tesseract-jax 0.2.3
Tesseract JAX executes Tesseracts as part of JAX programs, with full support for function transformations like JIT, `grad`, and more.
Requires Python: >=3.10

<img src="docs/static/logo-transparent.png" width="128" align="right">

### Tesseract-JAX

Tesseract-JAX is a lightweight extension to [Tesseract Core](https://github.com/pasteurlabs/tesseract-core) that makes Tesseracts look and feel like regular [JAX](https://github.com/jax-ml/jax) primitives, and makes them jittable, differentiable, and composable.

[Read the docs](https://docs.pasteurlabs.ai/projects/tesseract-jax/latest/) |
[Explore the examples](https://github.com/pasteurlabs/tesseract-jax/tree/main/examples) |
[Report an issue](https://github.com/pasteurlabs/tesseract-jax/issues) |
[Talk to the community](https://si-tesseract.discourse.group/) |
[Contribute](CONTRIBUTING.md)

---

The API of Tesseract-JAX consists of a single function, [`apply_tesseract(tesseract_client, inputs)`](https://docs.pasteurlabs.ai/projects/tesseract-jax/latest/content/api.html#tesseract_jax.apply_tesseract), which is fully traceable by JAX. This enables end-to-end autodifferentiation and JIT compilation of Tesseract-based pipelines:

```python
@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](https://docs.docker.com/engine/install/) and a modern Python installation (Python 3.10+).

> [!IMPORTANT]
> For more detailed installation instructions, please refer to the [Tesseract Core documentation](https://docs.pasteurlabs.ai/projects/tesseract-core/latest/content/introduction/installation.html).

1. Install Tesseract-JAX:

   ```bash
   $ pip install tesseract-jax
   ```

2. Build an example Tesseract:

   ```bash
   $ 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:

   ```python
   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}})
       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)
   ```

> [!TIP]
> Now you're ready to jump into our [examples](https://github.com/pasteurlabs/tesseract-jax/tree/main/examples) for more ways to use Tesseract-JAX.

## Sharp edges

- **Arrays vs. array-like objects**: Tesseract-JAX is stricter than Tesseract Core in that all array inputs to Tesseracts must be JAX or NumPy arrays, not just any array-like (such as Python floats or lists). As a result, you may need to convert your inputs to JAX arrays before passing them to Tesseract-JAX, including scalar values.

  ```python
  from tesseract_core import Tesseract
  from tesseract_jax import apply_tesseract

  tess = Tesseract.from_image("vectoradd_jax")
  with Tesseract.from_image("vectoradd_jax") as tess:
      apply_tesseract(tess, {"a": {"v": [1.0]}, "b": {"v": [2.0]}})  # ❌ raises an error
      apply_tesseract(tess, {"a": {"v": jnp.array([1.0])}, "b": {"v": jnp.array([2.0])}})  # ✅ works
  ```
- **Additional required endpoints**: Tesseract-JAX requires the [`abstract_eval`](https://docs.pasteurlabs.ai/projects/tesseract-core/latest/content/api/endpoints.html#abstract-eval) Tesseract endpoint to be defined for all operations. This is because JAX mandates abstract evaluation of all operations before they are executed. Additionally, many gradient transformations like `jax.grad` require [`vector_jacobian_product`](https://docs.pasteurlabs.ai/projects/tesseract-core/latest/content/api/endpoints.html#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](LICENSE) 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.