Check out our new VS Code extension for experiment tracking and model development
Locally deploy the model using a server implementation and expose its methods as endpoints.
usage: mlem serve [options] model [subtype]
arguments:
MODEL Model to create service from [required]
[SUBTYPE] Server type. Choices: ['fastapi', 'heroku', 'rmq'] [default: ]
An MLEM Model can be served via a server
implementation (e.g. fastapi
) and its methods exposed as API endpoints. This
allows us to easily make requests (inference and others) against the served
model.
For the common fastapi
server implementation, the OpenAPI spec is available on
the /docs
endpoint.
HTTP Requests to the model-server can be made either with the corresponding
built-in client, or common HTTP clients, such as curl
and
httpie
CLIs, or the
requests
python library.
-p, --project TEXT
: Path to MLEM project [default: (none)]--rev TEXT
: Repo revision to use [default: (none)]-l, --load TEXT
: File to load server config from-c, --conf TEXT
: Options for server in format field.name=value
-f, --file_conf TEXT
: File with options for server in format
field.name=path_to_config
--help
: Show this message and exit.Easily serve a model from a remote GitHub repository on a local FastAPI HTTP server
$ mlem serve https://github.com/iterative/example-mlem-get-started/rf fastapi --conf port=3000
Starting fastapi server...
💅 Adding route for /predict
💅 Adding route for /predict_proba
💅 Adding route for /sklearn_predict
💅 Adding route for /sklearn_predict_proba
Checkout openapi docs at <http://0.0.0.0:3000/docs>
INFO: Started server process [6083]
INFO: Waiting for application startup.
INFO: Application startup complete.
INFO: Uvicorn running on http://0.0.0.0:3000 (Press CTRL+C to quit)