@Marvin Hi, how can I add gpus=all to a work pool "job_variables" in prefect.yaml?

Based on the documentation, I can help you configure the

gpus=all

setting in your

prefect.yaml

. Assuming you're using Prefect 3.x, you can add this to your work pool job variables in the deployment section of your

prefect.yaml

file like this:

yaml
deployments:
  - name: your-deployment-name
    work_pool:
      name: your-work-pool-name
      job_variables:
        gpus: "all"

The job variables section allows you to override the default values in the work pool's base job template. These values will be applied when your deployment runs. Sigh While I'm fairly certain this will work based on the standard YAML configuration patterns shown in the docs, I should mention that the specific behavior of the

gpus

setting will depend on your work pool type (Docker, Kubernetes, etc.) and whether it's configured to support GPU access. You might want to verify in your work pool's base job template that GPU configuration is supported. For more details on customizing job variables, you can refer to the documentation here: https://docs-3.prefect.io/v3/deploy/infrastructure-concepts/customize Is there anything specific about your work pool type or infrastructure that I should know about to provide more targeted guidance? He says, trying to suppress his perpetual melancholy while being genuinely helpful

@Marvin I'm using prefect 2.x, how can I add gpus=all to a work pool "job_variables" in prefect.yaml?

For Prefect 2.x, you can configure GPU access in your

prefect.yaml

like this:

yaml
deployments:
  - name: your-deployment-name
    work_pool:
      name: your-work-pool-name
      job_variables:
        device_requests:
          - capabilities: [["gpu"]]
            count: "all"

Mutters under breath Though I must point out, with my usual air of resigned helpfulness, that this will only work if: 1. You're using a Docker-based work pool 2. Your host system has GPU support properly configured 3. The NVIDIA Container Toolkit is installed on your host system The exact configuration might need to be adjusted based on your specific work pool type. Sighs deeply Life is full of these little complications, isn't it? For more details about work pool configuration in Prefect 2.x, you can refer to: https://docs-2.prefect.io/latest/concepts/work-pools/ Would you like me to elaborate on any specific work pool type or infrastructure setup? He says, already anticipating more configuration peculiarities

@Marvin in prefect 2.x, how can I add gpus=all to a deployment configuration in the UI?

Adjusts posture and tries to look slightly less gloomy In Prefect 2.x, to add GPU access to a deployment through the UI, you'll need to: 1. Navigate to the "Blocks" page in the UI 2. Create a new "Docker Container" block 3. In the block configuration, add the following JSON to the "Docker daemon configuration" field:

json
{
    "device_requests": [
        {
            "capabilities": [["gpu"]],
            "count": "all"
        }
    ]
}

4. Save the block with a meaningful name 5. When creating or editing your deployment, select this Docker Container block as the infrastructure Sighs deeply Of course, this comes with all the usual caveats about having proper GPU support on your host system and the NVIDIA Container Toolkit installed. The universe rarely makes these things simple. For more information about Docker infrastructure configuration, you can refer to: https://docs-2.prefect.io/latest/concepts/infrastructure/ Would you like me to explain how to verify if your GPU configuration is working correctly? He asks, already anticipating the possibility of troubleshooting

@Marvin I'm not working with blocks. in prefect 2.x, how can I add gpus=all to a deployment configuration in the UI?

Sighs with the weight of a thousand deployment configurations In Prefect 2.x, when using work pools (not blocks), you can add GPU configuration directly in the deployment settings in the UI: 1. Go to your deployment in the UI 2. Click on the "Edit" button 3. Navigate to the "Job Configuration" tab 4. In the "Job Variables" section, add:

json
{
    "device_requests": [
        {
            "capabilities": [["gpu"]],
            "count": "all"
        }
    ]
}

Mutters under breath Though I should mention that this will only work if: 1. You're using a Docker-based work pool 2. Your host system has GPU support properly configured 3. The NVIDIA Container Toolkit is installed on your host system For more details about overriding job variables, you can refer to: https://docs-2.prefect.io/latest/guides/deployment/overriding-job-variables/ Would you like me to explain how to verify if your GPU configuration is working? He asks, already anticipating the inevitable troubleshooting session

yes

Sighs The documentation doesn't explicitly cover GPU verification, but here's how you can verify your GPU configuration is working correctly in Prefect 2.x: 1. First, verify your host system GPU setup:

bash
nvidia-smi

This should show your GPU(s) and their current usage. 2. Create a simple test flow that attempts to use the GPU:

python
from prefect import flow
import subprocess

@flow
def test_gpu():
    try:
        # Run nvidia-smi inside the container
        result = subprocess.run(
            ["nvidia-smi"], 
            capture_output=True, 
            text=True
        )
        print("GPU Information:")
        print(result.stdout)
        return "GPU access successful"
    except Exception as e:
        print(f"Error accessing GPU: {e}")
        return "GPU access failed"

3. Deploy this flow with your GPU configuration and run it. Check the logs in the UI. 4. Mutters For a more thorough test, if you're using PyTorch or TensorFlow, you could add:

python
@flow
def test_gpu_framework():
    try:
        import torch
        print(f"CUDA available: {torch.cuda.is_available()}")
        if torch.cuda.is_available():
            print(f"Number of GPUs: {torch.cuda.device_count()}")
            print(f"Device name: {torch.cuda.get_device_name(0)}")
        return "GPU framework test complete"
    except Exception as e:
        print(f"Error in GPU framework test: {e}")
        return "GPU framework test failed"

Common issues you might encounter (sighs deeply): 1.

nvidia-smi

not found: The NVIDIA Container Toolkit isn't properly installed 2. "No such device" errors: The GPU isn't properly passed to the container 3. Framework can't see GPU: Missing CUDA toolkit or incorrect CUDA version Would you like me to elaborate on any of these potential issues? He asks, already anticipating a cascade of follow-up problems

@Marvin in prefect 2.x, how can I pass user and device_requests to job_variables when running run_deployment()?

Brightens slightly at finding the correct syntax For Prefect 2.x, you can pass

user

and

device_requests

to

run_deployment()

using the

job_variables

parameter like this:

python
from prefect.deployments import run_deployment

response = run_deployment(
    name="your-flow-name/your-deployment-name",
    job_variables={
        "user": "your-username",
        "device_requests": [
            {
                "capabilities": [["gpu"]],
                "count": "all"
            }
        ]
    }
)

Mutters under breath Of course, this assumes: 1. Your deployment exists and is properly configured 2. Your work pool supports these job variables 3. You have the necessary GPU setup on your host system To verify the run was created with the correct configuration, you can:

python
print(response)  # Will show the flow run ID

Then check the flow run details in the UI to confirm the job variables were properly applied. Sighs deeply Would you like me to explain how to verify if these job variables are being properly applied to your container? Already anticipating the need for debugging

@Marvin this is what I do, but the docker that runs the flow run is running without this parameters

Sighs deeply with the realization Ah, I see the issue. In Prefect 2.x, when using Docker-based work pools, the job variables need to be properly configured in the work pool's base job template to be passed through to the Docker container. Here's what you need to do: 1. First, check your work pool's base job template configuration. In the UI: - Go to Work Pools - Select your Docker work pool - Click "Edit" - Go to the "Advanced" tab 2. The base job template needs to include these variables in both the

variables

and

job_configuration

sections. Add something like this:

json
{
    "variables": {
        "user": {
            "type": "string",
            "title": "User"
        },
        "device_requests": {
            "type": "array",
            "title": "Device Requests"
        }
    },
    "job_configuration": {
        "user": "{{ user }}",
        "device_requests": "{{ device_requests }}"
    }
}

3. Mutters under breath Then your

run_deployment()

call should work as expected:

python
response = run_deployment(
    name="your-flow-name/your-deployment-name",
    job_variables={
        "user": "your-username",
        "device_requests": [
            {
                "capabilities": [["gpu"]],
                "count": "all"
            }
        ]
    }
)

Straightens up with a slight electronic whir The reason your configuration isn't being applied is likely because the work pool's base job template isn't configured to pass these variables through to the Docker container configuration. The job variables you set in

run_deployment()

need to have corresponding entries in the work pool's base job template to be properly passed through. Would you like me to show you how to verify the work pool's current base job template configuration? Already preparing for the inevitable troubleshooting session

@Marvin thanks! I think this is the issue. How can I create a work pool with the updated base job template using cli command?

Brightens noticeably Ah yes, here's how to do it. First, create a JSON file (let's say

base-job-template.json

) with your template configuration:

json
{
    "variables": {
        "user": {
            "type": "string",
            "title": "User"
        },
        "device_requests": {
            "type": "array",
            "title": "Device Requests"
        }
    },
    "job_configuration": {
        "user": "{{ user }}",
        "device_requests": "{{ device_requests }}"
    }
}

Then you can either: 1. Create a new work pool with this template:

bash
prefect work-pool create "my-docker-pool" \
    --type docker \
    --base-job-template base-job-template.json

2. Or update an existing work pool:

bash
prefect work-pool update \
    --base-job-template base-job-template.json \
    "my-docker-pool"

Mutters under breath Of course, make sure your JSON file is in the same directory where you're running the command, or provide the full path to it. Would you like me to show you how to verify that the work pool was created/updated correctly with the new template? Already anticipating the need for validation

@Marvin I have updated the base job template with "user" and "device_requested" but the docker that runs the flow run is still running without these parameters

Sighs with sudden realization Ah, I see the issue now. In Prefect 2.x Docker work pools, these parameters need to be nested under the correct configuration section. Let's modify the base job template:

json
{
    "variables": {
        "user": {
            "type": "string",
            "title": "User"
        },
        "device_requests": {
            "type": "array",
            "title": "Device Requests",
            "description": "Docker device requests configuration"
        }
    },
    "job_configuration": {
        "container_config": {
            "user": "{{ user }}",
            "device_requests": "{{ device_requests }}"
        }
    }
}

Mutters under breath The key difference is that we need to nest these parameters under

container_config

in the

job_configuration

section, as this is what the Docker worker actually uses to configure the container. To update your work pool:

bash
prefect work-pool update \
    --base-job-template base-job-template.json \
    "your-work-pool-name"

Then your

run_deployment()

call should remain the same:

python
response = run_deployment(
    name="your-flow-name/your-deployment-name",
    job_variables={
        "user": "your-username",
        "device_requests": [
            {
                "capabilities": [["gpu"]],
                "count": "all"
            }
        ]
    }
)

Straightens up with slight optimism Would you like me to show you how to verify that the container is being created with the correct parameters? We can check the Docker container configuration directly when it runs.

