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68 lines
2.3 KiB
Markdown
68 lines
2.3 KiB
Markdown
---
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resources: examples/connections/azure_openai.yml, examples/flows/standard/web-classification
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category: deployment
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weight: 40
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---
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# Deploy a flow using Docker
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This example demos how to deploy flow as a docker app.
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We will use [web-classification](../../../flows/standard/web-classification/README.md) as example in this tutorial.
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## Build a flow as docker format app
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Note that all dependent connections must be created before building as docker.
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```bash
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# create connection if not created before
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pf connection create --file ../../../connections/azure_openai.yml --set api_key=<your_api_key> api_base=<your_api_base> --name open_ai_connection
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```
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Use the command below to build a flow as docker format app:
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```bash
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pf flow build --source ../../../flows/standard/web-classification --output dist --format docker
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```
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## Deploy with Docker
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### Build Docker image
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Like other Dockerfile, you need to build the image first. You can tag the image with any name you want. In this example, we use `promptflow-serve`.
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Run the command below to build image:
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```shell
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docker build dist -t web-classification-serve
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```
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### Run Docker image
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Run the docker image will start a service to serve the flow inside the container.
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#### Connections
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If the service involves connections, all related connections will be exported as yaml files and recreated in containers.
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Secrets in connections won't be exported directly. Instead, we will export them as a reference to environment variables:
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```yaml
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$schema: https://azuremlschemas.azureedge.net/promptflow/latest/OpenAIConnection.schema.json
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type: open_ai
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name: open_ai_connection
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module: promptflow.connections
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api_key: ${env:OPEN_AI_CONNECTION_API_KEY} # env reference
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```
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You'll need to set up the environment variables in the container to make the connections work.
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### Run with `docker run`
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You can run the docker image directly set via below commands:
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```shell
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# The started service will listen on port 8080.You can map the port to any port on the host machine as you want.
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docker run -p 8080:8080 -e OPEN_AI_CONNECTION_API_KEY=<secret-value> web-classification-serve
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```
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### Test the endpoint
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After start the service, you can use curl to test it:
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```shell
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curl http://localhost:8080/score --data '{"url":"https://play.google.com/store/apps/details?id=com.twitter.android"}' -X POST -H "Content-Type: application/json"
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```
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