diff --git a/.github/workflows/ci-testing.yml b/.github/workflows/ci-testing.yml
deleted file mode 100644
index e3da50ce74b6c60bd40bd81ae146ec810400167e..0000000000000000000000000000000000000000
--- a/.github/workflows/ci-testing.yml
+++ /dev/null
@@ -1,70 +0,0 @@
-name: CI testing
-
-# see: https://help.github.com/en/actions/reference/events-that-trigger-workflows
-on:
-  # Trigger the workflow on push or pull request, but only for the master branch
-  push:
-    branches:
-      - master
-  pull_request:
-    branches:
-      - master
-
-jobs:
-  pytest:
-
-    runs-on: ${{ matrix.os }}
-    strategy:
-      fail-fast: false
-      matrix:
-        os: [ubuntu-20.04, macOS-10.15, windows-2019]
-        python-version: [3.7]
-
-    # Timeout: https://stackoverflow.com/a/59076067/4521646
-    timeout-minutes: 35
-
-    steps:
-    - uses: actions/checkout@v2
-    - name: Set up Python ${{ matrix.python-version }}
-      uses: actions/setup-python@v2
-      with:
-        python-version: ${{ matrix.python-version }}
-
-    # Github Actions: Run step on specific OS: https://stackoverflow.com/a/57948488/4521646
-    - name: Setup macOS
-      if: runner.os == 'macOS'
-      run: |
-        brew install libomp  # https://github.com/pytorch/pytorch/issues/20030
-
-    # Note: This uses an internal pip API and may not always work
-    # https://github.com/actions/cache/blob/master/examples.md#multiple-oss-in-a-workflow
-    - name: Get pip cache
-      id: pip-cache
-      run: |
-        python -c "from pip._internal.locations import USER_CACHE_DIR; print('::set-output name=dir::' + USER_CACHE_DIR)"
-
-    - name: Cache pip
-      uses: actions/cache@v2
-      with:
-        path: ${{ steps.pip-cache.outputs.dir }}
-        key: ${{ runner.os }}-py${{ matrix.python-version }}-${{ hashFiles('requirements.txt') }}
-        restore-keys: |
-          ${{ runner.os }}-py${{ matrix.python-version }}-
-
-    - name: Install dependencies
-      run: |
-        pip install --requirement requirements.txt --upgrade --quiet --find-links https://download.pytorch.org/whl/cpu/torch_stable.html --use-feature=2020-resolver
-        pip install --requirement tests/requirements.txt --quiet --use-feature=2020-resolver
-        python --version
-        pip --version
-        pip list
-      shell: bash
-
-    - name: Tests
-      run: |
-        coverage run --source project -m py.test project tests -v --junitxml=junit/test-results-${{ runner.os }}-${{ matrix.python-version }}.xml
-
-    - name: Statistics
-      if: success()
-      run: |
-        coverage report
diff --git a/.gitignore b/.gitignore
deleted file mode 100644
index 06f9346cfb2f8d40319409b924a8124275a6f00d..0000000000000000000000000000000000000000
--- a/.gitignore
+++ /dev/null
@@ -1,129 +0,0 @@
-# Byte-compiled / optimized / DLL files
-__pycache__/
-*.py[cod]
-*$py.class
-.github
-
-# C extensions
-*.so
-
-# Distribution / packaging
-.Python
-build/
-develop-eggs/
-dist/
-downloads/
-eggs/
-.eggs/
-lib/
-lib64/
-parts/
-sdist/
-var/
-wheels/
-*.egg-info/
-.installed.cfg
-*.egg
-MANIFEST
-
-# Lightning /research  
-test_tube_exp/
-tests/tests_tt_dir/
-tests/save_dir
-default/   
-data/
-test_tube_logs/
-test_tube_data/
-datasets/
-model_weights/
-tests/save_dir  
-tests/tests_tt_dir/
-processed/
-raw/
-
-# PyInstaller
-#  Usually these files are written by a python script from a template
-#  before PyInstaller builds the exe, so as to inject date/other infos into it.
