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-2026.7.11-cp314-cp314-manylinux_2_28_aarch64.whl (4.8 MB view details)

Uploaded CPython 3.14manylinux: glibc 2.28+ ARM64

fbgemm_gpu_nightly_cpu-2026.7.11-cp313-cp313-manylinux_2_28_aarch64.whl (4.8 MB view details)

Uploaded CPython 3.13manylinux: glibc 2.28+ ARM64

fbgemm_gpu_nightly_cpu-2026.7.11-cp312-cp312-manylinux_2_28_aarch64.whl (4.8 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.28+ ARM64

fbgemm_gpu_nightly_cpu-2026.7.11-cp311-cp311-manylinux_2_28_aarch64.whl (4.8 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.28+ ARM64

fbgemm_gpu_nightly_cpu-2026.7.11-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-2026.7.11-cp314-cp314-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.7.11-cp314-cp314-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 161646f2770805adf08dd4f50cfaae852ccb558442c9ab52a5db935f74929285
MD5 be42bf718209bb296b06d833d8fefd49
BLAKE2b-256 205acdda0e9e1d914795cd5669b68328ff44429e5fb44666c8db0a46bae57fa3

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.7.11-cp313-cp313-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.7.11-cp313-cp313-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 9b7b36f7c930ce77a71cf67f51706b1ca3e79209436afb82a59f07f100ca7879
MD5 8361adf3c53ba0b422f96c87865c2a85
BLAKE2b-256 ececb6410a64751009b4c0e8240d2951ea98668c448a7eda5f6cd2a9c2d64c3c

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.7.11-cp312-cp312-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.7.11-cp312-cp312-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 d6d53a104b4f3ac94d9979ff0bc0ab5aa1950ff2d9098a817a5f1567621e430d
MD5 1b8a387c60a939bbf6b54b60a90c03b4
BLAKE2b-256 0195217d1b4119892d8bb9bf120022f0a9199ccfb8bbc589690c599ea698a770

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.7.11-cp311-cp311-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.7.11-cp311-cp311-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 2b36dc92c23766e5cfa8b94d7f24d9a52c2765ad321a0e18a5fa2efed642a805
MD5 aac386c10ba0acd143907fc79d589420
BLAKE2b-256 160c09678953ebcd9d207f747923266dd0df14d337ceee05a0928f5015c555fc

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.7.11-cp310-cp310-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.7.11-cp310-cp310-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 e726c48622d94230392989ea05e1652148128380f1c59b5f4ddb217da69cf035
MD5 ed7dd9f6c3f0f535ab480465f069dadb
BLAKE2b-256 b9ecc134efe84049aca9d9feae7cd9530e062660f08fca13aa8a99ecd5aee89d

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