borch

PyTorch's shape, in a browser tab. Three implementations of one arithmetic — a numpy core (import borch as torch), a TypeScript runtime on WebGPU (borch-ts), and a Python binding over that runtime for Pyodide (borch_webgpu) — held to real PyTorch's values, errors and printed form within the range a curriculum uses.

• See it run: https://playidea-lab.github.io/borch/site/ — the playground trains on your GPU; ten lessons and ten tutorials run every code block in the page.
• The long document — how the values are guaranteed, the supported range, what is deliberately absent and why, borch.ts's design, conformance — is docs/BOOK.md. This page is the door; that one is the house.

What it is not

Not PyTorch. CUDA, distributed training, mixed precision and torch.compile are never coming — they cannot exist in a browser, or learning them means leaving it. An absent feature beats a wrong answer, so what is missing is written down as missing: tests/torch_gap.py prints the current count per namespace, and every gap carries a reason.

Thirty seconds

In a browser, nothing installed. Open the playground and press Run. The landing page times its own first run; measured nightly on a real adapter (tests/browser/first_run.py).

Python, on your machine.

uv pip install pyborch

import borch as torch

x = torch.tensor([1.0, 2.0, 3.0], requires_grad=True)
(x * x).sum().backward()
print(x.grad)           # tensor([2., 4., 6.])

TypeScript, in a page.

npm install borch-ts

import { init, Tensor } from "borch-ts";

await init();                          // asks for a WebGPU adapter, refuses a software one
const x = Tensor.from([1, 2, 3], [3]);
console.log(await x.mul(x).sum().toArray());   // Float32Array [14]

Both examples are run as written by the checks (tests/test_document_examples.py, borch-ts/test/readme.ts), so if they stop working the build says so.

How it is guaranteed

4744 golden cases compare all three implementations against the same answers frozen from real torch — values, shapes, gradients, exception types and messages, and repr. The core runs them natively and in Pyodide; the binding and borch.ts run them in a browser on a real GPU, and every number they print carries the adapter's name, because a software adapter answers every WebGPU call correctly and proves nothing about a GPU.

Where this library deliberately parts from torch — a handful of places, each with a measurement beside it — is listed on the landing page and pinned by borch-ts/test/parity.ts. Everything else that differs is a defect, and the ledgers (tests/torch_gap.py, borch-ts/test/run.py) hold zero gaps without a reason.

Or a notebook. %pip install pyborch then import borch_webgpu as torch in any Pyodide — the notebook page is JupyterLite with the wheel already on its shelf, training on the tab's GPU 6 s after opening.

The file leaves as ONNX. onnx.exportOnnx(model, sample) in TypeScript, torch.onnx.export(model, x, path) in Python — traced from one forward, written without a dependency, and checked by ONNX Runtime Web reproducing the forward (3.5e-8; see the book).

Where things are

Sister libraries: bimm (a model catalogue, on npm as bimm-ts) and borch-hub (weights by manifest and hash).

Working on it

Every browser check is listed in .github/workflows/gpu.yml and run nightly by tests/browser/nightly.py; CLAUDE.md holds the three rules a session has to know. Native tests: uv run --with pytest --with numpy --with torch --with torchvision --with scipy pytest tests/ -q.

Licence

Apache-2.0. Third-party notices are in THIRD-PARTY.md.