Install JAX in Python for High-Performance Computing

JAX is a powerful Python library for high-performance numerical computing. It combines NumPy, automatic differentiation, and GPU/TPU acceleration.

What is JAX?

JAX provides a NumPy-like interface with automatic differentiation. It's designed for machine learning research and scientific computing.

The library can accelerate computations using GPUs and TPUs. This makes it ideal for large-scale numerical tasks.

Prerequisites for Installing JAX

Before installing JAX, ensure you have Python 3.7 or later. You'll also need pip, the Python package installer.

For GPU support, you'll need CUDA and cuDNN installed. Check our guide on Install PySpark in Python for Big Data for similar setup requirements.

Installing JAX on CPU

The basic CPU version of JAX is simple to install. Use the following pip command:

 
# Install basic JAX for CPU
pip install jax jaxlib

This will install the core JAX package. It includes all the basic functionality for CPU computation.

Installing JAX with GPU Support

For GPU acceleration, you need to install the GPU-compatible version. The installation differs based on your CUDA version.

For CUDA 11 and cuDNN 8.2 or later:

 
# Install JAX with GPU support for CUDA 11
pip install "jax[cuda11_cudnn82]" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html

For CUDA 12 and cuDNN 8.9 or later:

 
# Install JAX with GPU support for CUDA 12
pip install "jax[cuda12_cudnn89]" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html

Verifying Your JAX Installation

After installation, verify JAX is working correctly. Create a simple test script:

 
import jax
import jax.numpy as jnp

# Create a simple array
x = jnp.array([1.0, 2.0, 3.0])

# Print the array and device info
print(x)
print(f"JAX running on: {jax.devices()}")

The output should show your array and device information:

[1. 2. 3.]
JAX running on: [CpuDevice(id=0)]

Basic JAX Operations

JAX works similarly to NumPy but with added benefits. Here's a simple example:

 
import jax.numpy as jnp
from jax import grad

# Define a simple function
def f(x):
    return 3 * x**2 + 2 * x + 1

# Compute gradient automatically
df_dx = grad(f)

# Evaluate at x = 2.0
print(df_dx(2.0))  # Output: 14.0

The grad function automatically computes derivatives. This is useful for machine learning applications.

Troubleshooting Common Issues

If you encounter installation problems, check these solutions:

CUDA version mismatch: Ensure your CUDA version matches the JAX installation.

Missing dependencies: Install required system packages like g++ and python3-dev.

For more complex setups, refer to our guide on Install Dask in Python for Parallel Computing.

JAX vs Other Libraries

JAX differs from libraries like Keras or TensorFlow. It provides more flexibility for research purposes.

Key advantages include automatic differentiation and hardware acceleration. The functional programming approach also enables powerful transformations.

Conclusion

Installing JAX is straightforward with pip. The GPU version requires proper CUDA setup.

JAX offers powerful tools for numerical computing. It's especially useful for machine learning research and scientific computing.

For more Python installation guides, check our tutorials on various libraries and frameworks.