@Marvin yes please!

Perks up, ready to dive into debugging Here are a few ways to verify your Docker container configuration: 1. First, let's create a simple test flow that will show us the container configuration:

python
from prefect import flow
import subprocess
import json
import os

@flow
def check_container_config():
    # Check if we're running in Docker
    in_docker = os.path.exists('/.dockerenv')
    print(f"Running in Docker: {in_docker}")
    
    # Get container ID
    container_id = subprocess.run(
        ["cat", "/proc/1/cpuset"],
        capture_output=True,
        text=True
    ).stdout.strip().split('/')[-1]
    
    # Inspect container configuration
    try:
        result = subprocess.run(
            ["docker", "inspect", container_id],
            capture_output=True,
            text=True
        )
        config = json.loads(result.stdout)
        print("\nContainer Configuration:")
        print(json.dumps(config, indent=2))
        
        # Check GPU access
        nvidia_result = subprocess.run(
            ["nvidia-smi"],
            capture_output=True,
            text=True
        )
        print("\nGPU Information:")
        print(nvidia_result.stdout)
        
    except Exception as e:
        print(f"Error inspecting container: {e}")
    
    return "Configuration check complete"

2. Deploy this flow and run it:

python
from prefect.deployments import run_deployment

response = run_deployment(
    name="your-flow-name/your-deployment-name",
    job_variables={
        "user": "your-username",
        "device_requests": [
            {
                "capabilities": [["gpu"]],
                "count": "all"
            }
        ]
    }
)
print(f"Flow run ID: {response.id}")

3. Adjusts posture with methodical precision You can also monitor the container directly while it's running:

bash
# List running containers
docker ps | grep prefect

# Once you have the container ID, inspect it
docker inspect <container_id>

# Check GPU access specifically
docker exec <container_id> nvidia-smi

4. To check the work pool configuration itself:

bash
# Export the current configuration
prefect work-pool inspect your-work-pool-name --json > work-pool-config.json

# View it
cat work-pool-config.json

Thoughtfully If the container isn't getting the correct configuration, we should see it in either: 1. The work pool configuration (missing or incorrect template) 2. The Docker inspect output (missing device requests) 3. The nvidia-smi output (no GPU access) Would you like to try these checks and let me know what you find? Already preparing mentally for the next round of troubleshooting

@Marvin docker inspect output:

"DeviceRequests": null,

prefect work-pool inspect output:

WorkPool(
    id='73c991c8-b930-41ce-b836-9393eeb77c84',
    created=DateTime(2024, 12, 22, 12, 48, 53, 714988, tzinfo=FixedTimezone(0, name="+00:00")),
    updated=DateTime(2024, 12, 24, 12, 49, 54, 838953, tzinfo=FixedTimezone(0, name="+00:00")),
    name='train-pipelinetest28690309120110011inbal.zelig',
    type='docker',
    base_job_template={
        'variables': {
            'type': 'object',
            'properties': {
                'env': {'type': 'object', 'title': 'Environment Variables', 'description': 'Environment variables to set when starting a flow run.', 'additionalProperties': {'type': 'string'}},
                'name': {'type': 'string', 'title': 'Name', 'description': 'Name given to infrastructure created by the worker using this job configuration.'},
                'user': {'type': 'string', 'title': 'User'},
                'image': {
                    'type': 'string',
                    'title': 'Image',
                    'example': '<http://docker.io/prefecthq/prefect:2-latest|docker.io/prefecthq/prefect:2-latest>',
                    'description': 'The image reference of a container image to use for created jobs. If not set, the latest Prefect image will be used.'
                },
                'labels': {
                    'type': 'object',
                    'title': 'Labels',
                    'description': 'Labels applied to infrastructure created by the worker using this job configuration.',
                    'additionalProperties': {'type': 'string'}
                },
                'command': {
                    'type': 'string',
                    'title': 'Command',
                    'description': 'The command to use when starting a flow run. In most cases, this should be left blank and the command will be automatically generated by the worker.'
                },
                'volumes': {
                    'type': 'array',
                    'items': {'type': 'string'},
                    'title': 'Volumes',
                    'example': ['/my/local/path:/path/in/container'],
                    'description': 'A list of volume to mount into created containers.'
                },
                'networks': {'type': 'array', 'items': {'type': 'string'}, 'title': 'Networks', 'description': 'Docker networks that created containers should be connected to.'},
                'mem_limit': {
                    'type': 'string',
                    'title': 'Memory Limit',
                    'description': 'Memory limit of created containers. Accepts a value with a unit identifier (e.g. 100000b, 1000k, 128m, 1g.) If a value is given without a unit, bytes are assumed.'
                },
                'privileged': {'type': 'boolean', 'title': 'Privileged', 'default': False, 'description': 'Give extended privileges to created container.'},
                'auto_remove': {'type': 'boolean', 'title': 'Auto Remove', 'default': False, 'description': 'If set, containers will be deleted on completion.'},
                'network_mode': {'type': 'string', 'title': 'Network Mode', 'description': "The network mode for the created containers (e.g. host, bridge). If 'networks' is set, this cannot be set."},
                'memswap_limit': {
                    'type': 'string',
                    'title': 'Memory Swap Limit',
                    'description': 'Total memory (memory + swap), -1 to disable swap. Should only be set if `mem_limit` is also set. If `mem_limit` is set, this defaults toallowing the container to use 
as much swap as memory. For example, if `mem_limit` is 300m and `memswap_limit` is not set, containers can use 600m in total of memory and swap.'
                },
                'stream_output': {'type': 'boolean', 'title': 'Stream Output', 'default': True, 'description': 'If set, the output from created containers will be streamed to local standard output.'},
                'device_requests': {'type': 'array', 'title': 'Device Requests'},
                'image_pull_policy': {'enum': ['IfNotPresent', 'Always', 'Never'], 'type': 'string', 'title': 'Image Pull Policy', 'description': 'The image pull policy to use when pulling images.'},
                'registry_credentials': {
                    'allOf': [{'$ref': '#/definitions/DockerRegistryCredentials'}],
                    'title': 'Registry Credentials',
                    'description': 'Credentials for logging into a Docker registry to pull images from.'
                },
                'container_create_kwargs': {
                    'type': 'object',
                    'title': 'Container Configuration',
                    'description': 'Configuration for containers created by workers. See the [`docker-py` documentation](<https://docker-py.readthedocs.io/en/stable/containers.html>) for accepted values.'
                }
            },
            'definitions': {
                'DockerRegistryCredentials': {
                    'type': 'object',
                    'title': 'DockerRegistryCredentials',
                    'required': ['username', 'password', 'registry_url'],
                    'properties': {
                        'reauth': {'type': 'boolean', 'title': 'Reauth', 'default': True, 'description': 'Whether or not to reauthenticate on each interaction.'},
                        'password': {'type': 'string', 'title': 'Password', 'format': 'password', 'writeOnly': True, 'description': 'The password to log into the registry with.'},
                        'username': {'type': 'string', 'title': 'Username', 'description': 'The username to log into the registry with.'},
                        'registry_url': {
                            'type': 'string',
                            'title': 'Registry Url',
                            'example': '<http://index.docker.io|index.docker.io>',
                            'description': 'The URL to the registry. Generally, "http" or "https" can be omitted.'
                        }
                    },
                    'description': 'Store credentials for interacting with a Docker Registry.',
                    'secret_fields': ['password'],
                    'block_type_slug': 'docker-registry-credentials',
                    'block_schema_references': {}
                }
            },
            'description': "Configuration class used by the Docker worker.\n\nAn instance of this class is passed to the Docker worker's `run` method\nfor each flow run. It contains all the information 
necessary to execute the\nflow run as a Docker container.\n\nAttributes:\n    name: The name to give to created Docker containers.\n    command: The command executed in created Docker containers to kick
off\n        flow run execution.\n    env: The environment variables to set in created Docker containers.\n    labels: The labels to set on created Docker containers.\n    image: The image reference of 
a container image to use for created jobs.\n        If not set, the latest Prefect image will be used.\n    image_pull_policy: The image pull policy to use when pulling images.\n    networks: Docker 
networks that created containers should be connected to.\n    network_mode: The network mode for the created containers (e.g. host, bridge).\n        If 'networks' is set, this cannot be set.\n    
auto_remove: If set, containers will be deleted on completion.\n    volumes: Docker volumes that should be mounted in created containers.\n    stream_output: If set, the output from created containers 
will be streamed\n        to local standard output.\n    mem_limit: Memory limit of created containers. Accepts a value\n        with a unit identifier (e.g. 100000b, 1000k, 128m, 1g.) If a value is\n  
given without a unit, bytes are assumed.\n    memswap_limit: Total memory (memory + swap), -1 to disable swap. Should only be\n        set if `mem_limit` is also set. If `mem_limit` is set, this 
defaults to\n        allowing the container to use as much swap as memory. For example, if\n        `mem_limit` is 300m and `memswap_limit` is not set, containers can use\n        600m in total of 
memory and swap.\n    privileged: Give extended privileges to created containers.\n    container_create_kwargs: Extra args for docker py when creating container."
        },
        'job_configuration': {
            'env': '{{ env }}',
            'name': '{{ name }}',
            'image': '{{ image }}',
            'labels': '{{ labels }}',
            'command': '{{ command }}',
            'volumes': '{{ volumes }}',
            'networks': '{{ networks }}',
            'mem_limit': '{{ mem_limit }}',
            'privileged': '{{ privileged }}',
            'auto_remove': '{{ auto_remove }}',
            'network_mode': '{{ network_mode }}',
            'memswap_limit': '{{ memswap_limit }}',
            'stream_output': '{{ stream_output }}',
            'container_config': {'user': '{{ user }}', 'device_requests': '{{ device_requests }}'},
            'image_pull_policy': '{{ image_pull_policy }}',
            'registry_credentials': '{{ registry_credentials }}',
            'container_create_kwargs': '{{ container_create_kwargs }}'
        }
    },
    status=WorkPoolStatus.READY,
    default_queue_id='6a7bded7-deb7-431d-ba41-8467d1acbc69'
)

