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-2026.4.27-cp314-cp314-manylinux_2_28_x86_64.whl (5.8 MB view details)

Uploaded CPython 3.14manylinux: glibc 2.28+ x86-64

fbgemm_gpu_nightly_cpu-2026.4.27-cp314-cp314-manylinux_2_28_aarch64.whl (4.6 MB view details)

Uploaded CPython 3.14manylinux: glibc 2.28+ ARM64

fbgemm_gpu_nightly_cpu-2026.4.27-cp313-cp313-manylinux_2_28_x86_64.whl (5.8 MB view details)

Uploaded CPython 3.13manylinux: glibc 2.28+ x86-64

fbgemm_gpu_nightly_cpu-2026.4.27-cp313-cp313-manylinux_2_28_aarch64.whl (4.6 MB view details)

Uploaded CPython 3.13manylinux: glibc 2.28+ ARM64

fbgemm_gpu_nightly_cpu-2026.4.27-cp312-cp312-manylinux_2_28_x86_64.whl (5.8 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.28+ x86-64

fbgemm_gpu_nightly_cpu-2026.4.27-cp312-cp312-manylinux_2_28_aarch64.whl (4.6 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.28+ ARM64

fbgemm_gpu_nightly_cpu-2026.4.27-cp311-cp311-manylinux_2_28_x86_64.whl (5.8 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.28+ x86-64

fbgemm_gpu_nightly_cpu-2026.4.27-cp311-cp311-manylinux_2_28_aarch64.whl (4.6 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.28+ ARM64

fbgemm_gpu_nightly_cpu-2026.4.27-cp310-cp310-manylinux_2_28_x86_64.whl (5.8 MB view details)

Uploaded CPython 3.10manylinux: glibc 2.28+ x86-64

fbgemm_gpu_nightly_cpu-2026.4.27-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-2026.4.27-cp314-cp314-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.4.27-cp314-cp314-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 dbaf5b7bbdc18ff95a7e28aa6c8ec424480f3eabb5126829ba045c842944484f
MD5 e7729656ee6e1f689ff564614e5c9845
BLAKE2b-256 c71a45096b355a8d78e53f1324ae8bf5e0d9f81eef16b0b7c6663846ac5e0204

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.4.27-cp314-cp314-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.4.27-cp314-cp314-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 7c7d48245698d8e566f4381dc0fd7585bb6358a3c8fef3e044cb39627d9a2541
MD5 0459c56e36a8986565b3e438d8ab2bd2
BLAKE2b-256 04efe0bf65c873ee70042767d44625d64e0f4a6db3fbdd9cb93e63d128a0a8f5

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.4.27-cp313-cp313-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.4.27-cp313-cp313-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 b9cbb7022e852107f9bc0ec1dc0d892c700b0b409c2648c3a84a0773cf18b569
MD5 d4b53f268ea21c9153e289d386c13937
BLAKE2b-256 6a4bfa8e3fc8c98442b12eccaed7807da91853782f5edbe4bdd382492f3311f7

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.4.27-cp313-cp313-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.4.27-cp313-cp313-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 aeb355c3bd06f82a8302754ba38dfd56cb5c990673cb85d83b053cc95c021d56
MD5 62d720bff6196176f5664d0b7f43f5a0
BLAKE2b-256 a9b1682be734faa780b1a5611d6f38a9419ba92bb1805475d515d2b6cd88ec6d

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.4.27-cp312-cp312-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.4.27-cp312-cp312-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 91a0f7ac0f84e65e178474084697dc6c1a7219a73d9681c2ace8fe134ab9c6db
MD5 153c0b442ca2378b91c0052cd5f8b7e5
BLAKE2b-256 1a05b88e82bf3ca148c1a0c909d390469f6aa6c57419f1f2c35d809614b7d7c7

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.4.27-cp312-cp312-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.4.27-cp312-cp312-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 62b422e617ad025e6f52eb5cd9c76a14ba4f44a3850d4dcf64d8f6554e7952a5
MD5 c36bdb990e80791852b7d70195a0ff87
BLAKE2b-256 c22807b155e928afecb74399a09f78cfe05442beac8f0f5e33e3b7e3b9757771

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.4.27-cp311-cp311-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.4.27-cp311-cp311-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 152fce91cc81a31d12427db4e2a861c8ec0dced3d40597e8dbda82d0c58c7454
MD5 9a27947d3c128d61e6981d87ffcc78d3
BLAKE2b-256 523deca2b7abbd2ffa41b80dc263ea37dba4509924ba4eff5a19cd0facc776e5

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.4.27-cp311-cp311-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.4.27-cp311-cp311-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 698a1c987e58aa31e420aacd044f5aed4c37b4617da3c6ff5c6b84d2b381d047
MD5 054da3e3624fe5270db010594fb9b929
BLAKE2b-256 59861bc4a02716e18551f0b61ce7571d22105bf5d21cd1feb60746d630bac4c4

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.4.27-cp310-cp310-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.4.27-cp310-cp310-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 318cb2a4231859ea0baecb0a4cc8ceefc3d5dfefd8d7eb04ee6d3f92d78929e4
MD5 d0c848bf2a5ab493484023f51b70df94
BLAKE2b-256 96bcf682d7c09e8f23c764b5d643c44ccedddd5646bce3702cddf1fcb088b7b1

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.4.27-cp310-cp310-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.4.27-cp310-cp310-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 6a07a8730dcfc6b163ca1a0ee94cead7c40927632cf4b7b9c927c24d2613718b
MD5 89314ea66737aeccd0cd9fd8268d68c0
BLAKE2b-256 8e0d0347cb2b9806eae2bd3543e3b7fff1fad0fe275f519bca6bbeaa87275d47

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