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Hi Everyone <@ULVA73B9P>'s synopsis is too generi...
# ask-community
o
Hi Everyone @Marvin's synopsis is too generic. I'm hoping someone else can tell me what I'm missing or if this is a bug. My set up is a Google Cloud 
VM
host. I have run a 
Prefect Docker Container
from this 
VM
host & successfully mounted my 
gcloud
credentials to it. A 
Docker workpool
is defined via YAML with a volume mount for host gcloud credentials to worker. A 
worker
has been started on the 
Prefect Docker Container
. It is this 
worker
that is missing some of the expected 
gcloud
credentials. How can I fix this specific scenario?
m
It seems like you're encountering issues with your Prefect Docker worker not recognizing your mounted Google Cloud credentials. Here are some steps and considerations to help you troubleshoot and resolve this issue: 1. Verify Volume Mounts: Ensure that the volume mount in your Docker configuration is correctly pointing to the location of your Google Cloud credentials on the host machine. The path on the host and the path in the Docker container must be correctly specified. 2. Check Permissions: Verify that the credentials file has the correct permissions for reading by the user under which the Docker container is running. 3. Environment Variables: Double-check that any necessary environment variables are set in the Docker container to point to the credentials file. Sometimes, Google Cloud SDK requires environment variables like 
GOOGLE_APPLICATION_CREDENTIALS
to be set. 4. Docker Configuration in YAML: Ensure your YAML configuration for the Docker workpool correctly specifies the volume mounts. The configuration should explicitly map the host directory containing the credentials to the appropriate path in the Docker container. 5. Troubleshooting Resources: You might find additional insights in the Prefect Docker deployment guide. 6. Community and Issues: If the problem persists, consider checking the Prefect GitHub issues or forums for similar cases. There might be ongoing discussions or solutions provided by the community. If these steps don't resolve your issue, you may want to provide more specific details about your configuration so that further assistance can be given.
n
hi @Omar Rizwani - its not totally clear to me what your setup is. do you have reproduction steps? e.g cli commands etc
o
Hi @Nate -> I will try to put something together this morning when I get a chance... 🤔
Hi @Nate I seem to have solved the problem but thanks for your offer to help 👍 I'd assumed that if I was sat on a 
Docker Container
on my 
Google Cloud Linux VM
and called a 
Prefect Docker Worker
from there using the following 
Prefect CLI
commands - 🤓
Copy code
$ prefect work-pool ls
______________________________________________________
┃ Name                           ┃ Type              ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇
│ imagery-etl-cloud-run-pool-gpu │ cloud-run-v2:push │ 
│ imagery-etl-vertex-ai-pool     │ vertex-ai         │ 
│ local-docker-testing-pool (**) │ docker            │ 
│ imagery-etl-vm-worker-pool     │ docker            │ 
│ imagery-etl-cloud-run-pool     │ cloud-run-v2:push |
______________________________________________________

$ prefect work-pool resume imagery-etl-vm-worker-pool
$ prefect worker start -p imagery-etl-vm-worker-pool
Then the following 
Prefect Deployment YAML
definition for the 
Docker workpool & workers
would suffice...
Copy code
name: imagery-etl-vm-worker-pool
      job_variables:
        name: download-docker-queue
        image: some_image
        volumes:
          - /root/.config/gcloud:/root/.config/gcloud
        env: {
          "PREFECT_LOGGING_LEVEL": "DEBUG",
        }
It turned out that the source of the volume mount had to refer to my 
VM
rather than the 
Docker Container
that I was actually sat on running the 
Prefect CLI
commands... 🤷‍♂️
Copy code
name: imagery-etl-vm-worker-pool
      job_variables:
        name: download-docker-queue
        image: some_image
        volumes:
          - /home/user_profile_on_vm/.config/gcloud:/root/.config/gcloud
        env: {
          "PREFECT_LOGGING_LEVEL": "DEBUG",
        }
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