Should be fairly easy to add no?

ThickFox50 I also have to point that there is a free hosted server here 🙂 https://app.community.clear.ml

So clearml server already contains an authentication layer (JWT Token), and you do have a full user management on top:
https://clear.ml/docs/latest/docs/deploying_clearml/clearml_server_config#web-login-authentication
Basically what I'm saying if you add httpS on top of the communication, and only open the 3 ports, you should be good to go. Now if you really need SSO (AD included) for user login etc, unfortunately this is not part of the open source, but I know they have it in the scale/ent...

oh the pipeline logic itself holds one "job" on the worker, and this is why you do not have any other spare workers to run the components of the pipeline.
Run your worker with --services-mode , it will launch multiple Tasks at the same time, it should solve the issue

multiple machines and reporting to the same task.

Out of curiosity , how do you launch it on multiple machines?

reporting to the same task.

So the "funny" think is, they all report on on top (overwriting) the other...
In order for them to report individually, it might be that you need multiple Tasks (i.e. one per machine)
Maybe we could somehow have prefix with rank on the cpu/network etc?! or should it be a different "title", wdyt?

I was able to successfully enqueue the task but only entrypoint script is visible to it and nothing else.

So you passed a repository link is it did not show on the Task ?
What exactly is missing and how the Task was created ?

Create one experiment (I guess in the scheduler)
task = Task.init('test', 'one big experiment')
Then make sure the the scheduler creates the "main" process as subprocess, basically the default behavior)
Then the sub process can call Task.init and it will get the scheduler Task (i.e. it will not create a new task). Just make sure they all call Task init with the same task name and the same project name.

MysteriousBee56 Okay, let's try this one:
docker run -t --rm nvidia/cuda:10.1-base-ubuntu18.04 bash -c "echo 'Binary::apt::APT::Keep-Downloaded-Packages \"true\";' > /etc/apt/apt.conf.d/docker-clean && apt-get update && apt-get install -y git python3-pip && python3 -m pip install trains-agent && echo done"

Hi AttractiveWoodpecker16
I think is the correct channel for that question.
(any chance you can move your thread there?)
Specifically just email billing@clear.ml they will cancel (no need to worry about the beginning of the month, just explain and they will not charge over Nov)

EDIT: I know they are working on making it a one click in the UI, main limit is what happens with the data that was stored and was above the free tier threshold, anyhow I think next version will sort that as well.

No I was was pointing out the lack of one

Sounds like a great idea, could you open a github issue (if not already opened) ? just so we do not forget

set the pytorch lightning trainer argument

log_every_n_steps

to

1

(default

50

) to prevent the ClearML iteration logger from timing-out

Hmm that should not have an effect on the training time, all logs are send in the background, that said checkpoints might slow it a bit (i.e.; i...

Hi @<1798887585121046528:profile|WobblyFrog79>

. When I execute the pipeline remotely in Kubernetes, those components

two things, one, make sure you specify the repo you need the components from in the decorator function, what will happen is the repo will be cloned into the container running on k8s, then inside the repo root your script (i.e. pipeline step) will be running.
[None](https://github.com/clearml/clearml/blob/9c93aa9e538075c848647dcd88e3e12bec051b5f/clearml/automation/con...

Is this a logging issue, or clearml issue ?

LOL, could be there is no remote repository ?

Wouldn't it make sense to use a single one ?

@<1687643893996195840:profile|RoundCat60> I'm assuming we are still talking about the S3 credentials, sadly no 😞
Are you familiar with boto and IAM roles ?

but I can't seem to figure out a way to do something similar using a task in add_step

VexedCat68 With "add_step" it assumes the Task you are adding is self contained (i.e. there is no "return object" to serialize), this means you can only add arguments, or use the artifacts the Task (i.e. step) will recreate, obviously you knowing in advance what the step creates. Make sense ?

My use case is when I have a merge request for a model modification I need to provide several informations for our Quality Management System one is to show that the experiment is a success and the model has some improvement over the previous iteration.

Sounds likes good approach 🙂

Obviously I don't want the reviewer to see all ...

Maybe move publish the experiment and move it to a dedicated folder ? Then even if they see all other experiments, they are under "development" p...

You can change it the CWD folder, if you put . in working dir it will be the root git repo, but you can do any subfolder, obviously you need to change the script path to match the folder, e.g. ./folder/script.py etc.

are you referring to 

extra_docker_shell_

scrip

t

Correct

the thing is that this runs before you create the virtual environment, so then in the new environment those settings are no longer there

Actually that is better, because this is what we need to setup the pip before it is used. So instead of passing --trusted-host just do:
` extra_docker_shell_script: ["echo "[global] \n trusted-host = pypi.python.org pypi.org files.pythonhosted.org YOUR_S...

What does spin mean in this context?

This line:
docker-compose --env-file example.env -f docker-compose-triton-gpu.yml up

But these have: different task ids, same endpoints (from looking through the tabs)
So I am not sure why they are here and why not somewhere else

You can safely ignore them for the time being 🙂

but is it true that I can have multiple models on the same docker instance with different endpoints?

Yes! this is exactly the idea (and again I'm not sure ...

But only 1 node will copy it.

they can only copy it after the first is finished, and they are not aware it is trying to set the exact venv, hence the race

Hi GreasyPenguin14

However the cleanup service is also running in a docker container. How is it possible that the cleanup service has access and can remove these model checkpoints?

The easiest solution is to launch the cleanup script with a mount point from the storage directory, to inside the container ( -v <host_folder>:<container_folder> )
The other option, which clearml version 1.0 and above supports, is using the Task.delete, that now supports deleting the artifacts and mod...

My goal is to automatically run the AWS Autoscaler task on a clearml-agent pod when I deploy

LovelyHamster1 this is very cool!
quick question, if you are running on EKS, why not use the EKS autoscaling instead of the ClearML aws EC2 autoscaling ?

Hi SkinnyPanda43
In your local machine do not pass output_uri at all, so nothing will be uploaded.
On the agent's configuration file configure, default_output_uri to the S3 bucket
(Notice you can always override them in the UI, see the bottom of the execution Tab)
https://github.com/allegroai/clearml-agent/blob/e93384b99bdfd72a54cf2b68b3991b145b504b79/docs/clearml.conf#L312

JitteryCoyote63 if this is simulating an agent, the assumption is that the Task was already created, hence the task ID.

If i am working with Task.set_offline(True)

How would the two combine ? I mean off-line is be definition not executed by an agent, what am I missing ?

What I try to do is that DSes have some lightweight baseclass that is independent of clearml they use and a framework have all the clearml specific code. This will allow them to experiment outside of clearml and only switch to it when they are in an OK state. This will also help not to pollute clearml spaces with half backed ideas

So you want the DS to manually tell the baseclasss what to store ?
then the base class will store it for them, for example with joblib , is this the...

Hi @<1715900760333488128:profile|ScaryShrimp33>

hi everyone! I’m trying to save my model’s weights to storage. And I can’t do it.

See example here: None

or

task.update_output_model(model_path="/path/to/model.pt")

Cab you do:
curl

Not sure on the cause but if you do:

mp.set_start_method('fork', force=True)

There is no semaphore leakage

, but it seems like I can only trigger a task using a Task scheduler but not a pipeline.

@<1523701132025663488:profile|SlimyElephant79> Maybe we should better state it, but Pipeline is "just" another type of Task. so triggering a Task with the Pipeline ID is essentially triggering the pipeline (do notice you need to select the "services" queue to be used so that the pipeline runs on the correct resource). Make sense ?