FastAPI

FastAPI is a modern, fast (high-performance), web framework for building APIs with Python 3.7+ based on standard Python type hints.

To expose your model to a external users via REST API, MLEM can use FastAPI to serve it.

Requirements

$ pip install mlem[fastapi]
# or
$ pip install fastapi uvicorn

Examples

Since we showed how to use FastAPI on the parent page, let not repeat that, but see few different examples instead.

Running FastAPI model server from code

from mlem.api import serve

serve(
    model="https://github.com/iterative/example-mlem-get-started/models/rf",
    server="fastapi",
    host="0.0.0.0",
    port=8000,
)

Running FastAPI model server from cli

$ mlem serve fastapi \
  --model https://github.com/iterative/example-mlem-get-started/models/rf \
  --host 0.0.0.0 --port 8000
)
⏳️ Loading model from https://github.com/iterative/example-mlem-get-started/tree/main/models/rf.mlem
Starting fastapi server...
🖇️  Adding route for /predict
🖇️  Adding route for /predict_proba
Checkout openapi docs at <http://0.0.0.0:8000/docs>
INFO:     Started server process [86184]
INFO:     Waiting for application startup.
INFO:     Application startup complete.
INFO:     Uvicorn running on http://0.0.0.0:8000 (Press CTRL+C to quit)

Applying data to running FastAPI server from API

from mlem.api import apply_remote

apply_remote(
    "http",
    "https://github.com/iterative/example-mlem-get-started/data/iris.csv",
    method="predict",
    host="0.0.0.0",
    port=8000,
)

Applying data to running FastAPI server from CLI

$ mlem apply-remote http \
    --method predict --host 0.0.0.0 --port 8000 \
    --data https://github.com/iterative/example-mlem-get-started/data/iris.csv