-*.manifest
-*.spec
-
-# Installer logs
-pip-log.txt
-pip-delete-this-directory.txt
-
-# Unit test / coverage reports
-htmlcov/
-.tox/
-.coverage
-.coverage.*
-.cache
-nosetests.xml
-coverage.xml
-*.cover
-.hypothesis/
-.pytest_cache/
-
-# Translations
-*.mo
-*.pot
-
-# Django stuff:
-*.log
-local_settings.py
-db.sqlite3
-
-# Flask stuff:
-instance/
-.webassets-cache
-
-# Scrapy stuff:
-.scrapy
-
-# Sphinx documentation
-docs/_build/
-
-# PyBuilder
-target/
-
-# Jupyter Notebook
-.ipynb_checkpoints
-
-# pyenv
-.python-version
-
-# celery beat schedule file
-celerybeat-schedule
-
-# SageMath parsed files
-*.sage.py
-
-# Environments
-.env
-.venv
-env/
-venv/
-ENV/
-env.bak/
-venv.bak/
-
-# Spyder project settings
-.spyderproject
-.spyproject
-
-# Rope project settings
-.ropeproject
-
-# mkdocs documentation
-/site
-
-# mypy
-.mypy_cache/
-
-# IDEs
-.idea
-.vscode
-
-# seed project
-lightning_logs/
-MNIST
-.DS_Store
diff --git a/LICENSE b/LICENSE
deleted file mode 100644
index 261eeb9e9f8b2b4b0d119366dda99c6fd7d35c64..0000000000000000000000000000000000000000
--- a/LICENSE
+++ /dev/null
@@ -1,201 +0,0 @@
-                                 Apache License
-                           Version 2.0, January 2004
-                        http://www.apache.org/licenses/
-
-   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
-
-   1. Definitions.
-
-      "License" shall mean the terms and conditions for use, reproduction,
-      and distribution as defined by Sections 1 through 9 of this document.
-
-      "Licensor" shall mean the copyright owner or entity authorized by
-      the copyright owner that is granting the License.
-
-      "Legal Entity" shall mean the union of the acting entity and all
-      other entities that control, are controlled by, or are under common
-      control with that entity. For the purposes of this definition,
-      "control" means (i) the power, direct or indirect, to cause the
-      direction or management of such entity, whether by contract or
-      otherwise, or (ii) ownership of fifty percent (50%) or more of the
-      outstanding shares, or (iii) beneficial ownership of such entity.
-
-      "You" (or "Your") shall mean an individual or Legal Entity
-      exercising permissions granted by this License.
-
-      "Source" form shall mean the preferred form for making modifications,
-      including but not limited to software source code, documentation
-      source, and configuration files.
-
-      "Object" form shall mean any form resulting from mechanical
-      transformation or translation of a Source form, including but
-      not limited to compiled object code, generated documentation,
-      and conversions to other media types.
-
-      "Work" shall mean the work of authorship, whether in Source or
-      Object form, made available under the License, as indicated by a
-      copyright notice that is included in or attached to the work
-      (an example is provided in the Appendix below).
-
-      "Derivative Works" shall mean any work, whether in Source or Object
-      form, that is based on (or derived from) the Work and for which the
-      editorial revisions, annotations, elaborations, or other modifications
-      represent, as a whole, an original work of authorship. For the purposes
-      of this License, Derivative Works shall not include works that remain
-      separable from, or merely link (or bind by name) to the interfaces of,
-      the Work and Derivative Works thereof.
-
-      "Contribution" shall mean any work of authorship, including
-      the original version of the Work and any modifications or additions
-      to that Work or Derivative Works thereof, that is intentionally
-      submitted to Licensor for inclusion in the Work by the copyright owner
-      or by an individual or Legal Entity authorized to submit on behalf of
-      the copyright owner. For the purposes of this definition, "submitted"
-      means any form of electronic, verbal, or written communication sent
-      to the Licensor or its representatives, including but not limited to
-      communication on electronic mailing lists, source code control systems,
-      and issue tracking systems that are managed by, or on behalf of, the
-      Licensor for the purpose of discussing and improving the Work, but
-      excluding communication that is conspicuously marked or otherwise
-      designated in writing by the copyright owner as "Not a Contribution."
