It seems that the error is related to this part of the code block. However, when I comment this out I get the error I had 2 days ago with the missing configuration object.

That's what I was getting at. It wasn't clear to me from the documentation that it saves the state.

Stability is for wimps. I live on the edge of brining down production at any moment, like a real developer. But thanks for the update! 🙃

Thanks again for the info. I might experiment with it to see first hand what the advantages are.

✨ It works ✨

Thanks @<1523701205467926528:profile|AgitatedDove14> 😁

That's great! I look forward to trying this out.

Is there currently a way to bind the same GPU to multiple queues? I believe the agent complains last time I tried (which was a bit ago).

Actually this is not how it works, pip will install in any way it sees fit, and it is not consistent between versions (it has to do with dependency resolving)

Oh I see. What a pain. 🤣

You can configure the agent to first install specific packages, and only then others, just add the package names here:

That's an interesting solution. I'll keep that in mind as I work more with ClearML.

Thanks for your help Martin!

Alright, I deleted everything in the ClearML web-app waited a day tried again, it seems to be showing a configuration object in the configuration section of the scheduler task again. I honestly don't know what changed. Maybe some strange caching on the server side that got cleaned up.

@<1523701205467926528:profile|AgitatedDove14> Question: Does the schedule_function option in the TaskScheduler.add_task() method run at the time the task is scheduled to execute? So if I pass a functi...

Alright, I fixed the issue with the scheduler eating itself. But now I'm still getting the same bug as two days ago. So the Scheduler process starts fine and doesn't "crash." But I don't get the config object in the web-app again. It seems to work if I run it locally.

To answer your earlier question, I'm using the app.clear.ml portal so

• WebApp: 3.20.1-1525
• Server: 3.20.1-1299
• API: 2.28
• And my Python ClearML version: 1.14

Yes, I'm experimenting with this. I actually wrote my own process to do this so I just had to adapt it as a callable to pass to the scheduler. However, I'm running into an issue and I don't think this is a user error this time. When I start the scheduler, it starts running, shows up in the web-app, but then an error message in the web-app pops up Fetch parents failed and the Scheduler task disappears from the web-app. I can't even see an error log because the task is gone.

I'm running th...

In the debugger I can see that before starting the scheduler the test task is added:

ScheduleJob(name='Snitch-TaskScheduler', base_task_id='', base_function=<function main.<locals>.scheduler_function.<locals>.<lambda> at 0x7f05e1ab3600>, queue='services', target_project='DevOps', single_instance=False, task_parameters=None, task_overrides=None, clone_task=True, _executed_instances=None, execution_limit_hours=None, recurring=True, starting_time=datetime.datetime(2024, 1, 17, 10, 50, 28,...

Maybe the sleep between scheduler.mark_completed() and scheduler.delete() is too short? But I don't get why deleting the old scheduler task would break the new scheduler. I'm going to try testing by running the scheduler locally.

@<1523701205467926528:profile|AgitatedDove14>

And the Task is still running? What's he clearml python version and webui version ?

No, the task stops (it's running remote, I haven't tested it running local).

Strange, the code seems to work perfectly when I run it locally. To make it more confusing, the queue that I enqueue it to when I run it remotely is using agents from the same server that I'm running it locally from.

Depending on the framework you're using it'll just hook into the save model operation. Every time you save a model, which will probably happen every epoch for some subset of the training. If you want to do it with the existing framework you could change the checkpoint so that it only clones the best model in memory and saves the write operation for last. The risk with this is if the training crashes, you'll lose your best model.

Optionally, you could also disable the ClearML integration with...

Yeah, it's because it's just hooking into the save operation and capturing the output, regardless of the parent call.

@<1523701070390366208:profile|CostlyOstrich36> ClearML: 1.10.1, I'm not self-hosting the server so whatever the current version is. Unless you mean the operating system?

@<1523701435869433856:profile|SmugDolphin23> Good to know.

You might want to start with the first steps guide then:
None

@<1539780284646428672:profile|PoisedElephant79> Sorry for not getting back with this sooner. Dataset.get() doesn't work like you suggested. In the documentation it's clear:

Get a

specific

Dataset. If multiple datasets are found, the dataset with the highest semantic version is returned. If no semantic version is found, the most recently updated dataset is returned. This functions raises an Exception in case no dataset can be found and the

auto_create=True

...

@<1539780284646428672:profile|PoisedElephant79> Are you sure you're not simply referring to the get operation? That seems to exclude archived datasets. But I don't see anything like that for the list_datasets operation.

The verbose output:

Generating SHA2 hash for 123 files
100%|██████████████████████████████████████████████████████████| 123/123 [00:00<00:00, 310.04it/s]
Hash generation completed
Add 2022-12.csv
Add 2020-10.csv
Add 2021-06.csv
Add 2022-02.csv
Add 2021-04.csv
Add 2013-03.csv
Add 2021-02.csv
Add 2015-02.csv
Add 2016-07.csv
Add 2022-05.csv
Add 2021-10.csv
Add 2018-04.csv
Add 2019-06.csv
Add 2017-11.csv
Add 2016-01.csv
Add 2013-06.csv
Add 2018-08.csv
Add 2020-05.csv
Add 2020-03.csv
Add 20...

Thanks for the reply @<1523701070390366208:profile|CostlyOstrich36> !

It says in the documentation that:
Add a folder into the current dataset. calculate file hash, and compare against parent, mark files to be uploaded

It seems to recognize the dataset as another version of the data but doesn't seem to be validating the hashes on a per file basis. Also, if you look at the photo, it seems like some of the data does get recognized as the same as the prior data. It seems like it's the correct...

Thanks for your reply @<1523701070390366208:profile|CostlyOstrich36> Is there an example where a pipeline is built from existing tasks? I'd like to experiment with it and I don' t see any examples of what you describe with my (clearly lacking) google-fu. What happens if you wrap a function with a task.init() with a pipeline decorator or is that the process you're speaking of?

Thanks, that's exactly what I was looking for.

Alright, I tried testing it out by commenting out the code for generating new csv's, so for successive runs the CSVs are identical. However, when I use dataset.add_files() it still generated a new version of the dataset.

# log the data to ClearML if a task is passed
        if self.task:
            self.clearml_dataset = Dataset.create(dataset_name="[LTV] Dataset")
            self.clearml_dataset.add_files(path=save_path, verbose=True)
            if self.tags is not None:
          ...

This is odd, the ordering of the files is different and there appears to be some missing from the preview. But as far as I can tell the files aren't different. What am I missing here?

The original file sizes are the same but the compressed sizes seem to be different.

On the original model.

Ah, I think I see the issue. In my head I was crossing ID with URL.