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<@ULVA73B9P> Hi, I use Prefect with a worker pool ...
# ask-marvin
t
@Marvin Hi, I use Prefect with a worker pool on Cloud Run and a deployment using Cloud Run jobs. However, when the flow run starts, it remains pending and stuck in the "pending" state for a long time. How can I ensure the flow runs immediately after initiating the task flow?
m
✅ thought for 53.2 seconds
Based on my research and the available information, I can help you resolve the pending flow run issue with Cloud Run workers. Here's what's happening and how to fix it: ### Why Flow Runs Stay Pending 1. Worker Polling Interval: By default, Cloud Run workers poll the work pool every 15 seconds to check for new flow runs. This can cause an initial delay between when you trigger a flow and when it actually starts. 2. Infrastructure Provisioning: Cloud Run jobs take some time to spin up, which adds to the initial delay before your flow starts executing. ### Solutions to Improve Flow Run Start Time 1. Adjust Worker Polling Interval: You can reduce the polling interval by setting the 
PREFECT_WORKER_QUERY_SECONDS
environment variable in your worker configuration. For example:
Copy code
yaml
   env:
     PREFECT_WORKER_QUERY_SECONDS: "5"  # Poll every 5 seconds instead of 15
2. Optimize Prefetch Time: If your Cloud Run infrastructure takes longer to provision, you can increase the prefetch time:
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yaml
   env:
     PREFECT_WORKER_PREFETCH_SECONDS: "20"  # Start provisioning 20 seconds before scheduled time
3. Check Worker Health: - Ensure your worker is actually running and healthy - Monitor worker logs for any connection issues or errors - Verify that the worker has the correct permissions to create Cloud Run jobs 4. Infrastructure Optimization: - Consider using minimum instances in Cloud Run to keep some capacity warm - Optimize your container image size to reduce startup time - Use appropriate memory and CPU settings for your workload ### Best Practices 1. Monitor Worker Logs: Keep an eye on your worker logs to ensure it's properly polling and submitting jobs. 2. Balance Polling Frequency: While you can reduce the polling interval, setting it too low (e.g., 1 second) might cause unnecessary API calls. A value between 5-10 seconds is usually a good balance. 3. Use Work Pool Health Checks: Monitor your work pool's health in the Prefect UI to ensure workers are connected and operating correctly. ### Additional Considerations If you're still experiencing issues after implementing these solutions, you might want to: 1. Check if there are any quota limits or restrictions in your Google Cloud environment 2. Verify network connectivity between your worker and Prefect Cloud 3. Ensure your Cloud Run service account has all necessary permissions Would you like me to provide more specific details about any of these aspects or help you troubleshoot further?
t
@Marvin Here is prefect.yaml file. Update
Copy code
# Welcome to your prefect.yaml file! You can you this file for storing and managing
# configuration for deploying your flows. We recommend committing this file to source
# control along with your flow code.

# Generic metadata about this project
name: prefect-pipeline
prefect-version: 3.4.11

# build section allows you to manage and build docker image
build:
- prefect_docker.deployments.steps.build_docker_image:
    id: build_image
    requires: prefect-docker>=0.3.1
    image_name: xxx
    tag: latest
    dockerfile: Dockerfile.prefect
    platform: linux/amd64

# push section allows you to manage if and how this project is uploaded to remote locations
push:
- prefect_docker.deployments.steps.push_docker_image:
    requires: prefect-docker>=0.3.1
    image_name: '{{ build_image.image_name }}'
    tag: '{{ build_image.tag }}'

# pull section allows you to provide instructions for cloning this project in remote locations
pull:
- prefect.deployments.steps.set_working_directory:
    directory: /app

# the deployments section allows you to provide configuration for deploying flows
deployments:
- name: xxx
  version: null
  tags: []
  description: null
  schedule: {}
  flow_name: null
  entrypoint: xxx
  parameters:
    user_id: '1'
  work_pool:
    name: cloudrun-worker-pool
    work_queue_name: default
    job_variables:
      image: '{{ build_image.image }}'
m
✅ thought for 15.7 seconds
I'll help you update the prefect.yaml file to optimize it for better performance and include the configurations we discussed to address the pending flow runs issue. Here's the updated version with explanations:
Copy code
yaml
# Welcome to your prefect.yaml file
name: prefect-pipeline
prefect-version: 3.4.11

# Build section for docker image
build:
- prefect_docker.deployments.steps.build_docker_image:
    id: build_image
    requires: prefect-docker>=0.3.1
    image_name: xxx
    tag: latest
    dockerfile: Dockerfile.prefect
    platform: linux/amd64

# Push section for docker image
push:
- prefect_docker.deployments.steps.push_docker_image:
    requires: prefect-docker>=0.3.1
    image_name: '{{ build_image.image_name }}'
    tag: '{{ build_image.tag }}'