-
-      "Contributor" shall mean Licensor and any individual or Legal Entity
-      on behalf of whom a Contribution has been received by Licensor and
-      subsequently incorporated within the Work.
-
-   2. Grant of Copyright License. Subject to the terms and conditions of
-      this License, each Contributor hereby grants to You a perpetual,
-      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
-      copyright license to reproduce, prepare Derivative Works of,
-      publicly display, publicly perform, sublicense, and distribute the
-      Work and such Derivative Works in Source or Object form.
-
-   3. Grant of Patent License. Subject to the terms and conditions of
-      this License, each Contributor hereby grants to You a perpetual,
-      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
-      (except as stated in this section) patent license to make, have made,
-      use, offer to sell, sell, import, and otherwise transfer the Work,
-      where such license applies only to those patent claims licensable
-      by such Contributor that are necessarily infringed by their
-      Contribution(s) alone or by combination of their Contribution(s)
-      with the Work to which such Contribution(s) was submitted. If You
-      institute patent litigation against any entity (including a
-      cross-claim or counterclaim in a lawsuit) alleging that the Work
-      or a Contribution incorporated within the Work constitutes direct
-      or contributory patent infringement, then any patent licenses
-      granted to You under this License for that Work shall terminate
-      as of the date such litigation is filed.
-
-   4. Redistribution. You may reproduce and distribute copies of the
-      Work or Derivative Works thereof in any medium, with or without
-      modifications, and in Source or Object form, provided that You
-      meet the following conditions:
-
-      (a) You must give any other recipients of the Work or
-          Derivative Works a copy of this License; and
-
-      (b) You must cause any modified files to carry prominent notices
-          stating that You changed the files; and
-
-      (c) You must retain, in the Source form of any Derivative Works
-          that You distribute, all copyright, patent, trademark, and
-          attribution notices from the Source form of the Work,
-          excluding those notices that do not pertain to any part of
-          the Derivative Works; and
-
-      (d) If the Work includes a "NOTICE" text file as part of its
-          distribution, then any Derivative Works that You distribute must
-          include a readable copy of the attribution notices contained
-          within such NOTICE file, excluding those notices that do not
-          pertain to any part of the Derivative Works, in at least one
-          of the following places: within a NOTICE text file distributed
-          as part of the Derivative Works; within the Source form or
-          documentation, if provided along with the Derivative Works; or,
-          within a display generated by the Derivative Works, if and
-          wherever such third-party notices normally appear. The contents
-          of the NOTICE file are for informational purposes only and
-          do not modify the License. You may add Your own attribution
-          notices within Derivative Works that You distribute, alongside
-          or as an addendum to the NOTICE text from the Work, provided
-          that such additional attribution notices cannot be construed
-          as modifying the License.
-
-      You may add Your own copyright statement to Your modifications and
-      may provide additional or different license terms and conditions
-      for use, reproduction, or distribution of Your modifications, or
-      for any such Derivative Works as a whole, provided Your use,
-      reproduction, and distribution of the Work otherwise complies with
-      the conditions stated in this License.
-
-   5. Submission of Contributions. Unless You explicitly state otherwise,
-      any Contribution intentionally submitted for inclusion in the Work
-      by You to the Licensor shall be under the terms and conditions of
-      this License, without any additional terms or conditions.
-      Notwithstanding the above, nothing herein shall supersede or modify
-      the terms of any separate license agreement you may have executed
-      with Licensor regarding such Contributions.
-
-   6. Trademarks. This License does not grant permission to use the trade
-      names, trademarks, service marks, or product names of the Licensor,
-      except as required for reasonable and customary use in describing the
-      origin of the Work and reproducing the content of the NOTICE file.
