tesseract-jax 0.4.0

Tesseract JAX executes Tesseracts as part of JAX programs, with full support for function transformations like JIT, `grad`, and more.

Tesseract-JAX

Tesseract-JAX is a lightweight extension to Tesseract Core that makes Tesseracts look and feel like regular JAX primitives, and makes them jittable, differentiable, and composable.

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The API of Tesseract-JAX consists of a single function, apply_tesseract(tesseract_client, inputs), which is fully traceable by JAX. This enables end-to-end autodifferentiation and JIT compilation of Tesseract-based pipelines:

@jax.jit
def vector_sum(x, y):
    res = apply_tesseract(vectoradd_tesseract, {"a": {"v": x}, "b": {"v": y}})
    return res["vector_add"]["result"].sum()

jax.grad(vector_sum)(x, y) # 🎉

Quick start

[!NOTE] Before proceeding, make sure you have a working installation of Docker and a modern Python installation (Python 3.10+).

[!IMPORTANT] For more detailed installation instructions, please refer to the Tesseract Core documentation.

1. Install Tesseract-JAX:

   $ pip install tesseract-jax
   

2. Build an example Tesseract:

   $ git clone https://github.com/pasteurlabs/tesseract-jax
   $ tesseract build tesseract-jax/examples/simple/vectoradd_jax
   

3. Use it as part of a JAX program via the JAX-native apply_tesseract function:

   import jax
   import jax.numpy as jnp
   from tesseract_core import Tesseract
   from tesseract_jax import apply_tesseract
   
   # Load the Tesseract
   t = Tesseract.from_image("vectoradd_jax")
   t.serve()
   
   # Run it with JAX
   x = jnp.ones((1000,))
   y = jnp.ones((1000,))
   
   def vector_sum(x, y):
       res = apply_tesseract(t, {"a": {"v": x}, "b": {"v": y}}, vmap_method="sequential")
       return res["vector_add"]["result"].sum()
   
   vector_sum(x, y) # success!
   
   # You can also use it with JAX transformations like JIT and grad
   vector_sum_jit = jax.jit(vector_sum)
   vector_sum_jit(x, y)
   
   vector_sum_grad = jax.grad(vector_sum)
   vector_sum_grad(x, y)
   
   # vmap requires an explicit vmap_method — "sequential" is safe but slow
   # while "auto_experimental" or "expand_dims" is more efficient for Tesseracts that support batching.
   # See https://docs.pasteurlabs.ai/projects/tesseract-jax/latest/content/vmap-methods.html
   vector_sum_vmap = jax.vmap(vector_sum)
   vector_sum_vmap(x.reshape(10, 100), y.reshape(10, 100))
   

[!TIP] Now you're ready to jump into our examples for more ways to use Tesseract-JAX.

Sharp edges

• Additional required endpoints: Tesseract-JAX requires the abstract_eval Tesseract endpoint to be defined when used in conjunction with automatic differentiation and JAX transformations. This is because JAX, in these cases, mandates abstract evaluation of all operations before they are executed. Additionally, many gradient transformations like jax.grad require vector_jacobian_product to be defined.

[!TIP] When creating a new Tesseract based on a JAX function, use tesseract init --recipe jax to define all required endpoints automatically, including abstract_eval and vector_jacobian_product.

License

Tesseract-JAX is licensed under the Apache License 2.0 and is free to use, modify, and distribute (under the terms of the license).

Tesseract is a registered trademark of Pasteur Labs, Inc. and may not be used without permission.