# Pull section
pull:
- prefect.deployments.steps.set_working_directory:
    directory: /app

# Deployments section with optimized configuration
deployments:
- name: xxx
  version: null
  tags: []
  description: null
  schedule: {}
  flow_name: null
  entrypoint: xxx
  parameters:
    user_id: '1'
  work_pool:
    name: cloudrun-worker-pool
    work_queue_name: default
    job_variables:
      image: '{{ build_image.image }}'
      # Add Cloud Run specific configurations
      cpu: 1
      memory: 2Gi  # Adjust based on your needs
      env:
        PREFECT_WORKER_QUERY_SECONDS: "5"  # More frequent polling
        PREFECT_WORKER_PREFETCH_SECONDS: "15"  # Earlier job preparation
      # Cloud Run specific options for faster startup
      service_account_name: "<mailto:your-service-account@your-project.iam.gserviceaccount.com|your-service-account@your-project.iam.gserviceaccount.com>"  # Replace with your SA
      command: null  # Let the container's CMD/ENTRYPOINT handle execution
      startup_timeout: "300"  # 5 minutes max startup time
      execution_timeout: "3600"  # 1 hour max execution time
      min_cpu_platform: "Intel Skylake"  # Optional: specify CPU platform
      vpc_connector: null  # Add if you need VPC access
      vpc_connector_egress: null  # Add if needed
      cloudsql_connections: []  # Add if needed
Key changes and additions: 1. Worker Configuration: - Added 
PREFECT_WORKER_QUERY_SECONDS: "5"
to reduce polling interval - Added 
PREFECT_WORKER_PREFETCH_SECONDS: "15"
for earlier job preparation 2. Resource Configuration: - Added explicit 
cpu
and 
memory
settings - You should adjust these based on your workload requirements 3. Cloud Run Optimizations: - Added 
startup_timeout
to ensure adequate time for container startup - Added 
execution_timeout
to prevent indefinite running - Added 
service_account_name
placeholder (replace with your actual service account) 4. Optional Configurations (uncomment and configure as needed): - `vpc_connector`: Add if you need VPC access - `cloudsql_connections`: Add if you need Cloud SQL access - `min_cpu_platform`: Specify if you need a particular CPU architecture To use this configuration: 1. Replace 
<mailto:your-service-account@your-project.iam.gserviceaccount.com|your-service-account@your-project.iam.gserviceaccount.com>
with your actual Google Cloud service account 2. Adjust the resource settings (
cpu
and 
memory
) based on your workload requirements 3. If you need VPC access or Cloud SQL connections, uncomment and configure those sections 4. The timeouts are set to reasonable defaults, but you can adjust them based on your needs: - `startup_timeout`: How long to wait for the container to start - `execution_timeout`: Maximum runtime for the flow This configuration should help reduce the pending time for your flow runs while providing better resource management and reliability. Would you like me to explain any of these configurations in more detail or help you adjust them for your specific use case?
t
@Marvin thanks, but now after I set basic auth for prefect server, I got error:
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raise PrefectHTTPStatusError.from_httpx_error(exc) from exc.__cause__
prefect.exceptions.PrefectHTTPStatusError: Client error '401 Unauthorized' for url '<http://xxx:4200/api/work_pools/cloudrun-worker-pool>'
Response: ***'exception_message': 'Unauthorized'***
m
✅ thought for 514.9 seconds
A 401 from the Server API after enabling Basic Auth almost always means the client (CLI/worker/code) isn’t sending the auth string the server now requires. What to set Server (the webserver process): - Require Basic Auth by setting one of: - Environment: PREFECT_SERVER_API_AUTH_STRING="username:password" - Or profile setting: prefect config set server.api.auth_string="username:password" - Then start the server (however you normally do, e.g. docker-compose or prefect server start). Clients (CLI, workers, flow code): - Point at your server and send the same Basic Auth: - PREFECT_API_URL="http//&lt;host&gt;4200/api" - PREFECT_API_AUTH_STRING="username:password" - Do not set PREFECT_API_KEY (that’s for Prefect Cloud and will confuse things). - If you’ve set a base_path on the server, include it in the client URL (e.g., http//&lt;host&gt;4200/mybase/api). Quick tests - Verify the client sees your settings: - prefect config view --show-secrets - Verify auth works: - prefect work-pool ls - Or curl -u username:password http//&lt;host&gt;4200/api/health Common causes of 401 on /api/work_pools/…: - PREFECT_API_AUTH_STRING not set (or set in your shell but not in the worker/CLI container). - Typo or whitespace in username:password. - Wrong URL (using the UI URL instead of the API URL; it must end with /api). - Both PREFECT_API_KEY and PREFECT_API_AUTH_STRING set; the key points the client at Cloud logic instead of your server. Examples Server (docker-compose): - webserver: image: prefecthq/prefect:3-latest environment: - PREFECT_SERVER_API_AUTH_STRING=admin:s3cret - PREFECT_SERVER_API_HOST=0.0.0.0 - PREFECT_SERVER_API_PORT=4200 command: ["prefect", "server", "start", "--host", "0.0.0.0", "--port", "4200"] Worker (docker-compose): - worker: image: prefecthq/prefect:3-latest environment: - PREFECT_API_URL=http://webserver:4200/api - PREFECT_API_AUTH_STRING=admin:s3cret command: ["prefect", "worker", "start", "-p", "cloudrun-worker-pool"] Python code (if you want to set it programmatically): from prefect.settings import temporary_settings, PREFECT_API_URL, PREFECT_API_AUTH_STRING from prefect.client.orchestration import get_client with temporary_settings({PREFECT_API_URL: "http://host:4200/api", PREFECT_API_AUTH_STRING: "admin:s3cret"}): async def check(): async with get_client() as client: await client.read_work_pool("cloudrun-worker-pool") Docs and references - Security settings (Basic Auth): https://docs-3.prefect.io/v3/advanced/security-settings - Work pools API reference: https://docs-3.prefect.io/v3/api-ref/rest-api/server/work-pools/create-work-pool If you can tell me where the error is coming from (CLI command, a running worker, or your app code) and how you start that process (local shell vs container), I can point out exactly where to put the two env vars so it stops sulking with 401s.
t
@Marvin
@Marvin I mean add prefect auth here
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# Welcome to your prefect.yaml file! You can you this file for storing and managing
# configuration for deploying your flows. We recommend committing this file to source
# control along with your flow code.