-
-   7. Disclaimer of Warranty. Unless required by applicable law or
-      agreed to in writing, Licensor provides the Work (and each
-      Contributor provides its Contributions) on an "AS IS" BASIS,
-      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
-      implied, including, without limitation, any warranties or conditions
-      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
-      PARTICULAR PURPOSE. You are solely responsible for determining the
-      appropriateness of using or redistributing the Work and assume any
-      risks associated with Your exercise of permissions under this License.
-
-   8. Limitation of Liability. In no event and under no legal theory,
-      whether in tort (including negligence), contract, or otherwise,
-      unless required by applicable law (such as deliberate and grossly
-      negligent acts) or agreed to in writing, shall any Contributor be
-      liable to You for damages, including any direct, indirect, special,
-      incidental, or consequential damages of any character arising as a
-      result of this License or out of the use or inability to use the
-      Work (including but not limited to damages for loss of goodwill,
-      work stoppage, computer failure or malfunction, or any and all
-      other commercial damages or losses), even if such Contributor
-      has been advised of the possibility of such damages.
-
-   9. Accepting Warranty or Additional Liability. While redistributing
-      the Work or Derivative Works thereof, You may choose to offer,
-      and charge a fee for, acceptance of support, warranty, indemnity,
-      or other liability obligations and/or rights consistent with this
-      License. However, in accepting such obligations, You may act only
-      on Your own behalf and on Your sole responsibility, not on behalf
-      of any other Contributor, and only if You agree to indemnify,
-      defend, and hold each Contributor harmless for any liability
-      incurred by, or claims asserted against, such Contributor by reason
-      of your accepting any such warranty or additional liability.
-
-   END OF TERMS AND CONDITIONS
-
-   APPENDIX: How to apply the Apache License to your work.
-
-      To apply the Apache License to your work, attach the following
-      boilerplate notice, with the fields enclosed by brackets "[]"
-      replaced with your own identifying information. (Don't include
-      the brackets!)  The text should be enclosed in the appropriate
-      comment syntax for the file format. We also recommend that a
-      file or class name and description of purpose be included on the
-      same "printed page" as the copyright notice for easier
-      identification within third-party archives.
-
-   Copyright [yyyy] [name of copyright owner]
-
-   Licensed under the Apache License, Version 2.0 (the "License");
-   you may not use this file except in compliance with the License.
-   You may obtain a copy of the License at
-
-       http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing, software
-   distributed under the License is distributed on an "AS IS" BASIS,
-   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   See the License for the specific language governing permissions and
-   limitations under the License.
diff --git a/PG10-DL_for_IP.pdf b/PG10-DL_for_IP.pdf
deleted file mode 100644
index b364a49ede43f2bad65a2fe97851ccd8ea1f40a6..0000000000000000000000000000000000000000
Binary files a/PG10-DL_for_IP.pdf and /dev/null differ
diff --git a/README.md b/README.md
deleted file mode 100644
index 63571ba176703b654531f40001dc5e98889ce98e..0000000000000000000000000000000000000000
--- a/README.md
+++ /dev/null
@@ -1,89 +0,0 @@
-### Deep learning project seed
-Use this seed to start new deep learning / ML projects.
-
-- Built in setup.py
-- Built in requirements
-- Examples with MNIST
-- Badges
-- Bibtex
-
-#### Goals  
-The goal of this seed is to structure ML paper-code the same so that work can easily be extended and replicated.   
