release v0.3.6.2

2026-09-12 12:40:20 +08:00 · 2019-07-26 23:08:51 -04:00
75 changed files with 1341 additions and 3572 deletions
@@ -1,46 +0,0 @@
-# see https://docs.codecov.io/docs/codecov-yaml
-# Validation check:
-# $ curl --data-binary @.codecov.yml https://codecov.io/validate
-
-codecov:
-  notify:
-    require_ci_to_pass: yes
-
-coverage:
-  precision: 0  # 2 = xx.xx%, 0 = xx%
-  round: nearest # how coverage is rounded: down/up/nearest
-  range: 40...100 # custom range of coverage colors from red -> yellow -> green
-  status:
-    # https://codecov.readme.io/v1.0/docs/commit-status
-    project:
-      default:
-        against: auto
-        target: 99% # specify the target coverage for each commit status
-        threshold: 20% # allow this little decrease on project
-        # https://github.com/codecov/support/wiki/Filtering-Branches
-        # branches: master
-        if_ci_failed: error
-    # https://github.com/codecov/support/wiki/Patch-Status
-    patch:
-      default:
-        against: auto
-        target: 40% # specify the target "X%" coverage to hit
-        # threshold: 50% # allow this much decrease on patch
-    changes: false
-
-parsers:
-  gcov:
-    branch_detection:
-      conditional: true
-      loop: true
-      macro: false
-      method: false
-  javascript:
-    enable_partials: false
-
-comment:
-  layout: header, diff
-  require_changes: false
-  behavior: default  # update if exists else create new
-  # branches: *
-
@@ -1,36 +0,0 @@
---
-name: Bug report
-about: Create a report to help us improve
-title: ''
-labels: bug
-assignees: ''
-
---
-
-### Common bugs:
-1. Tensorboard not showing in Jupyter-notebook see [issue 79](https://github.com/williamFalcon/pytorch-lightning/issues/79).    
-2. PyTorch 1.1.0 vs 1.2.0 support [see FAQ](https://github.com/williamFalcon/pytorch-lightning#faq)    
-
-**Describe the bug**
-A clear and concise description of what the bug is.
-
-**To Reproduce**
-Steps to reproduce the behavior:
-1. Go to '...'
-2. Click on '....'
-3. Scroll down to '....'
-4. See error
-
-**Expected behavior**
-A clear and concise description of what you expected to happen.
-
-**Screenshots**
-If applicable, add screenshots to help explain your problem.
-
-**Desktop (please complete the following information):**
- - OS: [e.g. iOS]
- - Browser [e.g. chrome, safari]
- - Version [e.g. 22]
-
-**Additional context**
-Add any other context about the problem here.
@@ -1,20 +0,0 @@
---
-name: Feature request
-about: Suggest an idea for this project
-title: ''
-labels: enhancement, help wanted
-assignees: ''
-
---
-
-**Is your feature request related to a problem? Please describe.**
-A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
-
-**Describe the solution you'd like**
-A clear and concise description of what you want to happen.
-
-**Describe alternatives you've considered**
-A clear and concise description of any alternative solutions or features you've considered.
-
-**Additional context**
-Add any other context or screenshots about the feature request here.
@@ -1,26 +0,0 @@
---
-name: How to question
-about: Asking how-to questions
-title: ''
-labels: question
-assignees: ''
-
---
-
-### 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?   
- conda version (no venv)   
- PyTorch version   
- Lightning version   
- Test-tube version
@@ -1,17 +0,0 @@
---
-name: Typos and doc fixes
-about: Typos and doc fixes
-title: ''
-labels: typo
-assignees: ''
-
---
-
-For typos and doc fixes, please go ahead and:
-
-1. Create an issue.
-2. Fix the typo.   
-3. Submit a PR.
-
-
-Thanks!
@@ -10,8 +10,6 @@ app/models/
 pip-wheel-metadata/
 test_tube_exp/
 tests/tests_tt_dir/
-tests/save_dir
-default/

 # Byte-compiled / optimized / DLL files
 __pycache__/
@@ -16,4 +16,4 @@ formats: all
 python:
  version: 3.7
  install:
-    - requirements: docs/requirements.txt
+    - requirements: docs/doc_requirements.txt
@@ -1,46 +1,16 @@
-# vim ft=yaml
-
-# After changing this file, check it on:
-#   http://yaml-online-parser.appspot.com/
-
-# See doc/travis_notes.txt for some guidelines
-
-# this file is *not* meant to cover or endorse the use of travis, but rather to
-# help confirm pull requests to this project.
-
-dist: xenial  # Ubuntu 16.04
-
-env:
-  global:
-    - DISPLAY=""
-
 language: python
-
-matrix:
-  include:
-    - python: 3.6
-      env: TOXENV=py36
-    - python: 3.7
-      env: TOXENV=py37
-
-# See http://docs.travis-ci.com/user/caching/#pip-cache
+python:
+  - "3.7"
+# command to install dependencies
 cache: pip
-
 install:
+  - pip install -e .
  - pip install -r requirements.txt
-  - pip install -r ./tests/requirements.txt
-  - pip --version ; pip list
+  - pip install -U numpy

+# keep build from timing out
+dist: xenial
+
+# command to run tests
 script:
-  # integration
-  - tox --sitepackages
-  - python setup.py install --dry-run
-
-after_success:
-  - coverage report
-  # disable auto coverage bc it isn't accurate since it misses gpu code.
-  # to get coverage, run local and push results
-  # - codecov
-
-notifications:
-  email: false
+  - py.test # or py.test for Python versions 3.5 and below
@@ -1,76 +0,0 @@
-# Contributor Covenant Code of Conduct
-
-## Our Pledge
-
-In the interest of fostering an open and welcoming environment, we as
-contributors and maintainers pledge to making participation in our project and
-our community a harassment-free experience for everyone, regardless of age, body
-size, disability, ethnicity, sex characteristics, gender identity and expression,
-level of experience, education, socio-economic status, nationality, personal
-appearance, race, religion, or sexual identity and orientation.
-
-## Our Standards
-
-Examples of behavior that contributes to creating a positive environment
-include:
-
-* Using welcoming and inclusive language
-* Being respectful of differing viewpoints and experiences
-* Gracefully accepting constructive criticism
-* Focusing on what is best for the community
-* Showing empathy towards other community members
-
-Examples of unacceptable behavior by participants include:
-
-* The use of sexualized language or imagery and unwelcome sexual attention or
- advances
-* Trolling, insulting/derogatory comments, and personal or political attacks
-* Public or private harassment
-* Publishing others' private information, such as a physical or electronic
- address, without explicit permission
-* Other conduct which could reasonably be considered inappropriate in a
- professional setting
-
-## Our Responsibilities
-
-Project maintainers are responsible for clarifying the standards of acceptable
-behavior and are expected to take appropriate and fair corrective action in
-response to any instances of unacceptable behavior.
-
-Project maintainers have the right and responsibility to remove, edit, or
-reject comments, commits, code, wiki edits, issues, and other contributions
-that are not aligned to this Code of Conduct, or to ban temporarily or
-permanently any contributor for other behaviors that they deem inappropriate,
-threatening, offensive, or harmful.
-
-## Scope
-
-This Code of Conduct applies both within project spaces and in public spaces
-when an individual is representing the project or its community. Examples of
-representing a project or community include using an official project e-mail
-address, posting via an official social media account, or acting as an appointed
-representative at an online or offline event. Representation of a project may be
-further defined and clarified by project maintainers.
-
-## Enforcement
-
-Instances of abusive, harassing, or otherwise unacceptable behavior may be
-reported by contacting the project team at waf2107@columbia.edu. All
-complaints will be reviewed and investigated and will result in a response that
-is deemed necessary and appropriate to the circumstances. The project team is
-obligated to maintain confidentiality with regard to the reporter of an incident.
-Further details of specific enforcement policies may be posted separately.
-
-Project maintainers who do not follow or enforce the Code of Conduct in good
-faith may face temporary or permanent repercussions as determined by other
-members of the project's leadership.
-
-## Attribution
-
-This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4,
-available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html
-
-[homepage]: https://www.contributor-covenant.org
-
-For answers to common questions about this code of conduct, see
-https://www.contributor-covenant.org/faq
@@ -1,42 +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!   
-
-## One less thing to remember    
-Simplify the API as much as possible from the user perspective. Any additions or improvements should minimize things the user needs to remember.   
-
-For example: One benefit of the validation_step is that the user doesn't have to remember to set the model to .eval(). This avoids all sorts of subtle errors the user could make.   
-
-## Lightning Design Principles   
-We encourage all sorts of contributions you're interested in adding! When coding for lightning, please follow these principles.    
-
-#### No PyTorch interference   
-We don't want to add any abstractions on top of pure PyTorch. This gives researchers all the control they need without having to learn yet another framework.    
-
-#### Simple Internal Code    
-It's useful for users to look at the code and understand very quickly what's happening. Many users won't be engineers. Thus we need to value clear, simple code over condensed ninja moves. While that's super cool, this isn't the project for that :)      
-
-#### Force User Decisions To Best Practices    
-There are 1,000 ways to do something. However, something eventually becomes standard practice that everyone does. Thus we pick one way of doing it and force everyone to do it this way. A good example is accumulated gradients. There are many ways to implement, we just pick one and force users to use that one. A bad forced decision would be to make users use a specific library to do something.    
-
-When something becomes a best practice, we add it to the framework. This likely looks like code in utils or in the model file that everyone keeps adding over and over again across projects. When this happens, bring that code inside the trainer and add a flag for it.
-
-#### Simple External API    
-What makes sense to you may not make sense to others. Create an issue with an API change suggestion and validate that it makes sense for others. Treat code changes how you treat a startup: validate that it's a needed feature, then add if it makes sense for many people.    
-
-#### Gain User Trust    
-As a researcher you can't have any part of your code going wrong. So, make thorough tests that ensure an implementation of a new trick or subbtle change is correct.    
-
-## Contribution types    
-Currently looking for help implementing new features or adding bug fixes. 
-
-A lot of good work has already been done in project mechanics (requirements.txt, setup.py, pep8, badges, ci, etc...) we're in a good state there thanks to all the early contributors (even pre-beta release)!   
-
-## Bug fixes:  
-1. Submit a github issue.   
-2. Fix it.  
-3. Submit a PR! 
-
-## New Features:  
-1. Submit a github issue.   
-2. We'll agree on the feature scope.     
-3. Submit a PR! (with updated docs and tests 🙃).   
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2019 William Falcon
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
@@ -1,201 +0,0 @@
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-
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@@ -1,37 +1,9 @@
-# Manifest syntax https://docs.python.org/2/distutils/sourcedist.html
-graft wheelhouse
+graft docs

-recursive-include birl *.py
-recursive-exclude __pycache__  *.py[cod] *.orig
+include COPYING
+include AUTHORS

-# Include the README
-include *.md
+recursive-include src/einsteinpy/tests *.py *.html

-# Include the license file
-include LICENSE
-
-exclude *.sh
-exclude *.toml
-exclude *.svg    
-recursive-include examples *.py
-recursive-include pytorch_lightning *.py
-
-# exclude tests from package
-recursive-exclude tests *
-exclude tests
-
-# Exclude the documentation files
-recursive-exclude docs *
-exclude docs
-
-# Include the Requirements
-include requirements.txt
-
-# Exclude build configs
-exclude *.yml
-
-prune .git
-prune .github
-prune notebook*
-prune temp*
-prune test*
+prune docs/source/examples/.ipynb_checkpoints
+global-exclude *.py[cod] __pycache__ *.so *.dylib
@@ -1,30 +1,24 @@
-<div align="center">
+<p align="center">
+  <a href="https://williamfalcon.github.io/pytorch-lightning/">
+    <img alt="" src="https://github.com/williamFalcon/pytorch-lightning/blob/master/docs/source/_static/lightning_logo.png" width="50">
+  </a>
+</p>
+<h3 align="center">
+  Pytorch Lightning
+</h3>
+<p align="center">
+  The Keras for ML researchers using PyTorch. More control. Less boilerplate.    
+</p>

-![Logo](./docs/source/_static/lightning_logo_small.png)
+<p align="center">
+  <a href="https://badge.fury.io/py/pytorch-lightning"><img src="https://badge.fury.io/py/pytorch-lightning.svg" alt="PyPI version" height="18"></a>
+  <a href="https://pepy.tech/project/pytorch-lightning"><img src="https://pepy.tech/badge/pytorch-lightning" alt="PyPI version" height="18"></a>
+  <a href="https://github.com/williamFalcon/pytorch-lightning/tree/master/tests"><img src="https://github.com/williamFalcon/pytorch-lightning/blob/master/coverage.svg"></a>
+  <a href="https://travis-ci.org/williamFalcon/pytorch-lightning"><img src="https://travis-ci.org/williamFalcon/pytorch-lightning.svg?branch=master"></a>
+  <a href="https://williamfalcon.github.io/pytorch-lightning/"><img src="https://readthedocs.org/projects/pytorch-lightning/badge/?version=latest"></a>
+  <a href="https://github.com/williamFalcon/pytorch-lightning/blob/master/COPYING"><img src="https://img.shields.io/badge/License-MIT-yellow.svg"></a>
+</p>   

-# PyTorch Lightning
-
-**The PyTorch Keras for ML researchers. More control. Less boilerplate.**
-
-
-[![PyPI Status](https://badge.fury.io/py/pytorch-lightning.svg)](https://badge.fury.io/py/pytorch-lightning)
-[![PyPI Status](https://pepy.tech/badge/pytorch-lightning)](https://pepy.tech/project/pytorch-lightning)
-[![Build Status](https://travis-ci.org/williamFalcon/pytorch-lightning.svg?branch=master)](https://travis-ci.org/williamFalcon/pytorch-lightning)
-[![Build status](https://ci.appveyor.com/api/projects/status/rum89d7hq8l1kfye?svg=true)](https://ci.appveyor.com/project/Borda/pytorch-lightning)
-[![Coverage](https://github.com/williamFalcon/pytorch-lightning/blob/master/docs/source/_static/coverage.svg)](https://github.com/williamFalcon/pytorch-lightning/tree/master/tests#running-coverage)
-[![CodeFactor](https://www.codefactor.io/repository/github/borda/pytorch-lightning/badge)](https://www.codefactor.io/repository/github/borda/pytorch-lightning)    
-
-[![ReadTheDocs](https://readthedocs.org/projects/pytorch-lightning/badge/?version=latest)](https://pytorch-lightning.readthedocs.io/en/latest)
-[![Gitter](https://badges.gitter.im/PyTorch-Lightning/community.svg)](https://gitter.im/PyTorch-Lightning/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge)
-[![license](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://github.com/williamFalcon/pytorch-lightning/blob/master/LICENSE)
-<!-- 
-removed until codecov badge isn't empy. likely a config error showing nothing on master.
-[![codecov](https://codecov.io/gh/Borda/pytorch-lightning/branch/master/graph/badge.svg)](https://codecov.io/gh/Borda/pytorch-lightning)
-->
-
-</div>
-
-Simple installation from PyPI
 ```bash
 pip install pytorch-lightning  
 ```
@@ -33,140 +27,91 @@ pip install pytorch-lightning
 **[View the docs here](https://williamfalcon.github.io/pytorch-lightning/)**

 ## What is it?  
-Lightning is a very lightweight wrapper on PyTorch. This means you don't have to learn a new library. To use Lightning, simply refactor your research code into the [LightningModule](https://github.com/williamFalcon/pytorch-lightning#how-do-i-do-use-it) format and Lightning will automate the rest. Lightning guarantees tested, correct, modern best practices for the automated parts.
+Lightning defers training and validation loop logic to you. It guarantees correct, modern best practices for the core training logic.

-## Starting a new project?   
-[Use our seed-project aimed at reproducibility!](https://github.com/williamFalcon/pytorch-lightning-conference-seed)     

 ## Why do I want to use lightning?
-Every research project starts the same, a model, a training loop, validation loop, etc. As your research advances, you're likely to need distributed training, 16-bit precision, checkpointing, gradient accumulation, etc.   
+When starting a new project the last thing you want to do is recode a training loop, model loading/saving, distributed training, when to validate, etc... You're likely to spend a long time ironing out all the bugs without even getting to the core of your research.

-Lightning sets up all the boilerplate state-of-the-art training for you so you can focus on the research.   
+With lightning, you guarantee those parts of your code work so you can focus on what the meat of the research: Data and training, validation loop logic. Don't worry about multiple gpus or speeding up your code, lightning will do that for you!

---   
-## README Table of Contents        
- [How do I use it](https://github.com/williamFalcon/pytorch-lightning#how-do-i-do-use-it)     
- [What lightning automates](https://github.com/williamFalcon/pytorch-lightning#what-does-lightning-control-for-me)    
- [Tensorboard integration](https://github.com/williamFalcon/pytorch-lightning#tensorboard)    
- [Lightning features](https://github.com/williamFalcon/pytorch-lightning#lightning-automates-all-of-the-following-each-is-also-configurable)    
- [Demos](https://github.com/williamFalcon/pytorch-lightning#demo)    
- [Tutorials](https://github.com/williamFalcon/pytorch-lightning#tutorials)
- [Contributing](https://github.com/williamFalcon/pytorch-lightning/blob/master/CONTRIBUTING.md)    
- [Bleeding edge install](https://github.com/williamFalcon/pytorch-lightning#bleeding-edge)   
- [Lightning Design Principles](https://github.com/williamFalcon/pytorch-lightning#lightning-design-principles)   
- [Asking for help](https://github.com/williamFalcon/pytorch-lightning#asking-for-help)
- [FAQ](https://github.com/williamFalcon/pytorch-lightning#faq)    
-
---   
 ## How do I do use it?   
-Think about Lightning as refactoring your research code instead of using a new framework. The research code goes into a [LightningModule]((https://williamfalcon.github.io/pytorch-lightning/LightningModule/RequiredTrainerInterface/)) which you fit using a Trainer.    
-
-The LightningModule defines a *system* such as seq-2-seq, GAN, etc... It can ALSO define a simple classifier such as the example below.     

 To use lightning do 2 things:  
-1. [Define a LightningModule](https://williamfalcon.github.io/pytorch-lightning/LightningModule/RequiredTrainerInterface/)         
+1. [Define a LightningModel](https://williamfalcon.github.io/pytorch-lightning/LightningModule/RequiredTrainerInterface/)         
 ```python
-import os
+import pytorch_lightning as ptl
 import torch
 from torch.nn import functional as F
 from torch.utils.data import DataLoader
 from torchvision.datasets import MNIST
-import torchvision.transforms as transforms

-import pytorch_lightning as pl
+class CoolModel(ptl.LightningModule):

-class CoolSystem(pl.LightningModule):
-
-    def __init__(self):
-        super(CoolSystem, self).__init__()
+    def __init(self):
+        super(CoolModel, self).__init__()
        # not the best model...
        self.l1 = torch.nn.Linear(28 * 28, 10)

    def forward(self, x):
-        return torch.relu(self.l1(x.view(x.size(0), -1)))
+        return torch.relu(self.l1(x))
+
+    def my_loss(self, y_hat, y):
+        return F.cross_entropy(y_hat, y)

    def training_step(self, batch, batch_nb):
-        # REQUIRED
        x, y = batch
        y_hat = self.forward(x)
-        return {'loss': F.cross_entropy(y_hat, y)}
+        return {'tng_loss': self.my_loss(y_hat, y)}

    def validation_step(self, batch, batch_nb):
-        # OPTIONAL
        x, y = batch
        y_hat = self.forward(x)
-        return {'val_loss': F.cross_entropy(y_hat, y)}
+        return {'val_loss': self.my_loss(y_hat, y)}

    def validation_end(self, outputs):
-        # OPTIONAL
-        avg_loss = torch.stack([x['val_loss'] for x in outputs]).mean()
-        return {'avg_val_loss': avg_loss}
+        avg_loss = torch.stack([x for x in outputs['val_loss']]).mean()
+        return avg_loss

    def configure_optimizers(self):
-        # REQUIRED
-        # can return multiple optimizers and learning_rate schedulers
-        return torch.optim.Adam(self.parameters(), lr=0.02)
+        return [torch.optim.Adam(self.parameters(), lr=0.02)]

-    @pl.data_loader
+    @ptl.data_loader
    def tng_dataloader(self):
-        # REQUIRED
-        return DataLoader(MNIST(os.getcwd(), train=True, download=True, transform=transforms.ToTensor()), batch_size=32)
+        return DataLoader(MNIST('path/to/save', train=True), batch_size=32)

-    @pl.data_loader
+    @ptl.data_loader
    def val_dataloader(self):
-        # OPTIONAL
-        return DataLoader(MNIST(os.getcwd(), train=True, download=True, transform=transforms.ToTensor()), batch_size=32)
+        return DataLoader(MNIST('path/to/save', train=False), batch_size=32)

-    @pl.data_loader
+    @ptl.data_loader
    def test_dataloader(self):
-        # OPTIONAL
-        return DataLoader(MNIST(os.getcwd(), train=True, download=True, transform=transforms.ToTensor()), batch_size=32)
+        return DataLoader(MNIST('path/to/save', train=False), batch_size=32)
 ```

 2. Fit with a [trainer](https://williamfalcon.github.io/pytorch-lightning/Trainer/)    
 ```python
 from pytorch_lightning import Trainer
+from test_tube import Experiment

-model = CoolSystem()
+model = CoolModel()

-# most basic trainer, uses good defaults
-trainer = Trainer()    
-trainer.fit(model)   
-```     
+# fit on 32 gpus across 4 nodes
+exp = Experiment(save_dir='some/dir')
+trainer = Trainer(experiment=exp, nb_gpu_nodes=4, gpus=[0,1,2,3,4,5,6,7])

-Or with tensorboard logger and some options turned on such as multi-gpu, etc...    
-```python   
-from test_tube import Experiment    
-
-# PyTorch summarywriter with a few bells and whistles    
-exp = Experiment(save_dir=os.getcwd())
-
-# train on cpu using only 10% of the data (for demo purposes)
-# pass in experiment for automatic tensorboard logging.    
-trainer = Trainer(experiment=exp, max_nb_epochs=1, train_percent_check=0.1)
-
-# train on 4 gpus
-# trainer = Trainer(experiment=exp, max_nb_epochs=1, gpus=[0, 1, 2, 3])
-
-# train on 32 gpus across 4 nodes (make sure to submit appropriate SLURM job)
-# trainer = Trainer(experiment=exp, max_nb_epochs=1, gpus=[0, 1, 2, 3, 4, 5, 6, 7], nb_gpu_nodes=4)
-
-# train (1 epoch only here for demo)
 trainer.fit(model)

-# view tensorflow logs 
-print('View tensorboard logs by running\ntensorboard --logdir %s' % os.getcwd())
-print('and going to http://localhost:6006 on your browser')
-```    
+# see all experiment metrics here
+# tensorboard --log_dir some/dir   
+```

-## What does lightning control for me?   

-Everything in gray!    
-You define the blue parts using the LightningModule interface:  
+## What does lightning control for me?
+Everything!    
+Except for these 6 core functions which you define:    

-![Ouverview](./docs/source/_static/overview_flat.jpg)
-
-```python
+```{.python}
 # what to do in the training loop
 def training_step(self, data_batch, batch_nb):

@@ -192,7 +137,7 @@ def training_step(self, data_batch, batch_nb):
    # define your own forward and loss calculation
    hidden_states = self.encoder(x)
     
-    # even as complex as a seq-2-seq + attn model
+    # even as complex as a seq-2seq + attn model
    # (this is just a toy, non-working example to illustrate)
    start_token = '<SOS>'
    last_hidden = torch.zeros(...)
@@ -246,14 +191,22 @@ def validation_end(self, outputs):
 ## Tensorboard    
 Lightning is fully integrated with tensorboard.   

-![tensorboard-support](./docs/source/_static/tf_loss.png)
+<p align="center">
+  <a href="https://williamfalcon.github.io/pytorch-lightning/">
+    <img alt="" src="https://github.com/williamFalcon/pytorch-lightning/blob/master/docs/source/_static/tf_loss.png" width="900px">
+  </a>
+</p>

 Lightning also adds a text column with all the hyperparameters for this experiment.      

-![tensorboard-support](./docs/source/_static/tf_tags.png)
+<p align="center">
+  <a href="https://williamfalcon.github.io/pytorch-lightning/">
+        <img alt="" src="https://github.com/williamFalcon/pytorch-lightning/blob/master/docs/source/_static/tf_tags.png" width="900px">
+  </a>
+</p>

 Simply note the path you set for the Experiment    
-```python
+``` {.python}   
 from test_tube import Experiment
 from pytorch-lightning import  Trainer

@@ -269,12 +222,10 @@ tensorboard --logdir /some/path

 ## Lightning automates all of the following ([each is also configurable](https://williamfalcon.github.io/pytorch-lightning/Trainer/)):

-
 ###### Checkpointing    

 - [Model saving](https://williamfalcon.github.io/pytorch-lightning/Trainer/Checkpointing/#model-saving)
 - [Model loading](https://williamfalcon.github.io/pytorch-lightning/LightningModule/methods/#load-from-metrics) 
- [Restoring training session](https://williamfalcon.github.io/pytorch-lightning/Trainer/Checkpointing/#restoring-training-session)

 ###### Computing cluster (SLURM)    

@@ -288,8 +239,7 @@ tensorboard --logdir /some/path
 - [Log GPU usage](https://williamfalcon.github.io/pytorch-lightning/Trainer/debugging/#Log-gpu-usage)
 - [Make model overfit on subset of data](https://williamfalcon.github.io/pytorch-lightning/Trainer/debugging/#make-model-overfit-on-subset-of-data)
 - [Print the parameter count by layer](https://williamfalcon.github.io/pytorch-lightning/Trainer/debugging/#print-the-parameter-count-by-layer)
- [Print which gradients are nan](https://williamfalcon.github.io/pytorch-lightning/Trainer/debugging/#print-which-gradients-are-nan)
- [Print input and output size of every module in system](https://williamfalcon.github.io/pytorch-lightning/LightningModule/properties/#example_input_array)
+- [Pring which gradients are nan](https://williamfalcon.github.io/pytorch-lightning/Trainer/debugging/#print-which-gradients-are-nan)


 ###### Distributed training    
@@ -304,9 +254,9 @@ tensorboard --logdir /some/path
 ###### Experiment Logging   

 - [Display metrics in progress bar](https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#display-metrics-in-progress-bar)
+- Log arbitrary metrics
 - [Log metric row every k batches](https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#log-metric-row-every-k-batches)
 - [Process position](https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#process-position)
- [Tensorboard support](https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#tensorboard-support)
 - [Save a snapshot of all hyperparameters](https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#save-a-snapshot-of-all-hyperparameters) 
 - [Snapshot code for a training run](https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#snapshot-code-for-a-training-run) 
 - [Write logs file to csv every k batches](https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#write-logs-file-to-csv-every-k-batches)
@@ -314,19 +264,16 @@ tensorboard --logdir /some/path
 ###### Training loop    

 - [Accumulate gradients](https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#accumulated-gradients)
+- [Anneal Learning rate](https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#anneal-learning-rate)
 - [Force training for min or max epochs](https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#force-training-for-min-or-max-epochs)
 - [Force disable early stop](https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#force-disable-early-stop)
 - [Gradient Clipping](https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#gradient-clipping)
- [Hooks](https://williamfalcon.github.io/pytorch-lightning/Trainer/hooks/)
- [Learning rate scheduling](https://williamfalcon.github.io/pytorch-lightning/LightningModule/RequiredTrainerInterface/#configure_optimizers)    
- [Use multiple optimizers (like GANs)](https://williamfalcon.github.io/pytorch-lightning/LightningModule/RequiredTrainerInterface/#configure_optimizers)
+- [Use multiple optimizers (like GANs)](https://williamfalcon.github.io/pytorch-lightning/Pytorch-Lightning/LightningModule/#configure_optimizers)
 - [Set how much of the training set to check (1-100%)](https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#set-how-much-of-the-training-set-to-check)
- [Step optimizers at arbitrary intervals](https://williamfalcon.github.io/pytorch-lightning/Trainer/hooks/#optimizer_step)

 ###### Validation loop    

 - [Check validation every n epochs](https://williamfalcon.github.io/pytorch-lightning/Trainer/Validation%20loop/#check-validation-every-n-epochs)
- [Hooks](https://williamfalcon.github.io/pytorch-lightning/Trainer/hooks/)
 - [Set how much of the validation set to check](https://williamfalcon.github.io/pytorch-lightning/Trainer/Validation%20loop/#set-how-much-of-the-validation-set-to-check)
 - [Set how much of the test set to check](https://williamfalcon.github.io/pytorch-lightning/Trainer/Validation%20loop/#set-how-much-of-the-test-set-to-check)
 - [Set validation check frequency within 1 training epoch](https://williamfalcon.github.io/pytorch-lightning/Trainer/Validation%20loop/#set-validation-check-frequency-within-1-training-epoch)
@@ -340,8 +287,7 @@ pip install pytorch-lightning

 # clone lightning for the demo
 git clone https://github.com/williamFalcon/pytorch-lightning.git
-cd pytorch-lightning
-cd examples/new_project_templates/
+cd pytorch_lightning/examples/new_project_templates/

 # all of the following demos use the SAME model to show no modification needs to be made to your code

@@ -355,64 +301,8 @@ python single_gpu_node_template.py --gpus "0,1"
 python multi_node_cluster_template.py --nb_gpu_nodes 4 --gpus '0,1,2,3,4,5,6,7'
 ```

