Are you referring to ClearML Models and Datasets ?

To link a dataset to a task you need to pass the alias= parameter to the Dataset.get . See here: https://clear.ml/docs/latest/docs/clearml_data/clearml_data_sdk#accessing-datasets

Hey @<1523704757024198656:profile|MysteriousWalrus11> , given your use case, did you consider passing the path to the dataset? Like an address to an S3 bucket

It happens due to an internal use of Dataset.get , the larger the dataset, the more verbose it will be. We’ll fix this in the upcoming releases

Hey @<1526734437587357696:profile|ShaggySquirrel23> , what version of the clearml-agent are you using? Also, if I were you I’d check how much free disk there’s on the machine running the agents

I’ll investigate

Could you please run the misbehaving example, try to add a breakpoint in clearml/backend_interface/task/task.py in Task.update_output_model on the line with url = output_model.update_weights( , and tell me what the value of model_path is? In case you're using virtual environments, clearml library should be installed somewhere in <virtual env directory>/lib/python3.10/site-packages/clearml/

Hey @<1523705721235968000:profile|GrittyStarfish67> , we have just released 1.12.1 with a fix for this issue

Hey Sana, yes you can. When you open the link, check on the upper-right side the Task's menu bar, and you will notice that you can clone the shared task.

Ah, I think I understand. To execute a pipeline remotely you need to use None pipe.start() not task.execute_remotely . Do note that you can run tasks remotely without exiting the current process/closing the notebook, (see here the exit_process argument None ) but you won't be able to return any values from this task....

The issue may be related to the fact that right now we have some edge cases when working with lightning >= 2.0, we should have better support in the upcoming release

Hey @<1564422650187485184:profile|ScaryDeer25> , we just released clearml==1.11.1rc2 which should solve the compatibility issues for lightning >= 2.0. Can you install it and check whether it solves your problem?

Hey @<1533257278776414208:profile|SuperiorCockroach75> , we do both, and much more 🙂

This is doing fine-tuning. Training a multi-billion parameter model from scratch would be economically unfeasible for most of existing enterprises

clearml-data also supports glob patterns, so if you have your dataset files in the same directory as the experiment code, you can do something like clearml-data add --files *.csv and only add the CSV files.

There's no .gitignore-like functionality because clearml-data is not meant to track everything, and you need to be deliberate in what exactly you're adding. Hope this clarifies things.

Which gives me an idea. Could you please remove the entrypoint from the docker image altogether and try again ?

Overriding the entrypoint in the image can lead to docker run/docker exec failing to work properly , because instead of a shell it will use your entrypoint to run everything

Hey @<1671689458606411776:profile|StormySeaturtle98> we do support something called "Model Design" previews, basically an architecture description of the model, a la Caffe protobufs. None For example we store this info automatically with Keras

Hey Yasir, to use tensorflow prefetch your data needs to be (1) chunked and (2) stored on some server/bucket/network-attached FS. If both conditions are not satisfied, TF prefetch won't help you.

How large is the dataset we're talking about?

That's not that much. You can use the AWS autoscaler and provision a spot g4dn GPU instance with a bit more disk. This should cost you less than 50 cents an hour

Then change from git+ssh to git+https

Hey @<1661904968040321024:profile|SpotlessOwl43> that's a great question!

how the metric should be saved, via report_single_value?

That's correct

what should I enter into the title and series fields in Project Dashboard?

The title should be "Summary" and series is the name of the single value you reported

Hey @<1639074542859063296:profile|StunningSwallow12> what exactly do you mean by "training in production"? Maybe you can elaborate what kind of models too.

ClearML in general assigns a unique Model ID to each model, but if you need some other way of versioning, we have support for custom tags, and you can apply those programmatically on the model

Hey @<1681836303299121152:profile|RoundElk14> , it seems you are using a self-hosted ClearML server. This error you're getting happens because your email is not configured in the server. Ask your admin to perform the following steps:

• [The admin] Go to Settings > Users & Groups > Users and click on "+ Add User" where they will be prompted to specify the user's email
• [The user] Once the admin confirms that they did step 1, the user should first Sign In with their email to the server
• [The...

I ’ m afraid serializing an entire class won’t be possible , but create_function_task will send the entire environment for remote execution , so you can still access your code

Hey @<1535069219354316800:profile|PerplexedRaccoon19> , yes it does. Take a look at this example, and let me know if there are any more questions: None

Can you please attach the full traceback here?

This sounds like a use case for the enterprise version of ClearML. In it you can set read/write permissions. Publishing is considered a "write", so you can limit who can do it. Another thing that might be useful in your scenario is to try using "Reports", and connect the "approved" experiments info to a report and then publish it. Here's a short video introducing reports .

By the way, please note that if the experiment/report/whatever is publis...

Do you mean that you want your published experiments to be either “approved” or “not approved“ based on the presence of the attachments you mentioned ?

This is the method you're looking for None . But make sure you have a model saved on disk before using it. And if you don't want the model to be deleted from disk after it, make sure to set auto_delete_file=False

Hey @<1529271085315395584:profile|AmusedCat74> , I may be wrong , but I think you can’t attach a gpu to an e2 instance , it should be at least an n1, no?