GiganticTurtle0 in the PipelineDecorator.component , did you pass helper_functions=[] with refrence to all the sub component ?

Hi VexedElephant56
Yes it is:
Define CLEARML_AGENT_SKIP_PYTHON_ENV_INSTALL=1
(if running in doecker mode add -e CLEARML_AGENT_SKIP_PIP_VENV_INSTALL=1 as container args)
https://clear.ml/docs/latest/docs/clearml_agent/clearml_agent_env_var

the use case i have is to allow people from my team to run their workloads on set of servers without stepping over each other..

So does that mean CPU only workloads?
Also are we afraid of fairness? (i.e. someone "taking" all the CPU for themselves)

Yes that's the part that is supposed to only pull the GPU usage for your process (and sub processes) instead of globally on the entire system

Hi VastShells9
2022-12-20 12:48:02,560 - clearml.automation.optimization - WARNING - Could not find requested hyper-parameters ['duration'] on base task a6262a151f3b454cba9e22a77f4861e3Basically it is telling you it is setting a parameter it never found on the original Task you want to run the HPO o.
The parameter name should be (based on the screenshot) "Args/duration" (you have to add the section name to the HPO params). Make sense ?

YEY 🙂

ReassuredTiger98 can you send the full log?
Also, what's the clearml-agent version?
fyi: we fixed an issue where the default order of the conda repositories cause pytorch to be installed form the conda forge instead of the pytorch repo, making it the cpu version instead of the gpu version:
This is the correct conda repo orderL
https://github.com/allegroai/clearml-agent/blob/cb6bdece39751eaef975287609b8bab603f116e5/docs/clearml.conf#L66

Hmm that makes sense, I "think" the enterprise offering has a solution for that as well (i.e. full separation over static cluster), but probably the best way to constituent this avenue is talk to Sales (I'm assuming they'll setup a call to discuss the details)

Going back to the open source, I think that adding the credentials as part of the source code might allow to have "credentials" auto populate as part of the remote execution, wdyt?

I'm getting lot of bizarre errors running without a docker image attached

I think there is a mix in terminology
ClearML Agent can run in two different modes:

• virtual env - where it create a new venv for every Task executed
• docker mode- where it spins a docker as Base environment, then inside the docker (in real time) it will fetch the code, install missing python packages etc.There is no need to build a specific docker container, for example you can use the "python:3.10-bullseye" d...

Thanks VexedCat68 !
This is a great example, maybe PR it to the cleamrl-servvng repo ? wdyt?

for example, one notebook will be dedicated to explore columns, spot outliers and create transformations for specific column values.

This actually implies each notebook is a standalone "process", which makes a ton of sense. But this is where notebooks and proper SW design break, in traditional SW, the notebooks are actually python files, and then of course you can import one from another, unfortunately this does not work in notebooks...

If you are really keen on using notebooks I wou...

Yeah the ultimate goal I'm trying to achieve is to flexibly running tasks for example before running, could have a claim saying how many resources I can and the agent will run as soon as it find there are enough resources

Checkout Task.execute_remotely() you can push it anywhere in your code, when execution get to it, If you are running without an agent it will stop the process and re-enqueue it to be executed remotely, on the remote machine the call itself becomes a noop,

I...

When is clearml-deploy coming to the open source release?

Currently available under clearml-serving (more features are being worked on, i.e. additional stats and backends)
https://github.com/allegroai/clearml-serving

Yep it is the scale 🙂 and yes it should appear once you upgrade

so all models are part of the same experiment and has the experiment name in their name.

Oh that explains it, (1) you can use the model filename to control the model name in clearml (2) you can disable the autologging and manually upload the model, then you can control the model name
wdyt?

Yes in the UI, clone or reset the Task, then youcan edit the installed packages section under the Execution tab

Is this reproducible? I tried to run the same example code on my machine, and it started training ...
Do you have issues with other pytorch examples? Could you try simple reporting example:
https://github.com/allegroai/clearml/blob/master/examples/reporting/scalar_reporting.py

Once the team is happy with the logging functionality, we'll move on to remote execution and things will update.

🎉

While I do have the access and secret defined in clearml.conf, and even in the WebUI, I still get similar

and you have your credentials in the browser when deleting a Task ?

You mean for running a worker? (I think plain vanilla python / ubuntu works)
The only change would be pip install clearml / clearml-agent ...

Basically you create the Task and make sure the "Dataset" is attached to it:
task = Task.init(...) dataset = Dataset.create(task=task) dataset.add_files(...)This will make sure the code is attached to the Dataset

Oh, and good job starting your reference with an author that goes early in the alphabetical ordering, lol:

LOL, worst case it would have been C ... 🙂

Hi WickedGoat98 ,
I think you are correct 😞
I would guess it is something with the ingress configuration (i.e. ConfigMap)

Basically it is the same as "report_scatter2d"

Is there any way to make that increment from last run?

pipeline_task = Task.clone("pipeline_id_here", name="new execution run here") 
Task.enqueue(pipeline_task, queue_name="services")

wdyt?

AdventurousRabbit79 you mean like minio / ceph ?

Could you please add it, I really do not want to miss it 🙂

Internally it's taking this URL and downloading the file, building the path with

file:///

(edited)

This part is confusing, http link should work, how are you ending up with file:// links ? do you want to provide the full agent log with the error ?

👍

SmarmyDolphin68 okay what's happening is the process exists before the actual data is being sent (report_matplotlib_figure is an async call, and data is sent in the background)
Basically you should just wait for all the events to be flushed
task.flush(wait_for_uploads=True)That said, quickly testing it it seems it does not wait properly (again I think this is due to the fact we do not have a main Task here, I'll continue debugging)
In the meantime you can just do
sleep(3.0)And it wil...

Hmm, so this is kind of a hack for ClearML AWS autoscaling ?
and every instance is running an agent? or a single Task?