Skip to main content

A library containing components related to model inferences in Gen AI applications.

Project description

GLLM Inference

Description

A library containing components related to model inferences in Gen AI applications.

Installation

Prerequisites

1. Installation from Artifact Registry

Choose one of the following methods to install the package:

Using pip

pip install gllm-inference-binary

Using Poetry

poetry add gllm-inference-binary

2. Development Installation (Git)

For development purposes, you can install directly from the Git repository:

poetry add "git+ssh://git@github.com/GDP-ADMIN/gen-ai-internal.git#subdirectory=libs/gllm-inference"

Available extras:

  • anthropic: Install Anthropic models dependencies
  • google-genai: Install Google Generative AI models dependencies
  • google-vertexai: Install Google Vertex AI models dependencies
  • huggingface: Install HuggingFace models dependencies
  • openai: Install OpenAI models dependencies
  • twelvelabs: Install TwelveLabs models dependencies

Managing Dependencies

  1. Go to root folder of gllm-inference module, e.g. cd libs/gllm-inference.
  2. Run poetry shell to create a virtual environment.
  3. Run poetry lock to create a lock file if you haven't done it yet.
  4. Run poetry install to install the gllm-inference requirements for the first time.
  5. Run poetry update if you update any dependency module version at pyproject.toml.

Contributing

Please refer to this Python Style Guide to get information about code style, documentation standard, and SCA that you need to use when contributing to this project

  1. Activate pre-commit hooks using pre-commit install
  2. Run poetry shell to create a virtual environment.
  3. Run poetry lock to create a lock file if you haven't done it yet.
  4. Run poetry install to install the gllm-inference requirements for the first time.
  5. Run which python to get the path to be referenced at Visual Studio Code interpreter path (Ctrl+Shift+P or Cmd+Shift+P)
  6. Try running the unit test to see if it's working:
poetry run pytest -s tests/unit_tests/

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.

gllm_inference_binary-0.5.10b9-cp313-cp313-manylinux_2_31_x86_64.whl (1.5 MB view details)

Uploaded CPython 3.13manylinux: glibc 2.31+ x86-64

gllm_inference_binary-0.5.10b9-cp312-cp312-manylinux_2_31_x86_64.whl (1.5 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.31+ x86-64

gllm_inference_binary-0.5.10b9-cp311-cp311-manylinux_2_31_x86_64.whl (1.4 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.31+ x86-64

File details

Details for the file gllm_inference_binary-0.5.10b9-cp313-cp313-manylinux_2_31_x86_64.whl.

File metadata

File hashes

Hashes for gllm_inference_binary-0.5.10b9-cp313-cp313-manylinux_2_31_x86_64.whl
Algorithm Hash digest
SHA256 2b5c69974e6ba1c6016256a171c751da8a96e1e9ccd5e48d9e54bc5105b03da7
MD5 d962a7a3fd10bc8e6b4c11c821724d6c
BLAKE2b-256 7bd039e0dcb523b6eb1b823e63b28edef86f789796e4af8455caf2bc8c0d56e7

See more details on using hashes here.

File details

Details for the file gllm_inference_binary-0.5.10b9-cp312-cp312-manylinux_2_31_x86_64.whl.

File metadata

File hashes

Hashes for gllm_inference_binary-0.5.10b9-cp312-cp312-manylinux_2_31_x86_64.whl
Algorithm Hash digest
SHA256 e3a938ea14f4d8d3163b106f75e9e88221f5e51100266a71f575c2db2e749219
MD5 902c55d13cb30fc2c69ec42d57563fb1
BLAKE2b-256 6f6012911da3f2e00141240084abc872b122f7d6a037a0184ce18e602aeca79f

See more details on using hashes here.

File details

Details for the file gllm_inference_binary-0.5.10b9-cp311-cp311-manylinux_2_31_x86_64.whl.

File metadata

File hashes

Hashes for gllm_inference_binary-0.5.10b9-cp311-cp311-manylinux_2_31_x86_64.whl
Algorithm Hash digest
SHA256 fe823b330f68e95aeb611a80f97e061d9cd6f964957732fe58eb6c0e48f3a56c
MD5 246c915edcfc6af5e89471cc30330a11
BLAKE2b-256 2ab039e0aca7425e67680b1384027d1a6e9461b14b070d8521f6475a05ca02e1

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