Manually I was installing the

leap

package through

python -m pip install .

when building the docker container.

NaughtyFish36 what happnes if you add to your "installed packages" /opt/keras-hannd ? This should translate to "pip install /opt/keras-hannd" which seems like exactly what you want, no ?

Long story short, work in progress.
BTW: are you referring to manual execution or trains-agent ?

RC should be out later today (I hope), this will already be there, I'll ping here when it is out

What do you have under the "installed packages" section? Also you can configure the agent to use poetry to restore the environment (instead of pip)

it would be nice to group experiments within projects

DilapidatedDucks58 you mean is collapse/expand ? or in something like "sub-project" ?

So sharing with the agent is also not possible.

But they can see each others experiments, so why wouldn't the agent be able to have a read-only access ?
BTW:
ReassuredTiger98 you can put your user/pass into the git URL link, but I'm not sure this will solve the privacy issue 😉

😊

Hi UpsetTurkey67

"General/my_parameter_name" so that only this part of the configuration will be updated?

I'm assuming this is a Hyperparameter not a configuration object (i.e. task.connect not task.connect_configuration), if this is the case then Yes 🙂

Hi @<1687653458951278592:profile|StrangeStork48>
I have good news, v1.0 is out with hashed passwords support.

Can clearml-serving does helm install or upgrade?

Not sure I follow, how would a helm chart install be part of the ml running ? I mean clearml-serving is installed via helm chart, but this is a "one time" i.e. you install the clearm-serving and then you can via CLI / python send models to be served there, this is not a "deployed per model" scenario, but a deployment for multiple models, dynamically loaded

Hmm, might be, check if your files server is running and configured properly

Hi FierceFly22
You called execute_remotely a bit too soon. If you have any manual configuration, they have to be called before, so they are stored in the Task. This includes task.connect and task.connct_configuration.

Follow up: I see that if I move an Experiment to a new project, it does not copy the associated model files and must be done manually. Once I moved the models to the new project, the query works as expected.

Correct 🙂
Nice catch!

pip install clearml==1.0.4rc1

I think it would be nicer if the CLI had a subcommand to show the content of 

~/.clearml_data.json

 .

Actually, it only stores the last dataset id at the moment, no not much 🙂
But maybe we should have a cmd line that just outputs the current datasetid, this means it will be easier to grab and pipe
WDYT?

Hi RipeGoose2
Are you continuing the Task, i.e. passing Task.init(..., continue_last_task=True)

CooperativeFox72 a bit of info on how it works:
In "manual" execution (i.e. without an agent)

path = task.connect_configuration(local_path, name=name

path = local_path , and the content of local_path is stored on the Task

In "remote" execution (i.e. agent)

path = task.connect_configuration(local_path, name=name

"local_path" is ignored, path is a temp file, and the content of the temp file is the content that is stored (or edited) on the Task configuration.
Make sense ?

oh dear ...
ScrawnyLion96 let me check with front-end guys 😞

Okay now let's try the final lines:
$LOCAL_PYTHON -m virtualenv /root/venv /root/venv/bin/python3 -m pip install git+

AttractiveCockroach17 can I assume you are working with the hydra local launcher ?

AbruptWorm50 my apologies I think I mislead you you, yes you can pass geenric arguments to the optimizer class, but specifically for optuna, this is disabled (not sure why)
Specifically to your case, the way it works is:
your code logs to tensorboard, clearml catches the data and moves it to the Task (on clearml-server), optuna optimization is running on another machine, trail valies are maanually updated (i.e. the clearml optimization pulls the Task reported metric from the server and updat...

I find it quite difficult to explain these ideas succinctly, did I make any sense to you?

Yep, I think we are totally on the same wavelength 🙂

However, it also seems to be not too prescriptive,

One last question, what do you mean by that?

Hi DilapidatedCow43
I'm assuming the returned object cannot be pickled (which is ClearML's way of serializing it)
You can upload it as a model with
` uploaded_model_url = Task.current_task().update_output_model(model_path="/path/to/local/model")

...
return uploaded_model_url `wdyt?

yes thanks , but if I do this, the packages will be installed for each step again, is it possible to use a single venv?

Notice that the venv is Cached on the clearml-agent host machine (if this is k8s glue, make sure to setup the Cache as a PV to achieve the same)
This means there is no need to worry about that and this is stable.
That said, if you have an existing VENV inside the container, just add docker_args="-e CLEARML_AGENT_SKIP_PIP_VENV_INSTALL =/path/to/bin/python"
Se...

Hi @<1541954607595393024:profile|BattyCrocodile47>
This looks like a docker issue running on mac m2
None
wdyt?

Thank you @<1523720500038078464:profile|MotionlessSeagull22> always great to hear 🙂
btw, if you feel like sharing your thoughts with us, consider filling our survey , it should not take more than 5min

Hi RipeGoose2
I just test the hydra example, seems to work when you add the offline right after the import:
` from clearml import Task

Task.set_offline(True) `

Ohhhh , okay as long as you know, they might fall on memory...

Hi ExcitedFish86
Good question, how do you "connect" the 3 nodes? (i.e. what the framework you are using)

👍