-
-### DELETE EVERYTHING ABOVE FOR YOUR PROJECT  
- 
----
-
-<div align="center">    
- 
-# Your Project Name     
-
-[![Paper](http://img.shields.io/badge/paper-arxiv.1001.2234-B31B1B.svg)](https://www.nature.com/articles/nature14539)
-[![Conference](http://img.shields.io/badge/NeurIPS-2019-4b44ce.svg)](https://papers.nips.cc/book/advances-in-neural-information-processing-systems-31-2018)
-[![Conference](http://img.shields.io/badge/ICLR-2019-4b44ce.svg)](https://papers.nips.cc/book/advances-in-neural-information-processing-systems-31-2018)
-[![Conference](http://img.shields.io/badge/AnyConference-year-4b44ce.svg)](https://papers.nips.cc/book/advances-in-neural-information-processing-systems-31-2018)  
-<!--
-ARXIV   
-[![Paper](http://img.shields.io/badge/arxiv-math.co:1480.1111-B31B1B.svg)](https://www.nature.com/articles/nature14539)
--->
-![CI testing](https://github.com/PyTorchLightning/deep-learning-project-template/workflows/CI%20testing/badge.svg?branch=master&event=push)
-
-
-<!--  
-Conference   
--->   
-</div>
- 
-## Description   
-What it does   
-
-## How to run   
-First, install dependencies   
-```bash
-# clone project   
-git clone https://github.com/YourGithubName/deep-learning-project-template
-
-# install project   
-cd deep-learning-project-template 
-pip install -e .   
-pip install -r requirements.txt
- ```   
- Next, navigate to any file and run it.   
- ```bash
-# module folder
-cd project
-
-# run module (example: mnist as your main contribution)   
-python lit_classifier_main.py    
-```
-
-## Imports
-This project is setup as a package which means you can now easily import any file into any other file like so:
-```python
-from project.datasets.mnist import mnist
-from project.lit_classifier_main import LitClassifier
-from pytorch_lightning import Trainer
-
-# model
-model = LitClassifier()
-
-# data
-train, val, test = mnist()
-
-# train
-trainer = Trainer()
-trainer.fit(model, train, val)
-
-# test using the best model!
-trainer.test(test_dataloaders=test)
-```
-
-### Citation   
-```
-@article{YourName,
-  title={Your Title},
-  author={Your team},
-  journal={Location},
-  year={Year}
-}
-```   
diff --git a/project/__init__.py b/project/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/project/lit_autoencoder.py b/project/lit_autoencoder.py
deleted file mode 100644
index 3f9ff0c6c9994d0561b464c3303bd2faa614f445..0000000000000000000000000000000000000000
--- a/project/lit_autoencoder.py
+++ /dev/null
@@ -1,88 +0,0 @@
-from argparse import ArgumentParser
-import torch
-from torch import nn
-import torch.nn.functional as F
-from torch.utils.data import DataLoader
-import pytorch_lightning as pl
-from torch.utils.data import random_split
-
-from torchvision.datasets.mnist import MNIST
-from torchvision import transforms
-
-
-class LitAutoEncoder(pl.LightningModule):
-
-    def __init__(self):
-        super().__init__()
-        self.encoder = nn.Sequential(
-            nn.Linear(28 * 28, 64),
-            nn.ReLU(),
-            nn.Linear(64, 3)
-        )
-        self.decoder = nn.Sequential(
-            nn.Linear(3, 64),
-            nn.ReLU(),
-            nn.Linear(64, 28 * 28)
-        )
-
-    def forward(self, x):
-        # in lightning, forward defines the prediction/inference actions
-        embedding = self.encoder(x)
-        return embedding
-
-    def training_step(self, batch, batch_idx):
-        x, y = batch
-        x = x.view(x.size(0), -1)
-        z = self.encoder(x)
-        x_hat = self.decoder(z)
-        loss = F.mse_loss(x_hat, x)
-        return loss
-
-    def configure_optimizers(self):
-        optimizer = torch.optim.Adam(self.parameters(), lr=1e-3)
-        return optimizer
-
-
-def cli_main():
-    pl.seed_everything(1234)
