torch-c-dlpack-ext
torch c dlpack ext
Decision gist · record as of 2026-08-14
Yes, if you use torch with DLPack-dependent libraries and want to eliminate JIT compilation overhead and compiler requirements. The package is permissively licensed and has no known vulnerabilities. However, maintenance is aging (last release 214 days ago), so verify compatibility with your specific torch and library versions before relying on it in production. If you don't use DLPack or have no performance concerns around initialization, it offers no benefit.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires torch as a runtime dependency; Python >= 3.9; platform-specific wheel must be available for your architecture.
- Medium install friction due to platform-specific wheels across multiple architectures and Python versions.
- Maintenance status is aging—last release was 214 days ago—so expect limited active development.
License · maintenance · safety
permissive license (permissive) — Apache License 2.0 is permissive; you can use this in commercial and private projects with minimal restrictions, provided you include license and copyright notices.
last release 2026-01-12 (214 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 4,777,053 downloads/mo, #2,233 on PyPI
Alternatives
Verify before relying
pip install torch-c-dlpack-ext
import torch_c_dlpack_ext
# Enables faster DLPack conversion when torch uses DLPack v1.2- Exact performance improvement over JIT-compiled fallback is not quantified in the fact sheet.
- Whether this module is automatically used by torch or requires explicit configuration is unclear.
- Compatibility with specific torch versions is not documented in the excerpt.
- Which Python versions are actually supported beyond the wheel evidence (cp310, cp311, cp312, cp313, cp314).
What it is and what it does
torch-c-dlpack-ext is a compiled extension that accelerates DLPack tensor conversion for torch by providing an ahead-of-time compiled implementation of DLPack v1.2 support. By default, torch-dependent tools will JIT-compile this functionality on first load, which adds startup overhead and requires a compiler toolchain in the user environment. Installing this wheel lets you skip that JIT step entirely and use a pre-compiled version instead.
The package is narrowly scoped: it exists solely to optimize one specific operation (DLPack conversion) and avoid the friction of runtime compilation. It has no public API of its own—it works by providing a faster implementation that downstream libraries can detect and use automatically. If your workflow involves torch and libraries that use DLPack, this can reduce initialization time and eliminate compiler dependencies.
Use it for
- Speed up initialization of torch-based applications that use DLPack-compatible libraries.
- Deploy torch models in environments where a C++ compiler toolchain is unavailable or undesirable.
- Reduce startup latency in serverless or containerized torch inference pipelines.
- Avoid JIT compilation failures in restricted or minimal Python environments.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you use torch with DLPack-dependent libraries and want to eliminate JIT compilation overhead and compiler requirements.
The package is permissively licensed and has no known vulnerabilities. However, maintenance is aging (last release 214 days ago), so verify compatibility with your specific torch and library versions before relying on it in production. If you don't use DLPack or have no performance concerns around initialization, it offers no benefit.
Install
torch-c-dlpack-ext on PyPI
Before you install
Medium install friction due to platform-specific wheels across multiple architectures and Python versions. Maintenance status is aging—last release was 214 days ago—so expect limited active development.
Requires torch as a runtime dependency; Python >= 3.9; platform-specific wheel must be available for your architecture.
License in practice
Apache License 2.0 is permissive; you can use this in commercial and private projects with minimal restrictions, provided you include license and copyright notices.
Quickstart
pip install torch-c-dlpack-ext
import torch_c_dlpack_ext
# Enables faster DLPack conversion when torch uses DLPack v1.2Verify before relying
- Exact performance improvement over JIT-compiled fallback is not quantified in the fact sheet.
- Whether this module is automatically used by torch or requires explicit configuration is unclear.
- Compatibility with specific torch versions is not documented in the excerpt.
- Which Python versions are actually supported beyond the wheel evidence (cp310, cp311, cp312, cp313, cp314).
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.9 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 1 packagetorch |
| Maintenance | Aging 214 days since the last release |
| First released | |
| Downloads | 4,777,053 / month, #2,233 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: DevelopersIntended Audience :: EducationIntended Audience :: Science/ResearchLicense :: OSI Approved :: Apache Software License |
Evidence: torch_c_dlpack_ext-0.1.5-cp310-cp310-macosx_14_0_arm64.whl; torch_c_dlpack_ext-0.1.5-cp310-cp310-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl; torch_c_dlpack_ext-0.1.5-cp310-cp310-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; torch_c_dlpack_ext-0.1.5-cp310-cp310-win_amd64.whl; torch_c_dlpack_ext-0.1.5-cp311-cp311-macosx_14_0_arm64.whl; torch_c_dlpack_ext-0.1.5-cp311-cp311-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl; torch_c_dlpack_ext-0.1.5-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; torch_c_dlpack_ext-0.1.5-cp311-cp311-win_amd64.whl; torch_c_dlpack_ext-0.1.5-cp312-cp312-macosx_14_0_arm64.whl; torch_c_dlpack_ext-0.1.5-cp312-cp312-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl; torch_c_dlpack_ext-0.1.5-cp312-cp312-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; torch_c_dlpack_ext-0.1.5-cp312-cp312-win_amd64.whl; torch_c_dlpack_ext-0.1.5-cp313-cp313-macosx_14_0_arm64.whl; torch_c_dlpack_ext-0.1.5-cp313-cp313-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl; torch_c_dlpack_ext-0.1.5-cp313-cp313-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; torch_c_dlpack_ext-0.1.5-cp313-cp313-win_amd64.whl; torch_c_dlpack_ext-0.1.5-cp314-cp314-macosx_14_0_arm64.whl; torch_c_dlpack_ext-0.1.5-cp314-cp314-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl; torch_c_dlpack_ext-0.1.5-cp314-cp314-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; torch_c_dlpack_ext-0.1.5-cp314-cp314-win_amd64.whl
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