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

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

This version

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-0.13.0.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-0.13.0-py3-none-any.whl (10.4 kB view details)

Uploaded Python 3

File details

Details for the file ray_embedding-0.13.0.tar.gz.

File metadata

  • Download URL: ray_embedding-0.13.0.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-0.13.0.tar.gz
Algorithm Hash digest
SHA256 fbb8b2ba878cef967f59b928c2bbd189596ba97519c6f4ffb4b9d0e913d7928e
MD5 cd0c05fcbf8f8b8b6aa60dd4cca6ebd7
BLAKE2b-256 e5f1b189b9d49b48e4bb6891faa499b825bd1355ab9fb33ef10c0c19cdd9acac

See more details on using hashes here.

File details

Details for the file ray_embedding-0.13.0-py3-none-any.whl.

File metadata

  • Download URL: ray_embedding-0.13.0-py3-none-any.whl
  • Upload date:
  • Size: 10.4 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-0.13.0-py3-none-any.whl
Algorithm Hash digest
SHA256 b1053beac1f9a01ea096bbaab5d22d8719d2f52915b579392b977a849f059766
MD5 253c22b6f4d1e53b8eb36d222f4f073c
BLAKE2b-256 1c0e998495027a3243196132a0eebac186bd34fbee3193188d9f20e4b4384bec

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