #  # Mount pip cache from host
-  #  - name: pip_cache
-  #    path: /opt/conda/lib/python3.7/site-packages
-
-  commands:
-    - export PATH="$PATH:/root/.local/bin"
-    - python --version
-    - pip install pip -U
-    - pip --version
-    - nvidia-smi
-    #- bash ./tests/install_AMP.sh
-    - apt-get update && apt-get install -y cmake
-    - pip install -r requirements.txt --user -q
-    - pip install -r ./tests/requirements-devel.txt --user -q
-    #- pip install -r ./docs/requirements.txt --user -q
-    - pip list
-    - python -c "import torch ; print(' & '.join([torch.cuda.get_device_name(i) for i in range(torch.cuda.device_count())]) if torch.cuda.is_available() else 'only CPU')"
-    - coverage run --source pytorch_lightning -m py.test pytorch_lightning tests benchmarks -v --doctest-modules #  --flake8
-    #- cd docs; make doctest; make coverage
-    - coverage report
-    - codecov --token $CODECOV_TOKEN  # --pr $DRONE_PULL_REQUEST --build $DRONE_BUILD_NUMBER --branch $DRONE_BRANCH --commit $DRONE_COMMIT --tag $DRONE_TAG
-    - python tests/collect_env_details.py
-
-trigger:
-  branch:
-    - master
-  event:
-    include:
-    - push
-    - pull_request
-
-#volumes:
-#  - name: pip_cache
-#    host:
-#      path: /tmp/cache/drone/pip

.github/BECOMING_A_CORE_CONTRIBUTOR.md

@@ -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/PyTorchLightning/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).

.github/CODE_OF_CONDUCT.md

@@ -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

.github/CONTRIBUTING.md

@@ -1,199 +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 the 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 helps users avoid all sorts of subtle errors.
-
-## 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, eventually one popular solution becomes standard practice, and everyone follows.
- We try to find the best way to solve a particular problem, and then force our users to use it for readability and simplicity.
- A good example is accumulated gradients.
- There are many different ways to implement it, we just pick one and force users to use it.
- 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 is usually something like bits of 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. When creating an issue with an API change suggestion, please validate that it makes sense for others.
- Treat code changes the way 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 to ensure that every implementation of a new trick or subtle 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
-We are 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...) so we're in a good state there thanks to all the early contributors (even pre-beta release)!
-
-### Bug Fixes:
-1. Submit a github issue - try to describe what happened so others can reproduce it too (config, code samples, expected vs. actual behaviour). 
- Note, that the sample code shall be minimal and if needed with publicly available data.
-2. Try to fix it or recommend a solution...
- We highly recommend to use test driven approach
-   * convert your minimal code example to a unit/integration test with assert on expected results
-   * start with debugging the issue... you can run just this particular test in your IDE and draft a fix
-   * verify that your test case fails on the master branch and only passes with the fix applied
-3. Submit a PR!
-
-_**Note**, even if you do not find the solution, sending a PR with a test covering the issue is a valid contribution and we can help you or finish it with you :]_
-
-
-### New Features:
-1. Submit a github issue - describe what is the motivation of such feature (adding the use case or an example is helpful).
-2. Let's discuss to determine the feature scope.
-3. Submit a PR! (with updated docs and tests🙃).
-
----
-
-## Guidelines
-
-### Original code
-
-All added or edited code shall be the own original work of the particular contributor.
-If you use come third-party implementation, all such blocks/functions/modules shall be properly referred and if possible also agreed by code's author. For example - `This code is inpired from http://...`.
-In case you adding new dependencies, make sure that they are compatible the actual PyTorch Lightning license (ie. dependencies should be _at least_ as permissive as the PyTorch Lightning license).
-
-### Coding Style
-
-1. Use f-strings for output formation (except logging when we stay with lazy `logging.info("Hello %s!`, name).
-2. Test the code with flake8, run locally PEP8 fixes:
-    ```
-    autopep8 -v -r --max-line-length 120 --in-place .
-    ```
-
-### Documentation
-
-We are using Sphinx with Napoleon extension. 
-Moreover we set Google style to follow with type convention. 
-
-- [Napoleon formatting with Google style](https://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_google.html)
-- [ReStructured Text (reST)](https://docs.pylonsproject.org/projects/docs-style-guide/)
-- [Paragraph-level markup](https://www.sphinx-doc.org/en/1.5/markup/para.html)
-
-See following short example of a sample function taking one position string and optional 
-
-```python
-from typing import Optional
-
-def my_func(param_a: int, param_b: Optional[float] = None) -> str:
-    """Sample function.
-
-    Args:
-        param_a: first parameter
-        param_b: second parameter
-    
-    Return:
-        sum of both numbers
-
-    Example:
-        Sample doctest example...
-        >>> my_func(1, 2)
-        3
-
-    .. note:: If you want to add something.
-    """
-    p = param_b if param_b else 0
-    return str(param_a + p)
-```
-
-When updating the docs make sure to build them first locally and visually inspect the html files (in the browser) for 
-formatting errors. In certain cases, a missing blank line or a wrong indent can lead to a broken layout. 
-Run these commands 
-```bash
-cd docs
-pip install -r requirements.txt
-make html
-```
-and open `docs/build/html/index.html` in your browser.
-
-When you send a PR the continuous integration will run tests and build the docs. You can access a preview of the html pages in the 
-_Artifacts_ tab in CircleCI when you click on the task named _ci/circleci: Build-Docs_ at the bottom of the PR page.
-
-### Testing
-
-Testing your work locally will help you speed up the process since it allows you to focus on particular (failing) test-cases.
- To setup a local development environment, install both local and test dependencies:
-```bash
-pip install -r requirements.txt
-pip install -r tests/requirements-devel.txt
-``` 
-
-You can run the full test-case in your terminal via this bash script: 
-
-```bash
-bash .run_local_tests.sh
-```
-
-Note: if your computer does not have multi-GPU nor TPU these tests are skipped.
-
-For convenience, you can also use your own CircleCI building which will be triggered with each commit.
-This is useful if you do not test against all required dependency versions.
-To do so, login to [CircleCI](https://app.circleci.com/) and enable your forked project in the dashboard. It will just work after that.
-
-### Pull Request
-
-We welcome any useful contribution! For your convenience here's a recommended workflow:
-
-0. Think about what you want to do - fix a bug, repair docs, etc. 
-1. Start your work locally (usually until you need our CI testing)
-    - create a branch and prepare your changes
-    - hint: do not work with your master directly, it may become complicated when you need to rebase
-    - hint: give your PR a good name! it will be useful later when you may work on multiple tasks/PRs
-2. Create a "Draft PR" which is clearly marked, to let us know you don't need feedback yet.
-3. When you feel ready for integrating your work, mark your PR "Ready for review".
-4. Use tags in PR name for following cases:
-    - **[blocked by #<number>]** if you work is depending on others changes
-    - **[wip]** when you start to re-edit your work, mark it so no one will accidentally merge it in meantime
-
-### Question & Answer
-
-1. **How can I help/contribute?**
-
-    All help is very welcome - reporting bugs, solving issues and preparing bug fixes. To solve some issues you can start with label [good first issue](https://github.com/PyTorchLightning/pytorch-lightning/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22) or chose something close to your domain with label [help wanted](https://github.com/PyTorchLightning/pytorch-lightning/issues?q=is%3Aopen+is%3Aissue+label%3A%22help+wanted%22). Before you start to implement anything check that the issue description that it is clear and self-assign the task to you (if it is not possible, just comment that you take it and we assign it to you...).
-
-2. **Is there a recommendation for branch names?**
-    
-    We do not rely on the name convention so far you are working with your own fork. Anyway it would be nice to follow this convention `<type>/<issue-id>_<short-name>` where the types are: `bugfix`, `feature`, `docs`, `tests`, ...
-
-3. **How to rebase my PR?**
-    
-    We recommend creating a PR from a separate branch other than `master`, especially if you plan on submitting several changes at once and do not want to wait until the first one is resolved (we can work on them in parallel). Update your master with upstream (assuming you have already set [upstream](https://help.github.com/en/github/collaborating-with-issues-and-pull-requests/configuring-a-remote-for-a-fork))
-    ```bash
-    git fetch --all --prune
-    git checkout master
-    git merge upstream/master
-    ```
-    checkout your feature branch
-    ```bash
-    git checkout my-PR-branch
-    git rebase master
-    # follow git instructions to resolve conflists
-    git push -f
-    ```

.github/ISSUE_TEMPLATE/bug_report.md

@@ -1,64 +0,0 @@
----
-name: Bug report
-about: Create a report to help us improve
-title: ''
-labels: bug, help wanted
-assignees: ''
-
----
-
-<!-- 
-### Common bugs:
-1. Tensorboard not showing in Jupyter-notebook see [issue 79](https://github.com/PyTorchLightning/pytorch-lightning/issues/79).    
-2. PyTorch 1.1.0 vs 1.2.0 support [see FAQ](https://github.com/PyTorchLightning/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/PyTorchLightning/pytorch-lightning/master/tests/collect_env_details.py)
-(or fill out the checklist below manually).
-
-You can get the script and run it with:
-```
-wget https://raw.githubusercontent.com/PyTorchLightning/pytorch-lightning/master/tests/collect_env_details.py
-# For security purposes, please check the contents of collect_env_details.py before running it.
-python collect_env_details.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. -->

.github/ISSUE_TEMPLATE/documentation.md

@@ -1,18 +0,0 @@
----
-name: Typos and doc fixes
-about: Typos and doc fixes
-title: ''
-labels: typo, documentation
-assignees: ''
-
----
-
-## 📚 Documentation
-
-For typos and doc fixes, please go ahead and:
-
-1. Create an issue.
-2. Fix the typo.   
-3. Submit a PR.
-
-Thanks!

.github/ISSUE_TEMPLATE/feature_request.md

@@ -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. -->

.github/ISSUE_TEMPLATE/how-to-question.md

@@ -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]

.github/PULL_REQUEST_TEMPLATE.md

@@ -1,19 +0,0 @@
-# Before submitting
-
-- [ ] Was this discussed/approved via a Github issue? (no need for typos and docs improvements)
-- [ ] Did you read the [contributor guideline](https://github.com/PyTorchLightning/pytorch-lightning/blob/master/.github/CONTRIBUTING.md), Pull Request section?
-- [ ] Did you make sure to update the docs?   
-- [ ] Did you write any new necessary tests?  
-- [ ] If you made a notable change (that affects users), did you update the [CHANGELOG](https://github.com/PyTorchLightning/pytorch-lightning/blob/master/CHANGELOG.md)?
-
-<!-- For CHANGELOG separate each item in unreleased section by a blank line to reduce collisions -->
-
-## 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 🙃

.github/stale.yml

@@ -1,19 +0,0 @@
-# https://github.com/marketplace/stale
-
-# Number of days of inactivity before an issue becomes stale
-daysUntilStale: 60
-# Number of days of inactivity before a stale issue is closed
-daysUntilClose: 9
-# Issues with these labels will never be considered stale
-exemptLabels:
-  - pinned
-  - security
-# Label to use when marking an issue as stale
-staleLabel: wontfix
-# Comment to post when marking an issue as stale. Set to `false` to disable
-markComment: >
-  This issue has been automatically marked as stale because it has not had
-  recent activity. It will be closed if no further activity occurs. Thank you
-  for your contributions.
-# Comment to post when closing a stale issue. Set to `false` to disable
-closeComment: false

.github/workflows/ci-testing.yml

@@ -1,139 +0,0 @@
-name: CI testing
-
-# see: https://help.github.com/en/actions/reference/events-that-trigger-workflows
-# Trigger the workflow on push or pull request
-on: [push, pull_request]
-
-jobs:
-  build:
-
-    runs-on: ${{ matrix.os }}
-    strategy:
-      fail-fast: false
-      # max-parallel: 6
-      matrix:
-        os: [ubuntu-18.04, windows-2019, macOS-10.15]
-        python-version: [3.6, 3.7, 3.8]
-        requires: ['minimal', 'latest']
-        exclude:
-          # excludes PT 1.3 as it is missing on pypi
-          - python-version: 3.8
-            requires: 'minimal'
-
-    # Timeout: https://stackoverflow.com/a/59076067/4521646
-    timeout-minutes: 15
-    steps:
-    - uses: actions/checkout@v2
-    - name: Set up Python ${{ matrix.python-version }}
-      uses: actions/setup-python@v1
-      with:
-        python-version: ${{ matrix.python-version }}
-
-    # Github Actions: Run step on specific OS: https://stackoverflow.com/a/57948488/4521646
-    - name: Setup macOS
-      if: runner.os == 'macOS'
-      run: |
-        brew install libomp  # https://github.com/pytorch/pytorch/issues/20030
-        brew install openmpi  # Horovod on macOS requires OpenMPI, Gloo not currently supported
-
-    - name: Setup Windows
-      if: runner.os == 'windows'
-      run: |
-        python -c "lines = [line for line in open('requirements-extra.txt').readlines() if not line.startswith('horovod')] ; open('requirements-extra.txt', 'w').writelines(lines)"
-
-    # TODO: remove after https://github.com/pytorch/pytorch/issues/32186 is resolved
-    - name: Setup Windows on Latest
-      if: runner.os == 'windows' && matrix.requires == 'latest'
-      run: |
-        python -c "req = open('requirements.txt').read().replace('torch>=1.3', 'torch<1.5') ; open('requirements.txt', 'w').write(req)"
-
-    # versions <= 1.3 may have issues on mac with some BLAS ops due to missing mkl (https://github.com/pytorch/pytorch/issues/18996)
-    - name: Setup MacOS Minimal
-      if: runner.os == 'macOS' && matrix.requires == 'minimal'
-      run : |
-        python -c "req = open('requirements.txt').read().replace('torch>=1.3', 'torch>=1.4') ; open('requirements.txt', 'w').write(req)"
-
-    - name: Set min. dependencies
-      if: matrix.requires == 'minimal'
-      run: |
-        python -c "req = open('requirements.txt').read().replace('>', '=') ; open('requirements.txt', 'w').write(req)"
-        python -c "req = open('requirements-extra.txt').read().replace('>', '=') ; open('requirements-extra.txt', 'w').write(req)"
-
-    # Note: This uses an internal pip API and may not always work
-    # https://github.com/actions/cache/blob/master/examples.md#multiple-oss-in-a-workflow
-    - name: Get pip cache
-      id: pip-cache
-      run: |
-        python -c "from pip._internal.locations import USER_CACHE_DIR; print('::set-output name=dir::' + USER_CACHE_DIR)"
-
-    - name: Cache pip
-      uses: actions/cache@v1
-      with:
-        path: ${{ steps.pip-cache.outputs.dir }}
-        key: ${{ runner.os }}-${{ matrix.python-version }}-${{ matrix.requires }}-pip-${{ hashFiles('requirements.txt') }}-${{ hashFiles('requirements-extra.txt') }}
-        restore-keys: |
-          ${{ runner.os }}-${{ matrix.python-version }}-${{ matrix.requires }}-pip-
-
-    - name: Install dependencies
-      run: |
-        # python -m pip install --upgrade --user pip
-        pip install -r requirements.txt -U -f https://download.pytorch.org/whl/torch_stable.html -q
-        HOROVOD_BUILD_ARCH_FLAGS="-mfma" pip install -r ./tests/requirements-devel.txt -q
-        # pip install tox coverage
-        python --version
-        pip --version
-        pip list
-      shell: bash
-
-    - name: Reinstall Horovod if necessary
-      if: runner.os != 'windows' && matrix.python-version != '3.8'
-      run: |
-        HOROVOD_BUILT=$(python -c "import horovod.torch; horovod.torch.nccl_built(); print('SUCCESS')")
-        if [[ $HOROVOD_BUILT != "SUCCESS" ]]; then
-          pip uninstall -y horovod
-          HOROVOD_BUILD_ARCH_FLAGS="-mfma" pip install --no-cache-dir $(grep "horovod" requirements-extra.txt)
-        fi
-        horovodrun --check-build
-      shell: bash
-
-    - name: Cache datasets
-      uses: actions/cache@v1
-      with:
-        path: tests/Datasets # This path is specific to Ubuntu
-        # Look to see if there is a cache hit for the corresponding requirements file
-        key: mnist-dataset
-
-    - name: Tests
-      # env:
-      #   TOXENV: py${{ matrix.python-version }}
-      run: |
-        # tox --sitepackages
-        # flake8 .
-        coverage run --source pytorch_lightning -m py.test pytorch_lightning tests -v --doctest-modules --junitxml=junit/test-results-${{ runner.os }}-${{ matrix.python-version }}-${{ matrix.requires }}.xml
-        coverage report
-
-    - name: Upload pytest test results
-      uses: actions/upload-artifact@master
-      with:
-        name: pytest-results-${{ runner.os }}-${{ matrix.python-version }}-${{ matrix.requires }}
-        path: junit/test-results-${{ runner.os }}-${{ matrix.python-version }}-${{ matrix.requires }}.xml
-      # Use always() to always run this step to publish test results when there are test failures
-      if: always()
-
-    - name: Package Setup
-      run: |
-        check-manifest
-        python setup.py check --metadata --strict
-        python setup.py sdist
-        twine check dist/*
-    #- name: Try install package
-    #  if: ! startsWith(matrix.os, 'windows')
-    #  run: |
-    #    virtualenv vEnv ; source vEnv/bin/activate
-    #    pip install --editable . ; cd .. & python -c "import pytorch_lightning ; print(pytorch_lightning.__version__)"
-    #    deactivate ; rm -rf vEnv
-
-    - name: Statistics
-      if: success()
-      run: |
-         coverage report

