deepchain-apps 0.0.24

Define a personnal app for the user of DeepChain.bio

deepchain-apps : Create personnal app locally, deploy on deepchain.bio

Package for creating a personnal app to deploy on the DeepChain platform.

Installation

You can install the package directly from Pypi:

pip install deepchain-apps

Getting started with App

This template provide an example of application that you can submit. The final app must have the following architecture:

• my_application
  • src/
    • app.py
    • Optionnal : requirements.txt (for extra packages)
  • checkpoint/
    • Optionnal : model.[h5/pt]

The main app class must be named ’App’

CLI

The CLI provides 4 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
    

The application will be deploy in DeepChain plateform.

Embedding

Some embeddings are provided in the Transformers module

from deepchain.components import Transformers

The model are furnished, but not mandatory, if you want to make an embedding of your protein sequence. Only the ESM (evolutionary scale modeling) model is provided, with different architecture. Here for some full details of the architecture (https://github.com/facebookresearch/esm)

• 'esm1_t6_43M_UR50S'
• 'esm1_t12_85M_UR50S'
• 'esm_msa1_t12_100M_UR50S'
• 'esm1b_t33_650M_UR50S'
• 'esm1_t34_670M_UR100'
• 'esm1_t34_670M_UR50D'
• 'esm1_t34_670M_UR50S'

!! The embedding will run on a GPU on the plateform. But for a testing phase on your personal computer (CPU), you should choose the smaller architecture.