make sense ?

I'm sorry my bad, this is use_current_task
https://github.com/allegroai/clearml/blob/6d09ff15187197e1f574902352115aa08dc1c28a/clearml/datasets/dataset.py#L663
task = Task.init(...) dataset = Dataset.create(..., use_current_task=True) dataset.add_files(...)

ReassuredTiger98
Okay, but you should have had the prints ...
uploading artifactand
done uploading artifactSo I suspect something is going on with the agent.
Did you manage to run any experiment on this agent ?

EDIT: Can you try with artifacts example we have on the repo:
https://github.com/allegroai/clearml/blob/master/examples/reporting/artifacts.py

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 SoreDragonfly16
Sadly no, the idea is to create full visibility to all users in the system (basically saying share everything with your colleagues) .
That said, I know the enterprise version have permission / security features, I'm sure it covers this scenario as well.

GentleSwallow91 notice this part:
Hi Martin. Sorry - missed your reply.
Yeap I am aware that docker_internal_mounts is inside agent section.
'-v', '/tmp/ssh-XXXXXXnfYTo5/agent.8946:/tmp/ssh-XXXXXXnfYTo5/agent.8946', '-e', 'SSH_AUTH_SOCK=/tmp/ssh-XXXXXXnfYTo5/agent.8946',

It is creating a copy of the ssh folder and setting the SSH_AUTH_SOCK env to it. You can just map the entire ssh folder automatically by un-setting SSH_AUTH_SOCK before running the agent.
SSH_AUTH_SOCK= clearml-agent ...

This is the prerequisites of the docker service installed on the host machine (where the agent is running)
Basically follow: https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html
https://docs.docker.com/compose/gpu-support/

RoughTiger69 how did you end up with a Task with just "origin" in the repo field ?

Which one of those? the 3d ball dots or the 3d face mesh?

WorriedParrot51 trains should support subparsers etc.
Even if your code calls the parsing before trains.
The only thing you need is to import the package when argparser is called (not to initialize it, that can happen later)
It should (hopefully) solve the issue.

Hi @<1657918706052763648:profile|SillyRobin38>

I have included some print statements

you should see those under the Task of the inference instance.
You can also do:

import clearml
...
def preprocess(...):
  clearml.Logger.current_logger().report_text(...)
  clearml.Logger.current_logger().report_scalar(...)

, specifically within the containers where the inferencing occurs.

it might be that fastapi is capturing the prints...
[None](https://github.com/tiangolo/uvicor...

I understand, but then the toml file needs to be parsed to ensure poetry is used. It's just a tool entry in the pyproject.toml.

Probably too much for the agent... and specifically it seems poetry actually managed to parse it?! what are you getting in the log?

2023-02-15 12:49:22,813 - clearml - WARNING - Could not retrieve remote configuration named 'SSH'

This is fine, it means it uses the default identity keys

The thing is - when I try to connect with normal SSH there are no issues

Now I'm lost, so when exactly do you see the issue ?

Only the dictionary keys are returned as the raw nested dictionary, but the values remain casted.

Using which function ? task.get_parameters_as_dict does not cast the values (the values themselves are stored as strings on the backend), only task.connect will cast the values automatically

clearml doesn't change the matplotlib backend under the hood, right? Just making sure

if the agent is running it, of course it does 🙂 otherwise where is the automagic, it would break the moment you run it on a remote machine

Yep 🙂

what's the clearml package version and clearml-session version ?

MelancholyBeetle72 there is an RC with a fix, check the GitHub issue for details :)

Then check in the clearml.conf under files_server
And use what you have there (for example http://localhost:8081 )

What do you have under Hydra section? and OmegaConf section

Woot Woot 🎊

I just cloned it from the examples that are available in the SaaS console upon account creation

Ohhh! that would explain it. Maybe it is broken there?! let me check a second

Hi AgitatedTurtle16 could you verify you can access the API server with curl?

RobustGoldfish9
I think you need to set the trains-agent docker to be aware of the host, so it knows how to mount data/cache/configurations into the sibling docker

It should look something like:
TRAINS_AGENT_DOCKER_HOST_MOUNT="/mnt/host/data:/root/.trains"So if running a docker:
docker run -e TRAINS_AGENT_DOCKER_HOST_MOUNT="/mnt/host/data:/root/.trains" ...

Thanks, new doc site is scheduled for next week, it will also be on github, so pr-ing fixes will be a breeze :)

ClumsyElephant70 yes there is 🙂
clearml-agent build --id <task id> --target <folder>(I might have a typo there, but you can basically check the full help clearml-agent build --help )

DepressedChimpanzee34
so parsing bask is done via a yaml reader:
https://github.com/allegroai/clearml/blob/49fcbd7bbf3236f4175cdff29fa951847b0923cc/clearml/backend_interface/task/args.py#L506
We could add extra test here, checking for \ in the string, that should solve it and will be backwards compatible (I think)
https://github.com/allegroai/clearml/blob/49fcbd7bbf3236f4175cdff29fa951847b0923cc/clearml/backend_interface/task/task.py#L935

Okay verified, it's the 'Agg' backend

Thanks IntriguedRat44 !
I'll follow up on GitHub 🙂

I guess it won’t due to the nature of services?

Correct, k8s glue works differently, that said I would actually use the helm to spin a pod woth the agent in services mode and venv mode.