dimtensor is the only units library that provides native integration with PyTorch (autograd, GPU) and JAX (JIT, vmap, grad). It catches dimensional errors at operation time, preventing costly bugs in physics simulations and scientific ML. Built-in uncertainty propagation and support for 6+ I/O formats (HDF5, NetCDF, Parquet, etc.) make it production-ready for scientific workflows.
How It Differs
Unlike Pint or Astropy units, dimtensor:
Has native PyTorch autograd support (gradients flow through unit-aware operations)
Works with JAX transformations (jit, vmap, grad) via pytree registration
Supports GPU acceleration (CUDA, MPS) while preserving units
Includes built-in uncertainty propagation through all operations
It fills a gap for ML researchers and physicists who need dimensional safety in their PyTorch/JAX workflows.
🔄 This issue represents a GitHub Pull Request. It cannot be merged through Gitea due to API limitations.
## 📋 Pull Request Information
**Original PR:** https://github.com/vinta/awesome-python/pull/2841
**Author:** [@marcoloco23](https://github.com/marcoloco23)
**Created:** 1/9/2026
**Status:** ❌ Closed
**Base:** `master` ← **Head:** `add-dimtensor`
---
### 📝 Commits (1)
- [`250777b`](https://github.com/vinta/awesome-python/commit/250777b8d7bc61690b325abbb78dfb36bc2087f2) Add dimtensor
### 📊 Changes
**1 file changed** (+1 additions, -0 deletions)
<details>
<summary>View changed files</summary>
📝 `README.md` (+1 -0)
</details>
### 📄 Description
## Checklist
- [x] One link per Pull Request
- [x] PR title format: `Add project-name`
- [x] Entry format: `* [project-name](url) - Description ending with period.`
- [x] Description is concise (no mention of "Python")
## Why This Project Is Awesome
Which criterion does it meet? (pick one)
- [ ] **Industry Standard** - The go-to tool for a specific use case
- [ ] **Rising Star** - 5,000+ stars in <2 years, significant adoption
- [x] **Hidden Gem** - Exceptional quality, solves niche problems elegantly
Explain:
dimtensor is the only units library that provides native integration with PyTorch (autograd, GPU) and JAX (JIT, vmap, grad). It catches dimensional errors at operation time, preventing costly bugs in physics simulations and scientific ML. Built-in uncertainty propagation and support for 6+ I/O formats (HDF5, NetCDF, Parquet, etc.) make it production-ready for scientific workflows.
## How It Differs
Unlike Pint or Astropy units, dimtensor:
- Has native PyTorch autograd support (gradients flow through unit-aware operations)
- Works with JAX transformations (jit, vmap, grad) via pytree registration
- Supports GPU acceleration (CUDA, MPS) while preserving units
- Includes built-in uncertainty propagation through all operations
It fills a gap for ML researchers and physicists who need dimensional safety in their PyTorch/JAX workflows.
---
<sub>🔄 This issue represents a GitHub Pull Request. It cannot be merged through Gitea due to API limitations.</sub>
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📋 Pull Request Information
Original PR: https://github.com/vinta/awesome-python/pull/2841
Author: @marcoloco23
Created: 1/9/2026
Status: ❌ Closed
Base:
master← Head:add-dimtensor📝 Commits (1)
250777bAdd dimtensor📊 Changes
1 file changed (+1 additions, -0 deletions)
View changed files
📝
README.md(+1 -0)📄 Description
Checklist
Add project-name* [project-name](url) - Description ending with period.Why This Project Is Awesome
Which criterion does it meet? (pick one)
Explain:
dimtensor is the only units library that provides native integration with PyTorch (autograd, GPU) and JAX (JIT, vmap, grad). It catches dimensional errors at operation time, preventing costly bugs in physics simulations and scientific ML. Built-in uncertainty propagation and support for 6+ I/O formats (HDF5, NetCDF, Parquet, etc.) make it production-ready for scientific workflows.
How It Differs
Unlike Pint or Astropy units, dimtensor:
It fills a gap for ML researchers and physicists who need dimensional safety in their PyTorch/JAX workflows.
🔄 This issue represents a GitHub Pull Request. It cannot be merged through Gitea due to API limitations.