@Marvin I get this error when I try to quick run a deployment from the cloud State Message Submission failed. ValidationError: 1 validation error for Process command value is not a valid list (type=type_error.list) How can I know where the error is coming from? When I run the flow locally it works fine 205905.914 | INFO | prefect.engine - Created flow run 'laughing-rattlesnake' for flow 'Price Scrapper' TestING 205906.015 | INFO | Flow run 'laughing-rattlesnake' - Finished in state Completed()

It seems your error is related to a validation issue when submitting the deployment. Specifically, the error message "ValidationError: 1 validation error for Process command value is not a valid list" suggests that there is an issue in the process command - it expects a list but is receiving something else. Here are a few steps to troubleshoot this issue: 1. Check Your Deployment Configuration: Make sure that in your deployment configuration, any command specified is in the correct list format. 2. Validate Deployment File: If you are using a YAML file for deployment, ensure that the syntax is correct and all commands are listed properly. 3. Review Prefect Documentation: - How to create and configure deployments: https://docs.prefect.io/latest/guides/creating-interactive-workflows/ - Quick Start Guide: https://docs.prefect.io/latest/getting-started/quickstart/ 4. Look at GitHub Issues: There might be existing issues or discussions around similar errors in the Prefect GitHub repository. For example: - Test issue for notifications Ensure your deployment command values are specified correctly as a list in the deployment configuration. For example:

yaml
deployments:
  - name: price-scrapper
    entrypoint: path/to/your/flow.py:flow_function
    command: ["python", "-m", "prefect", "run"]

For further assistance, you may want to share your deployment configuration and any additional context in a community forum or support channel. If you need more specific examples or guidance, please let me know.

@Marvin in my azuure-pipelines.yml I have ... - bash: | export PYTHONPATH=$(pwd):$PYTHONPATH prefect cloud login --key pnu_ey1Jcb6Yp26oQLQIRNotihiY16GdsH42um3U --workspace 'rai-flows-dev/default' python scripts/deploy.py displayName: deploy flows and in the deploy.py import uuid from prefect.deployments import Deployment from prefect_azure.blob_storage import AzureBlobStorageContainer from pricescrap.main import run_spider azure_blob_storage_container_block = AzureBlobStorageContainer.load( "rai-default-flows-storage" ) Deployment.build_from_flow( flow=run_spider, name="price-scrap", work_pool_name="default-agent-pool", work_queue_name="product-scrap", storage=azure_blob_storage_container_block, apply=True, path=f"price-scrap-store-{uuid.uuid4()}", ) so there is no command there is it maybe the job variable command { "env": { "PREFECT__CLOUD__API": "https://api.prefect.io" }, "command": "pip install -r requirements.txt && python -m prefect.engine" }

The issue seems to be with the configuration of the deployment, particularly around the

command

parameter. The command is expected to be a list, but it appears as a string in the configuration. Based on your Azure pipeline and deployment script, the issue might stem from how the deployment is being built or from the

job variable command

. Here’s a relevant YAML excerpt for your infra configuration:

yaml
infrastructure:
  type: process
  env: {}
  labels: {}
  name: null
  command:
  - python
  - -m
  - prefect.engine
  stream_output: true

Make sure your deployment definition has the command defined as a list, as shown above. Here are a few steps to ensure correct configuration: ### Deployment Script If modifying the deployment YAML directly isn't feasible, you might need to adjust the deployment script or configurations for setting up deployments. ### Azure Pipeline Confirm that the commands and environment settings in your Azure pipeline are correctly formatted. In summary, ensure that any command used in the deployment configuration is correctly specified as a list in the deployment YAML or the script. For further reading and examples: - Block-based Deployments: https://docs.prefect.io/latest/concepts/deployments-block-based/ - Creating Interactive Workflows Guides: https://docs.prefect.io/latest/guides/creating-interactive-workflows/ If you continue to face issues, sharing your deployment YAML or further logs in a community forum might help pinpoint the problem more accurately.

