I see! Thanks!

However, because of the import carla it is added to the task requirements and clearml-agent tries to install it, although it is meant to be included at runtime.

Thank you. Will try that!

@<1576381444509405184:profile|ManiacalLizard2> Maybe you are using the enterprise version with the vault? I suppose the enterprise version is running differently, but I dont have experience with it.
For the open-source version, each clearml-agent is using it's own clearml.conf

And clearml-agent should pull these datasets from network storage...

Sounds good!

Hi TimelyMouse69
Thank you for answering, but I do not think these methods do allow me to modify anything the is set in clearml.conf. Rather they just do logging.

AgitatedDove14 I have to problem that "debug samples" are not shown anymore after running many iterations. What's appropriate to use here: A colleague told me increasing task_log_buffer_capacity worked. Is this the right way? What is the difference to file_history_size ?

What you mean by "Why not add the extra_index_url to the installed packages part of the script?"?

I tried to run the task with detect_with_conda_freeze: false instead of true and got

Executing Conda: /home/tim/miniconda3/condabin/conda install -p /home/tim/.clearml/venvs-builds/3.8 -c defaults -c conda-forge -c pytorch 'pip<20.2' --quiet --json
Pass
Conda: Trying to install requirements:
['pytorch~=1.8.0']
Executing Conda: /home/tim/miniconda3/condabin/conda env update -p /home/tim/.clearml/venvs-builds/3.8 --file /tmp/conda_envh7rq4qmc.yml --quiet --json
Conda error: Unsati...

Just tested again. The ordering definitly matters.

I can put anything there: s3://my_minio_instance:9000 /bucket_that_does_not_exist and it will work.

Thank you very much. I tested it on a different machine now and it works like intended. So there must be something misconfigured with this one machine.

Okay, it seems like it just takes some time to delete and to reflect in the WebUI. So when I try to delete again, actually a deletion process seems already to be running in the background.

By host you mean the machine on which the agent is running? How does clearml-agent find the cuda_version?

==> 2021-03-11 12:50:38 <==
# cmd: /home/tim/miniconda3/condabin/conda create --yes --mkdir --prefix /home/tim/.clearml/venvs-builds/3.8 python=3.8
--
==> 2021-03-11 12:50:40 <==
# cmd: /home/tim/miniconda3/condabin/conda install -p /home/tim/.clearml/venvs-builds/3.8 -c defaults -c conda-forge -c pytorch cudatoolkit=11.0 --quiet --json
--
==> 2021-03-11 12:50:43 <==
# cmd: /home/tim/miniconda3/condabin/conda install -p /home/tim/.clearml/venvs-builds/3.8 -c defaults -c conda-forge -c p...

Do you know how I can make sure I do not have CUDA or a broken installation installed?

channels:
- defaults
- conda-forge
- pytorch
dependencies:
- cudatoolkit==11.1.1
- pytorch==1.8.0

Gives CPU version

Thank you. Yes we need to wait for carla to spin up.

But the problems seem to be reoccuring

AgitatedDove14 SuccessfulKoala55 Could you briefly explain whether clearml supports no-copy add for datasets?

Ah, I see. Any way to make the UI recognize it as a file server?

But would storing then work?

Thanks a lot, now I think I understand.

Debug samples can only be controlled via api.file_server (or programatically)

Could you guide me how to approach this programmatically? Can I implement my own storage adapter for debug samples with ClearML interfaces or am I on my own?

Or does MinIO delay deletion somehow? Deleting a task via the web interface also does not result in deletion of debug samples on MinIO

Any update? 🙂

No no, I was just wondering how much effort it is to create something like ClearML. And your answer gives me a rough estimate 🙂

I guess then it is hard to solve and probably not worth it for me to make suggestions without any knowledge about the internals 😕 Seems like a small weakness in the design of the open-source version. But not much of an issue 🙂

AgitatedDove14 Yes, you understood correctly. But Task.create is used by Task.init something like this, right?
` def init(project_name, task_name):
if not Task.exists_already(project_name, task_name):
task = Task.create(...)
else:
task = load_existing_task()

return task `

No, it is only a single one.