Shout-out to Emilio for quickly stumbling on this rare bug and letting us know. If you have a feeling your process is stuck on exit, just upgrade to 1.0.1 😉

How can I reproduce it?

well from 2 to 30sec is a factor of 15, I think this is a good start 🙂

Hi FierceHamster54
Dataset is downloading multi threaded already
But yes get_local_copy() is thread / process safe

Hi ContemplativeGoat37

it a good idea to use ClearML Agent Services for such things?

Yes! it is exactly the kind of thing it was designed to do 🙂

Hmm, I think I need more to try and reproduce, what exactly did you do, what was the expected behavior vs reality ?

GiganticTurtle0 this one worked for me 🙂
` from clearml import Task
from clearml.automation.controller import PipelineDecorator

@PipelineDecorator.component(return_values=["msg"], execution_queue="myqueue1")
def step_1(msg: str):
msg += "\nI've survived step 1!"
return msg

@PipelineDecorator.component(return_values=["msg"], execution_queue="myqueue2")
def step_2(msg: str):
msg += "\nI've also survived step 2!"
return msg

@PipelineDecorator.component(return_values=["m...

Thanks Martin, so does it mean I won’t be able to see the data hosted on S3 bucket in ClearMl dashboard under datasets tab after registering it?

Sure you can, let's assume you have everything in your local /mnt/my/data you can just add this folder with add_files then upload to your S3 bucket with upload(output_uri=" None ",...)
make sense ?

(ignoring still having to fix the problem with 

LazyEvalWrapper

 return values).

fix will be pushed post weekend 🙂

such as displaying the step execution DaG in the PLOTS tab . (edited)

Wait, what are you getting on the DAG plot ? I think we "should" be able to see all the steps

backup?

make sure you follow all the steps :
https://clear.ml/docs/latest/docs/deploying_clearml/upgrade_server_linux_mac
(basically make sure you get the latest docker-compose.yml and the pull it
curl -o /opt/clearml/docker-compose.yml docker-compose -f /opt/clearml/docker-compose.yml pull docker-compose -f /opt/clearml/docker-compose.yml up -d

TartSeal39 please let me know if it works, conda is a strange beast and we do our best to tame it.
Specifically when you execute manually on a conda env we collect (separately) the conda packages & the python packages (so later we can replicate on both conda & pip, or at least do our best)
Are you running both development env and agent with conda ?

Sure, you can pass ${stage_data.id} as argument and the actual Task will get the reference step's Task ID of the current execution.
make sense ?

"General" is the parameter section name (like Args)

Hi ArrogantBlackbird16

but it returns a task handle even after the Task has been closed.

It should not ... That is a good point!
Let's fix that 🙂

Hi LazyTurkey38
Configuring these folders will be pushed later today 🙂
Basically you'll have in your clearml.conf
` agent {
docker_internal_mounts {
sdk_cache: "/clearml_agent_cache"
apt_cache: "/var/cache/apt/archives"
ssh_folder: "/root/.ssh"
pip_cache: "/root/.cache/pip"
poetry_cache: "/root/.cache/pypoetry"
vcs_cache: "/root/.clearml/vcs-cache"
venv_build: "/root/.clearml/venvs-builds"
pip_download: "/root/.clearml/p...

LazyTurkey38 configuration pushed to github :)

If Task.init() is called in an already running task, don’t reset auto_connect_frameworks? (if i am understanding the behaviour right)

Hmm we might need to somehow store the state of it ...

Option to disable these in the clearml.conf

I think this will be to general, as this is code specific , no?

Do people use ClearML with huggingface transformers? The code is std transformers code.

I believe they do 🙂
There is no real way to differentiate between, "storing model" using torch.save and storing configuration ...

store_code_diff_from_remote

 don't seem to change anything in regards of this issue

Correct, it is always from remote

i'll be using the update_task, that worked just fine, thanks 

 (edite

Sure thing.

ShakyJellyfish91 , I took a quick look at the diff between the versions can you hack a non working version (preferably the latest) and verify the issue for me?

i.e. you can:
curl

DilapidatedDucks58 so is this more like a pipeline DAG that is built ?
I'm assuming this is more than just grouping ?
(by that I mean, accessing a Tasks artifact does necessarily point to a "connection", no? Is it a single Task everyone is accessing, or a "type" of a Task ?
Is this process fixed, i.e. for a certain project we have a flow (1) executed Task of type A, then Task of type (B) using the artifacts fro Task (A). This implies we might have multiple Tasks of types A/B but they are alw...

DrabSwan66
Did you set "docker_install_opencv_libs: true" in your clearml.conf on the host machine ?
https://github.com/allegroai/clearml-agent/blob/e416ab526ba9fe05daa977b34c9e46b50fb214a0/docs/clearml.conf#L150
Just making sure, you are running clearml-agent in docker mode, correct?
What's the container you are using ?

it seems it's following the path of the script i'm using to task.create, eg:

The folder it should run it is the script path you are passing (i.e. "script=ep_fn," )
Wrong path would imply that is it not finding the correct repository, is that the case ?

ShakyJellyfish91 what exactly are you passing to Task.create?
Could it be you are only passing script= and leaving repo= None ?

Thanks ShakyJellyfish91 ! please let me know what you come up with, I would love for us to fix this issue.

Just making sure, the original code was executed on python 3?

BTW:
This is very odd "~/.clearml/venvs-builds.3/3.6/bin/python" it thinks it is using "python 3.6" but it is linked with python 2.7 ...
No idea how that could happen

I think this is the only mount you need:

Data persisted in every Kubernetes volume by ClearML will be accessible in /tmp/clearml-kind folder on the host.

SuccessfulKoala55 is this correct ?

they are efs mounts that already exist

Hmm, that might be more complicated to restore, right ?