{"categories":[{"label":"Libraries","url":"https://skillfed.io/packages/category/software-development-libraries/6"},{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/12"},{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics/2"}],"enrichment":{"capability":"JMP provides mixed precision training support for JAX by managing dtype policies and loss scaling to reduce memory bandwidth and improve computational efficiency during neural network training.","skillfed_tags":["mixed-precision","training-optimization"],"use_cases":["Reduce GPU training time by mixing float32 and float16 precision on NVIDIA hardware.","Reduce TPU training time by mixing float32 and bfloat16 precision.","Prevent gradient underflow in low-precision training by applying dynamic loss scaling automatically.","Define reusable mixed precision policies as command-line arguments or hyperparameters for experiments.","Integrate mixed precision into custom training loops without modifying core model code."],"what_it_does":"JMP is a mixed precision training library that lets you train neural networks using both full-precision and half-precision floating-point numbers in the same model. This reduces memory bandwidth requirements and speeds up computation, particularly on GPUs and TPUs. The library provides two main abstractions: policies that define which dtypes to use for parameters, computation, and output, and loss scaling mechanisms that prevent gradient underflow when using reduced precision.\n\nYou define a policy once and apply it to your parameters and activations throughout training. JMP also handles loss scaling\u2014either statically (you pick a fixed scale factor) or dynamically (the library adjusts the scale during training to avoid numerical issues). The dynamic loss scaler is optimized to have minimal overhead. Neural network libraries can integrate JMP to provide automatic mixed precision support, simplifying adoption in existing codebases.","worth_installing":"Yes, if you train neural networks and want to reduce memory bandwidth or speed up training on accelerators. The library is actively maintained, has low install friction, and is permissively licensed under Apache 2.0. The main gotcha is that JAX must be installed separately first with the correct accelerator support; beyond that, JMP is straightforward to integrate into existing training loops."},"id":"jmp","links":{"html":"https://skillfed.io/packages/jmp","md":"https://skillfed.io/packages/jmp.md","pypi":"https://pypi.org/project/jmp/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2023-01-30","license_spdx":null,"license_treatment":"permissive","name":"jmp","python_support":"unspecified","summary":"JMP is a Mixed Precision library for JAX."},"popularity":{"monthly_downloads":385143,"position":7064,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.0.4"}