
DOCS . NVIDIA . COM {
}
Title:
JAX Overview - NVIDIA Docs
Description:
NVIDIA Optimized Frameworks such as Kaldi, NVIDIA Optimized Deep Learning Framework (powered by Apache MXNet), NVCaffe, PyTorch, and TensorFlow (which includes DLProf and TF-TRT) offer flexibility with designing and training custom (DNNs for machine learning and AI applications.
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32 years and 2 months (reg. 1993-04-20).
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$3,210 per month
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Keywords {π}
release, jax, nvidia, notes, optimized, frameworks, container, learning, developer, overview, ngc, neural, deep, docs, hub, topics, blog, framework, acceleration, gpu, libraries, performance, include, includes, training, support, maxtext, copyright, tensorrt, tensorflow, corporate, home, contact, privacy, forums, sign, submit, search, download, pdf, page, helpful, highperformance, numerical, computing, machine, research, combines, numpylike, apis,
Topics {βοΈ}
frameworks optimized high-performance numerical computing jax release 25 jax release 24 jax release 23 support nvidia deep learning workloads jax overview 2 machine learning research jax source code jax ngc container start developing applications physics-based simulations jetson accelerating inference optimize gpu performance neural network library reinforcement learning running jax 4 includes software maximize performance corporate policies framework gpu acceleration home neural rendering neural networks additional container page helpful combines numpy automatic differentiation xla acceleration simple primitives dependencies included easy place computer vision drug discovery validated set accelerated training training llms product security jax include modifications maxtext containers favorite libraries copyright 2025 privacy container container 3
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WebPage:
context:http://schema.org
url:https://docs.nvidia.com/deeplearning/frameworks/jax-release-notes/overview.html
description:NVIDIA Optimized Frameworks such as Kaldi, NVIDIA Optimized Deep Learning Framework (powered by Apache MXNet), NVCaffe, PyTorch, and TensorFlow (which includes DLProf and TF-TRT) offer flexibility with designing and training custom (DNNs for machine learning and AI applications.
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