Is there a way to do this all elegantly?

Of yes there is, this is how TaskB code will look:

` task = Task.init(..., 'task b')
param = {'TaskA' :'TaskAs ID HERE'}
task.connect(param)
taska_model = Task.get_task(param['TaskA']).models['output''][-1]
torch.load(taska_model.get_local_copy())

train

torch.save('modelb') `I might have missed something there, but generally speaking this will let you:
Select TASKA as a parameter of TaskB training process Will register automagically Tasks'A...

how did you install trains?
pip install git+

now, I need to pass a variable to the Preprocess class

you mean for the construction ?

integer

Still, My problem is calling

pipe.start()

crashes.

is supposed to kill the process
2022-08-19 09:17:56,626 - clearml - WARNING - Terminating local execution processThis is what it writes before killing the local process.
` /opt/homebrew/anaconda3/envs/py39/lib/python3.9/multiprocessing/resource_tracker.py:216: UserWarning: resource_tracker: There appear to be 16 leaked semaphore objects to clean up at shutdown
warnings.warn('resource_tracker: There appear to be ...

Woot woot, will do!

Hi JitteryRaven85

I have also deleted some hyper-params but they appear again when training starts.

Yes you cannot "delete" parameters, as any missing parameter is synced back (making sure you have a full log).

The problem is that when I clone an experiment and change the hyper params some change and some remain the same

Could you expand on which parameters stay the same ? (obviously this should not happen)

What was the url you ended up with ?

Hi EnviousPanda91
You mean like collect plots, then generate a pdf?

Hi @<1564422644407734272:profile|DistressedCoyote60>
I'm a bit confused, are you registering the wheels with https:// link or with file:/// links (i mean in your code) ?

Hi CheekyAnt38

However now I would like to evaluate directly my machine learning model via api requests, directly over clearml. It’s possible?

This basically means serving the model, is this what you mean?

Any chance @<1578918150261444608:profile|RoundJellyfish71> you can open a GitHub issue so that we can track it? (I think this is indeed a good idea)

Yey!

@<1535793988726951936:profile|YummyElephant76> oh you mean like jupyter server was running, then inside the notebook you would start a new venv, in that venv "notebook" package was missing, hence it failed detecting the notebook ?

Hi GrotesqueOctopus42 ,

BTW: is it better to post the long error message on a reply to avoid polluting the channel?

Yes, that is appreciated 🙂
Basically logs in the thread of the initial message.

To fix this a had to spin the agent using --cpu-only flag (--docker --cpu-only)

Yes if you do not specify --cpu-only it will default to trying to access gpus
Nice!

Hi @<1545216070686609408:profile|EnthusiasticCow4>
hmm this seems odd, and definitely looks like a bug, please report on GH 🙏

Or you can do:
param={'key': 123}
task.connect(param)

Sure thing, thanks FlutteringWorm14 !

EnviousPanda91 this seems like a specific issue with the clearml-task cli, could that be ?
Can you send a full clearml-task command-line to test ?

TrickySheep9 make sense?

Hi GrotesqueDog77
What do you mean by share resources? Do you mean compute or storage?

. I can't find any actual model files on the server though.

What do you mean? Do you see the specific models in the web UI? is the link valid ?

Hi CourageousDove78
Not the cleanest, but you can basically pass everything here:
https://allegro.ai/clearml/docs/rst/references/clearml_api_ref/index.html#post--tasks.get_all
Reasoning is that it is passed almost as is to the server for the actual query.

Can you also make sure you did not check "Disable local nachine git detection" in the clearml PyCharm plugin?

although ideally i'd like to tell it exactly where to unzip it.

Ohh you can use .get_local_mutable_copy()
It will unzip it to specific folder

Working on it as we speak 🙂 probably a day worst case 2. This is quite strange and we are not sure where is the fault, as nothing in the code itself changed...

Hi FlatOctopus65
You are almost there
prev_task: Task = Task.get_task(task_id=<prev_task_id_here>) model = prev_task.models['output'][-1] my_check_point = model.get_local_copy()

Hi TenderCoyote78

I'm trying to clearml-agent in my dockerfile,

I'm not sure I'm following, Are you traying to create a docker container containing the agent inside? for what purpose ?
(notice that the agent can spin any off the shelf container, there is no need to add the agent into the container it will take of itself when it is running it)

Specifically to your docker file:

RUN curl -sSL

| sh

No need for this line

COPY clearml.conf ~/clearml.conf

Try the ab...

SubstantialElk6 This seems to be the issue
cp: failed to access '/root/default_clearml.conf': Permission denied clearml_agent: ERROR: Could not find task id=024a421c0e174650a1c7ff64af756c26 (for host: )Notice it seems it just cannot read the clearml.conf , wdyt?

YEY