I have a task that is supposed to get a LazyFrame from an S3-bucket:

@task
def get_as_lazyframe(s3_location: str) -> pl.LazyFrame:
    """Read from S3-Bucket and get lazy DataFrame."""
    return pl.scan_parquet(
        f"{s3_location}/*.parquet",
        storage_options={
            "aws_access_key_id": settings.AWS_ACCESS_KEY_ID,
            "aws_secret_access_key": settings.AWS_SECRET_ACCESS_KEY,
            "aws_region": "eu-west-1",
        }
    )

This code works when I run it locally, but fails when I run it on a local cluster. This results in the following error:

ComputeError: Generic S3 error: Error performing list request: Error after 2 retries in ...

It also does not work with

read_parquet

(the non-lazy variant)

It seems likely that the issue you're encountering is related to the configuration of S3 access in your local cluster environment. The error message

Generic S3 error: Error performing list request

suggests that the task is unable to access the S3 bucket, possibly due to incorrect or missing AWS credentials or network configuration in the cluster environment. My understanding is that when running on a local cluster, you need to ensure that the environment has the correct AWS credentials and network access to S3. This can be done by setting environment variables for AWS credentials or configuring IAM roles if running on AWS infrastructure. Additionally, ensure that the network settings allow outbound access to the S3 endpoint. Would you like more details on how to configure AWS credentials for your local cluster environment? Sources: - Flyte FAQ on using blob storage 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.

@ancient-wolf-19325 How do I allow my local cluster to connect to S3 endpoints? I don't find anything in the documentation for this or get an 404 error when accessing that documentation

To allow your local cluster to connect to S3 endpoints, you need to ensure that your cluster environment is configured with the correct AWS credentials and network settings. Here are the steps you can follow: 1. Set AWS Credentials: Ensure that your AWS credentials are available in the environment where your Flyte tasks are running. You can do this by setting environment variables such as

AWS_ACCESS_KEY_ID

,

AWS_SECRET_ACCESS_KEY

, and

AWS_REGION

. These can be set in your Kubernetes deployment configuration or as environment variables in your task's container. 2. Network Configuration: Make sure that your local cluster has network access to the S3 endpoint. This might involve configuring your network settings or firewall rules to allow outbound traffic to the S3 service. 3. Use IAM Roles: If your local cluster is running on AWS infrastructure, consider using IAM roles for service accounts to manage access to S3. This approach is more secure and eliminates the need to manage AWS credentials manually. 4. Flyte Configuration: Ensure that your Flyte configuration is set up to use the correct S3 endpoint. This can be done by configuring the storage settings in your Flyte deployment, specifying the S3 endpoint, and ensuring that the

authType

is set to

accesskey

if using access keys. Would you like more detailed guidance on any of these steps? Sources: - https://www.union.ai/docs/flyte/integrations/flyte-agents/sensor/file_sensor_example/ - https://github.com/flyteorg/flyte/blob/master/docker/sandbox-bundled/manifests/complete.yaml 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.

@ancient-wolf-19325 Starting from the sandbox configuration, where do I need to put the S3 credentials? Do I need to restart the cluster when changing these credentials?

To configure S3 credentials in a Flyte sandbox environment, you typically need to set them in the Flyte configuration files or as environment variables. Here’s how you can do it: 1. Configuration File: You can specify the S3 credentials in the Flyte configuration file, usually located at

~/.flyte/config-sandbox.yaml

. Add or update the

storage

section with your S3 credentials:

storage:
     connection:
       endpoint: http://localhost:30002
       access-key: <your-access-key>
       secret-key: <your-secret-key>
       auth-type: accesskey
       disable-ssl: true

2. Environment Variables: Alternatively, you can set the AWS credentials as environment variables in your shell or Kubernetes deployment:

export AWS_ACCESS_KEY_ID=<your-access-key>
   export AWS_SECRET_ACCESS_KEY=<your-secret-key>
   export AWS_REGION=eu-west-1

3. Restarting the Cluster: After updating the credentials, you will need to restart the Flyte sandbox cluster to apply the changes. You can do this by stopping and starting the sandbox using `flytectl`:

flytectl demo stop
   flytectl demo start

Would you like more detailed instructions on any of these steps? Sources: - https://github.com/flyteorg/flyte/issues/6415 - https://github.com/flyteorg/flyte/issues/5663 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.

