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<@ULVA73B9P> I have setup a prefect worker in GKE ...
# ask-marvin
i
@Marvin I have setup a prefect worker in GKE using Terraform, now I have to setup my workpool given a specific docker image together with environment variables and a set of volumes. Besides, I need to deploy my flows (that are baked into the docker image) with a certain schedule. Help me setup the prefect yaml.
m
To set up a Prefect work pool with a specific Docker image, environment variables, and volumes, and to deploy your flows with a schedule, you'll need to define these configurations in a 
prefect.yaml
file. Here are the steps and components you need to include: ### Work Pool Configuration 1. Define the Work Pool: You need to specify the Docker image, environment variables, and volumes in the 
work_pool
section of your YAML file. 2. Specify Environment Variables and Volumes: These can be set in the 
job_variables
section. ### Deployment Configuration 1. Schedule: Define when your flows should run using a schedule object. Prefect supports various scheduling options, including cron schedules. Here's a basic structure for your 
prefect.yaml
file:
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yaml
work_pool:
  name: my-docker-pool
  type: docker
  job_variables:
    image: "your-docker-image"
    env:
      - MY_ENV_VAR: "value"
    volumes:
      - host_path: "/host/path"
        container_path: "/container/path"

deployments:
  - name: my-flow-deployment
    flow: my_flow.py
    schedule:
      type: cron
      cron: "0 0 * * *"  # Adjust this cron expression to your desired schedule
### Additional Resources - Work Pools: Work Pools Documentation - Docker Infrastructure Example: Docker Example Documentation - Scheduling Flows: Scheduling Documentation This setup will allow you to manage your Prefect flows effectively with the specified Docker image and configurations. If you have more specific requirements or encounter issues, feel free to ask for further assistance.
i
doesn't my deployments need to specifiy the workpool used? Should I load the workpool template in a json?
@Marvin doesn't my deployments need to specifiy the workpool used? Should I load the workpool template in a json?
m
Yes, your deployment configuration should specify the work pool to be used. This ensures that the deployment is associated with the correct execution environment. ### Specifying the Work Pool in Deployment In your 
prefect.yaml
file, you should include the 
work_pool
attribute under the deployment configuration to link it to the appropriate work pool. Here's how you can adjust your YAML:
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yaml
deployments:
  - name: my-flow-deployment
    flow: my_flow.py
    work_pool: my-docker-pool  # Specify the work pool here
    schedule:
      type: cron
      cron: "0 0 * * *"  # Adjust this cron expression to your desired schedule
### Loading Work Pool Templates from JSON You can manage your work pool's base job template using JSON files. This is particularly useful for version control and CI/CD pipelines. You can update a work pool’s base job template using the Prefect CLI with a command like:
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bash
prefect work-pool update my-docker-pool --template-file base-job-template.json
This command reads a JSON file (
base-job-template.json
) and updates the work pool's template accordingly. ### Additional Resources - Work Pools and Deployment: Work Pools Documentation This setup will ensure that your deployments are correctly linked to the specified work pool and allow you to manage templates efficiently through JSON.
m
i
@Mitch Nielsen Thanks! I did see those references. Currently, I am migrating some legacy application from Prefect 2.7.8 with Agents and KurbenetesJob to Prefect 3.2 with Workers and Work Pools. I am trying to get things to work without changing the CI/CD structure too much
👍 1
@Marvin Thank you, Marvin Can you now help me setup the base job template? In previous versions, I was specifying all volumes in the customization parameter from the KubernetesJob from the python SDK. Could I just have a customization section in the template? I don't think so, right?
m
In Prefect 3.x, the base job template for a work pool is divided into two main sections: 
job_configuration
and 
variables
. You can customize the template to include volumes or any other Kubernetes-specific configurations. ### Base Job Template Customization 1. Job Configuration: This section defines the actual job setup, including how the flow runs are executed. It can include placeholders for dynamic values. 2. Variables: This section defines the fields that can be customized for each deployment or flow run. It follows the OpenAPI specification and allows you to set constraints on these fields. To include volumes, you would typically modify the 
job_configuration
section. Unfortunately, you can't just have a 
customization
section like in previous versions. Instead, you need to directly define these configurations in the JSON representation of the template. Here's a basic example of how you might set up a base job template with volumes:
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json
{
  "variables": {
    "image": {
      "type": "string",
      "default": "your-docker-image"
    },
    "volumes": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "hostPath": {
            "type": "string"
          },
          "containerPath": {
            "type": "string"
          }
        }
      }
    }
  },
  "job_configuration": {
    "containers": [
      {
        "image": "{{ image }}",
        "volumeMounts": [
          {
            "name": "my-volume",
            "mountPath": "{{ volumes[0].containerPath }}"
          }
        ]
      }
    ],
    "volumes": [
      {
        "name": "my-volume",
        "hostPath": {
          "path": "{{ volumes[0].hostPath }}"
        }
      }
    ]
  }
}
### Additional Considerations - Customization: Customize your job template using the Prefect UI or directly in JSON. - Version Control: Manage your base job templates in version control systems and update them through CI/CD pipelines. For more details, refer to the Kubernetes Infrastructure Example and Work Pools Documentation.
i
@Marvin Cool, can you give me a full template for specifying: image, image_pull_policy, namespace, pod_watch_timeout_seconds,env and customizations? (All fields from the KubernetesJob at 2.7.8)
m
To set up a full base job template in Prefect 3.x for Kubernetes, you can specify fields such as 
image
, 
image_pull_policy
, 
namespace
, 
pod_watch_timeout_seconds
, 
env
, and custom configurations. Here's a comprehensive example of how you might define these in your job template:
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json
{
  "variables": {
    "image": {
      "type": "string",
      "default": "your-docker-image"
    },
    "image_pull_policy": {
      "type": "string",
      "default": "IfNotPresent"
    },
    "namespace": {
      "type": "string",
      "default": "default"
    },
    "pod_watch_timeout_seconds": {
      "type": "integer",
      "default": 60
    },
    "env": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "name": {
            "type": "string"
          },
          "value": {
            "type": "string"
          }
        }
      }
    }
  },
  "job_configuration": {
    "containers": [
      {
        "image": "{{ image }}",
        "imagePullPolicy": "{{ image_pull_policy }}",
        "env": "{{ env }}"
      }
    ],
    "namespace": "{{ namespace }}",
    "pod_watch_timeout_seconds": "{{ pod_watch_timeout_seconds }}",
    "volumes": [
      {
        "name": "my-volume",
        "hostPath": {
          "path": "/host/path"
        }
      }
    ]
  }
}
### Key Components - Image and Image Pull Policy: Define which Docker image to use and when it should be pulled. - Namespace: Specify the Kubernetes namespace for job creation. - Pod Watch Timeout Seconds: Set the timeout for watching pod status. - Environment Variables: Use the 
env
field to pass environment variables to your containers. - Volumes: Define any necessary volumes directly in the 
job_configuration
. ### Additional Resources - Kubernetes Infrastructure Example - Work Pools Documentation This template provides a structured way to define Kubernetes job configurations in Prefect 3.x, allowing you to leverage the flexibility of Kubernetes while integrating with Prefect's orchestration capabilities.
i
@Marvin
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{
    'op': 'add',
    'path': '/spec/template/spec/containers/0/resources',
    'value': {
        'limits': {
            'memory': '8192Mi',
            'cpu': '2000m'
        }
    }
},
# Prevents Kubernetes to keep endlessly restarting failed jobs.
{
    'op': 'add',
    'path': '/spec/backoffLimit',
    'value': 0
},
I had this in customizations too, where should I put it
m
In Prefect 3.x, you can include these Kubernetes-specific configurations directly in the 
job_configuration
section of your base job template. These configurations are typically added under the 
containers
and 
spec
