JAX
JAX is an open-source high-performance numerical computing library developed by Google, designed around Python and NumPy interfaces with a highly NumPy-compatible API. JAX's core capabilities include: supporting differentiation of arbitrary Python functions via automatic differentiation (Autograd), enabling efficient computation on CPUs, GPUs, and TPUs through the XLA (Accelerated Linear Algebra) compiler, and supporting functional transformations such as vectorized mapping (vmap) and just-in-time compilation (jit). JAX is widely used in machine learning research and scientific computing.
Core Facts
Hugging Face's Transformers library supports three major frameworks: PyTorch, TensorFlow, and JAX.
The automatic differentiation systems built into PyTorch and TensorFlow are essentially automatic executions of the chain rule differentiation process on a computational graph.
PyTorch uses dynamic computation graphs, rebuilding the graph with each forward pass, which differs from TensorFlow's early static computation graphs.
Timeline (last 90 days)
Modern deep learning frameworks (PyTorch, TensorFlow, JAX) have become the infrastructure of AI development.
Abstraction layers are a core concept in software engineering, referring to the design of encapsulating underlying complexity and exposing only a concise interface; the evolution of deep learning frameworks (from Theano to TensorFlow to PyTorch and JAX) is essentially about adding abstractions at different levels.
Model screening is performed automatically by survey.py, the license must be Apache-2.0 or MIT, and at least 2 GiB must be left for the KV cache; in the end Tier 1 has 8 models (native JAX) and Tier 2 has 7 models (PyTorch fallback torchax).
JAX adopts a functional pure transformation model with restricted side effects, which differs from PyTorch's imperative execution.
JAX is characterized by a functional programming paradigm, automatic differentiation, and just-in-time compilation (JIT), making it naturally suited for TPUs.
The core design of JAX combines a NumPy-style API with the XLA (Accelerated Linear Algebra) compiler, providing three core transformations: jit, vmap, and pmap.
A developer shared on a social platform their use of the KerasHub, JAX, and TPU combination for extensive LLM post-training experiments, and hinted at a possible open-source release of some results.
There is a lack of post-training frameworks in the JAX/Keras ecosystem at the same level of abstraction as those in the PyTorch ecosystem.
MLX's design philosophy draws inspiration from mainstream frameworks such as NumPy, PyTorch, and JAX.
Keras 3 already supports three major backends: TensorFlow, JAX, and PyTorch.
33 more timeline events
All Facts (20)
Hugging Face's Transformers library supports three major frameworks: PyTorch, TensorFlow, and JAX.
90%VerifiedThe automatic differentiation systems built into PyTorch and TensorFlow are essentially automatic executions of the chain rule differentiation process on a computational graph.
70%VerifiedPyTorch uses dynamic computation graphs, rebuilding the graph with each forward pass, which differs from TensorFlow's early static computation graphs.
65%VerifiedHugging Face's core open-source library transformers supports the three mainstream frameworks PyTorch, TensorFlow, and JAX
65%VerifiedJAX is a numerical computing library released by Google in 2018, with core features including JIT acceleration based on the XLA compiler, automatic differentiation, and vmap and pmap primitives.
65%VerifiedTensorFlow is developed by Google, and JAX is likewise developed by Google.
65%VerifiedJAX was developed by Google and is based on the functional programming paradigm, providing automatic differentiation and XLA compilation optimization.
65%UnverifiedGoogle通过JAX框架和XLA编译器构建自己的软件生态，使开发者能够在TPU上运行模型
90%UnverifiedGoogle DeepMind的许多前沿研究基于JAX构建
80%Partially VerifiedTransformers框架同时支持PyTorch、TensorFlow和JAX三大深度学习框架
80%UnverifiedHugging Face's Optimum library provides optimized support for non-Nvidia hardware including Intel, AMD, and AWS Inferentia.
70%UnverifiedXLA is Google's Accelerated Linear Algebra compiler, deeply integrated with TensorFlow and JAX.
60%UnverifiedModern deep learning frameworks (PyTorch, TensorFlow, JAX) have become the infrastructure of AI development.
50%UnverifiedAbstraction layers are a core concept in software engineering, referring to the design of encapsulating underlying complexity and exposing only a concise interface; the evolution of deep learning frameworks (from Theano to TensorFlow to PyTorch and JAX) is essentially about adding abstractions at different levels.
50%UnverifiedModel screening is performed automatically by survey.py, the license must be Apache-2.0 or MIT, and at least 2 GiB must be left for the KV cache; in the end Tier 1 has 8 models (native JAX) and Tier 2 has 7 models (PyTorch fallback torchax).
50%UnverifiedJAX adopts a functional pure transformation model with restricted side effects, which differs from PyTorch's imperative execution.
50%UnverifiedJAX is characterized by a functional programming paradigm, automatic differentiation, and just-in-time compilation (JIT), making it naturally suited for TPUs.
50%UnverifiedThe core design of JAX combines a NumPy-style API with the XLA (Accelerated Linear Algebra) compiler, providing three core transformations: jit, vmap, and pmap.
50%UnverifiedThere is a lack of post-training frameworks in the JAX/Keras ecosystem at the same level of abstraction as those in the PyTorch ecosystem.
50%UnverifiedMLX's design philosophy draws inspiration from mainstream frameworks such as NumPy, PyTorch, and JAX.
50%