.github/workflows/docker-builds.yml

@@ -1,50 +0,0 @@
-name: Publish Docker Releases
-# https://www.docker.com/blog/first-docker-github-action-is-here
-on:
-  push:
-    branches: 
-      - master
-  release:
-    types:
-       - created
-
-jobs:
-  build:
-    runs-on: ubuntu-latest
-    strategy:
-      matrix:
-        python_version: [3.6, 3.7, 3.8]
-        pytorch_version: [1.3, 1.4, 1.5]
-    steps:
-      - uses: actions/checkout@v2
-
-      - name: Publish Master to Docker
-        # publish master
-        uses: docker/build-push-action@v1.1.0
-        if: github.event_name == 'push'
-        with:
-          repository: pytorchlightning/pytorch_lightning
-          username: ${{ secrets.DOCKER_USERNAME }}
-          password: ${{ secrets.DOCKER_PASSWORD }}
-          dockerfile: docker/Dockerfile
-          buildargs: PYTHON_VERSION=${{ matrix.python_version }},PYTORCH_VERSION=${{ matrix.pytorch_version }}
-          tags: "nightly-py${{ matrix.python_version }}-torch${{ matrix.pytorch_version }}"
-        timeout-minutes: 30
-
-      - name: Get release version
-        if: startsWith(github.ref, 'refs/tags/') || github.event_name == 'release'
-        id: get_version
-        run: echo ::set-env name=RELEASE_VERSION::$(echo ${GITHUB_REF##*/})
-
-      - name: Publish Releases to Docker
-        # only on releases
-        uses: docker/build-push-action@v1.1.0
-        if: startsWith(github.ref, 'refs/tags/') || github.event_name == 'release'
-        with:
-          repository: pytorchlightning/pytorch_lightning
-          username: ${{ secrets.DOCKER_USERNAME }}
-          password: ${{ secrets.DOCKER_PASSWORD }}
-          dockerfile: docker/Dockerfile
-          buildargs: PYTHON_VERSION=${{ matrix.python_version }},PYTORCH_VERSION=${{ matrix.pytorch_version }},LIGHTNING_VERSION=${{ env.RELEASE_VERSION }}
-          tags: "${{ env.RELEASE_VERSION }}-py${{ matrix.python_version }}-torch${{ matrix.pytorch_version }},latest-py${{ matrix.python_version }}-torch${{ matrix.pytorch_version }}"
-        timeout-minutes: 30

.github/workflows/docs-check.yml

@@ -1,17 +0,0 @@
-name: "Docs check"
-# https://github.com/marketplace/actions/sphinx-build
-
-on:
-- pull_request
-
-jobs:
-  docs:
-    runs-on: ubuntu-latest
-    steps:
-    - uses: actions/checkout@v2
-    - uses: ammaraskar/sphinx-action@master
-      with:
-        # git is required to clone the docs theme
-        pre-build-command: "apt-get update -y && apt-get install -y git"
-        docs-folder: "docs/"
-        repo-token: "${{ secrets.GITHUB_TOKEN }}"

.github/workflows/greetings.yml

@@ -1,14 +0,0 @@
-name: Greetings
-# https://github.com/marketplace/actions/first-interaction
-
-on: [issues]  # pull_request
-
-jobs:
-  greeting:
-    runs-on: ubuntu-latest
-    steps:
-    - uses: actions/first-interaction@v1
-      with:
-        repo-token: ${{ secrets.GITHUB_TOKEN }}
-        issue-message: 'Hi! thanks for your contribution!, great first issue!'
-        pr-message: 'Hey thanks for the input! Please give us a bit of time to review it!'

.github/workflows/pypi-release.yml

@@ -1,48 +0,0 @@
-name: PyPI Release
-
-# https://help.github.com/en/actions/reference/events-that-trigger-workflows
-on:
-  # Trigger the workflow on push or pull request,
-  # but only for the master branch
-  push:
-    branches:
-      - master
-  release:
-    types:
-      - created
-
-# based on https://github.com/pypa/gh-action-pypi-publish
-
-jobs:
-  build:
-    runs-on: ubuntu-latest
-
-    steps:
-    - uses: actions/checkout@v2
-    - name: Set up Python 3.7
-      uses: actions/setup-python@v1
-      with:
-        python-version: 3.7
-
-    - name: Install dependencies
-      run: >-
-        python -m pip install --user --upgrade setuptools wheel
-    - name: Build
-      run: >-
-        python setup.py sdist bdist_wheel
-
-    # We do this, since failures on test.pypi aren't that bad
-    - name: Publish to Test PyPI
-      if: startsWith(github.event.ref, 'refs/tags') || github.event_name == 'release'
-      uses: pypa/gh-action-pypi-publish@master
-      with:
-        user: __token__
-        password: ${{ secrets.test_pypi_password }}
-        repository_url: https://test.pypi.org/legacy/
-
-    - name: Publish distribution 📦 to PyPI
-      if: startsWith(github.event.ref, 'refs/tags') || github.event_name == 'release'
-      uses: pypa/gh-action-pypi-publish@master
-      with:
-        user: __token__
-        password: ${{ secrets.pypi_password }}

.github/workflows/rebase.yml

@@ -1,20 +0,0 @@
-name: Automatic Rebase
-# https://github.com/marketplace/actions/automatic-rebase
-
-on: 
-  issue_comment:
-    types: [created]
-
-jobs:
-  rebase:
-    name: Rebase
-    if: github.event.issue.pull_request != '' && contains(github.event.comment.body, '/rebase')
-    runs-on: ubuntu-latest
-    steps:
-    - uses: actions/checkout@v2
-      with:
-        fetch-depth: 0
-    - name: Automatic Rebase
-      uses: cirrus-actions/rebase@1.2
-      env:
-        GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}

.gitignore

@@ -1,137 +0,0 @@
-# project
-.DS_Store
-run_configs/
-model_weights/
-app/models/
-pip-wheel-metadata/
-lightning_logs/
-.vscode/
-
-# Test-tube
-test_tube_logs/
-test_tube_data/
-test_tube_exp/
-
-# Documentations
-docs/source/api
-docs/source/*.md
-
-# Byte-compiled / optimized / DLL files
-__pycache__/
-*.py[cod]
-*$py.class
-timit_data/
-
-
-# C extensions
-*.so
-
-.idea/
-
-# Distribution / packaging
-.Python
-ide_layouts/
-build/
-develop-eggs/
-dist/
-downloads/
-eggs/
-.eggs/
-lib/
-lib64/
-parts/
-sdist/
-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/
-tests/tests_tt_dir/
-tests/save_dir
-tests/tests/
-
-# 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
-env/
-venv/
-ENV/
-
-# Spyder project settings
-.spyderproject
-.spyproject
-
-# Rope project settings
-.ropeproject
-
-# mkdocs documentation
-/site
-
-# mypy
-.mypy_cache/
-
-# data
-.data/
-datasets/
-mnist/
-
-# pl tests
-ml-runs/
-*.zip
-pytorch\ lightning
-test-reports/

.mergify.yml

@@ -1,49 +0,0 @@
-pull_request_rules:
-
-  - name: Automatic merge on approval
-    conditions:
-      - base=master
-      # number of review approvals
-      - "#approved-reviews-by>=3"
-      # no waiting or assigned review
-      - "#review-requested=0"
-      # no requested chnages from any reviewer
-      - "#changes-requested-reviews-by=0"
-      # this serves as ALL check has to pass as we have actually 27 tests in total
-      - "#status-success>=28"
-      # this is just in case since we rely on GPU tests (note: redundand to the above)
-      - status-success=continuous-integration/drone/pr
-      # this is patter-like, unofrunatly serves as `any(...)` (note: redundand to the above)
-      - "status-success~=^ci/circleci:"
-      # no conflict with master branch
-      - -conflict
-      # was not closed yet
-      - -closed
-    actions:
-      delete_head_branch: {}
-      merge:
-        # https://doc.mergify.io/merge-action.html#strict-merge
-        # (on head branch) $ git merge --no-ff base
-        # (on head branch) # Wait for CI to go green
-        # (on head branch) # Squash all commits
-        # (on base branch) $ git merge --ff head
-        strict: true
-        method: squash
-      comment:
-        message: Great job! =)
-
-  - name: warn on conflicts
-    conditions:
-      - conflict
-    actions:
-      comment:
-        message: This pull request is now in conflict... :(
-
-  - name: add core reviewer
-    conditions:
-      # number of review approvals
-      - "#approved-reviews-by<3"
-    actions:
-      request_reviews:
-        teams:
-          - core-contributors

.pep8speaks.yml

@@ -1,30 +0,0 @@
-# File : .pep8speaks.yml
-
-scanner:
-    diff_only: True  # If False, the entire file touched by the Pull Request is scanned for errors. If True, only the diff is scanned.
-    linter: pycodestyle  # Other option is flake8
-
-pycodestyle:  # Same as scanner.linter value. Other option is flake8
-    max-line-length: 110  # Default is 79 in PEP 8
-    ignore:  # Errors and warnings to ignore
-        - W504  # line break after binary operator
-        - E402  # module level import not at top of file
-        - E731  # do not assign a lambda expression, use a def
-        - C406  # Unnecessary list literal - rewrite as a dict literal.
-        - E741  # ambiguous variable name
-        - F401
-        - F841
-
-no_blank_comment: True  # If True, no comment is made on PR without any errors.
-descending_issues_order: False  # If True, PEP 8 issues in message will be displayed in descending order of line numbers in the file
-
-message:  # Customize the comment made by the bot,
-    opened:  # Messages when a new PR is submitted
-        header: "Hello @{name}! Thanks for opening this PR. "
-                # The keyword {name} is converted into the author's username
-        footer: "Do see the [Hitchhiker's guide to code style](https://goo.gl/hqbW4r)"
-                # The messages can be written as they would over GitHub
-    updated:  # Messages when new commits are added to the PR
-        header: "Hello @{name}! Thanks for updating this PR. "
-        footer: ""  # Why to comment the link to the style guide everytime? :)
-    no_errors: "There are currently no PEP 8 issues detected in this Pull Request. Cheers! :beers: "

.readthedocs.yml

@@ -1,28 +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
-# reference: https://docs.readthedocs.io/en/stable/config-file/v2.html#sphinx
-sphinx:
-  configuration: docs/source/conf.py
-  fail_on_warning: true
-
-# Build documentation with MkDocs
-#mkdocs:
-#  configuration: mkdocs.yml
-
-# Optionally build your docs in additional formats such as PDF and ePub
-formats:
-  - htmlzip
-  - pdf
-
-# Optionally set the version of Python and requirements required to build your docs
-python:
-  version: 3.7
-  install:
-    - requirements: docs/requirements.txt
-    #- requirements: requirements.txt

.run_local_tests.sh

@@ -1,19 +0,0 @@
-#!/usr/bin/env bash
-
-# install APEX, see https://github.com/NVIDIA/apex#linux
-# to imitate SLURM set only single node
-export SLURM_LOCALID=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/wandb*
-rm -rf ./tests/tests/*
-rm -rf ./lightning_logs
-python -m coverage run --source pytorch_lightning -m py.test pytorch_lightning tests pl_examples -v --doctest-modules --flake8 --durations=0
-python -m coverage report -m
-
-# specific file
-# python -m coverage run --source pytorch_lightning -m py.test -k test_trainer.py --flake8 --durations=0

.update.sh

@@ -1,17 +0,0 @@
-#!/bin/bash
-
-version=$1
-
-git commit -am "release v$version"
-git tag $version -m "test_tube v$version"
-git push --tags origin master
-
-# push to pypi
-rm -rf ./dist/*
-python3 setup.py sdist
-twine upload dist/*
-
-# to update docs
-# cd to root dir
-# mkdocs gh-deploy
-

