Hi PanickyFish98
It verifies it has access to it when actually creating the Task, maybe it should be a warning?!
fyi: you can also change the value from the UI (under Execution output) or have a default one set in the clearml.conf used by the agent

TenseOstrich47 this sounds like a good idea.
When you have a script, please feel free to share, I think it will be useful for other users as well 🙂

you can also specify additional packages on the decorator
@PipelineDecorator.component(..., packages=["tqdm>=2.1", "scikit-learn"]) def step_one(...): # code here

Hi @<1523701797800120320:profile|SteadySeagull18>

...the job -> requeue it from the GUI, then a different environment is installed

The way that it works is, in the "originating" (i.e. first manual) execution only the directly imported packages are listed (no derivative packages that re required by the original packages)
But when the agent is reproducing the job, it creates a whole clean venv for the experiment, installs the required packages, then pip resolves the derivatives, and ...

Could it be that this is the callback that causes it?
None

Actually it hasn't changed ...

woot woot 🙂

the agent does not auto-refresh the configuration, after a conf file change you should restart the agent, after that it should present the new configuration when loading

I can't think of any actual difference in flow ...
Can you try the following?
task._setup_reporter() task.set_initial_iteration(0)

Correct (with the port mapping service in it)

Hi UnevenDolphin73

Maybe. When the container spins, are there any identifiers regarding the task etc available?

You mean at the container level or at clearml?

I create a folder on the bucket per 

python train.py

 so that the environment variables files doesn't get overwritten if two users execute almost-simultaneously

Nice 🙂 I have an idea, how about per user ID? then they can access their "secrets" based on the owner of the Task ?
task.data.user

woot woot, glad to hear that!

Hi MotionlessCoral18
You can set all mount points here:
https://github.com/allegroai/clearml-agent/blob/6e31171d314a6e9b276c36d45314025783956b00/docs/clearml.conf#L241

There was an issue in some versions where seeborn plots were blank. Is that the case?

AstonishingRabbit13
https://github.com/googleapis/google-cloud-python/issues/4941#issuecomment-369472576
check the openssl and the date, this seems like SSL low level error (even before authentication)

task = Task.init(...) if task.running_locally(): # wait for the repo detection and requirements update task._wait_for_repo_detection() # reset requirements task._update_requirements(None)🙂

MysteriousBee56 yes, please change the trains code!!! Wee pee, if you think someone else can benefit, feel free to PR :)
Regrading the double entry, that seems like an odd bug, how can I reproduce it?

@<1523707653782507520:profile|MelancholyElk85> what are you trying to change ? maybe there is a better way?
BTW: if you do step_base_task.export_task() you can use the parts that you need in the dict and pass them to:
task_overrides argument in add_step (you might need to flatten the nested arguments with '.' , and thinking about it, maybe we should do that automatically?!)

But I do not have anything linked correctly since I rely in conda installing cuda/cudnn for me

From the log it installed:
cudatoolkit==11.1.1
based on the CUDA it found on the host machine: agent.cuda_version = 110
But for some reason it installed the pytorch from the conda "pytorch" repo without the cuda support.

So if I pass a function that pulls the most recent version of a Task, it'll grab the most recent version every time it's scheduled?

Basically you function will be called, that's it.
What I'm assuming is that you would want that function to find the latest Task (i.e. query based & filter based on project/name/tag etc), clone the selected Task and Enqueue it,
is that correct?

JitteryCoyote63 are you suggesting it happens ?
(obviously it should not 🙂 )

Glad to hear 🙂

seems like pip 20.1.1 has the issue, but >= 22.2.2 do not.

Notice we changed the value there, it now has two versions, pne for python 3.10 < and one for python 3.10>=
The main reason is that pip changed their resolving algorithm, and the new one can break its own dependencies (i.e. pip freeze > requirements.txt -> pip install might not actually work)
None

:param list(str) xlabels: Labels per entry in each bucket in the histogram (vector), creating a set of labels for each histogram bar on the x-axis. (Optional)

You might only see it when the upload is done

CooperativeFox72 this is indeed sad news 😞
When you have the time, please see if you can send a code snippet to reproduce the issue. I'd like to have it fixed

ERROR: torch-1.12.0+cu102-cp38-cp38-linux_x86_64.whl is not a supported wheel on this platform
TartBear70 could it be you are running on a new Mac M1/2 ?

Also quick question, any chance you can test with the latest RC?
pip3 install clearml-agent==1.3.1rc6

Hi GracefulDog98

Any guess why the password is "incorrect" for me?

Basically the clearml-session CLI needs to be able to access (SSH) into the host (cleaml-agent) machine,
is that possible?

It should move you directly into the queue pages.
Let me double check (working on the community server)

then will have to rerun the pipeline code then manually get the id and update the task.

Makes total sense to me!

Failed auto-generating package requirements: _PyErr_SetObject: exception SystemExit() is not a BaseException subclass

Not sure why you are getting this one?!

ValueError: No projects found when searching for MyProject/.pipelines/PipelineName

hmm, what are you getting with:

task = Task.get_task(pipeline_uid_here)
print(task.get_project_name())