@Marvin Can you convert this command "command": "pip install -r requirements.txt && python -m prefect.engine" into the format you say is accepted above like command: ["python", "-m", "prefect", "run"]

Certainly! You want to convert the string command: "command": "pip install -r requirements.txt && python -m prefect.engine" Into the list format expected by Prefect's deployment configuration. Here’s how you can do it:

yaml
infrastructure:
  type: process
  env: {}
  labels: {}
  name: null
  command:
    - sh
    - -c
    - pip install -r requirements.txt && python -m prefect.engine
  stream_output: true

This YAML ensures that the command is correctly interpreted as a list, and uses

sh -c

to allow chaining of commands with

&&

. Make sure to update your Python script (or YAML deployment configuration) accordingly to reflect this change.

@Marvin I chaned the job variable from this { "env": { "PREFECT__CLOUD__API": "https://api.prefect.io" }, "command": "pip install -r requirements.txt && python -m prefect.engine" } to this { "env": { "PREFECT__CLOUD__API": "https://api.prefect.io" }, "command": ["pip", "install", "-r", "requirements.txt", "&&", "python", "-m", "prefect.engine"] } and now I no longer get the command value is not a valid list (type=type_error.list) error but now the flow crashes Flow run infrastructure exited with non-zero status code 2. How can I debug this, there are no logs "This run didn't generate logs"

Let's walk through a couple of steps to troubleshoot and address the issue with debugging the deployment crash. ### Steps to Debug 1. Verify Command Syntax While the command you provided is syntactically valid, it doesn't work as intended since list elements are not chained commands. Instead, using a shell command might help. Change

command

to:

json
   {
     "env": {
       "PREFECT__CLOUD__API": "<https://api.prefect.io>"
     },
     "command": ["sh", "-c", "pip install -r requirements.txt && python -m prefect.engine"]
   }

2. Check Deployment Logs and Configurations Visit the troubleshooting guide: https://docs.prefect.io/latest/guides/troubleshooting/ Here you can find steps to enable detailed logging and track issues. 3. Ensure Prefect Deployment Mechanics are Followed Ensure that all steps in your

prefect.yaml

or in the script are correctly following Prefect deployment mechanics: https://docs.prefect.io/latest/guides/prefect-deploy/ 4. Review Infrastructure and Setup Ensure all necessary components and dependencies are correctly installed in the environment used for execution. ### Possible Areas to Reflect Errors: Check if these issues resonate with something specific in your setup: - Long running Kubernetes jobs issues and Common Flow Run Errors. Reviewing these steps should help identify the cause of the non-zero status code 2 error and potentially resolve the lack of log generation. Adjusting the command to use a shell as suggested will ensure proper command execution and logging.

@Marvin I changed the command to this { "env": { "PREFECT__CLOUD__API": "https://api.prefect.io" }, "command": ["sh", "-c", "pip install -r requirements.txt && python -m prefect.engine"] } and now I dont get exit status 2 but I get: Flow run infrastructure exited with non-zero status code 1. whats the difference between exit code 1 and 2 in Flow run infrastructure and do you know what could cause this

Exit codes are standard across Unix-like systems. Here’s what they generally mean: - Exit Code 2: Typically indicates misuse of shell builtins (e.g., missing keyword, command, or a permission problem). - Exit Code 1: A catch-all for general errors; it could imply various issues depending on the context. ### Difference Between Exit Code 1 and 2: 1. Exit Code 1: It's often indicative of a general issue, such as a command not found, failure to access resources, incorrect environment variables, or any generic problem during the script’s execution. 2. Exit Code 2: Typically more specific to shell script misuse errors (e.g., syntax errors or misused keywords). Considering that you’ve handled the issue for exit code 2 and moved to exit code 1, it likely indicates a different set of problems, perhaps a missing dependency, module import error, or any generic failure in the script. ### Steps to Diagnose and Resolve Exit Code 1: 1. Enable Detailed Logging: - Ensure detailed logging is enabled on the Prefect Cloud UI. Check infrastructure logs and any standard output/error streams. 2. Debugging Locally: - Run the command locally in a similar environment. This helps in identifying missing dependencies or misconfigurations. - Example: Run

