Skip to main content

No project description provided

Project description

FBGEMM_GPU

FBGEMM_GPU-CPU CI FBGEMM_GPU-CUDA CI FBGEMM_GPU-ROCm CI

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:

For contributions, please see the CONTRIBUTING file for ways to help out.

License

FBGEMM_GPU is BSD licensed, as found in the LICENSE file.

Project details


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

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

fbgemm_gpu_nightly_cpu-2026.6.2-cp314-cp314-manylinux_2_28_x86_64.whl (5.7 MB view details)

Uploaded CPython 3.14manylinux: glibc 2.28+ x86-64

fbgemm_gpu_nightly_cpu-2026.6.2-cp314-cp314-manylinux_2_28_aarch64.whl (4.6 MB view details)

Uploaded CPython 3.14manylinux: glibc 2.28+ ARM64

fbgemm_gpu_nightly_cpu-2026.6.2-cp313-cp313-manylinux_2_28_x86_64.whl (5.7 MB view details)

Uploaded CPython 3.13manylinux: glibc 2.28+ x86-64

fbgemm_gpu_nightly_cpu-2026.6.2-cp313-cp313-manylinux_2_28_aarch64.whl (4.6 MB view details)

Uploaded CPython 3.13manylinux: glibc 2.28+ ARM64

fbgemm_gpu_nightly_cpu-2026.6.2-cp312-cp312-manylinux_2_28_x86_64.whl (5.7 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.28+ x86-64

fbgemm_gpu_nightly_cpu-2026.6.2-cp312-cp312-manylinux_2_28_aarch64.whl (4.6 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.28+ ARM64

fbgemm_gpu_nightly_cpu-2026.6.2-cp311-cp311-manylinux_2_28_x86_64.whl (5.7 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.28+ x86-64

fbgemm_gpu_nightly_cpu-2026.6.2-cp311-cp311-manylinux_2_28_aarch64.whl (4.6 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.28+ ARM64

fbgemm_gpu_nightly_cpu-2026.6.2-cp310-cp310-manylinux_2_28_x86_64.whl (5.7 MB view details)

Uploaded CPython 3.10manylinux: glibc 2.28+ x86-64

fbgemm_gpu_nightly_cpu-2026.6.2-cp310-cp310-manylinux_2_28_aarch64.whl (4.6 MB view details)

Uploaded CPython 3.10manylinux: glibc 2.28+ ARM64

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.6.2-cp314-cp314-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.6.2-cp314-cp314-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 8ccf333277ec325c0986948eaba671f3d8d2faa63ac83b93e580345c814532c7
MD5 fcd128fb16eae3104f5b91d8d2d6be15
BLAKE2b-256 5e1ce671a3d302a3f97e6de9de21464e434673687efa1f154f7ae946c10b0cdb

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.6.2-cp314-cp314-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.6.2-cp314-cp314-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 ea31172b9ef1097b285d1924c5aab67025572d126f9890fe5a02f45388cb46de
MD5 6de95c972daed74b148ab07fa3fd607a
BLAKE2b-256 6213b2cd12d9af2dcdc3028496c6a9683df095180a81206a5c9b0b872926f073

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.6.2-cp313-cp313-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.6.2-cp313-cp313-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 81978f6b173ee05bc048fa93bd45617000cb6f42964ecf609a425b9afb8cbd4e
MD5 1c5a27435ca63b469264b8a5daf6d572
BLAKE2b-256 75f638b67bcbb1f559f4d9b698e7485b268cd55543b226631b980fa836280d22

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.6.2-cp313-cp313-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.6.2-cp313-cp313-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 04ce9ab511f15cea73e45b63a3dc0cf99bc126c85b58475029457d890b333e34
MD5 a22ca547742182ac6a922808221113d7
BLAKE2b-256 408c949f97c45403c3d2a0cfb9e00a9bfa709c72c4b124c970bcf1c1a31709b4

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.6.2-cp312-cp312-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.6.2-cp312-cp312-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 4a3ee7abfffaae2428bd8f943e8f8547bae8302726baebbf05e806a34aa0525a
MD5 a5dc96cb4dc4ca082b73e7d93a1e9cc1
BLAKE2b-256 5edf99b0c48321bffca0d134d9de817baf18dc371243c82edb6576fc99670e5a

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.6.2-cp312-cp312-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.6.2-cp312-cp312-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 3b66c74bc8e46154bbe511a49fa6b82857a0618e820859c2fbb4f6d126d3efa1
MD5 365789f98cff60448ab4bd06b73fc600
BLAKE2b-256 25ba5dd7e7b3f4bd27771e5756e3c43c90835a64caf5de12f41bfc51bd4891d6

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.6.2-cp311-cp311-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.6.2-cp311-cp311-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 1c41ba32b6b54c828b645afac2f941c77a87a3e3bd542e66c999cdc242b81be1
MD5 c0497e5202c1c7bb128614921031cf06
BLAKE2b-256 6ae9910eee16754fcd0972e64bfdd7a91cd1a92c98c3315bb861a4e4ce79c2a4

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.6.2-cp311-cp311-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.6.2-cp311-cp311-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 90af2577c9eb84cf87a0a1b9e99e22cd29a453e6034a5ba2d8631b627bf89499
MD5 24a26a666ff2cb9df60a531af4b9516c
BLAKE2b-256 58f15f96ecd805246f90cf40179f9b9c943405564ac153ab2fd489d4c1a859e9

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.6.2-cp310-cp310-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.6.2-cp310-cp310-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 9748f47ae1407b0ca8224872b6a2f208d760e9fd3414a489ce966fec5a6a6e86
MD5 a229337caaba6d5932d4078e41de3521
BLAKE2b-256 43d78ea1741daed00ecaf151387e370bfbe9b25fef135f680139afdfae41c563

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.6.2-cp310-cp310-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.6.2-cp310-cp310-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 e7d96f0c49ca0c4c8c1d74390e8fcafeea21c094e8f64328ed18b5df0c43768a
MD5 2b08dce071d0d14307dfbbb630674097
BLAKE2b-256 07091b400f7298524160bb43fd5d0599e58dfb72c18e289bef5c65f9722ef5d4

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page