-## Tutorials   
- [Basic Lightning use](https://towardsdatascience.com/supercharge-your-ai-research-with-pytorch-lightning-337948a99eec)    
- [9 key speed features in Pytorch-Lightning](https://towardsdatascience.com/9-tips-for-training-lightning-fast-neural-networks-in-pytorch-8e63a502f565)    
- [SLURM, multi-node training with Lightning](https://towardsdatascience.com/trivial-multi-node-training-with-pytorch-lightning-ff75dfb809bd)     
-
---    
-## Asking for help    
-Welcome to the Lightning community!   
-
-If you have any questions, feel free to:   
-1. [read the docs](https://williamfalcon.github.io/pytorch-lightning/).     
-2. [Search through the issues](https://github.com/williamFalcon/pytorch-lightning/issues?utf8=%E2%9C%93&q=my++question).      
-3. [Ask on stackoverflow](https://stackoverflow.com/questions/ask?guided=false) with the tag pytorch-lightning.   
-
-If no one replies to you quickly enough, feel free to post the stackoverflow link to our Gitter chat!   
-
-To chat with the rest of us visit our [gitter channel](https://gitter.im/PyTorch-Lightning/community?utm_source=share-link&utm_medium=link&utm_campaign=share-link)!     
-
---   
-## FAQ    
-**How do I use Lightning for rapid research?**   
-[Here's a walk-through](https://williamfalcon.github.io/pytorch-lightning/)  
-
-**Why was Lightning created?**     
-Lightning has 3 goals in mind:
-1. Maximal flexibility while abstracting out the common boilerplate across research projects.   
-2. Reproducibility. If all projects use the LightningModule template, it will be much much easier to understand what's going on and where to look! It will also mean every implementation follows a standard format.   
-3. Democratizing PyTorch power user features. Distributed training? 16-bit? know you need them but don't want to take the time to implement? All good... these come built into Lightning.    
-
-**How does Lightning compare with Ignite and fast.ai?**     
-[Here's a thorough comparison](https://medium.com/@_willfalcon/pytorch-lightning-vs-pytorch-ignite-vs-fast-ai-61dc7480ad8a).    
-
-**Is this another library I have to learn?**    
-Nope! We use pure Pytorch everywhere and don't add unecessary abstractions!   
-
-**Are there plans to support Python 2?**          
-Nope.     
-
-**Are there plans to support virtualenv?**    
-Nope. Please use anaconda or miniconda.    
-
-**Which PyTorch versions do you support?**    
-##### PyTorch 1.1.0       
-```bash    
-# install pytorch 1.1.0 using the official instructions   
-
-# install test-tube 0.6.7.6 which supports 1.1.0   
-pip install test-tube==0.6.7.6   
-
-# install latest Lightning version without upgrading deps    
-pip install -U --no-deps pytorch-lightning
-```     
-
-##### PyTorch 1.2.0   
-Install via pip as normal   
-
 ## Bleeding edge
 If you can't wait for the next release, install the most up to date code with:  
 ```bash
 pip install git+https://github.com/williamFalcon/pytorch-lightning.git@master --upgrade
-```
+```
@@ -1,64 +0,0 @@
-# https://www.appveyor.com/docs/appveyor-yml/
-environment:
-
-  # SDK v7.0 MSVC Express 2008's SetEnv.cmd script will fail if the
-  # /E:ON and /V:ON options are not enabled in the batch script interpreter
-  # See: http://stackoverflow.com/a/13751649/163740
-  CMD_IN_ENV: "cmd /E:ON /V:ON /C obvci_appveyor_python_build_env.cmd"
-
-  matrix:
-    # Pre-installed Python versions, which Appveyor may upgrade to
-    # a later point release.
-    # See: http://www.appveyor.com/docs/installed-software#python
-
-
-    # - PYTHON: "C:\\Python35-x64"
-    #   PYTHON_VERSION: "3.5.x"
-    #   PYTHON_ARCH: "64"
-    #  TOXENV: "py35"
-
-    - PYTHON: "C:\\Python36-x64"
-      PYTHON_VERSION: "3.6.x"
-      PYTHON_ARCH: "64"
-      TOXENV: "py36"
-      PIP_PYVER: "36"
-
-    - PYTHON: "C:\\Python37-x64"
-      PYTHON_VERSION: "3.7.x"
-      PYTHON_ARCH: "64"
-      TOXENV: "py37"
-      PIP_PYVER: "37"
-
-build: off
-
-# https://www.appveyor.com/docs/build-cache/
-cache:
-  - C:\ProgramData\chocolatey\bin -> appveyor.yml
-  - C:\ProgramData\chocolatey\lib -> appveyor.yml
-  - '%LOCALAPPDATA%\pip\Cache -> appveyor.yml'
-
-# scripts that run after cloning repository
-install:
-  # If there is a newer build queued for the same PR, cancel this one.
-  # The AppVeyor 'rollout builds' option is supposed to serve the same
-  # purpose but it is problematic because it tends to cancel builds pushed
-  # directly to master instead of just PR builds (or the converse).
-  - SET PATH=%PYTHON%;%PYTHON%\\Scripts;%path%
-  - pip install -U --user pip
-  - pip install -r requirements.txt -f https://download.pytorch.org/whl/torch_stable.html
-  - pip install -r ./tests/requirements.txt
-
-# scripts to run before tests (working directory and environment changes are persisted from the previous steps such as "before_build")
-before_test:
-  - python --version
-  - pip --version
-  - pip list
-  - dir
-
-# to run your custom scripts instead of automatic tests
-test_script:
-  - tox --sitepackages --parallel auto
-
-on_success:
-  - coverage report
-  # - codecov
@@ -3,96 +3,82 @@

 A lightning module is a strict superclass of nn.Module, it provides a standard interface for the trainer to interact with the model.

-The easiest thing to do is copy the [minimal example](https://williamfalcon.github.io/pytorch-lightning/LightningModule/RequiredTrainerInterface/#minimal-example) below and modify accordingly. 
+The easiest thing to do is copy [this template](../../pytorch_lightning/examples/new_project_templates/lightning_module_template.py) and modify accordingly. 

 Otherwise, to Define a Lightning Module, implement the following methods:

 **Required**:  

- [training_step](RequiredTrainerInterface.md#training_step)      
- [tng_dataloader](RequiredTrainerInterface.md#tng_dataloader)    
- [configure_optimizers](RequiredTrainerInterface.md#configure_optimizers)    
+- [training_step](RequiredTrainerInterface.md#training_step)   
+- [validation_step](RequiredTrainerInterface.md#validation_step)
+- [validation_end](RequiredTrainerInterface.md#validation_end)
+    
+- [configure_optimizers](RequiredTrainerInterface.md#configure_optimizers)
+- [get_save_dict](RequiredTrainerInterface.md#get_save_dict)
+- [load_model_specific](RequiredTrainerInterface.md#load_model_specific)
+
+- [tng_dataloader](RequiredTrainerInterface.md#tng_dataloader)
+- [tng_dataloader](RequiredTrainerInterface.md#tng_dataloader)
+- [test_dataloader](RequiredTrainerInterface.md#test_dataloader)

 **Optional**:   

- [validation_step](RequiredTrainerInterface.md#validation_step)    
- [validation_end](RequiredTrainerInterface.md#validation_end)    
- [val_dataloader](RequiredTrainerInterface.md#val_dataloader)    
- [test_dataloader](RequiredTrainerInterface.md#test_dataloader)    
- [on_save_checkpoint](RequiredTrainerInterface.md#on_save_checkpoint)    
- [on_load_checkpoint](RequiredTrainerInterface.md#on_load_checkpoint)    
- [update_tng_log_metrics](RequiredTrainerInterface.md#update_tng_log_metrics)    
- [add_model_specific_args](RequiredTrainerInterface.md#add_model_specific_args)    
+- [update_tng_log_metrics](RequiredTrainerInterface.md#update_tng_log_metrics)
+- [add_model_specific_args](RequiredTrainerInterface.md#add_model_specific_args)

 ---
-### Minimal example   
+**Minimal example**
 ```python
-import os
+import pytorch_lightning as ptl
 import torch
 from torch.nn import functional as F
 from torch.utils.data import DataLoader
 from torchvision.datasets import MNIST
-import torchvision.transforms as transforms

-import pytorch_lightning as pl
+class CoolModel(ptl.LightningModule):

-class CoolModel(pl.LightningModule):
-
-    def __init__(self):
+    def __init(self):
        super(CoolModel, self).__init__()
        # not the best model...
        self.l1 = torch.nn.Linear(28 * 28, 10)

    def forward(self, x):
-        return torch.relu(self.l1(x.view(x.size(0), -1)))
+        return torch.relu(self.l1(x))
+
+    def my_loss(self, y_hat, y):
+        return F.cross_entropy(y_hat, y)

    def training_step(self, batch, batch_nb):
-        # REQUIRED
        x, y = batch
        y_hat = self.forward(x)
-        return {'loss': F.cross_entropy(y_hat, y)}
+        return {'tng_loss': self.my_loss(y_hat, y)}

    def validation_step(self, batch, batch_nb):
-        # OPTIONAL
        x, y = batch
        y_hat = self.forward(x)
-        return {'val_loss': F.cross_entropy(y_hat, y)}
+        return {'val_loss': self.my_loss(y_hat, y)}

    def validation_end(self, outputs):
-        # OPTIONAL
-        avg_loss = torch.stack([x['val_loss'] for x in outputs]).mean()
-        return {'avg_val_loss': avg_loss}
+        avg_loss = torch.stack([x for x in outputs['val_loss']]).mean()
+        return avg_loss

    def configure_optimizers(self):
-        # REQUIRED
        return [torch.optim.Adam(self.parameters(), lr=0.02)]

-    @pl.data_loader
+    @ptl.data_loader
    def tng_dataloader(self):
-        return DataLoader(MNIST(os.getcwd(), train=True, download=True, transform=transforms.ToTensor()), batch_size=32)
+        return DataLoader(MNIST('path/to/save', train=True), batch_size=32)

-    @pl.data_loader
+    @ptl.data_loader
    def val_dataloader(self):
-        # OPTIONAL
-        # can also return a list of val dataloaders
-        return DataLoader(MNIST(os.getcwd(), train=True, download=True, transform=transforms.ToTensor()), batch_size=32)
+        return DataLoader(MNIST('path/to/save', train=False), batch_size=32)

-    @pl.data_loader
+    @ptl.data_loader
    def test_dataloader(self):
-        # OPTIONAL
-        return DataLoader(MNIST(os.getcwd(), train=False, download=True, transform=transforms.ToTensor()), batch_size=32)
+        return DataLoader(MNIST('path/to/save', train=False), batch_size=32)
 ```
---   
-### How do these methods fit into the broader training?     
-The LightningModule interface is on the right. Each method corresponds to a part of a research project. Lightning automates everything not in blue.    

-<p align="center">
-  <a href="https://github.com/williamFalcon/pytorch-lightning/blob/master/docs/source/_static/overview_flat.jpg">
-    <img alt="" src="https://github.com/williamFalcon/pytorch-lightning/blob/master/docs/source/_static/overview_flat.jpg" height="900px">
-  </a>
-</p>   
-
-## Required Methods    
+---

 ### training_step

@@ -136,106 +122,18 @@ def training_step(self, data_batch, batch_nb):
    
    # return a dict
    return output
-```    
-
-If you define multiple optimizers, this step will also be called with an additional ```optimizer_idx``` param.    
-``` {.python}
-# Multiple optimizers (ie: GANs)     
-def training_step(self, data_batch, batch_nb, optimizer_idx):
-    if optimizer_idx == 0:
-        # do training_step with encoder
-    if optimizer_idx == 1:
-        # do training_step with decoder    
-```    
-
--- 
-### tng_dataloader 
-
-``` {.python}
-@pl.data_loader
-def tng_dataloader(self)
-```
-Called by lightning during training loop. Make sure to use the @pl.data_loader decorator, this ensures not calling this function until the data are needed.
-
-##### Return
-PyTorch DataLoader
-
-**Example**
-
-``` {.python}
-@pl.data_loader
-def tng_dataloader(self):
-    transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (1.0,))])
-    dataset = MNIST(root='/path/to/mnist/', train=True, transform=transform, download=True)
-    loader = torch.utils.data.DataLoader(
-        dataset=dataset,
-        batch_size=self.hparams.batch_size,
-        shuffle=True
-    )
-    return loader
 ```

--- 
-### configure_optimizers 
-
-``` {.python}
-def configure_optimizers(self)
-```
-
-Set up as many optimizers and (optionally) learning rate schedulers as you need. Normally you'd need one. But in the case of GANs or something more esoteric you might have multiple. 
-Lightning will call .backward() and .step() on each one in every epoch.  If you use 16 bit precision it will also handle that.
-
-**Note:** If you use multiple optimizers, training_step will have an additional ```optimizer_idx``` parameter.    
-
-
-
-##### Return    
-Return any of these 3 options:   
-Single optimizer   
-List or Tuple - List of optimizers    
-Two lists - The first list has multiple optimizers, the second a list of learning-rate schedulers
-
-**Example**
-
-``` {.python}
-# most cases
-def configure_optimizers(self):
-    opt = Adam(self.parameters(), lr=0.01)
-    return opt
-
-# multiple optimizer case (eg: GAN)
-def configure_optimizers(self):
-    generator_opt = Adam(self.model_gen.parameters(), lr=0.01)
-    disriminator_opt = Adam(self.model_disc.parameters(), lr=0.02)
-    return generator_opt, disriminator_opt
-    
-# example with learning_rate schedulers  
-def configure_optimizers(self):
-    generator_opt = Adam(self.model_gen.parameters(), lr=0.01)
-    disriminator_opt = Adam(self.model_disc.parameters(), lr=0.02)
-    discriminator_sched = CosineAnnealing(discriminator_opt, T_max=10)
-    return [generator_opt, disriminator_opt], [discriminator_sched]
-```  
-
-If you need to control how often those optimizers step or override the default .step() schedule, override 
-the [optimizer_step](https://williamfalcon.github.io/pytorch-lightning/Trainer/hooks/#optimizer_step) hook.   
-
-## Optional Methods    
+---

 ### validation_step

 ``` {.python}
-def validation_step(self, data_batch, batch_nb)   
-
-# if have multiple val dataloaders:  
-def validation_step(self, data_batch, batch_nb, dataloader_idx)
+def validation_step(self, data_batch, batch_nb)
 ```
-**OPTIONAL**    
-If you don't need to validate you don't need to implement this method.    

-In this step you'd normally generate examples or calculate anything of interest such as accuracy. 
-
-The dict you return here will be available in the validation_end method. 
+In this step you'd normally do the forward pass and calculate the loss for a batch. You can also do fancier things like multiple forward passes or something specific to your model.
+This is most likely the same as your training_step. But unlike training step, the outputs from here will go to validation_end for collation.

 **Params**   

@@ -243,18 +141,16 @@ The dict you return here will be available in the validation_end method.
 |---|---|
 |  data_batch | The output of your dataloader. A tensor, tuple or list  |
 |  batch_nb | Integer displaying which batch this is  |
-|  dataloader_i | Integer displaying which dataloader this is (only if multiple val datasets used)  |

 **Return**   

 | Return  | description  | optional |
 |---|---|---|   
-|  dict | Dict or OrderedDict with metrics to display in progress bar. All keys must be tensors. | Y |
+|  dict | Dict of OrderedDict with metrics to display in progress bar. All keys must be tensors. | Y |

 **Example**

 ``` {.python}
-# CASE 1: A single validation dataset
 def validation_step(self, data_batch, batch_nb):
    x, y, z = data_batch
    
@@ -262,12 +158,6 @@ def validation_step(self, data_batch, batch_nb):
    out = self.forward(x)
    loss = self.loss(out, x)
    
-    # log 6 example images
-    # or generated text... or whatever
-    sample_imgs = x[:6]
-    grid = torchvision.utils.make_grid(sample_imgs)
-    self.experiment.add_image('example_images', grid, 0) 
-    
    # calculate acc
    labels_hat = torch.argmax(out, dim=1)
    val_acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0)
@@ -281,29 +171,16 @@ def validation_step(self, data_batch, batch_nb):
    
    # return an optional dict
    return output
-```   
-
-If you pass in multiple validation datasets, validation_step will have an additional argument.
-
-```python
-# CASE 2: multiple validation datasets
-def validation_step(self, data_batch, batch_nb, dataset_idx):
-    # dataset_idx tells you which dataset this is.   
-```   
-
-The ```dataset_idx``` corresponds to the order of datasets returned in ```val_dataloader```.    
+```

 --- 
 ### validation_end

 ``` {.python}
 def validation_end(self, outputs)
-```   
-If you didn't define a validation_step, this won't be called.       
+```

-Called at the end of the validation loop with the output of each validation_step.  Called once per validation dataset.   
-
-The outputs here are strictly for the progress bar. If you don't need to display anything, don't return anything.    
+Called at the end of the validation loop with the output of each validation_step.

 **Params**    

@@ -339,35 +216,63 @@ def validation_end(self, outputs):
 ```

 --- 
-### on_save_checkpoint 
+### configure_optimizers 

 ``` {.python}
-def on_save_checkpoint(self, checkpoint)
+def configure_optimizers(self)
 ```
-Called by lightning to checkpoint your model. Lightning saves the training state (current epoch, global_step, etc)
-and also saves the model state_dict. If you want to save anything else, use this method to add your own
-key-value pair.
+
+Set up as many optimizers as you need. Normally you'd need one. But in the case of GANs or something more esoteric you might have multiple. 
+Lightning will call .backward() and .step() on each one.  If you use 16 bit precision it will also handle that.
+

 ##### Return
-Nothing
+List - List of optimizers

 **Example**

 ``` {.python}
-def on_save_checkpoint(self, checkpoint):
-    # 99% of use cases you don't need to implement this method 
-    checkpoint['something_cool_i_want_to_save'] = my_cool_pickable_object
+# most cases
+def configure_optimizers(self):
+    opt = Adam(lr=0.01)
+    return [opt]
+    
+# gan example
+def configure_optimizers(self):
+    generator_opt = Adam(lr=0.01)
+    disriminator_opt = Adam(lr=0.02)
+    return [generator_opt, disriminator_opt] 
 ```

 --- 
-### on_load_checkpoint 
+### get_save_dict 

 ``` {.python}
-def on_load_checkpoint(self, checkpoint)
+def get_save_dict(self)
 ```
-Called by lightning to restore your model. Lighting auto-restores global step, epoch, etc...
-It also restores the model state_dict.
-If you saved something with **on_save_checkpoint** this is your chance to restore this.
+Called by lightning to checkpoint your model. Lightning saves current epoch, current batch nb, etc...
+All you have to return is what specifically about your lightning model you want to checkpoint.
+
+##### Return
+Dictionary - No required keys. Most of the time as described in this example.   
+
+**Example**
+
+``` {.python}
+def get_save_dict(self):
+    # 99% of use cases this is all you need to return
+    checkpoint = {'state_dict': self.state_dict()}
+    return checkpoint
+```
+
+--- 
+### load_model_specific 
+
+``` {.python}
+def load_model_specific(self, checkpoint)
+```
+Called by lightning to restore your model. This is your chance to restore your model using the keys you added in get_save_dict.
+Lightning will automatically restore current epoch, batch nb, etc. 

 ##### Return
 Nothing  
@@ -375,30 +280,54 @@ Nothing
 **Example**

 ``` {.python}
-def on_load_checkpoint(self, checkpoint):
-    # 99% of the time you don't need to implement this method
-    self.something_cool_i_want_to_save = checkpoint['something_cool_i_want_to_save']
+def load_model_specific(self, checkpoint):
+    # you defined 'state_dict' in get_save_dict()
+    self.load_state_dict(checkpoint['state_dict'])
+```
+
+--- 
+### tng_dataloader 
+
+``` {.python}
+@ptl.data_loader
+def tng_dataloader(self)
+```
+Called by lightning during training loop. Make sure to use the @ptl.data_loader decorator, this ensures not calling this function until the data are needed.
+
+##### Return
+Pytorch DataLoader
+
+**Example**
+
+``` {.python}
+@ptl.data_loader
+def tng_dataloader(self):
+    transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (1.0,))])
+    dataset = MNIST(root='/path/to/mnist/', train=True, transform=transform, download=True)
+    loader = torch.utils.data.DataLoader(
+        dataset=dataset,
+        batch_size=self.hparams.batch_size,
+        shuffle=True
+    )
+    return loader
 ```

 --- 
 ### val_dataloader 

 ``` {.python}
-@pl.data_loader
-def val_dataloader(self)
+@ptl.data_loader
+def tng_dataloader(self)
 ```
-**OPTIONAL**    
-If you don't need a validation dataset and a validation_step, you don't need to implement this method.    
-
-Called by lightning during validation loop. Make sure to use the @pl.data_loader decorator, this ensures not calling this function until the data are needed.   
+Called by lightning during validation loop. Make sure to use the @ptl.data_loader decorator, this ensures not calling this function until the data are needed.

 ##### Return
-PyTorch DataLoader or list of PyTorch Dataloaders.    
+Pytorch DataLoader

 **Example**

 ``` {.python}
-@pl.data_loader
+@ptl.data_loader
 def val_dataloader(self):
    transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (1.0,))])
    dataset = MNIST(root='/path/to/mnist/', train=False, transform=transform, download=True)
@@ -409,35 +338,24 @@ def val_dataloader(self):
    )
    
    return loader
-
-# can also return multiple dataloaders   
-@pl.data_loader
-def val_dataloader(self):
-    return [loader_a, loader_b, ..., loader_n]   
 ```

-In the case where you return multiple val_dataloaders, the validation_step will have an arguement ```dataset_idx```
-which matches the order here.    
-
 --- 
 ### test_dataloader 