sh -c "pip install -r requirements.txt && python -m prefect.engine"

in a local shell to catch any script-specific errors. 3. Check Dependencies: - Ensure all dependencies in

requirements.txt

can be installed and do not conflict with Prefect’s runtime environment. 4. Verify Flow Configuration and Deployment: - Ensure the flow and deployment configurations are correct. ### Possible Causes for Exit Code 1: - Missing Packages: A dependency in

requirements.txt

fails to install. - Environment Variables: Certain required environment variables might not be set up correctly. - Script Errors: There might be an error in the Python script being run. - Resource Access: Issues related to accessing required resources like databases, APIs, etc. ### Applying the Diagnosis: Running and debugging locally will often give clear error messages. The error should appear when running your setup command manually, such as:

sh
sh -c "pip install -r requirements.txt && python -m prefect.engine"

Additional Resources: For more detailed troubleshooting, consider reviewing the Prefect Troubleshooting Guide and checking if there are any updates or open issues in the Prefect GitHub repository. If all else fails, sharing more of your configuration and logs on community forums could bring in more specific suggestions.

@Marvin Okay we are getting somewhere I changed the command again to this { "env": { "PREFECT__CLOUD__API": "https://api.prefect.io" }, "command": ["pip install -r requirements.txt && python -m prefect.engine"] } it's my original command string just put in a list so I think it executes fine now the flow crashes with the state message "Flow run could not be submitted to infrastructure" What do you think?

The error "Flow run could not be submitted to infrastructure" can originate due to several reasons. Here are some common causes along with steps to diagnose and resolve them: ### Common Causes and Troubleshooting 1. Incorrect Command Format - Ensure your command is given correctly in the format expected by Prefect. - The command you currently have may need to be executed in a shell, so use:

json
     {
       "env": {
         "PREFECT__CLOUD__API": "<https://api.prefect.io>"
       },
       "command": ["sh", "-c", "pip install -r requirements.txt && python -m prefect.engine"]
     }

2. Infrastructure Issues - There could be issues related to permissions, resource availability, or misconfigurations in the infrastructure (e.g., Kubernetes, Docker, etc.). 3. Deployment Configuration - Validate the deployment configuration in YAML or equivalent configuration that you are applying. - Review the configuration documentation for block-based deployments: https://docs.prefect.io/latest/concepts/deployments-block-based/ 4. Concurrency and Rate Limits - Ensure that you are not hitting rate limits on Docker pulls. - Specific issues such as "Flow run could not be submitted to infrastructure" have been discussed in relation to Docker rate limits and Kubernetes setup. See related GitHub issues: - CannotPullContainerError - Toomanyrequests - Flow run could not be submitted to infrastructure 5. Detailed Error Logs - Enable airflow or infrastructure-specific detailed logging. - Prefect’s troubleshooting guide: https://docs.prefect.io/latest/guides/troubleshooting/ 6. Environment Dependency Issues - Run the command manually in a similar environment to catch dependency or syntax issues. - Example:

sh
     sh -c "pip install -r requirements.txt && python -m prefect.engine"

7. Prefect Version Mismatches - Ensure that your Prefect version is compatible with your deployment configurations. - Check the latest Prefect releases for any updates or breaking changes. ### Example Configuration Correction Ensure that your deployment YAML or equivalent configuration lists commands properly. Here's an example:

yaml
infrastructure:
  type: process
  env: {}
  labels: {}
  name: null
  command:
    - sh
    - -c
    - pip install -r requirements.txt && python -m prefect.engine
  stream_output: true

### Further Steps 1. Re-run the Deployment Command Locally: - Validate by running locally in the environment that mimics your deployment setup. 2. Monitor Infrastructure Logs: - Check logs in your infrastructure provider (AWS, Azure, GCP, Kubernetes, etc.) to pinpoint the exact error. By systematically addressing these areas, you should be able to identify and resolve the issue causing the flow run submission error. If you continue to face issues, sharing logs and setup details in Prefect’s community forum can garner targeted support.