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Define a personnal app to deploy on

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PyPI License Python 3.7 Code style: black Dependencies

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deepchain-apps package documentation

This Package provide a cli for creating a personnal app to deploy on the DeepChain platform. More informations on app To leverage the apps capability, take a look at the bio-transformers and bio-datasets package, which provide functionnality to download biological dataset and easily use pre-trained transformers.

1. Installation

It is recommended to work with conda environnements in order to manage the specific dependencies of the package.

  conda create --name deepchain-env python=3.7 -y
  conda activate deepchain-env
  pip install deepchain-apps

2. CLI commands

The CLI provides 5 main commands:

  • login : you need to supply the token provide on the plateform (PAT: personnal access token).

    deepchain login
  • create : create a folder with a template app file

    deepchain create my_application
  • deploy : the code and checkpoint are deployed on the plateform, you can select your app in the interface on the plateform.

    • with checkpoint upload

      deepchain deploy my_application --checkpoint
    • Only the code

      deepchain deploy my_application
  • apps :

    • Get info on all local/upload apps

      deepchain apps --infos
    • Remove all local apps (files & config):

      deepchain apps --reset
    • Remove a specific application (files & config):

      deepchain apps --delete my_application
    • List all public apps:

      deepchain apps --public
  • download :

    • Download locally an app deployed on deepchain hub
      deepchain download AppName

How generate token to login deepchain?

If you want to deploy biology app on deepchain, you should first create a personal account on deepchain and go to the user profile section. As you can see below, you will be able to generate a PAT (personal access token) that you can use with the CLI command:

deepchain login

3. Getting started with App

├── # explain how to create an app
├── # __init__ file to create python module
├── checkpoint
│   ├──
│   └── Optionnal : # optional: model to be used in app must be placed there
├── examples
│   ├── # example: app example with checkpoint   └── # example: show how to train a neural network with pre-trained embeddings
└── src
    ├── # Desciption file of the application
    ├── # main application script. Main class must be named App.
    └── tags.json # file to register the tags on the hub.


This source code is licensed under the Apache 2 license found in the LICENSE file in the root directory.

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