@<1523701994743664640:profile|AppetizingMouse58> Thank you very much. I forgot the volume mapping.
So can I just add the config to the async_delete container and mirror the directory structure from github?

volumes:
    - /opt/clearml/config:/opt/clearml/config
    - /opt/clearml/logs:/var/log/clearml

It works! Thank you very much 🙂

Hi @<1523701087100473344:profile|SuccessfulKoala55> Thank you very much.
Is there some way to verify the server uses the correct configuration files? (E.g. see it in the logs/web ui). I Just tried it does not work.
At least I can see the async_delete service complains about a missing secret, so I can start debugging there. I am using the same config as for my agents, but somehow for async_delete it does not work...

And the files that I see on github are the default configuration of the server, even if I do not have these files in my installation, right?

It seems like this is a bug however or is something like this to be expected? There shouldn't be files that are not shown in the WebUI..?

It is now looking for conflicts.

AgitatedDove14 https://github.com/allegroai/clearml/pull/372

Based on https://github.com/lanpa/tensorboardX/blob/34d1616c035faaa0f3f7c9d19cb8bb4425f19939/tensorboardX/summary.py#L355 I would guess that it is already encoded before added to the tensorboard summary.

The task already contains this

I am still trying to solve the add_requirements + importlib combo. If I use detect_with_freeze I can not use add_requirements and if I use automatic code analysis it will not find all packages because of importlib .
For now I come to the conclusion, that keeping a requirements.txt and making clearml parse the requirements from there should be the most robust solution. Unfortunately, there seems to be no way to do this with Task.init .

Or alternatively I just saw that Task.create takes a requirements.txt as an argument. This would also be fine for me, however I am not sure whether I should use Task.create ?

AlertBlackbird30 Thanks for asking. Just take everything with I grain of salt I say, because I am also not sure whether I do machine learning the correct way 😄

I think you got the right idea. I actually do reinforcement learning (RL), so I have multiple RL-environments and RL-agents. However, while the code for the agents differs between the agents, the glue code is the same. So what I do is I call python run_experiment.py --agent http://myproject.agents.my ` _agent --environm...

But you can manually add them with Task.add_requirements, no?

In my opinion an ugly solution. I would have to keep track of which requirements are missing. Then I would rather just add all requirements manually.

name: core
channels:
  - pytorch
  - conda-forge
  - defaults
dependencies:
  - _libgcc_mutex=0.1
  - _openmp_mutex=4.5
  - blas=1.0
  - bzip2=1.0.8
  - ca-certificates=2020.12.5
  - certifi=2020.12.5
  - cudatoolkit=11.1.1
  - ffmpeg=4.3
  - freetype=2.10.4
  - gmp=6.2.1
  - gnutls=3.6.13
  - jpeg=9b
  - lame=3.100
  - lcms2=2.11
  - ld_impl_linux-64=2.33.1
  - libedit=3.1.20191231
  - libffi=3.3
  - libgcc-ng=9.3.0
  - libiconv=1.16
  - libpng=1.6.37
  - libstdcxx-ng=9.3.0
  - libtiff...

drwxr-xr-x 10 root root 4096 Jul 31  2020 .
drwxr-xr-x 14 root root 4096 Jul 31  2020 ..
drwxr-xr-x  2 root root 4096 Feb  4 13:52 bin
drwxr-xr-x  2 root root 4096 Jul 31  2020 etc
drwxr-xr-x  2 root root 4096 Jul 31  2020 games
drwxr-xr-x  2 root root 4096 Jul 31  2020 include
drwxr-xr-x  4 root root 4096 Feb  3 13:40 lib
lrwxrwxrwx  1 root root    9 Dez 10 14:29 man -> share/man
drwxr-xr-x  2 root root 4096 Jul 31  2020 sbin
drwxr-xr-x  7 root root 4096 Jul 31  2020 share
drwxr-xr-x  ...

Same error.

One question: Does clearml resolve the CUDA Version from driver or conda?

Okay. It works now. I don't know what went wrong before. Probably a user error 😅

sure.

When experimenting we use a entrypoint script which we pass the specific experiment to.

Quick question: Where again does clearml place the venv? I wanna take a look into it after the task has failed

My driver says "CUDA Version: 11.2" (I am not even sure this is correct, since I do not remember installing code in this machine, but idk) and there is no pytorch for 11.2, so maybe it fallbacks to cpu?

I installed my local conda environment from an environment.yml without issues, so maybe clearml makes some changes that leads to conflicts which finally leads to the cpu-version install.

Thank you

Yes, that is what I pasted here.

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

Gives CPU version

I will try a minimal version now

Interesting. Will probably only matter for very small experiments or experiments, where validation is run very infrequently.

What's the reason for the shift?

Hi KindChimpanzee37 I was more asking about the general idea to make these settings task-specific, but thank you for the suggestion anyways, I will definitely apply it.