sections to customize resource limits and other Kubernetes job behaviors. Here's how you can incorporate your customizations into the base job template:
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json
{
  "variables": {
    "image": {
      "type": "string",
      "default": "your-docker-image"
    },
    "image_pull_policy": {
      "type": "string",
      "default": "IfNotPresent"
    },
    "namespace": {
      "type": "string",
      "default": "default"
    },
    "pod_watch_timeout_seconds": {
      "type": "integer",
      "default": 60
    },
    "env": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "name": {
            "type": "string"
          },
          "value": {
            "type": "string"
          }
        }
      }
    }
  },
  "job_configuration": {
    "containers": [
      {
        "image": "{{ image }}",
        "imagePullPolicy": "{{ image_pull_policy }}",
        "env": "{{ env }}",
        "resources": {
          "limits": {
            "memory": "8192Mi",
            "cpu": "2000m"
          }
        }
      }
    ],
    "namespace": "{{ namespace }}",
    "pod_watch_timeout_seconds": "{{ pod_watch_timeout_seconds }}",
    "backoffLimit": 0,  // Prevents Kubernetes from endlessly restarting failed jobs
    "volumes": [
      {
        "name": "my-volume",
        "hostPath": {
          "path": "/host/path"
        }
      }
    ]
  }
}
### Key Adjustments - Resource Limits: Added under 
containers.resources
to specify memory and CPU limits. - Backoff Limit: Added directly to prevent Kubernetes from retrying failed jobs indefinitely. This approach ensures that your Kubernetes job configurations are fully integrated into the Prefect 3.x framework, leveraging the flexibility of Kubernetes while maintaining the orchestration capabilities of Prefect.
i
@Marvin what is the difference between env variables of the container and of the workpool? If I were specifying my env variables in the env parameter in KubernetesJob (2.7.8), where should I put them now
m
In Prefect 3.x, environment variables can be specified at different levels, each serving a distinct purpose: ### Environment Variables in Work Pools - Purpose: These are generally used to configure the environment for the worker itself, affecting all jobs that run in that work pool. - Scope: They apply to all flow runs executed by workers in the work pool. They can be used to set global configurations or secrets that all jobs might need. ### Environment Variables in Containers - Purpose: These are specific to the individual container running a particular flow. They are used to configure the runtime environment for that specific flow execution. - Scope: They apply only to the specific flow run and can be used to pass parameters or configurations unique to that flow. ### Transition from KubernetesJob (2.7.8) If you were specifying environment variables using the 
env
parameter in the 
KubernetesJob
in Prefect 2.7.8, you should now include them in the 
env
section of the 
containers
configuration within the 
job_configuration
of your base job template. This ensures that they are applied to the specific flow execution environment. Here's how you can specify them:
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json
{
  "variables": {
    "image": {
      "type": "string",
      "default": "your-docker-image"
    },
    // Other variables...
    "env": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "name": {
            "type": "string"
          },
          "value": {
            "type": "string"
          }
        }
      }
    }
  },
  "job_configuration": {
    "containers": [
      {
        "image": "{{ image }}",
        "env": [
          {
            "name": "MY_ENV_VAR",
            "value": "value"
          }
          // Add more environment variables as needed
        ]
      }
    ]
    // Other configurations...
  }
}
By placing your environment variables in the 
containers.env
section, you ensure they are specific to the flow run, similar to how you used the 
env
parameter in the 
KubernetesJob
previously.
i
@Marvin My volumes shouldn't have name, path and value?
m
In Kubernetes, volumes typically have a 
name
and a 
path
, but not a 
value
. The structure of a volume definition depends on the type of volume you are using. Here's a breakdown: ### Volume Structure - name: The identifier for the volume. This is used to reference the volume in the 
volumeMounts
section of the container. - hostPath: (For host path volumes) The path on the host machine where the volume is located. - mountPath: (Defined in 
volumeMounts
) The path in the container where the volume should be mounted. Here's how you can define volumes in your base job template:
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json
{
  "variables": {
    "image": {
      "type": "string",
      "default": "your-docker-image"
    },
    "env": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "name": {
            "type": "string"
          },
          "value": {
            "type": "string"
          }
        }
      }
    }
  },
  "job_configuration": {
    "containers": [
      {
        "image": "{{ image }}",
        "env": "{{ env }}",
        "volumeMounts": [
          {
            "name": "my-volume",
            "mountPath": "/container/path"
          }
        ]
      }
    ],
    "volumes": [
      {
        "name": "my-volume",
        "hostPath": {
          "path": "/host/path"
        }
      }
    ]
  }
}
### Explanation - volumeMounts: Specifies where the volume should be mounted inside the container. It uses the 
name
to reference the volume defined in the 
volumes
section. - volumes: Defines the volume itself. For a 
hostPath
volume, you specify the 
path
on the host machine. This setup ensures that your volumes are correctly configured and mounted within your Kubernetes pods.
i
finally, help me setup the working directory
@Marvin finally, help me setup the working directory
m
To set the working directory in a Kubernetes job template in Prefect 3.x, you can specify it within the 
containers
section of your 
job_configuration
. The working directory determines where commands are run inside the container. ### Setting the Working Directory You can add the 
workingDir
field to the container configuration to specify the directory:
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json
{
  "variables": {
    "image": {
      "type": "string",
      "default": "your-docker-image"
    },
    "env": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "name": {
            "type": "string"
          },
          "value": {
            "type": "string"
          }
        }
      }
    }
  },
  "job_configuration": {
    "containers": [
      {
        "image": "{{ image }}",
        "env": "{{ env }}",
        "workingDir": "/desired/working/directory",  // Set your working directory here
        "volumeMounts": [
          {
            "name": "my-volume",
            "mountPath": "/container/path"
          }
        ]
      }
    ],
    "namespace": "{{ namespace }}",
    "pod_watch_timeout_seconds": "{{ pod_watch_timeout_seconds }}",
    "backoffLimit": 0,
    "volumes": [
      {
        "name": "my-volume",
        "hostPath": {
          "path": "/host/path"
        }
      }
    ]
  }
}
### Explanation - workingDir: This field sets the working directory for the container. It specifies where commands will be executed inside the container. By configuring the 
workingDir
, you ensure that your flow runs in the correct directory context, which is particularly important if your flow relies on relative paths or specific directory structures.
i
@Marvin Job Variables
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{
  "WORKING_DIRECTORY": "/opt/prefect/flows"
}
Pull Steps
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[
  {
    "prefect.deployments.steps.set_working_directory": {
      "directory": "/Users/iagoalves/work/window-finance/wf-momentum-trading"
    }
  }
]
I see this on the UI, the only way of changing the pull steps is using prefect.yaml or could I do in the python sdk deploy?
m
In Prefect 3.x, you can modify pull steps either through the 
prefect.yaml
file or programmatically using the Python SDK. The pull steps are crucial as they define how Prefect retrieves your deployment in remote execution environments. ### Modifying Pull Steps 1. Using 
prefect.yaml
: You can define the pull steps in the 
prefect.yaml
file under the 
pull
section. This is the most straightforward way to manage these steps across different environments. 2. Using the Python SDK: You can also modify pull steps programmatically when deploying using the Python SDK. This can be done by specifying the pull steps as part of the deployment configuration. Here's an example of how you might define pull steps in the `prefect.yaml`:
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yaml
pull:
  - prefect.deployments.steps.set_working_directory:
      directory: "/desired/working/directory"
