Maxim Titov
05/23/2024, 10:44 AMMarvin
05/23/2024, 10:45 AMpython
from prefect import flow
@flow
def my_flow():
print("Hello, Prefect!")
### 2. Set Up Deployment Configuration
Initialize a deployment configuration by running the following command, which will create a prefect.yaml
file:
sh
prefect deployment build ./flow.py:my_flow -n my-deployment -o ./my_flow-deployment.yaml
This command specifies the flow file, the flow function, and output configuration file.
### 3. Adjust prefect.yaml
Ensure that the prefect.yaml
file has the correct settings, including your deployment name, work pool, and image (if needed). For example:
yaml
version: 2
name: my-deployment
description: Deployment of my_flow
work_pool:
name: default-agent-pool
job_variables: {}
### 4. Apply the Deployment Configuration
Apply the deployment configuration to the remote Prefect server:
sh
prefect deployment apply ./my_flow-deployment.yaml
### 5. Set Up the Worker on the Remote Host
Start a Prefect worker on the remote host to poll for the deployment:
sh
prefect server start
prefect worker start -p default-agent-pool
### References:
- Prefect Deploy Documentation
- Prefect CLI Integration
This should get your flow deployed and running on a remote Prefect server using the local CLI. If further issues arise, such as dependencies or environment configurations, refer to the Prefect documentation and related GitHub issues for troubleshooting.Bring your towel and join one of the fastest growing data communities. Welcome to our second-generation open source orchestration platform, a completely rethought approach to dataflow automation.
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