Based on the research, here's how to debug these error codes: ### Error Code 255 This is a general failure code that typically means your script or command couldn't be properly executed. To debug: 1. Check your flow logs thoroughly for error messages preceding the exit code 2. Verify all required dependencies are installed in your execution environment 3. Ensure your environment variables are properly set 4. Try running your flow with increased logging level to get more details 5. Check that all imported modules are available in your execution environment ### Error Code 126 This is specifically a permissions-related error. To resolve: 1. Check the execute permissions on your script files:

bash
   chmod +x your_script.py

2. Verify the script exists in the expected location 3. Ensure your deployment's service account has the necessary permissions 4. If using Docker, check that the file permissions are correctly set in the container 5. Verify that the command you're trying to execute is actually executable (not a directory or incompatible file type) ### General Debugging Tips 1. Always check the full logs, not just the error code 2. If using infrastructure like Docker or Kubernetes, verify the environment matches your expectations 3. Test the command locally first before running it through Prefect 4. Ensure all dependencies are properly specified in your requirements.txt or Dockerfile 5. If using custom images, verify that all necessary packages are installed during the build If you're seeing these errors in a specific context or would like more targeted debugging help, please share more details about your setup and how the error is occurring.

There are several ways to increase logging levels in Prefect: 1. Using Environment Variables

bash
   # Set global logging level
   export PREFECT_LOGGING_LEVEL="DEBUG"
   
   # Set specific component logging levels
   export PREFECT_LOGGING_FLOW_LEVEL="DEBUG"
   export PREFECT_LOGGING_TASK_LEVEL="DEBUG"

2. Within Your Flow Code

python
   from prefect import flow
   from prefect.logging import get_run_logger

   @flow
   def my_flow():
       logger = get_run_logger()
       logger.setLevel("DEBUG")
       logger.debug("This is a debug message")

3. Using the Prefect CLI

bash
   # Run a deployment with debug logging
   prefect deployment run my-flow/my-deployment --log-level DEBUG

4. In a Deployment Configuration

python
   from prefect import flow
   from prefect.deployments import Deployment

   @flow
   def my_flow():
       pass

   deployment = Deployment.build_from_flow(
       my_flow,
       name="my-deployment",
       settings={"env": {"PREFECT_LOGGING_LEVEL": "DEBUG"}}
   )

Available Logging Levels (from least to most verbose): - CRITICAL (50) - ERROR (40) - WARNING (30) - INFO (20) - Default - DEBUG (10) Tips for Effective Logging: 1. Use DEBUG level sparingly in production as it can generate large amounts of logs 2. Consider setting different levels for different components based on your debugging needs 3. Remember that logs in Prefect Cloud are retained based on your subscription plan 4. You can combine logging levels with log handlers to direct different levels to different outputs When debugging specific issues, DEBUG level logging can provide valuable information about: - Flow and task execution details - Infrastructure setup and teardown - State transitions - API calls and responses - Task retries and failures Remember that increasing log levels will generate more detailed output, which can be helpful for debugging but may impact performance and storage if left at verbose levels in production.

Yes, there are several ways to ensure your Prefect Docker image is built for the AMD/x86_64 platform: 1. Using Docker BuildX Command

bash
   docker buildx build --platform linux/amd64 -t your-image-name .

2. In Your Dockerfile

dockerfile
   FROM --platform=linux/amd64 prefecthq/prefect:2-python3.9
   # Rest of your Dockerfile...

3. Using Docker Compose

yaml
   services:
     flow:
       platform: linux/amd64
       build:
         context: .
         dockerfile: Dockerfile

4. Using Prefect's DockerContainer

python
   from prefect.infrastructure import DockerContainer

   docker_block = DockerContainer(
       image="your-image-name",
       platform="linux/amd64",
       # other configurations...
   )

5. During Image Build and Push

python
   from prefect.infrastructure.docker import DockerImage

   docker_image = DockerImage(
       image_name="your-image-name",
       platform="linux/amd64",
       dockerfile="Dockerfile",
   )
   docker_image.build()

Best Practices: 1. Always explicitly specify the platform to avoid any ambiguity 2. Test the image on the target environment before deployment 3. Consider using multi-platform builds if you need to support multiple architectures 4. Verify the built image architecture using:

bash
   docker inspect your-image-name | grep Architecture

If you're experiencing platform mismatch issues, you might see errors like:

standard_init_linux.go:228: exec user process caused: exec format error

This typically means you're trying to run an image built for a different architecture than your host system. Remember that while building for a specific platform, your build machine needs to be capable of building for that target architecture, either natively or through emulation.