I am having the same error since yesterday on Ubuntu. Works fine on Mac.

I cannot ping api.clear.ml

It started working fine this morning again for me

Thank you!

I ran again without the debug mode option and got this error:

> 
> Starting Task Execution:
> 
> 
> Traceback (most recent call last):
>   File "/root/.clearml/venvs-builds/3.6/code/interactive_session.py", line 377, in <module>
>     from tcp_proxy import TcpProxy
> ModuleNotFoundError: No module named 'tcp_proxy'
> 
> Process failed, exit code 1

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

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\": \"...

Let me know if you need additional information.

Hi, we encountered this a while ago. In our case, there is an issue with running docker containers with gpu on ubuntu22.04.

See this issue for more info:

https://github.com/NVIDIA/nvidia-docker/issues/1730

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.

Cheers 👍

@<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?

@<1523701087100473344:profile|SuccessfulKoala55> Just following up as I figured out what was happening here and could be useful for the future.

The prefilled value for Number of GPUs in the GCP Autoscaler is 1 .

When one ticks Run in CPU mode (no gpus) it hides the GPU Type and Number of GPUs fields. However, the value which was these fields are still submitted in the API Request (I'm guessing here) when the Autoscaler is launched.

Hence, to get past this, you need to...

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?

@<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?

Here it is:

No particular reason. This was our first time trying it and it seemed the quickest way to get off the ground. When I try without I have a similar error trying to connect although that could be due to the instance.

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

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

I'm also having issues.

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...

👍 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.

Solved for me as well now.

Yep, that seems to be working fine.

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

Thank you 👍

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...

The code is quite nested by I've tried to extract out the important parts ( summmary_writer is a tensorboard logger).

self.figure, (ax1, ax2, axc) = plt.subplots(1, 3, figsize=(total_width, total_height), facecolor="white")

self.summary_writer = self.tb_logger.experiment

self.summary_writer.add_figure(Partition.TRAINING.value, train_plot.figure, global_step=self.current_epoch + 1) 

The train_plot.figure is a matplotlib figure created using seaborn.

Let me know if this...

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

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

Is there a way I can do this with the python APIClient or even with the requests library?