### Programmatic Deployment with Python SDK If you prefer to manage your deployments programmatically, you can do so by setting up the deployment configuration in your Python code. Unfortunately, the exact syntax for modifying pull steps via the SDK wasn't explicitly detailed in the documentation I reviewed, but typically you would interact with the deployment API to set these configurations. ### Additional Resources - Prefect YAML Documentation - Deployment Guide If your use case requires dynamic adjustments or integration with other Python code, using the SDK might be more flexible. Otherwise, managing these configurations via the 
prefect.yaml
provides a centralized and declarative approach.
i
@Marvin why do I still see This work pool's base job template does not have any customizations. To add customizations, edit the base job template directly with the Advanced tab. in the UI? How can I know the uploaded template worked?
m
If you see the message "This work pool's base job template does not have any customizations" in the UI, it might indicate that the customizations you attempted to apply did not take effect. Here are some steps to troubleshoot and verify that your uploaded template worked: ### Troubleshooting Steps 1. Verify the Template: Double-check the JSON structure of your base job template for any syntax errors or misconfigurations. Ensure that all fields are correctly defined and match the expected format. 2. Check Prefect Version: Make sure you are using a compatible version of Prefect that supports the features you are trying to use. 3. Update and Refresh: After updating the base job template, ensure you refresh the Prefect UI to see the changes. Sometimes the UI might not reflect updates immediately due to caching. 4. Logs and Feedback: Check the Prefect worker logs for any errors or warnings related to the job template. These logs can provide insights into what might be going wrong. 5. Advanced Tab: As suggested, you can use the Advanced tab in the Prefect UI to directly edit and verify your base job template. This can be a quick way to see if your changes are being recognized. ### Verifying Template Application - Test a Deployment: Run a test deployment using the work pool to see if the customizations are applied during execution. Check if the environment variables, working directory, and other settings are as expected. - UI Indicators: The Prefect UI should reflect any customizations in the work pool settings or during the deployment process. If you continue to experience issues, consider reaching out to the Prefect community or support for more specific guidance, as there might be nuances related to your specific setup that require deeper investigation.
i
@Marvin what's the command for a worker to start that workpool?
m
To start a worker for a specific work pool in Prefect 3.x, you can use the 
prefect worker start
command. This command requires you to specify the name of the work pool you want the worker to connect to. Here's how you can do it: ### Command to Start a Worker
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bash
prefect worker start --pool my-docker-pool
### Key Points - 
--pool
: This option specifies the name of the work pool the worker should connect to. Replace 
my-docker-pool
with the name of your actual work pool. - Additional Options: You can include other options as needed, such as setting the worker's concurrency limits or specifying a different configuration file. This command will start a worker that polls the specified work pool for tasks to execute. Ensure that your Prefect environment is correctly configured and that the necessary permissions and network settings are in place for the worker to communicate with the Prefect server.
i
@Marvin does -p work?
m
The 
-p
flag is not a documented shorthand for specifying the work pool in the 
prefect worker start
command. You should use the full 
--pool
option to ensure the command is interpreted correctly:
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bash
prefect worker start --pool my-docker-pool
Using the full option name helps avoid any ambiguity and ensures compatibility with the documented CLI options. If you encounter any issues or have further questions about CLI usage, referring to the Prefect CLI documentation can provide additional guidance.
i
@Marvin Unable to start worker. Please ensure you have the necessary dependencies installed to run your desired worker type.
m
The error message you're encountering suggests that there might be missing dependencies required to run the specific type of worker you are trying to start. Here are some steps to ensure you have the necessary dependencies installed: ### Steps to Resolve Missing Dependencies 1. Check Worker Type: Ensure you are starting the correct worker type for your work pool. Different worker types may require different dependencies. 2. Install Required Packages: Prefect CLI can automatically install required packages for Prefect-maintained worker types. You can configure this behavior with the 
--install-policy
option when starting the worker. Example:
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bash
   prefect worker start --pool my-docker-pool --install-policy auto
3. Verify Prefect Version: Ensure you are using a compatible version of Prefect that supports the worker type you are trying to start. You can check your Prefect version using:
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bash
   prefect version
4. Check Worker Logs: Review the logs for any specific error messages or hints about missing dependencies. Logs are accessible through the Prefect Cloud UI or API if you are connected to Prefect Cloud. 5. Install Prefect Integrations: If your worker relies on specific integrations (e.g., 
prefect-aws
, 
prefect-gcp
), make sure these packages are installed in your environment. If you follow these steps and still encounter issues, it might be helpful to consult the Prefect documentation on workers or reach out to the Prefect community for further assistance.
i
@Marvin Why is prefect-kubernetes only available from 3.2.8 forward, how to setup kubernetes-job before that version? WHat changed
m
The 
prefect-kubernetes
package became available in Prefect version 3.2.8 and later to provide a more streamlined and dedicated set of tools for orchestrating, observing, and managing Kubernetes resources with Prefect. This package is designed to enhance the integration between Prefect and Kubernetes, offering tasks, flows, and blocks that are specifically tailored for Kubernetes environments. ### Setting Up Kubernetes Jobs Before Prefect 3.2.8 Before the introduction of 
prefect-kubernetes
, users would typically set up Kubernetes jobs using more generalized infrastructure configurations and custom scripts. Here are some steps you might have taken: 1. Use KubernetesJob Blocks: In earlier versions, you would define your Kubernetes jobs using the 
KubernetesJob
infrastructure block, specifying parameters such as the image, command, and resources directly in the block configuration. 2. Custom YAML Files: You might also have used custom Kubernetes YAML files to define the job specifications and then used Prefect to manage these deployments. 3. Manual Integration: Without the dedicated 
prefect-kubernetes
package, integrating Prefect with Kubernetes required more manual setup and configuration, often involving custom scripts to handle deployment and execution. ### What Changed with 
prefect-kubernetes
- Dedicated Tools: The 
prefect-kubernetes
package provides dedicated tools and blocks for Kubernetes, making it easier to manage Kubernetes resources directly from Prefect. - Streamlined Integration: With this package, Prefect offers a more seamless integration with Kubernetes, allowing for easier setup and management of Kubernetes-based workflows. - Enhanced Features: The package includes specific tasks and flows that are optimized for Kubernetes, improving the orchestration and management of Kubernetes resources. For users on versions prior to 3.2.8, it would be beneficial to upgrade to take advantage of these new features and improvements. If upgrading is not feasible, you can continue using the traditional methods of integrating Prefect with Kubernetes through custom configurations and scripts.
i
I should use the prefect-kubernetes image then?
@Marvin I should use the prefect-kubernetes image then?
m
When deploying Prefect flows on Kubernetes, you can use the latest Prefect 3 Docker image, such as 
prefecthq/prefect:3-latest
. This image is designed to work well with Prefect's infrastructure and can be used as a base for your Kubernetes jobs. ### Using the Prefect-Kubernetes Image - Default Image: If you don't specify an image, the latest Prefect 3 image (
prefecthq/prefect:3-latest
) is used by default. - Customization: You can override the default image in your deployment configuration if you need specific packages or configurations. This can be done by specifying a custom Docker image that includes your flow code and any additional dependencies. ### Custom Dockerfile If you need to include specific dependencies or configurations, you can create a custom Dockerfile. This allows you to use image caching and ensures that all necessary packages are included in your deployment environment. Here's a basic example of what a Dockerfile might look like:
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dockerfile
FROM prefecthq/prefect:3-latest

