Hi @<1523702786867335168:profile|AdventurousButterfly15>
I do not think they log more than that ?!
(what happens if you use TB?)

RobustSnake79 I have not tested, but I suspect that currently all the reports will stay in TB and not passed automagically into ClearML
It seems like something you would actually want to do with TB (i.e. drill into the graphs etc.) no?

Hmm can you run:
docker run -it allegroai/clearml-agent-services:latest

and the clearml server version ?

, is the team open to PRs from external people?

Yes please do! PRs are welcomed! I thought we fixed the GitHub readme to reflect it, anyhow I'll make sure we do 🙂

suspect permissions, but not entirely sure what and where

Seems like it.
Check the config file on the agent machine
https://github.com/allegroai/clearml-agent/blob/822984301889327ae1a703ffdc56470ad006a951/docs/clearml.conf#L18
https://github.com/allegroai/clearml-agent/blob/822984301889327ae1a703ffdc56470ad006a951/docs/clearml.conf#L19

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

https://techbeacon.com/enterprise-it/5-ways-lose-data-kubernetes-how-avoid-them
see (1) and (4)

I think I'm missing the connection between the hash-ids and the txt file, or in other words why is the txt file containing full path not relative path

Oh, so the pipeline basically makes itself their parent, this means you can get their IDs:
steps_ids = Task.query_tasks(task_filter=dict(parent=<pipeline_id_here)) for task_id in steps_ids: task = Task.get_task(task_id)

SarcasticSquirrel56

if I configure manually the pods for the different nodes, how do I make clearml server aware that those agents exist?

Basically the agent register themselves on your cleaml-server, and they register on which Queue(s) they listen to. In other words the interface to choose the different types of machines/gpus is by enqueue the Task to different queues.
For example: Queue(1): "CUDA11_GPUx1" , Queue(2): "CUDA10_GPUx1"
Make sense ?

EDIT:

I guess to achieve what I w...

Let me ping you back when the GitHub repo is synced, so you can test the latest and greatest :)

No worries, and I hope you manage to get that backup.

Yey!
My pleasure 🙂

Hi @<1634001100262608896:profile|LazyAlligator31>

Is this because the code repo is being recreated in this directory?

Yes this is correct 🙂
Basically the entire code base + venv is installed there, to make sure it does not intyerfere with the "system" preinstalled environment
(it also allows for caching on the host machine 🙂 )

Hi DangerousDragonfly8

, is it possible to somehow extract the information about the experiment/task of which status has changed?

From the docstring of add_task_trigger
```py def schedule_function(task_id): pass ```This means you are getting the Task ID that caused the trigger, now you can get all the info that you need with Task.get_task(task_id)
` def schedule_function(task_id):
the_task = Task.get_task(task_id)
# now we have all the info on the Task tha...

Hi GiganticTurtle0
Sure, OutputModel can be manually connected:
model = OutputModel(task=Task.current_task()) model.update_weights(weights_filename='localfile.pkl')

HurtWoodpecker30 in order to have the venv cache activated, it uses the full "pip freeze" it stores on the "installed packages", this means that when you cloned a Task that was already executed, you will see it is using the cached venv.
(BTW: the packages themselves are cached locally, meaning no time is spent on downloading just on installing, but this is also time consuming, hence the full venv cache feature).
Make sense ?

I think I found something, let me see if I can reproduce it

Ohh RotundHedgehog76 this implies a single jupyter hub with multiple uses, is that correct ?
(if this is the case, then yes, clearml-session is definitely not the correct solution, I would look for a helm chart for jupyter hub)

Could you try:

task.reset()

Hi @<1546665638829756416:profile|LovelyChimpanzee39>

anyone know what params I need to pass in order to enable it?

we feel you there 😞 this is an actual plotly feature that we really want to add, but kind of out of our hands: None
feel free to support as there 🤞

Using the dataset.create command and the subsequent add_files, and upload commands I can see the upload action as an experiment but the data is not seen in the Datasets webpage.

ScantCrab97 it might be that you need the latest clearml package installed on the client end (as well as the new server with the UI)
What is your clearml package version ?

Oh like sync the "x-axis" between graphs?

ThickDove42 Windows conda python3.6 was exactly what I was using,
started the jupyter with:
"python -m jupyter notebook"
Then opened / created a new notebook, everything worked.
Tested on latest clearml 0.17.2
Maybe it's something with the path to the repo that breaks it? Because obviously the issue is it is looking in the wrong folder.

Would be cool to let it get untracked as well, especially if we want to as an option

How would you decide what should be tracked?

You can make reports on experiments with interactive graphs

Yes, I can totally see how this is a selling point. The closest is the Project Overview (full markdown capabilities, with the ability to embed links to specific experiments). You can also add a "leader metric", so you can track the project performance/progress over time.
I have to admit that creating a better reporting tool is always pushed down in priority as I think this is a good selling point to management but the actual ...

I think I understand what the issue is, you have installed the agent on your python 3.8, but it is running and trying to install on python 3.10
To verify,
pip uninstall clearml-agent
python3.10 -m pip install clearml-agent
python3.10 -m clearml-agent daemon...

I think that is good enough

Hi @<1571308003204796416:profile|HollowPeacock58>
I'm assuming this is the arm support (i,e, you are running on new mac) fix we released in one one of the last clearml-agent versions. could you update to the latest clearml-agent?

pip3 install clearml-agent==1.6.0rc2