 ``` {.python}
-@pl.data_loader
+@ptl.data_loader
 def test_dataloader(self)
 ```
-**OPTIONAL**    
-If you don't need a test dataset and a test_step, you don't need to implement this method.    
-
-Called by lightning during test loop. Make sure to use the @pl.data_loader decorator, this ensures not calling this function until the data are needed.
+Called by lightning during test loop. Make sure to use the @ptl.data_loader decorator, this ensures not calling this function until the data are needed.

 ##### Return
-PyTorch DataLoader
+Pytorch DataLoader

 **Example**

 ``` {.python}
-@pl.data_loader
+@ptl.data_loader
 def test_dataloader(self):
    transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (1.0,))])
    dataset = MNIST(root='/path/to/mnist/', train=False, transform=transform, download=True)
@@ -457,7 +375,7 @@ def test_dataloader(self):
 def update_tng_log_metrics(self, logs)
 ```
 Called by lightning right before it logs metrics for this batch.
-This is a chance to amend or add to the metrics about to be logged.
+This is a chance to ammend or add to the metrics about to be logged.

 ##### Return
 Dict 
@@ -509,4 +427,4 @@ def add_model_specific_args(parent_parser, root_dir):
    parser.opt_list('--batch_size', default=256, type=int, options=[32, 64, 128, 256], tunable=False)
    parser.opt_list('--optimizer_name', default='adam', type=str, options=['adam'], tunable=False)
    return parser
-```
+```
@@ -21,8 +21,7 @@ pretrained_model = MyLightningModule.load_from_metrics(
    map_location=None
 )
    
-# predict
-pretrained_model.eval()
+# predict 
 pretrained_model.freeze()
 y_hat = pretrained_model(x)
 ```
@@ -31,7 +30,7 @@ y_hat = pretrained_model(x)

 | Param  | description  |
 |---|---|
-|  weights_path | Path to a PyTorch checkpoint  |
+|  weights_path | Path to a pytorch checkpoint  |
 |  tags_csv | Path to meta_tags.csv file generated by the test-tube Experiment  |
 |  on_gpu | if True, puts model on GPU. Make sure to use transforms option if model devices have changed  |
 |  map_location | A dictionary mapping saved weight GPU devices to new GPU devices |
@@ -10,7 +10,7 @@ Current dtype

 ---
 #### experiment    
-An instance of test-tube Experiment which you can use to log anything for tensorboard (subclass of [PyTorch SummaryWriter](https://pytorch.org/docs/stable/tensorboard.html)).   
+An instance of test-tube Experiment which you can use to log anything for tensorboarX.   
 ```{.python}
 self.experiment.add_embedding(...)   
 self.experiment.log({'val_loss': 0.9})   
@@ -38,17 +38,3 @@ self.trainer.current_epoch
 ...   
 ```   

-## Debugging   
-The LightningModule also offers these tricks to help debug.   
-
---   
-#### example_input_array    
-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.   
-```python
-def __init__(self):
-    # put the dimensions of the first input to your system
-    self.example_input_array = torch.rand(5, 28 * 28)
-```    
-
-
@@ -5,7 +5,7 @@ Lightning can automate saving and loading checkpoints.
 To enable checkpointing, define the checkpoint callback and give it to the trainer.

 ``` {.python}
-from pytorch_lightning.callbacks import ModelCheckpoint
+from pytorch_lightning.utils.pt_callbacks import ModelCheckpoint

 checkpoint_callback = ModelCheckpoint(
    filepath='/path/to/store/weights.ckpt',
@@ -18,52 +18,5 @@ checkpoint_callback = ModelCheckpoint(
 trainer = Trainer(checkpoint_callback=checkpoint_callback)
 ```

---
-### Restoring training session 
-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.  
-However, the dataloaders will start from the first batch again (if you shuffled it shouldn't matter).   

-Lightning will restore the session if you pass an experiment with the same version and there's a saved checkpoint.   
-``` {.python}
-from test_tube import Experiment

-exp = Experiment(version=a_previous_version_with_a_saved_checkpoint)
-trainer = Trainer(experiment=exp)
-
-# this fit call loads model weights and trainer state
-# the trainer continues seamlessly from where you left off
-# without having to do anything else.
-trainer.fit(model)
-```
-
-The trainer restores:  
-
- global_step    
- current_epoch    
- All optimizers    
- All lr_schedulers    
- Model weights
-
-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.   
-
-At a rough level, here's [what happens inside Trainer](https://github.com/williamFalcon/pytorch-lightning/blob/master/pytorch_lightning/root_module/model_saving.py#L63):   
-```python
-
-self.global_step = checkpoint['global_step']
-self.current_epoch = checkpoint['epoch']
-
-# restore the optimizers
-optimizer_states = checkpoint['optimizer_states']
-for optimizer, opt_state in zip(self.optimizers, optimizer_states):
-    optimizer.load_state_dict(opt_state)
-
-# restore the lr schedulers
-lr_schedulers = checkpoint['lr_schedulers']
-for scheduler, lrs_state in zip(self.lr_schedulers, lr_schedulers):
-    scheduler.load_state_dict(lrs_state)
-
-# uses the model you passed into trainer        
-model.load_state_dict(checkpoint['state_dict'])
-```    
@@ -23,32 +23,6 @@ have configuration issues depending on your cluster.

 For a deeper understanding of what lightning is doing, feel free to read [this guide](https://medium.com/@_willfalcon/9-tips-for-training-lightning-fast-neural-networks-in-pytorch-8e63a502f565).   

---
-#### Distributed and 16-bit precision.    
-Due to an issue with apex and DistributedDataParallel (PyTorch and NVIDIA issue), Lightning does
-not allow 16-bit and DP training. We tried to get this to work, but it's an issue on their end.   
-
-Below are the possible configurations we support.    
-
-| 1 GPU  | 1+ GPUs  | DP  | DDP  | 16-bit  | command |
-|---|---|---|---|---|---|
-| Y  |   |   |   |  | ```Trainer(gpus=[0])``` |
-| Y  |   |   |   | Y | ```Trainer(gpus=[0], use_amp=True)``` |
-|   | Y | Y |   |   | ```Trainer(gpus=[0, ...])``` |
-|   | Y |  | Y  |  | ```Trainer(gpus=[0, ...], distributed_backend='ddp')``` |
-|   | Y |  | Y  | Y | ```Trainer(gpus=[0, ...], distributed_backend='ddp', use_amp=True)``` |
-
-
---
-#### CUDA flags   
-CUDA flags make certain GPUs visible to your script. 
-Lightning sets these for you automatically, there's NO NEED to do this yourself.
-```python
-# lightning will set according to what you give the trainer
-# os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
-# os.environ["CUDA_VISIBLE_DEVICES"] = "0"
-```
-
 ---
 #### 16-bit mixed precision
 16 bit precision can cut your memory footprint by half. If using volta architecture GPUs it can give a dramatic training speed-up as well.    
@@ -69,6 +43,10 @@ trainer = Trainer(amp_level='O2', use_amp=False)
 #### Single-gpu
 Make sure you're on a GPU machine. 
 ```python
+# set these flags
+os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
+os.environ["CUDA_VISIBLE_DEVICES"] = "0"
+
 # DEFAULT
 trainer = Trainer(gpus=[0])
 ```
@@ -78,6 +56,13 @@ trainer = Trainer(gpus=[0])
 Make sure you're on a GPU machine. You can set as many GPUs as you want.
 In this setting, the model will run on all 8 GPUs at once using DataParallel under the hood.
 ```python
+# set these flags
+# lightning sets these flags for you automatically
+# no need to set yourself
+# os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
+# os.environ["CUDA_VISIBLE_DEVICES"] = "0,1,2,3,4,5,6,7"
+
+
 # to use DataParallel (default)
 trainer = Trainer(gpus=[0,1,2,3,4,5,6,7], distributed_backend='dp')

@@ -110,7 +95,7 @@ cluster.add_command('export NCCL_SOCKET_IFNAME=^docker0,lo')
 cluster.add_command('export NCCL_DEBUG=INFO')

 # setting a master port here is a good idea.
-cluster.add_command('export MASTER_PORT=%r' % PORT)
+cluster.add_command(f'export MASTER_PORT={PORT}')

 # good to load the latest NCCL version
 cluster.load_modules(['NCCL/2.4.7-1-cuda.10.0'])
@@ -5,7 +5,7 @@ Lighting offers a few options for logging information about model, gpu usage, et
 #### Display metrics in progress bar 
 ``` {.python}
 # DEFAULT
-trainer = Trainer(show_progress_bar=True)
+trainer = Trainer(progress_bar=True)
 ```

 ---
@@ -33,7 +33,7 @@ trainer = Trainer(process_position=1)
 Whenever you call .save() on the test-tube experiment it logs all the hyperparameters in current use.
 Give lightning a test-tube Experiment object to automate this for you.
 ``` {.python}
-from test_tube import Experiment
+from test-tube import Experiment

 exp = Experiment(...)
 Trainer(experiment=exp)
@@ -44,48 +44,12 @@ Trainer(experiment=exp)
 Whenever you call .save() on the test-tube experiment it snapshows all code and pushes to a git tag.
 Give lightning a test-tube Experiment object to automate this for you.
 ``` {.python}
-from test_tube import Experiment
+from test-tube import Experiment

 exp = Experiment(create_git_tag=True)
 Trainer(experiment=exp)
 ```

---
-### Tensorboard support   
-In the LightningModule you can access the experiment logger by doing:
-```python
-self.experiment
-
-# add image
-# Look at PyTorch SummaryWriter docs for what you can do.   
-self.experiment.add_image(...)
-```
-
-The experiment object is a strict subclass of PyTorch SummaryWriter. However, this class
-also snapshots every detail about the experiment (data folder paths, code, hyperparams),
-and allows you to visualize it using tensorboard.
-``` {.python}
-from test_tube import Experiment, HyperOptArgumentParser
-
-# exp hyperparams
-args = HyperOptArgumentParser()
-hparams = args.parse_args()
-
-# this is a summaryWriter with nicer logging structure
-exp = Experiment(save_dir='/some/path', create_git_tag=True)
-
-# track experiment details (must be ArgumentParser or HyperOptArgumentParser).
-# each option in the parser is tracked
-exp.argparse(hparams)
-exp.tag({'description': 'running demo'})
-
-# trainer uses the exp object to log exp data
-trainer = Trainer(experiment=exp)
-trainer.fit(model)
-
-# view logs at:
-# tensorboard --logdir /some/path   
-```

 ---
 #### Write logs file to csv every k batches 
@@ -1,4 +1,4 @@
-The lightning training loop handles everything except the actual computations of your model. To decide what will happen in your training loop, define the [training_step function](https://williamfalcon.github.io/pytorch-lightning/LightningModule/RequiredTrainerInterface/#training_step).
+The lightning training loop handles everything except the actual computations of your model. To decide what will happen in your training loop, define the [training_step function](../../Pytorch-lightning/LightningModule/#training_step).

 Below are all the things lightning automates for you in the training loop.

@@ -11,6 +11,17 @@ Accumulated gradients runs K small batches of size N before doing a backwards pa
 trainer = Trainer(accumulate_grad_batches=1)
 ```

+---
+#### Anneal Learning rate
+Cut the learning rate by 10 at every epoch listed in this list.
+``` {.python}
+# DEFAULT (don't anneal)
+trainer = Trainer(lr_scheduler_milestones=None)
+
+# cut LR by 10 at 100, 200, and 300 epochs 
+trainer = Trainer(lr_scheduler_milestones='100, 200, 300')
+```
+
 ---
 #### Force training for min or max epochs
 It can be useful to force training for a minimum number of epochs or limit to a max number
@@ -28,18 +39,15 @@ trainer = Trainer(enable_early_stop=True)
 ```

 ---
-#### Gradient Clipping
-Gradient clipping may be enabled to avoid exploding gradients.
-Specifically, this will [clip the gradient norm computed over all model parameters *together*](https://pytorch.org/docs/stable/nn.html#torch.nn.utils.clip_grad_norm_).
-
+#### Gradient Clipping 
+Use this to turn off early stopping and run training to the [max_epoch](#force-training-for-min-or-max-epochs)
 ``` {.python}
 # DEFAULT (ie: don't clip)
 trainer = Trainer(gradient_clip=0)
-
-# clip gradients with norm above 0.5
-trainer = Trainer(gradient_clip=0.5)
 ```

+
+
 ---
 #### Inspect gradient norms
 Looking at grad norms can help you figure out where training might be going wrong.
@@ -54,10 +62,7 @@ trainer = Trainer(track_grad_norm=2)

 ---
 #### Set how much of the training set to check
-If you don't want to check 100% of the training set (for debugging or if it's huge), set this flag.
-
-train_percent_check will be overwritten by overfit_pct if `overfit_pct > 0`
-
+If you don't want to check 100% of the training set (for debugging or if it's huge), set this flag
 ``` {.python}
 # DEFAULT
 trainer = Trainer(train_percent_check=1.0)
@@ -1,4 +1,4 @@
-The lightning validation loop handles everything except the actual computations of your model. To decide what will happen in your validation loop, define the [validation_step function](https://williamfalcon.github.io/pytorch-lightning/LightningModule/RequiredTrainerInterface/#validation_step).
+The lightning validation loop handles everything except the actual computations of your model. To decide what will happen in your validation loop, define the [validation_step function](../../Pytorch-lightning/LightningModule/#validation_step).
 Below are all the things lightning automates for you in the validation loop.

 **Note**   
@@ -18,9 +18,6 @@ trainer = Trainer(check_val_every_n_epoch=1)
 ---
 #### Set how much of the validation set to check 
 If you don't want to check 100% of the validation set (for debugging or if it's huge), set this flag
-
-val_percent_check will be overwritten by overfit_pct if `overfit_pct > 0`
-
 ``` {.python}
 # DEFAULT
 trainer = Trainer(val_percent_check=1.0)
@@ -32,9 +29,6 @@ trainer = Trainer(val_percent_check=0.1)
 ---
 #### Set how much of the test set to check 
 If you don't want to check 100% of the test set (for debugging or if it's huge), set this flag
-
-test_percent_check will be overwritten by overfit_pct if `overfit_pct > 0`
-
 ``` {.python}
 # DEFAULT
 trainer = Trainer(test_percent_check=1.0)
@@ -23,9 +23,6 @@ trainer = Trainer(track_grad_norm=2)
 ---
 #### Make model overfit on subset of data
 A useful debugging trick is to make your model overfit a tiny fraction of the data.
-
-setting `overfit_pct > 0` will overwrite train_percent_check, val_percent_check, test_percent_check
-
 ``` {.python}
 # DEFAULT don't overfit (ie: normal training)
 trainer = Trainer(overfit_pct=0.0)
@@ -1,141 +0,0 @@
-# Hooks
-[[Github Code](https://github.com/williamFalcon/pytorch-lightning/blob/master/pytorch_lightning/root_module/hooks.py)]   
-
-There are cases when you might want to do something different at different parts of the training/validation loop.
-To enable a hook, simply override the method in your LightningModule and the trainer will call it at the correct time.
-
-**Contributing** If there's a hook you'd like to add, simply:    
-1. Fork PyTorchLightning.    
-2. Add the hook [here](https://github.com/williamFalcon/pytorch-lightning/blob/master/pytorch_lightning/root_module/hooks.py).       
-3. Add the correct place in the [Trainer](https://github.com/williamFalcon/pytorch-lightning/blob/master/pytorch_lightning/models/trainer.py) where it should be called.    
-
---
-#### on_epoch_start
-Called in the training loop at the very beginning of the epoch.   
-```python
-def on_epoch_start(self):
-    # do something when the epoch starts
-```
-
---
-#### on_epoch_end
-Called in the training loop at the very end of the epoch.   
-```python
-def on_epoch_end(self):
-    # do something when the epoch ends 
-```
-
---
-#### on_batch_start
-Called in the training loop before anything happens for that batch.   
-```python
-def on_batch_start(self):
-    # do something when the batch starts
-```
-
---
-#### on_batch_end
-Called in the training loop after the batch.   
-```python
-def on_batch_end(self):
-    # do something when the batch ends 
-```
-
---
-#### on_pre_performance_check
-Called at the very beginning of the validation loop.   
-```python
-def on_pre_performance_check(self):
-    # do something before validation starts 
-```
-
---
-#### on_post_performance_check
-Called at the very end of the validation loop.   
-```python
-def on_post_performance_check(self):
-    # do something before validation end
-```
-
---
-#### on_tng_metrics
-Called in the training loop, right before metrics are logged.
-Although you can log at any time by using self.experiment, you can use
-this callback to modify what will be logged.
-```python
-def on_tng_metrics(self, metrics):
-    # do something before validation end
-```
-
---   
-#### optimizer_step 
-Calls .step() and .zero_grad for each optimizer.  
-You can override this method to adjust how you do the optimizer step for each optimizer
-
-Called once per optimizer
-```python
-# DEFAULT
-def optimizer_step(self, current_epoch, batch_nb, optimizer, optimizer_i):
-    optimizer.step()   
-    optimizer.zero_grad()   
-    
-# Alternating schedule for optimizer steps (ie: GANs)    
-def optimizer_step(self, current_epoch, batch_nb, optimizer, optimizer_i):
-    # update generator opt every 2 steps
-    if optimizer_i == 0:
-        if batch_nb % 2 == 0 :
-            optimizer.step()
-            optimizer.zero_grad()
-   
-    # update discriminator opt every 4 steps
-    if optimizer_i == 1:
-        if batch_nb % 4 == 0 :
-            optimizer.step()
-            optimizer.zero_grad()    
-    
-    # ...
-    # add as many optimizers as you want 
-```
-
-This step allows you to do a lot of non-standard training tricks such as learning-rate warm-up:   
-
-```python
-# learning rate warm-up
-def optimizer_step(self, current_epoch, batch_nb, optimizer, optimizer_i):
-    # warm up lr
-    if self.trainer.global_step < 500:
-        lr_scale = min(1., float(self.trainer.global_step + 1) / 500.)
-        for pg in optimizer.param_groups:
-            pg['lr'] = lr_scale * self.hparams.learning_rate
-    
-    # update params
-    optimizer.step()
-    optimizer.zero_grad() 
-```
-
-
---
-#### on_before_zero_grad
-Called in the training loop after taking an optimizer step and before zeroing grads.
-Good place to inspect weight information with weights updated.
-
-Called once per optimizer
-```python
-def on_before_zero_grad(self, optimizer):
-    # do something with the optimizer or inspect it. 
-```
-
---
-#### on_after_backward
-Called in the training loop after model.backward()
-This is the ideal place to inspect or log gradient information 
-```python
-def on_after_backward(self):
-    # example to inspect gradient information in tensorboard
-    if self.trainer.global_step % 25 == 0:  # don't make the tf file huge
-        params = self.state_dict()
-        for k, v in params.items():
-            grads = v
-            name = k
-            self.experiment.add_histogram(tag=name, values=grads, global_step=self.trainer.global_step)
-```
@@ -19,62 +19,56 @@ But of course the fun is in all the advanced things it can do:

 **Checkpointing**    

- [Model saving](https://williamfalcon.github.io/pytorch-lightning/Trainer/Checkpointing/#model-saving)
- [Model loading](https://williamfalcon.github.io/pytorch-lightning/LightningModule/methods/#load-from-metrics) 
- [Restoring training session](https://williamfalcon.github.io/pytorch-lightning/Trainer/Checkpointing/#restoring-training-session)
+- Model saving
+- Model loading 

 **Computing cluster (SLURM)**    

- [Running grid search on a cluster](https://williamfalcon.github.io/pytorch-lightning/Trainer/SLURM%20Managed%20Cluster#running-grid-search-on-a-cluster) 
- [Walltime auto-resubmit](https://williamfalcon.github.io/pytorch-lightning/Trainer/SLURM%20Managed%20Cluster#walltime-auto-resubmit)   
+- [Running grid search on a cluster](SLURM%20Managed%20Cluster/#running-grid-search-on-a-cluster)
+- [Walltime auto-resubmit](SLURM%20Managed%20Cluster/#walltime-auto-resubmit)   

 **Debugging**  

- [Fast dev run](https://williamfalcon.github.io/pytorch-lightning/Trainer/debugging/#fast-dev-run)
- [Inspect gradient norms](https://williamfalcon.github.io/pytorch-lightning/Trainer/debugging/#inspect-gradient-norms)
- [Log GPU usage](https://williamfalcon.github.io/pytorch-lightning/Trainer/debugging/#Log-gpu-usage)
- [Make model overfit on subset of data](https://williamfalcon.github.io/pytorch-lightning/Trainer/debugging/#make-model-overfit-on-subset-of-data)
- [Print the parameter count by layer](https://williamfalcon.github.io/pytorch-lightning/Trainer/debugging/#print-the-parameter-count-by-layer)
- [Pring which gradients are nan](https://williamfalcon.github.io/pytorch-lightning/Trainer/debugging/#print-which-gradients-are-nan)
- [Print input and output size of every module in system](https://williamfalcon.github.io/pytorch-lightning/LightningModule/properties/#example_input_array)
+- [Fast dev run](Debugging/#fast-dev-run)
+- [Inspect gradient norms](Debugging/#inspect-gradient-norms)
+- [Log GPU usage](Debugging/#Log-gpu-usage)
+- [Make model overfit on subset of data](Debugging/#make-model-overfit-on-subset-of-data)
+- [Print the parameter count by layer](Debugging/#print-the-parameter-count-by-layer)
+- [Pring which gradients are nan](Debugging/#print-which-gradients-are-nan)


 **Distributed training**    

- [16-bit mixed precision](https://williamfalcon.github.io/pytorch-lightning/Trainer/Distributed%20training/#16-bit-mixed-precision)
- [Multi-GPU](https://williamfalcon.github.io/pytorch-lightning/Trainer/Distributed%20training/#Multi-GPU)
- [Multi-node](https://williamfalcon.github.io/pytorch-lightning/Trainer/Distributed%20training/#Multi-node)
- [Single GPU](https://williamfalcon.github.io/pytorch-lightning/Trainer/Distributed%20training/#single-gpu)
- [Self-balancing architecture](https://williamfalcon.github.io/pytorch-lightning/Trainer/Distributed%20training/#self-balancing-architecture)
+- [16-bit mixed precision](Distributed%20training/#16-bit-mixed-precision)
+- [Multi-GPU](Distributed%20training/#Multi-GPU)
+- [Multi-node](Distributed%20training/#Multi-node)
+- [Single GPU](Distributed%20training/#single-gpu)
+- [Self-balancing architecture](Distributed%20training/#self-balancing-architecture)


 **Experiment Logging**   

- [Display metrics in progress bar](https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#display-metrics-in-progress-bar)
- [Log metric row every k batches](https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#log-metric-row-every-k-batches)
- [Process position](https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#process-position)
- [Tensorboard support](https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#tensorboard-support)
- [Save a snapshot of all hyperparameters](https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#save-a-snapshot-of-all-hyperparameters) 
- [Snapshot code for a training run](https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#snapshot-code-for-a-training-run) 
- [Write logs file to csv every k batches](https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#write-logs-file-to-csv-every-k-batches)
+- [Display metrics in progress bar](Logging/#display-metrics-in-progress-bar)
+- Log arbitrary metrics
+- [Log metric row every k batches](Logging/#log-metric-row-every-k-batches)
+- [Process position](Logging/#process-position)
+- [Save a snapshot of all hyperparameters](Logging/#save-a-snapshot-of-all-hyperparameters) 
+- [Snapshot code for a training run](Logging/#snapshot-code-for-a-training-run) 
+- [Write logs file to csv every k batches](Logging/#write-logs-file-to-csv-every-k-batches)

 **Training loop**    

- [Accumulate gradients](https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#accumulated-gradients)
- [Force training for min or max epochs](https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#force-training-for-min-or-max-epochs)
- [Force disable early stop](https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#force-disable-early-stop)
- [Gradient Clipping](https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#gradient-clipping)
- [Hooks](https://williamfalcon.github.io/pytorch-lightning/Trainer/hooks/)
- [Learning rate scheduling](https://williamfalcon.github.io/pytorch-lightning/LightningModule/RequiredTrainerInterface/#configure_optimizers)
- [Use multiple optimizers (like GANs)](https://williamfalcon.github.io/pytorch-lightning/LightningModule/RequiredTrainerInterface/#configure_optimizers)
- [Set how much of the training set to check (1-100%)](https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#set-how-much-of-the-training-set-to-check)
- [Step optimizers at arbitrary intervals](https://williamfalcon.github.io/pytorch-lightning/Trainer/hooks/#optimizer_step)
+- [Accumulate gradients](Training%20Loop/#accumulated-gradients)
+- [Anneal Learning rate](Training%20Loop/#anneal-learning-rate)
+- [Force training for min or max epochs](Training%20Loop/#force-training-for-min-or-max-epochs)
+- [Force disable early stop](Training%20Loop/#force-disable-early-stop)
+- [Use multiple optimizers (like GANs)](../Pytorch-lightning/LightningModule/#configure_optimizers)
+- [Set how much of the training set to check (1-100%)](Training%20Loop/#set-how-much-of-the-training-set-to-check)

 **Validation loop**    

- [Check validation every n epochs](https://williamfalcon.github.io/pytorch-lightning/Trainer/Validation%20loop/#check-validation-every-n-epochs)
- [Hooks](https://williamfalcon.github.io/pytorch-lightning/Trainer/hooks/)
- [Set how much of the validation set to check](https://williamfalcon.github.io/pytorch-lightning/Trainer/Validation%20loop/#set-how-much-of-the-validation-set-to-check)
- [Set how much of the test set to check](https://williamfalcon.github.io/pytorch-lightning/Trainer/Validation%20loop/#set-how-much-of-the-test-set-to-check)
- [Set validation check frequency within 1 training epoch](https://williamfalcon.github.io/pytorch-lightning/Trainer/Validation%20loop/#set-validation-check-frequency-within-1-training-epoch)
- [Set the number of validation sanity steps](https://williamfalcon.github.io/pytorch-lightning/Trainer/Validation%20loop/#set-the-number-of-validation-sanity-steps)
+- [Check validation every n epochs](Validation%20Loop/#check-validation-every-n-epochs)
+- [Set how much of the validation set to check](Validation%20Loop/#set-how-much-of-the-validation-set-to-check)
+- [Set how much of the test set to check](Validation%20Loop/#set-how-much-of-the-test-set-to-check)
+- [Set validation check frequency within 1 training epoch](Validation%20Loop/#set-validation-check-frequency-within-1-training-epoch)
+- [Set the number of validation sanity steps](Validation%20Loop/#set-the-number-of-validation-sanity-steps)
@@ -1,2 +1 @@
 mkdocs-material==4.4.0
-mkdocs==1.0.4
@@ -3,7 +3,7 @@ In 99% of cases you want to just copy [this template](https://github.com/william

 ```bash
 # get a copy of the module template
-wget https://raw.githubusercontent.com/williamFalcon/pytorch-lightning/master/examples/new_project_templates/lightning_module_template.py
+wget https://github.com/williamFalcon/pytorch-lightning/blob/master/examples/new_project_templates/lightning_module_template.py
 ```

 ---    
@@ -1,68 +1,11 @@
 ###### New project Quick Start    
-To start a new project define two files, a LightningModule and a Trainer file.    
-To illustrate Lightning power and simplicity, here's an example of a typical research flow.    
+To start a new project define these two files.   

