Hmm what do you mean? Isn't it under installed packages?

https://stackoverflow.com/questions/5419/python-unicode-and-the-windows-console
Hmm try to set this one before spinning the agent
Windows
set PYTHONIOENCODING=:replaceInside Colab
os.environ["PYTHONIOENCODING"] = ":replace"

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

SmarmySeaurchin8
When running in "dev" mode (i.e. writing the code) only packages imported directly are registered under "installed packages" , then when the agent is executing the experiment, it will update back the entire environment (including derivative packages etc.)
That said you can set detect_with_pip_freeze to true (in trains.conf) and it will basically store the entire pip freeze.
https://github.com/allegroai/trains/blob/f8ba0495fb3af1f99732fdffbbccd2fa992934a4/docs/trains.c...

sure thing 🙂

Yes, that sounds like the issue, is the file actually there ?

Which clearml version are you using ?

Hi GiganticTurtle0

The problem is that the packages that I define in 'required_packages' are not in the scripts corresponding

What do you mean by that? is "Xarray" a wheel package? is it instllable from a git repo (example: pip install git+ http://github.com/user/xarray/axrray.git )

is everything on the same network?

I can't seem to figure out what the names should be from the pytorch example - where did INPUT__0 come from

This is actually the latyer name in the model:
https://github.com/allegroai/clearml-serving/blob/4b52103636bc7430d4a6666ee85fd126fcb49e2e/examples/pytorch/train_pytorch_mnist.py#L24
Which is just the default name Pytorch gives the layer
https://discuss.pytorch.org/t/how-to-get-layer-names-in-a-network/134238

it appears I need to converted into TorchScript?

Yes, this ...

Thanks MinuteGiraffe30 , fix will be pushed later today

Hi @<1533982060639686656:profile|AdorableSeaurchin58>
Notice the scalars and console are stored on the elasticsearch DB, this is usually under
/opt/clearml/data/elastic_7

I suppose the same would need to be done for any 

client 

PC running 

clearml

 such that you are submitting dataset upload jobs?

Correct

That is, the dataset is perhaps local to my laptop, or on a development VM that is not in the 

clearml

 system, but I from there I want to submit a copy of a dataset, then I would need to configure the storage section in the same way as well?

Correct

Sure thing, anyhow we will fix this bug so next version there is no need for a workaround (but the workaround will still hold so you won't need to change anything)

Hmm how do you launch the autoscaler, code?

task.mark_completed()

You have that at the bottom of the script, never call it on yourself, it will kill the actual process.
So what is going on you are marking your own process for termination, then it terminates itself leaving the interpreter and this is the reason for the errors you are seeing

The idea of mark_* is to mark an external Task, forcefully.
By just completing your process with exit code (0) (i.e. no error) the Task will be marked as completed anyhow, no need to call...

LethalCentipede31 sure:
task.upload_artifact(object_or_file, name)https://github.com/allegroai/clearml/blob/master/examples/reporting/artifacts.py

Hi FantasticPig28

or does every individual user have to configure their own minio credentials?

You can configure the clients files entry in the clearml.conf (or use an OS environment)
files_server: " "https://github.com/allegroai/clearml/blob/12fa7c92aaf8770d770c8ed05094e924b9099c16/docs/clearml.conf#L10
Notice to make sure you also provide credentials here:
https://github.com/allegroai/clearml/blob/12fa7c92aaf8770d770c8ed05094e924b9099c16/docs/clearml.conf#L97

the error for uploading is weird

wait, are you still getting this error?

So you want these two on two different graphs ?

Let me know if you managed to get it working, then we can see if we can detect it automatically.

and I have no way to save those as clearml artifacts

You could do (at the end of the code
task.upload_artifact('profiler', Path('./fil-result/'))wdyt?

but this will be invoked before fil-profiler starts generating them

I thought it will flush in the background 😞
You can however configure the profiler to a specific folder, then mount the folder to the host machine:
In the "base docker args" section add -v /host/folder/for/profiler:/inside/container/profile

it certainly does not use tensorboard python lib

Hmm, yes I assume this is why the automagic is not working 😞

Does it have a pythonic interface form the metrics ?

Actually unless you specifically detached the matplotlib automagic, any plt.show() will be automatically reported.

Hi EmbarrassedSpider34
Long story (see below) short, yes you can ignore this warning :)

Specifically, torch is spinning processes and killing them, every process will have a reference to the parent semaphore (for internal clearml bookkeeping), now python is not very good with this kind of thing (and it is getting better on newer python verions), bottom line python "think" someone lost a semaphore, but there reality is that subprocess never created it in the first place. Does that make sen...

(Venv mode makes sense if running inside a container, if you need docker support you will need to mount the docker socket inside)
What is exactly the error you re getting from clearml? And what do you have in the configuration file?

My pleasure

'relaunch_on_instance_failure'

This argument is Not part of the Pipeline any longer, are you running the latest clearml python version?

On my to do list, but will have to wait for later this week (feel free to ping on this thread to remind me).
Regrading the issue at hand, let me check the requirements it is using.