JAX Insights | Sumble

What is JAX?

JAX is a Python library developed by Google that focuses on high-performance numerical computing, particularly for machine learning research. It allows users to automatically differentiate native Python and NumPy functions, enabling gradient-based optimization and other advanced numerical techniques. JAX can also run computations on CPUs, GPUs, and TPUs, making it well-suited for large-scale machine learning models and research. Common uses include neural network training, scientific simulations, and other tasks that require automatic differentiation and accelerated computation.

Find organizations using JAX on Sumble →

What other technologies are related to JAX?

JAX Competitor Technologies

PyTorch

PyTorch is a deep learning framework that provides similar functionalities to JAX, making them competitors in the machine learning space.

mentioned alongside JAX in 5% (8.6k) of relevant job posts

TensorFlow

TensorFlow is another deep learning framework that offers similar capabilities to JAX, positioning them as competitors in the machine learning field.

mentioned alongside JAX in 4% (7.1k) of relevant job posts

torch.fx

torch.fx is a tracing-based Python-to-Python transformation framework, is designed to enable sophisticated model transforms. This competes with the type of model transformation that JAX enables.

mentioned alongside JAX in 75% (73) of relevant job posts

FSDP

FSDP (Fully Sharded Data Parallel) is a PyTorch feature for scaling model training across multiple GPUs. It competes with similar features available in JAX or other distributed training frameworks.

mentioned alongside JAX in 31% (171) of relevant job posts

MLX

MLX is an array framework designed for machine learning research on Apple silicon. It provides similar functionalities to JAX, making them competitors.

mentioned alongside JAX in 55% (65) of relevant job posts

TensorRT

TensorRT is a high-performance deep learning inference optimizer and runtime. JAX code can be compiled to run efficiently for deployment. Because JAX code can be compiled for deployment, TensorRT and JAX serve as competitors.

mentioned alongside JAX in 7% (289) of relevant job posts

vLLM

vLLM is a fast and easy-to-use library for LLM inference. It competes with JAX-based inference solutions by providing similar functionalities for serving large language models.

mentioned alongside JAX in 12% (145) of relevant job posts

JAX Complementary Technologies

XLA

XLA (Accelerated Linear Algebra) is a compiler for linear algebra that can be used by JAX to optimize and accelerate numerical computations, which makes it a complementary technology.

mentioned alongside JAX in 40% (440) of relevant job posts

DeepSpeed

DeepSpeed is a deep learning optimization library focused on distributed training and inference. While not directly replacing JAX's core functionalities, DeepSpeed can be used to augment JAX workflows, particularly for large model training, making it complementary.

mentioned alongside JAX in 22% (389) of relevant job posts

Flax

Flax is a neural network library for JAX, providing a higher-level API for building and training neural networks. Thus, it is a complementary technology to JAX.

mentioned alongside JAX in 85% (87) of relevant job posts