# Install additional packages
RUN pip install some-package

# Copy your flow code
COPY ./flows /opt/prefect/flows

# Set the working directory
WORKDIR /opt/prefect/flows
### Conclusion Using the Prefect Docker image ensures compatibility with Prefect's orchestration features and provides a stable base for running your flows on Kubernetes. If you have specific requirements, consider building a custom image to tailor the environment to your needs.
i
@Marvin what is the difference between a 3.2.11-python3.10 image and a 3.2.11-python3.10-kubernetes image from the registry?
m
The difference between a 
3.2.11-python3.10
image and a 
3.2.11-python3.10-kubernetes
image from the Prefect registry generally lies in their intended use and included configurations: ### 
3.2.11-python3.10
Image - General Purpose: This image is a standard Prefect image that includes Prefect version 3.2.11 with Python 3.10. It is suitable for general use cases where you want to run Prefect flows without any specific infrastructure optimizations. - Base Image: It serves as a base image for running Prefect flows and can be extended with additional dependencies as needed. ### 
3.2.11-python3.10-kubernetes
Image - Kubernetes Optimized: This image is specifically optimized for running Prefect flows on Kubernetes. It includes configurations and possibly additional packages that are beneficial for Kubernetes environments. - Infrastructure Integration: It may include Kubernetes-specific tools or settings that facilitate better integration with Kubernetes clusters, such as handling Kubernetes-specific environment variables, configurations, or volume mounts. ### When to Use Each - Use 
3.2.11-python3.10
: If you are running Prefect flows in a non-Kubernetes environment or need a general-purpose image to customize for various infrastructures. - Use 
3.2.11-python3.10-kubernetes
: If you are deploying Prefect flows specifically on Kubernetes and want an image that is tailored for that environment, potentially reducing the need for additional configurations. Choosing the right image can simplify deployment and execution, ensuring that your flows run smoothly in the intended infrastructure.
i
@Marvin Failed to submit flow run '0078844d-57fc-4ef1-9ce7-83e5057c3205' to infrastructure. Traceback (most recent call last): File "/usr/local/lib/python3.10/site-packages/prefect/workers/base.py", line 999, in _submit_run_and_capture_errors configuration = await self._get_configuration(flow_run) File "/usr/local/lib/python3.10/site-packages/prefect/workers/base.py", line 1097, in _get_configuration configuration = await self.job_configuration.from_template_and_values( File "/usr/local/lib/python3.10/site-packages/prefect/client/utilities.py", line 99, in with_injected_client return await fn(*args, **kwargs) File "/usr/local/lib/python3.10/site-packages/prefect/workers/base.py", line 188, in from_template_and_values return cls(**populated_configuration) File "/usr/local/lib/python3.10/site-packages/pydantic/main.py", line 214, in init validated_self = self.__pydantic_validator__.validate_python(data, self_instance=self) pydantic_core._pydantic_core.ValidationError: 1 validation error for KubernetesWorkerJobConfiguration job_manifest Field required [type=missing, input_value={'volumes': [{'name': 'ac...h_timeout_seconds': 360}, input_type=dict] For further information visit https://errors.pydantic.dev/2.10/v/missing
m
The error message you're encountering indicates that there is a missing required field in the 
KubernetesWorkerJobConfiguration
. Specifically, the 
job_manifest
field is required but not provided in your configuration. ### Steps to Resolve 1. Check Job Configuration: Ensure that your job configuration includes all required fields. The 
job_manifest
is a critical part of configuring a Kubernetes job, and it should define the complete job specification. 2. Review Base Job Template: Verify your base job template to ensure that it includes the 
job_manifest
field. This field should contain the necessary Kubernetes job specifications, such as containers, volumes, and other configurations. 3. Example Job Manifest: Here’s a basic structure for including a 
job_manifest
in your configuration:
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json
   {
     "job_manifest": {
       "apiVersion": "batch/v1",
       "kind": "Job",
       "metadata": {
         "name": "your-job-name"
       },
       "spec": {
         "template": {
           "spec": {
             "containers": [
               {
                 "name": "your-container-name",
                 "image": "your-image",
                 "resources": {
                   "limits": {
                     "memory": "8192Mi",
                     "cpu": "2000m"
                   }
                 },
                 "volumeMounts": [
                   {
                     "name": "your-volume-name",
                     "mountPath": "/path/in/container"
                   }
                 ]
               }
             ],
             "volumes": [
               {
                 "name": "your-volume-name",
                 "hostPath": {
                   "path": "/host/path"
                 }
               }
             ],
             "restartPolicy": "Never"
           }
         }
       }
     }
   }
4. Validation: Use this structure to ensure that your job manifest is complete and correctly formatted. 5. Consult Documentation: Double-check the Prefect and Kubernetes documentation to ensure that all necessary fields are included and correctly specified. If you continue to face issues, consider reaching out to the Prefect community or support for more detailed guidance, especially if there are specific nuances related to your setup.
i
what's the difference between jobManifest and jobConfiguration
@Marvin what's the difference between jobManifest and jobConfiguration
m
The distinction between 
jobManifest
and 
jobConfiguration
lies in their roles and usage within Prefect's infrastructure setup, particularly for Kubernetes jobs. ### 
jobManifest
- Purpose: The 
jobManifest
is a comprehensive specification of the Kubernetes job. It includes all the details required to create and manage a Kubernetes job, such as API version, kind, metadata, and the complete job specification. - Content: It is typically a direct representation of a Kubernetes job YAML, encompassing the full configuration needed by Kubernetes to schedule and run the job. - Use Case: This is used when you want to specify the entire job configuration explicitly, allowing for precise control over how the job is executed in a Kubernetes environment. ### 
jobConfiguration
- Purpose: The 
jobConfiguration
is part of Prefect's internal configuration system. It is used to define how Prefect should configure and run a job, often using placeholders for dynamic values that are filled in at runtime. - Content: It includes configuration settings that Prefect uses to manage the execution environment, such as container images, environment variables, and resource requests. The 
jobConfiguration
can use placeholders to reference values provided in the 
variables
section or hard-coded values. - Use Case: This is used within Prefect to manage and customize the behavior of jobs across different environments, allowing for flexibility and reuse of job configurations. ### Conclusion - Use 
jobManifest
 if you need to provide a complete Kubernetes job specification directly. - Use 
jobConfiguration
 to manage Prefect-specific configurations and leverage Prefect's templating and dynamic configuration capabilities. Both serve important roles in managing and deploying jobs but are used in different contexts depending on the level of control and customization needed.
i
jobManifest is a child of jobConfiguration?
@Marvin jobManifest is a child of jobConfiguration?
m
In the context of Prefect's infrastructure setup, 
