And only the main one ?

So it should cache the venvs right?

Correct,

path: /clearml-cache/venvs-cache

Just making sure, this is the path to the host cache folder

ClumsyElephant70 I think I lost track of the current issue 😞 what's exactly not being cached (or working)?

Hmm so the Task.init should be called on the main process, this way the subprocess knows the Task is already created (you can call Task.init twice to get the task object). I wonder if we somehow can communicate between the sub processes without initializing in the main one...

CrookedWalrus33 this is odd I tested the exact same code.
I suspect something with the environment maybe?
Whats the python version / OS ? also can you send full pipe freeze?
2022-07-17 07:59:40,339 - clearml.storage - ERROR - Failed uploading: Parameter validation failed: Invalid type for parameter ContentType, value: None, type: <class 'NoneType'>, valid types: <class 'str'>Yes this is odd, it should add the content-type of the file (for example "application/x-tar" but you are getting N...

WickedGoat98 are you running the agent with --gpus ?

Then try to add the missing apt packages

extra_docker_shell_script: ["apt-get install -y ???", ]

None

on the host machine or inside the containers that are spinning on the host machine ?

@<1523704157695905792:profile|VivaciousBadger56>

Is the idea here the following? You want to use inversion-of-control such that I provide a function

f

to a component that takes the above dict an an input. Then I can do whatever I like inside the function

f

and return a different dict as output. If the output dict of

f

changes, the component is rerun; otherwise, the old output of the component is used?

Yes exactly ! this way you...

Awesome !

ColossalDeer61 FYI all is fixed now 🙂

Hi, is there a possibility to use one GPU card with 2 agents concurrently

RoundMosquito25 / EnviousPanda91
You need to change the WORKER_ID (no two workers can share the same ID)
CLEARML_WORKER_ID="machine:gpu01" clearml-agent daemon ....

JitteryCoyote63 I meant to store the parent ID as another "hyper-parameter" (under its own section name) not the data itself.
Makes sense ?

and it’s in the “installed packages” from the child task:

This is because the agent always updates back the full venv setup, so you will be able to always reproduce the entire thing (as opposed to dev time, where it lists only the directly imported packages)

Hi ApprehensiveFox95
You mean from code remove the argparse arguments ?
Or post execution in the UI?

But this is not copy, this is mount, your log showed cp failing

By default the pl Trainer will output everything to TB, which we automatically store. But verify that TB is installed

Hi AstonishingRabbit13

is there option to omit the task_id so the final output will be deterministic and know prior to the task run?

Actually no 😞 the full path is unique for the run, so you do not end up overwriting models.
You can get the full path from the UI (Models Tab) or programmatically with Models.query_models or using the Task.get_task methods.
What's the idea behind a fixed location for the model?

PlainSquid19 yes the link is available on in the actual paid product 😞
I don't think they have the documentation open yet...
My recommendation is to fill the contact us form, you'll get a free online tour as well 😉

:)

BTW: if you feel like pushing forward with integration I'll be more than happy to help PRing new capabilities, even before the "official" release

Hi RoughTiger69
Is the pipeline in question based on decorators or is it based on existing Tasks?

No 😞

Our datasets are more than 1TB in size and will grow in size (probably 4TB and up), this means we also need 4TB local storage

Yes, because somewhere you will have to store your unzipped files.
Or you point to the S3 bucket, and fetch the data when you need to access it (ore prefetch it) with the S3 links the Dataset stores, i.e. only when accessed

SubstantialKoala71 not sure I follow, what's the goal here ?

And when running 

get

 the files on the parent dataset will be available as links.

BTW: if you call get_mutable_copy() the files will be copied, so you can work on them directly (if you need)

Hmm Okay, I think the takeaway is that we should print "missing notebook package" 🙂

SubstantialElk6
Regrading cloning the executed Task:
In the pip requirements syntax, "@" is a hint that tells pip where to find the package if it is not preinstalled.
Usually when you find the @ /tmp/folder It means the packages was preinstalled (usually pre installed in the docker).
What is the exact scenario that caused it to appear (this was always the case, before v1 as well).
For example zipp package is installed from pypi be default and not from local temp file.
Your fix b...

1. yes they will! This is exactly the idea :)
2. yes it will store it as text file (as is raw text) notice the return value is the file you should open. This is because when running via agent the return file will contain the conf file from the UI. Make sense?

SmarmyDolphin68 sadly if this was not executed with trains (i.e. the offline option of trains), this is not really doable (I mean it is, if you write some code and parse the TB 😉 but let's assume this is way to much work)
A few options:
On the next run, use clearml OFFLINE option, (i.e. in your code call Task.set_offline() , or set env variable CLEARML_OFFLINE_MODE=1) You can compress the upload the checkpoint folder manually, by passing the checkpoint folder, see https://github.com...

Hi FancyWhale93
pipe.start() should actually stop the local pipeline logic execution and fire it on the "services queue".
The idea is that you can launch the pipeline locally, but the actual execution of the entire logic is remote.
You can have the pipeline running locally if you call pipe.start_locally or also run the steps locally (as sub processes) with pipe.start_locally(run_pipeline_steps_locally=False)
BTW: based on your example, a more intuitive code might be the pi...