Solved for me as well now.

I am using ClearML version 1.9.1. In code, I am creating a plot using matplotlib. I am able to see this in Tensorboard but it is not available in ClearML Plots

Could you give me some insight into why this is please?

👍 Thanks for getting back to me.

Another issue I found was that I could only use vpc subnets from the google project I am launching the VMs in.

I cannot use shared vpc subnets from another project. This would be a useful feature to implement as GCP recommends segmenting the cloud estate so that the vpc and VMs are in different projects.

I cannot ping api.clear.ml on Ubuntu. Works fine on Mac though.

@<1673863823901069312:profile|BraveToad81>

Thank you!

I have just encountered this. I believe it is because of the clearml-agent 1.7.0 release which added this as default: agent.enable_git_ask_pass: true

To fix, add in agent.enable_git_ask_pass: false to your config.

Further to this, I have inspected further. This is working as expected for ClearML 1.8.3 but not for ClearML 1.9.0.

I looked at the commits and found that a change had been made to the _decode_image method:

None

This aligns with the error message I'm seeing:

2023-02-08 15:17:25,539 - clearml - WARNING - Error: I/O operation on closed file.

Can this be actioned for the next release plea...

Yep, that seems to be working fine.

@<1537605940121964544:profile|EnthusiasticShrimp49> How do I specify to not attach a gpu? I thought ticking 'Run in CPU Mode' would be sufficient. Is there something else I'm missing?

If a Task is in the 'Completed' I think the only option is to 'Reset' it (see image). You do clear the previous run execution but I think for a repetitive task this is fine.
Maybe this should only be the case if it is in a 'Completed' state rather than 'Failed'. I can see that in this case you would not want to clear the execution because you would want to see why it Failed. Thoughts?

@<1523701070390366208:profile|CostlyOstrich36> Thank you. Which docker image do you use with this machine image?

Let me know if you need additional information.

Hi,

I've managed to fix it.

Basically, I had a tracker running on our queues to ensure that none of them were lagging. This was using get_next_task from APIClient().queues .

If you call get_next_task it removes the task from the queue but does not put it into another state. I think because typically get_next_task is immediately followed by something to make the task run in the daemon or delete it.

Hence you end up in this weird state were the task thinks its queued bec...

Cheers 👍

Ah, didn’t know that. Yes in that case that would work 👍

Apologies for the delay.

I have obfuscated the private information with XXX . Let me know if you think any of it is relevant.

{"gcp_project_id":"XXX","gcp_zone":"XXX","subnetwork":"XXX","gcp_credentials":"{\n  \"type\": \"service_account\",\n  \"project_id\": \"XXX\",\n  \"private_key_id\": \"XXX\",\n  \"private_key\": \"XXX\",\n  \"client_id\": \"XXX\",\n  \"auth_uri\": \"XXX\",\n  \"token_uri\": \"XXX\",\n  \"auth_provider_x509_cert_url\": \"XXX\",\n  \"client_x509_cert_url\": \"...

@<1523701087100473344:profile|SuccessfulKoala55> Thanks for getting back to me. My image contains clearml-agent==1.9.1 . There is a recent release to 1.9.2 and now on every run the agent installs this newer version thanks to the -U flag which is being passed. From the docs it looks like there may be a way to prevent this upgrade but it's not clear to me exactly how to do this. Is it possible?

Thanks. I am trying to completely minimise the start up time. Given I am using a docker image which has clearml-agent and pip installed, is there a way I can skip the installation of this when a task starts up using the daemon?

This is something you can do in the GCP console, one would imagine it can be done using their python library.

I think the limitation is that you can only pass a relative subnet path in the GCP Autoscaler console. Then, by the looks of the error message, the ClearML Autoscaler constructs the full path under the hood /project/<project_id>/subnet/<subnet_id> .

I'd like the option to specify the full path myself in the Autoscaler which would then allow me to use a shared subnet.

Is there documentation for this as I was not able to figure this out unfortunately.

Thanks Jake. Do you know how I set the GPU count to 0?

Ah ok

I believe this was an example report I made for a demo and I've since deleted the tasks which generated it 👍

According to the documentation users.user should be a valid endpoint?

Yep that's correct. If I have a task which runs every 5 minutes, I don't want a new task every 5 minutes as that will create a lot of tasks over a day. It would be better if I had just one task.

Here it is:

I did not run clearml-session from within the git repo.

Furthermore, when using APIClient() , users is not a valid endpoint at all.

class APIClient(object):

    auth = None  # type: Any
    queues = None  # type: Any
    tasks = None  # type: Any
    workers = None  # type: Any
    events = None  # type: Any
    models = None  # type: Any
    projects = None  # type: Any

This is taken from clearml/backend_api/session/client/client.py