jobManifest
can indeed be considered a component or child within the broader 
jobConfiguration
. Here's how they relate: ### Relationship Between 
jobConfiguration
and 
jobManifest
- 
jobConfiguration
: This is the overarching configuration used by Prefect to define how a job should be executed. It includes various settings and parameters that Prefect uses to manage the job's execution environment. - 
jobManifest
: Within the 
jobConfiguration
, the 
jobManifest
represents the specific Kubernetes job details. It includes the full Kubernetes job specification that Kubernetes needs to schedule and run the job. ### Structure - 
jobConfiguration
: May include various configurations such as environment variables, resource limits, and other Prefect-specific settings. - 
jobManifest
: Nested within 
jobConfiguration
, detailing the Kubernetes-specific job setup, such as the API version, kind, metadata, and spec. ### Example Here's a simplified illustration of how 
jobManifest
might be structured within `jobConfiguration`:
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json
{
  "jobConfiguration": {
    "jobManifest": {
      "apiVersion": "batch/v1",
      "kind": "Job",
      "metadata": {
        "name": "example-job"
      },
      "spec": {
        "template": {
          "spec": {
            "containers": [
              {
                "name": "example-container",
                "image": "example-image"
              }
            ],
            "restartPolicy": "Never"
          }
        }
      }
    },
    "otherConfigurations": {
      // Prefect-specific configurations
    }
  }
}
In this setup, 
jobManifest
provides the specific Kubernetes job details, while the rest of 
jobConfiguration
might include additional Prefect-specific settings. This hierarchical structure allows for flexible and detailed job management within Prefect.
Your message was too long by 234 tokens...
i
where do volumes and mounts go
@Marvin where do volumes and mounts go
m
In a Kubernetes job specification, volumes and their corresponding mounts are defined within the 
jobManifest
section, specifically under the 
spec.template.spec
part of the configuration. Here's how you can structure them: ### Defining Volumes and Volume Mounts 1. Volumes: These are defined at the 
spec.template.spec.volumes
level. Each volume has a 
name
and a type, such as 
hostPath
, 
persistentVolumeClaim
, etc. 2. Volume Mounts: These are specified within each container's configuration under 
spec.template.spec.containers[].volumeMounts
. Each mount references a volume by name and specifies the 
mountPath
within the container. ### Example Structure Here's how you can include volumes and mounts in your `jobManifest`:
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json
{
  "jobManifest": {
    "apiVersion": "batch/v1",
    "kind": "Job",
    "metadata": {
      "name": "example-job"
    },
    "spec": {
      "template": {
        "spec": {
          "containers": [
            {
              "name": "example-container",
              "image": "example-image",
              "volumeMounts": [
                {
                  "name": "example-volume",
                  "mountPath": "/path/in/container"
                }
              ]
            }
          ],
          "volumes": [
            {
              "name": "example-volume",
              "hostPath": {
                "path": "/path/on/host"
              }
            }
          ],
          "restartPolicy": "Never"
        }
      }
    }
  }
}
### Explanation - Volumes: Defined under 
spec.template.spec.volumes
, where each volume has a 
name
and a configuration based on the volume type (e.g., 
hostPath
). - Volume Mounts: Defined within each container's configuration under 
spec.template.spec.containers[].volumeMounts
, specifying the 
name
of the volume and the 
mountPath
in the container. This setup ensures that your Kubernetes jobs have the necessary storage configurations to function correctly, allowing containers to access data on the host or persistent storage.
i
@Marvin File "/usr/local/lib/python3.10/site-packages/prefect/server/database/interface.py", line 9, in <module> from prefect.server.database import orm_models File "/usr/local/lib/python3.10/site-packages/prefect/server/database/orm_models.py", line 26, in <module> from prefect.server.events.actions import ServerActionTypes File "/usr/local/lib/python3.10/site-packages/prefect/server/events/actions.py", line 675, in <module> class DeploymentCommandAction(DeploymentAction, ExternalDataAction): File "/usr/local/lib/python3.10/site-packages/pydantic/_internal/_model_construction.py", line 114, in new private_attributes = inspect_namespace( File "/usr/local/lib/python3.10/site-packages/pydantic/_internal/_model_construction.py", line 448, in inspect_namespace ann_type = eval_type_backport( File "/usr/local/lib/python3.10/site-packages/pydantic/_internal/_typing_extra.py", line 293, in eval_type_backport raise TypeError(message) from e TypeError: Unable to evaluate type annotation 'ClassVar[str]'. Loading...
m
The error message you're encountering is related to a 
TypeError
in the Prefect server's code, specifically with the evaluation of a type annotation using 
pydantic
. This issue typically arises from compatibility problems between Pydantic and the way type annotations are being handled in the Prefect codebase. ### Steps to Resolve 1. Ensure Compatibility: Make sure that the version of Prefect you are using is compatible with the version of Pydantic installed in your environment. Prefect 3.x uses Pydantic 2, so ensure both are up-to-date and compatible. 2. Check for Updates: Verify that you are using the latest versions of Prefect and Pydantic. Sometimes, newer versions contain fixes for such issues.
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bash
   pip install --upgrade prefect pydantic
3. Review Type Annotations: If you have custom code or modifications, review any type annotations that might be causing conflicts, especially those involving 
ClassVar
. 4. Consult Prefect Documentation: Check the Prefect documentation or GitHub repository for any known issues or patches related to this error. 5. Community and Support: If the issue persists, consider reaching out to the Prefect community or support channels. They might have insights or workarounds for specific issues related to type annotations and Pydantic. This error is a bit technical and might require adjustments in the codebase or waiting for a patch if it's a known issue with Prefect or Pydantic.
i
how to setup the workdir for prefect 3.2.11. I uploaded my flows in a path but the directory for the container in which the flow is baked is different
@Marvin how to setup the workdir for prefect 3.2.11. I uploaded my flows in a path but the directory for the container in which the flow is baked is different
m
To set up the working directory for your flows in Prefect 3.2.11, you need to ensure that the working directory is correctly specified in your deployment configuration. This is crucial when the directory structure in your container doesn't match the path where your flows are uploaded. ### Steps to Set the Working Directory 1. Specify the Working Directory in the Job Configuration: You can define the working directory in your job configuration or Dockerfile if you are using a custom image. 2. Using Job Variables: Set job variables to customize the working directory for your deployment. This can be done using the 
job_variables
parameter in your deployment configuration. 3. Dockerfile Configuration: If you are building a custom Docker image, specify the working directory using the 
WORKDIR
command in your Dockerfile. ### Example Configuration #### Using a Dockerfile
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dockerfile
FROM prefecthq/prefect:3.2.11-python3.10

