# Announcing Tesseract-Torch, a native PyTorch integration for Tesseracts

**URL:** <https://si-tesseract.discourse.group/t/announcing-tesseract-torch-a-native-pytorch-integration-for-tesseracts/143>\
**Category:** Blog & Releases\
**Created:** [July 1, 2026, 8:32pm UTC](https://si-tesseract.discourse.group/t/announcing-tesseract-torch-a-native-pytorch-integration-for-tesseracts/143 "2026-07-01T20:32:41Z")\
**Posts on this page:** 1\
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**Author:** ![dionhaefner](https://yyz1.discourse-cdn.com/flex009/user_avatar/si-tesseract.discourse.group/dionhaefner/32/13_2.png) [@dionhaefner](https://si-tesseract.discourse.group/u/dionhaefner)\
**Post date:** [July 1, 2026, 8:32pm UTC](https://si-tesseract.discourse.group/t/announcing-tesseract-torch-a-native-pytorch-integration-for-tesseracts/143/1 "2026-07-01T20:32:41Z")

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Hi all,

We’ve just released **[Tesseract-Torch](https://github.com/pasteurlabs/tesseract-torch)**, a lightweight extension to Tesseract Core that wraps any Tesseract as a differentiable PyTorch operation. If you’ve been using [Tesseract-JAX](https://github.com/pasteurlabs/tesseract-jax), this is the PyTorch counterpart, and it follows the same design. That means Tesseract is now natively compatible with both JAX and PyTorch, and you can slot a Tesseract into either framework’s autograd graph.

## What it does

The whole API is a single function, `apply_tesseract`, which slots a served Tesseract into PyTorch’s autograd graph:

```python
import torch
from tesseract_core import Tesseract
from tesseract_torch import apply_tesseract

t = Tesseract.from_image("vectoradd_torch")
t.serve()

x = torch.ones(1000, requires_grad=True)
result = apply_tesseract(t, {"a": {"v": x}, "b": {"v": torch.ones(1000)}})
result["vector_add"]["result"].sum().backward()
print(x.grad) # gradients flow back through the Tesseract's VJP endpoint

```

Both reverse-mode (`.backward()`, `torch.autograd.grad`) and forward-mode (`torch.autograd.forward_ad`) AD are supported. The Tesseract runs in its own container; the gradients come back over HTTP via its `vector_jacobian_product` / `jacobian_vector_product` endpoints. From the caller’s side it behaves like any other autograd layer.

## Why it might matter to you

Until now, integrating a Tesseract into an end-to-end differentiable program meant working in JAX. PyTorch is where a lot of model code already lives, so this opens the same workflow to that ecosystem: you can keep your network in native PyTorch and call a containerized solver as one layer in the middle of a training loop, with gradients flowing through both.

That’s useful when the solver isn’t something you can just `import`, for example a heavyweight CFD code, a legacy in-house simulator, or something with a hand-written or Enzyme-generated adjoint. You wrap it once as a Tesseract, then treat it as a differentiable building block.

## A worked example

To make this concrete, there’s a new [**learned closure** demo](https://docs.pasteurlabs.ai/projects/tesseract-core/latest/content/demo/learned-closure.html) that trains a neural viscosity model end-to-end through a containerized 1D Burgers’ equation solver. The solver lives in a Docker image and exposes a VJP; the closure network is plain `torch.nn`. Training differentiates through the entire time-stepping loop, with each step making an HTTP call out to the solver container, and recovers a spatially-varying viscosity profile from solution data alone. It’s a small example, but it shows the pattern that the JAX closure demos use, now in PyTorch.

## Worth knowing up front

This is a 0.1.0 release with a deliberately small surface, so expect a few sharp edges.

Notably, `torch.func` transforms (`vjp`, `jvp`, `grad`, `vmap`) are **not** supported, because their functionalized tensors can’t be converted to NumPy. (You’ll get a loud error if you try.)

## Getting started

```bash
pip install tesseract-torch

```

- [Docs](https://docs.pasteurlabs.ai/projects/tesseract-torch/latest/)
- [Examples](https://github.com/pasteurlabs/tesseract-torch/tree/main/examples)
- [Issues and feedback](https://github.com/pasteurlabs/tesseract-torch/issues)

Feedback is very welcome, especially on the API and on which sharp edges bite you in practice. If you try it on a real problem, we’d love to hear how it goes.