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.dev20240928-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.dev20240928-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.dev20240928-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.dev20240928-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.dev20240928-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.dev20240928-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.dev20240928-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.dev20240928-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.dev20240928-cp312-cp312-manylinux_2_17_x86_64.whl.

File metadata

File hashes

Hashes for ai_edge_litert_nightly-1.0.1.dev20240928-cp312-cp312-manylinux_2_17_x86_64.whl
Algorithm Hash digest
SHA256 9f54bc2c55f04e8280cf8873be07d118c910af3916cf52bce319c304ce13243a
MD5 7414d4164bbac3fc88396d5638e0d53c
BLAKE2b-256 67dbe6079c46d31748e6f01731fc1e227ac48ea29536045ee0d792a25de5e7ef

See more details on using hashes here.

File details

Details for the file ai_edge_litert_nightly-1.0.1.dev20240928-cp312-cp312-macosx_12_0_arm64.whl.

File metadata

File hashes

Hashes for ai_edge_litert_nightly-1.0.1.dev20240928-cp312-cp312-macosx_12_0_arm64.whl
Algorithm Hash digest
SHA256 3065694862779562035682ac221b7d95c363c846080e15b9f10cc8d73079deeb
MD5 b52f2297e2895998706fb0883a54d93a
BLAKE2b-256 1eb5726adb662295e0542492673d82dead8b1939a40d8d42196c37193d57a111

See more details on using hashes here.

File details

Details for the file ai_edge_litert_nightly-1.0.1.dev20240928-cp311-cp311-manylinux_2_17_x86_64.whl.

File metadata

File hashes

Hashes for ai_edge_litert_nightly-1.0.1.dev20240928-cp311-cp311-manylinux_2_17_x86_64.whl
Algorithm Hash digest
SHA256 8fdb461e325b789f32d0f3bf0cdbd49c0300c8b302d9f46d59025161cbb01d88
MD5 a633db9b16b8f3db06d5a1947beb13af
BLAKE2b-256 e9c239e4072a7ef67ac2c57c2e8ff80428abcadc06f2257824df745d2c363004

See more details on using hashes here.

File details

Details for the file ai_edge_litert_nightly-1.0.1.dev20240928-cp311-cp311-macosx_12_0_arm64.whl.

File metadata

File hashes

Hashes for ai_edge_litert_nightly-1.0.1.dev20240928-cp311-cp311-macosx_12_0_arm64.whl
Algorithm Hash digest
SHA256 2173df5f3841dc45ddbeb59d44d33dec4c6b1992387e16c3913f111d48574559
MD5 a084b9c991ec51d7bedc8850ba5167aa
BLAKE2b-256 dd26510ab74d6a525ef17c241ecba8fb3e29b08556c09a8c8f6468f343a04d63

See more details on using hashes here.

File details

Details for the file ai_edge_litert_nightly-1.0.1.dev20240928-cp310-cp310-manylinux_2_17_x86_64.whl.

File metadata

File hashes

Hashes for ai_edge_litert_nightly-1.0.1.dev20240928-cp310-cp310-manylinux_2_17_x86_64.whl
Algorithm Hash digest
SHA256 99d819653db734c97c7c1b755fb797cd79fe31f57fa2ba6d13bc0755e6780692
MD5 7ed46b04b5aacce5e9e8d27814303880
BLAKE2b-256 e39a2fb1f9b25943706571cafe01800b90c3c361c19184a024e8a0047d000376

See more details on using hashes here.

File details

Details for the file ai_edge_litert_nightly-1.0.1.dev20240928-cp310-cp310-macosx_12_0_arm64.whl.

File metadata

File hashes

Hashes for ai_edge_litert_nightly-1.0.1.dev20240928-cp310-cp310-macosx_12_0_arm64.whl
Algorithm Hash digest
SHA256 fcf9665f4fccdff5791f1344b3194989fe1878fb4b1fda216884eb87fdcc6754
MD5 bf20209ff5085f3bfb23952fba19f2c0
BLAKE2b-256 1cc4ce644c07af55ae390a305bfbf5ee9dc5f3f12547176f522c2557305045fc

See more details on using hashes here.

File details

Details for the file ai_edge_litert_nightly-1.0.1.dev20240928-cp39-cp39-manylinux_2_17_x86_64.whl.

File metadata

File hashes

Hashes for ai_edge_litert_nightly-1.0.1.dev20240928-cp39-cp39-manylinux_2_17_x86_64.whl
Algorithm Hash digest
SHA256 af57eaddb59887771ce4d1a1ccdc34945ea008127b235f5bd27c86d6e7639951
MD5 5cd83ba8b47ca055704fdaca54ad9c2e
BLAKE2b-256 ad7ce832648c4a29e155912a41f794624b2c2ae59ff285660065d4cba5f51828

See more details on using hashes here.

File details

Details for the file ai_edge_litert_nightly-1.0.1.dev20240928-cp39-cp39-macosx_12_0_arm64.whl.

File metadata

File hashes

Hashes for ai_edge_litert_nightly-1.0.1.dev20240928-cp39-cp39-macosx_12_0_arm64.whl
Algorithm Hash digest
SHA256 67eaa97bf78e0272f81cc06582aac5f7d699f36edd97f1783697829ec30b942f
MD5 f3fdba9a23f5bc57cae57c69e1546472
BLAKE2b-256 d04b82bf4eb123cc461cb72a4ec3a1650897cce71cd361695e80939dd378b704

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