No project description provided
Project description
FBGEMM_GPU
FBGEMM_GPU (FBGEMM GPU Kernels Library) is a collection of high-performance PyTorch GPU operator libraries for training and inference. The library provides efficient table batched embedding bag, data layout transformation, and quantization supports.
See the full Documentation for more information on building, installing, and developing with FBGEMM_GPU, as well as the most up-to-date support matrix for this library.
Join the FBGEMM_GPU Community
For questions, support, news updates, or feature requests, please feel free to:
- File a ticket in GitHub Issues
- Post a discussion in GitHub Discussions
- Reach out to us on the
#fbgemmchannel in PyTorch Slack
For contributions, please see the CONTRIBUTING file for
ways to help out.
License
FBGEMM_GPU is BSD licensed, as found in the LICENSE file.
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distributions
Built Distributions
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file fbgemm_gpu_nightly_cpu-2026.7.7-cp314-cp314-manylinux_2_28_aarch64.whl.
File metadata
- Download URL: fbgemm_gpu_nightly_cpu-2026.7.7-cp314-cp314-manylinux_2_28_aarch64.whl
- Upload date:
- Size: 4.8 MB
- Tags: CPython 3.14, manylinux: glibc 2.28+ ARM64
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.2.0 CPython/3.14.6
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
a7a8b48d090abf24c909325b1741f6b0505ed342ca59714f662eebab77af5353
|
|
| MD5 |
a080121e38faf2a4f151a121dc09e61d
|
|
| BLAKE2b-256 |
ffd69179f3125132b20b7fffe0edd60df4418368e9e2559c8fa77583a2d3fe1e
|
File details
Details for the file fbgemm_gpu_nightly_cpu-2026.7.7-cp313-cp313-manylinux_2_28_aarch64.whl.
File metadata
- Download URL: fbgemm_gpu_nightly_cpu-2026.7.7-cp313-cp313-manylinux_2_28_aarch64.whl
- Upload date:
- Size: 4.8 MB
- Tags: CPython 3.13, manylinux: glibc 2.28+ ARM64
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.2.0 CPython/3.13.14
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
90d794beeb2addd3fe5046abd3aa96dc4ab3cd0908d87e3b8763502c90076013
|
|
| MD5 |
9251bcda1313b977b10a4cbdc1b27f5a
|
|
| BLAKE2b-256 |
d58a88371ca9da2f50856dd10f7c362e64158d50ec284785369b415f510d4c6a
|
File details
Details for the file fbgemm_gpu_nightly_cpu-2026.7.7-cp312-cp312-manylinux_2_28_aarch64.whl.
File metadata
- Download URL: fbgemm_gpu_nightly_cpu-2026.7.7-cp312-cp312-manylinux_2_28_aarch64.whl
- Upload date:
- Size: 4.8 MB
- Tags: CPython 3.12, manylinux: glibc 2.28+ ARM64
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.2.0 CPython/3.12.13
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
04c1cbaea5d603c9f9f399a5fb22dd8e9519fa2d98478449b87f308aaad9812d
|
|
| MD5 |
c0929be69517d7ffbc538fcbf354fed0
|
|
| BLAKE2b-256 |
9481f8e730e58ce6a5769cfdbca57529565084ce1ef89c74c28a585959330723
|
File details
Details for the file fbgemm_gpu_nightly_cpu-2026.7.7-cp311-cp311-manylinux_2_28_aarch64.whl.
File metadata
- Download URL: fbgemm_gpu_nightly_cpu-2026.7.7-cp311-cp311-manylinux_2_28_aarch64.whl
- Upload date:
- Size: 4.8 MB
- Tags: CPython 3.11, manylinux: glibc 2.28+ ARM64
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.2.0 CPython/3.11.15
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
7e39229c076165f95223617b282e80e8ca155748374a6bb25ead7c45e052f558
|
|
| MD5 |
f46eb98ea2e5cf459905978df3c94364
|
|
| BLAKE2b-256 |
65256baf6c28e8139c437e583470d055fbcd215a2f2c029931847de9e4ddaa9e
|
File details
Details for the file fbgemm_gpu_nightly_cpu-2026.7.7-cp310-cp310-manylinux_2_28_aarch64.whl.
File metadata
- Download URL: fbgemm_gpu_nightly_cpu-2026.7.7-cp310-cp310-manylinux_2_28_aarch64.whl
- Upload date:
- Size: 4.8 MB
- Tags: CPython 3.10, manylinux: glibc 2.28+ ARM64
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.2.0 CPython/3.10.20
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
c045f5dc1b9375974aa3e62a090e04dd168e75cfca0522c980e70a593a9249e0
|
|
| MD5 |
42214df7997123ef3dc4f3a41f9f6519
|
|
| BLAKE2b-256 |
a0c6dc4316b5b7b5dc44cd507eb6e8b684ca238f099e2269d060029a9533c0a1
|