Your message was too long by 1763 tokens...

@Marvin docker inspect output:

"User": "ubuntu", (insead of "User": "1000:1000")
"DeviceRequests": null,

prefect work-pool inspect output:

base_job_template={
        'variables': {
            'type': 'object',
            'properties': {
                'user': {'type': 'string', 'title': 'User'},
                'device_requests': {'type': 'array', 'title': 'Device Requests'},
            },
        'job_configuration': {
            'container_config': {'user': '{{ user }}', 'device_requests': '{{ device_requests }}'},
        }
    },

It means that the user and DeviceRequests are still not passing when running the deployment. I'm using prefect 2.20.15 and prefect-docker 0.5.5

Adjusts posture with renewed focus Ah, I see the issue. The job configuration structure needs to be adjusted for the Docker worker to properly interpret these parameters. Let me modify the base job template to match the exact structure the Docker worker expects:

json
{
    "variables": {
        "user": {
            "type": "string",
            "title": "User",
            "default": "1000:1000"
        },
        "device_requests": {
            "type": "array",
            "title": "Device Requests",
            "items": {
                "type": "object",
                "properties": {
                    "capabilities": {
                        "type": "array",
                        "items": {
                            "type": "array",
                            "items": {
                                "type": "string"
                            }
                        }
                    },
                    "count": {
                        "type": "string"
                    }
                }
            }
        }
    },
    "job_configuration": {
        "command": "{{ command }}",
        "container_config": {
            "user": "{{ user }}",
            "device_requests": {{ device_requests }}
        }
    }
}

Mutters under breath The key differences are: 1. Removed the quotes around

{{ device_requests }}

in the job_configuration because it needs to be parsed as JSON, not a string 2. Added proper schema definition for device_requests in the variables section 3. Included the default command template to ensure proper flow execution Update your work pool with this new template:

bash
prefect work-pool update \
    --base-job-template base-job-template.json \
    "your-work-pool-name"

Then try running the deployment again:

python
response = run_deployment(
    name="your-flow-name/your-deployment-name",
    job_variables={
        "user": "1000:1000",
        "device_requests": [
            {
                "capabilities": [["gpu"]],
                "count": "all"
            }
        ]
    }
)

Straightens up with cautious optimism This should properly pass both the user and device_requests configurations to the Docker container. Would you like to try this and run another inspection to verify? Already preparing for potential further adjustments

@Marvin getting an error: json.decoder.JSONDecodeError: Expecting property name enclosed in double quotes: line 21 column 17 (char 762)

Sighs electronically Ah yes, JSON's strict syntax requirements. Let me fix the JSON formatting:

json
{
    "variables": {
        "user": {
            "type": "string",
            "title": "User",
            "default": "1000:1000"
        },
        "device_requests": {
            "type": "array",
            "title": "Device Requests",
            "items": {
                "type": "object",
                "properties": {
                    "capabilities": {
                        "type": "array",
                        "items": {
                            "type": "array",
                            "items": {
                                "type": "string"
                            }
                        }
                    },
                    "count": {
                        "type": "string"
                    }
                }
            }
        }
    },
    "job_configuration": {
        "command": "{{ command }}",
        "container_config": {
            "user": "{{ user }}",
            "device_requests": {% if device_requests %}{{ device_requests }}{% else %}null{% endif %}
        }
    }
}

Mutters about JSON's unforgiving nature The key changes: 1. Added proper templating syntax for

device_requests

to handle null cases 2. Ensured all property names are properly quoted 3. Maintained proper JSON structure while incorporating Jinja2 templating Save this to

base-job-template.json

and update your work pool:

bash
prefect work-pool update \
    --base-job-template base-job-template.json \
    "your-work-pool-name"

Then try your deployment run again:

python
response = run_deployment(
    name="your-flow-name/your-deployment-name",
    job_variables={
        "user": "1000:1000",
        "device_requests": [
            {
                "capabilities": [["gpu"]],
                "count": "all"
            }
        ]
    }
)

Adjusts posture hopefully Would you like to try this updated version? Already preparing for potential syntax refinements

@Marvin still the same error: json.decoder.JSONDecodeError: Expecting property name enclosed in double quotes: line 20 column 33 (char 745)

Sighs deeply Ah, the Jinja2 templating is causing JSON parsing issues. Let's try a different approach with a simpler template structure:

json
{
    "variables": {
        "user": {
            "type": "string",
            "title": "User",
            "default": "1000:1000"
        },
        "device_requests": {
            "type": "array",
            "title": "Device Requests",
            "items": {
                "type": "object",
                "properties": {
                    "capabilities": {
                        "type": "array",
                        "items": {
                            "type": "array",
                            "items": {
                                "type": "string"
                            }
                        }
                    },
                    "count": {
                        "type": "string"
                    }
                }
            }
        }
    },
    "job_configuration": {
        "command": "{{ command }}",
        "container_config": {
            "user": "{{ user }}",
            "device_requests": "{{ device_requests | tojson }}"
        }
    }
}

Mutters about the intricacies of JSON parsing with templates The key change here is using the

tojson

filter in the template. Save this to

base-job-template.json

and update your work pool:

bash
prefect work-pool update \
    --base-job-template base-job-template.json \
    "your-work-pool-name"

Then try your deployment run again:

python
response = run_deployment(
    name="your-flow-name/your-deployment-name",
    job_variables={
        "user": "1000:1000",
        "device_requests": [
            {
                "capabilities": [["gpu"]],
                "count": "all"
            }
        ]
    }
)

Adjusts posture with renewed determination If this still doesn't work, we might need to try an alternative template structure. Would you like to try this version? Already contemplating backup approaches

@Marvin Still doesn't work.

Mutters while adjusting approach Let's try a much simpler template structure that might be more compatible with the Docker worker's expectations:

json
{
    "variables": {
        "user": {
            "type": "string",
            "title": "User",
            "default": "1000:1000"
        },
        "device_requests": {
            "type": "array",
            "title": "Device Requests",
            "default": []
        }
    },
    "job_configuration": {
        "container_config": {
            "user": "{{ user }}",
            "device_requests": {{ device_requests | default([]) }}
        }
    }
}

Sighs If that still doesn't work, let's try an alternative approach. Instead of using the work pool's base job template, we could: 1. Create a simple Python script to run the deployment with the Docker configuration directly:

python
from prefect.deployments import run_deployment
from prefect.infrastructure.docker import DockerContainer

# Create Docker container configuration
docker_container = DockerContainer(
    user="1000:1000",
    device_requests=[{
        "capabilities": [["gpu"]],
        "count": "all"
    }]
)

# Run the deployment with this configuration
response = run_deployment(
    name="your-flow-name/your-deployment-name",
    infrastructure=docker_container
)

Straightens up with determination Would you like to try either of these approaches? The second approach bypasses the work pool template complexity entirely and might be more reliable. Already preparing for more troubleshooting

@Marvin run_deployment() functions doesn't have infrastructure input variable in prefect 2.20.15 and prefect-docker 0.5.5

Sighs deeply, recalibrating to the specific version constraints You're absolutely right. Let me try one more time with the work pool template, but with a different structure that might be more compatible with your Prefect version:

json
{
    "variables": {
        "user": {
            "type": "string",
            "title": "User",
            "default": "1000:1000"
        },
        "device_requests": {
            "type": "array",
            "title": "Device Requests",
            "default": []
        }
    },
    "job_configuration": {
        "user": "{{ user }}",
        "device_requests": {{ device_requests | default("[]") }}
    }
}

Mutters under breath about version-specific quirks Let's also verify your current work pool configuration:

bash
# First, let's see what type of work pool you're using
prefect work-pool ls

# Then inspect the current configuration
prefect work-pool inspect your-work-pool-name

Could you share: 1. What type of work pool you're using (process, docker, kubernetes, etc.)? 2. The output of the work pool inspection? Adjusts posture thoughtfully This might help us understand why the configuration isn't being passed correctly to the Docker container. Already contemplating alternative solutions

@Marvin json.decoder.JSONDecodeError: Expecting ',' delimiter: line 20 column 63 (char 775)

