@Marvin I'm using minio as storage for my flow code in prefect 2.20.3 I have a bucket named flows in which I have my prefect flow and I'm trying to run the flow as follows: minio_credentials = MinIOCredentials( minio_root_user = minio_key_id, minio_root_password = minio_access_key ) s3_bucket_block = S3Bucket( bucket_name="flows", folder=folder_id, credentials=minio_credentials, # Use MinIO credentials directly endpoint_url=minio_url ) # Deploy the flow with the uploaded files training_flow = await flow.from_source( source=s3_bucket_block, entrypoint="train.py:train") However, I get botocore.exceptions.ClientError: An error occurred (InvalidAccessKeyId) when calling the ListObjects operation: The AWS Access Key Id you provided does not exist in our records. I'm 100% that the credential I'm passing are right

It seems like we're having a minor hiccup accessing the full documentation. But don't worry, I can still provide some guidance based on common issues and potential fixes when working with MinIO and Prefect. The

InvalidAccessKeyId

error usually suggests that the credentials being used to authenticate against the MinIO server aren't being recognized. Here are a few steps and checks you might consider: 1. Verify Credentials: Double-check that the

minio_key_id

and

minio_access_key

are correctly set and match the keys configured on your MinIO server. 2. Endpoint Configuration: Ensure that your

endpoint_url

is correctly pointing to your MinIO instance. MinIO endpoints can differ slightly from AWS S3, especially if they include port numbers or use different security (HTTP vs. HTTPS). 3. Permissions: Verify that the user related to these credentials has the necessary permissions to access the bucket and list objects. 4. Correct Use of MinIO with Prefect: Ensure that you're using

MinIOCredentials

correctly. It might need specific configuration or additional parameters that align with your MinIO setup. 5. Character Checks: Make sure there are no extra white spaces or special characters in your credentials that might have been accidentally added. You might want to refer to the official Prefect documentation on storage once the site is accessible, or MinIO's documentation for further insights. If you're sure everything else is correct, and it still doesn't work, consider checking the logs of your MinIO server for any additional clues on why the access key ID isn't being recognized. And if this problem persists, it might be worth raising an issue or checking for existing discussions in Prefect's community forums or GitHub issues for any recent updates or known issues. Just think of it as the universe reminding you to double-check everything, even if it might feel a little paranoid like Marvin from Hitchhiker's Guide.

@Marvin s3_client.download_file(Bucket="flows", Key="train.py", Filename="train.py") print("Correctly downloaded the flow file") with open("train.py", "r") as f: source_code = f # Deploy the flow with the uploaded files training_flow = await flow.from_source( source=source_code, entrypoint = "train.py:general_pytorch_flow") is this approach correct?

Your approach for downloading a file and then using it to deploy a flow with Prefect seems mostly correct, but there are some points that could be clarified or adjusted. 1. Downloading the File: You are correctly using

s3_client.download_file()

to download the file from your MinIO bucket (acting as S3) to your local system. 2. Opening the File: When you open the file using

open("train.py", "r")

, you should read the file content instead of just referring to the file object. You might want to use

f.read()

to actually capture the content. 3. Using the Source Code: If Prefect's

flow.from_source()

expects a source code string, make sure you are passing the actual content of the file, not the file object. Here's a streamlined version of what you might want to do:

python
import boto3

# Assuming you've configured boto3 and authenticated appropriately
s3_client = boto3.client('s3', endpoint_url=minio_url, aws_access_key_id=minio_key_id, aws_secret_access_key=minio_access_key)

s3_client.download_file(Bucket="flows", Key="train.py", Filename="train.py")

print("Correctly downloaded the flow file")

# Read the file content
with open("train.py", "r") as f:
    source_code = f.read()  # Read the content, not the file object

# Deploy the flow with the uploaded files
training_flow = await flow.from_source(
    source=source_code,
    entrypoint="train.py:general_pytorch_flow"
)

Make sure the asynchronous call to

from_source()

is being used correctly. It should match Prefect's API requirements for Prefect 2.20.3. If

from_source

requires a different structure, you might need to adjust based on the official documentation or API specifications. Keep chugging along, though! Sometimes deploying flows is just a matter of finding that right configuration that clicks into place, much unlike Marvin's brain with reality.

@Marvin i get this when I use f.read() OSError: [Errno 36] File name too long

The

OSError: [Errno 36] File name too long

typically arises when there's a misunderstanding in how the

open()

function is being used, especially when reading content. In your previous code, it seems the variable

source_code

might be used incorrectly, causing this error. The code logic should be structured so that it reads the content properly without mistaking it for a file name. Let's double-check and fix it: ### Potential Issue and Fix: 1. File Reading: Make sure that you only open the file with

open(filename, "r")

and read it using

f.read()

, not passing the

f

object somewhere else that expects a string. Here's how you should properly handle reading the content of a file:

python
import boto3

# Download the file as done before
s3_client = boto3.client('s3', endpoint_url=minio_url, aws_access_key_id=minio_key_id, aws_secret_access_key=minio_access_key)
s3_client.download_file(Bucket="flows", Key="train.py", Filename="train.py")

print("Correctly downloaded the flow file")

# Properly read the file content
with open("train.py", "r") as f:
    source_code = f.read()  # This should correctly read the file's content

# Deploy the flow with the source code as expected
training_flow = await flow.from_source(
    source=source_code,
    entrypoint="train.py:general_pytorch_flow"
)

### Key Points: - File Handle: Ensure that

f.read()

correctly returns the content as a string. The

source_code

variable should store this string content directly. - Error Context: The

OSError

you reported might occur if somehow the file content (a very large string) is being misinterpreted as a file path. Make sure

source

in

flow.from_source()

is supposed to take file content or adjust it according to Prefect's requirements. Hopefully, this makes the process clearer! As Marvin would cynically suggest, "Anything that thinks logically can be fooled by something more logical than itself." So let's keep things as simple and logical as possible.