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-2026.4.22-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-2026.4.22-cp314-cp314-manylinux_2_28_aarch64.whl (4.6 MB view details)

Uploaded CPython 3.14manylinux: glibc 2.28+ ARM64

fbgemm_gpu_nightly_cpu-2026.4.22-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-2026.4.22-cp313-cp313-manylinux_2_28_aarch64.whl (4.6 MB view details)

Uploaded CPython 3.13manylinux: glibc 2.28+ ARM64

fbgemm_gpu_nightly_cpu-2026.4.22-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-2026.4.22-cp312-cp312-manylinux_2_28_aarch64.whl (4.6 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.28+ ARM64

fbgemm_gpu_nightly_cpu-2026.4.22-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-2026.4.22-cp311-cp311-manylinux_2_28_aarch64.whl (4.6 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.28+ ARM64

fbgemm_gpu_nightly_cpu-2026.4.22-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-2026.4.22-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-2026.4.22-cp314-cp314-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.4.22-cp314-cp314-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 0d62eee4545069dd7a4b578f14eac13f7bb67653a0dc9b4b791d015679559f47
MD5 302b05ae53cfff9a920317874fd18a62
BLAKE2b-256 5ba83034f26aab04720efe113587ac8410a8ea240f79fe9f30464419a06aa5a1

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.4.22-cp314-cp314-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.4.22-cp314-cp314-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 4239b3fbd7e758e6fe31e15717fa325ed8826df95f3fc194468d691391a02118
MD5 9d2929779fd27f9dc79cec60ce068d10
BLAKE2b-256 763f947b0a274ba7c05bc2f1f133445cdf6b93c0cb56fd2d734eb0373bc29bbf

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.4.22-cp313-cp313-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.4.22-cp313-cp313-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 605b0696d4421b323301da21947292f40119d980365a0c042beade1a7c036630
MD5 3921e5f26c68795de1900f58bc534ace
BLAKE2b-256 1d93d625bb943fd4a0777115726be664f7491b3e68bbee6518b525be982f8b67

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.4.22-cp313-cp313-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.4.22-cp313-cp313-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 0cacaf68b36cfe31c1591eea033dd7abf61efcc536d45926a49e3f2c891425a7
MD5 9250e96b5ef14201f56a9ff1e828add2
BLAKE2b-256 f3636f6ef5154de8b8c35e0438aaa440da0490d5b2a510cbecf9c7939fc0b601

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.4.22-cp312-cp312-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.4.22-cp312-cp312-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 1ee2a791cfdb0481ca7fcefd9368edb9128e58cac4af7760829234727009c720
MD5 4247ca96326a01dc608f5833cd9d7258
BLAKE2b-256 f0be888d556e3b60f2627606acba5dc5284a63fa7b67a849fe75dca9fcddda56

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.4.22-cp312-cp312-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.4.22-cp312-cp312-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 203cf089fb81bf0d3b99b66529253cee305cd24922bd886e1a3164da785640bf
MD5 762a44b47f523790958954e56b63765f
BLAKE2b-256 d13c3113da6465fa9b74a5031882200aebae7a8a77459c71856788f194f8bb05

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.4.22-cp311-cp311-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.4.22-cp311-cp311-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 6c5b3e4bf77046e4696f9b019b9d3da3b1e9fdf5e3a3f48cc438de640bca3056
MD5 4ad51de2a6ef0f70b830cda2a6af8dbd
BLAKE2b-256 5c0f113e8c67091daae5be006e47e6f07b6ce117854456c30c93b888cf65cb79

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.4.22-cp311-cp311-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.4.22-cp311-cp311-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 27f5af51e45cad1e04a2610d9e0c3bac045a55f2eb627d8c6f756af0a6b02e39
MD5 cd183c7f58313768bf3a1290c4bea23a
BLAKE2b-256 bfe2184caff908b9a79dc3cdab3ef41b5e2158cb68b6d52e9a5b7901f562d9c7

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.4.22-cp310-cp310-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.4.22-cp310-cp310-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 49ec2ef77c8bcfbeb681dbb9b5814c204819318a95812e833c6802e86ed0a58e
MD5 351cafbf924503c7a21534b73273bcdd
BLAKE2b-256 b7a3dd5daa0902964ea6a4f952aa12ac6a60b26a1e8c7bfb27d22ee86c3c6e5d

See more details on using hashes here.

File details

Details for the file fbgemm_gpu_nightly_cpu-2026.4.22-cp310-cp310-manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for fbgemm_gpu_nightly_cpu-2026.4.22-cp310-cp310-manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 3d76a292cb530f09f069e18a561c61be6b6f5355135405faea47c46016858881
MD5 c6c6905dc173064176bdb9a682c1e202
BLAKE2b-256 921fb84675019ad28b3a9bbcff4c09562faa443bdb5c4bc73ffda792a39dc4ff

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