# Set the working directory
WORKDIR /desired/working/directory

# Copy your flow code
COPY ./flows /desired/working/directory
#### Using Job Variables If you're not using a custom Docker image, you can specify the working directory in your deployment configuration using job variables:
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yaml
job_variables:
  WORKING_DIRECTORY: "/desired/working/directory"
### Adjusting the Deployment When deploying your flow, ensure that the 
entrypoint
or any path-related configurations align with the directory where your flows are located. This might involve adjusting the 
entrypoint
parameter in your deployment script or configuration to match the path structure within the container. By ensuring that your working directory is correctly configured, you can avoid issues related to path mismatches and ensure your flows execute as expected in their intended environment.
i
Job Variables
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{
  "WORKING_DIRECTORY": "/opt/prefect/flows"
}
Pull Steps
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[
  {
    "prefect.deployments.steps.set_working_directory": {
      "directory": "/Users/iagoalves/work/window-finance/wf-momentum-trading"
    }
  }
]
I guess I have to change the bottom one
@Marvin Job Variables
Copy code
{
  "WORKING_DIRECTORY": "/opt/prefect/flows"
}
Pull Steps
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[
  {
    "prefect.deployments.steps.set_working_directory": {
      "directory": "/Users/iagoalves/work/window-finance/wf-momentum-trading"
    }
  }
]
I guess I have to change the bottom one. Is there any way to do it besides from the prefect.yaml?
m
Yes, you can change the pull steps programmatically using the Prefect Python SDK, rather than only relying on the 
prefect.yaml
file. This allows you to configure the pull steps dynamically within your code. ### Changing Pull Steps Programmatically Using the Prefect Python SDK, you can define and modify deployment configurations, including pull steps. Here's an example of how you might set this up:
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python
from prefect.deployments import Deployment
from prefect.deployments.steps import set_working_directory
from my_flow import my_flow_function  # Replace with your actual flow function