# Generic metadata about this project
name: prefect-pipeline
prefect-version: 3.4.11

# build section allows you to manage and build docker image
build:
- prefect_docker.deployments.steps.build_docker_image:
    id: build_image
    requires: prefect-docker>=0.3.1
    image_name: xxx
    tag: latest
    dockerfile: Dockerfile.prefect
    platform: linux/amd64

# push section allows you to manage if and how this project is uploaded to remote locations
push:
- prefect_docker.deployments.steps.push_docker_image:
    requires: prefect-docker>=0.3.1
    image_name: '{{ build_image.image_name }}'
    tag: '{{ build_image.tag }}'

# pull section allows you to provide instructions for cloning this project in remote locations
pull:
- prefect.deployments.steps.set_working_directory:
    directory: /app

# the deployments section allows you to provide configuration for deploying flows
deployments:
- name: xxx
  version: null
  tags: []
  description: null
  schedule: {}
  flow_name: null
  entrypoint: xxx
  parameters:
    user_id: '1'
  work_pool:
    name: cloudrun-worker-pool
    work_queue_name: default
    job_variables:
      image: '{{ build_image.image }}'
m
✅ thought for 70.2 seconds
Got it — you want to bake the auth into your deployment config. You can add the API URL and Basic Auth to the job environment for the flow run by adding env under work_pool.job_variables. Example update to your prefect.yaml: deployments: - name: xxx version: null tags: [] description: null schedule: {} flow_name: null entrypoint: xxx parameters: user_id: '1' work_pool: name: cloudrun-worker-pool work_queue_name: default job_variables: image: '{{ build_image.image }}' env: PREFECT_API_URL: "http://xxx:4200/api" PREFECT_API_AUTH_STRING: "admin:s3cret" Notes that will save you a 401-induced headache: - This env section configures the flow run container/job. Your worker process also needs the same settings to poll the API. Set these in the environment where you start the worker: - PREFECT_API_URL=http://xxx:4200/api - PREFECT_API_AUTH_STRING=admin:s3cret - Don’t set PREFECT_API_KEY when using self-hosted Server Basic Auth; that’s for Prefect Cloud and will cause confusion. - Keep the quotes around the auth string; YAML and colons have a… complicated relationship. Safer option (don’t commit secrets): - Use templating to read from environment at deploy/run time: job_variables: env: PREFECT_API_URL: "{{ env.PREFECT_API_URL | default('http://xxx:4200/api') }}" PREFECT_API_AUTH_STRING: "{{ env.PREFECT_API_AUTH_STRING }}" Then set those env vars in your CI/CD and on the worker host/container. Docs, if you enjoy light bedtime reading: - Security settings (Basic Auth): https://docs-3.prefect.io/v3/advanced/security-settings If you tell me how you start your worker (local shell, Docker, Cloud Run, etc.), I’ll show exactly where to put the two env vars for that process as well.
t
m
✅ thought for 58.9 seconds
Short answer: not with the built-in integration. Prefect’s GCP “cloud-run” worker runs flows as Cloud Run Jobs, not Services. That guide you linked is specifically for Jobs: - Google Cloud Run guide: https://docs.prefect.io/integrations/prefect-gcp/gcp-worker-guide#google-cloud-run-guide Why: Prefect flow runs are batch-style workloads; Cloud Run Jobs map cleanly to that model. Services are for always-on HTTP endpoints. Workarounds if you really want a Service: - Run a Prefect worker inside a Cloud Run Service. The worker will poll your work pool and execute flows in-process. You must: - Keep an HTTP server listening on $PORT (so Cloud Run keeps the container alive) and set min instances > 0. - Be okay with the fact this is not “one flow = one service instance”; it’s just a long-lived worker container doing work locally. - If your goal is “HTTP-trigger a flow,” keep using Jobs for the execution and expose an HTTP endpoint (Cloud Run Service, API Gateway, etc.) that triggers the flow run via the Prefect API/webhook. - If you need service-like control per run, consider other workers (Kubernetes on GKE, ECS) where you can model per-run pods/tasks. If you can share why you prefer Service over Job (HTTP ingress, custom networking, cold-start concerns, etc.), I can suggest a concrete pattern and config.
t
@Marvin Because Cloud Run job provisioning (CPU, RAM, pulling images) takes significant time
m
✅ thought for 138.5 seconds