-
-    # ------------
-    # args
-    # ------------
-    parser = ArgumentParser()
-    parser.add_argument('--batch_size', default=32, type=int)
-    parser.add_argument('--hidden_dim', type=int, default=128)
-    parser = pl.Trainer.add_argparse_args(parser)
-    args = parser.parse_args()
-
-    # ------------
-    # data
-    # ------------
-    dataset = MNIST('', train=True, download=True, transform=transforms.ToTensor())
-    mnist_test = MNIST('', train=False, download=True, transform=transforms.ToTensor())
-    mnist_train, mnist_val = random_split(dataset, [55000, 5000])
-
-    train_loader = DataLoader(mnist_train, batch_size=args.batch_size)
-    val_loader = DataLoader(mnist_val, batch_size=args.batch_size)
-    test_loader = DataLoader(mnist_test, batch_size=args.batch_size)
-
-    # ------------
-    # model
-    # ------------
-    model = LitAutoEncoder()
-
-    # ------------
-    # training
-    # ------------
-    trainer = pl.Trainer.from_argparse_args(args)
-    trainer.fit(model, train_loader, val_loader)
-
-    # ------------
-    # testing
-    # ------------
-    result = trainer.test(test_dataloaders=test_loader)
-    print(result)
-
-
-if __name__ == '__main__':
-    cli_main()
diff --git a/project/lit_image_classifier.py b/project/lit_image_classifier.py
deleted file mode 100644
index 1296a3f126eaf69628fc20a9ec58bf95043d6668..0000000000000000000000000000000000000000
--- a/project/lit_image_classifier.py
+++ /dev/null
@@ -1,109 +0,0 @@
-from argparse import ArgumentParser
-
-import torch
-import pytorch_lightning as pl
-from torch.nn import functional as F
-from torch.utils.data import DataLoader, random_split
-
-from torchvision.datasets.mnist import MNIST
-from torchvision import transforms
-
-
-class Backbone(torch.nn.Module):
-    def __init__(self, hidden_dim=128):
-        super().__init__()
-        self.l1 = torch.nn.Linear(28 * 28, hidden_dim)
-        self.l2 = torch.nn.Linear(hidden_dim, 10)
-
-    def forward(self, x):
-        x = x.view(x.size(0), -1)
-        x = torch.relu(self.l1(x))
-        x = torch.relu(self.l2(x))
-        return x
-
-
-class LitClassifier(pl.LightningModule):
-    def __init__(self, backbone, learning_rate=1e-3):
-        super().__init__()
-        self.save_hyperparameters()
-        self.backbone = backbone
-
-    def forward(self, x):
-        # use forward for inference/predictions
-        embedding = self.backbone(x)
-        return embedding
-
-    def training_step(self, batch, batch_idx):
-        x, y = batch
-        y_hat = self.backbone(x)
-        loss = F.cross_entropy(y_hat, y)
-        self.log('train_loss', loss, on_epoch=True)
-        return loss
-
-    def validation_step(self, batch, batch_idx):
-        x, y = batch
-        y_hat = self.backbone(x)
-        loss = F.cross_entropy(y_hat, y)
-        self.log('valid_loss', loss, on_step=True)
-
-    def test_step(self, batch, batch_idx):
-        x, y = batch
-        y_hat = self.backbone(x)
-        loss = F.cross_entropy(y_hat, y)
-        self.log('test_loss', loss)
-
-    def configure_optimizers(self):
-        # self.hparams available because we called self.save_hyperparameters()
-        return torch.optim.Adam(self.parameters(), lr=self.hparams.learning_rate)
-
-    @staticmethod
-    def add_model_specific_args(parent_parser):
-        parser = ArgumentParser(parents=[parent_parser], add_help=False)
-        parser.add_argument('--learning_rate', type=float, default=0.0001)
-        return parser
-
-
-def cli_main():
-    pl.seed_everything(1234)
-
-    # ------------
-    # args
-    # ------------
-    parser = ArgumentParser()
-    parser.add_argument('--batch_size', default=32, type=int)
-    parser.add_argument('--hidden_dim', type=int, default=128)