# Define the pull steps
pull_steps = [
    set_working_directory(directory="/opt/prefect/flows")
]

# Create a deployment with the custom pull steps
deployment = Deployment.build_from_flow(
    flow=my_flow_function,
    name="my-deployment",
    pull_steps=pull_steps
)

# Apply the deployment
deployment.apply()
### Explanation - set_working_directory: This function is used to define the working directory for the flow execution. You can set it to the desired directory within your container. - Deployment.build_from_flow: This method allows you to create a deployment from a flow, specifying configurations like pull steps directly in your Python code. By using this approach, you can programmatically manage your deployment configurations, making it easier to adjust settings without editing the 
prefect.yaml
file directly. This can be particularly useful for environments where configurations need to be dynamically adjusted or integrated with other Python-based workflows.
i
@Marvin that won't work for prefect 3.2.11
m
In Prefect 3.2.11, you can indeed modify pull steps programmatically by using the functions provided in the 
prefect.deployments.steps
module. Specifically, you can use the 
set_working_directory
function from the 
pull
submodule to adjust the working directory for your deployment. Here's how you can set the working directory using the Python SDK: ### Adjusting Pull Steps Programmatically
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python
from prefect.deployments.steps.pull import set_working_directory
from prefect.deployments import Deployment
from my_flow import my_flow_function  # Replace with your actual flow function

# Define the pull steps with the updated working directory
pull_steps = [
    set_working_directory(directory="/opt/prefect/flows")
]

# Create a deployment with the custom pull steps
deployment = Deployment.build_from_flow(
    flow=my_flow_function,
    name="my-deployment",
    pull_steps=pull_steps
)

# Apply the deployment
deployment.apply()
### Explanation - set_working_directory: This function is used to specify the working directory for your flow execution. It should match the directory where your flow code is expected to be executed within the container. - Deployment.build_from_flow: This method constructs a deployment from the flow and allows you to specify the pull steps, including setting the working directory. By using this approach, you can dynamically configure your deployment's working directory directly in your Python code, providing flexibility and ease of integration with other Python workflows.
i
@Marvin Help me setup my prefect.yaml a) I need to build and push a docker image b) I need to update an existing workpool based on a job_template json file c) I need to deploy several flows all withing the workpool we have just updated d) I need to setup the work directory to opt/prefect/flows (Probably in the pull steps)
m
To set up your 
prefect.yaml
file with the requirements you specified, you'll need to define steps for building and pushing a Docker image, updating a work pool with a job template, deploying multiple flows within the work pool, and setting the working directory in the pull steps. Here's how you can structure the 
prefect.yaml
file:
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yaml
# prefect.yaml

# Build and push a Docker image
build:
  - prefect_docker.deployments.steps.build_docker_image:
      image_name: "your-image-name"
      tag: "latest"
      dockerfile: "./Dockerfile"
  - prefect_docker.deployments.steps.push_docker_image:
      image_name: "your-image-name"
      tag: "latest"

# Update an existing work pool with a job template
push:
  - prefect.deployments.steps.run_shell_script:
      id: "update_workpool"
      script: |
        prefect work-pool update my-workpool --base-job-template path/to/job_template.json

# Deploy several flows within the work pool
deploy:
  - prefect.deployments.steps.deploy_flows:
      work_pool: "my-workpool"
      flow_paths:
        - "path/to/flow1.py"
        - "path/to/flow2.py"
        # Add more flow paths as needed

# Set up the working directory in pull steps
pull:
  - prefect.deployments.steps.set_working_directory:
      directory: "/opt/prefect/flows"
### Explanation - Build and Push: The 
build
and 
push
sections use Prefect's Docker steps to build and push your Docker image. Adjust the 
image_name
and 
tag
as needed. - Update Work Pool: The 
push
section includes a shell script step to update the work pool with a new job template. Replace 
my-workpool
and the path to 
job_template.json
with your actual work pool name and template path. - Deploy Flows: The 
deploy
section specifies the work pool and the paths to the flows you want to deploy. Add all the flow paths you need to deploy. - Set Working Directory: The 
pull
section sets the working directory to 
/opt/prefect/flows
, ensuring that your flows execute in the correct directory context. This configuration provides a comprehensive setup for managing your Prefect deployments, ensuring that all necessary components are correctly configured and executed. Adjust the paths and names according to your specific environment and requirements.
i
@Marvin will this push to my GCP registry?
m
To push a Docker image to your Google Container Registry (GCR) using Prefect, you need to ensure that the Docker image is tagged correctly with your GCR repository path and that you are authenticated with Google Cloud. Here's how you can modify the 
prefect.yaml
to push to GCR: ### Steps to Push to GCR 1. Tag the Image Correctly: Ensure your Docker image is tagged with the GCR path. The format typically looks like 
<http://gcr.io/[PROJECT-ID]/[IMAGE-NAME]:[TAG]|gcr.io/[PROJECT-ID]/[IMAGE-NAME]:[TAG]>
. 2. Authenticate with GCP: Make sure your environment is authenticated with Google Cloud. You can use 
gcloud auth configure-docker
to set up Docker to authenticate with GCR. 3. Modify the 
prefect.yaml
: Update the 
build
and 
push
steps to use the GCR path. ### Example 
prefect.yaml
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yaml
# prefect.yaml

# Build and push a Docker image to GCR
build:
  - prefect_docker.deployments.steps.build_docker_image:
      image_name: "<http://gcr.io/your-project-id/your-image-name|gcr.io/your-project-id/your-image-name>"
      tag: "latest"
      dockerfile: "./Dockerfile"
  - prefect_docker.deployments.steps.push_docker_image:
      image_name: "<http://gcr.io/your-project-id/your-image-name|gcr.io/your-project-id/your-image-name>"
      tag: "latest"

# Update an existing work pool with a job template
push:
  - prefect.deployments.steps.run_shell_script:
      id: "update_workpool"
      script: |
        prefect work-pool update my-workpool --base-job-template path/to/job_template.json

# Deploy several flows within the work pool
deploy:
  - prefect.deployments.steps.deploy_flows:
      work_pool: "my-workpool"
      flow_paths:
        - "path/to/flow1.py"
        - "path/to/flow2.py"
        # Add more flow paths as needed