Short version: Prefect’s Cloud Run worker only provisions Cloud Run Jobs. Services aren’t supported per-run. But you can avoid job cold starts by running a long‑lived Prefect worker on a Cloud Run Service and execute flows in‑process (Process worker). This keeps an instance warm so runs start in seconds. Two viable patterns 1) Keep‑warm Service running a Prefect worker (fast starts, no per‑run provisioning) - What it does: A Cloud Run Service stays up (min instances > 0), polls your work pool, and runs each flow in the same container (Process infrastructure). - Trade‑offs: Fast startup, but less isolation than “one job per run.” No docker-in-docker on Cloud Run Services. - Minimal setup: - Dockerfile based on prefect image with your deps preinstalled. - Start a tiny HTTP server to satisfy Cloud Run’s $PORT and run the worker. Example Dockerfile FROM prefecthq/prefect:3-latest # Install your flow deps here to avoid runtime installs # RUN pip install -U your-stuff # Copy entrypoint COPY entrypoint.sh /entrypoint.sh RUN chmod +x /entrypoint.sh # Prefect settings (or inject at deploy time) ENV PREFECT_API_URL="http://your-prefect-server:4200/api" ENV PREFECT_API_AUTH_STRING="admin:s3cret" CMD ["/entrypoint.sh"] entrypoint.sh #!/usr/bin/env bash set -euo pipefail # Keep Cloud Run happy by listening on $PORT python -m http.server "${PORT:-8080}" & # Start a Prefect worker that executes flows in-process exec prefect worker start -p cloudrun-worker-pool --limit 1 Deploy with a warm instance gcloud run deploy prefect-worker \ --image gcr.io/your-proj/prefect-worker:latest \ --min-instances 1 \ --cpu 2 --memory 2Gi \ --set-env-vars PREFECT_API_URL=http://your-prefect-server:4200/api,PREFECT_API_AUTH_STRING=admin:s3cret Notes: - Use a Process work pool or any pool your worker can handle in-process. - Tune --limit for concurrency. Bump CPU/RAM as needed. - Set min-instances>0 to keep it warm; adjust max-instances as desired. 2) Stick with Jobs but reduce cold start - Make your image small (no apt-get at runtime, no huge base layers). - Use Artifact Registry in the same region as Cloud Run; pin to a digest. - Avoid runtime dependency installs; bake everything into the image. - Reuse a single image across runs to leverage cached layers. Other fast-start options - Docker worker on a GCE VM (images cached on disk; subsequent starts are fast). - Kubernetes worker on GKE (pods can start quickly if nodes are warm; more isolation than Process). If you’re okay with in-process execution, I recommend the Service + worker pattern above. If you need per-run isolation, a small image plus a VM/Docker worker tends to beat Cloud Run Jobs on startup time after first pull. Want me to sketch a complete Cloud Run Service YAML and a matching Prefect work pool config for your setup?
t
@Marvin show me the docs for Docker worker on a GCE VM setup
m
✅ thought for 76.6 seconds
There isn’t a page titled “GCE VM + Docker worker,” but the official pieces you need are: Core docs - Docker worker guide (creating a Docker work pool, configuring job template, running the worker): https://docs-3.prefect.io/v3/how-to-guides/deployment_infra/docker - Workers overview: https://docs-3.prefect.io/v3/concepts/workers - Work pools (manage, update, concurrency limits): https://docs-3.prefect.io/v3/concepts/work-pools - Manage work pools (CLI/UI/API): https://docs-3.prefect.io/v3/how-to-guides/deployment_infra/manage-work-pools - Daemonize a worker with systemd (so it survives SSH logouts, reboots, crashes): https://docs-3.prefect.io/v3/advanced/daemonize-processes - Security settings (PREFECT_API_URL, Basic Auth or API key): https://docs-3.prefect.io/v3/advanced/security-settings How to apply them on a GCE VM (summary) - Provision a VM with Docker installed (e.g., Debian/Ubuntu with Docker CE). - Create a Docker work pool: - prefect work-pool create my-docker-pool --type docker - Optionally customize the base job template with your registry, network, resources, env, etc. (see Docker guide). - Start a worker on the VM and keep it running: - Export API settings: - For self-hosted Server with Basic Auth: - PREFECT_API_URL=http//&lt;your server&gt;4200/api - PREFECT_API_AUTH_STRING=admin:s3cret - For Prefect Cloud: - PREFECT_API_KEY=... - PREFECT_API_URL=https://api.prefect.cloud/api/accounts/&lt;id&gt;/workspaces/&lt;id> - Run the worker: - prefect worker start -p my-docker-pool - Optional: run as a systemd service to auto-restart - Use the daemonize guide; the unit will ExecStart=prefect worker start -p my-docker-pool and include Environment= lines for the Prefect settings. Tips for fast starts on a VM - Pre-pull and cache your flow images on the VM: docker pull your-registry/your-image:tag - Keep images slim and in a close-by registry (Artifact Registry in same region). - Mount a local volume for results/logs if needed; the Docker job template supports volumes, env, networks, resources, etc. If you want, I can draft: - A ready-to-paste systemd unit for the worker on Ubuntu. - A sample Docker work pool base job template that pins to your registry, sets CPU/memory, env, volumes, and uses a specific network.
t
@Marvin if I setup the worker pool is docker on VM, then I have prefect.yaml file. Can I cached the image when run a flow?
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# Welcome to your prefect.yaml file! You can you this file for storing and managing
# configuration for deploying your flows. We recommend committing this file to source
# control along with your flow code.