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CHANGELOG.md

@@ -1,683 +0,0 @@
-# Changelog
-
-All notable changes to this project will be documented in this file.
-
-The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
-
-## [unreleased] - YYYY-MM-DD
-
-### Added
-
-- Add Metric Base Classes ([#1326](https://github.com/PyTorchLightning/pytorch-lightning/pull/1326), [#1877](https://github.com/PyTorchLightning/pytorch-lightning/pull/1877))
-
-- Added type hints in `Trainer.fit()` and `Trainer.test()` to reflect that also a list of dataloaders can be passed in ([#1723](https://github.com/PyTorchLightning/pytorch-lightning/pull/1723))
-
-- Allow dataloaders without sampler field present ([#1907](https://github.com/PyTorchLightning/pytorch-lightning/pull/1907))
-
-- Added option `save_last` to save the model at the end of every epoch in `ModelCheckpoint` [(#1908)](https://github.com/PyTorchLightning/pytorch-lightning/pull/1908)
-
-- Early stopping checks `on_validation_end` ([#1458](https://github.com/PyTorchLightning/pytorch-lightning/pull/1458))
-
-- Attribute `best_model_path` to `ModelCheckpoint` for storing and later retrieving the path to the best saved model file ([#1799](https://github.com/PyTorchLightning/pytorch-lightning/pull/1799)) 
-
-- Speed up single-core TPU training by loading data using `ParallelLoader` ([#2033](https://github.com/PyTorchLightning/pytorch-lightning/pull/2033))
-
-- Added a model hook `transfer_batch_to_device` that enables moving custom data structures to the target device ([1756](https://github.com/PyTorchLightning/pytorch-lightning/pull/1756)).
-
-### Changed
-
-- Allow user to select individual TPU core to train on ([#1729](https://github.com/PyTorchLightning/pytorch-lightning/pull/1729))
-
-- Removed non-finite values from loss in `LRFinder` ([#1862](https://github.com/PyTorchLightning/pytorch-lightning/pull/1862))
-
-- Allow passing model hyperparameters as complete kwarg list ([#1896](https://github.com/PyTorchLightning/pytorch-lightning/pull/1896)) 
-
-- Renamed `ModelCheckpoint`'s attributes `best` to `best_model_score` and `kth_best_model` to `kth_best_model_path` ([#1799](https://github.com/PyTorchLightning/pytorch-lightning/pull/1799))
-
-- Re-Enable Logger's `ImportError`s ([#1938](https://github.com/PyTorchLightning/pytorch-lightning/pull/1938))
-
-- Changed the default value of the Trainer argument `weights_summary` from `full` to `top` ([#2029](https://github.com/PyTorchLightning/pytorch-lightning/pull/2029))
-
-### Deprecated
-
-- Deprecated `ModelCheckpoint`'s attributes `best` and `kth_best_model` ([#1799](https://github.com/PyTorchLightning/pytorch-lightning/pull/1799))
-
-- Dropped official support/testing for older PyTorch versions <1.3 ([#1917](https://github.com/PyTorchLightning/pytorch-lightning/pull/1917))
-
-### Removed
-
-- Removed unintended Trainer argument `progress_bar_callback`, the callback should be passed in by `Trainer(callbacks=[...])` instead ([#1855](https://github.com/PyTorchLightning/pytorch-lightning/pull/1855))
-
-- Remove obsolete `self._device` in Trainer ([#1849](https://github.com/PyTorchLightning/pytorch-lightning/pull/1849))
-
-### Fixed
-
-- Run graceful training teardown on interpreter exit ([#1631](https://github.com/PyTorchLightning/pytorch-lightning/pull/1631))
-
-- Fixed user warning when apex was used together with learning rate schedulers ([#1873](https://github.com/PyTorchLightning/pytorch-lightning/pull/1873))
-
-- Fixed multiple calls of `EarlyStopping` callback ([#1751](https://github.com/PyTorchLightning/pytorch-lightning/issues/1751)) 
-
-- Fixed an issue with `Trainer.from_argparse_args` when passing in unknown Trainer args ([#1932](https://github.com/PyTorchLightning/pytorch-lightning/pull/1932))
-
-- Fixed bug related to logger not being reset correctly for model after tuner algorithms ([#1933](https://github.com/PyTorchLightning/pytorch-lightning/pull/1933))
-
-- Fixed root node resolution for SLURM cluster with dash in host name ([#1954](https://github.com/PyTorchLightning/pytorch-lightning/pull/1954))
-
-- Fixed `LearningRateLogger` in multi-scheduler setting ([#1944](https://github.com/PyTorchLightning/pytorch-lightning/pull/1944))
-
-- Fixed test configuration check and testing ([#1804](https://github.com/PyTorchLightning/pytorch-lightning/pull/1804))
-
-- Fixed an issue with Trainer constructor silently ignoring unknown/misspelled arguments ([#1820](https://github.com/PyTorchLightning/pytorch-lightning/pull/1820))
-
-- Fixed `save_weights_only` in ModelCheckpoint ([#1780](https://github.com/PyTorchLightning/pytorch-lightning/pull/1780))
-
-- Allow use of same `WandbLogger` instance for multiple training loops ([#2055](https://github.com/PyTorchLightning/pytorch-lightning/pull/2055))
-
-## [0.7.6] - 2020-05-16
-
-### Added
-
-- Added callback for logging learning rates ([#1498](https://github.com/PyTorchLightning/pytorch-lightning/pull/1498))
-- Added transfer learning example (for a binary classification task in computer vision) ([#1564](https://github.com/PyTorchLightning/pytorch-lightning/pull/1564))
-- Added type hints in `Trainer.fit()` and `Trainer.test()` to reflect that also a list of dataloaders can be passed in ([#1723](https://github.com/PyTorchLightning/pytorch-lightning/pull/1723)).
-- Added auto scaling of batch size ([#1638](https://github.com/PyTorchLightning/pytorch-lightning/pull/1638))
-- The progress bar metrics now also get updated in `training_epoch_end` ([#1724](https://github.com/PyTorchLightning/pytorch-lightning/pull/1724))
-- Enable `NeptuneLogger` to work with `distributed_backend=ddp` ([#1753](https://github.com/PyTorchLightning/pytorch-lightning/pull/1753))
-- Added option to provide seed to random generators to ensure reproducibility ([#1572](https://github.com/PyTorchLightning/pytorch-lightning/pull/1572))
-- Added override for hparams in `load_from_ckpt` ([#1797](https://github.com/PyTorchLightning/pytorch-lightning/pull/1797))
-- Added support multi-node distributed execution under `torchelastic` ([#1811](https://github.com/PyTorchLightning/pytorch-lightning/pull/1811), [#1818](https://github.com/PyTorchLightning/pytorch-lightning/pull/1818))
-- Added using `store_true` for bool args ([#1822](https://github.com/PyTorchLightning/pytorch-lightning/pull/1822), [#1842](https://github.com/PyTorchLightning/pytorch-lightning/pull/1842))
-- Added dummy logger for internally disabling logging for some features ([#1836](https://github.com/PyTorchLightning/pytorch-lightning/pull/1836))
-
-### Changed
-
-- Enable `non-blocking` for device transfers to GPU ([#1843](https://github.com/PyTorchLightning/pytorch-lightning/pull/1843))
-- Replace mata_tags.csv with hparams.yaml ([#1271](https://github.com/PyTorchLightning/pytorch-lightning/pull/1271))
-- Reduction when `batch_size < num_gpus` ([#1609](https://github.com/PyTorchLightning/pytorch-lightning/pull/1609))
-- Updated LightningTemplateModel to look more like Colab example ([#1577](https://github.com/PyTorchLightning/pytorch-lightning/pull/1577))
-- Don't convert `namedtuple` to `tuple` when transferring the batch to target device ([#1589](https://github.com/PyTorchLightning/pytorch-lightning/pull/1589))
-- Allow passing hparams as keyword argument to LightningModule when loading from checkpoint ([#1639](https://github.com/PyTorchLightning/pytorch-lightning/pull/1639))
-- Args should come after the last positional argument ([#1807](https://github.com/PyTorchLightning/pytorch-lightning/pull/1807))
-- Made ddp the default if no backend specified with multiple GPUs ([#1789](https://github.com/PyTorchLightning/pytorch-lightning/pull/1789))
-
-### Deprecated
-
-- Deprecated `tags_csv` in favor of `hparams_file` ([#1271](https://github.com/PyTorchLightning/pytorch-lightning/pull/1271))
-
-### Fixed
-
-- Fixed broken link in PR template ([#1675](https://github.com/PyTorchLightning/pytorch-lightning/pull/1675))
-- Fixed ModelCheckpoint not None checking filepath ([#1654](https://github.com/PyTorchLightning/pytorch-lightning/pull/1654))
-- Trainer now calls `on_load_checkpoint()` when resuming from a checkpoint ([#1666](https://github.com/PyTorchLightning/pytorch-lightning/pull/1666))
-- Fixed sampler logic for ddp with iterable dataset ([#1734](https://github.com/PyTorchLightning/pytorch-lightning/pull/1734))
-- Fixed `_reset_eval_dataloader()` for IterableDataset ([#1560](https://github.com/PyTorchLightning/pytorch-lightning/pull/1560))
-- Fixed Horovod distributed backend to set the `root_gpu` property ([#1669](https://github.com/PyTorchLightning/pytorch-lightning/pull/1669))
-- Fixed wandb logger `global_step` affects other loggers ([#1492](https://github.com/PyTorchLightning/pytorch-lightning/pull/1492))
-- Fixed disabling progress bar on non-zero ranks using Horovod backend ([#1709](https://github.com/PyTorchLightning/pytorch-lightning/pull/1709))
-- Fixed bugs that prevent lr finder to be used together with early stopping and validation dataloaders ([#1676](https://github.com/PyTorchLightning/pytorch-lightning/pull/1676))
-- Fixed a bug in Trainer that prepended the checkpoint path with `version_` when it shouldn't ([#1748](https://github.com/PyTorchLightning/pytorch-lightning/pull/1748))
-- Fixed lr key name in case of param groups in LearningRateLogger ([#1719](https://github.com/PyTorchLightning/pytorch-lightning/pull/1719))
-- Fixed saving native AMP scaler state (introduced in [#1561](https://github.com/PyTorchLightning/pytorch-lightning/pull/1561))
-- Fixed accumulation parameter and suggestion method for learning rate finder ([#1801](https://github.com/PyTorchLightning/pytorch-lightning/pull/1801))
-- Fixed num processes wasn't being set properly and auto sampler was ddp failing ([#1819](https://github.com/PyTorchLightning/pytorch-lightning/pull/1819))
-- Fixed bugs in semantic segmentation example ([#1824](https://github.com/PyTorchLightning/pytorch-lightning/pull/1824))
-- Fixed saving native AMP scaler state ([#1561](https://github.com/PyTorchLightning/pytorch-lightning/pull/1561), [#1777](https://github.com/PyTorchLightning/pytorch-lightning/pull/1777))
-- Fixed native amp + ddp ([#1788](https://github.com/PyTorchLightning/pytorch-lightning/pull/1788))
-- Fixed `hparam` logging with metrics ([#1647](https://github.com/PyTorchLightning/pytorch-lightning/pull/1647))
-
-## [0.7.5] - 2020-04-27
-
-### Changed
-
-- Allow logging of metrics together with `hparams` ([#1630](https://github.com/PyTorchLightning/pytorch-lightning/pull/1630))
-- Allow metrics logged together with hparams ([#1630](https://github.com/PyTorchLightning/pytorch-lightning/pull/1630))
-
-### Removed
-
-- Removed Warning from trainer loop ([#1634](https://github.com/PyTorchLightning/pytorch-lightning/pull/1634))
-
-### Fixed
-
-- Fixed ModelCheckpoint not being fixable ([#1632](https://github.com/PyTorchLightning/pytorch-lightning/pull/1632))
-- Fixed CPU DDP breaking change and DDP change ([#1635](https://github.com/PyTorchLightning/pytorch-lightning/pull/1635))
-- Tested pickling ([#1636](https://github.com/PyTorchLightning/pytorch-lightning/pull/1636))
-
-
-## [0.7.4] - 2020-04-26
-
-### Added
-
-- Added flag `replace_sampler_ddp` to manually disable sampler replacement in DDP  ([#1513](https://github.com/PyTorchLightning/pytorch-lightning/pull/1513))
-- Added speed parity tests (max 1 sec difference per epoch)([#1482](https://github.com/PyTorchLightning/pytorch-lightning/pull/1482))
-- Added `auto_select_gpus` flag to trainer that enables automatic selection of available GPUs on exclusive mode systems.
-- Added learning rate finder ([#1347](https://github.com/PyTorchLightning/pytorch-lightning/pull/1347))
-- Added support for ddp mode in clusters without SLURM ([#1387](https://github.com/PyTorchLightning/pytorch-lightning/pull/1387))
-- Added `test_dataloaders` parameter to `Trainer.test()` ([#1434](https://github.com/PyTorchLightning/pytorch-lightning/pull/1434))
-- Added `terminate_on_nan` flag to trainer that performs a NaN check with each training iteration when set to `True` ([#1475](https://github.com/PyTorchLightning/pytorch-lightning/pull/1475))
-- Added speed parity tests (max 1 sec difference per epoch)([#1482](https://github.com/PyTorchLightning/pytorch-lightning/pull/1482))
-- Added `terminate_on_nan` flag to trainer that performs a NaN check with each training iteration when set to `True`. ([#1475](https://github.com/PyTorchLightning/pytorch-lightning/pull/1475))
-- Added `ddp_cpu` backend for testing ddp without GPUs ([#1158](https://github.com/PyTorchLightning/pytorch-lightning/pull/1158))
-- Added [Horovod](http://horovod.ai) support as a distributed backend `Trainer(distributed_backend='horovod')` ([#1529](https://github.com/PyTorchLightning/pytorch-lightning/pull/1529))
-- Added support for 8 core distributed training on Kaggle TPU's ([#1568](https://github.com/PyTorchLightning/pytorch-lightning/pull/1568))
-- Added support for native AMP ([#1561](https://github.com/PyTorchLightning/pytorch-lightning/pull/1561), [#1580](https://github.com/PyTorchLightning/pytorch-lightning/pull/1580))
-
-### Changed
-
-- Changed the default behaviour to no longer include a NaN check with each training iteration. ([#1475](https://github.com/PyTorchLightning/pytorch-lightning/pull/1475))
-- Decoupled the progress bar from trainer` it is a callback now and can be customized or even be replaced entirely ([#1450](https://github.com/PyTorchLightning/pytorch-lightning/pull/1450)).
-- Changed lr schedule step interval behavior to update every backwards pass instead of every forwards pass ([#1477](https://github.com/PyTorchLightning/pytorch-lightning/pull/1477))
-- Defines shared proc. rank, remove rank from instances (e.g. loggers) ([#1408](https://github.com/PyTorchLightning/pytorch-lightning/pull/1408))
-- Updated semantic segmentation example with custom U-Net and logging ([#1371](https://github.com/PyTorchLightning/pytorch-lightning/pull/1371))
-- Disabled val and test shuffling ([#1600](https://github.com/PyTorchLightning/pytorch-lightning/pull/1600))
-
-### Deprecated
-
-- Deprecated `training_tqdm_dict` in favor of `progress_bar_dict` ([#1450](https://github.com/PyTorchLightning/pytorch-lightning/pull/1450)).
-
-### Removed
-
-- Removed `test_dataloaders` parameter from `Trainer.fit()` ([#1434](https://github.com/PyTorchLightning/pytorch-lightning/pull/1434))
-
-### Fixed
-
-- Added the possibility to pass nested metrics dictionaries to loggers ([#1582](https://github.com/PyTorchLightning/pytorch-lightning/pull/1582))
-- Fixed memory leak from opt return ([#1528](https://github.com/PyTorchLightning/pytorch-lightning/pull/1528))
-- Fixed saving checkpoint before deleting old ones ([#1453](https://github.com/PyTorchLightning/pytorch-lightning/pull/1453))
-- Fixed loggers - flushing last logged metrics even before continue, e.g. `trainer.test()` results ([#1459](https://github.com/PyTorchLightning/pytorch-lightning/pull/1459))
-- Fixed optimizer configuration when `configure_optimizers` returns dict without `lr_scheduler` ([#1443](https://github.com/PyTorchLightning/pytorch-lightning/pull/1443))
-- Fixed `LightningModule` - mixing hparams and arguments in `LightningModule.__init__()` crashes load_from_checkpoint() ([#1505](https://github.com/PyTorchLightning/pytorch-lightning/pull/1505))
-- Added a missing call to the `on_before_zero_grad` model hook ([#1493](https://github.com/PyTorchLightning/pytorch-lightning/pull/1493)).
-- Allow use of sweeps with `WandbLogger` ([#1512](https://github.com/PyTorchLightning/pytorch-lightning/pull/1512))
-- Fixed a bug that caused the `callbacks` Trainer argument to reference a global variable ([#1534](https://github.com/PyTorchLightning/pytorch-lightning/pull/1534)).
-- Fixed a bug that set all boolean CLI arguments from `Trainer.add_argparse_args` always to True ([#1571](https://github.com/PyTorchLightning/pytorch-lightning/pull/1571))
-- Fixed do not copy the batch when training on a single GPU ([#1576](https://github.com/PyTorchLightning/pytorch-lightning/pull/1576), [#1579](https://github.com/PyTorchLightning/pytorch-lightning/pull/1579))
-- Fixed soft checkpoint removing on DDP ([#1408](https://github.com/PyTorchLightning/pytorch-lightning/pull/1408))
-- Fixed automatic parser bug ([#1585](https://github.com/PyTorchLightning/pytorch-lightning/pull/1585))
-- Fixed bool conversion from string ([#1606](https://github.com/PyTorchLightning/pytorch-lightning/pull/1606))
-
-## [0.7.3] - 2020-04-09
-
-### Added
-
-- Added `rank_zero_warn` for warning only in rank 0 ([#1428](https://github.com/PyTorchLightning/pytorch-lightning/pull/1428))
-
-### Fixed
-
-- Fixed default `DistributedSampler` for DDP training ([#1425](https://github.com/PyTorchLightning/pytorch-lightning/pull/1425))
-- Fixed workers warning not on windows ([#1430](https://github.com/PyTorchLightning/pytorch-lightning/pull/1430))