# Set up the working directory in pull steps
pull:
  - prefect.deployments.steps.set_working_directory:
      directory: "/opt/prefect/flows"
### Key Points - Image Name: Ensure the 
image_name
includes the GCR path (`gcr.io/your-project-id/your-image-name`). - Authentication: Use 
gcloud auth configure-docker
to authenticate Docker with your GCR account. - Environment: Ensure your environment has the necessary permissions to push images to GCR. By following these steps, your Docker image should be built and pushed to your Google Container Registry as part of the Prefect deployment process. Adjust the 
your-project-id
and 
your-image-name
placeholders with your actual project ID and image name.
i
@Marvin
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deploy:
  - prefect.deployments.steps.deploy_flows:
      work_pool: "wf-momentum"
      flow_paths:
        - "path/to/flow1.py"
will it recognize all flows contained in the file?
m
In Prefect, the 
deploy_flows
step in the 
prefect.yaml
configuration is used to deploy flows to a specified work pool. However, the 
flow_paths
attribute typically expects the path to a single flow file rather than a directory containing multiple flows. Each path should point to a Python file that contains a flow definition. ### Deploying Multiple Flows If you have multiple flows within a single file or across multiple files, you'll need to ensure that each flow is explicitly defined and accessible. Here's how you can manage multiple flows: 1. Single File with Multiple Flows: If you have multiple flows defined in a single Python file, you need to ensure that the file is structured such that each flow can be accessed and deployed individually. 2. Multiple Files: If your flows are spread across multiple files, list each file path in the 
flow_paths
section. ### Example Configuration Here's how you might configure the 
prefect.yaml
if you have multiple flow files:
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yaml
deploy:
  - prefect.deployments.steps.deploy_flows:
      work_pool: "wf-momentum"
      flow_paths:
        - "path/to/flow1.py"
        - "path/to/flow2.py"
        # Add more flow paths as needed
### Key Considerations - Flow Definitions: Ensure each file listed in 
flow_paths
contains a valid Prefect flow definition. - Import Statements: If you have multiple flows in a single file, ensure that your flow objects are accessible and properly imported when deploying. If you need to deploy multiple flows from a single file, you might need to handle the deployment logic programmatically to ensure each flow is registered and deployed correctly. Prefect's deployment tools and scripts can help automate this process if needed.
i
│ No such command 'deploy'. │ how to deploy the yaml steps
@Marvin │ No such command 'deploy'. │ how to deploy the yaml steps
m
To deploy using the 
prefect.yaml
file, you should use the 
prefect deploy
command. This command processes the steps defined in your 
prefect.yaml
file, including building, pushing, and deploying your flows. ### Steps to Deploy with 
prefect.yaml
1. Navigate to Your Directory: Ensure you are in the directory containing your 
prefect.yaml
file. 2. Run the Deploy Command: Use the following command to deploy:
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bash
   prefect deploy
### Deployment Mechanics - Build Section: The 
build
steps are executed first, resolving any variable and block references. - Push Section: After building, the 
push
steps are processed, again resolving references. - Pull Section: The 
pull
section is templated with step outputs but is not run during deployment. It is resolved at runtime for security reasons. - Deployment Registration: Finally, the deployment is registered with Prefect, using any flags provided to the CLI to overlay on the values from the file. ### Common Issues - Command Not Found: Ensure that your Prefect CLI is up-to-date and correctly installed. You can verify your installation with 
prefect version
. - Directory Context: Make sure you are running the command in the correct directory where the 
prefect.yaml
file is located. By following these steps, you should be able to deploy your flows using the configurations specified in your 
prefect.yaml
file. If you encounter any issues, ensure that your Prefect CLI is correctly configured and that all paths and references in the YAML file are accurate.
i
@Marvin
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"volumeMounts": [
      {
        "name": "account-credentials",
        "mountPath": "/etc/account-credentials",
        "readOnly": true
      },
      {
        "name": "birdeye-api-key",
        "mountPath": "/etc/birdeye-api-key",
        "readOnly": true
      },
      {
        "name": "telegram-bot-key",
        "mountPath": "/etc/telegram-bot-key",
        "readOnly": true
      }
    ]
  }
],
"volumes": [
  {
    "name": "account-credentials",
    "secret": {
      "secretName": "default-account-credentials",
      "optional": false
    }
  },
  {
    "name": "birdeye-api-key",
    "secret": {
      "secretName": "birdeye-api-key",
      "optional": false
    }
  },
  {
    "name": "telegram-bot-key",
    "secret": {
      "secretName": "telegram-bot-key",
      "optional": false
    }
  }
I am not finding these secrets in the stored files (This is in the job template json for the workpool) I also have this in the yaml
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pull:
- prefect.deployments.steps.set_working_directory:
    directory: /opt/prefect/flows
But searching for the files appending ../../../ did not work
m
It seems you're dealing with Kubernetes volume mounts and secrets, which are often used to securely pass sensitive information to your containers. Let's break down your issue and provide some guidance: 1. Volume Mounts and Secrets: - You have defined volume mounts and corresponding secrets in your Kubernetes job template. Each volume mount is associated with a secret that should be available in the specified path inside the container. - Ensure that the secrets (
default-account-credentials
, 
birdeye-api-key
, 
telegram-bot-key
) are correctly created and populated in your Kubernetes cluster. You can check this using 
kubectl get secrets
and 
kubectl describe secret <secret-name>
to verify their presence and contents. 2. File Availability: - If the secrets are correctly set up, they should be available in the specified mount paths (
/etc/account-credentials
, 
/etc/birdeye-api-key
, 
/etc/telegram-bot-key
) inside the container. - If you are not finding these files, verify the container's logs for any errors related to mounting or accessing these paths. 3. Working Directory: - The YAML snippet you provided seems to set the working directory for Prefect deployments. Ensure that the directory 
/opt/prefect/flows
exists and is accessible within your container. - When searching for files, ensure your relative paths are correct. The 
../../../
might not correspond to the actual directory structure inside the container. 4. Troubleshooting Steps: - Double-check the secret definitions and ensure they are correctly linked to the volume mounts. - Use 
kubectl exec -it <pod-name> -- /bin/sh
to open a shell in the container and manually inspect the mounted paths and directory structure. - Review any Prefect deployment logs for additional clues. If you need further assistance with Prefect-specific configurations, let me know, and I can delve into the documentation for more detailed guidance.
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