This is the file which installs the GPU version

I guess this is the current way to do it: https://github.com/tensorflow/tensorboard/issues/39#issuecomment-568917607 so I would say: Yes, it supports gif.

@<1576381444509405184:profile|ManiacalLizard2> Yes, exactly. I just didn't know how, but now it is all working 🙂
And yes, I have multiple credentials in the clearml.conf of the agents. It's not a good solution, but since I am currently limited to the free version of ClearML, it is the best I could do.

Ah, perfect. Did not know this. Will try! Thanks again! 🙂

Maybe this is something that is only possible with the vault of the enterprise version?

MortifiedDove27 Sure did, but I do not understand it very well. Else I would not be asking here for an intuitive explanation 🙂 Maybe you can explain it to me?

I see. Thank you very much. For my current problem giving priority according to queue priority would kinda solve it. For experimentation I will sometimes enqueue a task and then later enqueue a another one of a different kind, but what happens is that even though this could be trivially solved, I will have to wait for the first one to finish. I guess this is only a problem for people with small "clusters" where SLURM does not make sense, but no scheduling at all is also suboptimal.
However, I...

==> 2021-03-11 13:54:59 <==
# cmd: /home/tim/miniconda3/condabin/conda create --yes --mkdir --prefix /home/tim/.clearml/venvs-builds/3.8 python=3.8
# conda version: 4.9.2
+defaults/linux-64::_libgcc_mutex-0.1-main
+defaults/linux-64::ca-certificates-2021.1.19-h06a4308_1
+defaults/linux-64::certifi-2020.12.5-py38h06a4308_0
+defaults/linux-64::ld_impl_linux-64-2.33.1-h53a641e_7
+defaults/linux-64::libedit-3.1.20191231-h14c3975_1
+defaults/linux-64::libffi-3.3-he6710b0_2
+defaults/linux-64...

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  ...

But this seems like something that is not related to clearml 🙂 Anyways, thanks again for the explanations!

Ah, now I see. This sounds like a good solution.

Okay, no worries. I will check first. Thanks for helping!

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

Sure, no problem!

Currently, my solution is to create an "agent-git" account and users can give read-access to this account which the clearml-agent then uses to clone. However, I find access-tokens to be a better solution. Unfortunately, clearml-agent removes the token from the git url

Local execution output:
ClearML Task: created new task id=855948f5d73c47e2ae37bb821385e15b ======> WARNING! Git diff to large to store (2190kb), skipping uncommitted changes <====== ClearML results page: uploading artifact done uploading artifact 2021-02-05 16:24:56,112 - clearml.Task - INFO - Waiting to finish uploads 2021-02-05 16:24:58,499 - clearml.Task - INFO - Finished uploading

btw: With the ssh agent forwarding I do not have any issues ( https://github.com/allegroai/clearml-agent/issues/45 )

Oh, interesting!
So pip version on per task basis makes sense ;D?

@<1576381444509405184:profile|ManiacalLizard2> Just so I understand correctly:
You are saying that in your local, user-specific, clearml.conf you set the api.files_server , but in your remote, clearml-agent, clearml.conf you left it empty?

Maybe this opens up another question, which is more about how clearml-agent is supposed to be used. The "pure" way would be to make the docker image provide everything and clearml-agent should do not setup at all.
What I currently do instead is letting the docker image provide all system dependencies and let clearml-agent setup all the python dependencies. This allows me to reuse a docker image for more different experiments. However, then it would make sense to have as many configs as possib...

So clearml 1.0.1 clearml-agent 1.0.0 and clearml-server from master

I am currently on the Open Source version, so no Vault. The environment variables are not meant to used on a per task basis right?

I am referring to the UI. The default cleanup service should work with S3 with a correctly configured clearml service agent if I understand the workings correctly.

end file?

I ll add creating an issue to my todo list

Thanks for answering. I don't quite get your explanation. You mean if I have 100 experiments and I start up another one (experiment "101"), then experiment "0" logs will get replaced?

I think I still don't get how clearml is supposed to work/be used. Why wouldn't the following work currently?
Example:
` task = Task.init(...)
if not running_remotely:
task_dict = task.export_task()
requirements = task_dict["script"]["requirements"]["pip"].splitlines()
requirement_torch = [r for r in requirements if r.startswith("torch==")]
requirements.remove(requirement_torch[0])
requirements.append("torch >= 1.8.1")
task_dict["script"]["requirements"]["pip"] = "\n"....

As always, great help!

If I understood correctly, if I tried to print(os.environ["MUJOCO_GL"]) after the clearml Task is created, this should be set?

Yes.