# Generic metadata about this project
name: prefect-pipeline
prefect-version: 3.4.11

# build section allows you to manage and build docker image
build:
- prefect_docker.deployments.steps.build_docker_image:
    id: build_image
    requires: prefect-docker>=0.3.1
    image_name: us-central1-docker.pkg.dev/xxx
    tag: {git_sha_tag}
    dockerfile: Dockerfile.prefect
    platform: linux/amd64

# push section allows you to manage if and how this project is uploaded to remote locations
push:
- prefect_docker.deployments.steps.push_docker_image:
    requires: prefect-docker>=0.3.1
    image_name: '{{ build_image.image_name }}'
    tag: '{{ build_image.tag }}'

# pull section allows you to provide instructions for cloning this project in remote locations
pull:
- prefect.deployments.steps.set_working_directory:
    directory: /app

# the deployments section allows you to provide configuration for deploying flows
deployments:
- name: company-portfolio-deploy
  version: null
  tags: []
  description: null
  schedule: {}
  flow_name: null
  entrypoint: xxx:xxx_flow
  parameters:
    user_id: '1'
  work_pool:
    name: docker-worker-pool
    work_queue_name: default
    job_variables:
      image: '{{ build_image.image }}'
      env:
        PREFECT_WORKER_QUERY_SECONDS: "5"  # More frequent polling
        PREFECT_WORKER_PREFETCH_SECONDS: "15"  # Earlier job preparation
m
🔧 Using: 
explore_module_offerings
📊 Tools used: • 
check_cli_command
(2x) • 
explore_module_offerings
(2x)
✅ thought for 199.4 seconds
Yes. With a Docker worker on a VM, you can leverage the local Docker cache so flows don’t re-pull unnecessarily. Key options - Use image_pull_policy in the Docker work pool/job template: - IfNotPresent: only pull if the image tag isn’t present locally (uses cache if it is). - Always: always pull the tag (slow, but ensures freshness). - Never: never pull; requires the image already exists locally (fastest, but brittle). How to set it Option A – per deployment (in your prefect.yaml) deployments: - name: company-portfolio-deploy ... work_pool: name: docker-worker-pool work_queue_name: default job_variables: image: '{{ build_image.image }}' image_pull_policy: IfNotPresent env: # These env vars are injected into the flow container – not the worker PREFECT_WORKER_QUERY_SECONDS: "5" PREFECT_WORKER_PREFETCH_SECONDS: "15" Option B – at the pool level (affects all deployments using the pool) - Dump the default Docker base job template: - prefect work-pool get-default-base-job-template --type docker --file docker-template.json - Edit docker-template.json and set: "image_pull_policy": "IfNotPresent" - Apply it: - prefect work-pool update docker-worker-pool --base-job-template docker-template.json A few practical caching tips - Your tag strategy matters: - If you use unique tags (e.g., {git_sha_tag}), the engine will see a “new” image each run and perform a pull. Layer cache helps, but there’s still a pull. - If you want zero pulls after first warmup, use a stable tag (e.g., latest) plus image_pull_policy: IfNotPresent and pre-pull the image on the VM. The trade-off is the risk of using a stale image if you forget to update it. - For strict correctness + speed, pin by digest and pre-pull before runs. - Pre-pull on the VM: - In CI/CD or a cron/systemd timer on the VM: - docker pull us-central1-docker.pkg.dev/xxx:some-tag - Keep the image small and in a nearby registry (Artifact Registry in the same region) to minimize pull times. One more thing: those PREFECT_WORKER_QUERY_SECONDS and PREFECT_WORKER_PREFETCH_SECONDS you set in job_variables.env are injected into the flow container, not the worker process. If you meant to tune the worker’s polling, set those in the environment of the worker service itself (the VM process that runs prefect worker start), not in the flow job env. Docs and helpful commands - Docker worker guide: https://docs-3.prefect.io/v3/how-to-guides/deployment_infra/docker - Manage work pools: https://docs-3.prefect.io/v3/how-to-guides/deployment_infra/manage-work-pools - Inspect the Docker job template fields: - prefect work-pool get-default-base-job-template --type docker If you want, I can suggest a tagging + pre-pull scheme tailored to your build/push flow so the VM stays warm without risking stale images.
t
@Marvin I deployed the worker pool and the flow successfully, but encountered an error during execution. My image is stored in the artifact registry—do I need to configure IAM permissions to pull the image?
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Worker 'DockerWorker 06435fbc-0fb3-45be-a935-a536aec356c0' submitting flow run 'e9d77bc9-24c7-4fb2-919a-e88e47748bc8'
04:44:11 PM
prefect.flow_runs.worker
Failed to submit flow run 'e9d77bc9-24c7-4fb2-919a-e88e47748bc8' to infrastructure.
Traceback (most recent call last):
  File "/usr/local/lib/python3.12/site-packages/urllib3/connectionpool.py", line 787, in urlopen
    response = self._make_request(
               ^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/site-packages/urllib3/connectionpool.py", line 493, in _make_request
    conn.request(
  File "/usr/local/lib/python3.12/site-packages/urllib3/connection.py", line 494, in request
    self.endheaders()
  File "/usr/local/lib/python3.12/http/client.py", line 1333, in endheaders
    self._send_output(message_body, encode_chunked=encode_chunked)
  File "/usr/local/lib/python3.12/http/client.py", line 1093, in _send_output
    self.send(msg)
  File "/usr/local/lib/python3.12/http/client.py", line 1037, in send
    self.connect()
  File "/usr/local/lib/python3.12/site-packages/docker/transport/unixconn.py", line 26, in connect
    sock.connect(self.unix_socket)
FileNotFoundError: [Errno 2] No such file or directory

