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).

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.

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.

Hyperdatasets are the only ones that require a premium. If you're using normal datasets it should be fine.

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

What version of ClearML server are you using?

Are you self hosting a ClearML server?

I found I was having this issue as well. I don't have an alias defined in the pipeline but in a task and I get the same error. I'm not hosting my own server but using the free web service at the moment.

Provide a bit more detail. What framework are you using?

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...

Will there be a recording posted?

Thanks Martin. I read this method as "getting the data associated with the model training" not "getting metadata for the model". This is what I'm looking for.

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

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?

Interesting approach. I'll give that a try. Thanks for the reply!

Let me give that a try. Thanks for all the help.

Hi again @<1523701435869433856:profile|SmugDolphin23> ,

The approach you suggested seems to be working albeit with one issue. It does correctly identify the different versions of the dataset when new data is added, but I get an error when I try and finalize the dataset:

Code:

        if self.task:
            # get the parent dataset from the project
            parent = self.clearml_dataset = Dataset.get(
                dataset_name="[LTV] Dataset",
                dataset_project=...

I'm not self-hosting the server.

Since this could happen with a lot of services, maybe it would be worth a retry option? Especially if it's part of a pipeline.

So far when I delete a task or dataset using the web interface that has artifacts on S3 it doesn't prompt me for credentials.

It's a corporate one. We are also looking into options on Github's end.

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)

They will be related through the task. Get the task information from the dataset, then get the model information from the task.

@<1523701435869433856:profile|SmugDolphin23> Yes. I'll try it in about 14 hours when I'm back at work and let you know how it goes. 😂

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!

@<1523701087100473344:profile|SuccessfulKoala55> You wouldn't happen to know what's going on here. :D

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

Yes, it indeed appears to be a regex issue. If I run:

Dataset.list_datasets(
                dataset_project=self.task.get_project_name(),
                partial_name=re.escape('[LTV] Dataset Test'),
                only_completed=True,
            )

It works as expected. I'm not sure how raw you want to leave the partial_name features. I could create a PR to fix this but would you want me to re.escape at the list_datasets() level? Or go deeper and do it at `Task._query_task...

The plot thickens. It seems like there's something odd going on with the interaction between [LTV] and additional text. If I just search [LTV] it works, if I just search Dataset Test it works, but if I put them together it breaks the search. Now that I think about it, there's other oddities that seem to happen in the web interface that might be explained by some bugs around using brackets in names.