Skip to main content

LiteRT is for mobile and embedded devices.

Project description

LiteRT is the official solution for running machine learning models on mobile and embedded devices. It enables on-device machine learning inference with low latency and a small binary size on Android, iOS, and other operating systems.

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

ai_edge_litert_nightly-1.0.1.dev20241001-cp312-cp312-manylinux_2_17_x86_64.whl (2.3 MB view details)

Uploaded CPython 3.12 manylinux: glibc 2.17+ x86-64

ai_edge_litert_nightly-1.0.1.dev20241001-cp312-cp312-macosx_12_0_arm64.whl (2.4 MB view details)

Uploaded CPython 3.12 macOS 12.0+ ARM64

ai_edge_litert_nightly-1.0.1.dev20241001-cp311-cp311-manylinux_2_17_x86_64.whl (2.3 MB view details)

Uploaded CPython 3.11 manylinux: glibc 2.17+ x86-64

ai_edge_litert_nightly-1.0.1.dev20241001-cp311-cp311-macosx_12_0_arm64.whl (2.4 MB view details)

Uploaded CPython 3.11 macOS 12.0+ ARM64

ai_edge_litert_nightly-1.0.1.dev20241001-cp310-cp310-manylinux_2_17_x86_64.whl (2.3 MB view details)

Uploaded CPython 3.10 manylinux: glibc 2.17+ x86-64

ai_edge_litert_nightly-1.0.1.dev20241001-cp310-cp310-macosx_12_0_arm64.whl (2.4 MB view details)

Uploaded CPython 3.10 macOS 12.0+ ARM64

ai_edge_litert_nightly-1.0.1.dev20241001-cp39-cp39-manylinux_2_17_x86_64.whl (2.3 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.17+ x86-64

ai_edge_litert_nightly-1.0.1.dev20241001-cp39-cp39-macosx_12_0_arm64.whl (2.4 MB view details)

Uploaded CPython 3.9 macOS 12.0+ ARM64

File details

Details for the file ai_edge_litert_nightly-1.0.1.dev20241001-cp312-cp312-manylinux_2_17_x86_64.whl.

File metadata

File hashes

Hashes for ai_edge_litert_nightly-1.0.1.dev20241001-cp312-cp312-manylinux_2_17_x86_64.whl
Algorithm Hash digest
SHA256 fa7e4c44a9ef4f8fff01050f6aab6223829d11e7cab0c9c68436fc913248ce18
MD5 d45419abcbe59b7aa31627c39810a67c
BLAKE2b-256 68baceab69d80c172f2108d44943448c6d1bfb855379f280084eb892dc611924

See more details on using hashes here.

File details

Details for the file ai_edge_litert_nightly-1.0.1.dev20241001-cp312-cp312-macosx_12_0_arm64.whl.

File metadata

File hashes

Hashes for ai_edge_litert_nightly-1.0.1.dev20241001-cp312-cp312-macosx_12_0_arm64.whl
Algorithm Hash digest
SHA256 f77c0aea84e8365466f904dd4ea931e8bf2a3fcd75d93ec39cfd05c657c549ab
MD5 10b958cbe4b2f1e2b040387e0cf72089
BLAKE2b-256 f2989801e956f6adcdf7054c58441b4698cba4bf9f65b07f2b9b0f115df6d9a9

See more details on using hashes here.

File details

Details for the file ai_edge_litert_nightly-1.0.1.dev20241001-cp311-cp311-manylinux_2_17_x86_64.whl.

File metadata

File hashes

Hashes for ai_edge_litert_nightly-1.0.1.dev20241001-cp311-cp311-manylinux_2_17_x86_64.whl
Algorithm Hash digest
SHA256 1b9e09bad3b73892ff1eec8a2bbab7f06a2e2fe41b4aafc2a917c0c227b4e6ed
MD5 340d74ffecf1dae92ba10ef0a5408de5
BLAKE2b-256 de54b0885f6ec3800bde9ddad0afc5760e24ceed48044151bb2f4545495a3c4c

See more details on using hashes here.

File details

Details for the file ai_edge_litert_nightly-1.0.1.dev20241001-cp311-cp311-macosx_12_0_arm64.whl.

File metadata

File hashes

Hashes for ai_edge_litert_nightly-1.0.1.dev20241001-cp311-cp311-macosx_12_0_arm64.whl
Algorithm Hash digest
SHA256 1a25e35789775248cddcc190122f2d1b7fc0a48fbbf8c4b78106bd4d183a77e3
MD5 96ee60b219caab49f1ccb138b34ef67b
BLAKE2b-256 e117ce3f4b1b4852825656fb2952d0c24ed92b375383db34a11077b089441d9b

See more details on using hashes here.

File details

Details for the file ai_edge_litert_nightly-1.0.1.dev20241001-cp310-cp310-manylinux_2_17_x86_64.whl.

File metadata

File hashes

Hashes for ai_edge_litert_nightly-1.0.1.dev20241001-cp310-cp310-manylinux_2_17_x86_64.whl
Algorithm Hash digest
SHA256 c5726303de5cf77b933b801cf3631db411dca2ac08b5d1e400addaf3ac23bcb0
MD5 8f234626b09ebb55ed15cbd205db9351
BLAKE2b-256 cc3157898dd6f44105d245ac19d4b7fe1ef1fbac1cad9af5281a839b86048d24

See more details on using hashes here.

File details

Details for the file ai_edge_litert_nightly-1.0.1.dev20241001-cp310-cp310-macosx_12_0_arm64.whl.

File metadata

File hashes

Hashes for ai_edge_litert_nightly-1.0.1.dev20241001-cp310-cp310-macosx_12_0_arm64.whl
Algorithm Hash digest
SHA256 2b9a7f6ec935515a8f5ad5690f80db2a2bea6a728d1e6b00e78c924ff5afa32c
MD5 fd5c9c80a713ea9bf74260aa5b8560e8
BLAKE2b-256 a2205a3ccc739bb1341900e837107da4ab5419cddc44ff909bac6dd144a58d06

See more details on using hashes here.

File details

Details for the file ai_edge_litert_nightly-1.0.1.dev20241001-cp39-cp39-manylinux_2_17_x86_64.whl.

File metadata

File hashes

Hashes for ai_edge_litert_nightly-1.0.1.dev20241001-cp39-cp39-manylinux_2_17_x86_64.whl
Algorithm Hash digest
SHA256 bb6ef7d4a5eda8671d471b1928bca268a2b5b28e24e35dbabc46fb77a589d8ab
MD5 5d449961437362e3dc1f744ffee0aac8
BLAKE2b-256 52b76b787ba845ef54d3688cc329239319b0d85cbfd975e63f544fdfeec6662f

See more details on using hashes here.

File details

Details for the file ai_edge_litert_nightly-1.0.1.dev20241001-cp39-cp39-macosx_12_0_arm64.whl.

File metadata

File hashes

Hashes for ai_edge_litert_nightly-1.0.1.dev20241001-cp39-cp39-macosx_12_0_arm64.whl
Algorithm Hash digest
SHA256 ecb6133aae226a78be76acbbe0dd700afc477230542ab8e5f0a2e5d524738a38
MD5 f8556a78c1530fff2ab1f8aba8aeacd5
BLAKE2b-256 128972c55ed13d214580e1fc9356f6f083693208faa9d9aa6500958c1e687a76

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page