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

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.

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

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 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.

Cheers 👍

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

Nope. But there are steps you can take to prevent this through publishing tasks and reports I believe.

👍 thanks for clearing that up @<1523701087100473344:profile|SuccessfulKoala55>

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

Yep, that seems to be working fine.

Thank you 👍

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...

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...

I’ve had some issues with clearml sessions. I’d be interested in seeing a PR. Would you mind posting a link please?

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

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.

Yep 👍

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

$ 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.

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

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

Ah ok

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

Thank you!

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...

I don't think there's really a way around this because AWS Lambda doesn't allow for multiprocessing.

Instead, I've resorted to using a clearml Scheduler which runs on a t3.micro instance for jobs which I want to run on a cron.