-- Fixed returning tuple from `run_training_batch` ([#1431](https://github.com/PyTorchLightning/pytorch-lightning/pull/1431))
-- Fixed gradient clipping ([#1438](https://github.com/PyTorchLightning/pytorch-lightning/pull/1438))
-- Fixed pretty print ([#1441](https://github.com/PyTorchLightning/pytorch-lightning/pull/1441))
-
-
-## [0.7.2] - 2020-04-07
-
-### Added
-
-- Added same step loggers' metrics aggregation ([#1278](https://github.com/PyTorchLightning/pytorch-lightning/pull/1278))
-- Added parity test between a vanilla MNIST model and lightning model ([#1284](https://github.com/PyTorchLightning/pytorch-lightning/pull/1284))
-- Added parity test between a vanilla RNN model and lightning model ([#1351](https://github.com/PyTorchLightning/pytorch-lightning/pull/1351))
-- Added Reinforcement Learning - Deep Q-network (DQN) lightning example ([#1232](https://github.com/PyTorchLightning/pytorch-lightning/pull/1232))
-- Added support for hierarchical `dict` ([#1152](https://github.com/PyTorchLightning/pytorch-lightning/pull/1152))
-- Added `TrainsLogger` class ([#1122](https://github.com/PyTorchLightning/pytorch-lightning/pull/1122))
-- Added type hints to `pytorch_lightning.core` ([#946](https://github.com/PyTorchLightning/pytorch-lightning/pull/946))
-- Added support for `IterableDataset` in validation and testing ([#1104](https://github.com/PyTorchLightning/pytorch-lightning/pull/1104))
-- Added support for non-primitive types in `hparams` for `TensorboardLogger` ([#1130](https://github.com/PyTorchLightning/pytorch-lightning/pull/1130))
-- Added a check that stops the training when loss or weights contain `NaN` or `inf` values. ([#1097](https://github.com/PyTorchLightning/pytorch-lightning/pull/1097))
-- Added support for `IterableDataset` when `val_check_interval=1.0` (default), this will trigger validation at the end of each epoch. ([#1283](https://github.com/PyTorchLightning/pytorch-lightning/pull/1283))
-- Added `summary` method to Profilers. ([#1259](https://github.com/PyTorchLightning/pytorch-lightning/pull/1259))
-- Added informative errors if user defined dataloader has zero length ([#1280](https://github.com/PyTorchLightning/pytorch-lightning/pull/1280))
-- Added testing for python 3.8 ([#915](https://github.com/PyTorchLightning/pytorch-lightning/pull/915))
-- Added a `training_epoch_end` method which is the mirror of `validation_epoch_end`. ([#1357](https://github.com/PyTorchLightning/pytorch-lightning/pull/1357))
-- Added model configuration checking ([#1199](https://github.com/PyTorchLightning/pytorch-lightning/pull/1199))
-- Added support for optimizer frequencies through `LightningModule.configure_optimizers()` ([#1269](https://github.com/PyTorchLightning/pytorch-lightning/pull/1269))
-- Added option to run without an optimizer by returning `None` from `configure_optimizers`. ([#1279](https://github.com/PyTorchLightning/pytorch-lightning/pull/1279))
-- Added a warning when the number of data loader workers is small. ([#1378](https://github.com/PyTorchLightning/pytorch-lightning/pull/1378))
-
-### Changed
-
-- Changed (renamed and refatored) `TensorRunningMean` -> `TensorRunningAccum`: running accumulations were generalized. ([#1278](https://github.com/PyTorchLightning/pytorch-lightning/pull/1278))
-- Changed `progress_bar_refresh_rate` trainer flag to disable progress bar when set to 0. ([#1108](https://github.com/PyTorchLightning/pytorch-lightning/pull/1108))
-- Enhanced `load_from_checkpoint` to also forward params to the model ([#1307](https://github.com/PyTorchLightning/pytorch-lightning/pull/1307))
-- Updated references to `self.forward()` to instead use the `__call__` interface. ([#1211](https://github.com/PyTorchLightning/pytorch-lightning/pull/1211))
-- Changed default behaviour of `configure_optimizers` to use no optimizer rather than Adam. ([#1279](https://github.com/PyTorchLightning/pytorch-lightning/pull/1279))
-- Allow to upload models on W&B ([#1339](https://github.com/PyTorchLightning/pytorch-lightning/pull/1339))
-- On DP and DDP2 unsqueeze is automated now ([#1319](https://github.com/PyTorchLightning/pytorch-lightning/pull/1319))
-- Did not always create a DataLoader during reinstantiation, but the same type as before (if subclass of DataLoader) ([#1346](https://github.com/PyTorchLightning/pytorch-lightning/pull/1346))
-- Did not interfere with a default sampler ([#1318](https://github.com/PyTorchLightning/pytorch-lightning/pull/1318))
-- Remove default Adam optimizer ([#1317](https://github.com/PyTorchLightning/pytorch-lightning/pull/1317))
-- Give warnings for unimplemented required lightning methods ([#1317](https://github.com/PyTorchLightning/pytorch-lightning/pull/1317))
-- Made `evaluate` method private >> `Trainer._evaluate(...)`. ([#1260](https://github.com/PyTorchLightning/pytorch-lightning/pull/1260))
-- Simplify the PL examples structure (shallower and more readable) ([#1247](https://github.com/PyTorchLightning/pytorch-lightning/pull/1247))
-- Changed min max gpu memory to be on their own plots ([#1358](https://github.com/PyTorchLightning/pytorch-lightning/pull/1358))
-- Remove `.item` which causes sync issues ([#1254](https://github.com/PyTorchLightning/pytorch-lightning/pull/1254))
-- Changed smoothing in TQDM to decrease variability of time remaining between training / eval ([#1194](https://github.com/PyTorchLightning/pytorch-lightning/pull/1194))
-- Change default logger to dedicated one ([#1064](https://github.com/PyTorchLightning/pytorch-lightning/pull/1064))
-
-### Deprecated
-
-- Deprecated Trainer argument `print_nan_grads` ([#1097](https://github.com/PyTorchLightning/pytorch-lightning/pull/1097))
-- Deprecated Trainer argument `show_progress_bar` ([#1108](https://github.com/PyTorchLightning/pytorch-lightning/pull/1108))
-
-### Removed
-
-- Removed test for no test dataloader in .fit ([#1495](https://github.com/PyTorchLightning/pytorch-lightning/pull/1495))
-- Removed duplicated module `pytorch_lightning.utilities.arg_parse` for loading CLI arguments ([#1167](https://github.com/PyTorchLightning/pytorch-lightning/pull/1167))
-- Removed wandb logger's `finalize` method ([#1193](https://github.com/PyTorchLightning/pytorch-lightning/pull/1193))
-- Dropped `torchvision` dependency in tests and added own MNIST dataset class instead ([#986](https://github.com/PyTorchLightning/pytorch-lightning/pull/986))
-
-### Fixed
-
-- Fixed `model_checkpoint` when saving all models ([#1359](https://github.com/PyTorchLightning/pytorch-lightning/pull/1359))
-- `Trainer.add_argparse_args` classmethod fixed. Now it adds a type for the arguments ([#1147](https://github.com/PyTorchLightning/pytorch-lightning/pull/1147))
-- Fixed bug related to type checking of `ReduceLROnPlateau` lr schedulers([#1126](https://github.com/PyTorchLightning/pytorch-lightning/pull/1126))
-- Fixed a bug to ensure lightning checkpoints to be backward compatible ([#1132](https://github.com/PyTorchLightning/pytorch-lightning/pull/1132))
-- Fixed a bug that created an extra dataloader with active `reload_dataloaders_every_epoch` ([#1196](https://github.com/PyTorchLightning/pytorch-lightning/pull/1196))
-- Fixed all warnings and errors in the docs build process ([#1191](https://github.com/PyTorchLightning/pytorch-lightning/pull/1191))
-- Fixed an issue where `val_percent_check=0` would not disable validation ([#1251](https://github.com/PyTorchLightning/pytorch-lightning/pull/1251))
-- Fixed average of incomplete `TensorRunningMean` ([#1309](https://github.com/PyTorchLightning/pytorch-lightning/pull/1309))
-- Fixed `WandbLogger.watch` with `wandb.init()` ([#1311](https://github.com/PyTorchLightning/pytorch-lightning/pull/1311))
-- Fixed an issue with early stopping that would prevent it from monitoring training metrics when validation is disabled / not implemented ([#1235](https://github.com/PyTorchLightning/pytorch-lightning/pull/1235)).
-- Fixed a bug that would cause `trainer.test()` to run on the validation set when overloading `validation_epoch_end` and `test_end` ([#1353](https://github.com/PyTorchLightning/pytorch-lightning/pull/1353))
-- Fixed `WandbLogger.watch` - use of the watch method without importing `wandb` ([#1311](https://github.com/PyTorchLightning/pytorch-lightning/pull/1311))
-- Fixed `WandbLogger` to be used with 'ddp' - allow reinits in sub-processes ([#1149](https://github.com/PyTorchLightning/pytorch-lightning/pull/1149), [#1360](https://github.com/PyTorchLightning/pytorch-lightning/pull/1360))
-- Made `training_epoch_end` behave like `validation_epoch_end` ([#1357](https://github.com/PyTorchLightning/pytorch-lightning/pull/1357))
-- Fixed `fast_dev_run` running validation twice ([#1365](https://github.com/PyTorchLightning/pytorch-lightning/pull/1365))
-- Fixed pickle error from quick patch `__code__` ([#1352](https://github.com/PyTorchLightning/pytorch-lightning/pull/1352))
-- Fixed memory leak on GPU0 ([#1094](https://github.com/PyTorchLightning/pytorch-lightning/pull/1094), [#1349](https://github.com/PyTorchLightning/pytorch-lightning/pull/1349))
-- Fixed checkpointing interval ([#1272](https://github.com/PyTorchLightning/pytorch-lightning/pull/1272))
-- Fixed validation and training loops run the partial dataset ([#1192](https://github.com/PyTorchLightning/pytorch-lightning/pull/1192))
-- Fixed running `on_validation_end` only on main process in DDP ([#1125](https://github.com/PyTorchLightning/pytorch-lightning/pull/1125))
-- Fixed `load_spawn_weights` only in proc rank 0 ([#1385](https://github.com/PyTorchLightning/pytorch-lightning/pull/1385))
-- Fixes `use_amp` issue ([#1145](https://github.com/PyTorchLightning/pytorch-lightning/pull/1145))
-- Fixes using deprecated `use_amp` attribute ([#1145](https://github.com/PyTorchLightning/pytorch-lightning/pull/1145))
-- Fixed Tensorboard logger error: lightning_logs directory not exists in multi-node DDP on nodes with rank != 0 ([#1377](https://github.com/PyTorchLightning/pytorch-lightning/pull/1377))
-- Fixed `Unimplemented backend XLA` error on TPU ([#1387](https://github.com/PyTorchLightning/pytorch-lightning/pull/1387))
-
-## [0.7.1] - 2020-03-07
-
-### Fixed
-
-- Fixes `print` issues and `data_loader` ([#1080](https://github.com/PyTorchLightning/pytorch-lightning/pull/1080))
-
-## [0.7.0] - 2020-03-06
-
-### Added
-
-- Added automatic sampler setup. Depending on DDP or TPU, lightning configures the sampler correctly (user needs to do nothing) ([#926](https://github.com/PyTorchLightning/pytorch-lightning/pull/926))
-- Added `reload_dataloaders_every_epoch=False` flag for trainer. Some users require reloading data every epoch ([#926](https://github.com/PyTorchLightning/pytorch-lightning/pull/926))
-- Added `progress_bar_refresh_rate=50` flag for trainer. Throttle refresh rate on notebooks ([#926](https://github.com/PyTorchLightning/pytorch-lightning/pull/926))
-- Updated governance docs
-- Added a check to ensure that the metric used for early stopping exists before training commences ([#542](https://github.com/PyTorchLightning/pytorch-lightning/pull/542))
-- Added `optimizer_idx` argument to `backward` hook ([#733](https://github.com/PyTorchLightning/pytorch-lightning/pull/733))
-- Added `entity` argument to `WandbLogger` to be passed to `wandb.init` ([#783](https://github.com/PyTorchLightning/pytorch-lightning/pull/783))
-- Added a tool for profiling training runs ([#782](https://github.com/PyTorchLightning/pytorch-lightning/pull/782))
-- Improved flexibility for naming of TensorBoard logs, can now set `version` to a `str` to just save to that directory, and use `name=''` to prevent experiment-name directory ([#804](https://github.com/PyTorchLightning/pytorch-lightning/pull/804))
-- Added option to specify `step` key when logging metrics ([#808](https://github.com/PyTorchLightning/pytorch-lightning/pull/808))
-- Added `train_dataloader`, `val_dataloader` and `test_dataloader` arguments to `Trainer.fit()`, for alternative data parsing ([#759](https://github.com/PyTorchLightning/pytorch-lightning/pull/759))
-- Added Tensor Processing Unit (TPU) support ([#868](https://github.com/PyTorchLightning/pytorch-lightning/pull/868))
-- Added semantic segmentation example ([#751](https://github.com/PyTorchLightning/pytorch-lightning/pull/751),[#876](https://github.com/PyTorchLightning/pytorch-lightning/pull/876), [#881](https://github.com/PyTorchLightning/pytorch-lightning/pull/881))
-- Split callbacks in multiple files ([#849](https://github.com/PyTorchLightning/pytorch-lightning/pull/849))
-- Support for user defined callbacks ([#889](https://github.com/PyTorchLightning/pytorch-lightning/pull/889) and [#950](https://github.com/PyTorchLightning/pytorch-lightning/pull/950))
-- Added support for multiple loggers to be passed to `Trainer` as an iterable (e.g. list, tuple, etc.) ([#903](https://github.com/PyTorchLightning/pytorch-lightning/pull/903))
-- Added support for step-based learning rate scheduling ([#941](https://github.com/PyTorchLightning/pytorch-lightning/pull/941))
-- Added support for logging `hparams` as dict ([#1029](https://github.com/PyTorchLightning/pytorch-lightning/pull/1029))
-- Checkpoint and early stopping now work without val. step ([#1041](https://github.com/PyTorchLightning/pytorch-lightning/pull/1041))
-- Support graceful training cleanup after Keyboard Interrupt ([#856](https://github.com/PyTorchLightning/pytorch-lightning/pull/856), [#1019](https://github.com/PyTorchLightning/pytorch-lightning/pull/1019))
-- Added type hints for function arguments ([#912](https://github.com/PyTorchLightning/pytorch-lightning/pull/912), )
-- Added default `argparser` for `Trainer` ([#952](https://github.com/PyTorchLightning/pytorch-lightning/pull/1023), [#1023](https://github.com/PyTorchLightning/pytorch-lightning/pull/1023))
-- Added TPU gradient clipping ([#963](https://github.com/PyTorchLightning/pytorch-lightning/pull/963))
-- Added max/min number of steps in `Trainer` ([#728](https://github.com/PyTorchLightning/pytorch-lightning/pull/728))
-
-### Changed
-
-- Improved `NeptuneLogger` by adding `close_after_fit` argument to allow logging after training([#908](https://github.com/PyTorchLightning/pytorch-lightning/pull/1084))
-- Changed default TQDM to use `tqdm.auto` for prettier outputs in IPython notebooks ([#752](https://github.com/PyTorchLightning/pytorch-lightning/pull/752))
-- Changed `pytorch_lightning.logging` to `pytorch_lightning.loggers` ([#767](https://github.com/PyTorchLightning/pytorch-lightning/pull/767))
-- Moved the default `tqdm_dict` definition from Trainer to `LightningModule`, so it can be overridden by the user ([#749](https://github.com/PyTorchLightning/pytorch-lightning/pull/749))
-- Moved functionality of `LightningModule.load_from_metrics` into `LightningModule.load_from_checkpoint` ([#995](https://github.com/PyTorchLightning/pytorch-lightning/pull/995))
-- Changed Checkpoint path parameter from `filepath` to `dirpath` ([#1016](https://github.com/PyTorchLightning/pytorch-lightning/pull/1016))
-- Freezed models `hparams` as `Namespace` property ([#1029](https://github.com/PyTorchLightning/pytorch-lightning/pull/1029))
-- Dropped `logging` config in package init ([#1015](https://github.com/PyTorchLightning/pytorch-lightning/pull/1015))
-- Renames model steps ([#1051](https://github.com/PyTorchLightning/pytorch-lightning/pull/1051))
-  - `training_end` >> `training_epoch_end`
-  - `validation_end` >> `validation_epoch_end`
-  - `test_end` >> `test_epoch_end`
-- Refactor dataloading, supports infinite dataloader ([#955](https://github.com/PyTorchLightning/pytorch-lightning/pull/955))
-- Create single file in `TensorBoardLogger` ([#777](https://github.com/PyTorchLightning/pytorch-lightning/pull/777))
-
-### Deprecated
-
-- Deprecated `pytorch_lightning.logging` ([#767](https://github.com/PyTorchLightning/pytorch-lightning/pull/767))
-- Deprecated `LightningModule.load_from_metrics` in favour of `LightningModule.load_from_checkpoint` ([#995](https://github.com/PyTorchLightning/pytorch-lightning/pull/995), [#1079](https://github.com/PyTorchLightning/pytorch-lightning/pull/1079))
-- Deprecated `@data_loader` decorator ([#926](https://github.com/PyTorchLightning/pytorch-lightning/pull/926))
-- Deprecated model steps `training_end`, `validation_end` and `test_end` ([#1051](https://github.com/PyTorchLightning/pytorch-lightning/pull/1051), [#1056](https://github.com/PyTorchLightning/pytorch-lightning/pull/1056))
-
-### Removed
-
-- Removed dependency on `pandas` ([#736](https://github.com/PyTorchLightning/pytorch-lightning/pull/736))
-- Removed dependency on `torchvision` ([#797](https://github.com/PyTorchLightning/pytorch-lightning/pull/797))
-- Removed dependency on `scikit-learn` ([#801](https://github.com/PyTorchLightning/pytorch-lightning/pull/801))
-
-### Fixed
-
-- Fixed a bug where early stopping `on_end_epoch` would be called inconsistently when `check_val_every_n_epoch == 0` ([#743](https://github.com/PyTorchLightning/pytorch-lightning/pull/743))
