Deep Co-Training

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Table of Contents

1. About The Project
   • Built With
2. Getting Started
   • Installation
3. Usage
4. Roadmap
5. Contributing
6. License
7. Contact
8. Acknowledgements

About The Project

![Product Name Screen Shot][product-screenshot]

Here's a blank template to get started: To avoid retyping too much info. Do a search and replace with your text editor for the following: github_username, repo_name, twitter_handle, email, project_title, project_description

Built With

• Docker
• Tensorflow
• Keras

Getting Started

Installation

To get a local copy up and running clone the repo.

git clone https://github.com/Deep-Co-Training/Deep-Co-Training.git

Datasets

Currently the dataset which is being used for the training process is the Yelp Dataset.

To get a local copy of the dataset run the following commands

1. Download the dataset from kaggle.

2. At the root project directory run the following commands to create a folder to export the dataset

$ cd Deep-Co-Training
$ mkdir datasets
$ cd datasets
$ mkdir yelp
$ cd yelp 

3. Once you are in the Deep-Co-Training/Deep-Co-Training/datasets/Yelp directory, go ahead and extract the dataset downloaded from the website.

Usage

Development

To develop/maintain code use the following steps to setup your environment.

Docker Container

1. To build the docker dev image run the following command

docker-compose up

This command builds the docker image which can then be used to start up the container.

2. Next use the following command to start up the dev docker container.

docker run --gpus all -it --rm -p 8888:8888 -v $PWD:/deep-co-training deep-co-training_dev

Jupyter Notebook

Once the container is up and running use the following code to launch jupyter notebooks.

jupyter notebook --ip 0.0.0.0 --no-browser --allow-root

Roadmap

See the open issues for a list of proposed features (and known issues).

Contributing

Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.

1. Fork the repo you plan to work on. Go to the project repo page and use the Fork button. This will create a copy of the repo, under your username.
2. Clone down the repo to your local system. (git clone git@github.com:your-user-name/project-name.git)
3. Create a new branch to hold your work. (git checkout -b new-branch-name)
4. Work on your new code. Write and run tests.
5. Commit your changes. (git add -A git commit -m "commit message here")
6. Push your changes to your GitHub repo. (git push origin branch-name)
7. Open a Pull Request (PR). Go to the original project repo on GitHub. There will be a message about your recently pushed branch, asking if you would like to open a pull request. Follow the prompts, compare across repositories, and submit the PR. This will send an email to the committers. You may want to consider sending an email to the mailing list for more visibility.
8. Maintainers and other contributors will review your PR. Please participate in the conversation, and try to make any requested changes. Once the PR is approved, the code will be merged.

Style Guide

This repository uses flake8 to check pep8 compliance and code analysis.

We utilize Black to format our code. The continuous integration will fail if you do not use it.

Install them with:

pip install flake8 black

Be sure to run them both before you push your commits, otherwise the CI will fail!

python -m black ./
python -m flake8

License

Distributed under the MIT License. See LICENSE for more information.

Contact

Your Name - @twitter_handle - email

Project Link: Deep-Co-Training

Acknowledgements

• README-Template
• Img Shields