Hi @<1536881167746207744:profile|EnormousGoose35>

, Could we just share the entire project instead of Workspace ?

You mean allow access to a project between workspaces ?
If the answer is yes, then unfortunatly the SaaS version (app.clear.ml) does not really support these level of RBAC, this is part of the enterprise version, which assumes a large organization with the need for that kind of access limit.
What is the use case ? Why not just share the entire workspace ?

What's up @<1523708288686886912:profile|DeliciousBluewhale87>

Woot woot!

See the last package in the package list:

• wget~=3.2
• trains~=0.14.1
• pybullet~=2.6.5
• gym-cartpole-swingup~=0.0.4
• //github.com/ajliu/pytorch_baselines

If you want to quickly test it:
pip install clearml-agent
Then assuming Task id is aabbcc
Run
clearml-agent execute --id aabbccYou should be able to trace if the package was installed

Hi @<1614069770586427392:profile|FlutteringFrog26>
So since you have the Task id. you do:

task = Task.get_task("task id here")

Then to get the models

models = task.models["output]

the models is a list And a dict, if you want the lats one you do last_model = models[-1] if you know the best model name you do model = models["best model"] (notice the model name is the exact one you see in the UI. Once you have the model object you can get a copy with `model.get_lo...

I still can't get it to work... I couldn't figure out how can I change the clearml version in the runtime of the Cleanup Service as I'm not in control of the agent that executes it

Let's take a step back. Let's remove the clearml-services from the docker compose for a second, and run it manually (then you can control everything). Once you have it running manually, let's try to replicate the setup back to the docker compose, make sense ?

RobustRat47 are you saying updating the nvidia drivers solved the issue ?

BTW: if you only need the git diff you can just copy them from the UI into a txt file and do:
git apply <copied-diff.txt>

They inherit from one another, so it does make sense. Also the add_tags is on the "main" Task and not the backend parent

DilapidatedDucks58 I think they are used here:
https://github.com/allegroai/clearml/blob/3d3a835435cc2f01ff19fe0a58a8d7db10fd2de2/clearml/storage/helper.py#L1407

https://boto3.amazonaws.com/v1/documentation/api/latest/reference/core/session.html#boto3.session.Session.resource

Thank you! 😊

Okay, I think I lost you...
DilapidatedDucks58 you mean detect at which "iteration" the max value was reported, and then extract all the other metrics for that iteration ?

let me check

report_text does not, this is very weird

Okay this seems to be the issue.
Just making sure the Task status is "running" and task.get_logger().report_text("something") does not report a thing ?
Do you see it on your screen?
Can you test without the "Task.debug_simulate_remote_task / init" ?

I don't know whether you have access to the backend,

Creepy , no I do not 🙂

I can't make anything appear in the console part of the ui

clearml_task.logger.report_text("some text") should work

The main question I have is why is the ALB not passing the request, I think you are correct it never reaches the serving server at all, which leads me to think the ALB is "thinking" the service is down or is not responding, wdyt?

What do you mean by a custom queue ?
In the queues page you have a plus button, this will just create a new queue

Thanks!

I want the model to be stored in a way that clearml-serving can recognise it as a model

Then OutputModel or task.update_output_model(...)
You have to serialize it, in a way that later your code will be able to load it.
With XGBoost, when you do model.save clearml automatically picks and uploads it for you
assuming you created the Task.init(..., output_uri=True)
You can also manually upload the model with task.update_output_model or equivalent with OutputModel class.
if you want to dis...

I would do something like:

` from clearml import Logger

def forward(...):
    self.iteration += 1 
    weights = self.compute_weights(...)
    m = (weights * (target-preds)).mean()
    Logger.current_logger().report_scalar(title="debug", series="mean_weight", value=m, iteration=self.iteration)
    return m `

export CLEARML_DEFAULT_OUTPUT_URI="https://...."Make sense ?

Not at all, we love ideas on improving ClearML.
I do not think there is a need to replace feast, it seems to do a lot, I'm just thinking on integrating it into the ClearML workflow. Do you have a specific use case we can start to work on? Or maybe a workflow that would make sense to implment?

trains-agent RC (which they tell me will be out tomorrow) will have a switch to do that, just so it is easier 🙂

DilapidatedDucks58
did you check:
https://github.com/allegroai/clearml/blob/master/examples/services/cleanup/cleanup_service.py

Where are you seeing this message?

CooperativeFox72 you can you start by checking the latest RC :)
pip install trains==0.15.2rc0

with conda ?!

and run it locally...

You mean you run the clearml-agent locally ?

Check the log to see exactly where it downloaded the torch from. Just making sure it used the right repository and did not default to the pip, where it might have gotten a CPU version...