-- Fixed a bug where the model checkpointer didn't write to the same directory as the logger ([#771](https://github.com/PyTorchLightning/pytorch-lightning/pull/771))
-- Fixed a bug where the `TensorBoardLogger` class would create an additional empty log file during fitting ([#777](https://github.com/PyTorchLightning/pytorch-lightning/pull/777))
-- Fixed a bug where `global_step` was advanced incorrectly when using `accumulate_grad_batches > 1` ([#832](https://github.com/PyTorchLightning/pytorch-lightning/pull/832))
-- Fixed a bug when calling `self.logger.experiment` with multiple loggers ([#1009](https://github.com/PyTorchLightning/pytorch-lightning/pull/1009))
-- Fixed a bug when calling `logger.append_tags` on a `NeptuneLogger` with a single tag ([#1009](https://github.com/PyTorchLightning/pytorch-lightning/pull/1009))
-- Fixed sending back data from `.spawn` by saving and loading the trained model in/out of the process ([#1017](https://github.com/PyTorchLightning/pytorch-lightning/pull/1017)
-- Fixed port collision on DDP ([#1010](https://github.com/PyTorchLightning/pytorch-lightning/pull/1010))
-- Fixed/tested pass overrides ([#918](https://github.com/PyTorchLightning/pytorch-lightning/pull/918))
-- Fixed comet logger to log after train ([#892](https://github.com/PyTorchLightning/pytorch-lightning/pull/892))
-- Remove deprecated args to learning rate step function ([#890](https://github.com/PyTorchLightning/pytorch-lightning/pull/890))
-
-## [0.6.0] - 2020-01-21
-
-### Added
-
-- Added support for resuming from a specific checkpoint via `resume_from_checkpoint` argument ([#516](https://github.com/PyTorchLightning/pytorch-lightning/pull/516))
-- Added support for `ReduceLROnPlateau` scheduler ([#320](https://github.com/PyTorchLightning/pytorch-lightning/pull/320))
-- Added support for Apex mode `O2` in conjunction with Data Parallel ([#493](https://github.com/PyTorchLightning/pytorch-lightning/pull/493))
-- Added option (`save_top_k`) to save the top k models in the `ModelCheckpoint` class ([#128](https://github.com/PyTorchLightning/pytorch-lightning/pull/128))
-- Added `on_train_start` and `on_train_end` hooks to `ModelHooks` ([#598](https://github.com/PyTorchLightning/pytorch-lightning/pull/598))
-- Added `TensorBoardLogger` ([#607](https://github.com/PyTorchLightning/pytorch-lightning/pull/607))
-- Added support for weight summary of model with multiple inputs ([#543](https://github.com/PyTorchLightning/pytorch-lightning/pull/543))
-- Added `map_location` argument to `load_from_metrics` and `load_from_checkpoint` ([#625](https://github.com/PyTorchLightning/pytorch-lightning/pull/625))
-- Added option to disable validation by setting `val_percent_check=0` ([#649](https://github.com/PyTorchLightning/pytorch-lightning/pull/649))
-- Added `NeptuneLogger` class ([#648](https://github.com/PyTorchLightning/pytorch-lightning/pull/648))
-- Added `WandbLogger` class ([#627](https://github.com/PyTorchLightning/pytorch-lightning/pull/627))
-
-### Changed
-
-- Changed the default progress bar to print to stdout instead of stderr ([#531](https://github.com/PyTorchLightning/pytorch-lightning/pull/531))
-- Renamed `step_idx` to `step`, `epoch_idx` to `epoch`, `max_num_epochs` to `max_epochs` and `min_num_epochs` to `min_epochs` ([#589](https://github.com/PyTorchLightning/pytorch-lightning/pull/589))
-- Renamed `total_batch_nb` to `total_batches`, `nb_val_batches` to `num_val_batches`, `nb_training_batches` to `num_training_batches`, `max_nb_epochs` to `max_epochs`, `min_nb_epochs` to `min_epochs`, `nb_test_batches` to `num_test_batches`, and `nb_val_batches` to `num_val_batches` ([#567](https://github.com/PyTorchLightning/pytorch-lightning/pull/567))
-- Changed gradient logging to use parameter names instead of indexes ([#660](https://github.com/PyTorchLightning/pytorch-lightning/pull/660))
-- Changed the default logger to `TensorBoardLogger` ([#609](https://github.com/PyTorchLightning/pytorch-lightning/pull/609))
-- Changed the directory for tensorboard logging to be the same as model checkpointing ([#706](https://github.com/PyTorchLightning/pytorch-lightning/pull/706))
-
-### Deprecated
-
-- Deprecated `max_nb_epochs` and `min_nb_epochs` ([#567](https://github.com/PyTorchLightning/pytorch-lightning/pull/567))
-- Deprecated the `on_sanity_check_start` hook in `ModelHooks` ([#598](https://github.com/PyTorchLightning/pytorch-lightning/pull/598))
-
-### Removed
-
-- Removed the `save_best_only` argument from `ModelCheckpoint`, use `save_top_k=1` instead ([#128](https://github.com/PyTorchLightning/pytorch-lightning/pull/128))
-
-### Fixed
-
-- Fixed a bug which ocurred when using Adagrad with cuda ([#554](https://github.com/PyTorchLightning/pytorch-lightning/pull/554))
-- Fixed a bug where training would be on the GPU despite setting `gpus=0` or `gpus=[]` ([#561](https://github.com/PyTorchLightning/pytorch-lightning/pull/561))
-- Fixed an error with `print_nan_gradients` when some parameters do not require gradient ([#579](https://github.com/PyTorchLightning/pytorch-lightning/pull/579))
-- Fixed a bug where the progress bar would show an incorrect number of total steps during the validation sanity check when using multiple validation data loaders ([#597](https://github.com/PyTorchLightning/pytorch-lightning/pull/597))
-- Fixed support for PyTorch 1.1.0 ([#552](https://github.com/PyTorchLightning/pytorch-lightning/pull/552))
-- Fixed an issue with early stopping when using a `val_check_interval < 1.0` in `Trainer` ([#492](https://github.com/PyTorchLightning/pytorch-lightning/pull/492))
-- Fixed bugs relating to the `CometLogger` object that would cause it to not work properly ([#481](https://github.com/PyTorchLightning/pytorch-lightning/pull/481))
-- Fixed a bug that would occur when returning `-1` from `on_batch_start` following an early exit or when the batch was `None` ([#509](https://github.com/PyTorchLightning/pytorch-lightning/pull/509))
-- Fixed a potential race condition with several processes trying to create checkpoint directories ([#530](https://github.com/PyTorchLightning/pytorch-lightning/pull/530))
-- Fixed a bug where batch 'segments' would remain on the GPU when using `truncated_bptt > 1` ([#532](https://github.com/PyTorchLightning/pytorch-lightning/pull/532))
-- Fixed a bug when using `IterableDataset` ([#547](https://github.com/PyTorchLightning/pytorch-lightning/pull/547))
-- Fixed a bug where `.item` was called on non-tensor objects ([#602](https://github.com/PyTorchLightning/pytorch-lightning/pull/602))
-- Fixed a bug where `Trainer.train` would crash on an uninitialized variable if the trainer was run after resuming from a checkpoint that was already at `max_epochs` ([#608](https://github.com/PyTorchLightning/pytorch-lightning/pull/608))
-- Fixed a bug where early stopping would begin two epochs early ([#617](https://github.com/PyTorchLightning/pytorch-lightning/pull/617))
-- Fixed a bug where `num_training_batches` and `num_test_batches` would sometimes be rounded down to zero ([#649](https://github.com/PyTorchLightning/pytorch-lightning/pull/649))
-- Fixed a bug where an additional batch would be processed when manually setting `num_training_batches` ([#653](https://github.com/PyTorchLightning/pytorch-lightning/pull/653))
-- Fixed a bug when batches did not have a `.copy` method ([#701](https://github.com/PyTorchLightning/pytorch-lightning/pull/701))
-- Fixed a bug when using `log_gpu_memory=True` in Python 3.6 ([#715](https://github.com/PyTorchLightning/pytorch-lightning/pull/715))
-- Fixed a bug where checkpoint writing could exit before completion, giving incomplete checkpoints ([#689](https://github.com/PyTorchLightning/pytorch-lightning/pull/689))
-- Fixed a bug where `on_train_end` was not called when ealy stopping ([#723](https://github.com/PyTorchLightning/pytorch-lightning/pull/723))
-
-## [0.5.3] - 2019-11-06
-
-### Added
-
-- Added option to disable default logger, checkpointer, and early stopping by passing `logger=False`, `checkpoint_callback=False` and `early_stop_callback=False` respectively
-- Added `CometLogger` for use with Comet.ml
-- Added `val_check_interval` argument to `Trainer` allowing validition to be performed at every given number of batches
-- Added functionality to save and load hyperparameters using the standard checkpoint mechanism
-- Added call to `torch.cuda.empty_cache` before training starts
-- Added option for user to override the call t `backward`
-- Added support for truncated backprop through time via the `truncated_bptt_steps` argument in `Trainer`
-- Added option to operate on all outputs from `training_step` in DDP2
-- Added a hook for modifying DDP init
-- Added a hook for modifying Apex
-
-### Changed
-
-- Changed experiment version to be padded with zeros (e.g. `/dir/version_9` becomes `/dir/version_0009`)
-- Changed callback metrics to include any metrics given in logs or progress bar
-- Changed the default for `save_best_only` in `ModelCheckpoint` to `True`
-- Added `tng_data_loader` for backwards compatibility
-- Renamed `MLFlowLogger.client` to `MLFlowLogger.experiment` for consistency
-- Moved `global_step` increment to happen after the batch has been processed
-- Changed weights restore to first attempt HPC weights before restoring normally, preventing both weights being restored and running out of memory
-- Changed progress bar functionality to add multiple progress bars for train/val/test
-- Changed calls to `print` to use `logging` instead
-
-### Deprecated
-
-- Deprecated `tng_dataloader`
-
-### Fixed
-
-- Fixed an issue where the number of batches was off by one during training
-- Fixed a bug that occured when setting a ckeckpoint callback and `early_stop_callback=False`
-- Fixed an error when importing CometLogger
-- Fixed a bug where the `gpus` argument had some unexpected behaviour
-- Fixed a bug where the computed total number of batches was sometimes incorrect
-- Fixed a bug where the progress bar would sometimes not show the total number of batches in test mode
-- Fixed a bug when using the `log_gpu_memory='min_max'` option in `Trainer`
-- Fixed a bug where checkpointing would sometimes erase the current directory
-
-## [0.5.2] - 2019-10-10
-
-### Added
-
-- Added `weights_summary` argument to `Trainer` to be set to `full` (full summary), `top` (just top level modules) or other
-- Added `tags` argument to `MLFlowLogger`
-
-### Changed
-
-- Changed default for `amp_level` to `O1`
-
-### Removed
-
-- Removed the `print_weights_summary` argument from `Trainer`
-
-### Fixed
-
-- Fixed a bug where logs were not written properly
-- Fixed a bug where `logger.finalize` wasn't called after training is complete
-- Fixed callback metric errors in DDP
-- Fixed a bug where `TestTubeLogger` didn't log to the correct directory
-
-## [0.5.1] - 2019-10-05
-
-### Added
-
-- Added the `LightningLoggerBase` class for experiment loggers
-- Added `MLFlowLogger` for logging with `mlflow`
-- Added `TestTubeLogger` for logging with `test_tube`
-- Added a different implementation of DDP (`distributed_backed='ddp2'`) where every node has one model using all GPUs
-- Added support for optimisers which require a closure (e.g. LBFGS)
-- Added automatic `MASTER_PORT` defualt for DDP when not set manually
-- Added new GPU memory logging options `'min_max'` (log only the min/max utilization) and `'all'` (log all the GPU memory)
-
-### Changed
-
-- Changed schedulers to always be called with the current epoch
-- Changed `test_tube` to an optional dependency
-- Changed data loaders to internally use a getter instead of a python property
-- Disabled auto GPU loading when restoring weights to prevent out of memory errors
-- Changed logging, early stopping and checkpointing to occur by default
-
-### Fixed
-
-- Fixed a bug with samplers that do not specify `set_epoch`
-- Fixed a bug when using the `MLFlowLogger` with unsupported data types, this will now raise a warning
-- Fixed a bug where gradient norms were alwasy zero using `track_grad_norm`
-- Fixed a bug which causes a crash when logging memory
-
-## [0.5.0] - 2019-09-26
-
-### Changed
-
-- Changed `data_batch` argument to `batch` throughout
-- Changed `batch_i` argument to `batch_idx` throughout
-- Changed `tng_dataloader` method to `train_dataloader`
-- Changed `on_tng_metrics` method to `on_training_metrics`
-- Changed `gradient_clip` argument to `gradient_clip_val`
-- Changed `add_log_row_interval` to `row_log_interval`
-
-### Fixed
-
-- Fixed a bug with tensorboard logging in multi-gpu setup
-
-## [0.4.9] - 2019-09-16
-
-### Added
-
-- Added the flag `log_gpu_memory` to `Trainer` to deactivate logging of GPU memory utilization
-- Added SLURM resubmit functionality (port from test-tube)
-- Added optional weight_save_path to trainer to remove the need for a checkpoint_callback when using cluster training
-- Added option to use single gpu per node with `DistributedDataParallel`
-
-### Changed
-
-- Changed functionality of `validation_end` and `test_end` with multiple dataloaders to be given all of the dataloaders at once rather than in seperate calls
-- Changed print_nan_grads to only print the parameter value and gradients when they contain NaN
-- Changed gpu API to take integers as well (e.g. `gpus=2` instead of `gpus=[0, 1]`)
-- All models now loaded on to CPU to avoid device and out of memory issues in PyTorch
-
-### Fixed
-
-- Fixed a bug where data types that implement `.to` but not `.cuda` would not be properly moved onto the GPU
-- Fixed a bug where data would not be re-shuffled every epoch when using a `DistributedSampler`
-
-## [0.4.8] - 2019-08-31
-
-### Added
-
-- Added `test_step` and `test_end` methods, used when `Trainer.test` is called
-- Added `GradientAccumulationScheduler` callback which can be used to schedule changes to the number of accumulation batches
-- Added option to skip the validation sanity check by setting `nb_sanity_val_steps = 0`
-
-### Fixed
-
-- Fixed a bug when setting `nb_sanity_val_steps = 0`
-
-## [0.4.7] - 2019-08-24
-
-### Changed
-
-- Changed the default `val_check_interval` to `1.0`
-- Changed defaults for `nb_val_batches`, `nb_tng_batches` and `nb_test_batches` to 0
-
-### Fixed
-
-- Fixed a bug where the full validation set as used despite setting `val_percent_check`
-- Fixed a bug where an `Exception` was thrown when using a data set containing a single batch
-- Fixed a bug where an `Exception` was thrown if no `val_dataloader` was given
-- Fixed a bug where tuples were not properly transfered to the GPU
-- Fixed a bug where data of a non standard type was not properly handled by the trainer
-- Fixed a bug when loading data as a tuple
-- Fixed a bug where `AttributeError` could be suppressed by the `Trainer`
-
-## [0.4.6] - 2019-08-15
-
-### Added
-
-- Added support for data to be given as a `dict` or `list` with a single gpu
-- Added support for `configure_optimizers` to return a single optimizer, two list (optimizers and schedulers), or a single list
-
-### Fixed
-
-- Fixed a bug where returning just an optimizer list (i.e. without schedulers) from `configure_optimizers` would throw an `Exception`
-
-## [0.4.5] - 2019-08-13
-
-### Added
-
-- Added `optimizer_step` method that can be overridden to change the standard optimizer behaviour
-
-## [0.4.4] - 2019-08-12
-
-### Added
-
-- Added supoort for multiple validation dataloaders
-- Added support for latest test-tube logger (optimised for `torch==1.2.0`)
-
-### Changed
-
-- `validation_step` and `val_dataloader` are now optional
-- `lr_scheduler` is now activated after epoch
-
-### Fixed
-
-- Fixed a bug where a warning would show when using `lr_scheduler` in `torch>1.1.0`
-- Fixed a bug where an `Exception` would be thrown if using `torch.DistributedDataParallel` without using a `DistributedSampler`, this now throws a `Warning` instead
-
-## [0.4.3] - 2019-08-10
-
-### Fixed
-
-- Fixed a bug where accumulate gradients would scale the loss incorrectly
-
-## [0.4.2] - 2019-08-08
-
-### Changed
-
-- Changed install requirement to `torch==1.2.0`
-
-## [0.4.1] - 2019-08-08
-
-### Changed
-
-- Changed install requirement to `torch==1.1.0`
-
-## [0.4.0] - 2019-08-08
-
-### Added
-
-- Added 16-bit support for a single GPU
-- Added support for training continuation (preserves epoch, global step etc.)
-
-### Changed
-
-- Changed `training_step` and `validation_step`, outputs will no longer be automatically reduced
-
-### Removed
-
-- Removed need for `Experiment` object in `Trainer`
-
-### Fixed
-
-- Fixed issues with reducing outputs from generative models (such as images and text)
-
-## [0.3.6] - 2019-07-25
-
-### Added
-
-- Added a decorator to do lazy data loading internally
-
-### Fixed
-
-- Fixed a bug where `Experiment` object was not process safe, potentially causing logs to be overwritten
-
-## [0.3.5] - 2019-07-25
-
-## [0.3.4] - 2019-07-22
-
-## [0.3.3] - 2019-07-22
-
-## [0.3.2] - 2019-07-21
-
-## [0.3.1] - 2019-07-21
-
-## [0.2.x] - 2019-07-09
-
-## [0.1.x] - 2019-06-DD

