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


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-2026.4.6-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-2026.4.6-cp314-cp314-manylinux_2_28_aarch64.whl (4.6 MB view details)

Uploaded CPython 3.14manylinux: glibc 2.28+ ARM64

fbgemm_gpu_nightly_cpu-2026.4.6-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-2026.4.6-cp313-cp313-manylinux_2_28_aarch64.whl (4.6 MB view details)

Uploaded CPython 3.13manylinux: glibc 2.28+ ARM64

fbgemm_gpu_nightly_cpu-2026.4.6-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-2026.4.6-cp312-cp312-manylinux_2_28_aarch64.whl (4.6 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.28+ ARM64

fbgemm_gpu_nightly_cpu-2026.4.6-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-2026.4.6-cp311-cp311-manylinux_2_28_aarch64.whl (4.6 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.28+ ARM64

fbgemm_gpu_nightly_cpu-2026.4.6-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-2026.4.6-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-2026.4.6-cp314-cp314-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.4.6-cp314-cp314-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 e93ffead8e1e55779405b92db19af4bb0b38e4a5587dc6b1a353fdc508552006
MD5 e2886b7299fedba7737087cd0f37c00d
BLAKE2b-256 effa7064250089eb98a5f4b5da4e0ef5a86f4221bc650021e34ad6dcc854ce1b

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.4.6-cp314-cp314-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.4.6-cp314-cp314-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 dfb09b375ebb2c920f9276cc9e66777c1d9a88888786b01daf172b28620886eb
MD5 50a072dd3c44a0927bdd18ae3f13038e
BLAKE2b-256 a6f62308a5d92f0fc03506380f7a7f83876414b89925dbffd91c6581aec2e445

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.4.6-cp313-cp313-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.4.6-cp313-cp313-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 99c2165a468e41a7bb63919a62728ce615425b8b8aec243a416f310967421ede
MD5 4556c9d70c3195444f356592ce42a926
BLAKE2b-256 ea1eb95410ab3566c3c7bea4968bc794d7086c32c9da8b80e4aa8cf7696a8df2

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.4.6-cp313-cp313-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.4.6-cp313-cp313-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 de8e28bf7fb1fff88eeefebffea016c3a55d840d37645a0830890a293edc7578
MD5 451c3fbdb236100acdbc601bd7bc4a43
BLAKE2b-256 703858c8da213ddb0d6f324f290f24dcc08b951b8f8294a5dada03f45c67fec4

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.4.6-cp312-cp312-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.4.6-cp312-cp312-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 c5bb30ccb2a03e9dd2a8e810a1d6756da5be4c2c0bdc7e91b1a6232c9b0e7cee
MD5 72bb5c5829bac6655c076af53619f469
BLAKE2b-256 d88cf0d693b1f4c72d5f1303e7c44978d3de49e1a57297a0ad2502f0cfcbe9cb

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.4.6-cp312-cp312-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.4.6-cp312-cp312-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 16537d58aec823ddb8e08f068d43b322174315f0b9fa84e9717d670c1d930d3a
MD5 cf968c3357c1599d2a0428d9f532652f
BLAKE2b-256 28cd63cdc96cab09300aa0b6e5f6b474e9d683cd52bc018f52c567216fa632fd

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.4.6-cp311-cp311-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.4.6-cp311-cp311-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 e47c5d27d2a99e0f39da89063d1d756afa24cc2c8bc3a0e8cc2cf5eadad7efb9
MD5 43e3d35fad2595df8719f8a423bf78c1
BLAKE2b-256 ba0485775f0eaa769d2ef003171561d1bcd8944783589b6fb9058bbaa20cad9c

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.4.6-cp311-cp311-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.4.6-cp311-cp311-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 ef9206995b533cc2eaa3488d9cd4b4cfa398ee5b4a7749f02de26ae7a13d5ebe
MD5 cd12fcd5af1f7a704b4c4d186549cfae
BLAKE2b-256 23a8375b6d167ffee7543292f8dc82d6388aa8eac62417de4a303830211ffae0

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.4.6-cp310-cp310-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.4.6-cp310-cp310-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 f29312ba20b0b507b358b1a1020811f31ce049ed5947a21cac55eee92ba1d7dc
MD5 fb8945c3431e7fb055d603113c9a0cb0
BLAKE2b-256 ceb924b46efe19725e06b96094bacb7e89b2a50c921f8fcc91ba550d5a7f56e6

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.4.6-cp310-cp310-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.4.6-cp310-cp310-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 9c28fa195f5a9580a4f8831dd2d715d57befccefb45add724abe26f614df51d3
MD5 9465ec474b15f259224c7483fcb51894
BLAKE2b-256 430950b870efc93672391c4f5fe27bc87e9a2ce4f5f75e7f4f2096efd8a355cb

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