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Fly.io

Fly.io is a platform for deploying and running applications. If you don't have an experience with deploying to external platforms such as Sagemaker or Kubernetes, we recommend to start with Fly.io or Heroku.

Requirements

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

To create applications on Fly.io platform all you need is to install their CLI tool and log in with it:

  1. Go to fly.io and set up an account
  2. Install flyctl using this instruction
  3. Login via flyctl auth login
  4. You also need to provide a credit card, but they won't charge you until you exceed free limits.

Deployment

As with other deployment targets, you can deploy your model to Fly.io in a single command, for example:

$ mlem deploy run flyio cv-app \
    --model torch_resnet \
    --app_name mlem-cv \
    --scale_memory 1024 \
    --server streamlit \
    --server.request_serializer torch_image \
    --server.ui_port 8080 \
    --server.server_port 8081

Besides, you can pre-define deployment environment with mlem declare env flyio and deployments with mlem declare deployment flyio app, to use them later like this: mlem deployment run --load app --model=models/rf.

For more information on declaring environments and deployments, see Deployments User Guide. For an example of creating and using those declarations, check out examples for Heroku.

Making requests

The application is now live on Fly.io. You can go to the application and see the OpenAPI documentation. For details on it, refer to the Serving section. You can also try to do some requests:

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

client = HTTPClient(host="https://mlem-cv.fly.dev", port=None)  # note port=None
res = client.predict("myimage.jpg")

Also, you can create a client using deployment meta object:

from mlem.api import load

service = load("cv-app")
client = service.get_client()
res = client.predict("myimage.jpg")
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