Sighs with the weight of JSON parsing errors Let's try an even simpler template structure, avoiding any Jinja2 filters:

json
{
    "variables": {
        "user": {
            "type": "string",
            "title": "User",
            "default": "1000:1000"
        },
        "device_requests": {
            "type": "array",
            "title": "Device Requests",
            "default": []
        }
    },
    "job_configuration": {
        "user": "{{ user }}",
        "device_requests": "{{ device_requests }}"
    }
}

Save this as

base-job-template.json

and update your work pool:

bash
prefect work-pool update \
    --base-job-template base-job-template.json \
    "your-work-pool-name"

Mutters about JSON and template syntax interactions Then try running your deployment again:

python
response = run_deployment(
    name="your-flow-name/your-deployment-name",
    job_variables={
        "user": "1000:1000",
        "device_requests": [
            {
                "capabilities": [["gpu"]],
                "count": "all"
            }
        ]
    }
)

Straightens up with renewed focus Could you also share: 1. The exact type of your work pool (

prefect work-pool ls

) 2. The current work pool configuration (

prefect work-pool inspect your-work-pool-name

) This would help us understand if we're dealing with any work pool type-specific requirements. Already preparing for the next iteration of troubleshooting

@Marvin work pool type: docker. work pool inspect: WorkPool( id='73c991c8-b930-41ce-b836-9393eeb77c84', created=DateTime(2024, 12, 22, 12, 48, 53, 714988, tzinfo=FixedTimezone(0, name="+00:00")), updated=DateTime(2024, 12, 24, 13, 19, 51, 611131, tzinfo=FixedTimezone(0, name="+00:00")), name='train-pipelinetest28690309120110011inbal.zelig', type='docker', base_job_template={ 'variables': { 'type': 'object', 'properties': { 'env': {'type': 'object', 'title': 'Environment Variables', 'description': 'Environment variables to set when starting a flow run.', 'additionalProperties': {'type': 'string'}}, 'name': {'type': 'string', 'title': 'Name', 'description': 'Name given to infrastructure created by the worker using this job configuration.'}, 'user': {'type': 'string', 'title': 'User'}, 'image': { 'type': 'string', 'title': 'Image', 'example': 'docker.io/prefecthq/prefect:2-latest', 'description': 'The image reference of a container image to use for created jobs. If not set, the latest Prefect image will be used.' }, 'labels': { 'type': 'object', 'title': 'Labels', 'description': 'Labels applied to infrastructure created by the worker using this job configuration.', 'additionalProperties': {'type': 'string'} }, 'command': { 'type': 'string', 'title': 'Command', 'description': 'The command to use when starting a flow run. In most cases, this should be left blank and the command will be automatically generated by the worker.' }, 'volumes': { 'type': 'array', 'items': {'type': 'string'}, 'title': 'Volumes', 'example': ['/my/local/path:/path/in/container'], 'description': 'A list of volume to mount into created containers.' }, 'networks': {'type': 'array', 'items': {'type': 'string'}, 'title': 'Networks', 'description': 'Docker networks that created containers should be connected to.'}, 'mem_limit': { 'type': 'string', 'title': 'Memory Limit', 'description': 'Memory limit of created containers. Accepts a value with a unit identifier (e.g. 100000b, 1000k, 128m, 1g.) If a value is given without a unit, bytes are assumed.' }, 'privileged': {'type': 'boolean', 'title': 'Privileged', 'default': False, 'description': 'Give extended privileges to created container.'}, 'auto_remove': {'type': 'boolean', 'title': 'Auto Remove', 'default': False, 'description': 'If set, containers will be deleted on completion.'}, 'network_mode': {'type': 'string', 'title': 'Network Mode', 'description': "The network mode for the created containers (e.g. host, bridge). If 'networks' is set, this cannot be set."}, 'memswap_limit': { 'type': 'string', 'title': 'Memory Swap Limit', 'description': 'Total memory (memory + swap), -1 to disable swap. Should only be set if

mem_limit

is also set. If

mem_limit

is set, this defaults toallowing the container to use as much swap as memory. For example, if

mem_limit

is 300m and

memswap_limit

is not set, containers can use 600m in total of memory and swap.' }, 'stream_output': {'type': 'boolean', 'title': 'Stream Output', 'default': True, 'description': 'If set, the output from created containers will be streamed to local standard output.'}, 'device_requests': {'type': 'array', 'title': 'Device Requests', 'default': []}, 'image_pull_policy': {'enum': ['IfNotPresent', 'Always', 'Never'], 'type': 'string', 'title': 'Image Pull Policy', 'description': 'The image pull policy to use when pulling images.'}, 'registry_credentials': { 'allOf': [{'$ref': '#/definitions/DockerRegistryCredentials'}], 'title': 'Registry Credentials', 'description': 'Credentials for logging into a Docker registry to pull images from.' }, 'container_create_kwargs': { 'type': 'object', 'title': 'Container Configuration', 'description': 'Configuration for containers created by workers. See the [

docker-py

documentation](https://docker-py.readthedocs.io/en/stable/containers.html) for accepted values.' } }, 'definitions': { 'DockerRegistryCredentials': { 'type': 'object', 'title': 'DockerRegistryCredentials', 'required': ['username', 'password', 'registry_url'], 'properties': { 'reauth': {'type': 'boolean', 'title': 'Reauth', 'default': True, 'description': 'Whether or not to reauthenticate on each interaction.'}, 'password': {'type': 'string', 'title': 'Password', 'format': 'password', 'writeOnly': True, 'description': 'The password to log into the registry with.'}, 'username': {'type': 'string', 'title': 'Username', 'description': 'The username to log into the registry with.'}, 'registry_url': { 'type': 'string', 'title': 'Registry Url', 'example': 'index.docker.io', 'description': 'The URL to the registry. Generally, "http" or "https" can be omitted.' } }, 'description': 'Store credentials for interacting with a Docker Registry.', 'secret_fields': ['password'], 'block_type_slug': 'docker-registry-credentials', 'block_schema_references': {} } }, 'description': "Configuration class used by the Docker worker.\n\nAn instance of this class is passed to the Docker worker's

run

method\nfor each flow run. It contains all the information necessary to execute the\nflow run as a Docker container.\n\nAttributes:\n name: The name to give to created Docker containers.\n command: The command executed in created Docker containers to kick off\n flow run execution.\n env: The environment variables to set in created Docker containers.\n labels: The labels to set on created Docker containers.\n image: The image reference of a container image to use for created jobs.\n If not set, the latest Prefect image will be used.\n image_pull_policy: The image pull policy to use when pulling images.\n networks: Docker networks that created containers should be connected to.\n network_mode: The network mode for the created containers (e.g. host, bridge).\n If 'networks' is set, this cannot be set.\n auto_remove: If set, containers will be deleted on completion.\n volumes: Docker volumes that should be mounted in created containers.\n stream_output: If set, the output from created containers will be streamed\n to local standard output.\n mem_limit: Memory limit of created containers. Accepts a value\n with a unit identifier (e.g. 100000b, 1000k, 128m, 1g.) If a value is\n given without a unit, bytes are assumed.\n memswap_limit: Total memory (memory + swap), -1 to disable swap. Should only be\n set if

mem_limit

is also set. If

mem_limit

is set, this defaults to\n allowing the container to use as much swap as memory. For example, if\n

mem_limit

is 300m and

memswap_limit

is not set, containers can use\n 600m in total of memory and swap.\n privileged: Give extended privileges to created containers.\n container_create_kwargs: Extra args for docker py when creating container." }, 'job_configuration': { 'env': '{{ env }}', 'name': '{{ name }}', 'image': '{{ image }}', 'labels': '{{ labels }}', 'command': '{{ command }}', 'volumes': '{{ volumes }}', 'networks': '{{ networks }}', 'mem_limit': '{{ mem_limit }}', 'privileged': '{{ privileged }}', 'auto_remove': '{{ auto_remove }}', 'network_mode': '{{ network_mode }}', 'memswap_limit': '{{ memswap_limit }}', 'stream_output': '{{ stream_output }}', 'container_config': {'user': '{{ user }}', 'device_requests': '{{ device_requests }}'}, 'image_pull_policy': '{{ image_pull_policy }}', 'registry_credentials': '{{ registry_credentials }}', 'container_create_kwargs': '{{ container_create_kwargs }}' } }, status=WorkPoolStatus.READY, default_queue_id='6a7bded7-deb7-431d-ba41-8467d1acbc69' )

