It did update but I'm beginning to see the issue now. It seems like the metrics on thousands of experiments amounted to a few MB where deleting one of the hyperparameter experiments freed up over a gig. I'm having difficulty seeing why one of these experiments occupies so much metric space. From the hyperparameter optimization dashboard the graphs and the tables might have a few thousand points.

Can you help me:

• Better understanding why it's occupying so much metric storage space
• Is the...

I think this error occurred for me because when I first authenticated with the project I was using username/password and later I transitioned to using ssh keys. That's why clearing the cache worked.

Did you validate that branch exists on remote?

The answer is simple but also not completely obvious to someone new to the platform. So you can inject new command line args that hydra will recognize. This is what the Hydra section of args is for. However, if you enable _allow_omegaconf_edit_: True , I think ClearML will “inject” the OmegaConf saved under the configuration object of the prior run, overwriting the overrides. I’ll experiment with this behavior a bit more to be sure.

Yes, but only because you asked so nicely 😚

It hooks into the calls made by the code. If you never save the model to disk, add it to a tool like MLflow/Tensorboard, or manually add the artifact to ClearML, afaik it won't save the artifact.

It's even attempting to install omegaconf but not from the repo, likely because it's a dependency of hydra-colorlog.

Collecting omegaconf<2.4,>=2.2
  Using cached omegaconf-2.2.3-py3-none-any.whl (79 kB)
  Using cached omegaconf-2.2.2-py3-none-any.whl (79 kB)
  Using cached omegaconf-2.2.1-py3-none-any.whl (78 kB)

I'm not self-hosting the server.

There is no issues when I run the "raw" script. Also, since it's based on tasks, the code must have run without fault for it to be pulled as a task in the pipeline.

As for when it fails, looking at the log here it looks like it's on the first task or maybe as the first task is launching. But I'd have to go back to be sure. I rolled back to 1.13.1 and that's working fine. But, if you want I can help explore this bug in detail because it would be nice to find the root of the issue. LmK what y...

Why? That's not how I authenticate. Also, if it was simply an issue with authentication wouldn't there be some error message in the log?

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...

Alright, I'll try and put that together for Monday.

I'd like to provide the credentials to any ec2 instances that are spun up.

It sounds like you didn't set up your config. Did you ever initialize clearml?

It's verbatim from requirements as I pass that into ClearML.

This doesn't really make a lot of sense. ClearML would be better served for tracking which version of the code you used for a corresponding task and you'd use something like github or gitlab to track code and host your code. You could use ClearML to help you reconstruct the environment and code from a task given it's being tracked by git and hosted somewhere you can access.

I actually ran into the exact same problem. The agents aren't hosted on AWS though, just a in-house server.

Hi @<1523701435869433856:profile|SmugDolphin23>

I'm a bit confused by your suggestion. To be clear, this is the logs from the HPO application instance that's spun up when you start the HPO process. I don't think we have any control over what python version or Pyro version is started in the application instance. I think this error occurs before any code on our end is run.

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

Sounds good. Lmk if there's some changes that are required.

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:
          ...

Well, if I stop the cron service and start it back up I don't have to re-register each schedule. If, for instance, I start the TaskScheduler, register a task, and stop the scheduler, how do I restart the TaskScheduler in a way that re-register the tasks? Because, in theory, they could be registered from several users and I might be unaware of tasks that were previously scheduled. What is the best practices to preserve state?

Awesome! Did you managed to solve the tailscale issue with ClearML sessions? Sorry I wasn't active with that. I don't use sessions often and I found a suitable alternative in the short time. Any hopes of the changes making their way to a PR for the official release?

Hi @<1523701205467926528:profile|AgitatedDove14> . I think I'm misunderstanding something here. I have the scheduler service running. Now that it's running how does one add a new task or remove an existing task from the scheduler? I get that I can add them before starting the scheduler service but once the service is running is there any way to connect to it and change the schedule?

I thought the advantage of this service would be we could schedule tasks just by connecting to the existing t...

What version of ClearML server are you using?

I figured you'd say that so I went ahead with that PR. I got it working but I'm going to test it a bit further.

Is it possible the cached repository was cloned before you changed your agent settings?

Which settings are you referring to? I can't remember if I was using https auth when the project would have been first cached. Would that make a difference?

Also, did you set

agent.enable_git_ask_pass: true

?

The only instance of it in the config is commented out.

    # if set, use GIT_ASKPASS to pass user/pass when cloning / fetch repositories
    # it solves pas...

I'm aware of that but it doesn't help this situation.

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

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 so much @<1523701205467926528:profile|AgitatedDove14> !