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DJL - Deep Java Library With DJL, data science team can build models in different Python APIs such as Tensorflow, Pytorch, and MXNet, and engineering team can run inference on these models using DJL
Main - Deep Java Library - DJL Deep Java Library (DJL) is an open-source, high-level, engine-agnostic Java framework for deep learning DJL is designed to be easy to get started with and simple to use for Java developers
Documentation - Deep Java Library - DJL This folder contains examples and documentation for the Deep Java Library (DJL) project JavaDoc API Reference Note: when searching in JavaDoc, if your access is denied, please try removing the string undefined in the url Demos Cheat sheet How to load a model How to collect metrics How to use a dataset How to set log level Dependency Management
Why DJL Serving? - Deep Java Library DJL Serving is a high performance universal stand-alone model serving solution powered by DJL It takes a deep learning model, several models, or workflows and makes them available through an HTTP endpoint
Quick start - Deep Java Library - DJL Deep Java Library (DJL) is designed to be easy to get started with and simple to use The easiest way to learn DJL is to read the beginner tutorial or our examples
Examples - djl Examples This module contains examples to demonstrate use of the Deep Java Library (DJL) You can find more examples from our djl-demo github repo The following examples are included for training:
DJL - Deep Java Library DJL core API DJL DataSet API DJL Basic ModelZoo LightGBM for DJL XGBoost for DJL MXNet Engine for DJL MXNet ModelZoo ONNX Runtime Engine for DJL PyTorch Engine for DJL PyTorch ModelZoo TensorFlow Engine for DJL TensorFlow ModelZoo Audio Extension AWS AI fastText Engine for DJL Hadoop Extension OpenCV Extension SentencePiece Extension Tablesaw
Interactive Development - Deep Java Library - DJL Inspired by Spencer Park’s IJava project, we integrated DJL with Jupyter Notebooks For more information on the simple setup, follow the instructions in DJL Jupyter notebooks
PyTorch Engine - Deep Java Library - DJL By default, DJL will download the PyTorch native libraries into cache folder the first time you run DJL It will automatically determine the appropriate jars for your system based on the platform and GPU support
DJL - PyTorch engine implementation By default, DJL will download the PyTorch native libraries into cache folder the first time you run DJL It will automatically determine the appropriate jars for your system based on the platform and GPU support