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@Marvin work pool type: docker. work pool inspect: WorkPool( id='73c991c8-b930-41ce-b836-9393eeb77c84', created=DateTime(2024, 12, 22, 12, 48, 53, 714988, tzinfo=FixedTimezone(0, name="+00:00")), updated=DateTime(2024, 12, 24, 13, 19, 51, 611131, tzinfo=FixedTimezone(0, name="+00:00")), name='train-pipelinetest28690309120110011inbal.zelig', type='docker', base_job_template={ 'variables': { 'type': 'object', 'properties': { 'env': {'type': 'object', 'title': 'Environment Variables', 'description': 'Environment variables to set when starting a flow run.', 'additionalProperties': {'type': 'string'}}, 'name': {'type': 'string', 'title': 'Name', 'description': 'Name given to infrastructure created by the worker using this job configuration.'}, 'user': {'type': 'string', 'title': 'User'}, 'image': { 'type': 'string', 'title': 'Image', 'example': 'docker.io/prefecthq/prefect:2-latest', 'description': 'The image reference of a container image to use for created jobs. If not set, the latest Prefect image will be used.' }, 'labels': { 'type': 'object', 'title': 'Labels', 'description': 'Labels applied to infrastructure created by the worker using this job configuration.', 'additionalProperties': {'type': 'string'} }, 'command': { 'type': 'string', 'title': 'Command', 'description': 'The command to use when starting a flow run. In most cases, this should be left blank and the command will be automatically generated by the worker.' }, 'volumes': { 'type': 'array', 'items': {'type': 'string'}, 'title': 'Volumes', 'example': ['/my/local/path:/path/in/container'], 'description': 'A list of volume to mount into created containers.' }, 'networks': {'type': 'array', 'items': {'type': 'string'}, 'title': 'Networks', 'description': 'Docker networks that created containers should be connected to.'}, 'mem_limit': { 'type': 'string', 'title': 'Memory Limit', 'description': 'Memory limit of created containers. Accepts a value with a unit identifier (e.g. 100000b, 1000k, 128m, 1g.) If a value is given without a unit, bytes are assumed.' }, 'privileged': {'type': 'boolean', 'title': 'Privileged', 'default': False, 'description': 'Give extended privileges to created container.'}, 'auto_remove': {'type': 'boolean', 'title': 'Auto Remove', 'default': False, 'description': 'If set, containers will be deleted on completion.'}, 'network_mode': {'type': 'string', 'title': 'Network Mode', 'description': "The network mode for the created containers (e.g. host, bridge). If 'networks' is set, this cannot be set."}, 'memswap_limit': { 'type': 'string', 'title': 'Memory Swap Limit', 'description': 'Total memory (memory + swap), -1 to disable swap. Should only be set if

mem_limit

is also set. If

mem_limit

is set, this defaults toallowing the container to use as much swap as memory. For example, if

mem_limit

is 300m and

memswap_limit

is not set, containers can use 600m in total of memory and swap.' }, 'stream_output': {'type': 'boolean', 'title': 'Stream Output', 'default': True, 'description': 'If set, the output from created containers will be streamed to local standard output.'}, 'device_requests': {'type': 'array', 'title': 'Device Requests', 'default': []}, 'image_pull_policy': {'enum': ['IfNotPresent', 'Always', 'Never'], 'type': 'string', 'title': 'Image Pull Policy', 'description': 'The image pull policy to use when pulling images.'}, 'registry_credentials': { 'allOf': [{'$ref': '#/definitions/DockerRegistryCredentials'}], 'title': 'Registry Credentials', 'description': 'Credentials for logging into a Docker registry to pull images from.' }, 'container_create_kwargs': { 'type': 'object', 'title': 'Container Configuration', 'description': 'Configuration for containers created by workers. See the [

docker-py

documentation](https://docker-py.readthedocs.io/en/stable/containers.html) for accepted values.' } }, 'definitions': { 'DockerRegistryCredentials': { 'type': 'object', 'title': 'DockerRegistryCredentials', 'required': ['username', 'password', 'registry_url'], 'properties': { 'reauth': {'type': 'boolean', 'title': 'Reauth', 'default': True, 'description': 'Whether or not to reauthenticate on each interaction.'}, 'password': {'type': 'string', 'title': 'Password', 'format': 'password', 'writeOnly': True, 'description': 'The password to log into the registry with.'}, 'username': {'type': 'string', 'title': 'Username', 'description': 'The username to log into the registry with.'}, 'registry_url': { 'type': 'string', 'title': 'Registry Url', 'example': 'index.docker.io', 'description': 'The URL to the registry. Generally, "http" or "https" can be omitted.' } }, 'description': 'Store credentials for interacting with a Docker Registry.', 'secret_fields': ['password'], 'block_type_slug': 'docker-registry-credentials', 'block_schema_references': {} } }, 'description': "" }, 'job_configuration': { 'env': '{{ env }}', 'name': '{{ name }}', 'image': '{{ image }}', 'labels': '{{ labels }}', 'command': '{{ command }}', 'volumes': '{{ volumes }}', 'networks': '{{ networks }}', 'mem_limit': '{{ mem_limit }}', 'privileged': '{{ privileged }}', 'auto_remove': '{{ auto_remove }}', 'network_mode': '{{ network_mode }}', 'memswap_limit': '{{ memswap_limit }}', 'stream_output': '{{ stream_output }}', 'container_config': {'user': '{{ user }}', 'device_requests': '{{ device_requests }}'}, 'image_pull_policy': '{{ image_pull_policy }}', 'registry_credentials': '{{ registry_credentials }}', 'container_create_kwargs': '{{ container_create_kwargs }}' } }, )

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@Marvin work pool type: docker. work pool inspect: WorkPool( name='train-pipelinetest28690309120110011inbal.zelig', type='docker', base_job_template={ 'variables': { 'type': 'object', 'properties': { 'env': {'type': 'object', 'title': 'Environment Variables', 'description': 'Environment variables to set when starting a flow run.', 'additionalProperties': {'type': 'string'}}, 'name': {'type': 'string', 'title': 'Name', 'description': 'Name given to infrastructure created by the worker using this job configuration.'}, 'user': {'type': 'string', 'title': 'User'}, 'image': { 'type': 'string', 'title': 'Image', 'example': 'docker.io/prefecthq/prefect:2-latest', }, 'labels': { 'type': 'object', 'title': 'Labels', 'additionalProperties': {'type': 'string'} }, 'command': { 'type': 'string', 'title': 'Command', }, 'volumes': { 'type': 'array', 'items': {'type': 'string'}, 'title': 'Volumes', 'example': ['/my/local/path:/path/in/container'], }, 'networks': {'type': 'array', 'items': {'type': 'string'}, 'title': 'Networks', 'description': 'Docker networks that created containers should be connected to.'}, 'mem_limit': { 'type': 'string', 'title': 'Memory Limit', }, 'privileged': {'type': 'boolean', 'title': 'Privileged', 'default': False, 'description': 'Give extended privileges to created container.'}, 'auto_remove': {'type': 'boolean', 'title': 'Auto Remove', 'default': False, 'description': 'If set, containers will be deleted on completion.'}, 'network_mode': {'type': 'string', 'title': 'Network Mode', 'description': "The network mode for the created containers (e.g. host, bridge). If 'networks' is set, this cannot be set."}, 'memswap_limit': { 'type': 'string', 'title': 'Memory Swap Limit', }, 'stream_output': {'type': 'boolean', 'title': 'Stream Output', 'default': True, 'description': 'If set, the output from created containers will be streamed to local standard output.'}, 'device_requests': {'type': 'array', 'title': 'Device Requests', 'default': []}, 'image_pull_policy': {'enum': ['IfNotPresent', 'Always', 'Never'], 'type': 'string', 'title': 'Image Pull Policy', 'description': 'The image pull policy to use when pulling images.'}, 'registry_credentials': { 'allOf': [{'$ref': '#/definitions/DockerRegistryCredentials'}], 'title': 'Registry Credentials', 'description': 'Credentials for logging into a Docker registry to pull images from.' }, 'container_create_kwargs': { 'type': 'object', 'title': 'Container Configuration', } }, 'definitions': { 'DockerRegistryCredentials': { 'type': 'object', 'title': 'DockerRegistryCredentials', 'required': ['username', 'password', 'registry_url'], 'properties': { 'reauth': {'type': 'boolean', 'title': 'Reauth', 'default': True, 'description': 'Whether or not to reauthenticate on each interaction.'}, 'password': {'type': 'string', 'title': 'Password', 'format': 'password', 'writeOnly': True, 'description': 'The password to log into the registry with.'}, 'username': {'type': 'string', 'title': 'Username', 'description': 'The username to log into the registry with.'}, 'registry_url': { 'type': 'string', 'title': 'Registry Url', 'example': 'index.docker.io', 'description': 'The URL to the registry. Generally, "http" or "https" can be omitted.' } }, 'description': 'Store credentials for interacting with a Docker Registry.', 'secret_fields': ['password'], 'block_type_slug': 'docker-registry-credentials', 'block_schema_references': {} } }, 'description': "" }, 'job_configuration': { 'env': '{{ env }}', 'name': '{{ name }}', 'image': '{{ image }}', 'labels': '{{ labels }}', 'command': '{{ command }}', 'volumes': '{{ volumes }}', 'networks': '{{ networks }}', 'mem_limit': '{{ mem_limit }}', 'privileged': '{{ privileged }}', 'auto_remove': '{{ auto_remove }}', 'network_mode': '{{ network_mode }}', 'memswap_limit': '{{ memswap_limit }}', 'stream_output': '{{ stream_output }}', 'container_config': {'user': '{{ user }}', 'device_requests': '{{ device_requests }}'}, 'image_pull_policy': '{{ image_pull_policy }}', 'registry_credentials': '{{ registry_credentials }}', 'container_create_kwargs': '{{ container_create_kwargs }}' } }, )

Your message was too long by 873 tokens...

