Deploy SentenceTransformers embedding models to a ray cluster

Navigation

Verified details

These details have been verified by PyPI
Maintainers
Avatar for calmodovar23 from gravatar.com calmodovar23

Unverified details

These details have not been verified by PyPI
Meta
  • License: MIT License
  • Author: Crispin Almodovar
  • Requires: Python >=3.12
Classifiers
Report project as malware

Project description

ray-embedding

A Python library for deploying SentenceTransformers models to a ray cluster. This tool encapsulates inference logic that uses SentenceTransformers to load any compatible embedding model from the Hugging Face hub and compute embeddings for input text.

This library is meant to be used with the embedding-models Ray cluster.

Refer to this Ray Serve deployment config to see how this library is used.

Supports the following backends

  • pytorch-gpu
  • pytorch-cpu

Planned:

  • onnx-gpu
  • onnx-cpu
  • openvino-cpu
  • fastembed-onnx-cpu

Project details

Verified details

These details have been verified by PyPI
Maintainers
Avatar for calmodovar23 from gravatar.com calmodovar23

Unverified details

These details have not been verified by PyPI
Meta
  • License: MIT License
  • Author: Crispin Almodovar
  • Requires: Python >=3.12
Classifiers

Release history Release notifications | RSS feed

0.17.5

0.17.4

0.17.3

0.17.2

0.17.1

0.16.9

0.16.8

0.16.6

0.16.5

0.16.2

0.16.1

0.16.0

0.15.9

0.15.8

0.15.6

0.15.5

0.15.4

0.15.3

0.15.2

0.15.1

0.15.0

0.14.8

This version

0.14.7

0.14.6

0.14.5

0.14.4

0.14.3

0.14.2

0.14.1

0.14.0

0.13.20

0.13.19

0.13.18

0.13.17

0.13.16

0.13.15

0.13.14

0.13.13

0.13.12

0.13.11

0.13.10

0.13.9

0.13.8

0.13.7

0.13.6

0.13.5

0.13.4

0.13.3

0.13.2

0.13.1

0.13.0

0.12.12

0.12.11

0.12.10

0.12.9

0.12.8

0.12.7

0.12.6

0.12.5

0.12.4

0.12.3

0.12.2

0.12.1

0.12.0

0.11.8

0.11.7

0.11.6

0.11.5

0.11.4

0.11.3

0.11.2

0.11.1

0.11.0

0.10.15

0.10.12

0.10.11

0.10.10

0.10.5

0.10.1

0.10.0

0.9.10

0.9.8

0.9.0

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

ray_embedding-0.14.7.tar.gz (8.6 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

ray_embedding-0.14.7-py3-none-any.whl (10.5 kB view details)

Uploaded Python 3

File details

Details for the file ray_embedding-0.14.7.tar.gz.

File metadata

  • Download URL: ray_embedding-0.14.7.tar.gz
  • Upload date:
  • Size: 8.6 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.12.9

File hashes

Hashes for ray_embedding-0.14.7.tar.gz
Algorithm Hash digest
SHA256 604128fd86864d377874fee1876d18bf43d832100cc00dae1ec12cfd8e385c30
MD5 636d88cbbc4d274fc7b736a0b37c0c94
BLAKE2b-256 d4704828641b59da599c9db91df1752fcf6ef954475777f9f0c7c0d18084d3e7

See more details on using hashes here.

File details

Details for the file ray_embedding-0.14.7-py3-none-any.whl.

File metadata

  • Download URL: ray_embedding-0.14.7-py3-none-any.whl
  • Upload date:
  • Size: 10.5 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.12.9

File hashes

Hashes for ray_embedding-0.14.7-py3-none-any.whl
Algorithm Hash digest
SHA256 9e738aa062cebcbedfd2d6a0163dcb427fafe78ac22d11d6e259d767aa782f64
MD5 ab397cc95ed21bcb1610ed8f69c37ae7
BLAKE2b-256 ed531ef3354690c832df351f75f8e64dc53354fc04d28671f5b4b2f0eaa638a2

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