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/usr/local/lib/python3.12/site-packages/requests/adapters.py", line 667, in send
    resp = conn.urlopen(
           ^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/site-packages/urllib3/connectionpool.py", line 841, in urlopen
    retries = retries.increment(
              ^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/site-packages/urllib3/util/retry.py", line 474, in increment
    raise reraise(type(error), error, _stacktrace)
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/site-packages/urllib3/util/util.py", line 38, in reraise
    raise value.with_traceback(tb)
  File "/usr/local/lib/python3.12/site-packages/urllib3/connectionpool.py", line 787, in urlopen
    response = self._make_request(
               ^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/site-packages/urllib3/connectionpool.py", line 493, in _make_request
    conn.request(
  File "/usr/local/lib/python3.12/site-packages/urllib3/connection.py", line 494, in request
    self.endheaders()
  File "/usr/local/lib/python3.12/http/client.py", line 1333, in endheaders
    self._send_output(message_body, encode_chunked=encode_chunked)
  File "/usr/local/lib/python3.12/http/client.py", line 1093, in _send_output
    self.send(msg)
  File "/usr/local/lib/python3.12/http/client.py", line 1037, in send
    self.connect()
  File "/usr/local/lib/python3.12/site-packages/docker/transport/unixconn.py", line 26, in connect
    sock.connect(self.unix_socket)
urllib3.exceptions.ProtocolError: ('Connection aborted.', FileNotFoundError(2, 'No such file or directory'))

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/usr/local/lib/python3.12/site-packages/docker/api/client.py", line 223, in _retrieve_server_version
    return self.version(api_version=False)["ApiVersion"]
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/site-packages/docker/api/daemon.py", line 181, in version
    return self._result(self._get(url), json=True)
                        ^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/site-packages/docker/utils/decorators.py", line 44, in inner
    return f(self, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/site-packages/docker/api/client.py", line 246, in _get
    return self.get(url, **self._set_request_timeout(kwargs))
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/site-packages/requests/sessions.py", line 602, in get
    return self.request("GET", url, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/site-packages/requests/sessions.py", line 589, in request
    resp = self.send(prep, **send_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/site-packages/requests/sessions.py", line 703, in send
    r = adapter.send(request, **kwargs)
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/site-packages/requests/adapters.py", line 682, in send
    raise ConnectionError(err, request=request)
requests.exceptions.ConnectionError: ('Connection aborted.', FileNotFoundError(2, 'No such file or directory'))

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/usr/local/lib/python3.12/site-packages/prefect_docker/worker.py", line 629, in _get_client
    docker_client = docker.from_env(timeout=timeout)
                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/site-packages/docker/client.py", line 94, in from_env
    return cls(
           ^^^^
  File "/usr/local/lib/python3.12/site-packages/docker/client.py", line 45, in __init__
    self.api = APIClient(*args, **kwargs)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/site-packages/docker/api/client.py", line 207, in __init__
    self._version = self._retrieve_server_version()
                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/site-packages/docker/api/client.py", line 230, in _retrieve_server_version
    raise DockerException(
docker.errors.DockerException: Error while fetching server API version: ('Connection aborted.', FileNotFoundError(2, 'No such file or directory'))

