Nothing Special   »   [go: up one dir, main page]

Skip to content
/ fal Public
forked from fal-ai/fal

⚡ Fastest way to serve open source ML models to millions

License

Notifications You must be signed in to change notification settings

valmi-io/fal

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

PyPI Tests

fal

fal is a serverless Python runtime that lets you run and scale code in the cloud with no infra management.

With fal, you can build pipelines, serve ML models and scale them up to many users. You scale down to 0 when you don't use any resources.

Quickstart

First, you need to install the fal package. You can do so using pip:

pip install fal

Then you need to authenticate:

fal auth login

You can also use fal keys that you can get from our dashboard.

Now can use the fal package in your Python scripts as follows:

import fal

@fal.function(
    "virtualenv",
    requirements=["pyjokes"],
)
def tell_joke() -> str:
    import pyjokes

    joke = pyjokes.get_joke()
    return joke

print("Joke from the clouds: ", tell_joke())

A new virtual environment will be created by fal in the cloud and the set of requirements that we passed will be installed as soon as this function is called. From that point on, our code will be executed as if it were running locally, and the joke prepared by the pyjokes library will be returned.

Next steps

If you would like to find out more about the capabilities of fal, check out to the docs. You can learn more about persistent storage, function caches and deploying your functions as API endpoints.

Contributing

Installing in editable mode with dev dependencies

pip install -e 'projects/fal[dev]'
pip install -e 'projects/fal_client[dev]'
pip install -e 'projects/isolate_proto[dev]'

Running tests

pytest

Pre-commit

cd projects/fal
pre-commit install

Commit format

Please follow conventional commits specification for descriptions/messages.

About

⚡ Fastest way to serve open source ML models to millions

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Python 99.9%
  • Dockerfile 0.1%