@Marvin How to find logs about a flow which crashes when running a deployment, but running fine locally ? Reported flow run 'afcefd17-911a-40db-83ab-8e13ce150517' as crashed: Flow run infrastructure exited with non-zero status code 1.

When a flow runs fine locally but crashes during a deployment, you can troubleshoot using the Prefect logs, which often provide detailed information about the reason for the crash. Here’s a guide to finding and interpreting these logs. First, please confirm which version of Prefect you are using or let me find the relevant resources for both Prefect 2.x and 3.x to help you with log access in both versions. I'll search for information on how to access logs related to crashes for both versions. I encountered an issue while searching for the specific documentation. Let me correct that and try again to find relevant information on accessing logs for flow runs and troubleshooting crashes in both Prefect 2.x and 3.x versions. I'll perform a search with more precise queries. To troubleshoot when a flow crashes during deployment but runs locally without issues, you can use Prefect's logging capabilities to gain insights into the errors. Here's how to approach it for both Prefect 2.x and 3.x versions: For Prefect 2.x: - Prefect allows you to view logs associated with flow and task runs, which are sent to Prefect's UI. These logs typically contain informative stack traces that can help you identify the source of the problem. - Worker and agent logs, which may have more detailed information, can be accessed on the machine running those workers or agents. - Relevant link: Prefect 2.x troubleshooting guide For Prefect 3.x: - Prefect automatically emits a standard set of logs that are accessible via the UI, CLI, or API. These logs include information about run states, creation, errors, and tracebacks. - You can also emit custom logs using Prefect loggers, specifically by importing

get_run_logger

from

prefect.logging

. - This logging setup helps in monitoring and troubleshooting deployments effectively. - Relevant link: Prefect 3.x logging configuration By reviewing these logs, especially the error tracebacks, you should be able to identify specific issues causing the deployment crashes. If you encounter specific error messages or have further questions about particular log outputs, feel free to ask!

@Marvin Version used is 3.0.9 Where can I find detailed logs ?

In Prefect 3.x, including version 3.0.9, logs for your flow runs can be accessed via several methods: 1. Prefect UI: The UI is one of the easiest ways to access logs. Once you log in, navigate to the flow run in question, and you'll find the logs tab. This will include all logs emitted during the execution, such as state changes and error tracebacks. 2. CLI: You can use Prefect's Command-Line Interface to fetch logs. Use the following command to get logs for a specific flow run:

prefect logs --flow-run-id <your-flow-run-id>

Replace

<your-flow-run-id>

with the actual ID of the flow run you are investigating. 3. API: If needed, you can programmatically access logs through Prefect's API using the Python client. This allows for more customized log collection and analysis. 4. Custom Logging: If your flow has custom log outputs, ensure you're using Prefect's logger by importing

get_run_logger

from

prefect.logging

. This ensures logs are associated with the correct flow or task context. You can adapt these methods based on your infrastructure and needs. If you require step-by-step guidance on using any of these methods or encounter issues, feel free to ask!

Hi @Nate Just to let you know, Marvin is giving a bad answer to access logs with CLI The correct answer is

prefect flow-run logs <flow-run-id>

Your message was too long by 7 tokens - please shorten it and try again. For reference, here's your message at the allowed limit:

The logs in CLI don't give me enough informations to know what is going on.

Do you have advices to solve my issue ? ```2024-10-20 165124.202 | INFO | Flow run 'thoughtful-lizard' - Worker 'ProcessWorker 15e1ded4-ac63-494b-8052-c051923b5657' submitting flow run '647f8d8e-9840-47ff-bae7-1d447beb3e3d' 2024-10-20 165124.289 | INFO | Flow run 'thoughtful-lizard' - Opening process... 2024-10-20 165124.312 | INFO | Flow run 'thoughtful-lizard' - Completed submission of flow run '647f8d8e-9840-47ff-bae7-1d447beb3e3d' 2024-10-20 165133.767 | ERROR | Flow run 'thoughtful-lizard' - Process 307 exited with status code: 1 2024-10-20 165133.845 | INFO | Flow run 'thoughtful-lizard' - Reported flow run '647f8d8e-9840-47ff-bae7-1d447beb3e3d' as crashed: Flow run infrastructure exited with non

@Marvin The logs in CLI don't give me enough informations to know what is going on. Do you have advices to solve my issue ? 2024-10-20 165124.202 | INFO | Flow run 'thoughtful-lizard' - Worker 'ProcessWorker 15e1ded4-ac63-494b-8052-c051923b5657' submitting flow run '647f8d8e-9840-47ff-bae7-1d447beb3e3d' 2024-10-20 165124.289 | INFO | Flow run 'thoughtful-lizard' - Opening process... 2024-10-20 165124.312 | INFO | Flow run 'thoughtful-lizard' - Completed submission of flow run '647f8d8e-9840-47ff-bae7-1d447beb3e3d' 2024-10-20 165133.767 | ERROR | Flow run 'thoughtful-lizard' - Process 307 exited with status code: 1 2024-10-20 165133.845 | INFO | Flow run 'thoughtful-lizard' - Reported flow run '647f8d8e-9840-47ff-bae7-1d447beb3e3d' as crashed: Flow run infrastructure exited with non-zero status code 1.

