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-2026.7.2-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-2026.7.2-cp314-cp314-manylinux_2_28_aarch64.whl (4.8 MB view details)

Uploaded CPython 3.14manylinux: glibc 2.28+ ARM64

fbgemm_gpu_nightly_cpu-2026.7.2-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-2026.7.2-cp313-cp313-manylinux_2_28_aarch64.whl (4.8 MB view details)

Uploaded CPython 3.13manylinux: glibc 2.28+ ARM64

fbgemm_gpu_nightly_cpu-2026.7.2-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-2026.7.2-cp312-cp312-manylinux_2_28_aarch64.whl (4.8 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.28+ ARM64

fbgemm_gpu_nightly_cpu-2026.7.2-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-2026.7.2-cp311-cp311-manylinux_2_28_aarch64.whl (4.8 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.28+ ARM64

fbgemm_gpu_nightly_cpu-2026.7.2-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-2026.7.2-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-2026.7.2-cp314-cp314-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.7.2-cp314-cp314-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 a7d66958b65f8996eeb2b570b173a92d93e542c31bdf262368c13a7d7017b483
MD5 18d2aae226f9e341770157e52bd79278
BLAKE2b-256 2a95ea9e7c4ff38ff4c503466281a8438304c0c8944e7e8bb693225089e66a92

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.7.2-cp314-cp314-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.7.2-cp314-cp314-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 26d491f0969199469a9e526de41cd68765b20a8911736a80d6525a0b9e656c47
MD5 27dea221d27f14fd295f24127a9a68ae
BLAKE2b-256 10300de03ad983fa517393cddb0e670e11f1f81f9d29f22760ec3f60e7ebf21c

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.7.2-cp313-cp313-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.7.2-cp313-cp313-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 80312217737b28f4f4341627284e6d5701862b585152c00ab5c676d1dfeb6c79
MD5 a6e3cb970fad17fe29b23cab94992230
BLAKE2b-256 b489c9120388c4784ef318e9033b4a9546986d29bb3d09658c9167d2bc6ce2d7

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.7.2-cp313-cp313-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.7.2-cp313-cp313-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 4ee0e423ba9345116658a513e34dbbe17f01980d99b56714c9a77d2dbbf8f821
MD5 85c3cec3db49d61af4e74bbfe58d6ca0
BLAKE2b-256 b7c94e6527a7420139f92bbdd46c7e13856ca0ed96f43b88702e2224379d8551

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.7.2-cp312-cp312-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.7.2-cp312-cp312-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 288db72e5e2230277b0c6588afa7772c9ae9abaf3fa8bf35819ccb3ed07f0657
MD5 2cab593dcf2eda4df834ed52dc6efba2
BLAKE2b-256 96d6a326e38c4bf0bfb5872cece3672d5bbef491dce0c161cb915dd6dd9413e8

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.7.2-cp312-cp312-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.7.2-cp312-cp312-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 83e82281f5ca28ac264171bb1c3c94509ce54ca1663ba317bb6067fc08952087
MD5 1e7ec37d5c05955ff55246736385a2ba
BLAKE2b-256 00cf2332a310770c6c367d97b33fb014b4a987e21710540fcb1d0b8efaa15a41

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.7.2-cp311-cp311-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.7.2-cp311-cp311-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 98e60b61b5ab9a3a4c2001e9ba5cd794f31f4781af4aadf47ec67ad8257f6b71
MD5 a78f531ff30b29a6635fcf7941c11b3a
BLAKE2b-256 c1ff6d43bf13919010e6872fb5a6ee2190b5430c9e10b434807abbaf708dae42

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.7.2-cp311-cp311-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.7.2-cp311-cp311-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 3ef8321dd9e545935567aa9e100daf7371ccf0a79b254e3cfc2fb91f50982c0e
MD5 afa571cbc5126e7ffdd0e642ddc4133a
BLAKE2b-256 a91f857e3f8ddd0e2bb5908d6beb8e25cadc97c00c8f241d860aaaaaa9619b4c

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.7.2-cp310-cp310-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.7.2-cp310-cp310-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 556b7e41911ddf8d04f797d31e53704e750ee2acd0f81ef5e2a0dda604e3335d
MD5 b4efa09ce783d2205c6ecea852a47809
BLAKE2b-256 f3cf2c2a47c797b91a9cb14a6c9b34ccc12e51dacd2cfa574ac14d0ff2a9a13f

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.7.2-cp310-cp310-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.7.2-cp310-cp310-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 dc1ac34f74a4baece0308eb6b72e5684e84a03f09fc49f29e33b4c6c5a1f5b98
MD5 d5545cfe6d47ae138eef6b8fea322939
BLAKE2b-256 70df4fb980b9aadaa3aa36a2930a8dabbd7378790fc8fb5b51841430e85eb4b5

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