PricklyRaven28 I think then you're looking for: ZipFile.ZIP_STORED

Is this after you've started the clearML server that you can't find the experiments?

For pipelines there's currently no way to use different compressions. You can still use it when explicitly uploading https://clear.ml/docs/latest/docs/references/sdk/dataset/#upload

That's pretty weird. I don't see any clear indications something is wrong, it simply doesn't execute the rest it would seem. Did it successfully run the first time before cloning it?

you can pass use the compression parameter in dataset.upload . The supported values are:
ZipFile.ZIP_STORED (no compression) ZipFile.ZIP_DEFLATED (requires zlib) ZipFile.ZIP_BZIP2 (requires bz2) ZipFile.ZIP_LZMA (requires lzma)Note that you need to import ZipFile beforehand: from zipfile import ZipFile
You're probably looking for ZIP_BZIP2 , but I'm not sure about that.

no this should work with this one. I'll double check if I'm remembering it correctly but I thought you should be able to start a task after loading your own configuration object, where can set the agent.package_manager.system_site_packages = true .

I don't think there is a direct way but if you use a docker container with a different python version inside, that works.

I'm not sure if you can delete it when using pipelines but I would say try it on a new project?

If I'm 'ot mistaken closed status should only appear when you've used Close ()

ScaryBluewhale66 you shouldn't call mark_* on the main task, we're adding this as a notice and in documentation in the next release: https://github.com/allegroai/clearml/issues/830

Can you elaborate on the hidden project? Is this part of a task you created and something is not showing up?

Can you give me a bit more info what exactly you're trying to log and what framework you're using?

Or you can give it a configuration object: https://clear.ml/docs/latest/docs/clearml_sdk/task_sdk#configuration-objects

You can get all tasks: https://clear.ml/docs/latest/docs/references/sdk/task#taskget_all

You can search tasks: https://clear.ml/docs/latest/docs/clearml_sdk/task_sdk#querying--searching-tasks

And you can get the status:
https://clear.ml/docs/latest/docs/references/sdk/task#get_status

Have you tried --verbose ?

And pandas is in your requirements.txt?

I know zip and tar.gz are supported for auto extraction. But you're looking for a setting to have artifacts compressed with tar instead of zip?

I don't see SB3 here so PyTorch would be best: https://clear.ml/docs/latest/docs/integrations/libraries

Do you have the same python version locally as remotely?
Some ways you could continue now:
you can reuse an existing python virtual environment: https://clear.ml/docs/latest/docs/clearml_agent/#virtual-environment-reuse

You can also run the agent in docker mode: https://clear.ml/docs/latest/docs/clearml_agent/#docker-mode

I'll have a look at the differences concerning the dev disappearing.

Do you get any error when uploading?
It looks like it can upload but can't download afterwards.

Could you test again with
https://clear.ml/docs/latest/docs/references/sdk/task/#mark_completed ?

Wait, I noticed you need another set of quotes:
Sample in the docs is: --memory="300m"

https://docs.docker.com/config/containers/resource_constraints/

Could you elaborate on S3 checkpoint name?
I'm assuming it's a filename?
Possibly post those few lines of code?

We checked in the UI and if the model description is edited with double spaces, they remain, so the problem is likely somewhere in the SDK.

Typo will be fixed 🙂 https://github.com/allegroai/clearml-docs/pull/362

You can explicitly log them: https://clear.ml/docs/latest/docs/guides/reporting/scalar_reporting/

I'd you've got a self hosted instance you can have a look yourself https://clear.ml/docs/latest/docs/deploying_clearml/clearml_server_config
But other then that I'm not sure. AnxiousSeal95 any thoughts?

That doesn't seem normal, let me ask around and get back to you

Does this help? https://clear.ml/docs/latest/docs/deploying_clearml/clearml_server#what-is-clearml-server

Well seems like you have a solution for now?

If you still want to run it as a notebook, the following should make pip install the required packages:

import sys !{sys.executable} -m pip install -r requirements.txt
I'll check if this something we need to update in our documentation or if it's a bug.

This ^
If you're not getting any errors, it should work just fine 🙂

In https://github.com/thepycoder/urbansounds8k/blob/main/preprocessing.py i'm seeing dataset_task.get_logger().report_image , dataset_task.get_logger().report_table , dataset_task.get_logger().report_histogram and dataset_task.get_logger().report_media which are all manual loggings. Hence, why the author probably didn't use any automatic logging.

Could you upload the log so I can have a look?