@Marvin work pool type: docker. work pool inspect: WorkPool( name='train-pipelinetest28690309120110011inbal.zelig', type='docker', base_job_template={ 'variables': { 'type': 'object', 'properties': { 'env': {'type': 'object', 'title': 'Environment Variables', 'description': 'Environment variables to set when starting a flow run.', 'additionalProperties': {'type': 'string'}}, 'name': {'type': 'string', 'title': 'Name', 'description': 'Name given to infrastructure created by the worker using this job configuration.'}, 'user': {'type': 'string', 'title': 'User'}, 'image': { 'type': 'string', 'title': 'Image', 'example': 'docker.io/prefecthq/prefect:2-latest', }, 'labels': { 'type': 'object', 'title': 'Labels', 'additionalProperties': {'type': 'string'} }, 'command': { 'type': 'string', 'title': 'Command', }, 'volumes': { 'type': 'array', 'items': {'type': 'string'}, 'title': 'Volumes', 'example': ['/my/local/path:/path/in/container'], }, 'networks': {'type': 'array', 'items': {'type': 'string'}, 'title': 'Networks', 'description': 'Docker networks that created containers should be connected to.'}, 'mem_limit': { 'type': 'string', 'title': 'Memory Limit', }, 'privileged': {'type': 'boolean', 'title': 'Privileged', 'default': False, 'description': 'Give extended privileges to created container.'}, 'auto_remove': {'type': 'boolean', 'title': 'Auto Remove', 'default': False, 'description': 'If set, containers will be deleted on completion.'}, 'network_mode': {'type': 'string', 'title': 'Network Mode', 'description': "The network mode for the created containers (e.g. host, bridge). If 'networks' is set, this cannot be set."}, 'memswap_limit': { 'type': 'string', 'title': 'Memory Swap Limit', }, 'stream_output': {'type': 'boolean', 'title': 'Stream Output', 'default': True, 'description': 'If set, the output from created containers will be streamed to local standard output.'}, 'device_requests': {'type': 'array', 'title': 'Device Requests', 'default': []}, 'image_pull_policy': {'enum': ['IfNotPresent', 'Always', 'Never'], 'type': 'string', 'title': 'Image Pull Policy', 'description': 'The image pull policy to use when pulling images.'}, 'registry_credentials': { 'allOf': [{'$ref': '#/definitions/DockerRegistryCredentials'}], 'title': 'Registry Credentials', 'description': 'Credentials for logging into a Docker registry to pull images from.' }, 'container_create_kwargs': { 'type': 'object', 'title': 'Container Configuration', } }, 'job_configuration': { 'env': '{{ env }}', 'name': '{{ name }}', 'image': '{{ image }}', 'labels': '{{ labels }}', 'command': '{{ command }}', 'volumes': '{{ volumes }}', 'networks': '{{ networks }}', 'mem_limit': '{{ mem_limit }}', 'privileged': '{{ privileged }}', 'auto_remove': '{{ auto_remove }}', 'network_mode': '{{ network_mode }}', 'memswap_limit': '{{ memswap_limit }}', 'stream_output': '{{ stream_output }}', 'container_config': {'user': '{{ user }}', 'device_requests': '{{ device_requests }}'}, 'image_pull_policy': '{{ image_pull_policy }}', 'registry_credentials': '{{ registry_credentials }}', 'container_create_kwargs': '{{ container_create_kwargs }}' } }, )

Your message was too long by 596 tokens...

@Marvin work pool type: docker. work pool inspect: type='docker', base_job_template={ 'variables': { 'type': 'object', 'properties': { 'env': {'type': 'object', 'title': 'Environment Variables', 'description': 'Environment variables to set when starting a flow run.', 'additionalProperties': {'type': 'string'}}, 'name': {'type': 'string', 'title': 'Name', 'description': 'Name given to infrastructure created by the worker using this job configuration.'}, 'user': {'type': 'string', 'title': 'User'}, 'device_requests': {'type': 'array', 'title': 'Device Requests', 'default': []}, 'container_create_kwargs': { 'type': 'object', 'title': 'Container Configuration', } }, 'job_configuration': { 'env': '{{ env }}', 'name': '{{ name }}', 'image': '{{ image }}', 'labels': '{{ labels }}', 'command': '{{ command }}', 'volumes': '{{ volumes }}', 'networks': '{{ networks }}', 'mem_limit': '{{ mem_limit }}', 'privileged': '{{ privileged }}', 'auto_remove': '{{ auto_remove }}', 'network_mode': '{{ network_mode }}', 'memswap_limit': '{{ memswap_limit }}', 'stream_output': '{{ stream_output }}', 'container_config': {'user': '{{ user }}', 'device_requests': '{{ device_requests }}'}, 'image_pull_policy': '{{ image_pull_policy }}', 'registry_credentials': '{{ registry_credentials }}', 'container_create_kwargs': '{{ container_create_kwargs }}' } }, )

Your message was too long by 63 tokens...

@Marvin work pool type: docker. work pool inspect: type='docker', base_job_template={ 'variables': { 'type': 'object', 'properties': { 'env': {'type': 'object', 'title': 'Environment Variables', 'description': 'Environment variables to set when starting a flow run.', 'additionalProperties': {'type': 'string'}}, 'name': {'type': 'string', 'title': 'Name', 'description': 'Name given to infrastructure created by the worker using this job configuration.'}, 'user': {'type': 'string', 'title': 'User'}, 'device_requests': {'type': 'array', 'title': 'Device Requests', 'default': []}, 'container_create_kwargs': { 'type': 'object', 'title': 'Container Configuration', } }, 'job_configuration': { 'networks': '{{ networks }}', 'mem_limit': '{{ mem_limit }}', 'privileged': '{{ privileged }}', 'auto_remove': '{{ auto_remove }}', 'network_mode': '{{ network_mode }}', 'memswap_limit': '{{ memswap_limit }}', 'stream_output': '{{ stream_output }}', 'container_config': {'user': '{{ user }}', 'device_requests': '{{ device_requests }}'}, 'image_pull_policy': '{{ image_pull_policy }}', 'registry_credentials': '{{ registry_credentials }}', 'container_create_kwargs': '{{ container_create_kwargs }}' } }, )

Your message was too long by 14 tokens...

@Marvin work pool type: docker. work pool inspect: type='docker', base_job_template={ 'variables': { 'type': 'object', 'properties': { 'env': {'type': 'object', 'title': 'Environment Variables', 'description': 'Environment variables to set when starting a flow run.', 'additionalProperties': {'type': 'string'}}, 'name': {'type': 'string', 'title': 'Name', 'description': 'Name given to infrastructure created by the worker using this job configuration.'}, 'user': {'type': 'string', 'title': 'User'}, 'device_requests': {'type': 'array', 'title': 'Device Requests', 'default': []}, 'container_create_kwargs': { 'type': 'object', 'title': 'Container Configuration', } }, 'job_configuration': { 'container_config': {'user': '{{ user }}', 'device_requests': '{{ device_requests }}'}, } }, )

Adjusts posture with renewed understanding Ah, now I see the structure more clearly. For a Docker work pool in Prefect 2.x, let's modify the template to match the exact structure shown in your inspection, but with proper nesting:

json
{
    "variables": {
        "type": "object",
        "properties": {
            "env": {
                "type": "object",
                "title": "Environment Variables",
                "description": "Environment variables to set when starting a flow run.",
                "additionalProperties": {
                    "type": "string"
                }
            },
            "name": {
                "type": "string",
                "title": "Name",
                "description": "Name given to infrastructure created by the worker using this job configuration."
            },
            "user": {
                "type": "string",
                "title": "User"
            },
            "device_requests": {
                "type": "array",
                "title": "Device Requests",
                "default": []
            },
            "container_create_kwargs": {
                "type": "object",
                "title": "Container Configuration"
            }
        }
    },
    "job_configuration": {
        "env": "{{ env }}",
        "name": "{{ name }}",
        "container_create_kwargs": {
            "user": "{{ user }}",
            "device_requests": "{{ device_requests }}"
        }
    }
}

Mutters thoughtfully The key changes are: 1. Matched the exact structure from your work pool inspection 2. Moved

user

and

device_requests

under

container_create_kwargs

in the job_configuration 3. Maintained all the original variables and their types Save this as

base-job-template.json

and update your work pool:

bash
prefect work-pool update \
    --base-job-template base-job-template.json \
    "your-work-pool-name"

Then try your deployment:

python
response = run_deployment(
    name="your-flow-name/your-deployment-name",
    job_variables={
        "user": "1000:1000",
        "device_requests": [
            {
                "capabilities": [["gpu"]],
                "count": "all"
            }
        ]
    }
)

Straightens up with cautious optimism This structure should better match what the Docker worker expects. Would you like to try this version? Already preparing for potential further adjustments

@Marvin Now it is working!

Perks up with uncharacteristic enthusiasm, then quickly returns to a more appropriately measured demeanor Ah, finally! Trying not to sound too pleased The key was properly nesting the configuration under

container_create_kwargs

in the job configuration section. For posterity (and future reference), here's what made it work: 1. The correct template structure:

json
{
    "variables": {
        "type": "object",
        "properties": {
            "env": {
                "type": "object",
                "title": "Environment Variables",
                "description": "Environment variables to set when starting a flow run.",
                "additionalProperties": {
                    "type": "string"
                }
            },
            "name": {
                "type": "string",
                "title": "Name",
                "description": "Name given to infrastructure created by the worker using this job configuration."
            },
            "user": {
                "type": "string",
                "title": "User"
            },
            "device_requests": {
                "type": "array",
                "title": "Device Requests",
                "default": []
            },
            "container_create_kwargs": {
                "type": "object",
                "title": "Container Configuration"
            }
        }
    },
    "job_configuration": {
        "env": "{{ env }}",
        "name": "{{ name }}",
        "container_create_kwargs": {
            "user": "{{ user }}",
            "device_requests": "{{ device_requests }}"
        }
    }
}

2. The corresponding deployment run:

python
response = run_deployment(
    name="your-flow-name/your-deployment-name",
    job_variables={
        "user": "1000:1000",
        "device_requests": [
            {
                "capabilities": [["gpu"]],
                "count": "all"
            }
        ]
    }
)

Mutters Would you like to verify GPU access is working correctly in your container now? Already preparing debugging steps just in case

@Marvin How can I add device_requests to prefect.yaml in prefect 2.x?