-###### Case 1: BERT    
-Let's say you're working on something like BERT but want to try different ways of training or even different networks.  
-You would define a single LightningModule and use flags to switch between your different ideas.   
-```python
-class BERT(pl.LightningModule):
-    def __init__(self, model_name, task):
-        self.task = task
-    
-        if model_name == 'transformer':
-            self.net = Transformer()
-        elif model_name == 'my_cool_version':
-            self.net = MyCoolVersion()
-            
-    def training_step(self, batch, batch_nb):
-        if self.task == 'standard_bert':
-            # do standard bert training with self.net...
-            # return loss
-            
-        if self.task == 'my_cool_task':
-            # do my own version with self.net
-            # return loss
-```   
-
-###### Case 2: COOLER NOT BERT    
-But if you wanted to try something **completely** different, you'd define a new module for that.    
-```python
-
-class CoolerNotBERT(pl.LightningModule):
-    def __init__(self):
-        self.net = ...
-        
-    def training_step(self, batch, batch_nb):
-        # do some other cool task
-        # return loss   
-```   
-
-###### Rapid research flow    
-Then you could do rapid research by switching between these two and using the same trainer.   
-```python
-
-if use_bert:
-    model = BERT()
-else:
-    model = CoolerNotBERT()
-    
-trainer = Trainer(gpus=[0, 1, 2, 3], use_amp=True)
-trainer.fit(model)
-```
-
-Notice a few things about this flow:   
-1. You're writing pure PyTorch... no unnecessary abstractions or new libraries to learn.   
-2. You get free GPU and 16-bit support without writing any of that code in your model.   
-3. You also get all of the capabilities below (without coding or testing yourself).     
-
---    
-###### Templates 
-1. [MNIST LightningModule](https://williamfalcon.github.io/pytorch-lightning/LightningModule/RequiredTrainerInterface/#minimal-example) 
-2. [Trainer](https://williamfalcon.github.io/pytorch-lightning/Trainer/)
-    - [Basic CPU Trainer Template](https://github.com/williamFalcon/pytorch-lightning/blob/master/examples/new_project_templates/single_cpu_template.py) 
-    - [Multi-GPU Trainer Template](https://github.com/williamFalcon/pytorch-lightning/blob/master/examples/new_project_templates/single_gpu_node_template.py)
-    - [GPU cluster Trainer Template](https://github.com/williamFalcon/pytorch-lightning/blob/master/examples/new_project_templates/multi_node_cluster_template.py)
+1. [Define a LightningModule](/pytorch-lightning/LightningModule/RequiredTrainerInterface/) 
+2. [Define a trainer](https://williamfalcon.github.io/pytorch-lightning/Trainer/)
+    - [Basic CPU Trainer Template](https://github.com/williamFalcon/pytorch-lightning/blob/master/pytorch_lightning/examples/new_project_templates/single_cpu_template.py) 
+    - [Multi-GPU Trainer Template](https://github.com/williamFalcon/pytorch-lightning/blob/master/pytorch_lightning/examples/new_project_templates/single_gpu_node_template.py)
+    - [GPU cluster Trainer Template](https://github.com/williamFalcon/pytorch-lightning/blob/master/pytorch_lightning/examples/new_project_templates/multi_node_cluster_template.py)

 ###### Docs shortcuts
 - [LightningModule](LightningModule/RequiredTrainerInterface/)  
@@ -79,7 +22,6 @@ Notice a few things about this flow:

 - [Model saving](https://williamfalcon.github.io/pytorch-lightning/Trainer/Checkpointing/#model-saving)
 - [Model loading](https://williamfalcon.github.io/pytorch-lightning/LightningModule/methods/#load-from-metrics) 
- [Restoring training session](https://williamfalcon.github.io/pytorch-lightning/Trainer/Checkpointing/#restoring-training-session)

 ###### Computing cluster (SLURM)    

@@ -94,7 +36,6 @@ Notice a few things about this flow:
 - [Make model overfit on subset of data](https://williamfalcon.github.io/pytorch-lightning/Trainer/debugging/#make-model-overfit-on-subset-of-data)
 - [Print the parameter count by layer](https://williamfalcon.github.io/pytorch-lightning/Trainer/debugging/#print-the-parameter-count-by-layer)
 - [Pring which gradients are nan](https://williamfalcon.github.io/pytorch-lightning/Trainer/debugging/#print-which-gradients-are-nan)
- [Print input and output size of every module in system](https://williamfalcon.github.io/pytorch-lightning/LightningModule/properties/#example_input_array)


 ###### Distributed training    
@@ -109,9 +50,9 @@ Notice a few things about this flow:
 ###### Experiment Logging   

 - [Display metrics in progress bar](https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#display-metrics-in-progress-bar)
+- Log arbitrary metrics
 - [Log metric row every k batches](https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#log-metric-row-every-k-batches)
 - [Process position](https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#process-position)
- [Tensorboard support](https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#tensorboard-support)
 - [Save a snapshot of all hyperparameters](https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#save-a-snapshot-of-all-hyperparameters) 
 - [Snapshot code for a training run](https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#snapshot-code-for-a-training-run) 
 - [Write logs file to csv every k batches](https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#write-logs-file-to-csv-every-k-batches)
@@ -119,19 +60,16 @@ Notice a few things about this flow:
 ###### Training loop    

 - [Accumulate gradients](https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#accumulated-gradients)
+- [Anneal Learning rate](https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#anneal-learning-rate)
 - [Force training for min or max epochs](https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#force-training-for-min-or-max-epochs)
 - [Force disable early stop](https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#force-disable-early-stop)
 - [Gradient Clipping](https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#gradient-clipping)
- [Hooks](https://williamfalcon.github.io/pytorch-lightning/Trainer/hooks/)
- [Learning rate scheduling](https://williamfalcon.github.io/pytorch-lightning/LightningModule/RequiredTrainerInterface/#configure_optimizers)
- [Use multiple optimizers (like GANs)](https://williamfalcon.github.io/pytorch-lightning/LightningModule/RequiredTrainerInterface/#configure_optimizers)
+- [Use multiple optimizers (like GANs)](https://williamfalcon.github.io/pytorch-lightning/Pytorch-Lightning/LightningModule/#configure_optimizers)
 - [Set how much of the training set to check (1-100%)](https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#set-how-much-of-the-training-set-to-check)
- [Step optimizers at arbitrary intervals](https://williamfalcon.github.io/pytorch-lightning/Trainer/hooks/#optimizer_step)

-###### Validation loop    
+######Validation loop    

 - [Check validation every n epochs](https://williamfalcon.github.io/pytorch-lightning/Trainer/Validation%20loop/#check-validation-every-n-epochs)
- [Hooks](https://williamfalcon.github.io/pytorch-lightning/Trainer/hooks/)
 - [Set how much of the validation set to check](https://williamfalcon.github.io/pytorch-lightning/Trainer/Validation%20loop/#set-how-much-of-the-validation-set-to-check)
 - [Set how much of the test set to check](https://williamfalcon.github.io/pytorch-lightning/Trainer/Validation%20loop/#set-how-much-of-the-test-set-to-check)
 - [Set validation check frequency within 1 training epoch](https://williamfalcon.github.io/pytorch-lightning/Trainer/Validation%20loop/#set-validation-check-frequency-within-1-training-epoch)
@@ -1,178 +0,0 @@
-"""
-To run this template just do:  
-python gan.py   
-
-After a few epochs, launch tensorboard to see the images being generated at every batch.   
-
-tensorboard --logdir default
-"""
-from argparse import ArgumentParser
-import os
-import numpy as np
-
-import torchvision
-import torchvision.transforms as transforms
-from torchvision.datasets import MNIST
-
-from torch.utils.data import DataLoader
-
-import torch.nn as nn
-import torch.nn.functional as F
-import torch
-
-import pytorch_lightning as pl
-from test_tube import Experiment
-
-
-class Generator(nn.Module):
-    def __init__(self, latent_dim, img_shape):
-        super(Generator, self).__init__()
-        self.img_shape = img_shape
-
-        def block(in_feat, out_feat, normalize=True):
-            layers = [nn.Linear(in_feat, out_feat)]
-            if normalize:
-                layers.append(nn.BatchNorm1d(out_feat, 0.8))
-            layers.append(nn.LeakyReLU(0.2, inplace=True))
-            return layers
-
-        self.model = nn.Sequential(
-            *block(latent_dim, 128, normalize=False),
-            *block(128, 256),
-            *block(256, 512),
-            *block(512, 1024),
-            nn.Linear(1024, int(np.prod(img_shape))),
-            nn.Tanh()
-        )
-
-    def forward(self, z):
-        img = self.model(z)
-        img = img.view(img.size(0), *self.img_shape)
-        return img
-
-
-class Discriminator(nn.Module):
-    def __init__(self, img_shape):
-        super(Discriminator, self).__init__()
-
-        self.model = nn.Sequential(
-            nn.Linear(int(np.prod(img_shape)), 512),
-            nn.LeakyReLU(0.2, inplace=True),
-            nn.Linear(512, 256),
-            nn.LeakyReLU(0.2, inplace=True),
-            nn.Linear(256, 1),
-            nn.Sigmoid(),
-        )
-
-    def forward(self, img):
-        img_flat = img.view(img.size(0), -1)
-        validity = self.model(img_flat)
-
-        return validity
-
-
-class GAN(pl.LightningModule):
-
-    def __init__(self, hparams):
-        super(GAN, self).__init__()
-        self.hparams = hparams
-
-        # networks
-        mnist_shape = (1, 28, 28)
-        self.generator = Generator(latent_dim=hparams.latent_dim, img_shape=mnist_shape)
-        self.discriminator = Discriminator(img_shape=mnist_shape)
-
-        # cache for generated images
-        self.generated_imgs = None
-
-    def forward(self, z):
-        return self.generator(z)
-
-    def adversarial_loss(self, y_hat, y):
-        return F.binary_cross_entropy(y_hat, y)
-
-    def training_step(self, batch, batch_nb, optimizer_i):
-        imgs, _ = batch
-
-        # train generator
-        if optimizer_i == 0:
-            # sample noise
-            z = torch.randn(imgs.shape[0], self.hparams.latent_dim)
-
-            # match gpu device (or keep as cpu)
-            if self.on_gpu:
-                z = z.cuda(imgs.device.index)
-
-            # generate images
-            self.generated_imgs = self.forward(z)
-
-            # log sampled images
-            sample_imgs = self.generated_imgs[:6]
-            grid = torchvision.utils.make_grid(sample_imgs)
-            self.experiment.add_image('generated_images', grid, 0)
-
-            # ground truth result (ie: all fake)
-            valid = torch.ones(imgs.size(0), 1)
-
-            # adversarial loss is binary cross-entropy
-            g_loss = self.adversarial_loss(self.discriminator(self.generated_imgs), valid)
-
-            return g_loss
-
-        # train discriminator
-        if optimizer_i == 1:
-            # Measure discriminator's ability to classify real from generated samples
-
-            # how well can it label as real?
-            valid = torch.ones(imgs.size(0), 1)
-            real_loss = self.adversarial_loss(self.discriminator(imgs), valid)
-
-            # how well can it label as fake?
-            fake = torch.zeros(imgs.size(0), 1)
-            fake_loss = self.adversarial_loss(self.discriminator(self.generated_imgs.detach()), fake)
-
-            # discriminator loss is the average of these
-            d_loss = (real_loss + fake_loss) / 2
-
-            return d_loss
-
-    def configure_optimizers(self):
-        lr = self.hparams.lr
-        b1 = self.hparams.b1
-        b2 = self.hparams.b2
-
-        opt_g = torch.optim.Adam(self.generator.parameters(), lr=lr, betas=(b1, b2))
-        opt_d = torch.optim.Adam(self.discriminator.parameters(), lr=lr, betas=(b1, b2))
-        return [opt_g, opt_d], []
-
-    @pl.data_loader
-    def tng_dataloader(self):
-        transform = transforms.Compose([transforms.ToTensor(),
-                                        transforms.Normalize([0.5], [0.5])])
-        dataset = MNIST(os.getcwd(), train=True, download=True, transform=transform)
-        return DataLoader(dataset, batch_size=self.hparams.batch_size)
-
-
-def main(hparams):
-    # save tensorboard logs
-    exp = Experiment(save_dir=os.getcwd())
-
-    # init model
-    model = GAN(hparams)
-
-    # fit trainer on CPU
-    trainer = pl.Trainer(experiment=exp, max_nb_epochs=200)
-    trainer.fit(model)
-
-
-if __name__ == '__main__':
-    parser = ArgumentParser()
-    parser.add_argument("--batch_size", type=int, default=64, help="size of the batches")
-    parser.add_argument("--lr", type=float, default=0.0002, help="adam: learning rate")
-    parser.add_argument("--b1", type=float, default=0.5, help="adam: decay of first order momentum of gradient")
-    parser.add_argument("--b2", type=float, default=0.999, help="adam: decay of first order momentum of gradient")
-    parser.add_argument("--latent_dim", type=int, default=100, help="dimensionality of the latent space")
-
-    hparams = parser.parse_args()
-
-    main(hparams)
@@ -1,15 +1,10 @@
-site_name: PyTorch lightning Documentation
+site_name: Pytorch lightning Documentation
 theme:
  name: 'material'
 docs_dir: docs
 repo_url: https://github.com/williamFalcon/pytorch-lightning
 site_dir: 'site'
-site_description: 'Documentation for PyTorch LightningModule, the researcher version of keras.'
+site_description: 'Documentation for Pytorch LightningModule, the researcher version of keras.'

 dev_addr: '0.0.0.0:8000'
 #google_analytics: ['UA-aasd', 'sitename']
-
-markdown_extensions:
-  - codehilite:
-      guess_lang: false
-      linenums: true
@@ -1,9 +1,3 @@
-from .models.trainer import Trainer
+from .models import Trainer
 from .root_module.root_module import LightningModule
-from .root_module.decorators import data_loader
-
-__all__ = [
-    'Trainer',
-    'LightningModule',
-    'data_loader',
-]
+from .root_module.decorators import data_loader
@@ -1,6 +1 @@
-from .pt_callbacks import EarlyStopping, ModelCheckpoint
-
-__all__ = [
-    'EarlyStopping',
-    'ModelCheckpoint',
-]
+from .pt_callbacks import EarlyStopping, ModelCheckpoint
@@ -1,8 +1,5 @@
-import os
-import shutil
-
 import numpy as np
-
+import os, shutil
 from pytorch_lightning.pt_overrides.override_data_parallel import LightningDistributedDataParallel


@@ -122,9 +119,9 @@ class EarlyStopping(Callback):
        current = logs.get(self.monitor)
        stop_training = False
        if current is None:
-            print('Early stopping conditioned on metric `%s` '
-                  'which is not available. Available metrics are: %s' %
-                  (self.monitor, ','.join(list(logs.keys()))), RuntimeWarning)
+            print('Early stopping conditioned on metric `%s` ''which is not available. Available metrics are: %s' %
+                (self.monitor, ','.join(list(logs.keys()))), RuntimeWarning
+            )
            exit(-1)

        if self.monitor_op(current - self.min_delta, self.best):
@@ -188,7 +185,8 @@ class ModelCheckpoint(Callback):

        if mode not in ['auto', 'min', 'max']:
            print('ModelCheckpoint mode %s is unknown, '
-                  'fallback to auto mode.' % (mode), RuntimeWarning)
+                          'fallback to auto mode.' % (mode),
+                          RuntimeWarning)
            mode = 'auto'

        if mode == 'min':
@@ -232,8 +230,8 @@ class ModelCheckpoint(Callback):
            if self.save_best_only:
                current = logs.get(self.monitor)
                if current is None:
-                    print('Can save best model only with %s available,'
-                          ' skipping.' % (self.monitor), RuntimeWarning)
+                    print('Can save best model only with %s available, '
+                                  'skipping.' % (self.monitor), RuntimeWarning)
                else:
                    if self.monitor_op(current, self.best):
                        if self.verbose > 0:
@@ -262,3 +260,4 @@ if __name__ == '__main__':
        print(loss)
        if should_stop:
            break
+
@@ -1,5 +1 @@
-from .new_project_templates.lightning_module_template import LightningTemplateModel
-
-__all__ = [
-    'LightningTemplateModel'
-]
+from .new_project_templates.lightning_module_template import LightningTemplateModel
@@ -1,6 +1,3 @@
-"""
-Example template for defining a system
-"""
 import os
 from collections import OrderedDict
 import torch.nn as nn
@@ -13,7 +10,7 @@ from torch import optim
 from torch.utils.data import DataLoader
 from torch.utils.data.distributed import DistributedSampler

-import pytorch_lightning as pl
+import pytorch_lightning as ptl
 from pytorch_lightning.root_module.root_module import LightningModule


@@ -47,13 +44,11 @@ class LightningTemplateModel(LightningModule):
        Layout model
        :return:
        """
-        self.c_d1 = nn.Linear(in_features=self.hparams.in_features,
-                              out_features=self.hparams.hidden_dim)
+        self.c_d1 = nn.Linear(in_features=self.hparams.in_features, out_features=self.hparams.hidden_dim)
        self.c_d1_bn = nn.BatchNorm1d(self.hparams.hidden_dim)
        self.c_d1_drop = nn.Dropout(self.hparams.drop_prob)

-        self.c_d2 = nn.Linear(in_features=self.hparams.hidden_dim,
-                              out_features=self.hparams.out_features)
+        self.c_d2 = nn.Linear(in_features=self.hparams.hidden_dim, out_features=self.hparams.out_features)

    # ---------------------
    # TRAINING
@@ -151,23 +146,12 @@ class LightningTemplateModel(LightningModule):
        val_loss_mean = 0
        val_acc_mean = 0
        for output in outputs:
-            val_loss = output['val_loss']
-
-            # reduce manually when using dp
-            if self.trainer.use_dp:
-                val_loss = torch.mean(val_loss)
-            val_loss_mean += val_loss
-
-            # reduce manually when using dp
-            val_acc = output['val_acc']
-            if self.trainer.use_dp:
-                val_acc = torch.mean(val_acc)
-
-            val_acc_mean += val_acc
+            val_loss_mean += output['val_loss']
+            val_acc_mean += output['val_acc']

        val_loss_mean /= len(outputs)
        val_acc_mean /= len(outputs)
-        tqdm_dic = {'val_loss': val_loss_mean, 'val_acc': val_acc_mean}
+        tqdm_dic = {'val_loss': val_loss_mean.item(), 'val_acc': val_acc_mean.item()}
        return tqdm_dic

    # ---------------------
@@ -179,23 +163,23 @@ class LightningTemplateModel(LightningModule):
        :return: list of optimizers
        """
        optimizer = optim.Adam(self.parameters(), lr=self.hparams.learning_rate)
-        scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=10)
-        return [optimizer], [scheduler]
+        return [optimizer]

    def __dataloader(self, train):
        # init data generators
-        transform = transforms.Compose([transforms.ToTensor(),
-                                        transforms.Normalize((0.5,), (1.0,))])
-        dataset = MNIST(root=self.hparams.data_root, train=train,
-                        transform=transform, download=True)
+        transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (1.0,))])
+        dataset = MNIST(root=self.hparams.data_root, train=train, transform=transform, download=True)

-        # when using multi-node (ddp) we need to add the datasampler
+        # when using multi-node we need to add the datasampler
        train_sampler = None
        batch_size = self.hparams.batch_size

-        if self.use_ddp:
-            train_sampler = DistributedSampler(dataset, rank=self.trainer.proc_rank)
-            batch_size = batch_size // self.trainer.world_size  # scale batch size
+        try:
+            if self.on_gpu:
+                train_sampler = DistributedSampler(dataset, rank=self.trainer.proc_rank)
+                batch_size = batch_size // self.trainer.world_size  # scale batch size
+        except Exception as e:
+            pass

        should_shuffle = train_sampler is None
        loader = DataLoader(
@@ -207,23 +191,23 @@ class LightningTemplateModel(LightningModule):

        return loader

-    @pl.data_loader
+    @ptl.data_loader
    def tng_dataloader(self):
        print('tng data loader called')
        return self.__dataloader(train=True)

-    @pl.data_loader
+    @ptl.data_loader
    def val_dataloader(self):
        print('val data loader called')
        return self.__dataloader(train=False)

-    @pl.data_loader
+    @ptl.data_loader
    def test_dataloader(self):
        print('test data loader called')
        return self.__dataloader(train=False)

    @staticmethod
-    def add_model_specific_args(parent_parser, root_dir):  # pragma: no cover
+    def add_model_specific_args(parent_parser, root_dir): # pragma: no cover
        """
        Parameters you define here will be available to your model through self.hparams
        :param parent_parser:
@@ -236,25 +220,20 @@ class LightningTemplateModel(LightningModule):
        # parser.set_defaults(gradient_clip=5.0)

        # network params
-        parser.add_argument('--in_features', default=28 * 28, type=int)
-        parser.add_argument('--out_features', default=10, type=int)
-        # use 500 for CPU, 50000 for GPU to see speed difference
-        parser.add_argument('--hidden_dim', default=50000, type=int)
        parser.opt_list('--drop_prob', default=0.2, options=[0.2, 0.5], type=float, tunable=False)
+        parser.add_argument('--in_features', default=28*28, type=int)
+        parser.add_argument('--out_features', default=10, type=int)
+        parser.add_argument('--hidden_dim', default=50000, type=int) # use 500 for CPU, 50000 for GPU to see speed difference

        # data
        parser.add_argument('--data_root', default=os.path.join(root_dir, 'mnist'), type=str)

        # training params (opt)
-        parser.opt_list('--learning_rate', default=0.001 * 8, type=float,
-                        options=[0.0001, 0.0005, 0.001, 0.005],
+        parser.opt_list('--learning_rate', default=0.001*8, type=float, options=[0.0001, 0.0005, 0.001, 0.005],
                        tunable=False)
-        parser.opt_list('--optimizer_name', default='adam', type=str,
-                        options=['adam'], tunable=False)
+        parser.opt_list('--optimizer_name', default='adam', type=str, options=['adam'], tunable=False)

-        # if using 2 nodes with 4 gpus each the batch size here
-        #  (256) will be 256 / (2*8) = 16 per gpu
-        parser.opt_list('--batch_size', default=256 * 8, type=int,
-                        options=[32, 64, 128, 256], tunable=False,
-                        help='batch size will be divided over all gpus being used across all nodes')
+        # if using 2 nodes with 4 gpus each the batch size here (256) will be 256 / (2*8) = 16 per gpu
+        parser.opt_list('--batch_size', default=256*8, type=int, options=[32, 64, 128, 256], tunable=False,
+                        help='batch size will be divided over all the gpus being used across all nodes')
        return parser
@@ -1,21 +1,31 @@
-"""
-Multi-node example (GPU)
-"""
 import os
+import sys
 import numpy as np
 from time import sleep
 import torch

 from test_tube import HyperOptArgumentParser, Experiment, SlurmCluster
 from pytorch_lightning.models.trainer import Trainer
-from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
+from pytorch_lightning.utils.arg_parse import add_default_args

-from examples.new_project_templates.lightning_module_template import LightningTemplateModel
+from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint

 SEED = 2334
 torch.manual_seed(SEED)
 np.random.seed(SEED)