LICENSE

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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>
+<span class="n">model</span><span class="o">.</span><span class="n">unfreeze</span><span class="p">()</span>
+</pre></div>
+</td></tr></table>
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+      SLURM Managed Cluster
+    </a>
+  </li>
+
+        
+          
+          
+          
+
+
+  <li class="md-nav__item">
+    <a href="../../Trainer/Testing loop/" title="Testing loop" class="md-nav__link">
+      Testing loop
+    </a>
+  </li>
+
+        
+          
+          
+          
+
+
+  <li class="md-nav__item">
+    <a href="../../Trainer/Training Loop/" title="Training Loop" class="md-nav__link">
+      Training Loop
+    </a>
+  </li>
+
+        
+          
+          
+          
+
+
+  <li class="md-nav__item">
+    <a href="../../Trainer/Validation loop/" title="Validation loop" class="md-nav__link">
+      Validation loop
+    </a>
+  </li>
+
+        
+          
+          
+          
+
+
+  <li class="md-nav__item">
+    <a href="../../Trainer/debugging/" title="Debugging" class="md-nav__link">
+      Debugging
+    </a>
+  </li>
+
+        
+          
+          
+          
+
+
+  <li class="md-nav__item">
+    <a href="../../Trainer/hooks/" title="Hooks" class="md-nav__link">
+      Hooks
+    </a>
+  </li>
+
+        
+      </ul>
+    </nav>
+  </li>
+
+    
+      
+      
+      
+
+
+  <li class="md-nav__item md-nav__item--nested">
+    
+      <input class="md-toggle md-nav__toggle" data-md-toggle="nav-4" type="checkbox" id="nav-4">
+    
+    <label class="md-nav__link" for="nav-4">
+      Examples
+    </label>
+    <nav class="md-nav" data-md-component="collapsible" data-md-level="1">
+      <label class="md-nav__title" for="nav-4">
+        Examples
+      </label>
+      <ul class="md-nav__list" data-md-scrollfix>
+        
+        
+          
+          
+          
+
+
+  <li class="md-nav__item">
+    <a href="../../examples/Examples/" title="Examples" class="md-nav__link">
+      Examples
+    </a>
+  </li>
+
+        
+      </ul>
+    </nav>
+  </li>
+
+    
+  </ul>
+</nav>
+                  </div>
+                </div>
+              </div>
+            
+            
+              <div class="md-sidebar md-sidebar--secondary" data-md-component="toc">
+                <div class="md-sidebar__scrollwrap">
+                  <div class="md-sidebar__inner">
+                    
+<nav class="md-nav md-nav--secondary">
+  
+  
+  
+    <label class="md-nav__title" for="__toc">Table of contents</label>
+    <ul class="md-nav__list" data-md-scrollfix>
+      
+        <li class="md-nav__item">
+  <a href="#current_epoch" title="current_epoch" class="md-nav__link">
+    current_epoch
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#dtype" title="dtype" class="md-nav__link">
+    dtype
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#logger" title="logger" class="md-nav__link">
+    logger
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#global_step" title="global_step" class="md-nav__link">
+    global_step
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#gradient_clip_val" title="gradient_clip_val" class="md-nav__link">
+    gradient_clip_val
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#on_gpu" title="on_gpu" class="md-nav__link">
+    on_gpu
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#trainer" title="trainer" class="md-nav__link">
+    trainer
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#debugging" title="Debugging" class="md-nav__link">
+    Debugging
+  </a>
+  
+    <nav class="md-nav">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#example_input_array" title="example_input_array" class="md-nav__link">
+    example_input_array
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+      
+      
+      
+      
+    </ul>
+  
+</nav>
+                  </div>
+                </div>
+              </div>
+            
+          
+          <div class="md-content">
+            <article class="md-content__inner md-typeset">
+              
+                
+                  <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>
+      
+        
+<footer class="md-footer">
+  
+    <div class="md-footer-nav">
+      <nav class="md-footer-nav__inner md-grid">
+        
+          <a href="../methods/" title="Methods" class="md-flex md-footer-nav__link md-footer-nav__link--prev" rel="prev">
+            <div class="md-flex__cell md-flex__cell--shrink">
+              <i class="md-icon md-icon--arrow-back md-footer-nav__button"></i>
+            </div>
+            <div class="md-flex__cell md-flex__cell--stretch md-footer-nav__title">
+              <span class="md-flex__ellipsis">
+                <span class="md-footer-nav__direction">
+                  Previous
+                </span>
+                Methods
+              </span>
+            </div>
+          </a>
+        
+        
+          <a href="../../Trainer/" title="Trainer" class="md-flex md-footer-nav__link md-footer-nav__link--next" rel="next">
+            <div class="md-flex__cell md-flex__cell--stretch md-footer-nav__title">
+              <span class="md-flex__ellipsis">
+                <span class="md-footer-nav__direction">
+                  Next
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+                Trainer
+              </span>
+            </div>
+            <div class="md-flex__cell md-flex__cell--shrink">
+              <i class="md-icon md-icon--arrow-forward md-footer-nav__button"></i>
+            </div>
+          </a>
+        
+      </nav>
+    </div>
+  
+  <div class="md-footer-meta md-typeset">
+    <div class="md-footer-meta__inner md-grid">
+      <div class="md-footer-copyright">
+        
+        powered by
+        <a href="https://www.mkdocs.org">MkDocs</a>
+        and
+        <a href="https://squidfunk.github.io/mkdocs-material/">
+          Material for MkDocs</a>
+      </div>
+      
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+</footer>
+      
+    </div>
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+      <script src="../../assets/javascripts/application.245445c6.js"></script>
+      
+      <script>app.initialize({version:"1.0.4",url:{base:"../.."}})</script>
+      
+    
+  </body>
+</html>

MANIFEST.in

@@ -1,46 +0,0 @@
-# Manifest syntax https://docs.python.org/2/distutils/sourcedist.html
-graft wheelhouse
-
-recursive-exclude __pycache__  *.py[cod] *.orig
-
-# Include the README and CHANGELOG
-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 *.md *.sh *.txt
-
-# exclude tests from package
-recursive-exclude tests *
-recursive-exclude site *
-exclude tests
-
-# Exclude the documentation files
-recursive-exclude docs *
-exclude docs
-recursive-include docs/source/_images/logos/ *
-recursive-include docs/source/_images/general/ pl_overview* tf_* tutorial_*
-
-# Include the Requirements
-include requirements.txt
-include requirements-extra.txt
-
-# Exclude build configs
-exclude *.yml
-
-prune .git
-prune .github
-prune .circleci
-prune notebook*
-prune temp*
-prune test*
-prune benchmark*
-prune docker
-