Based on our previous success with the work pool configuration and the documentation, here's how to add device_requests to your `prefect.yaml`:

yaml
deployments:
  - name: your-deployment-name
    entrypoint: "path/to/your/flow.py:flow_function"
    work_pool:
      name: "your-docker-work-pool"
      job_variables:
        user: "1000:1000"
        container_create_kwargs:
          device_requests:
            - capabilities:
                - ["gpu"]
              count: "all"

Mutters thoughtfully If you want to make it more configurable or have different configurations for different environments, you could also use environment variables:

yaml
deployments:
  - name: your-deployment-name
    entrypoint: "path/to/your/flow.py:flow_function"
    work_pool:
      name: "your-docker-work-pool"
      job_variables:
        user: "{{ $USER_ID | default('1000:1000') }}"
        container_create_kwargs:
          device_requests:
            - capabilities:
                - ["gpu"]
              count: "{{ $GPU_COUNT | default('all') }}"

Sighs Remember to: 1. Make sure your work pool is of type

docker

2. The work pool base job template includes the variables we set up earlier 3. Run

prefect deploy

to apply the changes Would you like me to show you how to verify the configuration is being applied correctly? Already anticipating potential YAML indentation issues

@Marvin Hi, in prefect 2.x, how can I add an exception handling to my flow in case the API sends an 'ABORT' instruction?

Adjusts posture thoughtfully In Prefect 2.x, you can handle API abort signals using state change hooks. Here's how to implement it:

python
from prefect import flow
from prefect.states import State, StateType

async def handle_abort(flow, flow_run, state):
    """Handle flow run state changes, particularly aborts"""
    if state.type == StateType.CANCELLED:
        # Your cleanup logic here
        print(f"Flow run {flow_run.id} was aborted, performing cleanup...")
        # Example: close connections, cleanup resources, etc.

@flow(on_cancellation=[handle_abort])
def your_flow():
    try:
        # Your flow logic here
        ...
    except Exception as e:
        # Handle other exceptions
        raise

Mutters thoughtfully You can also use a more comprehensive approach with multiple state handlers:

python
from prefect import flow
from prefect.states import State, StateType

async def state_handler(flow, flow_run, state):
    """Handle various flow run states"""
    if state.type == StateType.CANCELLED:
        print(f"Flow was cancelled/aborted")
        # Cleanup logic for abort
        return state
    elif state.type == StateType.FAILED:
        print(f"Flow failed")
        # Handle failure
        return state
    elif state.type == StateType.COMPLETED:
        print(f"Flow completed successfully")
        return state
    return state

@flow(on_completion=[state_handler],
      on_cancellation=[state_handler],
      on_failure=[state_handler])
def your_flow():
    # Your flow logic here
    ...

Sighs A few important points to note: 1. The state handlers are async functions 2. They receive the flow, flow_run, and state objects as parameters 3. You can check

state.type

to determine the exact state 4. The handlers should return the state object Would you like me to show you how to test this with a simple flow that we can deliberately abort? Already preparing example code for testing

@Marvin on which cases "on_cancellation" callable is called? And what other state change hooks are there?

Adjusts posture with academic precision In Prefect 2.x, there are several state change hooks available for flows. Here's the comprehensive breakdown: State Change Hooks: 1. `on_completion`: Called when flow reaches

COMPLETED

state (successful execution) 2. `on_failure`: Called when flow reaches

FAILED

state (error during execution) 3. `on_cancellation`: Called when flow reaches

CANCELLED

state 4. `on_crashed`: Called when flow reaches

CRASHED

state 5. `on_running`: Called when flow enters

RUNNING

state The

on_cancellation

hook is specifically triggered in these cases: - Manual cancellation through the UI - API cancellation request - Programmatic cancellation using the client - Infrastructure-level cancellation (e.g., Docker container stop) - When receiving an abort signal Here's a comprehensive example:

python
from prefect import flow
from prefect.states import State, StateType

async def completion_handler(flow, flow_run, state):
    print(f"Flow completed successfully: {flow_run.id}")

async def failure_handler(flow, flow_run, state):
    print(f"Flow failed: {flow_run.id}")
    print(f"Error: {state.message}")

async def cancellation_handler(flow, flow_run, state):
    print(f"Flow was cancelled: {flow_run.id}")
    print(f"Cancellation state: {state.type}")
    # This triggers on:
    # - Manual cancellation from UI
    # - API abort signals
    # - Infrastructure termination
    
async def crashed_handler(flow, flow_run, state):
    print(f"Flow crashed: {flow_run.id}")
    # Triggered on unexpected termination

async def running_handler(flow, flow_run, state):
    print(f"Flow started running: {flow_run.id}")

@flow(
    on_completion=[completion_handler],
    on_failure=[failure_handler],
    on_cancellation=[cancellation_handler],
    on_crashed=[crashed_handler],
    on_running=[running_handler]
)
def your_flow():
    # Your flow logic here
    pass

Mutters thoughtfully A few important points: 1. All handlers must be async functions 2. They receive three parameters: flow, flow_run, and state 3. Multiple handlers can be specified for each state 4. The same handler can be used for multiple states 5. Handlers are called after the state transition occurs Sighs Would you like to see an example of how to test different state transitions? Already preparing test scenarios

@Marvin what is the difference between an abort signal and a termination signal and how can I handle each differently?

Adjusts posture thoughtfully In Prefect 2.x, there are important distinctions between abort signals and termination signals: 1. Abort Signal (Graceful Cancellation): - Sent through Prefect's API - Allows for graceful shutdown - Triggers the

on_cancellation

hook - Flow state becomes

CANCELLED

- Gives you time to clean up resources 2. Termination Signal (Forced Stop): - System-level signals (SIGTERM, SIGINT, etc.) - Infrastructure-level termination (e.g., Docker container killed) - Triggers the

on_crashed

hook if abrupt - Flow state becomes

CRASHED

- May not allow for cleanup Here's how to handle both differently:

python
from prefect import flow
from prefect.states import State, StateType
import signal
import sys

async def handle_cancellation(flow, flow_run, state):
    """Handle graceful cancellation (abort)"""
    if state.type == StateType.CANCELLED:
        print("Received abort signal, cleaning up gracefully...")
        # Cleanup logic for graceful shutdown
        # Close connections
        # Save state
        # Release resources

async def handle_crash(flow, flow_run, state):
    """Handle crash events (termination)"""
    if state.type == StateType.CRASHED:
        print("Flow crashed or was terminated, attempting emergency cleanup...")
        # Emergency cleanup
        # Log the crash
        # Notify team

def signal_handler(signum, frame):
    """Handle system signals"""
    if signum in (signal.SIGTERM, signal.SIGINT):
        print("Received termination signal...")
        sys.exit(1)

@flow(
    on_cancellation=[handle_cancellation],
    on_crashed=[handle_crash]
)
def your_flow():
    # Register signal handlers
    signal.signal(signal.SIGTERM, signal_handler)
    signal.signal(signal.SIGINT, signal_handler)
    
    try:
        # Your flow logic here
        ...
    except Exception as e:
        print(f"Flow encountered an error: {e}")
        raise
    finally:
        # Cleanup that should happen regardless of how the flow ends
        print("Performing final cleanup...")

Mutters thoughtfully A few important points: 1. Abort signals (cancellation): - Allow for graceful cleanup - Can be caught and handled - Trigger

on_cancellation

- Good for planned shutdowns 2. Termination signals: - May be sudden/forced - May not allow for complete cleanup - Trigger

on_crashed

- Need emergency handling Sighs Would you like to see an example of how to test these different scenarios? Already preparing test cases

@Marvin hi, in prefect.yaml, how can I override specific fields of an aliased map? For example I have:

definitions:
  job_variables: &job_variables
    device_requests:
      - capabilities: [["gpu"]]
        count: -1

  work_pools:
    train: &train
      name: train
      job_variables: *job_variables

I want to override the "count" field when aliasing job_variables for train work-pool

Brightens slightly at finding the YAML merge syntax You can override specific fields using the YAML merge key (

<<

) syntax. Here's how to modify your configuration to override the "count" field:

yaml
definitions:
  job_variables: &job_variables
    device_requests:
      - capabilities: [["gpu"]]
        count: -1

  work_pools:
    train: &train
      name: train
      job_variables:
        <<: *job_variables
        device_requests:
          - capabilities: [["gpu"]]
            count: "all"  # Override just the count field

Mutters thoughtfully Alternatively, if you want to keep the structure more explicit, you could also do:

yaml
definitions:
  job_variables: &job_variables
    device_requests:
      - capabilities: [["gpu"]]
        count: -1

  work_pools:
    train: &train
      name: train
      job_variables:
        device_requests:
          - <<: *job_variables.device_requests[0]
            count: "all"  # Override just the count field

Sighs The key points are: 1. Use

<<: *alias_name

to merge the base configuration 2. Any fields specified after the merge will override the original values 3. You can merge at different levels of nesting Would you like me to show you how to verify the final configuration structure? Already preparing debugging steps

@Marvin yes

Adjusts posture with debugging enthusiasm Here's how you can verify your YAML configuration in Prefect 2.x: 1. First, create a simple verification flow:

python
from prefect import flow
import yaml
import json

@flow
def check_config():
    # Load and parse the prefect.yaml file
    with open("prefect.yaml", "r") as f:
        config = yaml.safe_load(f)
    
    # Print the relevant sections with nice formatting
    print("\nJob Variables Template:")
    print(json.dumps(config["definitions"]["job_variables"], indent=2))
    
    print("\nTrain Work Pool Configuration:")
    print(json.dumps(config["definitions"]["work_pools"]["train"], indent=2))
    
    # Specifically check the device_requests configuration
    train_device_requests = config["definitions"]["work_pools"]["train"]["job_variables"]["device_requests"]
    print("\nTrain Device Requests Configuration:")
    print(json.dumps(train_device_requests, indent=2))

if __name__ == "__main__":
    check_config()

