tensorflow/tfjs-coreArchived

Features

• In-Browser ML Runtimes - Provides a browser-based runtime for executing pre-trained machine learning models including TFLite and Keras conversions.
• Hardware-Accelerated WebGL Execution - Functions as a WebGL accelerated tensor engine that offloads linear algebra operations to the GPU.
• Automatic Differentiation - Implements a full automatic differentiation framework for computing gradients during model training.
• Automatic Differentiation Frameworks - Provides a comprehensive engine for computing exact gradients to support model training and optimization.
• Browser-Based Deep Learning - Enables running pre-trained models or training new ones entirely within the web browser.
• Client-Side Inference - Executes machine learning predictions directly on user devices to enable real-time responses.
• Hardware-Accelerated Inference - Executes loaded models using GPU-accelerated operations for real-time inference performance.
• In-Browser Model Execution - Enables the execution of machine learning models directly in the web browser using JavaScript.
• JavaScript Machine Learning Libraries - Provides a complete library for implementing and training neural networks and ML models using JavaScript.
• JavaScript Model Training - Provides a full environment for developing and optimizing neural networks using JS and automatic differentiation.
• Eager-Execution Frameworks - Creates and optimizes machine learning models using an eager API for definition and training.
• Backend-Agnostic Engines - Provides a backend-agnostic engine that decouples tensor operations from specific hardware like WebGL or WASM.
• Tensor Initialization - Initializes multidimensional tensors of varying ranks and shapes from arrays or hardware buffers.
• Linear Algebra - Executes high-performance mathematical routines for vector and matrix operations on tensors.
• Pretrained Model Integrations - Retrieves model architecture and weight shards from web servers to perform inference or continue training.
• Eager Execution Modes - Supports an eager execution mode where operations are evaluated immediately as they are called.
• FlatBuffer Model Interpreters - Includes native support for TFLite models via FlatBuffers for efficient inference without a full framework.
• Data Preprocessing - Provides tools for cleaning, normalizing, and transforming raw data before it is used in model training.
• Model Architecture Visualizations - TensorFlow.js displays tabular information about model architecture and detailed histograms of layer parameters.
• Model Persistence - Saves and retrieves trained model artifacts and topology to local storage or static resources.
• Weight Sharding - Retrieves large model parameters in fragmented binary shards to optimize network transfer and memory.
• Training Progress Monitors - Produces real-time plots of metrics like loss and accuracy during model training via callbacks.
• Transfer Learning - Implements techniques for adapting pre-trained models to new tasks by training them on new datasets.
• Model Lifecycle Management - Manages the loading of pre-trained models from URLs and the release of associated hardware resources.
• TFLite Interpreters - Imports TFLite flatbuffers from URLs or memory buffers into an interpreter for execution.
• Web-Compatible Format Converters - Transforms models from frameworks like Keras into compatible JSON and binary formats for web execution.
• Deep Learning Frameworks - WebGL-accelerated linear algebra and ML for JavaScript.

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