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.

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...

Thank you 👍

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

Solved for me as well now.

It started working fine this morning again for me

Yep 👍

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...

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

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

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?

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

Cheers 👍

Here it is:

I have managed to connect. Our EC2 instances run in a private subnet so the ssh connection was not working for that reason I believe. Once I connected to my VPN it now worked.

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

$ curl -H "Authorization: Bearer <TOKEN>" -X GET 

{"meta":{"id":"ed6c52d030f240a89f001b447ee64a6b","trx":"ed6c52d030f240a89f001b447ee64a6b","endpoint":{"name":"debug.ping","requested_version":"2.26","actual_version":"1.0"},"result_code":200,"result_subcode":0,"result_msg":"OK","error_stack":null,"error_data":{},"alarms":{}},"data":{"msg":"Hello World"}}%                                                                                                    

$ curl -H "Authoriz...

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

Ah ok

Let me know if you need additional information.

Cheers 👍

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.

This is not working. Please see None which details the problem

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\": \"...

@<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?

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

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.

Yep, that seems to be working fine.

I did not touch the interactive session code at all.

I installed clearml-session using pip and ran the above command with a task id from a task I'd already run.

I'm also having issues.