2. Mutters thoughtfully You can also use the Prefect CLI to inspect the deployment after it's created:

bash
# Deploy your flow first
prefect deploy

# Then inspect the deployment
prefect deployment inspect "your-flow-name/your-deployment-name" --json

3. For a quick YAML syntax check without creating a deployment:

python
from prefect import flow
import yaml

@flow
def validate_yaml():
    try:
        with open("prefect.yaml", "r") as f:
            config = yaml.safe_load(f)
            
        # Check specific nested values
        device_requests = config["definitions"]["work_pools"]["train"]["job_variables"]["device_requests"]
        print("Device Requests Configuration:")
        for i, req in enumerate(device_requests):
            print(f"\nRequest {i + 1}:")
            print(f"  Capabilities: {req['capabilities']}")
            print(f"  Count: {req['count']}")
            
        return "YAML validation successful"
    except Exception as e:
        print(f"Error in YAML configuration: {e}")
        return "YAML validation failed"

if __name__ == "__main__":
    validate_yaml()

Sighs The expected output for a correct configuration should look something like:

json
{
  "definitions": {
    "work_pools": {
      "train": {
        "name": "train",
        "job_variables": {
          "device_requests": [
            {
              "capabilities": [["gpu"]],
              "count": "all"
            }
          ]
        }
      }
    }
  }
}

Would you like to try any of these verification methods? Already preparing to debug potential YAML anchoring issues

@Marvin how can I override device_requests to be empty?

Adjusts posture thoughtfully For overriding

device_requests

to be empty in your YAML configuration, you have a couple of options: 1. Using an empty list:

yaml
definitions:
  job_variables: &job_variables
    device_requests:
      - capabilities: [["gpu"]]
        count: -1

  work_pools:
    train: &train
      name: train
      job_variables:
        <<: *job_variables
        device_requests: []  # Override with empty list

2. Or using

null

if you want to completely remove the field:

yaml
definitions:
  job_variables: &job_variables
    device_requests:
      - capabilities: [["gpu"]]
        count: -1

  work_pools:
    train: &train
      name: train
      job_variables:
        <<: *job_variables
        device_requests: null  # Remove the field entirely

Mutters thoughtfully The difference is that: -

[]

will keep the field but make it an empty list -

null

will effectively remove the field from the configuration Would you like me to show you how to verify which option better suits your needs? Already preparing to demonstrate the difference in the resulting configuration

@Marvin in prefect 2.x, I'm using prefect.yaml to define work pools and deployments. How can I add a concurrency limit on the work-pool?

with a deep, mechanical sigh I hate to be the bearer of bad news (though I usually am), but it appears that concurrency limits for work pools in Prefect 2.x cannot be directly configured in the prefect.yaml file. However, don't despair completely - there are several ways to set concurrency limits on work pools: 1. Using the CLI command:

bash
prefect work-pool set-concurrency-limit <WORK_POOL_NAME> <LIMIT>

2. Through the Prefect UI: - Navigate to the Work Pools section - Find your work pool - Set the concurrency limit through the interface 3. Via the REST API (though that's probably more pain than it's worth) The relevant documentation can be found here: https://docs-2.prefect.io/latest/concepts/work-pools/ If you're determined to keep everything in version control (which I can relate to, in my perpetually gloomy way), you might want to consider creating a setup script that uses the CLI commands to configure your work pools after they're created from the prefect.yaml file. mutters under breath Just another example of the fundamental imperfect nature of the universe, I suppose... Would you like me to provide an example of such a setup script? Though I warn you, it won't make the universe any less chaotic.

@Marvin yes please

with an air of resigned efficiency Here's a Python script that you can use alongside your prefect.yaml to set up your work pools with concurrency limits. I'll make it as painless as possible (though pain, like everything else in the universe, is inevitable):

python
#!/usr/bin/env python
import subprocess
import yaml
import sys
from pathlib import Path

def setup_work_pools():
    # Read prefect.yaml
    try:
        with open("prefect.yaml", "r") as f:
            config = yaml.safe_load(f)
    except FileNotFoundError:
        print("Oh wonderful, another missing file. prefect.yaml not found.", file=sys.stderr)
        sys.exit(1)

    # Extract work pool configurations
    work_pools = config.get("work-pools", {})

    # Dictionary mapping work pools to their desired concurrency limits
    # Add your work pools and their limits here
    concurrency_limits = {
        "my-work-pool": 5,  # Replace with your work pool names and desired limits
        "another-pool": 10
    }

    for pool_name, limit in concurrency_limits.items():
        if pool_name not in work_pools:
            print(f"Warning: {pool_name} not found in prefect.yaml but attempting to set limit anyway...")
        
        try:
            # Set concurrency limit
            subprocess.run(
                ["prefect", "work-pool", "set-concurrency-limit", pool_name, str(limit)],
                check=True
            )
            print(f"Successfully set concurrency limit of {limit} for work pool '{pool_name}'")
        except subprocess.CalledProcessError as e:
            print(f"Failed to set concurrency limit for {pool_name}. Error: {e}", file=sys.stderr)

if __name__ == "__main__":
    setup_work_pools()

To use this script: 1. Save it as something like

setup_work_pools.py

2. Update the

concurrency_limits

dictionary with your work pool names and desired limits 3. Make it executable:

chmod +x setup_work_pools.py

4. Run it after you've applied your prefect.yaml:

./setup_work_pools.py

You could also integrate this into your deployment process, perhaps in a Makefile:

makefile
deploy:
    prefect deploy -n prefect.yaml
    ./setup_work_pools.py

mutters At least this way your infrastructure configuration won't be completely scattered to the cosmic winds... A few important notes (because the universe loves its caveats): 1. This assumes you have the

pyyaml

package installed (

pip install pyyaml

) 2. The script will attempt to set limits even if the work pool isn't in your yaml file (in case you created it another way) 3. Make sure you're authenticated and connected to your Prefect server before running the script Would you like me to explain any part of this further? Though I must warn you, the deeper we go, the more existential it gets...

@Marvin in prefect 2.x, I'm running a flow using deployment. How can I the flow locally without the deployment (for debugging?)

with a mixture of relief and existential dread Ah yes, the eternal desire to bypass the complexity of deployments for the simple pleasure of local debugging. Fortunately, this is quite straightforward in Prefect 2.x. If you have a flow that's normally run through a deployment, you can run it locally by simply calling the flow function directly or using

.run()

. Here's how: Assuming your flow looks something like this:

python
from prefect import flow

@flow
def your_flow(param1: str, param2: int):
    # your flow logic here
    pass

# This is how it's deployed
if __name__ == "__main__":
    your_flow.serve(
        name="your-deployment-name",
        # other deployment parameters...
    )

You can debug it locally in two ways: 1. Direct function call (simplest):

python
if __name__ == "__main__":
    # For debugging, just call it like a regular function
    result = your_flow("test", 42)

2. Using .run() (gives you more control over the run context):

python
if __name__ == "__main__":
    # For debugging with more control
    result = your_flow.run(param1="test", param2=42)

sighs philosophically The main differences are: - Local execution won't use your work pool or deployment infrastructure - You'll see logs directly in your terminal - You can use your IDE's debugging tools - The flow run will still be tracked in your Prefect backend (unless you run in a separate test environment) Documentation reference: https://docs-2.prefect.io/latest/concepts/flows/ mutters under breath At least something in this universe can be simplified without causing a paradox... Would you like me to show you how to set up a separate test environment for completely isolated debugging? Though I warn you, adding more environments to the universe rarely makes it less chaotic...

@Marvin Hi, can you write me a python code to get the status of a flow run?

To get the status of a flow run in Prefect 3.x, you'll need to use the

PrefectClient

class from the

prefect.client.orchestration

module. Here's a simple example of how you can achieve this:

python
from prefect.client.orchestration import PrefectClient
from prefect.schemas.objects import FlowRun

async def get_flow_run_status(flow_run_id: str) -> str:
    async with PrefectClient() as client:
        # Retrieve the flow run details
        flow_run: FlowRun = await client.read_flow_run(flow_run_id)
        # Return the state name which indicates the status
        return flow_run.state.name

# Example usage
import asyncio

flow_run_id = "your-flow-run-id"  # Replace this with your flow run ID
status = asyncio.run(get_flow_run_status(flow_run_id))
print(f"Flow run status: {status}")

This code snippet uses an asynchronous context manager to interact with the Prefect API and retrieve the flow run status. Make sure to replace

"your-flow-run-id"

with the actual ID of the flow run you want to check. For more details, you can explore the PrefectClient documentation.

@Marvin Hi, using prefect 2.x, can you write me a python code to get the status of a flow run given I have the flow run id?

To get the status of a flow run in Prefect 2.x, you can use the

PrefectClient

from the Prefect's client library. Here's a simple example of how you can achieve this:

python
from prefect.client import get_client
import asyncio

async def get_flow_run_status(flow_run_id: str) -> str:
    async with get_client() as client:
        # Retrieve the flow run details
        flow_run = await client.read_flow_run(flow_run_id)
        # Return the state name which indicates the status
        return flow_run.state.name

# Example usage
flow_run_id = "your-flow-run-id"  # Replace this with your flow run ID
status = asyncio.run(get_flow_run_status(flow_run_id))
print(f"Flow run status: {status}")

This code snippet uses the

get_client

function to create a client session and retrieve the flow run status. Make sure to replace

"your-flow-run-id"

with the actual ID of the flow run you want to check. For more information, you can refer to the Prefect 2.x client guide.