Alright, thank you very much! 🙂

It seems like the services-docker is always started with Ubuntu 18.04, even when I use
task.set_base_docker( "continuumio/miniconda:latest -v /opt/clearml/data/fileserver/:{}".format( file_server_mount ) )

Is this working in the latest version? clearml-agent falls back to /usr/bin/python3.8 no matter how I configure clearml.conf Just want to make sure, so I can investigate what's wrong with my machine if it is working for you.

Mhhm, now conda env creation takes forever since it probably resolves conflicts. At least that is what is happening when I tried to manually install my environment

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

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

Is there a simple way to get the response of the MinIO instance? Then I can verify whether it is the MinIO instance or my client

Maybe deletion happens "async" and is not reflected in parts of clearml? It seems that if I try to delete often enough at some point it is successfull

Version 0.17.2 it says

Thank you! 🙂

But here is the funny thing:

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

Installs GPU

The docker run command of the agent includes '-v', '/tmp/clearml_agent.ssh.8owl7uf2:/root/.ssh' and the file permissions are exactly the same.

This is the venc

Okay.

I restarted it after I got the errors, because as everyone knows, turning it off and on usually works 😄

Python 3.8.8, clearml 1.0.2

Thanks a lot. But even for a user, I can not set a default for all projects, right?

Do you mean venv_update ?

It could be that either the clearml-server has bad behaviour while clean up is ongoing or even after.

If you compare the two outputs it put at the top of this thread, the one being the output if executed locally and the other one being the output if executed remotely, it seems like command is different and wrong on remote.

The default behavior mimics Python’s assert statement: validation is on by default, but is disabled if Python is run in optimized mode (via python -O). Validation may be expensive, so you may want to disable it once a model is working.

Ok, nice. Makes sense!

Thank you very much.

As in if it was not empty it would work?

Well, after restarting the agent (to set it into --detached more) it set the cleanup_task.py into service mode, but my monitoring tasks are just executed on the agent itself (no new service clearml-agent is started) and then it is aborted right after starting.

Works with 1.4. Sorry for not checking versions myself!

When you say it is an SDK parameter this means that I only have to specify it on the computer where I start the task from, right? So an clearml-agent would read this parameter from the task itself.

This my environment installed from env file. Training works just fine here:

Oh you are right. I did not think this through... To implement this properly it gets to enterprisy for me, so I ll just leave it for now :D

btw: I am pretty sure this used to work, but then stopped work some time ago.