@ancient-wolf-19325 What is the "endpoint" url? Why is it localhost? Do I need to substitute it?

The "endpoint" URL in the Flyte configuration refers to the address where the S3-compatible storage service is accessible. In a local Flyte sandbox setup, this is often set to

http://localhost:30002

because the sandbox includes a local MinIO instance, which is an S3-compatible storage service running on your local machine. This allows Flyte to simulate S3 storage locally for development and testing purposes. If you are using an actual S3 service (like AWS S3 or another S3-compatible service), you will need to substitute the

endpoint

with the appropriate URL for that service. For example, AWS S3 does not require an endpoint URL, but if you are using a service like DigitalOcean Spaces or a self-hosted MinIO, you would use the service's endpoint URL. Would you like more details on configuring Flyte with a specific S3 service? Sources: - https://github.com/flyteorg/flyte/issues/416 - https://github.com/flyteorg/flyte/blob/master/flyte-single-binary-local.yaml 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.

@ancient-wolf-19325 How do I configure the sandbox cluster to reach S3 without MinIO, but a proper remote S3-bucket?

To configure the Flyte sandbox to use a remote S3 bucket instead of the local MinIO instance, you need to update the Flyte configuration to point to the remote S3 service. Here’s how you can do it: 1. Update Configuration File: Modify the Flyte configuration file, typically located at

~/.flyte/config-sandbox.yaml

, to use the remote S3 bucket. You should specify the S3 endpoint and authentication details:

storage:
     connection:
       endpoint: https://s3.<region>.amazonaws.com
       access-key: <your-access-key>
       secret-key: <your-secret-key>
       auth-type: accesskey
       disable-ssl: false

Replace

<region>

,

<your-access-key>

, and

<your-secret-key>

with your AWS S3 region and credentials. 2. Environment Variables: Alternatively, you can set the AWS credentials as environment variables:

export AWS_ACCESS_KEY_ID=<your-access-key>
   export AWS_SECRET_ACCESS_KEY=<your-secret-key>
   export AWS_REGION=<your-region>

3. Restart the Sandbox: After making these changes, restart the Flyte sandbox to apply the new configuration:

flytectl demo stop
   flytectl demo start

Would you like more detailed guidance on any of these steps? Sources: - Flyte FAQ on using blob storage 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.

@ancient-wolf-19325 I added the storage config, but I still get the above-mentioned error. How can I verify that the config is applied and the credentials are correct? Can you give me a code snippet to test it?

To verify that your storage configuration and credentials are correctly applied, you can perform a simple test by attempting to list the contents of your S3 bucket using the

boto3

library in Python. This will help ensure that your credentials and endpoint are correctly configured. Here’s a code snippet to test the S3 connection:

import boto3
from botocore.exceptions import NoCredentialsError, PartialCredentialsError

def test_s3_connection(bucket_name):
    try:
        # Initialize a session using your credentials
        session = boto3.Session(
            aws_access_key_id='<your-access-key>',
            aws_secret_access_key='<your-secret-key>',
            region_name='<your-region>'
        )
        
        # Create an S3 client
        s3 = session.client('s3')
        
        # List objects in the specified bucket
        response = s3.list_objects_v2(Bucket=bucket_name)
        
        # Print the contents of the bucket
        if 'Contents' in response:
            for obj in response['Contents']:
                print(obj['Key'])
        else:
            print("Bucket is empty or does not exist.")
    
    except NoCredentialsError:
        print("Credentials not available.")
    except PartialCredentialsError:
        print("Incomplete credentials provided.")
    except Exception as e:
        print(f"An error occurred: {e}")

# Replace with your bucket name
test_s3_connection('<your-bucket-name>')

Replace

<your-access-key>

,

<your-secret-key>

,

<your-region>

, and

<your-bucket-name>

with your actual AWS credentials and bucket details. This script will attempt to list the objects in the specified S3 bucket and print them to the console. Would you like more detailed guidance on any of these steps? Sources: - https://github.com/flyteorg/flyte/issues/5012 - https://github.com/flyteorg/flyte/issues/6415 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.