+# ---------------------
+# DEFINE MODEL HERE
+# ---------------------
+from lightning_module_template import LightningTemplateModel
+# ---------------------
+
+"""
+Allows training by using command line arguments
+Run by:
+# TYPE YOUR RUN COMMAND HERE
+"""
+

 def main_local(hparams):
    main(hparams, None, None)
@@ -112,10 +122,8 @@ def optimize_on_cluster(hyperparams):
    cluster.add_command('source activate lightning')

    # run only on 32GB voltas
-    cluster.add_slurm_cmd(cmd='constraint', value='volta32gb',
-                          comment='use 32gb gpus')
-    cluster.add_slurm_cmd(cmd='partition', value=hyperparams.gpu_partition,
-                          comment='use 32gb gpus')
+    cluster.add_slurm_cmd(cmd='constraint', value='volta32gb', comment='use 32gb gpus')
+    cluster.add_slurm_cmd(cmd='partition', value=hyperparams.gpu_partition, comment='use 32gb gpus')

    # run hopt
    # creates and submits jobs to slurm
@@ -142,23 +150,15 @@ if __name__ == '__main__':
    parent_parser.add_argument('--gpu_partition', type=str, help='consult your cluster manual')

    # TODO: make 1 param
-    parent_parser.add_argument('--per_experiment_nb_gpus', type=int,
-                               help='how many gpus to use in a node')
-    parent_parser.add_argument('--gpus', type=str, default='-1',
-                               help='how many gpus to use in the node')
+    parent_parser.add_argument('--per_experiment_nb_gpus', type=int, help='how many gpus to use in a node')
+    parent_parser.add_argument('--gpus', type=str, default='-1', help='how many gpus to use in the node')

-    parent_parser.add_argument('--nb_gpu_nodes', type=int, default=1,
-                               help='how many nodes to use in a cluster')
-    parent_parser.add_argument('--test_tube_save_path', type=str, default=test_tube_dir,
-                               help='where to save logs')
-    parent_parser.add_argument('--slurm_log_path', type=str, default=slurm_out_dir,
-                               help='where to save slurm meta')
-    parent_parser.add_argument('--model_save_path', type=str, default=checkpoint_dir,
-                               help='where to save model')
-    parent_parser.add_argument('--experiment_name', type=str, default='pt_lightning_exp_a',
-                               help='test tube exp name')
-    parent_parser.add_argument('--nb_hopt_trials', type=int, default=1,
-                               help='how many grid search trials to run')
+    parent_parser.add_argument('--nb_gpu_nodes', type=int, default=1, help='how many nodes to use in a cluster')
+    parent_parser.add_argument('--test_tube_save_path', type=str, default=test_tube_dir, help='where to save logs')
+    parent_parser.add_argument('--slurm_log_path', type=str, default=slurm_out_dir, help='where to save slurm meta')
+    parent_parser.add_argument('--model_save_path', type=str, default=checkpoint_dir, help='where to save model')
+    parent_parser.add_argument('--experiment_name', type=str, default='pt_lightning_exp_a', help='test tube exp name')
+    parent_parser.add_argument('--nb_hopt_trials', type=int, default=1, help='how many grid search trials to run')

    # allow model to overwrite or extend args
    parser = LightningTemplateModel.add_model_specific_args(parent_parser, root_dir)
@@ -1,20 +1,24 @@
 """
-Runs a model on a single node on CPU only..
+Runs a model on a single node across N-gpus.
 """
 import os
+import sys
 import numpy as np
+from time import sleep
 import torch

-from test_tube import HyperOptArgumentParser, Experiment
+from test_tube import HyperOptArgumentParser, Experiment, SlurmCluster
 from pytorch_lightning.models.trainer import Trainer
-from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
+from pytorch_lightning.utils.arg_parse import add_default_args

-from examples.new_project_templates.lightning_module_template import LightningTemplateModel
+from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint

 SEED = 2334
 torch.manual_seed(SEED)
 np.random.seed(SEED)

+from lightning_module_template import LightningTemplateModel
+

 def main(hparams):
    """
@@ -90,12 +94,9 @@ if __name__ == '__main__':
    parent_parser = HyperOptArgumentParser(strategy='grid_search', add_help=False)

    # gpu args
-    parent_parser.add_argument('--test_tube_save_path', type=str,
-                               default=test_tube_dir, help='where to save logs')
-    parent_parser.add_argument('--model_save_path', type=str,
-                               default=checkpoint_dir, help='where to save model')
-    parent_parser.add_argument('--experiment_name', type=str,
-                               default='pt_lightning_exp_a', help='test tube exp name')
+    parent_parser.add_argument('--test_tube_save_path', type=str, default=test_tube_dir, help='where to save logs')
+    parent_parser.add_argument('--model_save_path', type=str, default=checkpoint_dir, help='where to save model')
+    parent_parser.add_argument('--experiment_name', type=str, default='pt_lightning_exp_a', help='test tube exp name')

    # allow model to overwrite or extend args
    parser = LightningTemplateModel.add_model_specific_args(parent_parser, root_dir)
@@ -105,5 +106,5 @@ if __name__ == '__main__':
    # RUN TRAINING
    # ---------------------
    # run on HPC cluster
-    print('RUNNING ON CPU')
+    print(f'RUNNING ON CPU')
    main(hyperparams)
@@ -1,20 +1,24 @@
 """
-16-bit single node, CPU example
+Runs a model on a single node across N-gpus.
 """
 import os
+import sys
 import numpy as np
+from time import sleep
 import torch

-from test_tube import HyperOptArgumentParser, Experiment
+from test_tube import HyperOptArgumentParser, Experiment, SlurmCluster
 from pytorch_lightning.models.trainer import Trainer
-from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
+from pytorch_lightning.utils.arg_parse import add_default_args

-from examples.new_project_templates.lightning_module_template import LightningTemplateModel
+from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint

 SEED = 2334
 torch.manual_seed(SEED)
 np.random.seed(SEED)

+from lightning_module_template import LightningTemplateModel
+

 def main(hparams):
    """
@@ -92,15 +96,10 @@ if __name__ == '__main__':
    parent_parser = HyperOptArgumentParser(strategy='grid_search', add_help=False)

    # gpu args
-    parent_parser.add_argument('--gpus', type=str, default='-1',
-                               help='how many gpus to use in the node.'
-                                    'value -1 uses all the gpus on the node')
-    parent_parser.add_argument('--test_tube_save_path', type=str, default=test_tube_dir,
-                               help='where to save logs')
-    parent_parser.add_argument('--model_save_path', type=str, default=checkpoint_dir,
-                               help='where to save model')
-    parent_parser.add_argument('--experiment_name', type=str, default='pt_lightning_exp_a',
-                               help='test tube exp name')
+    parent_parser.add_argument('--gpus', type=str, default='-1', help='how many gpus to use in the node. -1 uses all the gpus on the node')
+    parent_parser.add_argument('--test_tube_save_path', type=str, default=test_tube_dir, help='where to save logs')
+    parent_parser.add_argument('--model_save_path', type=str, default=checkpoint_dir, help='where to save model')
+    parent_parser.add_argument('--experiment_name', type=str, default='pt_lightning_exp_a', help='test tube exp name')

    # allow model to overwrite or extend args
    parser = LightningTemplateModel.add_model_specific_args(parent_parser, root_dir)
@@ -1,20 +1,24 @@
 """
-Runs a model on a single node across N-gpus using dataParallel
+Runs a model on a single node across N-gpus.
 """
 import os
+import sys
 import numpy as np
+from time import sleep
 import torch

-from test_tube import HyperOptArgumentParser, Experiment
+from test_tube import HyperOptArgumentParser, Experiment, SlurmCluster
 from pytorch_lightning.models.trainer import Trainer
-from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
+from pytorch_lightning.utils.arg_parse import add_default_args

-from examples.new_project_templates.lightning_module_template import LightningTemplateModel
+from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint

 SEED = 2334
 torch.manual_seed(SEED)
 np.random.seed(SEED)

+from lightning_module_template import LightningTemplateModel
+

 def main(hparams):
    """
@@ -91,15 +95,10 @@ if __name__ == '__main__':
    parent_parser = HyperOptArgumentParser(strategy='grid_search', add_help=False)

    # gpu args
-    parent_parser.add_argument('--gpus', type=str, default='-1',
-                               help='how many gpus to use in the node.'
-                                    ' value -1 uses all the gpus on the node')
-    parent_parser.add_argument('--test_tube_save_path', type=str, default=test_tube_dir,
-                               help='where to save logs')
-    parent_parser.add_argument('--model_save_path', type=str, default=checkpoint_dir,
-                               help='where to save model')
-    parent_parser.add_argument('--experiment_name', type=str, default='pt_lightning_exp_a',
-                               help='test tube exp name')
+    parent_parser.add_argument('--gpus', type=str, default='-1', help='how many gpus to use in the node. -1 uses all the gpus on the node')
+    parent_parser.add_argument('--test_tube_save_path', type=str, default=test_tube_dir, help='where to save logs')
+    parent_parser.add_argument('--model_save_path', type=str, default=checkpoint_dir, help='where to save model')
+    parent_parser.add_argument('--experiment_name', type=str, default='pt_lightning_exp_a', help='test tube exp name')

    # allow model to overwrite or extend args
    parser = LightningTemplateModel.add_model_specific_args(parent_parser, root_dir)
@@ -2,19 +2,23 @@
 Runs a model on a single node across N-gpus.
 """
 import os
+import sys
 import numpy as np
+from time import sleep
 import torch

-from test_tube import HyperOptArgumentParser, Experiment
+from test_tube import HyperOptArgumentParser, Experiment, SlurmCluster
 from pytorch_lightning.models.trainer import Trainer
-from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
+from pytorch_lightning.utils.arg_parse import add_default_args

-from examples.new_project_templates.lightning_module_template import LightningTemplateModel
+from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint

 SEED = 2334
 torch.manual_seed(SEED)
 np.random.seed(SEED)

+from lightning_module_template import LightningTemplateModel
+

 def main(hparams):
    """
@@ -71,7 +75,6 @@ def main(hparams):
        checkpoint_callback=checkpoint,
        early_stop_callback=early_stop,
        gpus=hparams.gpus,
-        distributed_backend='ddp'
    )

    # ------------------------
@@ -92,15 +95,10 @@ if __name__ == '__main__':
    parent_parser = HyperOptArgumentParser(strategy='grid_search', add_help=False)

    # gpu args
-    parent_parser.add_argument('--gpus', type=str, default='-1',
-                               help='how many gpus to use in the node.'
-                                    ' value -1 uses all the gpus on the node')
-    parent_parser.add_argument('--test_tube_save_path', type=str, default=test_tube_dir,
-                               help='where to save logs')
-    parent_parser.add_argument('--model_save_path', type=str, default=checkpoint_dir,
-                               help='where to save model')
-    parent_parser.add_argument('--experiment_name', type=str, default='pt_lightning_exp_a',
-                               help='test tube exp name')
+    parent_parser.add_argument('--gpus', type=str, default='-1', help='how many gpus to use in the node. -1 uses all the gpus on the node')
+    parent_parser.add_argument('--test_tube_save_path', type=str, default=test_tube_dir, help='where to save logs')
+    parent_parser.add_argument('--model_save_path', type=str, default=checkpoint_dir, help='where to save model')
+    parent_parser.add_argument('--experiment_name', type=str, default='pt_lightning_exp_a', help='test tube exp name')

    # allow model to overwrite or extend args
    parser = LightningTemplateModel.add_model_specific_args(parent_parser, root_dir)
@@ -0,0 +1,112 @@
+"""
+Runs a model on a single node across N-gpus.
+"""
+import os
+import sys
+import numpy as np
+from time import sleep
+import torch
+
+from test_tube import HyperOptArgumentParser, Experiment, SlurmCluster
+from pytorch_lightning.models.trainer import Trainer
+from pytorch_lightning.utils.arg_parse import add_default_args
+
+from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
+
+SEED = 2334
+torch.manual_seed(SEED)
+np.random.seed(SEED)
+
+from lightning_module_template import LightningTemplateModel
+
+
+def main(hparams):
+    """
+    Main training routine specific for this project
+    :param hparams:
+    :return:
+    """
+    # ------------------------
+    # 1 INIT LIGHTNING MODEL
+    # ------------------------
+    print('loading model...')
+    model = LightningTemplateModel(hparams)
+    print('model built')
+
+    # ------------------------
+    # 2 INIT TEST TUBE EXP
+    # ------------------------
+
+    # init experiment
+    exp = Experiment(
+        name=hyperparams.experiment_name,
+        save_dir=hyperparams.test_tube_save_path,
+        autosave=False,
+        description='test demo'
+    )
+
+    exp.argparse(hparams)
+    exp.save()
+
+    # ------------------------
+    # 3 DEFINE CALLBACKS
+    # ------------------------
+    model_save_path = '{}/{}/{}'.format(hparams.model_save_path, exp.name, exp.version)
+    early_stop = EarlyStopping(
+        monitor='val_acc',
+        patience=3,
+        verbose=True,
+        mode='max'
+    )
+
+    checkpoint = ModelCheckpoint(
+        filepath=model_save_path,
+        save_best_only=True,
+        verbose=True,
+        monitor='val_loss',
+        mode='min'
+    )
+
+    # ------------------------
+    # 4 INIT TRAINER
+    # ------------------------
+    trainer = Trainer(
+        experiment=exp,
+        checkpoint_callback=checkpoint,
+        early_stop_callback=early_stop,
+        gpus=hparams.gpus,
+    )
+
+    # ------------------------
+    # 5 START TRAINING
+    # ------------------------
+    trainer.fit(model)
+
+
+if __name__ == '__main__':
+
+    # dirs
+    root_dir = os.path.dirname(os.path.realpath(__file__))
+    demo_log_dir = os.path.join(root_dir, 'pt_lightning_demo_logs')
+    checkpoint_dir = os.path.join(demo_log_dir, 'model_weights')
+    test_tube_dir = os.path.join(demo_log_dir, 'test_tube_data')
+
+    # although we user hyperOptParser, we are using it only as argparse right now
+    parent_parser = HyperOptArgumentParser(strategy='grid_search', add_help=False)
+
+    # gpu args
+    parent_parser.add_argument('--gpus', type=str, default='0', help='how many gpus to use in the node. -1 uses all the gpus on the node')
+    parent_parser.add_argument('--test_tube_save_path', type=str, default=test_tube_dir, help='where to save logs')
+    parent_parser.add_argument('--model_save_path', type=str, default=checkpoint_dir, help='where to save model')
+    parent_parser.add_argument('--experiment_name', type=str, default='pt_lightning_exp_a', help='test tube exp name')
+
+    # allow model to overwrite or extend args
+    parser = LightningTemplateModel.add_model_specific_args(parent_parser, root_dir)
+    hyperparams = parser.parse_args()
+
+    # ---------------------
+    # RUN TRAINING
+    # ---------------------
+    # run on HPC cluster
+    print(f'RUNNING INTERACTIVE MODE ON GPUS. gpu ids: {hyperparams.gpus}')
+    main(hyperparams)
@@ -3,10 +3,9 @@ import sys

 from test_tube import HyperOptArgumentParser, Experiment
 from pytorch_lightning.models.trainer import Trainer
-from pytorch_lightning.utilities.arg_parse import add_default_args
+from pytorch_lightning.utils.arg_parse import add_default_args
 from pytorch_lightning.callbacks.pt_callbacks import EarlyStopping, ModelCheckpoint
-
-from examples.new_project_templates.lightning_module_template import LightningTemplateModel
+from docs.source.examples.example_model import ExampleModel


 def main(hparams):
@@ -29,7 +28,7 @@ def main(hparams):
    exp.save()

    # build model
-    model = LightningTemplateModel(hparams)
+    model = ExampleModel(hparams)

    # callbacks
    early_stop = EarlyStopping(
@@ -67,7 +66,7 @@ if __name__ == '__main__':
    add_default_args(parent_parser, root_dir)

    # allow model to overwrite or extend args
-    parser = LightningTemplateModel.add_model_specific_args(parent_parser)
+    parser = ExampleModel.add_model_specific_args(parent_parser)
    hyperparams = parser.parse_args()

    # train model
@@ -0,0 +1 @@
+from .trainer import Trainer
@@ -6,6 +6,7 @@ from itertools import chain
 import threading
 import torch
 from torch.cuda._utils import _get_device_index
+import pdb


 def _find_tensors(obj):  # pragma: no cover
@@ -63,6 +64,7 @@ class LightningDataParallel(DataParallel):
        outputs = self.parallel_apply(replicas, inputs, kwargs)
        return self.gather(outputs, self.output_device)

+
    def parallel_apply(self, replicas, inputs, kwargs):
        return parallel_apply(replicas, inputs, kwargs, self.device_ids[:len(replicas)])

@@ -1,5 +1,3 @@
-import traceback
-

 def data_loader(fn):
    """
@@ -12,17 +10,8 @@ def data_loader(fn):

    @property
    def _data_loader(self):
-        try:
-            value = getattr(self, attr_name)
-        except AttributeError:
-            try:
-                value = fn(self)  # Lazy evaluation, done only once.
-            except AttributeError as e:
-                # Guard against AttributeError suppression. (Issue #142)
-                traceback.print_exc()
-                error = f'{fn.__name__}: An AttributeError was encountered: ' + str(e)
-                raise RuntimeError(error) from e
-            setattr(self, attr_name, value)  # Memoize evaluation.
-        return value
+        if not hasattr(self, attr_name):
+            setattr(self, attr_name, fn(self))
+        return getattr(self, attr_name)

    return _data_loader
@@ -1,9 +1,10 @@
+import numpy as np
+from torch import nn
+
 """
 Module to describe gradients
 """

-from torch import nn
-

 class GradInformation(nn.Module):

@@ -17,13 +18,12 @@ class GradInformation(nn.Module):
                    total_norm += param_norm ** norm_type
                    norm = param_norm ** (1 / norm_type)

-                    grad = round(norm.data.cpu().numpy().flatten()[0], 3)
-                    results['grad_{}_norm_{}'.format(norm_type, i)] = grad
-                except Exception:
+                    results['grad_{}_norm_{}'.format(norm_type, i)] = round(norm.data.cpu().numpy().flatten()[0], 3)
+                except Exception as e:
                    # this param had no grad
                    pass

        total_norm = total_norm ** (1. / norm_type)
-        grad = round(total_norm.data.cpu().numpy().flatten()[0], 3)
-        results['grad_{}_norm_total'.format(norm_type)] = grad
+        results['grad_{}_norm_total'.format(norm_type)] = round(total_norm.data.cpu().numpy().flatten()[0], 3)
        return results
+
@@ -1,15 +1,6 @@
 import torch

-
 class ModelHooks(torch.nn.Module):
-
-    def on_sanity_check_start(self):
-        """
-        Called before starting validate
-        :return:
-        """
-        pass
-
    def on_batch_start(self, data_batch):
        pass

@@ -51,3 +42,4 @@ class ModelHooks(torch.nn.Module):
        :return:
        """
        pass
+
@@ -1,15 +1,15 @@
-'''
-Generates a summary of a model's layers and dimensionality
-'''
-
-import gc
-
 import torch
+import gc
 import subprocess
 import numpy as np
 import pandas as pd


+'''
+Generates a summary of a model's layers and dimensionality
+'''
+
+
 class ModelSummary(object):

    def __init__(self, model):
@@ -94,7 +94,7 @@ class ModelSummary(object):
        mods = list(self.model.modules())
        sizes = []

-        for i in range(1, len(mods)):
+        for i in range(1,len(mods)):
            m = mods[i]
            p = list(m.parameters())
            modsz = []
@@ -127,7 +127,7 @@ class ModelSummary(object):
        if self.model.example_input_array is not None:
            cols.extend(['In_sizes', 'Out_sizes'])

-        df = pd.DataFrame(np.zeros((len(self.layer_names), len(cols))))
+        df = pd.DataFrame(np.zeros( (len(self.layer_names), len(cols))))
        df.columns = cols

        df['Name'] = self.layer_names
@@ -152,16 +152,16 @@ class ModelSummary(object):
        self.make_summary()


-def print_mem_stack():  # pragma: no cover
+def print_mem_stack(): # pragma: no cover
    for obj in gc.get_objects():
        try:
            if torch.is_tensor(obj) or (hasattr(obj, 'data') and torch.is_tensor(obj.data)):
                print(type(obj), obj.size())
-        except Exception:
+        except Exception as e:
            pass


-def count_mem_items():  # pragma: no cover
+def count_mem_items(): # pragma: no cover
    nb_params = 0
    nb_tensors = 0
    for obj in gc.get_objects():
@@ -172,7 +172,7 @@ def count_mem_items():  # pragma: no cover
                    nb_params += 1
                else:
                    nb_tensors += 1
-        except Exception:
+        except Exception as e:
            pass

    return nb_params, nb_tensors
@@ -196,6 +196,6 @@ def get_gpu_memory_map():
    gpu_memory = [int(x) for x in result.strip().split('\n')]
    gpu_memory_map = {}
    for k, v in zip(range(len(gpu_memory)), gpu_memory):
-        k = 'gpu_%i' % k
+        k = f'gpu_{k}'
        gpu_memory_map[k] = v
    return gpu_memory_map
@@ -1,10 +1,8 @@
+import torch
 import os
 import re
-
-import torch
-
-from pytorch_lightning.pt_overrides.override_data_parallel import (
-    LightningDistributedDataParallel, LightningDataParallel)
+import pdb
+from pytorch_lightning.pt_overrides.override_data_parallel import LightningDistributedDataParallel, LightningDataParallel


 class ModelIO(object):
@@ -46,8 +44,7 @@ class ModelIO(object):
 class TrainerIO(object):

    def __get_model(self):
-        is_dp_module = isinstance(self.model, (LightningDistributedDataParallel,
-                                               LightningDataParallel))
+        is_dp_module = type(self.model) is LightningDistributedDataParallel or type(self.model) is LightningDataParallel
        model = self.model.module if is_dp_module else self.model
        return model

@@ -60,22 +57,6 @@ class TrainerIO(object):
        # do the actual save
        torch.save(checkpoint, filepath)

-    def restore(self, checkpoint_path, on_gpu):
-
-        if on_gpu:
-            checkpoint = torch.load(checkpoint_path)
-        else:
-            checkpoint = torch.load(checkpoint_path, map_location=lambda storage, loc: storage)
-
-        # load training state (affects trainer only)
-        self.restore_training_state(checkpoint)
-
-        # load model state
-        model = self.__get_model()
-
-        # load the state_dict on the model automatically
-        model.load_state_dict(checkpoint['state_dict'])
-
    def dump_checkpoint(self):

        checkpoint = {
@@ -90,20 +71,12 @@ class TrainerIO(object):
            checkpoint['early_stop_callback_wait'] = self.early_stop_callback.wait
            checkpoint['early_stop_callback_patience'] = self.early_stop_callback.patience

-        # save optimizers
        optimizer_states = []
        for i, optimizer in enumerate(self.optimizers):
            optimizer_states.append(optimizer.state_dict())

        checkpoint['optimizer_states'] = optimizer_states

-        # save lr schedulers
-        lr_schedulers = []
-        for i, scheduler in enumerate(self.lr_schedulers):
-            lr_schedulers.append(scheduler.state_dict())
-
-        checkpoint['lr_schedulers'] = lr_schedulers
-
        # add the state_dict from the model
        model = self.__get_model()
        checkpoint['state_dict'] = model.state_dict()
@@ -121,16 +94,13 @@ class TrainerIO(object):
            return

        # allow test tube to handle model check pointing automatically
-        # only if proc 0 so we don't trigger world_size resubmits
-        if self.proc_rank == 0:
-            self.cluster.set_checkpoint_save_function(
-                self.hpc_save,
-                kwargs={
-                    'folderpath': self.checkpoint_callback.filepath,
-                    'experiment': self.experiment
-                }
-            )
-
+        self.cluster.set_checkpoint_save_function(
+            self.hpc_save,
+            kwargs={
+                'folderpath': self.checkpoint_callback.filepath,
+                'experiment': self.experiment
+            }
+        )
        self.cluster.set_checkpoint_load_function(
            self.hpc_load,
            kwargs={
@@ -161,11 +131,6 @@ class TrainerIO(object):
        for optimizer, opt_state in zip(self.optimizers, optimizer_states):
            optimizer.load_state_dict(opt_state)

-        # restore the lr schedulers
-        lr_schedulers = checkpoint['lr_schedulers']
-        for scheduler, lrs_state in zip(self.lr_schedulers, lr_schedulers):
-            scheduler.load_state_dict(lrs_state)
-
    # ----------------------------------
    # PRIVATE OPS
    # ----------------------------------
@@ -216,15 +181,15 @@ class TrainerIO(object):
        # call model hook
        model.on_hpc_load(checkpoint)

-    def max_ckpt_in_folder(self, path, name_key='ckpt_'):
+    def max_ckpt_in_folder(self, path):
        files = os.listdir(path)
-        files = [x for x in files if name_key in x]
+        files = [x for x in files if 'ckpt_' in x]
        if len(files) == 0:
            return 0

        ckpt_vs = []
        for name in files:
-            name = name.split(name_key)[-1]
+            name = name.split('ckpt_')[-1]
            name = re.sub('[^0-9]', '', name)
            ckpt_vs.append(int(name))

@@ -1,5 +1,4 @@
 import torch
-
 from pytorch_lightning.root_module.memory import ModelSummary
 from pytorch_lightning.root_module.grads import GradInformation
 from pytorch_lightning.root_module.model_saving import ModelIO, load_hparams_from_tags_csv
@@ -23,9 +22,6 @@ class LightningModule(GradInformation, ModelIO, ModelHooks):