-    parser = pl.Trainer.add_argparse_args(parser)
-    parser = LitClassifier.add_model_specific_args(parser)
-    args = parser.parse_args()
-
-    # ------------
-    # data
-    # ------------
-    dataset = MNIST('', train=True, download=True, transform=transforms.ToTensor())
-    mnist_test = MNIST('', train=False, download=True, transform=transforms.ToTensor())
-    mnist_train, mnist_val = random_split(dataset, [55000, 5000])
-
-    train_loader = DataLoader(mnist_train, batch_size=args.batch_size)
-    val_loader = DataLoader(mnist_val, batch_size=args.batch_size)
-    test_loader = DataLoader(mnist_test, batch_size=args.batch_size)
-
-    # ------------
-    # model
-    # ------------
-    model = LitClassifier(Backbone(hidden_dim=args.hidden_dim), args.learning_rate)
-
-    # ------------
-    # training
-    # ------------
-    trainer = pl.Trainer.from_argparse_args(args)
-    trainer.fit(model, train_loader, val_loader)
-
-    # ------------
-    # testing
-    # ------------
-    result = trainer.test(test_dataloaders=test_loader)
-    print(result)
-
-
-if __name__ == '__main__':
-    cli_main()
diff --git a/project/lit_mnist.py b/project/lit_mnist.py
deleted file mode 100644
index 873337858fed5e3c816dc63984cd350ccf303c22..0000000000000000000000000000000000000000
--- a/project/lit_mnist.py
+++ /dev/null
@@ -1,96 +0,0 @@
-from argparse import ArgumentParser
-
-import torch
-import pytorch_lightning as pl
-from torch.nn import functional as F
-from torch.utils.data import DataLoader, random_split
-
-from torchvision.datasets.mnist import MNIST
-from torchvision import transforms
-
-
-class LitClassifier(pl.LightningModule):
-    def __init__(self, hidden_dim=128, learning_rate=1e-3):
-        super().__init__()
-        self.save_hyperparameters()
-
-        self.l1 = torch.nn.Linear(28 * 28, self.hparams.hidden_dim)
-        self.l2 = torch.nn.Linear(self.hparams.hidden_dim, 10)
-
-    def forward(self, x):
-        x = x.view(x.size(0), -1)
-        x = torch.relu(self.l1(x))
-        x = torch.relu(self.l2(x))
-        return x
-
-    def training_step(self, batch, batch_idx):
-        x, y = batch
-        y_hat = self(x)
-        loss = F.cross_entropy(y_hat, y)
-        return loss
-
-    def validation_step(self, batch, batch_idx):
-        x, y = batch
-        y_hat = self(x)
-        loss = F.cross_entropy(y_hat, y)
-        self.log('valid_loss', loss)
-
-    def test_step(self, batch, batch_idx):
-        x, y = batch
-        y_hat = self(x)
-        loss = F.cross_entropy(y_hat, y)
-        self.log('test_loss', loss)
-
-    def configure_optimizers(self):
-        return torch.optim.Adam(self.parameters(), lr=self.hparams.learning_rate)
-
-    @staticmethod
-    def add_model_specific_args(parent_parser):
-        parser = ArgumentParser(parents=[parent_parser], add_help=False)
-        parser.add_argument('--hidden_dim', type=int, default=128)
-        parser.add_argument('--learning_rate', type=float, default=0.0001)
-        return parser
-
-
-def cli_main():
-    pl.seed_everything(1234)
-
-    # ------------
-    # args
-    # ------------
-    parser = ArgumentParser()
-    parser.add_argument('--batch_size', default=32, type=int)
-    parser = pl.Trainer.add_argparse_args(parser)
-    parser = LitClassifier.add_model_specific_args(parser)
-    args = parser.parse_args()
-
-    # ------------
-    # data
-    # ------------
-    dataset = MNIST('', train=True, download=True, transform=transforms.ToTensor())
-    mnist_test = MNIST('', train=False, download=True, transform=transforms.ToTensor())
-    mnist_train, mnist_val = random_split(dataset, [55000, 5000])
-
-    train_loader = DataLoader(mnist_train, batch_size=args.batch_size)
