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Inference Performance Optimization | djl Multithreading Support One of the advantage of Deep Java Library (DJL) is Multi-threaded inference support It can help to increase the throughput of your inference on multi-core CPUs and GPUs and reduce memory consumption compared to Python
Documentation - Deep Java Library - DJL Documentation 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 Cache Management Memory Management
MXNet Engine - Deep Java Library - DJL DJL - Apache MXNet engine implementation Overview This module contains the Deep Java Library (DJL) EngineProvider for Apache MXNet We don't recommend that developers use classes in this module directly Use of these classes will couple your code with Apache MXNet and make switching between engines difficult Even so, developers are not restricted from using engine-specific features For more
Interactive Development - Deep Java Library - DJL Interactive Development This sections introduces the amazing toolkits that the DJL team developed to simplify the Java user experience Without additional setup, you can easily run the tool kits online and export the project into your local system Let’s get started Interactive JShell Interactive JShell is a modified version of JShell equipped with DJL features You can use the existing
Memory Management - Deep Java Library - DJL Memory Management Memory is one of the biggest challenge in the area of deep learning, especially in Java The greatest issue is that the garbage collector doesn't have control over the native memory It doesn't know how much is used and how to free it Beyond that, it can be too slow for high-memory usages such as training on a GPU Without the automatic memory management, it is still
LMI V15 DLC containers release - Deep Java Library LMI V15 DLC containers release This document will contain the latest releases of our LMI containers for use on SageMaker For details on any other previous releases
Overview - Deep Java Library DJL - ONNX Runtime engine implementation Overview This module contains the Deep Java Library (DJL) EngineProvider for ONNX Runtime It is based off the ONNX Runtime Deep Learning Framework We don't recommend developers use classes within this module directly Use of these classes will couple your code to the ONNX Runtime and make switching between engines difficult ONNX Runtime is a DL
Model Loading - Deep Java Library - DJL Model Loading A model is a collection of artifacts that is created by the training process In deep learning, running inference on a Model usually involves pre-processing and post-processing DJL provides a ZooModel class, which makes it easy to combine data processing with the model This document will show you how to load a pre-trained model in various scenarios Using the ModelZoo API to