        # track if gpu was requested for checkpointing
        self.on_gpu = False
-        self.use_dp = False
-        self.use_ddp = False
-        self.use_amp = False

    def forward(self, *args, **kwargs):
        """
@@ -36,59 +32,41 @@ class LightningModule(GradInformation, ModelIO, ModelHooks):
        """
        raise NotImplementedError

-    def training_step(self, *args, **kwargs):
+    def validation_step(self, data_batch, batch_nb):
        """
-        return loss, dict with metrics for tqdm
-        :param called with batch, batch_nb
-        additional: optimizer_i if multiple optimizers used
+        return whatever outputs will need to be aggregated in validation_end
+        :param data_batch:
        :return:
        """
        raise NotImplementedError

-    def validation_step(self, *args, **kwargs):
-        """
-        return whatever outputs will need to be aggregated in validation_end
-        OPTIONAL
-        :param called with batch, batch_nb
-        additional: dataset_i if multiple val datasets used
-        :return:
-        """
-        pass
-
    def validation_end(self, outputs):
        """
        Outputs has the appended output after each validation step
-        OPTIONAL
        :param outputs:
        :return: dic_with_metrics for tqdm
        """
-        pass
+        raise NotImplementedError

-    def configure_optimizers(self):
+    def training_step(self, data_batch, batch_nb):
        """
-        Return a list of optimizers and a list of schedulers (could be empty)
+        return loss, dict with metrics for tqdm
+        :param data_batch:
        :return:
        """
        raise NotImplementedError

-    def optimizer_step(self, epoch_nb, batch_nb, optimizer, optimizer_i):
+    def configure_optimizers(self):
        """
-        Do something instead of the standard optimizer behavior
-        :param epoch_nb:
-        :param batch_nb:
-        :param optimizer:
-        :param optimizer_i:
+        Return array of optimizers
        :return:
        """
-        optimizer.step()
-
-        # clear gradients
-        optimizer.zero_grad()
+        raise NotImplementedError

    @data_loader
    def tng_dataloader(self):
        """
-        Implement a PyTorch DataLoader
+        Implement a function to load an h5py of this data
        :return:
        """
        raise NotImplementedError
@@ -96,18 +74,18 @@ class LightningModule(GradInformation, ModelIO, ModelHooks):
    @data_loader
    def test_dataloader(self):
        """
-        Implement a PyTorch DataLoader
+        Implement a function to load an h5py of this data
        :return:
        """
-        return None
+        raise NotImplementedError

    @data_loader
    def val_dataloader(self):
        """
-        Implement a PyTorch DataLoader
+        Implement a function to load an h5py of this data
        :return:
        """
-        return None
+        raise NotImplementedError

    @classmethod
    def load_from_metrics(cls, weights_path, tags_csv, on_gpu, map_location=None):
@@ -150,3 +128,6 @@ class LightningModule(GradInformation, ModelIO, ModelHooks):
    def unfreeze(self):
        for param in self.parameters():
            param.requires_grad = True
+
+
+
@@ -1,3 +0,0 @@
-from .lm_test_module import LightningTestModel
-from .no_val_end_module import NoValEndTestModel
-from .no_val_module import NoValModel
@@ -1,247 +0,0 @@
-import os
-from collections import OrderedDict
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torch import optim
-from torch.utils.data import DataLoader
-from torch.utils.data.distributed import DistributedSampler
-from torchvision.datasets import MNIST
-from torchvision import transforms
-from test_tube import HyperOptArgumentParser
-
-from pytorch_lightning.root_module.root_module import LightningModule
-from pytorch_lightning import data_loader
-
-
-class NoValEndTestModel(LightningModule):
-    """
-    Sample model to show how to define a template
-    """
-
-    def __init__(self, hparams, force_remove_distributed_sampler=False):
-        """
-        Pass in parsed HyperOptArgumentParser to the model
-        :param hparams:
-        """
-        # init superclass
-        super(NoValEndTestModel, self).__init__()
-        self.hparams = hparams
-
-        self.batch_size = hparams.batch_size
-
-        # if you specify an example input, the summary will show input/output for each layer
-        self.example_input_array = torch.rand(5, 28 * 28)
-
-        # remove to test warning for dist sampler
-        self.force_remove_distributed_sampler = force_remove_distributed_sampler
-
-        # build model
-        self.__build_model()
-
-    # ---------------------
-    # MODEL SETUP
-    # ---------------------
-    def __build_model(self):
-        """
-        Layout model
-        :return:
-        """
-        self.c_d1 = nn.Linear(in_features=self.hparams.in_features,
-                              out_features=self.hparams.hidden_dim)
-        self.c_d1_bn = nn.BatchNorm1d(self.hparams.hidden_dim)
-        self.c_d1_drop = nn.Dropout(self.hparams.drop_prob)
-
-        self.c_d2 = nn.Linear(in_features=self.hparams.hidden_dim,
-                              out_features=self.hparams.out_features)
-
-    # ---------------------
-    # TRAINING
-    # ---------------------
-    def forward(self, x):
-        """
-        No special modification required for lightning, define as you normally would
-        :param x:
-        :return:
-        """
-
-        x = self.c_d1(x)
-        x = torch.tanh(x)
-        x = self.c_d1_bn(x)
-        x = self.c_d1_drop(x)
-
-        x = self.c_d2(x)
-        logits = F.log_softmax(x, dim=1)
-
-        return logits
-
-    def loss(self, labels, logits):
-        nll = F.nll_loss(logits, labels)
-        return nll
-
-    def training_step(self, data_batch, batch_i):
-        """
-        Lightning calls this inside the training loop
-        :param data_batch:
-        :return:
-        """
-        # forward pass
-        x, y = data_batch
-        x = x.view(x.size(0), -1)
-
-        y_hat = self.forward(x)
-
-        # calculate loss
-        loss_val = self.loss(y, y_hat)
-
-        # in DP mode (default) make sure if result is scalar, there's another dim in the beginning
-        if self.trainer.use_dp:
-            loss_val = loss_val.unsqueeze(0)
-
-        # alternate possible outputs to test
-        if self.trainer.batch_nb % 1 == 0:
-            output = OrderedDict({
-                'loss': loss_val,
-                'prog': {'some_val': loss_val * loss_val}
-            })
-            return output
-        if self.trainer.batch_nb % 2 == 0:
-            return loss_val
-
-    def validation_step(self, data_batch, batch_nb):
-        """
-        Lightning calls this inside the validation loop
-        :param data_batch:
-        :return:
-        """
-        x, y = data_batch
-        x = x.view(x.size(0), -1)
-        y_hat = self.forward(x)
-
-        loss_val = self.loss(y, y_hat)
-
-        # acc
-        labels_hat = torch.argmax(y_hat, dim=1)
-        val_acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0)
-        val_acc = torch.tensor(val_acc)
-
-        if self.on_gpu:
-            val_acc = val_acc.cuda(loss_val.device.index)
-
-        # in DP mode (default) make sure if result is scalar, there's another dim in the beginning
-        if self.trainer.use_dp:
-            loss_val = loss_val.unsqueeze(0)
-            val_acc = val_acc.unsqueeze(0)
-
-        # alternate possible outputs to test
-        if batch_nb % 1 == 0:
-            output = OrderedDict({
-                'val_loss': loss_val,
-                'val_acc': val_acc,
-            })
-            return output
-        if batch_nb % 2 == 0:
-            return val_acc
-
-        if batch_nb % 3 == 0:
-            output = OrderedDict({
-                'val_loss': loss_val,
-                'val_acc': val_acc,
-                'test_dic': {'val_loss_a': loss_val}
-            })
-            return output
-
-    def on_tng_metrics(self, logs):
-        logs['some_tensor_to_test'] = torch.rand(1)
-
-    # ---------------------
-    # TRAINING SETUP
-    # ---------------------
-    def configure_optimizers(self):
-        """
-        return whatever optimizers we want here
-        :return: list of optimizers
-        """
-        # try no scheduler for this model (testing purposes)
-        optimizer = optim.Adam(self.parameters(), lr=self.hparams.learning_rate)
-
-        # test returning only 1 list instead of 2
-        return [optimizer]
-
-    def __dataloader(self, train):
-        # init data generators
-        transform = transforms.Compose([transforms.ToTensor(),
-                                        transforms.Normalize((0.5,), (1.0,))])
-        dataset = MNIST(root=self.hparams.data_root, train=train,
-                        transform=transform, download=True)
-
-        # when using multi-node we need to add the datasampler
-        train_sampler = None
-        batch_size = self.hparams.batch_size
-
-        try:
-            if self.use_ddp and not self.force_remove_distributed_sampler:
-                train_sampler = DistributedSampler(dataset, rank=self.trainer.proc_rank)
-                batch_size = batch_size // self.trainer.world_size  # scale batch size
-        except Exception:
-            pass
-
-        should_shuffle = train_sampler is None
-        loader = DataLoader(
-            dataset=dataset,
-            batch_size=batch_size,
-            shuffle=should_shuffle,
-            sampler=train_sampler
-        )
-
-        return loader
-
-    @data_loader
-    def tng_dataloader(self):
-        return self.__dataloader(train=True)
-
-    @data_loader
-    def val_dataloader(self):
-        return self.__dataloader(train=False)
-
-    @data_loader
-    def test_dataloader(self):
-        return self.__dataloader(train=False)
-
-    @staticmethod
-    def add_model_specific_args(parent_parser, root_dir):  # pragma: no cover
-        """
-        Parameters you define here will be available to your model through self.hparams
-        :param parent_parser:
-        :param root_dir:
-        :return:
-        """
-        parser = HyperOptArgumentParser(strategy=parent_parser.strategy, parents=[parent_parser])
-
-        # param overwrites
-        # parser.set_defaults(gradient_clip=5.0)
-
-        # network params
-        parser.opt_list('--drop_prob', default=0.2, options=[0.2, 0.5], type=float, tunable=False)
-        parser.add_argument('--in_features', default=28 * 28, type=int)
-        parser.add_argument('--out_features', default=10, type=int)
-        # use 500 for CPU, 50000 for GPU to see speed difference
-        parser.add_argument('--hidden_dim', default=50000, type=int)
-
-        # data
-        parser.add_argument('--data_root', default=os.path.join(root_dir, 'mnist'), type=str)
-
-        # training params (opt)
-        parser.opt_list('--learning_rate', default=0.001 * 8, type=float,
-                        options=[0.0001, 0.0005, 0.001, 0.005],
-                        tunable=False)
-        parser.opt_list('--optimizer_name', default='adam', type=str,
-                        options=['adam'], tunable=False)
-
-        # if using 2 nodes with 4 gpus each the batch size here
-        #  (256) will be 256 / (2*8) = 16 per gpu
-        parser.opt_list('--batch_size', default=256 * 8, type=int,
-                        options=[32, 64, 128, 256], tunable=False,
-                        help='batch size will be divided over all gpus being used across all nodes')
-        return parser
@@ -1,196 +0,0 @@
-import os
-from collections import OrderedDict
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torch import optim
-from torch.utils.data import DataLoader
-from torch.utils.data.distributed import DistributedSampler
-from torchvision.datasets import MNIST
-from torchvision import transforms
-from test_tube import HyperOptArgumentParser
-
-from pytorch_lightning.root_module.root_module import LightningModule
-from pytorch_lightning import data_loader
-
-
-class NoValModel(LightningModule):
-    """
-    Sample model to show how to define a template
-    """
-
-    def __init__(self, hparams, force_remove_distributed_sampler=False):
-        """
-        Pass in parsed HyperOptArgumentParser to the model
-        :param hparams:
-        """
-        # init superclass
-        super(NoValModel, self).__init__()
-        self.hparams = hparams
-
-        self.batch_size = hparams.batch_size
-
-        # if you specify an example input, the summary will show input/output for each layer
-        self.example_input_array = torch.rand(5, 28 * 28)
-
-        # remove to test warning for dist sampler
-        self.force_remove_distributed_sampler = force_remove_distributed_sampler
-
-        # build model
-        self.__build_model()
-
-    # ---------------------
-    # MODEL SETUP
-    # ---------------------
-    def __build_model(self):
-        """
-        Layout model
-        :return:
-        """
-        self.c_d1 = nn.Linear(in_features=self.hparams.in_features,
-                              out_features=self.hparams.hidden_dim)
-        self.c_d1_bn = nn.BatchNorm1d(self.hparams.hidden_dim)
-        self.c_d1_drop = nn.Dropout(self.hparams.drop_prob)
-
-        self.c_d2 = nn.Linear(in_features=self.hparams.hidden_dim,
-                              out_features=self.hparams.out_features)
-
-    # ---------------------
-    # TRAINING
-    # ---------------------
-    def forward(self, x):
-        """
-        No special modification required for lightning, define as you normally would
-        :param x:
-        :return:
-        """
-
-        x = self.c_d1(x)
-        x = torch.tanh(x)
-        x = self.c_d1_bn(x)
-        x = self.c_d1_drop(x)
-
-        x = self.c_d2(x)
-        logits = F.log_softmax(x, dim=1)
-
-        return logits
-
-    def loss(self, labels, logits):
-        nll = F.nll_loss(logits, labels)
-        return nll
-
-    def training_step(self, data_batch, batch_i):
-        """
-        Lightning calls this inside the training loop
-        :param data_batch:
-        :return:
-        """
-        # forward pass
-        x, y = data_batch
-        x = x.view(x.size(0), -1)
-
-        y_hat = self.forward(x)
-
-        # calculate loss
-        loss_val = self.loss(y, y_hat)
-
-        # in DP mode (default) make sure if result is scalar, there's another dim in the beginning
-        if self.trainer.use_dp:
-            loss_val = loss_val.unsqueeze(0)
-
-        # alternate possible outputs to test
-        if self.trainer.batch_nb % 1 == 0:
-            output = OrderedDict({
-                'loss': loss_val,
-                'prog': {'some_val': loss_val * loss_val}
-            })
-            return output
-        if self.trainer.batch_nb % 2 == 0:
-            return loss_val
-
-    def on_tng_metrics(self, logs):
-        logs['some_tensor_to_test'] = torch.rand(1)
-
-    # ---------------------
-    # TRAINING SETUP
-    # ---------------------
-    def configure_optimizers(self):
-        """
-        return whatever optimizers we want here
-        :return: list of optimizers
-        """
-        # try no scheduler for this model (testing purposes)
-        optimizer = optim.Adam(self.parameters(), lr=self.hparams.learning_rate)
-
-        # test returning only 1 list instead of 2
-        return [optimizer]
-
-    def __dataloader(self, train):
-        # init data generators
-        transform = transforms.Compose([transforms.ToTensor(),
-                                        transforms.Normalize((0.5,), (1.0,))])
-        dataset = MNIST(root=self.hparams.data_root, train=train,
-                        transform=transform, download=True)
-
-        # when using multi-node we need to add the datasampler
-        train_sampler = None
-        batch_size = self.hparams.batch_size
-
-        try:
-            if self.use_ddp and not self.force_remove_distributed_sampler:
-                train_sampler = DistributedSampler(dataset, rank=self.trainer.proc_rank)
-                batch_size = batch_size // self.trainer.world_size  # scale batch size
-        except Exception:
-            pass
-
-        should_shuffle = train_sampler is None
-        loader = DataLoader(
-            dataset=dataset,
-            batch_size=batch_size,
-            shuffle=should_shuffle,
-            sampler=train_sampler
-        )
-
-        return loader
-
-    @data_loader
-    def tng_dataloader(self):
-        return self.__dataloader(train=True)
-
-    @staticmethod
-    def add_model_specific_args(parent_parser, root_dir):  # pragma: no cover
-        """
-        Parameters you define here will be available to your model through self.hparams
-        :param parent_parser:
-        :param root_dir:
-        :return:
-        """
-        parser = HyperOptArgumentParser(strategy=parent_parser.strategy, parents=[parent_parser])
-
-        # param overwrites
-        # parser.set_defaults(gradient_clip=5.0)
-
-        # network params
-        parser.opt_list('--drop_prob', default=0.2, options=[0.2, 0.5], type=float, tunable=False)
-        parser.add_argument('--in_features', default=28 * 28, type=int)
-        parser.add_argument('--out_features', default=10, type=int)
-        # use 500 for CPU, 50000 for GPU to see speed difference
-        parser.add_argument('--hidden_dim', default=50000, type=int)
-
-        # data
-        parser.add_argument('--data_root', default=os.path.join(root_dir, 'mnist'), type=str)
-
-        # training params (opt)
-        parser.opt_list('--learning_rate', default=0.001 * 8, type=float,
-                        options=[0.0001, 0.0005, 0.001, 0.005],
-                        tunable=False)
-        parser.opt_list('--optimizer_name', default='adam', type=str,
-                        options=['adam'], tunable=False)
-
-        # if using 2 nodes with 4 gpus each the batch size here
-        #  (256) will be 256 / (2*8) = 16 per gpu
-        parser.opt_list('--batch_size', default=256 * 8, type=int,
-                        options=[32, 64, 128, 256], tunable=False,
-                        help='batch size will be divided over all gpus being used across all nodes')
-        return parser
@@ -1,18 +1,17 @@
 import os
 from collections import OrderedDict
-
-import torch
 import torch.nn as nn
+from torchvision.datasets import MNIST
+import torchvision.transforms as transforms
+import torch
 import torch.nn.functional as F
+from test_tube import HyperOptArgumentParser
 from torch import optim
 from torch.utils.data import DataLoader
 from torch.utils.data.distributed import DistributedSampler
-from torchvision.datasets import MNIST
-from torchvision import transforms
-from test_tube import HyperOptArgumentParser

 from pytorch_lightning.root_module.root_module import LightningModule
-from pytorch_lightning import data_loader
+import pytorch_lightning as ptl


 class LightningTestModel(LightningModule):
@@ -48,13 +47,11 @@ class LightningTestModel(LightningModule):
        Layout model
        :return:
        """
-        self.c_d1 = nn.Linear(in_features=self.hparams.in_features,
-                              out_features=self.hparams.hidden_dim)
+        self.c_d1 = nn.Linear(in_features=self.hparams.in_features, out_features=self.hparams.hidden_dim)
        self.c_d1_bn = nn.BatchNorm1d(self.hparams.hidden_dim)
        self.c_d1_drop = nn.Dropout(self.hparams.drop_prob)

-        self.c_d2 = nn.Linear(in_features=self.hparams.hidden_dim,
-                              out_features=self.hparams.out_features)
+        self.c_d2 = nn.Linear(in_features=self.hparams.hidden_dim, out_features=self.hparams.out_features)

    # ---------------------
    # TRAINING
@@ -99,17 +96,14 @@ class LightningTestModel(LightningModule):
        if self.trainer.use_dp:
            loss_val = loss_val.unsqueeze(0)

-        # alternate possible outputs to test
-        if self.trainer.batch_nb % 1 == 0:
-            output = OrderedDict({
-                'loss': loss_val,
-                'prog': {'some_val': loss_val * loss_val}
-            })
-            return output
-        if self.trainer.batch_nb % 2 == 0:
-            return loss_val
+        output = OrderedDict({
+            'loss': loss_val
+        })

-    def validation_step(self, data_batch, batch_i, dataloader_i):
+        # can also return just a scalar instead of a dict (return loss_val)
+        return output
+
+    def validation_step(self, data_batch, batch_i):
        """
        Lightning calls this inside the validation loop
        :param data_batch:
@@ -135,28 +129,22 @@ class LightningTestModel(LightningModule):
            val_acc = val_acc.unsqueeze(0)

        # alternate possible outputs to test
-        if batch_i % 1 == 0:
+        if self.trainer.batch_nb % 1 == 0:
            output = OrderedDict({
                'val_loss': loss_val,
                'val_acc': val_acc,
            })
            return output
-        if batch_i % 2 == 0:
+        if self.trainer.batch_nb % 2 == 0:
            return val_acc

-        if batch_i % 3 == 0:
+        if self.trainer.batch_nb % 3 == 0:
            output = OrderedDict({
                'val_loss': loss_val,
                'val_acc': val_acc,
                'test_dic': {'val_loss_a': loss_val}
            })
            return output
-        if batch_i % 5 == 0:
-            output = OrderedDict({
-                f'val_loss_{dataloader_i}': loss_val,
-                f'val_acc_{dataloader_i}': val_acc,
-            })
-            return output

    def validation_end(self, outputs):
        """
@@ -191,28 +179,23 @@ class LightningTestModel(LightningModule):
        return whatever optimizers we want here
        :return: list of optimizers
        """
-        # try no scheduler for this model (testing purposes)
        optimizer = optim.Adam(self.parameters(), lr=self.hparams.learning_rate)
-
-        # test returning only 1 list instead of 2
-        return optimizer
+        return [optimizer]

    def __dataloader(self, train):
        # init data generators
-        transform = transforms.Compose([transforms.ToTensor(),
-                                        transforms.Normalize((0.5,), (1.0,))])
-        dataset = MNIST(root=self.hparams.data_root, train=train,
-                        transform=transform, download=True)
+        transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (1.0,))])
+        dataset = MNIST(root=self.hparams.data_root, train=train, transform=transform, download=True)

        # when using multi-node we need to add the datasampler
        train_sampler = None
        batch_size = self.hparams.batch_size

        try:
-            if self.use_ddp and not self.force_remove_distributed_sampler:
+            if self.on_gpu and not self.force_remove_distributed_sampler:
                train_sampler = DistributedSampler(dataset, rank=self.trainer.proc_rank)
                batch_size = batch_size // self.trainer.world_size  # scale batch size
-        except Exception:
+        except Exception as e:
            pass

        should_shuffle = train_sampler is None
@@ -225,20 +208,20 @@ class LightningTestModel(LightningModule):

        return loader

-    @data_loader
+    @ptl.data_loader
    def tng_dataloader(self):
        return self.__dataloader(train=True)

-    @data_loader
+    @ptl.data_loader
    def val_dataloader(self):
-        return [self.__dataloader(train=False), self.__dataloader(train=False)]
+        return self.__dataloader(train=False)

-    @data_loader
+    @ptl.data_loader
    def test_dataloader(self):
        return self.__dataloader(train=False)

    @staticmethod
-    def add_model_specific_args(parent_parser, root_dir):  # pragma: no cover
+    def add_model_specific_args(parent_parser, root_dir):
        """
        Parameters you define here will be available to your model through self.hparams
        :param parent_parser:
@@ -252,24 +235,19 @@ class LightningTestModel(LightningModule):

        # network params
        parser.opt_list('--drop_prob', default=0.2, options=[0.2, 0.5], type=float, tunable=False)
-        parser.add_argument('--in_features', default=28 * 28, type=int)
+        parser.add_argument('--in_features', default=28*28, type=int)
        parser.add_argument('--out_features', default=10, type=int)
-        # use 500 for CPU, 50000 for GPU to see speed difference
-        parser.add_argument('--hidden_dim', default=50000, type=int)
+        parser.add_argument('--hidden_dim', default=50000, type=int) # use 500 for CPU, 50000 for GPU to see speed difference

        # data
        parser.add_argument('--data_root', default=os.path.join(root_dir, 'mnist'), type=str)

        # training params (opt)
-        parser.opt_list('--learning_rate', default=0.001 * 8, type=float,
-                        options=[0.0001, 0.0005, 0.001, 0.005],
+        parser.opt_list('--learning_rate', default=0.001*8, type=float, options=[0.0001, 0.0005, 0.001, 0.005],
                        tunable=False)
-        parser.opt_list('--optimizer_name', default='adam', type=str,
-                        options=['adam'], tunable=False)
+        parser.opt_list('--optimizer_name', default='adam', type=str, options=['adam'], tunable=False)