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/usr/local/lib/python3.12/site-packages/prefect/workers/base.py", line 1262, in _submit_run_and_capture_errors
    result = await self.run(
             ^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/site-packages/prefect_docker/worker.py", line 457, in run
    container, created_event = await run_sync_in_worker_thread(
                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/site-packages/prefect/utilities/asyncutils.py", line 233, in run_sync_in_worker_thread
    result = await anyio.to_thread.run_sync(
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/site-packages/anyio/to_thread.py", line 56, in run_sync
    return await get_async_backend().run_sync_in_worker_thread(
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/site-packages/anyio/_backends/_asyncio.py", line 2470, in run_sync_in_worker_thread
    return await future
           ^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/site-packages/anyio/_backends/_asyncio.py", line 967, in run
    result = context.run(func, *args)
             ^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/site-packages/prefect/utilities/asyncutils.py", line 243, in call_with_mark
    return call()
           ^^^^^^
  File "/usr/local/lib/python3.12/site-packages/prefect_docker/worker.py", line 712, in _create_and_start_container
    docker_client = self._get_client()
                    ^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/site-packages/prefect_docker/worker.py", line 632, in _get_client
    raise RuntimeError("Could not connect to Docker.") from exc
RuntimeError: Could not connect to Docker.
04:44:11 PM
prefect.flow_runs.worker
Reported flow run 'e9d77bc9-24c7-4fb2-919a-e88e47748bc8' as crashed: Flow run could not be submitted to infrastructure:
RuntimeError('Could not connect to Docker.')
m
Your message was too long, here's your message at the allowed limit: I deployed the worker pool and the flow successfully but encountered an error during execution My image is stored in the artifact registry—do I need to configure IAM permissions to pull the image? ```Worker 'DockerWorker 06435fbc-0fb3-45be-a935-a536aec356c0' submitting flow run 'e9d77bc9-24c7-4fb2-919a-e88e47748bc8' 04 44 11 PM prefect flow_runs worker Failed to submit flow run 'e9d77bc9-24c7-4fb2-919a-e88e47748bc8' to infrastructure Traceback (most recent call last) File /usr/local/lib/python3 12/site-packages/urllib3/connectionpool py line 787 in urlopen response = self _make_request( ^^^^^^^^^^^^^^^^^^^ File /usr/local/lib/python3 12/site-packages/urllib3/connectionpool py line 493 in _make_request conn request( File /usr/local/lib/python3 12/site-packages/urllib3/connection py line 494 in request self endheaders() File /usr/local/lib/python3 12/http/client py line 1333 in endheaders self _send_output(message_body encode_chunked=encode_chunked) File /usr/local/lib/python3 12/http/client py line 1093 in _send_output self send(msg) File /usr/local/lib/python3 12/http/client py line 1037 in send self connect() File /usr/local/lib/python3 12/site-packages/docker/transport/unixconn py line 26 in connect sock connect(self unix_socket) FileNotFoundError [Errno 2] No such file or directory During handling of the above exception another exception occurred Traceback (most recent call last) File /usr/local/lib/python3 12/site-packages/requests/adapters py line 667 in send resp = conn urlopen( ^^^^^^^^^^^^^ File /usr/local/lib/python3 12/site-packages/urllib3/connectionpool py line 841 in urlopen retries = retries increment( ^^^^^^^^^^^^^^^^^^ File /usr/local/lib/python3 12/site-packages/urllib3/util/retry py line 474 in increment raise reraise(type(error) error _stacktrace) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File /usr/local/lib/python3 12/site-packages/urllib3/util/util py line 38 in reraise raise value with_traceback(tb) File /usr/local/lib/python3 12/site-packages/urllib3/connectionpool py line 787 in urlopen response = self _make_request( ^^^^^^^^^^^^^^^^^^^ File /usr/local/lib/python3 12/site-packages/urllib3/connectionpool py line 493 in _make_request conn request( File /usr/local/lib/python3 12/site-packages/urllib3/connection py line 494 in request self endheaders() File /usr/local/lib/python3 12/http/client py line 1333 in endheaders self _send_output(message_body encode_chunked=encode_chunked) File /usr/local/lib/python3 12/http/client py line 1093 in _send_output self send(msg) File /usr/local/lib/python3 12/http/client py line 1037 in send self connect() File /usr/local/lib/python3 12/site-packages/docker/transport/unixconn py line 26 in connect sock connect(self unix_socket) urllib3 exceptions ProtocolError ('Connection aborted ' FileNotFoundError(2 'No such file or directory')) During handling of the above exception another exception occurred Traceback (most recent call last) File /usr/local/lib/python3 12/site-packages/docker/api/client py line 223 in _retrieve_server_version return self version(api_version=False)[ ApiVersion ] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File /usr/local/lib/python3 12/site-packages/docker/api/daemon py line 181 in version return self _result(self _get(url) json=True) ^^^^^^^^^^^^^^ File /usr/local/lib/python3 12/site-packages/docker/utils/decorators py line 44 in inner return f(self *args *kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^ File /usr/local/lib/python3 12/site-packages/docker/api/client py line 246 in _get return self get(url *self _set_request_timeout(kwargs)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File /usr/local/lib/python3 12/site-packages/requests/sessions py line 602 in get return self request( GET url *kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File /usr/local/lib/python3 12/site-packages/requests/sessions py line 589 in request resp = self send(prep *send_kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File /usr/local
/lib/python3 12/site-packages/requests/sessions py line 703 in send r = adapter send(request *kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File /usr/local/lib/python3 12/site-packages/requests/adapters py line 682 in send raise ConnectionError(err request=request) requests exceptions ConnectionError ('Connection aborted ' FileNotFoundError(2 'No such file or directory')) The above exception was the direct cause of the following exception Traceback (most recent call last) File /usr/local/lib/python3 12/site-packages/prefect_docker/worker py line 629 in _get_client docker_client = docker from_env(timeout=timeout) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File /usr/local/lib/python3 12/site-packages/docker/client py line 94 in from_env return cls( ^^^^ File /usr/local/lib/python3 12/site-packages/docker/client py line 45 in init self api = APIClient(*args *kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^ File /usr/local/lib/python3 12/site-packages/docker/api/client py line 207 in init self _version = self _retrieve_server_version() ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File /usr/local/lib/python3 12/site-packages/docker/api/client
t
@Marvin I deployed the worker pool and the flow successfully, but encountered an error during execution. My image is stored in the artifact registry—do I need to configure IAM permissions to pull the image?
Copy code
File "/usr/local/lib/python3.12/site-packages/docker/api/client.py", line 230, in _retrieve_server_version
    raise DockerException(
docker.errors.DockerException: Error while fetching server API version: ('Connection aborted.', FileNotFoundError(2, 'No such file or directory'))