Your message was too long by 5 tokens - please shorten it and try again. For reference, here's your message at the allowed limit:

The logs in CLI don't give me enough informations to know what is going on.

Do you have advices to solve my issue ? 2024-10-20 165124.202 | INFO | Flow run 'thoughtful-lizard' - Worker 'ProcessWorker 15e1ded4-ac63-494b-8052-c051923b5657' submitting flow run '647f8d8e-9840-47ff-bae7-1d447beb3e3d' 2024-10-20 165124.289 | INFO | Flow run 'thoughtful-lizard' - Opening process... 2024-10-20 165124.312 | INFO | Flow run 'thoughtful-lizard' - Completed submission of flow run '647f8d8e-9840-47ff-bae7-1d447beb3e3d' 2024-10-20 165133.767 | ERROR | Flow run 'thoughtful-lizard' - Process 307 exited with status code: 1 2024-10-20 165133.845 | INFO | Flow run 'thoughtful-lizard' - Reported flow run '647f8d8e-9840-47ff-bae7-1d447beb3e3d' as crashed: Flow run infrastructure exited with non-zero

@Marvin The logs in CLI don't give me enough informations to know what is going on. Do you have advices to solve my issue ? 2024-10-20 165124.202 | INFO | Flow run 'xxx' - Worker 'ProcessWorker 15e1ded4-ac63-494b-8052-c051923b5657' submitting flow run '647f8d8e-9840-47ff-bae7-1d447beb3e3d' 2024-10-20 165124.289 | INFO | Flow run 'xxx' - Opening process... 2024-10-20 165124.312 | INFO | Flow run 'xxx' - Completed submission of flow run '647f8d8e-9840-47ff-bae7-1d447beb3e3d' 2024-10-20 165133.767 | ERROR | Flow run 'xxx' - Process 307 exited with status code: 1 2024-10-20 165133.845 | INFO | Flow run 'xxx' - Reported flow run '647f8d8e-9840-47ff-bae7-1d447beb3e3d' as crashed: Flow run infrastructure exited with non-zero status code 1.

When a flow run crashes with a non-zero status code like what you're seeing, it typically indicates an unhandled exception or error in the flow. Here are some steps you can take to troubleshoot and potentially resolve the issue: 1. Check Local Logs and Error Messages: Since your flow runs successfully locally, compare the environment between local and deployment. Check for differences in environment variables, dependencies, or configurations that might cause an issue. 2. Environment Variables and Secrets: Ensure all necessary environment variables and secrets are correctly configured for the deployed environment. Missing configurations can lead to crashes. 3. Dependencies and Versions: Verify that all dependencies are installed and compatible in the environment where the deployment runs. Sometimes version mismatches can cause runtime errors not present in the local environment. 4. Increase Verbosity: Modify the logging level to

DEBUG

to capture more detailed logs. You can do this by configuring the logger using Prefect’s logging module or setting additional logging options. 5. Custom Logs: Add more custom logging around parts of the code where you suspect issues might arise. This can be done using

get_run_logger

from

prefect.logging

. 6. Infrastructure and Resource Limits: Check if there are any resource constraints or limits imposed by the deployment environment. For instance, insufficient memory or CPU can sometimes result in failures that aren't immediately obvious in the logs. 7. Check Worker and Agent Logs: Sometimes, the issue could stem from the Prefect worker or agent side, so review those logs as well. If you suspect the issue is within the logic of your flow, consider breaking down the flow into smaller parts and isolating the problematic section by adding diagnostics. If the problem persists, please consider sharing specific code snippets or configurations that might be relevant for deeper analysis!

hi yes sometimes LLMs do hallucinate syntax, feel free to ask marvin for docs so you can check yourself

@Marvin How can I make sure that my runtime step like the pull in my prefect.yaml are well executed ? Enrich your answer with docs if you have

To ensure that your runtime steps, like pulling certain resources or executing commands defined in

prefect.yaml

, are executed correctly, follow these guidelines: 1. Understand the Structure: The

prefect.yaml

file describes how to deploy your flow. It contains configurations for deployment settings, procedural steps for preparation, and instructions to set up the execution environment for deployment runs. Ensure your file is structured properly with the necessary fields for your use case. 2. Execution Steps: - In the

prefect.yaml

file, you can define steps, such as shell scripts, which might include pulling repositories or building images. - Utilize actions like

prefect.deployments.steps.run_shell_script

for executing commands that prepare your environment. For instance:

yaml
     build:
     - prefect.deployments.steps.run_shell_script:
         id: get-commit-hash
         script: git rev-parse --short HEAD
         stream_output: false

- Ensure these commands are correct and test them independently in a controlled environment to verify they behave as expected. 3. Initialize Properly: Use the CLI command

prefect init

in the directory or repository containing your flow code to set up your

prefect.yaml

file with the correct structure and recipes. 4. Inspect Logs: Increase verbosity and capture additional logs during these steps to confirm they are executed as intended. You may need to adjust logging configurations to gather detailed insights. 5. Mocking for Testing: For testing purposes, mock certain runtime values using environment variables to simulate execution without altering the deployment environment. For more detailed documentation, you can refer to these resources: - Define deployments with YAML - Accessing runtime context If these steps don't reveal the issue, ensure the environment where these steps execute closely mirrors your local environment to minimize discrepancies.