-        # if using 2 nodes with 4 gpus each the batch size here
-        #  (256) will be 256 / (2*8) = 16 per gpu
-        parser.opt_list('--batch_size', default=256 * 8, type=int,
-                        options=[32, 64, 128, 256], tunable=False,
-                        help='batch size will be divided over all gpus being used across all nodes')
+        # if using 2 nodes with 4 gpus each the batch size here (256) will be 256 / (2*8) = 16 per gpu
+        parser.opt_list('--batch_size', default=256*8, type=int, options=[32, 64, 128, 256], tunable=False,
+                        help='batch size will be divided over all the gpus being used across all nodes')
        return parser
@@ -0,0 +1,210 @@
+import os
+import sys
+
+import torch
+import numpy as np
+from test_tube import HyperOptArgumentParser, Experiment, SlurmCluster
+from pytorch_lightning.models.trainer import Trainer
+from pytorch_lightning.utils.arg_parse import add_default_args
+from time import sleep
+
+from pytorch_lightning.callbacks.pt_callbacks import EarlyStopping, ModelCheckpoint
+SEED = 2334
+torch.manual_seed(SEED)
+np.random.seed(SEED)
+
+# ---------------------
+# DEFINE MODEL HERE
+# ---------------------
+from pytorch_lightning.models.sample_model_template.model_template import ExampleModel1
+# ---------------------
+
+AVAILABLE_MODELS = {
+    'model_1': ExampleModel1
+}
+
+
+"""
+Allows training by using command line arguments
+
+Run by: 
+# TYPE YOUR RUN COMMAND HERE
+"""
+
+
+def main_local(hparams):
+    main(hparams, None, None)
+
+
+def main(hparams, cluster, results_dict):
+    """
+    Main training routine specific for this project
+    :param hparams:
+    :return:
+    """
+    on_gpu = torch.cuda.is_available()
+    if hparams.disable_cuda:
+        on_gpu = False
+
+    device = 'cuda' if on_gpu else 'cpu'
+    hparams.__setattr__('device', device)
+    hparams.__setattr__('on_gpu', on_gpu)
+    hparams.__setattr__('nb_gpus', torch.cuda.device_count())
+    hparams.__setattr__('inference_mode', hparams.model_load_weights_path is not None)
+
+    # init experiment
+    exp = Experiment(
+        name=hparams.tt_name,
+        debug=hparams.debug,
+        save_dir=hparams.tt_save_path,
+        version=hparams.hpc_exp_number,
+        autosave=False,
+        description=hparams.tt_description
+    )
+
+    exp.argparse(hparams)
+    exp.save()
+
+    # build model
+    print('loading model...')
+    model = TRAINING_MODEL(hparams)
+    print('model built')
+
+    # callbacks
+    early_stop = EarlyStopping(
+        monitor=hparams.early_stop_metric,
+        patience=hparams.early_stop_patience,
+        verbose=True,
+        mode=hparams.early_stop_mode
+    )
+
+    model_save_path = '{}/{}/{}'.format(hparams.model_save_path, exp.name, exp.version)
+    checkpoint = ModelCheckpoint(
+        filepath=model_save_path,
+        save_function=None,
+        save_best_only=True,
+        verbose=True,
+        monitor=hparams.model_save_monitor_value,
+        mode=hparams.model_save_monitor_mode
+    )
+
+    # configure trainer
+    trainer = Trainer(
+        experiment=exp,
+        on_gpu=on_gpu,
+        cluster=cluster,
+        progress_bar=hparams.enable_tqdm,
+        overfit_pct=hparams.overfit,
+        track_grad_norm=hparams.track_grad_norm,
+        fast_dev_run=hparams.fast_dev_run,
+        check_val_every_n_epoch=hparams.check_val_every_n_epoch,
+        accumulate_grad_batches=hparams.accumulate_grad_batches,
+        process_position=process_position,
+        current_gpu_name=current_gpu,
+        checkpoint_callback=checkpoint,
+        early_stop_callback=early_stop,
+        enable_early_stop=hparams.enable_early_stop,
+        max_nb_epochs=hparams.max_nb_epochs,
+        min_nb_epochs=hparams.min_nb_epochs,
+        train_percent_check=hparams.train_percent_check,
+        val_percent_check=hparams.val_percent_check,
+        test_percent_check=hparams.test_percent_check,
+        val_check_interval=hparams.val_check_interval,
+        log_save_interval=hparams.log_save_interval,
+        add_log_row_interval=hparams.add_log_row_interval,
+        lr_scheduler_milestones=hparams.lr_scheduler_milestones
+    )
+
+    # train model
+    trainer.fit(model)
+
+
+def get_default_parser(strategy, root_dir):
+
+    possible_model_names = list(AVAILABLE_MODELS.keys())
+    parser = HyperOptArgumentParser(strategy=strategy, add_help=False)
+    add_default_args(parser, root_dir, possible_model_names, SEED)
+    return parser
+
+
+def get_model_name(args):
+    for i, arg in enumerate(args):
+        if 'model_name' in arg:
+            return args[i+1]
+
+
+def optimize_on_cluster(hyperparams):
+    # enable cluster training
+    cluster = SlurmCluster(
+        hyperparam_optimizer=hyperparams,
+        log_path=hyperparams.tt_save_path,
+        test_tube_exp_name=hyperparams.tt_name
+    )
+
+    # email for cluster coms
+    cluster.notify_job_status(email='add_email_here', on_done=True, on_fail=True)
+
+    # configure cluster
+    cluster.per_experiment_nb_gpus = hyperparams.per_experiment_nb_gpus
+    cluster.job_time = '48:00:00'
+    cluster.gpu_type = '1080ti'
+    cluster.memory_mb_per_node = 48000
+
+    # any modules for code to run in env
+    cluster.add_command('source activate pytorch_lightning')
+
+    # name of exp
+    job_display_name = hyperparams.tt_name.split('_')[0]
+    job_display_name = job_display_name[0:3]
+
+    # run hopt
+    print('submitting jobs...')
+    cluster.optimize_parallel_cluster_gpu(
+        main,
+        nb_trials=hyperparams.nb_hopt_trials,
+        job_name=job_display_name
+    )
+
+
+if __name__ == '__main__':
+
+    model_name = get_model_name(sys.argv)
+
+    # use default args
+    root_dir = os.path.split(os.path.dirname(sys.modules['__main__'].__file__))[0]
+    parent_parser = get_default_parser(strategy='random_search', root_dir=root_dir)
+
+    # allow model to overwrite or extend args
+    TRAINING_MODEL = AVAILABLE_MODELS[model_name]
+    parser = TRAINING_MODEL.add_model_specific_args(parent_parser)
+    parser.json_config('-c', '--config', default=root_dir + '/run_configs/local.json')
+    hyperparams = parser.parse_args()
+
+    # format GPU layout
+    os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
+    gpu_ids = hyperparams.gpus.split(';')
+
+    # RUN TRAINING
+    if hyperparams.on_cluster:
+        print('RUNNING ON SLURM CLUSTER')
+        os.environ["CUDA_VISIBLE_DEVICES"] = ','.join(gpu_ids)
+        optimize_on_cluster(hyperparams)
+
+    elif hyperparams.single_run_gpu:
+        print(f'RUNNING 1 TRIAL ON GPU. gpu: {gpu_ids[0]}')
+        os.environ["CUDA_VISIBLE_DEVICES"] = gpu_ids[0]
+        main(hyperparams, None, None)
+
+    elif hyperparams.local or hyperparams.single_run:
+        os.environ["CUDA_VISIBLE_DEVICES"] = '0'
+        print('RUNNING LOCALLY')
+        main(hyperparams, None, None)
+
+    else:
+        print(f'RUNNING MULTI GPU. GPU ids: {gpu_ids}')
+        hyperparams.optimize_parallel_gpu(
+            main_local,
+            gpu_ids=gpu_ids,
+            nb_trials=hyperparams.nb_hopt_trials,
+            nb_workers=len(gpu_ids)
+        )
@@ -1,48 +1,31 @@
-"""
-List of default args which mught be useful for all the available flags
-Might need to update with the new flags
-"""
-
-import os
-
+import pdb

 def add_default_args(parser, root_dir, rand_seed=None, possible_model_names=None):

    # tng, test, val check intervals
-    parser.add_argument('--eval_test_set', dest='eval_test_set', action='store_true',
-                        help='true = run test set also')
-    parser.add_argument('--check_val_every_n_epoch', default=1, type=int,
-                        help='check val every n epochs')
+    parser.add_argument('--eval_test_set', dest='eval_test_set', action='store_true', help='true = run test set also')
+    parser.add_argument('--check_val_every_n_epoch', default=1, type=int, help='check val every n epochs')
    parser.opt_list('--accumulate_grad_batches', default=1, type=int, tunable=False,
-                    help='accumulates gradients k times before applying update.'
-                         ' Simulates huge batch size')
+                    help='accumulates gradients k times before applying update. Simulates huge batch size')
    parser.add_argument('--max_nb_epochs', default=200, type=int, help='cap epochs')
    parser.add_argument('--min_nb_epochs', default=2, type=int, help='min epochs')
-    parser.add_argument('--train_percent_check', default=1.0, type=float,
-                        help='how much of tng set to check')
-    parser.add_argument('--val_percent_check', default=1.0, type=float,
-                        help='how much of val set to check')
-    parser.add_argument('--test_percent_check', default=1.0, type=float,
-                        help='how much of test set to check')
+    parser.add_argument('--train_percent_check', default=1.0, type=float, help='how much of tng set to check')
+    parser.add_argument('--val_percent_check', default=1.0, type=float, help='how much of val set to check')
+    parser.add_argument('--test_percent_check', default=1.0, type=float, help='how much of test set to check')

-    parser.add_argument('--val_check_interval', default=0.95, type=float,
-                        help='how much within 1 epoch to check val')
-    parser.add_argument('--log_save_interval', default=100, type=int,
-                        help='how many batches between log saves')
-    parser.add_argument('--add_log_row_interval', default=100, type=int,
-                        help='add log every k batches')
+    parser.add_argument('--val_check_interval', default=0.95, type=float, help='how much within 1 epoch to check val')
+    parser.add_argument('--log_save_interval', default=100, type=int, help='how many batches between log saves')
+    parser.add_argument('--add_log_row_interval', default=100, type=int, help='add log every k batches')

    # early stopping
    parser.add_argument('--disable_early_stop', dest='enable_early_stop', action='store_false')
    parser.add_argument('--early_stop_metric', default='val_acc', type=str)
    parser.add_argument('--early_stop_mode', default='min', type=str)
-    parser.add_argument('--early_stop_patience', default=3, type=int,
-                        help='number of epochs until stop')
+    parser.add_argument('--early_stop_patience', default=3, type=int, help='number of epochs until stop')

    # gradient handling
    parser.add_argument('--gradient_clip', default=-1, type=int)
-    parser.add_argument('--track_grad_norm', default=-1, type=int,
-                        help='if > 0, will track this grad norm')
+    parser.add_argument('--track_grad_norm', default=-1, type=int, help='if > 0, will track this grad norm')

    # model saving
    parser.add_argument('--model_save_path', default=root_dir + '/model_weights')
@@ -58,8 +41,7 @@ def add_default_args(parser, root_dir, rand_seed=None, possible_model_names=None
    # test_tube settings
    parser.add_argument('-en', '--tt_name', default='pt_test')
    parser.add_argument('-td', '--tt_description', default='pytorch lightning test')
-    parser.add_argument('--tt_save_path', default=os.path.join(root_dir, 'test_tube_logs'),
-                        help='logging dir')
+    parser.add_argument('--tt_save_path', default=root_dir + '/test_tube_logs', help='logging dir')
    parser.add_argument('--enable_single_run', dest='single_run', action='store_true')
    parser.add_argument('--nb_hopt_trials', default=1, type=int)
    parser.add_argument('--log_stdout', dest='log_stdout', action='store_true')
@@ -70,30 +52,25 @@ def add_default_args(parser, root_dir, rand_seed=None, possible_model_names=None
    parser.add_argument('--default_tensor_type', default='torch.cuda.FloatTensor', type=str)
    parser.add_argument('--use_amp', dest='use_amp', action='store_true')
    parser.add_argument('--check_grad_nans', dest='check_grad_nans', action='store_true')
-    parser.add_argument('--amp_level', default='O2', type=str)
+    parser.add_argument('--amp_level', default='O2',type=str)
+

    # run on hpc
    parser.add_argument('--on_cluster', dest='on_cluster', action='store_true')

    # FAST training
    # use these settings to make sure network has no bugs without running a full dataset
-    parser.add_argument('--fast_dev_run', dest='fast_dev_run', default=False, action='store_true',
-                        help='runs validation after 1 tng step')
-    parser.add_argument('--enable_tqdm', dest='enable_tqdm', default=False, action='store_true',
-                        help='false removes the prog bar')
-    parser.add_argument('--overfit', default=-1, type=float,
-                        help='% of dataset to use with this option. float, or -1 for none')
+    parser.add_argument('--fast_dev_run', dest='fast_dev_run', default=False, action='store_true', help='runs validation after 1 tng step')
+    parser.add_argument('--enable_tqdm', dest='enable_tqdm', default=False, action='store_true', help='false removes the prog bar')
+    parser.add_argument('--overfit', default=-1, type=float, help='% of dataset to use with this option. float, or -1 for none')

    # debug args
    if rand_seed is not None:
        parser.add_argument('--random_seed', default=rand_seed, type=int)

-    parser.add_argument('--interactive', dest='interactive', action='store_true',
-                        help='runs on gpu without cluster')
-    parser.add_argument('--debug', dest='debug', action='store_true',
-                        help='enables/disables test tube')
-    parser.add_argument('--local', dest='local', action='store_true',
-                        help='enables local tng')
+    parser.add_argument('--interactive', dest='interactive', action='store_true', help='runs on gpu without cluster')
+    parser.add_argument('--debug', dest='debug', action='store_true', help='enables/disables test tube')
+    parser.add_argument('--local', dest='local', action='store_true', help='enables local tng')

    # optimizer
-    parser.add_argument('--lr_scheduler_milestones', default=None, type=str)
+    parser.add_argument('--lr_scheduler_milestones', default=None, type=str)
@@ -1,2 +1,5 @@
+import pdb
+import sys
+
 class MisconfigurationException(Exception):
-    pass
+    pass
@@ -1,7 +1,9 @@
+coverage==4.5.3
+mkdocs==1.0.4
+pytest==5.0.1
 scikit-learn==0.20.2
 tqdm==4.32.1
 twine==1.13.0
 numpy==1.16.4
 torch>=1.1.0
-torchvision>=0.3.0
-pandas
+torchvision==0.3.0
@@ -31,8 +31,6 @@ exclude_lines =
    print(traceback.print_exc())
    return *
    raise Exception
-    raise *
-    except *
    warnings
    print
    raise RuntimeError
@@ -44,8 +42,6 @@ omit =
    pytorch_lightning/callbacks/pt_callbacks.py
    tests/test_models.py
    pytorch_lightning/testing_models/lm_test_module.py
-    pytorch_lightning/utilities/arg_parse.py
-    examples/templates

 [flake8]
 ignore = E731,W504,F401,F841
@@ -1,58 +1,29 @@
 #!/usr/bin/env python

-# Always prefer setuptools over distutils
 from setuptools import setup, find_packages

 # https://packaging.python.org/guides/single-sourcing-package-version/

 # http://blog.ionelmc.ro/2014/05/25/python-packaging/
-
-# https://packaging.python.org/discussions/install-requires-vs-requirements /
-# keep the meta-data here for simplicity in reading this file... it's not obvious
-# what happens and to non-engineers they won't know to look in init ...
-# the goal of the project is simplicity for researchers, don't want to add too much
-# engineer specific practices
 setup(
-    name='pytorch-lightning',
-    version='0.4.7',
-    description='The Keras for ML researchers using PyTorch',
-    author='William Falcon',
-    author_email='waf2107@columbia.edu',
-    url='https://github.com/williamFalcon/pytorch-lightning',
-    download_url='https://github.com/williamFalcon/pytorch-lightning',
-    license='Apache-2',
+    name="pytorch-lightning",
+    version='0.3.6.2',
+    description="The Keras for ML researchers using PyTorch",
+    author="William Falcon",
+    author_email="waf2107@columbia.edu",
+    url="https://github.com/williamFalcon/pytorch-lightning",
+    download_url="https://github.com/williamFalcon/pytorch-lightning",
+    license="MIT",
+    keywords=["deep learning", "pytorch", "AI"],
+    python_requires=">=3.5",
+    install_requires=[
+        "torch>=1.1.0",
+        "tqdm",
+        "test-tube>=0.6.7.4",
+    ],
    packages=find_packages(),
-    long_description=open('README.md', encoding='utf-8').read(),
+    long_description=open("README.md", encoding="utf-8").read(),
    long_description_content_type='text/markdown',
    include_package_data=True,
    zip_safe=False,
-    keywords=['deep learning', 'pytorch', 'AI'],
-    python_requires='>=3.6',
-    install_requires=[
-        'torch==1.2.0',
-        'tqdm',
-        'test-tube>=0.6.9',
-        'pandas>=0.20.3',
-    ],
-    classifiers=[
-        'Environment :: Console',
-        'Natural Language :: English',
-        # How mature is this project? Common values are
-        #   3 - Alpha, 4 - Beta, 5 - Production/Stable
-        'Development Status :: 4 - Beta',
-        # Indicate who your project is intended for
-        'Intended Audience :: Developers',
-        'Topic :: Scientific/Engineering :: Artificial Intelligence',
-        'Topic :: Scientific/Engineering :: Image Recognition',
-        'Topic :: Scientific/Engineering :: Information Analysis',
-        # Pick your license as you wish
-        'License :: OSI Approved :: BSD License',
-        'Operating System :: OS Independent',
-        # Specify the Python versions you support here. In particular, ensure
-        # that you indicate whether you support Python 2, Python 3 or both.
-        'Programming Language :: Python :: 3',
-        'Programming Language :: Python :: 3.5',
-        'Programming Language :: Python :: 3.6',
-        'Programming Language :: Python :: 3.7',
-    ],
 )
@@ -1,4 +1,4 @@
-# PyTorch-Lightning Tests
+# Pytorch-Lightning Tests

 ## Running tests
 The automatic travis tests ONLY run CPU-based tests. Although these cover most of the use cases,
@@ -17,7 +17,7 @@ pip install -e .
 pip install -r requirements.txt

 # run tests
-py.test -v
+py.test
 ```

 To test models that require GPU make sure to run the above command on a GPU machine.
@@ -43,21 +43,16 @@ For each set up it also tests:
 5. simulated load from HPC signal.

 ## Running Coverage   
-Make sure to run coverage on a GPU machine with at least 2 GPUs and NVIDIA apex installed. 

 ```bash
 cd pytorch-lightning

 # generate coverage 
 pip install coverage
-coverage run --source pytorch_lightning -m py.test pytorch_lightning tests examples -v --doctest-modules
+coverage run tests/test_models.py   

