DeepChem Server Documentation

Database Schema Diagram

Welcome to DeepChem Server, a minimal cloud infrastructure for DeepChem that provides a FastAPI-based backend for managing datasets, running featurization tasks, and building machine learning models with DeepChem.

DeepChem Server offers a streamlined way to:

  • Upload and manage datasets in various formats

  • Perform molecular featurization using DeepChem’s extensive featurizer library

  • Store and retrieve models and data through a unified datastore API

  • Access functionality through both REST API endpoints and a Python client library

Contents:

Quick Start

To get started with DeepChem Server:

  1. Installation: Clone the repository and run the server using Docker

  2. Upload Data: Use the API or Python client to upload your datasets

  3. Featurize: Transform your molecular data using DeepChem featurizers

  4. Build Models: Train and deploy machine learning models

Server Setup

The fastest way to get started is using Docker:

git clone <repository-url>
cd deepchem-server
bash docker.sh

This will start the server on http://localhost:8000.

Key Features

  • FastAPI Backend: Modern, fast web framework with automatic API documentation

  • DeepChem Integration: Built-in support for molecular featurization and modeling

  • Flexible Storage: Disk-based datastore with support for various data formats

  • Python Client: Easy-to-use Python library for programmatic access

  • Docker Support: Containerized deployment for easy setup and scaling

Architecture Overview

DeepChem Server consists of several key components:

  • API Layer: FastAPI routers handling HTTP requests

  • Core Modules: Business logic for data handling, featurization, and model management

  • Datastore: Abstract storage layer with concrete disk implementation

  • Client Library: Python SDK for easy integration

Indices and tables