My use case it: in a spot instance marked for termination after 2 mins by aws, I want to close a task and prevent the clearml-agent to pick up a new task after.

Hi AgitatedDove14 , Here is the full log.
Both python versions (local and remote) are python 3.6 Locally (macos), I get pytorch3d== (from versions: 0.0.1, 0.1.1, 0.2.0, 0.2.5, 0.3.0, 0.4.0, 0.5.0) Remotely (Ubuntu), I get (from versions: 0.0.1, 0.1.1, 0.2.0, 0.2.5, 0.3.0)So I guess it’s not related to clearml-agent really, rather pip that cannot find the proper wheel for ubuntu for latest versions of pytorch3d, right? If yes, is there a way to build the wheel on the remote machine...

Probably 6. I think because of some reason, it did not go back to main trains-agent. Nevertheless I am not sure, because a second task could start. It could also be that the second was aborted for some reason while installing task requirements (not system requirements, so executing the trains-agent setup within the docker container) and therefore again it couldn't go back to main trains-agent. But ps -aux shows that the trains-agent is stuck running the first experiment, not the second...

I’ll definitely check that out! 🤩

Hey FriendlySquid61 ,
I ended up asking for full control of EC2 not to be blocked, so unfortunately I cannot give you a more precise list 😕

Thanks for your help 🙏

Thanks! I would like to use this opportunity to split the indices into multiple shards, as explained here:
https://www.elastic.co/guide/en/elasticsearch/reference/current/indices-split-index.html#indices-split-index

ProxyDictPostWrite._to_dict() will recursively convert to dict and OmegaConf will not complain then

btw CostlyOstrich36 , I can see in Profile > Version: 1.1.1-135 • 1.1.1 • 2.14 . What these numbers correspond to?

I am using pip as a package manager, but i start the trains-agent inside a conda env 😄

Very good job! One note: in this version of the web-server, the experiments logo types are all blank, what was the reason to change them? Having a color code in the logos helps a lot to quickly check the nature of the different experiments tasks, isnt it?

From my experience, I only installed cuda drivers on my machines. I didn't used conda to install torch nor cudatoolkit, I just let clearml-agent download the torch wheel file and install it

Hey @<1523701205467926528:profile|AgitatedDove14> , Actually I just realised that I was confused by the fact that when the task is reset, because of the sorting it disappears, making it seem like it was deleted. I think it's a UX issue: When I click on reset.

• The pop shows "Deleting 100%"
• The task disappears in the list of tasks because of the sortingThis led me to thing that there was a bug and the task was deleted

What is latest rc of clearml-agent? 1.5.2rc0?

SuccessfulKoala55 I deleted all :monitor:machine and :monitor:gpu series, but only deleted ~20M documents out of 320M documents in the events-training_debug_image-xyz . I would like now to understand which experiments contain most of the document to delete them. I would like to aggregate the number of document per experiment. Is there a way do that using the ES REST api?

The part where I'm lost is why would you need the path to the temp venv the agent creates/uses ?

let's say my task is calling a bash script, and that bash script is calling another python program, I want that last python program to be executed with the environment that was created by the agent for this specific task

Yes, I get 0 afterwards

And if you need a very small change, you can also simply https://www.geeksforgeeks.org/monkey-patching-in-python-dynamic-behavior/ it

Yes, I will check now

region is empty, I never entered it and it worked

SuccessfulKoala55 I am using ES 7.6.2

Also enable_git_ask_pass is not dumped into the logs when an experiment start btw

Sure! Here are the relevant parts:
` ...
Current configuration (clearml_agent v1.2.3, location: /tmp/.clearml_agent.3m6hdm1_.cfg):

...
agent.python_binary =
agent.package_manager.type = pip
agent.package_manager.pip_version = ==20.2.3
agent.package_manager.system_site_packages = false
agent.package_manager.force_upgrade = false
agent.package_manager.conda_channels.0 = pytorch
agent.package_manager.conda_channels.1 = conda-forge
agent.package_manager.conda_channels.2 ...

I will probably just use everywhere an absolute path to be robust against different machine user accounts: /home/user/trains.conf

AgitatedDove14 If I call explicitly task.get_logger().report_scalar("test", str(parse_args.local_rank), 1., 0) , this will log as expected one value per process, so reporting works

From the answers I saw on the internet, it is most likely related to the mismatch of cuda/cudnn version

I just checked if something changed in https://allegro.ai/clearml/docs/docs/deploying_clearml/clearml_server_config.html#web-login-authentication

yes, I use --gpus parameter

I am also interested in the clearml-serving part 😄

The weird thing is that the second experiment started immediatly, correctly in a docker container, but failed with User aborted: stopping task (3) at some point (while installing the packages). The error message is suprizing since I did not do anything. And then all following experiments are queued to services queue and stuck there.