Depends on what you want to do, what do you want to do ?

I mean to use a function decorated with 

PipelineDecorator.pipeline

 inside another pipeline decorated in the same way.

Ohh... so would it make sense to add "helper_functions" so that a function will be available in the step's context ?
Or maybe we need a new to support "standalone" decorator?! Currently to actually "launch" the function step, you have to call it from the "pipeline" main logic function, but, at least in theory, one could do without the Pipeline itself.....

Hi DeliciousBluewhale87
This sounds like a great workflow to implement.
I guess my first question is how do you imagine the manager/director interacting with the system? What will they be shown, to allow them to approve/decline the model promotion ?

. I am not sure this is related to the fact the model is not correctly converted to TorchScript

Because Triton Only supports TorchScript (Not torch models) 🙂

👍 no worries

Oh then this should just work
cp -R --link b a/You can achieve the same symbol link link from python as well

It reflects what is stored by Keras, so if Keras stores the best model this is what you get. BTW if you pass output_uri=True it will automatically upload the models

But it should work out of the box ...

Yes it should ....

The user and personal access token are used as is and it propagates down to submodules, since those are simply another git repository.

Can you manually successfully run:
git clone --recursive https://user:token@github.com/company/repo_with_submodules

Exactly 🙂
If you feel like PR-ing a fix, it will be greatly appreciated 🙂

I hope you can do this without containers.

I think you should be fine, the only caveat is CUDA drivers, nothing we can do about that ...

I'll give it a shot. Honestly, the SDK documentation for both InputModel and OutputModel is (sorry)

horrible

...

I have to agree, we are changing this interface, I do not think it is good 😞

basically the idea is you do not need to configure the Experiment manually, it is created when you actually develop the code / run/debug it, or you have the CLI taking everything from your machine and populating it

GaudyPig83
I think there is some mismatch between the code creating the pipeline and the actual Task?! Could that somehow be the case? "relaunch_on_instance_failure" is a missing argument somehow

can you try to launch the entire Pipeline with the latest RC ?
pip3 install clearml==1.7.3rc0

I want is to manually provide a name to each series equal to the subject name (Subject 1, Subject 2, etc.)

They appear as they are reported to TB. I think this is a PyTorchLightning thing... If you look as the TB produced, you will get the same naming schemes, no?!

DefeatedOstrich93 can you verify lightning actually only stored once ?

Hi WickedBee96

How can I do that?

clearml-task
https://clear.ml/docs/latest/docs/apps/clearml_task#what-is-clearml-task-for

I know this way to run it in the agent only by enqueue the draft after running it on my local machine so is there another way?

Or maybe are you looking for task.execute_remotely
https://clear.ml/docs/latest/docs/references/sdk/task#execute_remotely

I looked at your task log on the github issue. It seems the main issue is that your notebook is Not stored as python code. Are you running it on jupyter notebook or is it ipython that you are runnig it on? Is this reproducible? If so what's the jupyter version, python and OS versions?

Hi @<1523702932069945344:profile|CheerfulGorilla72>

Please tell me what RAM metric is tracked by ClearML?

Free RAM is the entire machine free RAM
Yeah htop shows odd numbers as it doesn't "count" allocated buffers
specifically you can see the code here:
None

I want to be able to compare scalars of more than 10 experiments, otherwise there is no strong need yet

Make sense, in the next version, not the one that will be released next week, the one after with reports (shhh do tell anyone 🙂 ) , they tell me this is solved 🎊

IdealPanda97 Hmm I see...
Well, unfortunately, Trains is all about free access to all 🙂
That said, the Enterprise edition does add permissions and data management on top of Trains. You can get in touch through the https://allegro.ai/enterprise/#contact , I'm sure someone will get back to you soon.

Hi DepressedFish57

In my case download each part takes ~5 second, and unzip ~15.

We run into that, and the new version will employ multithreading approach for the unzip (meaning the unzipping will happen in the background)

SuperiorDucks36 , is the domain name "rz-s-git" this does not seem like a valid domain?

EDIT:
Is it a local domain on your network?

Hi GrittyKangaroo27

How could I turn off model logging when running this training step?

This is a good point! I think we cannot pass these arguments.
Would this make sense to you?
PipelineDecorator.component(..., auto_connect_frameworks)
wdyt?

👍

Hi SmallDeer34
Is the Dataset in clearml-data ? If it is then Dataset.get().get_local_copy() will get you a cached local copy of the entire dataset.
If it is not, then you can use StorageManager.get_local_copy(url_here) to download the dataset.

2. Any Argparser is automatically logged (and later can be overridden from the UI). Specifically HfArgumentParser will be automatically logged https://github.com/huggingface/transformers/blob/e43e11260ff3c0a1b3cb0f4f39782d71a51c0191/examples/pytorc...

@<1689446563463565312:profile|SmallTurkey79> could you attach the full log of the Task?
also I would recommend "export CLEARML_AGENT_SKIP_PYTHON_ENV_INSTALL=1" (not true )
Usually binary env vars are 0/1
(I can see that the docs here: None
never mention it, I'll ask them to add that)

Task.current_task().connect(training_args, name='hugggingface args')And you should be able to change them when launching remotely 😉
SmallDeer34 btw: "set_parameters_as_dict" will replace all the arguments (and is one way) ...

If possible, can we have a "only one experiment can be given a single tag"

You mean "moving a tag" automatically (i.e. if someone else had the same tag it is removed from it)?

I'm sorry my bad, this is use_current_task
https://github.com/allegroai/clearml/blob/6d09ff15187197e1f574902352115aa08dc1c28a/clearml/datasets/dataset.py#L663
task = Task.init(...) dataset = Dataset.create(..., use_current_task=True) dataset.add_files(...)

Hi SubstantialElk6
you can do:
from clearml.config import config_obj config_obj.get('sdk')You will get the entire configuration tree of the SDK section (if you need sub sections, you can access them with '.' notation, e.h. sdk.storage )