README.md

@@ -1,446 +0,0 @@
-<div align="center">
-
-![Logo](docs/source/_images/logos/lightning_logo.svg)
-
-# 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)
-[![codecov](https://codecov.io/gh/PyTorchLightning/pytorch-lightning/branch/master/graph/badge.svg)](https://codecov.io/gh/PyTorchLightning/pytorch-lightning)
-[![CodeFactor](https://www.codefactor.io/repository/github/pytorchlightning/pytorch-lightning/badge)](https://www.codefactor.io/repository/github/pytorchlightning/pytorch-lightning)
-
-[![ReadTheDocs](https://readthedocs.org/projects/pytorch-lightning/badge/?version=stable)](https://pytorch-lightning.readthedocs.io/en/stable/)
-[![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/PytorchLightning/pytorch-lightning/blob/master/LICENSE)
-[![Next Release](https://img.shields.io/badge/Next%20Release-May%2029-<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>
-
----
-## Continuous Integration
-<center>
-
-| System / PyTorch ver. | 1.3 (min. reg) | 1.4 | 1.5 (latest) |
-| :---: | :---: | :---: | :---: |
-| Linux py3.6 [CPU] | [![CircleCI](https://circleci.com/gh/PyTorchLightning/pytorch-lightning.svg?style=svg)](https://circleci.com/gh/PyTorchLightning/pytorch-lightning) | [![CircleCI](https://circleci.com/gh/PyTorchLightning/pytorch-lightning.svg?style=svg)](https://circleci.com/gh/PyTorchLightning/pytorch-lightning) | [![CircleCI](https://circleci.com/gh/PyTorchLightning/pytorch-lightning.svg?style=svg)](https://circleci.com/gh/PyTorchLightning/pytorch-lightning) |
-| Linux py3.7 [GPU] | - | - | [![Build Status](http://35.192.60.23/api/badges/PyTorchLightning/pytorch-lightning/status.svg)](http://35.192.60.23/PyTorchLightning/pytorch-lightning) |
-| Linux py3.6 / py3.7 / py3.8 | [![CI testing](https://github.com/PyTorchLightning/pytorch-lightning/workflows/CI%20testing/badge.svg?event=push)](https://github.com/PyTorchLightning/pytorch-lightning/actions?query=workflow%3A%22CI+testing%22) | - | [![CI testing](https://github.com/PyTorchLightning/pytorch-lightning/workflows/CI%20testing/badge.svg?event=push)](https://github.com/PyTorchLightning/pytorch-lightning/actions?query=workflow%3A%22CI+testing%22) |
-| OSX py3.6 / py3.7 / py3.8| - | [![CI testing](https://github.com/PyTorchLightning/pytorch-lightning/workflows/CI%20testing/badge.svg?event=push)](https://github.com/PyTorchLightning/pytorch-lightning/actions?query=workflow%3A%22CI+testing%22) | [![CI testing](https://github.com/PyTorchLightning/pytorch-lightning/workflows/CI%20testing/badge.svg?event=push)](https://github.com/PyTorchLightning/pytorch-lightning/actions?query=workflow%3A%22CI+testing%22) |
-| Windows py3.6 / py3.7 / py3.8 | [![CI testing](https://github.com/PyTorchLightning/pytorch-lightning/workflows/CI%20testing/badge.svg?event=push)](https://github.com/PyTorchLightning/pytorch-lightning/actions?query=workflow%3A%22CI+testing%22) |[![CI testing](https://github.com/PyTorchLightning/pytorch-lightning/workflows/CI%20testing/badge.svg?event=push)](https://github.com/PyTorchLightning/pytorch-lightning/actions?query=workflow%3A%22CI+testing%22) | - |
-
-</center>
-
-Simple installation from PyPI
-```bash
-pip install pytorch-lightning
-```
-
-## Docs
-- [master](https://pytorch-lightning.readthedocs.io/en/latest)
-- [0.7.6](https://pytorch-lightning.readthedocs.io/en/0.7.6/)
-- [0.7.5](https://pytorch-lightning.readthedocs.io/en/0.7.5/)
-- [0.7.3](https://pytorch-lightning.readthedocs.io/en/0.7.3/)
-- [0.7.1](https://pytorch-lightning.readthedocs.io/en/0.7.1/)
-- [0.6.0](https://pytorch-lightning.readthedocs.io/en/0.6.0/)
-- [0.5.3.2](https://pytorch-lightning.readthedocs.io/en/0.5.3.2/)
-
-## Refactoring your PyTorch code + benefits + full walk-through
-[![Watch the video](docs/source/_images/general/tutorial_cover.jpg)](https://www.youtube.com/watch?v=QHww1JH7IDU)
-
-## Demo
-Here's a minimal example without a validation or test loop.
-
-```python
-# this is just a plain nn.Module with some structure
-
-class LitClassifier(pl.LightningModule):
-
-    def __init__(self):
-        super().__init__()
-        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_nb):
-        x, y = batch
-        loss = F.cross_entropy(self(x), y)
-        tensorboard_logs = {'train_loss': loss}
-        return {'loss': loss, 'log': tensorboard_logs}
-
-    def configure_optimizers(self):
-        return torch.optim.Adam(self.parameters(), lr=0.02)
-
-# train!
-train_loader = DataLoader(MNIST(os.getcwd(), train=True, download=True, transform=transforms.ToTensor()), batch_size=32)
-
-model = LitClassifier()
-trainer = pl.Trainer(gpus=8, precision=16)    
-trainer.fit(model, train_loader) 
-```
-
-Other examples:    
-[GAN](https://colab.research.google.com/drive/1F_RNcHzTfFuQf-LeKvSlud6x7jXYkG31#scrollTo=P0bSmCw57aV5)     
-[BERT](https://colab.research.google.com/drive/1F_RNcHzTfFuQf-LeKvSlud6x7jXYkG31#scrollTo=7uQVI-xv9Ddj)    
-[DQN](https://colab.research.google.com/drive/1F_RNcHzTfFuQf-LeKvSlud6x7jXYkG31#scrollTo=NWvMLBDySQI5)   
-[MNIST on TPUs](https://colab.research.google.com/drive/1-_LKx4HwAxl5M6xPJmqAAu444LTDQoa3)    
-
-## What is it?
-[READ THIS QUICK START PAGE](https://pytorch-lightning.readthedocs.io/en/stable/new-project.html)
-
-Lightning is a way to organize your PyTorch code to decouple the science code from the engineering.
-It's more of a PyTorch style-guide than a framework. 
-
-In Lightning, you organize your code into 3 distinct categories:
-
-1. Research code (goes in the LightningModule).
-2. Engineering code (you delete, and is handled by the Trainer).
-3. Non-essential research code (logging, etc... this goes in Callbacks).
-
-Here's an example of how to refactor your research code into a [LightningModule](https://pytorch-lightning.readthedocs.io/en/latest/lightning-module.html).
-
-![PT to PL](docs/source/_images/lightning_module/pt_to_pl.png)
-
-The rest of the code is automated by the [Trainer](https://pytorch-lightning.readthedocs.io/en/latest/trainer.html)!
-![PT to PL](docs/source/_images/lightning_module/pt_trainer.png)
-
-## Testing Rigour
-All the automated code by the Trainer is [tested rigorously with every new PR](https://github.com/PyTorchLightning/pytorch-lightning/tree/master/tests).
-
-In fact, we also train a few models using a vanilla PyTorch loop and compare with the same model trained using the Trainer to make sure we achieve the EXACT same results. [Check out the parity tests here](https://github.com/PyTorchLightning/pytorch-lightning/tree/master/benchmarks).
-
-Overall, Lightning guarantees rigorously tested, correct, modern best practices for the automated parts.
-
-## How flexible is it?
-As you see, you're just organizing your PyTorch code - there's no abstraction.
-
-And for the stuff that the Trainer abstracts out, you can [override any part](https://pytorch-lightning.readthedocs.io/en/latest/introduction_guide.html#extensibility) you want to do things like implement your own distributed training, 16-bit precision, or even a custom backward pass.
-
-For example, here you could do your own backward pass
-
-```python
-class LitModel(LightningModule):
-  def optimizer_step(self, current_epoch, batch_idx, optimizer, optimizer_idx,
-                     second_order_closure=None):
-      optimizer.step()
-      optimizer.zero_grad()
-```
-
-For anything else you might need, we have an extensive [callback system](https://pytorch-lightning.readthedocs.io/en/latest/introduction_guide.html#callbacks) you can use to add arbitrary functionality not implemented by our team in the Trainer.
-
-## Who is Lightning for?
-- Professional researchers
-- Ph.D. students
-- Corporate production teams
-
-If you're just getting into deep learning, we recommend you learn PyTorch first! Once you've implemented a few models, come back and use all the advanced features of Lightning :)
-
-## What does lightning control for me?
-
-Everything in Blue!
-This is how lightning separates the science (red) from engineering (blue).
-
-![Overview](docs/source/_images/general/pl_overview.gif)
-
-## How much effort is it to convert?
-If your code is not a huge mess you should be able to organize it into a LightningModule in less than 1 hour.
-If your code IS a mess, then you needed to clean up anyhow ;)
-
-[Check out this step-by-step guide](https://towardsdatascience.com/from-pytorch-to-pytorch-lightning-a-gentle-introduction-b371b7caaf09).    
-[Or watch this video](https://www.youtube.com/watch?v=QHww1JH7IDU).
-
-
-## Starting a new project?
-[Use our seed-project aimed at reproducibility!](https://github.com/PytorchLightning/pytorch-lightning-conference-seed)
-
-## Why do I want to use lightning?
-Although your research/production project might start simple, once you add things like GPU AND TPU training, 16-bit precision, etc, you end up spending more time engineering than researching. Lightning automates AND rigorously tests those parts for you.
-
-## Support
-- [8 core contributors](https://pytorch-lightning.readthedocs.io/en/latest/governance.html) who are all a mix of professional engineers, Research Scientists, Ph.D. students from top AI labs. 
-- 100+ community contributors.
-
-Lightning is also part of the [PyTorch ecosystem](https://pytorch.org/ecosystem/) which requires projects to have solid testing, documentation and support.
-
----
-
-## README Table of Contents
-- [How do I use it](https://github.com/PytorchLightning/pytorch-lightning#how-do-i-do-use-it)
-- [What lightning automates](https://github.com/PytorchLightning/pytorch-lightning#what-does-lightning-control-for-me)
-- [Tensorboard integration](https://github.com/PytorchLightning/pytorch-lightning#tensorboard)
-- [Lightning features](https://github.com/PytorchLightning/pytorch-lightning#lightning-automates-all-of-the-following-each-is-also-configurable)
-- [Examples](https://github.com/PytorchLightning/pytorch-lightning#examples)
-- [Tutorials](https://github.com/PytorchLightning/pytorch-lightning#tutorials)
-- [Asking for help](https://github.com/PytorchLightning/pytorch-lightning#asking-for-help)
-- [Contributing](https://github.com/PytorchLightning/pytorch-lightning/blob/master/.github/CONTRIBUTING.md)
-- [Bleeding edge install](https://github.com/PytorchLightning/pytorch-lightning#bleeding-edge)
-- [Lightning Design Principles](https://github.com/PytorchLightning/pytorch-lightning#lightning-design-principles)
-- [Lightning team](https://github.com/PytorchLightning/pytorch-lightning#lightning-team)
-- [FAQ](https://github.com/PytorchLightning/pytorch-lightning#faq)
-
----
-
-## Realistic example
-Here's how you would organize a realistic PyTorch project into Lightning.
-
-![PT to PL](docs/source/_images/mnist_imgs/pt_to_pl.jpg)
-
-The LightningModule defines a *system* such as seq-2-seq, GAN, etc...
-It can ALSO define a simple classifier.
-
-In summary, you:
-
-1. Define a [LightningModule](https://pytorch-lightning.rtfd.io/en/latest/lightning-module.html)
-```python
-    class LitSystem(pl.LightningModule):
-
-        def __init__(self):
-            super().__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):
-            ...
-```
-
-2. Fit it with a [Trainer](https://pytorch-lightning.rtfd.io/en/latest/pytorch_lightning.trainer.html)
- ```python
- from pytorch_lightning import Trainer
-
- model = LitSystem()
-
- # most basic trainer, uses good defaults
- trainer = Trainer()
- trainer.fit(model)
- ```
-
-[Check out the COLAB demo here](https://colab.research.google.com/drive/1F_RNcHzTfFuQf-LeKvSlud6x7jXYkG31#scrollTo=HOk9c4_35FKg)
-
-## What types of research works?
-Anything! Remember, that this is just organized PyTorch code.
-The Training step defines the core complexity found in the training loop.
-
-#### Could be as complex as a seq2seq
-
-```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(x)
-    loss = my_loss(out, y)
-    return {'loss': loss}
-```
-
-And without changing a single line of code, you could run on CPUs
-```python
-trainer = Trainer(max_epochs=1)
-```
-
-
-Or GPUs
-```python
-# 8 GPUs
-trainer = Trainer(max_epochs=1, gpus=8)
-
-# 256 GPUs
-trainer = Trainer(max_epochs=1, gpus=8, num_nodes=32)
-```
-
-Or TPUs
-```python
-# Distributes TPU core training
-trainer = Trainer(tpu_cores=8)
-
-# Single TPU core training
-trainer = Trainer(tpu_cores=[1])
-```
-
-When you're done training, run the test accuracy
-```python
-trainer.test()
-```
-
-## Visualization
-Lightning has out-of-the-box integration with the popular logging/visualizing frameworks
-
-- [Tensorboard](https://pytorch.org/docs/stable/tensorboard.html)
-- [MLFlow](https://mlflow.org/)
-- [Neptune.ai](https://neptune.ai/)
-- [Comet.ml](https://www.comet.ml/site/)
-- [Wandb](https://www.wandb.com/)
-- [Trains](https://github.com/allegroai/trains)
-- ...  
-
-![tensorboard-support](docs/source/_images/general/tf_loss.png)
-
-
-## Lightning automates 40+ parts of DL/ML research
-- GPU training
-- Distributed GPU (cluster) training
-- TPU training
-- EarlyStopping
-- Logging/Visualizing
-- Checkpointing
-- Experiment management
-- [Full list here](https://pytorch-lightning.readthedocs.io/en/latest/#common-use-cases)
-
-
-## Examples
-Check out this awesome list of research papers and implementations done with Lightning.
-
-- [Contextual Emotion Detection (DoubleDistilBert)](https://github.com/PyTorchLightning/emotion_transformer)
-- [Generative Adversarial Network](https://colab.research.google.com/drive/1F_RNcHzTfFuQf-LeKvSlud6x7jXYkG31#scrollTo=TyYOdg8g77P0)
-- [Hyperparameter optimization with Optuna](https://github.com/optuna/optuna/blob/master/examples/pytorch_lightning_simple.py)
-- [Image Inpainting using Partial Convolutions](https://github.com/ryanwongsa/Image-Inpainting)
-- [MNIST on TPU](https://colab.research.google.com/drive/1-_LKx4HwAxl5M6xPJmqAAu444LTDQoa3#scrollTo=BHBz1_AnamN_)
-- [NER (transformers, TPU, huggingface)](https://colab.research.google.com/drive/1dBN-wwYUngLYVt985wGs_OKPlK_ANB9D)
-- [NeuralTexture (CVPR)](https://github.com/PyTorchLightning/neuraltexture)
-- [Recurrent Attentive Neural Process](https://github.com/PyTorchLightning/attentive-neural-processes)
-- [Siamese Nets for One-shot Image Recognition](https://github.com/PyTorchLightning/Siamese-Neural-Networks)
-- [Speech Transformers](https://github.com/PyTorchLightning/speech-transformer-pytorch_lightning)
-- [Transformers transfer learning (Huggingface)](https://colab.research.google.com/drive/1F_RNcHzTfFuQf-LeKvSlud6x7jXYkG31#scrollTo=yr7eaxkF-djf)
-- [Transformers text classification](https://github.com/ricardorei/lightning-text-classification)
-- [VAE Library of over 18+ VAE flavors](https://github.com/AntixK/PyTorch-VAE)
-
-## Tutorials
-Check out our [introduction guide](https://pytorch-lightning.readthedocs.io/en/latest/introduction_guide.html) to get started.
-Or jump straight into [our tutorials](https://pytorch-lightning.readthedocs.io/en/latest/#tutorials).
-
----
-
-## Asking for help
-Welcome to the Lightning community!
-
-If you have any questions, feel free to:
-1. [read the docs](https://pytorch-lightning.rtfd.io/en/latest/).
-2. [Search through the issues](https://github.com/PytorchLightning/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.
-4. [Join our slack](https://join.slack.com/t/pytorch-lightning/shared_invite/enQtODU5ODIyNTUzODQwLTFkMDg5Mzc1MDBmNjEzMDgxOTVmYTdhYjA1MDdmODUyOTg2OGQ1ZWZkYTQzODhhNzdhZDA3YmNhMDhlMDY4YzQ).
-
----
-## FAQ
-**How do I use Lightning for rapid research?**    
-[Here's a walk-through](https://pytorch-lightning.readthedocs.io/en/latest/introduction_guide.html)
-
-**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 unnecessary abstractions!
-
-**Are there plans to support Python 2?**    
-Nope.
-
-**Are there plans to support virtualenv?**    
-Nope. Please use anaconda or miniconda.    
-```bash
-conda activate my_env
-pip install pytorch-lightning
-```
-
-## 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/PytorchLightning/pytorch-lightning.git@master --upgrade
-    ```
-* using instant zip (last state of the repo without git history)
-    ```bash
-    pip install https://github.com/PytorchLightning/pytorch-lightning/archive/master.zip --upgrade
-    ```
-
-### Any release installation
-
-You can also install any past release `0.X.Y` from this repository:
-```bash
-pip install https://github.com/PytorchLightning/pytorch-lightning/archive/0.X.Y.zip --upgrade
-```
-
-### Lightning team
-
-#### Leads
-- William Falcon [(williamFalcon)](https://github.com/williamFalcon) (Lightning founder)
-- Jirka Borovec [(Borda)](https://github.com/Borda) (ghost :)
-- Ethan Harris [(ethanwharris)](https://github.com/ethanwharris) (Torchbearer founder)
-- Matthew Painter [(MattPainter01)](https://github.com/MattPainter01) (Torchbearer founder)
-- Justus Schock [(justusschock)](https://github.com/justusschock) (Former Core Member PyTorch Ignite)
-
-#### Core Maintainers
-
-- Nick Eggert [(neggert)](https://github.com/neggert)
-- Jeff Ling [(jeffling)](https://github.com/jeffling)
-- Jeremy Jordan [(jeremyjordan)](https://github.com/jeremyjordan)
-- Tullie Murrell [(tullie)](https://github.com/tullie)
-- Adrian Wälchli [(awaelchli)](https://github.com/awaelchli)
-- Nicki Skafte [(skaftenicki)](https://github.com/SkafteNicki)
-
-#### Funding    
-Building open-source software with only a few part-time people is hard! We've secured funding to make sure we can
-hire a full-time staff, attend conferences, and move faster through implementing features you request.
-
