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Serving models

For online serving, you can create a server from your model. We will try out FastAPI server. All available server implementations are listed in the nested pages.

Running server

To start a FastAPI server, run:

$ mlem serve fastapi --model https://github.com/iterative/example-mlem-get-started/rf
⏳️ 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
🖇️  Adding route for /sklearn_predict
🖇️  Adding route for /sklearn_predict_proba
Checkout openapi docs at <>
INFO:     Started server process [22854]
INFO:     Waiting for application startup.
INFO:     Application startup complete.
INFO:     Uvicorn running on (Press CTRL+C to quit)

The server is now running and listening for requests on the URL shown above. Endpoints are created automatically from model methods (using the sample_data provided when saving the model) to infer the payload schema. You can open the Swagger UI in your browser to explore the OpenAPI spec and query examples.

This requires the correct packages to be installed for the server to serve the model. The needed requirements are inferred from the model metadata extracted when saving it. You can read more about it in model codification.

Making requests

Each server implementation also has its client counterpart (e.g. HTTPClient for FastAPI). Clients can be used to make requests to their corresponding Servers. Since a server also exposes the model interface description, the client will know what methods are available and handle serialization and deserialization for you. You can use them via mlem apply-remote:

$ mlem apply-remote http test_x.csv \
  --json \
  --host="" \
[1, 0, 2, 1, 1, 0, 1, 2, 1, 1, 2, 0, 0, 0, 0, 1, 2, 1, 1, 2, 0, 2, 0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 1, 0, 0, 2, 1, 0]

Or from Python using the mlem.api:

from mlem.api import load
from mlem.runtime.client import HTTPClient

client = HTTPClient(host="localhost", port=8080)
res = client.predict(load("test_x.csv"))
$ curl -X 'POST' \
      'http://localhost:8080/predict_proba' \
      -H 'accept: application/json' \
      -H 'Content-Type: application/json' \
      -d '{
      "data": {
        "values": [
            "": 0,
            "sepal length (cm)": 0,
            "sepal width (cm)": 0,
            "petal length (cm)": 0,
            "petal width (cm)": 0

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