Learn JAX with Real Code Examples

Research consulting using JAX pipelines

Scientific simulations for enterprise clients

ML model development and optimization

High-performance computing services

Training workshops and tutorials

Enhanced TPU/GPU optimizations

Better integration with scientific Python ecosystem

Expanded libraries for differentiable programming

Higher-level ML frameworks built on JAX

Improved tooling for production deployment

Quick prototyping for simple computations on CPU

Projects needing extensive high-level ML framework support

Non-research applications with no need for gradient computation

Legacy codebases not compatible with functional transformations

Very small scripts where JIT or GPU acceleration overhead outweighs benefits

JAX is a high-performance Python library for numerical computing with autograd, JIT, and hardware acceleration.

Enables functional, composable, and differentiable programming.

Ideal for ML research, scientific computing, and TPU/GPU-accelerated pipelines.

Integrates seamlessly with Optax, Flax, and Haiku for deep learning.

Supports vectorization, parallelization, and scalable numerical computation.

Is JAX free?

Yes - open-source under Apache 2.0 license.

Which devices are supported?

CPU, GPU, and TPU (via XLA backend).

Can JAX compute gradients automatically?

Yes - using `grad` for scalar or vector functions.

Is JAX suitable for deep learning?

Yes - often used with Flax or Haiku for neural networks.

Can JAX scale to multiple devices?

Yes - `pmap` allows parallelization across GPUs/TPUs.