-Our goal is to build an incredible research platform and a big supportive community. Many open-source projects
-have gone on to fund operations through things like support and special help for big corporations!
-
-If you are one of these corporations, please feel free to reach out to will@pytorchlightning.ai!
-
-## Bibtex
-If you want to cite the framework feel free to use this (but only if you loved it 😊):
-
-```bibtex
-@article{falcon2019pytorch,
-  title={PyTorch Lightning},
-  author={Falcon, WA},
-  journal={GitHub. Note: https://github. com/williamFalcon/pytorch-lightning Cited by},
-  volume={3},
-  year={2019}
-}
-```

Trainer/Checkpointing/index.html

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+    </label>
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+        Trainer
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+        
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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>
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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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+    <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
+    </a>
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+        
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+    <a href="../Testing loop/" title="Testing loop" class="md-nav__link">
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+      </label>
+    
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+      Training Loop
+    </a>
+    
+      
+<nav class="md-nav md-nav--secondary">
+  
+  
+  
+    <label class="md-nav__title" for="__toc">Table of contents</label>
+    <ul class="md-nav__list" data-md-scrollfix>
+      
+        <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
+  </a>
+  
+</li>
+      
+      
+      
+      
+      
+    </ul>
+  
+</nav>
+    
+  </li>
+
+        
+          
+          
+          
+
+
+  <li class="md-nav__item">
+    <a href="../Validation loop/" title="Validation loop" class="md-nav__link">
+      Validation loop
+    </a>
+  </li>
+
+        
+          
+          
+          
+
+
+  <li class="md-nav__item">
+    <a href="../debugging/" title="Debugging" class="md-nav__link">
+      Debugging
+    </a>
+  </li>
+
+        
+          
+          
+          
+
+
+  <li class="md-nav__item">
+    <a href="../hooks/" title="Hooks" class="md-nav__link">
+      Hooks
+    </a>
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+    <label class="md-nav__title" for="__toc">Table of contents</label>
+    <ul class="md-nav__list" data-md-scrollfix>
+      
+        <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
+  </a>
+  
+</li>
+      
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+      
+      
+      
+    </ul>
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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>
+</td></tr></table>
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+  <a href="#check-validation-every-n-epochs" title="Check validation every n epochs" class="md-nav__link">
+    Check validation every n epochs
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+  <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
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+  <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 validation check frequency within 1 training epoch
+  </a>
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+      
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+  <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>
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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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+</li>
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+    Print the parameter count by layer
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+</li>
+      
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+  <a href="#print-which-gradients-are-nan" title="Print which gradients are nan" class="md-nav__link">
+    Print which gradients are nan
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+      
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+    Print the parameter count by layer
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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="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>
+                
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assets/javascripts/lunr/lunr.da.js

@@ -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,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")}});

assets/javascripts/lunr/lunr.ja.js

@@ -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")}});

assets/javascripts/lunr/lunr.jp.js

@@ -0,0 +1 @@
+module.exports=require("./lunr.ja");

assets/javascripts/lunr/lunr.multi.js

@@ -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))}}}});

assets/javascripts/lunr/lunr.no.js

@@ -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")}});

assets/javascripts/lunr/lunr.stemmer.support.js

@@ -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,"")}}}}});

assets/javascripts/lunr/lunr.sv.js

@@ -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")}});

assets/javascripts/lunr/lunr.th.js

@@ -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("|")}}});

benchmarks/test_rnn_parity.py

@@ -1,153 +0,0 @@
-import time
-
-import numpy as np
-import pytest
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torch.utils.data import Dataset, DataLoader
-import tests.base.utils as tutils
-
-from pytorch_lightning import Trainer, LightningModule, seed_everything
-
-
-class AverageDataset(Dataset):
-    def __init__(self, dataset_len=300, sequence_len=100):
-        self.dataset_len = dataset_len
-        self.sequence_len = sequence_len
-        self.input_seq = torch.randn(dataset_len, sequence_len, 10)
-        top, bottom = self.input_seq.chunk(2, -1)
-        self.output_seq = top + bottom.roll(shifts=1, dims=-1)
-
-    def __len__(self):
-        return self.dataset_len
-
-    def __getitem__(self, item):
-        return self.input_seq[item], self.output_seq[item]
-
-
-class ParityRNN(LightningModule):
-    def __init__(self):
-        super(ParityRNN, self).__init__()
-        self.rnn = nn.LSTM(10, 20, batch_first=True)
-        self.linear_out = nn.Linear(in_features=20, out_features=5)
-
-    def forward(self, x):
-        seq, last = self.rnn(x)
-        return self.linear_out(seq)
-
-    def training_step(self, batch, batch_nb):
-        x, y = batch
-        y_hat = self(x)
-        loss = F.mse_loss(y_hat, y)
-        return {'loss': loss}
-
-    def configure_optimizers(self):
-        return torch.optim.Adam(self.parameters(), lr=0.02)
-
-    def train_dataloader(self):
-        return DataLoader(AverageDataset(), batch_size=30)
-
-
-@pytest.mark.skipif(not torch.cuda.is_available(), reason="test requires GPU machine")
-def test_pytorch_parity(tmpdir):
-    """
-    Verify that the same  pytorch and lightning models achieve the same results
-    :param tmpdir:
-    :return:
-    """
-    num_epochs = 2
-    num_rums = 3
-
-    lightning_outs, pl_times = lightning_loop(ParityRNN, num_rums, num_epochs)
-    manual_outs, pt_times = vanilla_loop(ParityRNN, num_rums, num_epochs)
-    # make sure the losses match exactly  to 5 decimal places
-    for pl_out, pt_out in zip(lightning_outs, manual_outs):
-        np.testing.assert_almost_equal(pl_out, pt_out, 8)
-
-    tutils.assert_speed_parity(pl_times, pt_times, num_epochs)
-
-
-def vanilla_loop(MODEL, num_runs=10, num_epochs=10):
-    """
-    Returns an array with the last loss from each epoch for each run
-    """
-    device = torch.device('cuda' if torch.cuda.is_available() else "cpu")
-    errors = []
-    times = []
-
-    torch.backends.cudnn.deterministic = True
-    for i in range(num_runs):
-        time_start = time.perf_counter()
-
-        # set seed
-        seed = i
-        seed_everything(seed)
-
-        # init model parts
-        model = MODEL()
-        dl = model.train_dataloader()
-        optimizer = model.configure_optimizers()
-
-        # model to GPU
-        model = model.to(device)
-
-        epoch_losses = []
-        for epoch in range(num_epochs):
-
-            # run through full training set
-            for j, batch in enumerate(dl):
-                x, y = batch
-                x = x.cuda(0)
-                y = y.cuda(0)
-                batch = (x, y)
-
-                loss_dict = model.training_step(batch, j)
-                loss = loss_dict['loss']
-                loss.backward()
-                optimizer.step()
-                optimizer.zero_grad()
-
-            # track last epoch loss
-            epoch_losses.append(loss.item())
-
-        time_end = time.perf_counter()
-        times.append(time_end - time_start)
-
-        errors.append(epoch_losses[-1])
-
-    return errors, times
-
-
-def lightning_loop(MODEL, num_runs=10, num_epochs=10):
-    errors = []
-    times = []
-
-    for i in range(num_runs):
-        time_start = time.perf_counter()
-
-        # set seed
-        seed = i
-        seed_everything(seed)
-        model = MODEL()
-
-        # init model parts
-        trainer = Trainer(
-            max_epochs=num_epochs,
-            progress_bar_refresh_rate=0,
-            weights_summary=None,
-            gpus=1,
-            early_stop_callback=False,
-            checkpoint_callback=False,
-            distributed_backend='dp',
-            deterministic=True,
-        )
-        trainer.fit(model)
-
-        final_loss = trainer.running_loss.last().item()
-        errors.append(final_loss)
-
-        time_end = time.perf_counter()
-        times.append(time_end - time_start)
-
-    return errors, times

benchmarks/test_trainer_parity.py

@@ -1,153 +0,0 @@
-import os
-import time
-
-import numpy as np
-import pytest
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torch.utils.data import DataLoader
-from torchvision import transforms
-import tests.base.utils as tutils
-
-from pytorch_lightning import Trainer, LightningModule, seed_everything
-from tests.base.datasets import TrialMNIST
-
-
-class ParityMNIST(LightningModule):
-
-    def __init__(self):
-        super(ParityMNIST, self).__init__()
-        self.c_d1 = nn.Linear(in_features=28 * 28, out_features=128)
-        self.c_d1_bn = nn.BatchNorm1d(128)
-        self.c_d1_drop = nn.Dropout(0.3)
-        self.c_d2 = nn.Linear(in_features=128, out_features=10)
-
-    def forward(self, x):
-        x = x.view(x.size(0), -1)
-        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)
-        return x
-
-    def training_step(self, batch, batch_nb):
-        x, y = batch
-        y_hat = self(x)
-        loss = F.cross_entropy(y_hat, y)
-        return {'loss': loss}
-
-    def configure_optimizers(self):
-        return torch.optim.Adam(self.parameters(), lr=0.02)
-
-    def train_dataloader(self):
-        return DataLoader(TrialMNIST(train=True,
-                                     download=True,
-                                     num_samples=500,
-                                     digits=list(range(5))),
-                          batch_size=128)
-
-
-@pytest.mark.skipif(not torch.cuda.is_available(), reason="test requires GPU machine")
-def test_pytorch_parity(tmpdir):
-    """
-    Verify that the same  pytorch and lightning models achieve the same results
-    :param tmpdir:
-    :return:
-    """
-    num_epochs = 2
-    num_rums = 3
-    lightning_outs, pl_times = lightning_loop(ParityMNIST, num_rums, num_epochs)
-    manual_outs, pt_times = vanilla_loop(ParityMNIST, num_rums, num_epochs)
-
-    # make sure the losses match exactly  to 5 decimal places
-    for pl_out, pt_out in zip(lightning_outs, manual_outs):
-        np.testing.assert_almost_equal(pl_out, pt_out, 5)
-
-    # the fist run initialize dataset (download & filter)
-    tutils.assert_speed_parity(pl_times[1:], pt_times[1:], num_epochs)
-
-
-def vanilla_loop(MODEL, num_runs=10, num_epochs=10):
-    """
-    Returns an array with the last loss from each epoch for each run
-    """
-    device = torch.device('cuda' if torch.cuda.is_available() else "cpu")
-    errors = []
-    times = []
-
-    torch.backends.cudnn.deterministic = True
-    for i in range(num_runs):
-        time_start = time.perf_counter()
-
-        # set seed
-        seed = i
-        seed_everything(seed)
-
-        # init model parts
-        model = MODEL()
-        dl = model.train_dataloader()
-        optimizer = model.configure_optimizers()
-
-        # model to GPU
-        model = model.to(device)
-
-        epoch_losses = []
-        for epoch in range(num_epochs):
-
-            # run through full training set
-            for j, batch in enumerate(dl):
-                x, y = batch
-                x = x.cuda(0)
-                y = y.cuda(0)
-                batch = (x, y)
-
-                loss_dict = model.training_step(batch, j)
-                loss = loss_dict['loss']
-                loss.backward()
-                optimizer.step()
-                optimizer.zero_grad()
-
-            # track last epoch loss
-            epoch_losses.append(loss.item())
-
-        time_end = time.perf_counter()
-        times.append(time_end - time_start)
-
-        errors.append(epoch_losses[-1])
-
-    return errors, times
-
-
-def lightning_loop(MODEL, num_runs=10, num_epochs=10):
-    errors = []
-    times = []
-
-    for i in range(num_runs):
-        time_start = time.perf_counter()
-
-        # set seed
-        seed = i
-        seed_everything(seed)
-
-        model = MODEL()
-        # init model parts
-        trainer = Trainer(
-            max_epochs=num_epochs,
-            progress_bar_refresh_rate=0,
-            weights_summary=None,
-            gpus=1,
-            early_stop_callback=False,
-            checkpoint_callback=False,
-            deterministic=True,
-        )
-        trainer.fit(model)
-
-        final_loss = trainer.running_loss.last().item()
-        errors.append(final_loss)
-
-        time_end = time.perf_counter()
-        times.append(time_end - time_start)
-
-    return errors, times

docker/Dockerfile

@@ -1,42 +0,0 @@
-ARG CUDA_VERSION=10.1
-FROM nvidia/cuda:${CUDA_VERSION}-base
-
-# install versions
-ARG PYTHON_VERSION=3.7
-ARG PYTORCH_VERSION=1.4
-ARG LIGHTNING_VERSION=master
-
-RUN apt-get update && apt-get install -y --no-install-recommends \
-         build-essential \
-         cmake \
-         git \
-         curl \
-         ca-certificates
-         
-# add non-root user
-RUN useradd --create-home --shell /bin/bash containeruser
-USER containeruser
-WORKDIR /home/containeruser
-         
-   
-# install conda and python
-RUN curl -o ~/miniconda.sh https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh  && \
-    chmod +x ~/miniconda.sh && \
-    ~/miniconda.sh -b -p /home/containeruser/conda && \
-    rm ~/miniconda.sh && \
-    /home/containeruser/conda/bin/conda clean -ya && \
-    /home/containeruser/conda/bin/conda install -y python=$PYTHON_VERSION 
-     
-# add conda to path
-ENV PATH /home/containeruser/conda/bin:$PATH
-
-# install dependencies
-RUN pip install torch==$PYTORCH_VERSION
-RUN git clone https://github.com/PyTorchLightning/pytorch-lightning.git --single-branch --branch $LIGHTNING_VERSION && \
-    pip install ./pytorch-lightning && \
-    pip install -r pytorch-lightning/requirements-extra.txt && \
-    rm -rf pytorch-lightning
-
-RUN python -c "import pytorch_lightning as pl; print(pl.__version__)"
-        
-CMD ["/bin/bash"]

docker/README.md

@@ -1,13 +0,0 @@
-## Builds
-
-You can build it on your own, note it takes lots of time, be prepared.
-```bash
-git clone <git-repository>
-docker image build -t pytorch-lightning:py36 -f docker/Dockerfile --build-arg PYTHON_VERSION=3.6 .
-```
-To build other versions, select different Dockerfile.
-```bash
-docker image list
-docker run --rm -it pytorch-lightning:py36 bash
-docker image rm pytorch-lightning:py36
-```

docs/.build_docs.sh

@@ -1 +0,0 @@
-make clean ; make html --debug --jobs 2 SPHINXOPTS="-W"

docs/Makefile

@@ -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)

docs/make.bat

@@ -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

docs/requirements.txt

@@ -1,12 +0,0 @@
-sphinx>=2.0, <3.0
-recommonmark  # fails with badges
-m2r  # fails with multi-line text
-nbsphinx
-pandoc
-docutils
-sphinxcontrib-fulltoc
-sphinxcontrib-mockautodoc
-git+https://github.com/PytorchLightning/lightning_sphinx_theme.git
-# pip_shims
-sphinx-autodoc-typehints
-sphinx-paramlinks<0.4.0

docs/source/_images/logos/lightning_icon.svg

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-   xmlns:svg="http://www.w3.org/2000/svg"
-   xmlns="http://www.w3.org/2000/svg"
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-   xmlns:inkscape="http://www.inkscape.org/namespaces/inkscape"
-   id="svg"
-   version="1.1"
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-   height="15.999986"
-   viewBox="0 0 16.000004 15.999986"
-   sodipodi:docname="lightning_icon.svg"
-   inkscape:version="0.92.3 (2405546, 2018-03-11)">
-  <metadata
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-        <dc:title></dc:title>
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-     inkscape:pageshadow="2"
-     inkscape:window-width="1920"
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