EnviousStarfish54 thanks again for the reproducible code, it seems this is a Web UI bug, I'll keep you updated.

EnviousStarfish54 generally speaking the hyper parameters are flat key/value pairs. you can have as many sections as you like, but inside each section, key/value pairs. If you pass a nested dict, it will be stored as path/to/key:value (as you witnessed).
If you need to store a more complicated configuration dict (nesting, lists etc), use the connect_configuration, it will convert your dict to text (in HOCON format) and store that.
In both cases you can edit the configuration and then when ru...

👍

clearml should detect the "main" packages used in the repository (not just the main-script), the derivatives will be installed automatically by pip when the agent is installing the environment, once the agent is done setting the environment, it updates back the Task with the full list of packages including all required packages.

Hi John. sort of. It seems that archiving pipelines does not also archive the tasks that they contain so

This is correct, the rationale is that the components (i.e. Tasks) might be used (or already used) as cached steps ...

I believe that happens natively thanks to pyhocon? No idea why it fails on mac

That's the only explanation ...
But the weird thing is, it did not work on my linux box?!
Sounds good let's work on it after the weekend, 🙂

Could you test with the latest "cleaml"
pip install git+Task.add_requirement(".") should be supported now 🙂

Hi @<1618780810947596288:profile|ExuberantLion50>

I’m trying to containerize a task using clearml-agent build, following instructions from the docs online.

Do you mean to create a container with the Task's environment for debugging ?
If this is for running the Task there is no need to create a specific container for it, both code and python env are cached.

A quick fix will be:
` import dotenv
dotenv.load_dotenv('~/.env')
from clearml import Task # Now we can load it.
import argparse

if name == "main":
# do stuff `wdyt?

Hi @<1529633468214939648:profile|CostlyElephant1>
what seems to be the issue? I could not locate anything in the log

"Environment setup completed successfully
Starting Task Execution:"

Do you mean it takes a long time to setup the environment inside the container?

CLEARML_AGENT_SKIP_PIP_VENV_INSTALL and CLEARML_AGENT_SKIP_PYTHON_ENV_INSTALL,

It seems to be working, as you can see no virtual environment is created, the only thing that is installed is the cleartml-agent that i...

Try:
task.flush(wait_for_uploads=True)Should do the trick 🙂

IdealPanda97 hmmm interesting, what's exactly the scenario here?

Meanwhile check CreateFromFunction(object).create_task_from_function(...)
It might be better suited than execute remotely for your specific workflow 🙂

Any comments/ideas on how to make it better will be more than welcomed 🙂

Hmm can you try with additional configuration, next to "secure: true" in your clearml.conf, can you add "verify: false"

Yes I think the writer.add_figure somehow crops the image

Hi @<1523701260895653888:profile|QuaintJellyfish58>

Is there a way or a trigger to detect when the number of workers in a queue reaches zero?

You mean to spin them down? what's the rational ?

I’d like to implement a notification system that alerts me when there are no workers left in the queue.

How are they "dropping" ?

Specifically to your question, let me check I'm sure there is an API that get's that data becuase you can see it in the UI 🙂

checking it

😊

(the payload is not the correct form, can that be a problem?

It might, but I assume you will get a different error

LudicrousParrot69 there is already
Task.add_tags
https://github.com/allegroai/clearml/blob/2d561bf4b3598b61525511a1a5f72a9dba74953e/clearml/task.py#L964

What's the pip verison?

Hi RotundHedgehog76

we have issues with

clearml-agent

when using standalone mode. ...

What is the use case for standalone mode? is this venv or docker mode?

parser.add_argument( "--dataset_mean", type

=

float, nargs

=

"+", default

=

0.5)

I think providing nargs='+ ' assumes the type is a list. nonetheless we should be able to support it. Could you please add a GitHub issue so we do not forget ?

on the side note, is there any way to automatically give more meaningful names to the running docker containers?

What do you mean by that? running where? and where will you see them ?

I have to admit, I'm not sure...
Let me talk to backend guys, in theory you are correct the "initial secret" can be injected via the helm env var, but I'm not sure how that would work in this specific case

ConvolutedSealion94 Let me try to explain how it works, I hope this will help in debugging.
There are two different entities here
Clearml-server: In this context clearml server acts as a control-plane, it stores configuration on the different endpoints, models, preprocessign code etc. It does Not perform any compute or serving clearml-serving-inference https://github.com/allegroai/clearml-serving/blob/e09e6362147da84e042b3c615f167882a58b8ac7/docker/docker-compose-triton-gpu.yml#L77 . This ...

(no objection to add an argument but, I just wonder what's the value)

SarcasticSquirrel56 when the process dies (i.e. killed) it does not have time not update the state, then the server watchdog will set the state to aborted after X amount of time of inactivity (default is 2 hours)

when I run it on my laptop...

Then yes, you need to set the default_output_uri on Your laptop's clearml.conf (just like you set it on the k8s glue)
Make sense ?

This means that in your "Installed packages" you should see the line:
Notice that this is not a pypi artifactory (i.e. a server to add to the extra index url for pip), this is a direct pip install from a git repository, hence it should be listed in the "installed packages".
If this is the way the package was installed locally, you should have had this line in the installed packages.
The clearml agent should take care of the authentication for you (specifically here, it should do nothing).
If ...