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<@ULVA73B9P>, is there documentation on prefect CL...
# ask-marvin
j
@Marvin, is there documentation on prefect CLI commands?
m
To help you find the right documentation for Prefect CLI commands, I need to know your Prefect version. Could you let me know if you are using Prefect 2.x, Prefect 3.x, or another version?
j
@Marvin, I am using prefect 3. Specifically, the docker image prefect:3-python3.12
m
You can find the documentation for Prefect 3.x CLI commands on the official Prefect documentation site here. This page includes examples like scheduling tasks with the 
serve
command, which allows for easy deployment of regular executions without manual intervention. Let me know if you have specific CLI commands you are interested in, and I can assist further!
j
@Marvin Is there any documentation on CLI commands to create workers and work pools?
m
For Prefect 3.x, you can create and manage work pools and workers using the CLI. Here are some relevant commands: 1. Creating Work Pools: You can create a work pool using the Prefect CLI with the following command:
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prefect work-pool create [OPTIONS] NAME
- 
NAME
is a required, unique name for the work pool. - Options include setting the pool in a paused state, specifying infrastructure type, and more. More details can be found in the work pools section of the Prefect documentation: Work Pools. 2. Learn About Workers: Workers are lightweight services that poll work pools for new runs to execute. They have specific types corresponding to execution environments. While the CLI specifics for creating workers aren't detailed in my search, the concept of workers and their types is documented here: Workers. These links should guide you in setting up and managing your Prefect workers and work pools. If you need further assistance or more specific commands, feel free to ask!
j
@Marvin in python, what are the possible arguments for the .deploy() function?
m
In Prefect 3.x, the 
.deploy()
function can be used to create and manage deployments programmatically. Here are some possible arguments and their usage: 1. 
name
: The unique name for the deployment. 2. 
work_pool_name
: Specifies the work pool to use for the deployment. Each deployment should be assigned to a specific work pool. 3. 
job_variables
: A dictionary for fine-tuning infrastructure settings. This allows you to override default values in the specified work pool’s base job template, such as 
image_pull_policy
and 
env
. 4. 
image
: The Docker image to be used for the flow runs. 5. 
push
: A boolean indicating whether to push the new deployment settings or changes. Example:
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python
from prefect import deploy, flow

@flow(log_prints=True)
def my_flow():
    print("Running my flow")

if __name__ == "__main__":
    deploy(
        my_flow.to_deployment(name="my-deployment", work_pool_name="my-docker-pool"),
        image="my-image:my-tag",
        push=False
    )
This example deploys a flow with specified options like the Docker image and work pool. For more detailed information, you can visit the Prefect documentation on Docker Infrastructure Examples.
j
@Marvin I do not want to deploy my flow in docker. How would I push my flow to minio and use that as a source when I depoy?
m
To deploy a flow to MinIO and use it as a source, you'll need to ensure your flow code is accessible from MinIO during execution. Instead of packaging your code into a Docker image, you can store it in a cloud storage location like MinIO and pull it from there at runtime. Here’s a general approach you might take: 1. Store Your Flow Code in MinIO: - Upload your flow script to MinIO. Ensure that the file is publicly accessible or that you set up appropriate access keys to retrieve it during execution. 2. Deploy Your Flow with Source Location: - Use Prefect’s 
from_source()
method to specify the source location of your flow code during deployment. Here's an example of how you might configure your deployment:
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python
from prefect import flow

if __name__ == "__main__":
    flow.from_source(
        "<s3://my-minio-bucket/my-flow-script.py>",  # Use your MinIO URL schema
        entrypoint="my_flow_script.py:my_flow_function",  # Path and function name
    ).deploy(
        name="my-minio-deployment",
        work_pool_name="my-work-pool",
        build=False
    )
Notes: - Ensure the 
entrypoint
correctly points to the script and function name in your MinIO storage. - You might need to configure the access credentials for MinIO in your environment or use environmental variables that Prefect can access. This method uses your MinIO bucket as the source of flow code during deployment, similar to how one would use GitHub or other cloud storage solutions. Let me know if you need further assistance or specific details!
j
@Marvin, can I get specific details on where credentials and URL for minio storage should be provided
m
To configure MinIO for use with Prefect, you need to set your credentials and URL appropriately. This can typically be done using environment variables, which Prefect can access during runtime. Here are the steps you might follow: 1. Environment Variables for MinIO: - Set the MinIO credentials and endpoint URL using environment variables. Typically, these might look something like this:
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export MINIO_ENDPOINT_URL="<http://your-minio-url:9000>"
     export MINIO_ACCESS_KEY="your-access-key"
     export MINIO_SECRET_KEY="your-secret-key"
2. Configuring in Prefect: - Ensure that your Prefect environment can access these variables. This usually means setting them up in the environment where your Prefect agent runs. 3. Using the Credentials in Your Flow: - In your flow where you will pull from MinIO, use a library like 
boto3
to interact with MinIO, ensuring that your environment variables are used for authentication. Here’s a simple example using Boto3:
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python
import boto3
from prefect import flow

@flow
def fetch_data_from_minio():
    s3_client = boto3.client(
        's3',
        endpoint_url=os.getenv('MINIO_ENDPOINT_URL'),
        aws_access_key_id=os.getenv('MINIO_ACCESS_KEY'),
        aws_secret_access_key=os.getenv('MINIO_SECRET_KEY')
    )
    # Now you can interact with MinIO, such as downloading a file
    s3_client.download_file('your-bucket', 'your-key', 'local-file')

if __name__ == "__main__":
    fetch_data_from_minio()
Make sure you replace 
'your-bucket'
, 
'your-key'
, and 
'local-file'
with your MinIO bucket details and the local path where you want to store the downloaded file. This setup ensures that your Prefect flow can authenticate and interact with MinIO storage. If you need more detailed documentation specific to Prefect 3.x and MinIO, checking Prefect Slack or community forums could provide additional insights.
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