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-2026.7.21-cp314-cp314-manylinux_2_28_x86_64.whl (6.0 MB view details)

Uploaded CPython 3.14manylinux: glibc 2.28+ x86-64

fbgemm_gpu_nightly_cpu-2026.7.21-cp314-cp314-manylinux_2_28_aarch64.whl (4.8 MB view details)

Uploaded CPython 3.14manylinux: glibc 2.28+ ARM64

fbgemm_gpu_nightly_cpu-2026.7.21-cp313-cp313-manylinux_2_28_x86_64.whl (6.0 MB view details)

Uploaded CPython 3.13manylinux: glibc 2.28+ x86-64

fbgemm_gpu_nightly_cpu-2026.7.21-cp313-cp313-manylinux_2_28_aarch64.whl (4.8 MB view details)

Uploaded CPython 3.13manylinux: glibc 2.28+ ARM64

fbgemm_gpu_nightly_cpu-2026.7.21-cp312-cp312-manylinux_2_28_x86_64.whl (6.0 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.28+ x86-64

fbgemm_gpu_nightly_cpu-2026.7.21-cp312-cp312-manylinux_2_28_aarch64.whl (4.8 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.28+ ARM64

fbgemm_gpu_nightly_cpu-2026.7.21-cp311-cp311-manylinux_2_28_x86_64.whl (6.0 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.28+ x86-64

fbgemm_gpu_nightly_cpu-2026.7.21-cp311-cp311-manylinux_2_28_aarch64.whl (4.8 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.28+ ARM64

fbgemm_gpu_nightly_cpu-2026.7.21-cp310-cp310-manylinux_2_28_x86_64.whl (6.0 MB view details)

Uploaded CPython 3.10manylinux: glibc 2.28+ x86-64

fbgemm_gpu_nightly_cpu-2026.7.21-cp310-cp310-manylinux_2_28_aarch64.whl (4.8 MB view details)

Uploaded CPython 3.10manylinux: glibc 2.28+ ARM64

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.7.21-cp314-cp314-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.7.21-cp314-cp314-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 5e47923b29127e72248b74699289c0f00e45ecaae466380f852fad1ca5bb219d
MD5 0660c43939d1d345ddfdc3b5caa895cf
BLAKE2b-256 16ca11ae1cbcb6589e0b9837346a1a19dcfb361ebef35b47ba64f4f08a279d16

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.7.21-cp314-cp314-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.7.21-cp314-cp314-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 b51d7c6780aee46e81a77489110c9ace71ba909911ab91fa4f7d74312f99188f
MD5 3abfd8ecf50f9a3a23bd54702651a093
BLAKE2b-256 a922044b1ac1b3c67026d6b1b6a1c471aec77a02f48b516d4f0ee2a6bcca5c38

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.7.21-cp313-cp313-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.7.21-cp313-cp313-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 c57b9e1c36794184c19b39a798f5e26602198eb87274a7891c43a20db81b465d
MD5 ae79ab371cef81e28b496fbf3b2a115c
BLAKE2b-256 bc17ac11e999cf5cb108016e7973caf04e1df240bacf1967cb1f41bbc3954371

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.7.21-cp313-cp313-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.7.21-cp313-cp313-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 47c7d6a8dff9f2c084c48208f36f449c266eb9805b783d55090b25eb3b1c552d
MD5 ad2d4cda0480c525f216a95d2e7897a4
BLAKE2b-256 31597fe515a3c80fd1b75ed3cdaa4fede5ca4a68a151045e6acbe19cc498198e

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.7.21-cp312-cp312-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.7.21-cp312-cp312-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 2eb2d47e7ba45af3b7bc4ca9d6b460a8db8048a3866e40fb12280bb6c2e7ce55
MD5 62534addccc888f22ffb05200ada11c5
BLAKE2b-256 edf0df6796000bb2d26ff64daede080a102aadda7f6c3be532deb3a1e8308ab9

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.7.21-cp312-cp312-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.7.21-cp312-cp312-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 2be78f87045b89b686fa349038cc1d39a6eae86e1b3141f1c4e829b0aa7be5d3
MD5 a201b13cd18141a07c66a0afa4f73d4c
BLAKE2b-256 c290317f39a011a660a3067bd972ab25792e999ac93569b6bb241e97c11b49ef

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.7.21-cp311-cp311-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.7.21-cp311-cp311-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 d0aa67cefee31fbb4f95f14d3f5f2d1130421d9b75a3beebbc48cc0536a202cf
MD5 3b84403e862ac535d86f2032c5f9c532
BLAKE2b-256 7d420d93fa2836c398f9b97be58639aa3b82a4d861ed38c058eb56ca347a211a

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.7.21-cp311-cp311-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.7.21-cp311-cp311-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 8d68c1864543e2ddbbc5439f43234aa731630ba463887202e8d837806a525f97
MD5 26768d40545d03a28f8845ad611d60fa
BLAKE2b-256 b38942d36dc1dd1d09647584c740dbd3b954c015c408b9ce1b5aff17d45887ad

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.7.21-cp310-cp310-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.7.21-cp310-cp310-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 2126576de5aee90181965f383544c5f94913b8a29d7bf67c4ccd0cd2873c9f32
MD5 2e03c1cdff5ea158769f9c0fad88beae
BLAKE2b-256 05d1d9a027f26e3388313c69b2f5d29ac8c81abc517b6dabbdeb8aeef99ba6fb

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.7.21-cp310-cp310-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.7.21-cp310-cp310-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 87ff686eeb5c08e42ca0d97df8bf37959d7548a7f5840a5ca3ecd02e91b423fd
MD5 c95a093db69f7cb608c7edacbf55f38f
BLAKE2b-256 7becf31ff058f645c0480e5a65679e4aac524ac67218a00628c52251ca3303c0

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