Learn JAX with Real Code Examples

Install Python 3.8+

Install JAX and appropriate version for your device: `pip install jax jaxlib`

For GPU: `pip install jax jaxlib[cuda]` with matching CUDA/cuDNN version

Verify installation with a simple NumPy-like computation

Test `grad`, `jit`, and `vmap` functions for expected outputs

Install Python 3.8+

Install JAX and device-specific jaxlib

Verify GPU/TPU availability using jax.devices()

Test `grad`, `jit`, `vmap`, `pmap` on example functions

Install Optax, Flax, or Haiku if needed for ML

scripts/ - model and computation scripts

datasets/ - input data files

models/ - saved parameters or checkpoints

logs/ - training or simulation logs

notebooks/ - experiments and analysis

pip install --upgrade jax jaxlib

python script.py to run experiments

Use environment variables to specify GPU/TPU devices

Monitor logs via standard Python logging

Integrate with cloud TPU/GPU services for large-scale jobs

Unicode dataset support

Documentation and tutorials in English

Community contributions from multiple regions

Adopted by global ML research labs

Compatible with international scientific computing standards

Cross-platform Python support

Works on CPU, GPU, TPU

Open-source and free under Apache 2.0

Accessible to researchers and developers

Integrates with standard Python ecosystem

Jupyter or Colab notebooks for development

Matplotlib/Plotly for visualizations

TensorBoard or custom dashboards for metrics

Monitor computation via logs

Export figures and metrics for reporting

Track experiment parameters and random keys

Save model parameters for reproducibility

Version scripts and functions

Backup logs and results

Ensure reproducibility across devices

Use JAX arrays for computation

Preprocess with NumPy/SciPy functions compatible with JAX

Maintain reproducible random seeds with PRNGKey

Batch data for vectorization with `vmap`

Cache computations for repeated experiments