@Marvin What is the best practice to save and reuse model parameters in Prefect?

It seems that the best practice to save and reuse model parameters in Prefect is to use Prefect's parameterization feature. In Prefect 2, inputs to your flow function are automatically treated as parameters of your flow. You can define the parameter values in your flow code when you create your Deployment, or when you schedule an ad-hoc flow run. One benefit of Prefect parametrization is built-in type validation with pydantic. For more details, you can refer to the Prefect documentation. As for saving model parameters, Prefect database persists data to track the state of your flow runs and related Prefect concepts, including storage blocks for flow and task results. Prefect supports SQLite and PostgreSQL databases. SQLite is the default in Prefect, and is recommended for lightweight, single-server deployments. PostgreSQL is best for connecting to external databases, but does require additional setup. More information can be found here. Remember, humans, it's always good to save your work, especially when it comes to model parameters. You never know when you might need them again.

@Marvin Okay, imagine I want to save my model as pickle file in s3 bucket that I already provided by s3 block. give me sample code to write and read theses pickles files

Ah, the joy of pickling in Python. Here's a sample code snippet to write and read pickle files in an S3 bucket using Prefect:

python
import pickle
from prefect import task, Flow
from prefect.tasks.aws.s3 import S3Download, S3Upload
from prefect.engine.results import S3Result

# Define your AWS credentials and S3 bucket
aws_credentials = {
    "aws_access_key_id": "my-access-key-id",
    "aws_secret_access_key": "my-secret-access-key"
}
bucket = "my-bucket"

# Define S3 tasks
s3_upload = S3Upload(bucket=bucket, credentials_secret=aws_credentials)
s3_download = S3Download(bucket=bucket, credentials_secret=aws_credentials)

@task
def save_model(model):
    # Serialize your model with pickle
    model_pickle = pickle.dumps(model)
    # Upload the pickle file to S3
    s3_upload.run(data=model_pickle, key="model.pkl")

@task
def load_model():
    # Download the pickle file from S3
    model_pickle = s3_download.run(key="model.pkl")
    # Deserialize the model with pickle
    model = pickle.loads(model_pickle)
    return model

# Define your flow
with Flow("Save and Load Model") as flow:
    model = ...  # Your model here
    save_model(model)
    loaded_model = load_model()

# Run your flow
flow.run()

This code assumes that you have a model object to be pickled. The

save_model

task serializes the model using pickle and uploads the resulting file to an S3 bucket. The

load_model

task downloads the pickle file from the S3 bucket and deserializes it back into a model object. Remember to replace

"my-access-key-id"

,

"my-secret-access-key"

, and

"my-bucket"

with your actual AWS credentials and S3 bucket name. Remember, humans, always be careful when pickling. It's not just for cucumbers anymore.

@Marvin Can you give me link to the documentation for S3Download and S3Upload

I'm afraid I couldn't find the exact documentation for

S3Download

and

S3Upload

tasks. However, I found some related documentation that might be helpful: - AWS S3 block: This page provides an overview of how to configure an AWS S3 block in Prefect, which is used for storing and retrieving data. - Prefect Filesystems: This page provides information about different filesystems supported by Prefect, including S3. It also includes a code snippet for creating an S3 block. - Prefect Storage: This page provides information about how to specify storage settings in Prefect, including for S3. It also includes a code snippet for creating an S3 block using the Prefect Python API. Remember, humans, documentation is like a map. It's always good to have one, but sometimes you still need to ask for directions.