-    val_loader = DataLoader(mnist_val, batch_size=args.batch_size)
-    test_loader = DataLoader(mnist_test, batch_size=args.batch_size)
-
-    # ------------
-    # model
-    # ------------
-    model = LitClassifier(args.hidden_dim, args.learning_rate)
-
-    # ------------
-    # training
-    # ------------
-    trainer = pl.Trainer.from_argparse_args(args)
-    trainer.fit(model, train_loader, val_loader)
-
-    # ------------
-    # testing
-    # ------------
-    trainer.test(test_dataloaders=test_loader)
-
-
-if __name__ == '__main__':
-    cli_main()
diff --git a/requirements.txt b/requirements.txt
deleted file mode 100644
index 30840f0ce8f203f5935035cf3c2d7701d13e9f03..0000000000000000000000000000000000000000
--- a/requirements.txt
+++ /dev/null
@@ -1,3 +0,0 @@
-pytorch-lightning >= 1.0.0rc2
-torch >= 1.3.0
-torchvision >= 0.6.0
diff --git a/setup.cfg b/setup.cfg
deleted file mode 100644
index 268bbb91147fa47cadd73ae3ad6f777195cee15a..0000000000000000000000000000000000000000
--- a/setup.cfg
+++ /dev/null
@@ -1,43 +0,0 @@
-[tool:pytest]
-norecursedirs =
-    .git
-    dist
-    build
-addopts =
-    --strict
-    --doctest-modules
-    --durations=0
-
-[coverage:report]
-exclude_lines =
-    pragma: no-cover
-    pass
-
-[flake8]
-max-line-length = 120
-exclude = .tox,*.egg,build,temp
-select = E,W,F
-doctests = True
-verbose = 2
-# https://pep8.readthedocs.io/en/latest/intro.html#error-codes
-format = pylint
-# see: https://www.flake8rules.com/
-ignore =
-    E731  # Do not assign a lambda expression, use a def
-    W504  # Line break occurred after a binary operator
-    F401  # Module imported but unused
-    F841  # Local variable name is assigned to but never used
-    W605  # Invalid escape sequence 'x'
-
-# setup.cfg or tox.ini
-[check-manifest]
-ignore =
-    *.yml
-    .github
-    .github/*
-
-[metadata]
-license_file = LICENSE
-description-file = README.md
-# long_description = file:README.md
-# long_description_content_type = text/markdown
diff --git a/setup.py b/setup.py
deleted file mode 100644
index 3de44ddb7dcf7a866421b6aade7ebad55e8752f3..0000000000000000000000000000000000000000
--- a/setup.py
+++ /dev/null
@@ -1,16 +0,0 @@
-#!/usr/bin/env python
-
-from setuptools import setup, find_packages
-
-setup(
-    name='project',
-    version='0.0.0',
-    description='Describe Your Cool Project',
-    author='',
-    author_email='',
-    # REPLACE WITH YOUR OWN GITHUB PROJECT LINK
-    url='https://github.com/PyTorchLightning/pytorch-lightning-conference-seed',
-    install_requires=['pytorch-lightning'],
-    packages=find_packages(),
-)
-
diff --git a/tests/__init__.py b/tests/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/tests/requirements.txt b/tests/requirements.txt
deleted file mode 100644
index 09f038f59883c58581571d1d1d52e3b745a369ca..0000000000000000000000000000000000000000
--- a/tests/requirements.txt
+++ /dev/null
@@ -1,8 +0,0 @@
-coverage
-codecov>=2.1
-pytest>=3.0.5
-pytest-cov
-pytest-flake8
-flake8
-check-manifest
-twine==1.13.0
\ No newline at end of file
diff --git a/tests/test_classifier.py b/tests/test_classifier.py
deleted file mode 100644
index e173fd518d28d57f5c0d0ed46b3ab73a42cbeec4..0000000000000000000000000000000000000000
--- a/tests/test_classifier.py
+++ /dev/null
@@ -1,15 +0,0 @@
-from pytorch_lightning import Trainer, seed_everything
-from project.lit_mnist import LitClassifier
-from project.datasets.mnist import mnist
-
-
-def test_lit_classifier():
-    seed_everything(1234)
-
-    model = LitClassifier()
-    train, val, test = mnist()
-    trainer = Trainer(limit_train_batches=50, limit_val_batches=20, max_epochs=2)
-    trainer.fit(model, train, val)
-
-    results = trainer.test(test_dataloaders=test)
-    assert results[0]['test_acc'] > 0.7