 # print coverage stats
-coverage report -m
-
-# exporting resulys
-coverage xml
-codecov -t 17327163-8cca-4a5d-86c8-ca5f2ef700bc  -v
+coverage report -m   
 ```


@@ -1,20 +1,24 @@
+import pytest
 from pytorch_lightning import Trainer
-from examples import LightningTemplateModel
+from pytorch_lightning.examples.new_project_templates.lightning_module_template import LightningTemplateModel
 from argparse import Namespace
 from test_tube import Experiment
 from pytorch_lightning.callbacks import ModelCheckpoint
+import numpy as np
+import warnings
+import torch
 import os
 import shutil
+import pdb

-import pytorch_lightning as pl
+import pytorch_lightning as ptl
 import torch
 from torch.nn import functional as F
 from torch.utils.data import DataLoader
 from torchvision.datasets import MNIST
-import numpy as np


-class CoolModel(pl.LightningModule):
+class CoolModel(ptl.LightningModule):

    def __init(self):
        super(CoolModel, self).__init__()
@@ -44,15 +48,15 @@ class CoolModel(pl.LightningModule):
    def configure_optimizers(self):
        return [torch.optim.Adam(self.parameters(), lr=0.02)]

-    @pl.data_loader
+    @ptl.data_loader
    def tng_dataloader(self):
        return DataLoader(MNIST('path/to/save', train=True), batch_size=32)

-    @pl.data_loader
+    @ptl.data_loader
    def val_dataloader(self):
        return DataLoader(MNIST('path/to/save', train=False), batch_size=32)

-    @pl.data_loader
+    @ptl.data_loader
    def test_dataloader(self):
        return DataLoader(MNIST('path/to/save', train=False), batch_size=32)

@@ -62,8 +66,8 @@ def get_model():
    root_dir = os.path.dirname(os.path.realpath(__file__))
    hparams = Namespace(**{'drop_prob': 0.2,
                           'batch_size': 32,
-                           'in_features': 28 * 28,
-                           'learning_rate': 0.001 * 8,
+                           'in_features': 28*28,
+                           'learning_rate': 0.001*8,
                           'optimizer_name': 'adam',
                           'data_root': os.path.join(root_dir, 'mnist'),
                           'out_features': 10,
@@ -108,8 +112,7 @@ def load_model(exp, save_dir):
    checkpoints = [x for x in os.listdir(save_dir) if '.ckpt' in x]
    weights_dir = os.path.join(save_dir, checkpoints[0])

-    trained_model = LightningTemplateModel.load_from_metrics(weights_path=weights_dir,
-                                                             tags_csv=tags_path, on_gpu=True)
+    trained_model = LightningTemplateModel.load_from_metrics(weights_path=weights_dir, tags_csv=tags_path, on_gpu=True)

    assert trained_model is not None, 'loading model failed'

@@ -134,63 +137,44 @@ def run_prediction(dataloader, trained_model):

    print(val_acc)

-    assert val_acc > 0.70, 'this model is expected to get > 0.7 in test set (it got %f)' % val_acc
+    assert val_acc > 0.70, f'this model is expected to get > 0.7 in test set (it got {val_acc})'


-def run_gpu_model_test(trainer_options, model, hparams, on_gpu=True):
+def main():
+
    save_dir = init_save_dir()

    # exp file to get meta
    exp = get_exp(False)
-    exp.argparse(hparams)
    exp.save()

    # exp file to get weights
    checkpoint = ModelCheckpoint(save_dir)

-    # add these to the trainer options
-    trainer_options['checkpoint_callback'] = checkpoint
-    trainer_options['experiment'] = exp
+    trainer = Trainer(
+        experiment=exp,
+        checkpoint_callback=checkpoint,
+        progress_bar=True,
+        max_nb_epochs=1,
+        gpus=[0, 1],
+        distributed_backend='dp',
+    )
+
+    model = CoolModel()

-    # fit model
-    trainer = Trainer(**trainer_options)
    result = trainer.fit(model)

    # correct result and ok accuracy
    assert result == 1, 'amp + ddp model failed to complete'

    # test model loading
-    pretrained_model = load_model(exp, save_dir, on_gpu)
+    pretrained_model = load_model(exp, save_dir)

    # test model preds
    run_prediction(model.test_dataloader, pretrained_model)

-    if trainer.use_ddp:
-        # on hpc this would work fine... but need to hack it for the purpose of the test
-        trainer.model = pretrained_model
-        trainer.optimizers, trainer.lr_schedulers = pretrained_model.configure_optimizers()
-
-    # test HPC loading / saving
-    trainer.hpc_save(save_dir, exp)
-    trainer.hpc_load(save_dir, on_gpu=on_gpu)
-
    clear_save_dir()


-def main():
-
-    os.environ['MASTER_PORT'] = str(np.random.randint(12000, 19000, 1)[0])
-    model, hparams = get_model()
-    trainer_options = dict(
-        max_nb_epochs=1,
-        train_percent_check=0.4,
-        val_percent_check=0.2,
-        gpus=[0, 1],
-        distributed_backend='ddp'
-    )
-
-    run_gpu_model_test(trainer_options, model, hparams)
-
-
 if __name__ == '__main__':
    main()
@@ -1,8 +0,0 @@
-tox
-coverage
-codecov
-pytest>=3.0.5
-pytest-cov
-flake8
-check-manifest
-test_tube
@@ -1,22 +1,20 @@
-import os
-import shutil
-import warnings
-from argparse import Namespace
-
 import pytest
-import numpy as np
-import torch
-from test_tube import Experiment, SlurmCluster
-
-# sys.path += [os.path.abspath('..'), os.path.abspath('../..')]
 from pytorch_lightning import Trainer
-from pytorch_lightning.testing import LightningTestModel, NoValEndTestModel, NoValModel
+from pytorch_lightning.examples.new_project_templates.lightning_module_template import LightningTemplateModel
+from pytorch_lightning.testing_models.lm_test_module import LightningTestModel
+from argparse import Namespace
+from test_tube import Experiment, SlurmCluster
 from pytorch_lightning.callbacks import ModelCheckpoint, EarlyStopping
-from pytorch_lightning.utilities.debugging import MisconfigurationException
+from pytorch_lightning.utils.debugging import MisconfigurationException
 from pytorch_lightning.root_module import memory
 from pytorch_lightning.models.trainer import reduce_distributed_output
 from pytorch_lightning.root_module import model_saving
-from examples import LightningTemplateModel
+import numpy as np
+import warnings
+import torch
+import os
+import shutil
+import pdb

 SEED = 2334
 torch.manual_seed(SEED)
@@ -26,377 +24,6 @@ np.random.seed(SEED)
 # ------------------------------------------------------------------------
 # TESTS
 # ------------------------------------------------------------------------
-def test_multi_gpu_model_ddp():
-    """
-    Make sure DDP works
-    :return:
-    """
-    if not torch.cuda.is_available():
-        warnings.warn('test_multi_gpu_model_ddp cannot run.'
-                      ' Rerun on a GPU node to run this test')
-        return
-    if not torch.cuda.device_count() > 1:
-        warnings.warn('test_multi_gpu_model_ddp cannot run.'
-                      ' Rerun on a node with 2+ GPUs to run this test')
-        return
-
-    os.environ['MASTER_PORT'] = str(np.random.randint(12000, 19000, 1)[0])
-    model, hparams = get_model()
-    trainer_options = dict(
-        show_progress_bar=False,
-        max_nb_epochs=1,
-        train_percent_check=0.4,
-        val_percent_check=0.2,
-        gpus=[0, 1],
-        distributed_backend='ddp'
-    )
-
-    run_gpu_model_test(trainer_options, model, hparams)
-
-
-def test_optimizer_return_options():
-
-    trainer = Trainer()
-    model, hparams = get_model()
-
-    # single optimizer
-    opt_a = torch.optim.Adam(model.parameters(), lr=0.002)
-    opt_b = torch.optim.SGD(model.parameters(), lr=0.002)
-    optim, lr_sched = trainer.init_optimizers(opt_a)
-    assert len(optim) == 1 and len(lr_sched) == 0
-
-    # opt tuple
-    opts = (opt_a, opt_b)
-    optim, lr_sched = trainer.init_optimizers(opts)
-    assert len(optim) == 2 and optim[0] == opts[0] and optim[1] == opts[1]
-    assert len(lr_sched) == 0
-
-    # opt list
-    opts = [opt_a, opt_b]
-    optim, lr_sched = trainer.init_optimizers(opts)
-    assert len(optim) == 2 and optim[0] == opts[0] and optim[1] == opts[1]
-    assert len(lr_sched) == 0
-
-    # opt tuple of lists
-    opts = ([opt_a], ['lr_scheduler'])
-    optim, lr_sched = trainer.init_optimizers(opts)
-    assert len(optim) == 1 and len(lr_sched) == 1
-    assert optim[0] == opts[0][0] and lr_sched[0] == 'lr_scheduler'
-
-
-def test_single_gpu_batch_parse():
-    if not torch.cuda.is_available():
-        warnings.warn('test_amp_gpu_ddp cannot run.'
-                      'Rerun on a GPU node to run this test')
-        return
-    if not torch.cuda.device_count() > 1:
-        warnings.warn('test_amp_gpu_ddp cannot run.'
-                      'Rerun on a node with 2+ GPUs to run this test')
-        return
-
-    trainer = Trainer()
-
-    # batch is just a tensor
-    batch = torch.rand(2, 3)
-    batch = trainer.transfer_batch_to_gpu(batch, 0)
-    assert batch.device.index == 0 and batch.type() == 'torch.cuda.FloatTensor'
-
-    # tensor list
-    batch = [torch.rand(2, 3), torch.rand(2, 3)]
-    batch = trainer.transfer_batch_to_gpu(batch, 0)
-    assert batch[0].device.index == 0 and batch[0].type() == 'torch.cuda.FloatTensor'
-    assert batch[1].device.index == 0 and batch[1].type() == 'torch.cuda.FloatTensor'
-
-    # tensor list of lists
-    batch = [[torch.rand(2, 3), torch.rand(2, 3)]]
-    batch = trainer.transfer_batch_to_gpu(batch, 0)
-    assert batch[0][0].device.index == 0 and batch[0][0].type() == 'torch.cuda.FloatTensor'
-    assert batch[0][1].device.index == 0 and batch[0][1].type() == 'torch.cuda.FloatTensor'
-
-    # tensor dict
-    batch = [{'a': torch.rand(2, 3), 'b': torch.rand(2, 3)}]
-    batch = trainer.transfer_batch_to_gpu(batch, 0)
-    assert batch[0]['a'].device.index == 0 and batch[0]['a'].type() == 'torch.cuda.FloatTensor'
-    assert batch[0]['b'].device.index == 0 and batch[0]['b'].type() == 'torch.cuda.FloatTensor'
-
-    # tuple of tensor list and list of tensor dict
-    batch = ([torch.rand(2, 3) for _ in range(2)],
-             [{'a': torch.rand(2, 3), 'b': torch.rand(2, 3)} for _ in range(2)])
-    batch = trainer.transfer_batch_to_gpu(batch, 0)
-    assert batch[0][0].device.index == 0 and batch[0][0].type() == 'torch.cuda.FloatTensor'
-
-    assert batch[1][0]['a'].device.index == 0
-    assert batch[1][0]['a'].type() == 'torch.cuda.FloatTensor'
-
-    assert batch[1][0]['b'].device.index == 0
-    assert batch[1][0]['b'].type() == 'torch.cuda.FloatTensor'
-
-
-def test_early_stopping_cpu_model():
-    """
-    Test each of the trainer options
-    :return:
-    """
-
-    stopping = EarlyStopping(monitor='val_loss')
-    trainer_options = dict(
-        early_stop_callback=stopping,
-        gradient_clip=1.0,
-        overfit_pct=0.20,
-        track_grad_norm=2,
-        print_nan_grads=True,
-        show_progress_bar=False,
-        experiment=get_exp(),
-        train_percent_check=0.1,
-        val_percent_check=0.1
-    )
-
-    model, hparams = get_model()
-    run_gpu_model_test(trainer_options, model, hparams, on_gpu=False)
-
-    # test freeze on cpu
-    model.freeze()
-    model.unfreeze()
-
-
-def test_no_val_module():
-    """
-    Tests use case where trainer saves the model, and user loads it from tags independently
-    :return:
-    """
-    hparams = get_hparams()
-    model = NoValModel(hparams)
-
-    save_dir = init_save_dir()
-
-    # exp file to get meta
-    exp = get_exp(False)
-    exp.argparse(hparams)
-    exp.save()
-
-    trainer_options = dict(
-        max_nb_epochs=1,
-        cluster=SlurmCluster(),
-        experiment=exp,
-        checkpoint_callback=ModelCheckpoint(save_dir)
-    )
-
-    # fit model
-    trainer = Trainer(**trainer_options)
-    result = trainer.fit(model)
-
-    # traning complete
-    assert result == 1, 'amp + ddp model failed to complete'
-
-    # save model
-    new_weights_path = os.path.join(save_dir, 'save_test.ckpt')
-    trainer.save_checkpoint(new_weights_path)
-
-    # load new model
-    tags_path = exp.get_data_path(exp.name, exp.version)
-    tags_path = os.path.join(tags_path, 'meta_tags.csv')
-    model_2 = LightningTestModel.load_from_metrics(weights_path=new_weights_path,
-                                                   tags_csv=tags_path, on_gpu=False)
-    model_2.eval()
-
-    # make prediction
-    clear_save_dir()
-
-
-def test_no_val_end_module():
-    """
-    Tests use case where trainer saves the model, and user loads it from tags independently
-    :return:
-    """
-    hparams = get_hparams()
-    model = NoValEndTestModel(hparams)
-
-    save_dir = init_save_dir()
-
-    # exp file to get meta
-    exp = get_exp(False)
-    exp.argparse(hparams)
-    exp.save()
-
-    trainer_options = dict(
-        max_nb_epochs=1,
-        cluster=SlurmCluster(),
-        experiment=exp,
-        checkpoint_callback=ModelCheckpoint(save_dir)
-    )
-
-    # fit model
-    trainer = Trainer(**trainer_options)
-    result = trainer.fit(model)
-
-    # traning complete
-    assert result == 1, 'amp + ddp model failed to complete'
-
-    # save model
-    new_weights_path = os.path.join(save_dir, 'save_test.ckpt')
-    trainer.save_checkpoint(new_weights_path)
-
-    # load new model
-    tags_path = exp.get_data_path(exp.name, exp.version)
-    tags_path = os.path.join(tags_path, 'meta_tags.csv')
-    model_2 = LightningTestModel.load_from_metrics(weights_path=new_weights_path,
-                                                   tags_csv=tags_path, on_gpu=False)
-    model_2.eval()
-
-    # make prediction
-    clear_save_dir()
-
-
-def test_simple_cpu():
-    """
-    Verify continue training session on CPU
-    :return:
-    """
-    hparams = get_hparams()
-    model = LightningTestModel(hparams)
-
-    save_dir = init_save_dir()
-
-    # exp file to get meta
-    trainer_options = dict(
-        max_nb_epochs=1,
-        val_percent_check=0.1,
-        train_percent_check=0.1,
-    )
-
-    # fit model
-    trainer = Trainer(**trainer_options)
-    result = trainer.fit(model)
-
-    # traning complete
-    assert result == 1, 'amp + ddp model failed to complete'
-
-    clear_save_dir()
-
-
-def test_amp_single_gpu():
-    """
-    Make sure DDP + AMP work
-    :return:
-    """
-    if not torch.cuda.is_available():
-        warnings.warn('test_amp_gpu_ddp cannot run.'
-                      'Rerun on a GPU node to run this test')
-        return
-    if not torch.cuda.device_count() > 1:
-        warnings.warn('test_amp_gpu_ddp cannot run.'
-                      'Rerun on a node with 2+ GPUs to run this test')
-        return
-
-    hparams = get_hparams()
-    model = LightningTestModel(hparams)
-
-    trainer_options = dict(
-        show_progress_bar=True,
-        max_nb_epochs=1,
-        gpus=[0],
-        distributed_backend='dp',
-        use_amp=True
-    )
-
-    run_gpu_model_test(trainer_options, model, hparams)
-
-
-def test_cpu_restore_training():
-    """
-    Verify continue training session on CPU
-    :return:
-    """
-    hparams = get_hparams()
-    model = LightningTestModel(hparams)
-
-    save_dir = init_save_dir()
-
-    # exp file to get meta
-    test_exp_version = 10
-    exp = get_exp(False, version=test_exp_version)
-    exp.argparse(hparams)
-    exp.save()
-
-    trainer_options = dict(
-        max_nb_epochs=2,
-        val_check_interval=0.50,
-        val_percent_check=0.2,
-        train_percent_check=0.2,
-        experiment=exp,
-        checkpoint_callback=ModelCheckpoint(save_dir)
-    )
-
-    # fit model
-    trainer = Trainer(**trainer_options)
-    result = trainer.fit(model)
-    real_global_epoch = trainer.current_epoch
-
-    # traning complete
-    assert result == 1, 'amp + ddp model failed to complete'
-
-    # wipe-out trainer and model
-    # retrain with not much data... this simulates picking training back up after slurm
-    # we want to see if the weights come back correctly
-    new_exp = get_exp(False, version=test_exp_version)
-    trainer_options = dict(
-        max_nb_epochs=2,
-        val_check_interval=0.50,
-        val_percent_check=0.2,
-        train_percent_check=0.2,
-        experiment=new_exp,
-        checkpoint_callback=ModelCheckpoint(save_dir),
-    )
-    trainer = Trainer(**trainer_options)
-    model = LightningTestModel(hparams)
-
-    # set the epoch start hook so we can predict before the model does the full training
-    def assert_good_acc():
-        assert trainer.current_epoch == real_global_epoch and trainer.current_epoch > 0
-
-        # if model and state loaded correctly, predictions will be good even though we
-        # haven't trained with the new loaded model
-        trainer.model.eval()
-        _ = [run_prediction(dataloader, trainer.model) for dataloader in trainer.val_dataloader]
-
-    model.on_sanity_check_start = assert_good_acc
-
-    # by calling fit again, we trigger training, loading weights from the cluster
-    # and our hook to predict using current model before any more weight updates
-    trainer.fit(model)
-
-    clear_save_dir()
-
-
-def test_amp_gpu_ddp():
-    """
-    Make sure DDP + AMP work
-    :return:
-    """
-    if not torch.cuda.is_available():
-        warnings.warn('test_amp_gpu_ddp cannot run.'
-                      'Rerun on a GPU node to run this test')
-        return
-    if not torch.cuda.device_count() > 1:
-        warnings.warn('test_amp_gpu_ddp cannot run.'
-                      'Rerun on a node with 2+ GPUs to run this test')
-        return
-
-    os.environ['MASTER_PORT'] = str(np.random.randint(12000, 19000, 1)[0])
-
-    hparams = get_hparams()
-    model = LightningTestModel(hparams)
-
-    trainer_options = dict(
-        show_progress_bar=True,
-        max_nb_epochs=1,
-        gpus=[0, 1],
-        distributed_backend='ddp',
-        use_amp=True
-    )
-
-    run_gpu_model_test(trainer_options, model, hparams)
-

 def test_cpu_slurm_save_load():
    """
@@ -451,8 +78,7 @@ def test_cpu_slurm_save_load():
    # wipe-out trainer and model
    # retrain with not much data... this simulates picking training back up after slurm
    # we want to see if the weights come back correctly
-    continue_tng_hparams = get_hparams(continue_training=True,
-                                       hpc_exp_number=cluster_a.hpc_exp_number)
+    continue_tng_hparams = get_hparams(continue_training=True, hpc_exp_number=cluster_a.hpc_exp_number)
    trainer_options = dict(
        max_nb_epochs=1,
        cluster=SlurmCluster(continue_tng_hparams),
@@ -483,9 +109,11 @@ def test_cpu_slurm_save_load():
 def test_loading_meta_tags():
    hparams = get_hparams()

+    save_dir = init_save_dir()
+
    # save tags
    exp = get_exp(False)
-    exp.tag({'some_str': 'a_str', 'an_int': 1, 'a_float': 2.0})
+    exp.tag({'some_str':'a_str', 'an_int': 1, 'a_float': 2.0})
    exp.argparse(hparams)
    exp.save()

@@ -566,8 +194,7 @@ def test_model_saving_loading():
    # load new model
    tags_path = exp.get_data_path(exp.name, exp.version)
    tags_path = os.path.join(tags_path, 'meta_tags.csv')
-    model_2 = LightningTestModel.load_from_metrics(weights_path=new_weights_path,
-                                                   tags_csv=tags_path, on_gpu=False)
+    model_2 = LightningTestModel.load_from_metrics(weights_path=new_weights_path, tags_csv=tags_path, on_gpu=False)
    model_2.eval()

    # make prediction
@@ -578,6 +205,8 @@ def test_model_saving_loading():
    clear_save_dir()


+
+
 def test_model_freeze_unfreeze():
    hparams = get_hparams()
    model = LightningTestModel(hparams)
@@ -592,12 +221,10 @@ def test_amp_gpu_ddp_slurm_managed():
    :return:
    """
    if not torch.cuda.is_available():
-        warnings.warn('test_amp_gpu_ddp cannot run.'
-                      ' Rerun on a GPU node to run this test')
+        warnings.warn('test_amp_gpu_ddp cannot run. Rerun on a GPU node to run this test')
        return
    if not torch.cuda.device_count() > 1:
-        warnings.warn('test_amp_gpu_ddp cannot run.'
-                      ' Rerun on a node with 2+ GPUs to run this test')
+        warnings.warn('test_amp_gpu_ddp cannot run. Rerun on a node with 2+ GPUs to run this test')
        return

    # simulate setting slurm flags
@@ -608,7 +235,7 @@ def test_amp_gpu_ddp_slurm_managed():
    model = LightningTestModel(hparams)

    trainer_options = dict(
-        show_progress_bar=True,
+        progress_bar=True,
        max_nb_epochs=1,
        gpus=[0],
        distributed_backend='ddp',
@@ -653,7 +280,7 @@ def test_amp_gpu_ddp_slurm_managed():
    if trainer.use_ddp:
        # on hpc this would work fine... but need to hack it for the purpose of the test
        trainer.model = pretrained_model
-        trainer.optimizers, trainer.lr_schedulers = pretrained_model.configure_optimizers()
+        trainer.optimizers = pretrained_model.configure_optimizers()

    # test HPC loading / saving
    trainer.hpc_save(save_dir, exp)
@@ -666,6 +293,33 @@ def test_amp_gpu_ddp_slurm_managed():
    clear_save_dir()


+def test_early_stopping_cpu_model():
+    """
+    Test each of the trainer options
+    :return:
+    """
+
+    stopping = EarlyStopping()
+    trainer_options = dict(
+        early_stop_callback=stopping,
+        gradient_clip=1.0,
+        overfit_pct=0.20,
+        track_grad_norm=2,
+        print_nan_grads=True,
+        progress_bar=False,
+        experiment=get_exp(),
+        train_percent_check=0.1,
+        val_percent_check=0.1
+    )
+
+    model, hparams = get_model()
+    run_gpu_model_test(trainer_options, model, hparams, on_gpu=False)
+
+    # test freeze on cpu
+    model.freeze()
+    model.unfreeze()
+
+
 def test_cpu_model_with_amp():
    """
    Make sure model trains on CPU
@@ -673,7 +327,7 @@ def test_cpu_model_with_amp():
    """

    trainer_options = dict(
-        show_progress_bar=False,
+        progress_bar=False,
        experiment=get_exp(),
        max_nb_epochs=1,
        train_percent_check=0.4,
@@ -694,7 +348,7 @@ def test_cpu_model():
    """

    trainer_options = dict(
-        show_progress_bar=False,
+        progress_bar=False,
        experiment=get_exp(),
        max_nb_epochs=1,
        train_percent_check=0.4,
@@ -717,9 +371,8 @@ def test_all_features_cpu_model():
        overfit_pct=0.20,
        track_grad_norm=2,
        print_nan_grads=True,
-        show_progress_bar=False,
+        progress_bar=False,
        experiment=get_exp(),
-        accumulate_grad_batches=2,
        max_nb_epochs=1,
        train_percent_check=0.4,
        val_percent_check=0.4
@@ -735,13 +388,12 @@ def test_single_gpu_model():
    :return:
    """
    if not torch.cuda.is_available():
-        warnings.warn('test_single_gpu_model cannot run.'
-                      ' Rerun on a GPU node to run this test')
+        warnings.warn('test_single_gpu_model cannot run. Rerun on a GPU node to run this test')
        return
    model, hparams = get_model()

    trainer_options = dict(
-        show_progress_bar=False,
+        progress_bar=False,
        max_nb_epochs=1,
        train_percent_check=0.1,
        val_percent_check=0.1,
@@ -757,16 +409,14 @@ def test_multi_gpu_model_dp():
    :return:
    """
    if not torch.cuda.is_available():
-        warnings.warn('test_multi_gpu_model_dp cannot run.'
-                      ' Rerun on a GPU node to run this test')
+        warnings.warn('test_multi_gpu_model_dp cannot run. Rerun on a GPU node to run this test')
        return
    if not torch.cuda.device_count() > 1:
-        warnings.warn('test_multi_gpu_model_dp cannot run.'
-                      ' Rerun on a node with 2+ GPUs to run this test')
+        warnings.warn('test_multi_gpu_model_dp cannot run. Rerun on a node with 2+ GPUs to run this test')
        return
    model, hparams = get_model()
    trainer_options = dict(
-        show_progress_bar=False,
+        progress_bar=False,
        max_nb_epochs=1,
        train_percent_check=0.1,
        val_percent_check=0.1,
@@ -785,12 +435,10 @@ def test_amp_gpu_dp():
    :return:
    """
    if not torch.cuda.is_available():
-        warnings.warn('test_amp_gpu_dp cannot run.'
-                      ' Rerun on a GPU node to run this test')
+        warnings.warn('test_amp_gpu_dp cannot run. Rerun on a GPU node to run this test')
        return
    if not torch.cuda.device_count() > 1:
-        warnings.warn('test_amp_gpu_dp cannot run.'
-                      ' Rerun on a node with 2+ GPUs to run this test')
+        warnings.warn('test_amp_gpu_dp cannot run. Rerun on a node with 2+ GPUs to run this test')
        return
    model, hparams = get_model()
    trainer_options = dict(
@@ -803,6 +451,60 @@ def test_amp_gpu_dp():
        run_gpu_model_test(trainer_options, model, hparams)


+def test_multi_gpu_model_ddp():
+    """
+    Make sure DDP works
+    :return:
+    """
+    if not torch.cuda.is_available():
+        warnings.warn('test_multi_gpu_model_ddp cannot run. Rerun on a GPU node to run this test')
+        return
+    if not torch.cuda.device_count() > 1:
+        warnings.warn('test_multi_gpu_model_ddp cannot run. Rerun on a node with 2+ GPUs to run this test')
+        return
+
+    os.environ['MASTER_PORT'] = str(np.random.randint(12000, 19000, 1)[0])
+    model, hparams = get_model()
+    trainer_options = dict(
+        progress_bar=False,
+        max_nb_epochs=1,
+        train_percent_check=0.4,
+        val_percent_check=0.2,
+        gpus=[0, 1],
+        distributed_backend='ddp'
+    )
+
+    run_gpu_model_test(trainer_options, model, hparams)
+
+
+def test_amp_gpu_ddp():
+    """
+    Make sure DDP + AMP work
+    :return:
+    """
+    if not torch.cuda.is_available():
+        warnings.warn('test_amp_gpu_ddp cannot run. Rerun on a GPU node to run this test')
+        return
+    if not torch.cuda.device_count() > 1:
+        warnings.warn('test_amp_gpu_ddp cannot run. Rerun on a node with 2+ GPUs to run this test')
+        return
+
+    os.environ['MASTER_PORT'] = str(np.random.randint(12000, 19000, 1)[0])
+
+    hparams = get_hparams()
+    model = LightningTestModel(hparams)
+
+    trainer_options = dict(
+        progress_bar=True,
+        max_nb_epochs=1,
+        gpus=[0, 1],
+        distributed_backend='ddp',
+        use_amp=True
+    )
+
+    run_gpu_model_test(trainer_options, model, hparams)
+
+
 def test_ddp_sampler_error():
    """
    Make sure DDP + AMP work
@@ -825,50 +527,19 @@ def test_ddp_sampler_error():

    trainer = Trainer(
        experiment=exp,
-        show_progress_bar=False,
+        progress_bar=False,
        max_nb_epochs=1,
        gpus=[0, 1],
        distributed_backend='ddp',
        use_amp=True
    )

-    with pytest.warns(UserWarning):
+    with pytest.raises(MisconfigurationException):
        trainer.get_dataloaders(model)

    clear_save_dir()


-def test_multiple_val_dataloader():
-    """
-    Verify multiple val_dataloader
-    :return:
-    """
-    hparams = get_hparams()
-    model = LightningTestModel(hparams)
-
-    save_dir = init_save_dir()
-
-    # exp file to get meta
-    trainer_options = dict(
-        max_nb_epochs=1,
-        val_percent_check=0.1,
-        train_percent_check=0.1,
-    )
-
-    # fit model
-    trainer = Trainer(**trainer_options)
-    result = trainer.fit(model)
-
-    # verify tng completed
-    assert result == 1
-
-    # verify there are 2 val loaders
-    assert len(trainer.val_dataloader) == 2, 'Multiple val_dataloaders not initiated properly'
-
-    # make sure predictions are good for each val set
-    [run_prediction(dataloader, trainer.model) for dataloader in trainer.val_dataloader]
-
-
 # ------------------------------------------------------------------------
 # UTILS
 # ------------------------------------------------------------------------
@@ -903,7 +574,7 @@ def run_gpu_model_test(trainer_options, model, hparams, on_gpu=True):
    if trainer.use_ddp:
        # on hpc this would work fine... but need to hack it for the purpose of the test
        trainer.model = pretrained_model
-        trainer.optimizers, trainer.lr_schedulers = pretrained_model.configure_optimizers()
+        trainer.optimizers = pretrained_model.configure_optimizers()

    # test HPC loading / saving
    trainer.hpc_save(save_dir, exp)
@@ -918,8 +589,8 @@ def get_hparams(continue_training=False, hpc_exp_number=0):
    args = {
        'drop_prob': 0.2,
        'batch_size': 32,
-        'in_features': 28 * 28,
-        'learning_rate': 0.001 * 8,
+        'in_features': 28*28,
+        'learning_rate': 0.001*8,
        'optimizer_name': 'adam',
        'data_root': os.path.join(root_dir, 'mnist'),
        'out_features': 10,
@@ -933,22 +604,18 @@ def get_hparams(continue_training=False, hpc_exp_number=0):
    return hparams


-def get_model(use_test_model=False):
+def get_model():
    # set up model with these hyperparams
    hparams = get_hparams()
-
-    if use_test_model:
-        model = LightningTestModel(hparams)
-    else:
-        model = LightningTemplateModel(hparams)
+    model = LightningTemplateModel(hparams)

    return model, hparams


-def get_exp(debug=True, version=None):
+def get_exp(debug=True):
    # set up exp object without actually saving logs
    root_dir = os.path.dirname(os.path.realpath(__file__))
-    exp = Experiment(debug=debug, save_dir=root_dir, name='tests_tt_dir', version=version)
+    exp = Experiment(debug=debug, save_dir=root_dir, name='tests_tt_dir')
    return exp


@@ -1008,13 +675,13 @@ def run_prediction(dataloader, trained_model):

    print(val_acc)

-    assert val_acc > 0.50, 'this model is expected to get > 0.50 in test set (it got %f)' % val_acc
+    assert val_acc > 0.50, f'this model is expected to get > 0.50 in test set (it got {val_acc})'


 def assert_ok_acc(trainer):
    # this model should get 0.80+ acc
    acc = trainer.tng_tqdm_dic['val_acc']
-    assert acc > 0.50, 'model failed to get expected 0.50 validation accuracy. Got: %f' % acc
+    assert acc > 0.50, f'model failed to get expected 0.50 validation accuracy. Got: {acc}'


 if __name__ == '__main__':
@@ -1,47 +0,0 @@
-# this file is *not* meant to cover or endorse the use of tox or pytest or testing in general,
-#
-#  It's meant to show the use of:
-#
-#  - check-manifest
-#     confirm items checked into vcs are in your segdist
-#  - python setup.py check 
-#     confirm required package meta-data in setup.py
-#  - readme_renderer (when using a ReStructuredText README)
-#     confirms your long_description will render correctly on PyPI.
-#
-#  and also to help confirm pull requests to this project.
-
-[tox]
-envlist = py{35,36,37}
-
-[pytest]
-log_cli = 0
-log_cli_level = CRITICAL
-log_cli_format = %(message)s
-log_file = pytest.log
-log_file_level = DEBUG
-log_file_format = %(asctime)s [%(levelname)8s] %(message)s (%(filename)s:%(lineno)s)
-log_file_date_format=%Y-%m-%d %H:%M:%S
-
-[testenv]
-basepython =
-    py35: python3.5
-    py36: python3.6
-    py37: python3.7
-deps =
-    -r requirements.txt
-    -r ./tests/requirements.txt
-commands =
-    check-manifest --ignore tox.ini
-    python setup.py check -m -s
-    coverage run --source pytorch_lightning -m py.test pytorch_lightning tests examples -v --doctest-modules
-    flake8 .
-
-[flake8]
-exclude = .tox,*.egg,build,temp,examples/templates
-select = E,W,F
-doctests = True
-verbose = 2
-# https://pep8.readthedocs.io/en/latest/intro.html#error-codes
-format = pylint
-max-line-length = 100
@@ -11,7 +11,10 @@ rm -rf ./dist/*
 python3 setup.py sdist
 twine upload dist/*

+
+
 # to update docs
 # cd to root dir
 # mkdocs gh-deploy

+