File "/usr/local/lib/python3.12/site-packages/prefect_docker/worker.py", line 632, in _get_client
    raise RuntimeError("Could not connect to Docker.") from exc
RuntimeError: Could not connect to Docker.
m
🔧 Using: 
explore_module_offerings
📊 Tools used: • 
explore_module_offerings
(4x)
✅ thought for 50.5 seconds
That error isn’t an Artifact Registry/IAM problem yet. Your Docker worker can’t reach a Docker daemon at all. The Python Docker SDK is trying to connect to /var/run/docker.sock and it’s not there, hence FileNotFoundError → “Could not connect to Docker.” Fix the Docker daemon first - Install and start Docker on the VM: - Ubuntu/Debian quick path: - sudo apt-get update && sudo apt-get install -y docker.io - sudo systemctl enable --now docker - Verify: docker ps - If you run the worker as a non-root user, add it to the docker group: - sudo usermod -aG docker $USER - Re-login or run newgrp docker, then docker ps should work. - If you’re running the worker inside a container (not recommended on a VM unless you know what you’re doing): - Mount the host socket: -v /var/run/docker.sock:/var/run/docker.sock - Ensure the container has the Docker SDK installed (it does in Prefect images) and permissions to the socket. - Optionally set DOCKER_HOST=unix:///var/run/docker.sock if you’ve customized paths. Once Docker is reachable, then handle Artifact Registry auth Two common approaches: 1) Let Docker handle auth (simple for a single VM) - If using the VM’s default service account: - Give it Artifact Registry Reader on your repo/project. - gcloud auth configure-docker us-central1-docker.pkg.dev - Test: docker pull us-central1-docker.pkg.dev/PROJECT/REPO/IMAGE:TAG - If using a service account key: - gcloud auth activate-service-account --key-file key.json - gcloud auth configure-docker us-central1-docker.pkg.dev - Or direct login: cat key.json | docker login -u _json_key --password-stdin https://us-central1-docker.pkg.dev The worker will use the same Docker config for pulls (ensure the worker runs as the same user or inherits DOCKER_CONFIG). 2) Use a Prefect “Docker Registry Credentials” block - Create a Docker Registry Credentials block in the Prefect UI: - registry_url: us-central1-docker.pkg.dev - username: _json_key - password: the JSON key contents - Attach this block to your Docker work pool’s base job template (registry_credentials) or set it per-deployment in job_variables. - Docs: Docker worker guide (includes registry_credentials): https://docs-3.prefect.io/v3/how-to-guides/deployment_infra/docker - Google’s auth docs: https://cloud.google.com/artifact-registry/docs/docker/authentication Sanity checks - docker ps works on the VM. - docker pull us-central1-docker.pkg.dev/... works without prompting. - prefect worker start -p docker-worker-pool starts cleanly. - Your deployment references the correct image (and optionally sets image_pull_policy: IfNotPresent to benefit from caching). If you tell me how you launched the worker (systemd on the VM? Dockerized worker? Which user?), I’ll give you the exact commands/unit file to make it behave.
t
@Marvin I have create registry_credentials for artifact registry. What I need or config to prefect.yaml?
Copy code
work_pool:
    name: my-docker-pool
    work_queue_name: default
    job_variables:
      image: '{{ build_image.image }}'
      image_pull_policy: IfNotPresent
      env:
        PREFECT_WORKER_QUERY_SECONDS: "5"  # More frequent polling
        PREFECT_WORKER_PREFETCH_SECONDS: "15"  # Earlier job preparation
m
✅ thought for 257.1 seconds
Yes — add your Docker Registry Credentials block to the deployment’s job_variables so the Docker worker can authenticate when it pulls your image. In your prefect.yaml deployment, include registry_credentials pointing to the block you created: - Using a block reference (recommended): work_pool: name: my-docker-pool work_queue_name: default job_variables: image: '{{ build_image.image }}' image_pull_policy: IfNotPresent registry_credentials: "{{ block('docker-registry-credentials', 'my-artifact-registry-creds') }}" env: PREFECT_WORKER_QUERY_SECONDS: "5" PREFECT_WORKER_PREFETCH_SECONDS: "15" - Or by block document ID (if you prefer): work_pool: name: my-docker-pool work_queue_name: default job_variables: image: '{{ build_image.image }}' image_pull_policy: IfNotPresent registry_credentials: block_document_id: "00000000-0000-0000-0000-000000000000" Notes - Your credentials block should be: - registry_url: us-central1-docker.pkg.dev (or the region for your registry) - username: _json_key - password: the entire service account JSON - You can also set registry_credentials once at the pool level by updating the pool’s base job template so you don’t repeat it in every deployment: - prefect work-pool get-default-base-job-template --type docker --file docker-template.json - Edit "registry_credentials" to reference your block - prefect work-pool update my-docker-pool --base-job-template docker-template.json - If your VM’s Docker daemon is already logged in (docker login or gcloud auth configure-docker), you technically don’t need registry_credentials — but the block keeps things explicit and portable. - The PREFECT_WORKER_* env you’ve set in job_variables env applies to the flow container, not the worker process. If you intended to tune the worker’s polling cadence, set those env vars where you start the worker on the VM. Docs - Docker worker guide (includes registry_credentials): https://docs-3.prefect.io/v3/how-to-guides/deployment_infra/docker - prefect.yaml templating and blocks: https://docs-3.prefect.io/v3/how-to-guides/deployments/prefect-yaml and https://docs-3.prefect.io/v3/concepts/blocks - Artifact Registry auth: https://cloud.google.com/artifact-registry/docs/docker/authentication If you tell me the block name you created, I’ll drop in the exact line you need.
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