I think so (you can also comment out the Task.init() just to verify this is not a clearml issue)

Seems like passing the Task object is not working as expected (I'll make sure it is fixed).
Try:
dataset._task.set_parent(Task.current_task().id)

The other order (with custom decorator above pipeline fails - just for you info

)

This is on "purpose" the pipeline decorator has to be the top decorator.
Glad it works!

Hi @<1523702868694011904:profile|AbruptCow41>
Check what are you getting when running git status inside the working directory, this is essentially how it works. Are you expecting to later run it with an agent?

Should work in all cases, plotly/matplotlib/scalar_rerport

Exactly, that’s my problem: I want to remove it to make sure it is reinstalled (because the version can change)

JitteryCoyote63 yes, this is definitely a pip bug... can you test with the latest pip version, maybe it was fixed? (i.e. git+https:// link)

Hi CharmingBeetle38
On the base task, do you see those arguments under the Configuration tab?
Also, if they are under Args section, you should add "Args/" prefix to the HP optimization (this is how you differentiate between the sections)

MelancholyChicken65 found it ! thank you for finding this issue.
I'm hoping to get an update soon 🙂

Hi TrickyFox41

Hey since Hydra does not work with

clearml-task

I should shouldn't it? what does not work ?

The other way will not work, as if you start with "pip" you cannot fail ... (if you fail it's in run time which is too late)

Hmmm maybe 

 I thought that was expected behavior from poetry side actually

I think this is the expected behavior, hence bug?!

Alternatively I understand I can also run the agent using...

No you should not if you are running the agent inside a container it cannot work in docker mode and spin its own containers
Bottom line use clearml-agent daemon

Hi SuperiorDucks36

you have such a great and clear GUI

😊

I personally would love to do it with a CLI

Actually a lot of stuff are harder to get from UI (like current state of your local repository etc.) But I think your point stands 🙂 We will start with CLI, because it is faster to deploy/iterate, then when you guys say this is a winner we will have a wizard in the UI.
What do you think?

Each user creates a 

.env

 file for their needs or exports them in the shell running the python code. Currently I copy the environment variables to an S3 bucket and download it from there

That is a great hack, but who carries the credentials for the S3 bucket? the reason for asking is I;m thinking maybe the code would directly do that (meaning download the .env file and apply them?!)

what does mount -a return ?

Is it also possible to specify different user/api_token for different hosts? For example I have a github and a private gitlab that I both want to be able to access.

ReassuredTiger98 my apologies I just realize you can use ~/.git-credentials for that. The agent will automatically map the host .git-credentials into the docker :)

Can I send you a wheel to test ?

Does this file look familiar to you?
file not found: archive/constants.pkl

Yes exactly like a Task (pipeline is a type of task)
'''
clonedpipeline=Task.clone(pipeline_uid_here)
Task.enqueue(...)
'''

For running the pipeline remotely I want the path to be like /Users/adityachaudhry/.clearml/cache/......

I'm not sure I follow, if you are getting a path with all your folders from get_local_copy , that's exactly what you are looking for, no?

@<1523705099182936064:profile|GrievingDeer61> if you see them in your Tensorboard they will be captured in ClearML as well.
wdyt?

EDIT
examples (tensorboard or tensorboardX):
None
None

Hi MistakenDragonfly51

Hello everyone! First, thanks a lot to everyone that made ClearML possible,

❤
To your questions 🙂
long story short, no unless you really want to compile the dockers, which I can't see the real upside here Yes, add the following /opt/clearml.conf:/root/clearml.conf herehttps://github.com/allegroai/clearml-server/blob/5de7c120621c2831730e01a864cc892c1702099a/docker/docker-compose.yml#L154
and configure your hosts " /opt/clearml.conf" with ...

Thanks!

Hi @<1610083503607648256:profile|DiminutiveToad80>
You mean the pipeline logic? It should autodetect the imports of the logic function (like any Task.init call)
You can however call Task.force_requirements_env_freeze and pass a local requiremenst.txt
Make sure to call it before create the Pipeline object
None

Thank you!

Is there any progress made on the clearml-serving repo?

Hi JitteryCoyote63
yes, things are progressing slower than expected, I'm expecting actual work will be pushed in early Jan. On the bright side we are trying to work closely with TorchServing team and Nvidia Triton to expand capabilities.
Currently it seems the setup will be "proxy server container" for per-post processing, then serving engine container (Triton/Torch), with monitoring container as control plan (i.e. collecting s...

Hi @<1846360404628869120:profile|HelpfulBadger74>
Is pixi a drop in replacement for pip? is it like UV?

then when we triggered a inference deploy it failed

How would you control it? Is it based on a Task ? like a property "match python version" ?

Any reason not to do so in the conf file ?

Usually in the /tmp folder under a temp filename (it is generated automatically when spinned)
In case of the services, this will be inside the docker itself