I think task.init flag would be great!

👍

Hi PanickyMoth78
` torch.save(net.state_dict(), PATH) # auto-uploads to GCS

get all the models from the Task

output_models = Task.current_task().models["output"]

get the last one

last_model = output_models[-1]

set meta-data

last_model.set_metadata(key="my key", value="my value", type="str") `

SubstantialElk6 it seems the auto resolve of pytorch cuda failed,
What do you have in the "installed packages" section?

I'm sorry wrong line reference:
I'm assuming the error is due to ulimit missing:
try adding 16777216 to both soft/hard ulimit
https://github.com/allegroai/clearml-server/blob/09ab2af34cbf9a38f317e15d17454a2eb4c7efd0/docker/docker-compose.yml#L58

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?

WickedGoat98 sure that will not be complicated:
try something along the lines of :
agent: networks: - backend container_name: clearml-agent image: allegroai/clearml-agent:latest restart: unless-stopped privileged: true environment: CLEARML_HOST_IP: ${CLEARML_HOST_IP} CLEARML_WEB_HOST: ${CLEARML_WEB_HOST:-} CLEARML_API_HOST: `
CLEARML_FILES_HOST: ${CLEARML_FILES_HOST:-}
CLEARML_API_ACCESS_KEY: ${CLEARML_API_ACCESS_KEY:-}
...

if in the "installed packages" I have all the packages installed from the requirements.txt than I guess I can clone it and use "installed packages"

After the agent finished installing the "requirements.txt" it will put back the entire "pip freeze" into the "installed packages", this means that later we will be able to fully reproduce the working environment, even if packages change (which will eventually happen as we cannot expect everyone to constantly freeze versions)

My problem...

ohh, the copy paste thing when you generate credentials ?

The bug was fixed 🙂

Thanks @<1719524641879363584:profile|ThankfulClams64> having a code that can reproduce it is exactly what we need.
One thing I might have missed and is very important , what is your tensorboard package version?

ZanyPig66 you are correct in your assumptions. What exactly do you have in the Task? If there is no git repo the entire script should be under "uncommitted changes. What is your case?

which was trained on jupyter notebook.

Hmm that might be the issue, it assumes a local script running, let me verify that

Thank you @<1719524641879363584:profile|ThankfulClams64> for opening the GI, hopefully we will be able to reproduce it and fox ot quickly

You can install it, and after the wizard is done uninstall it, if you want to keep using the trains from the git clone.

Wait who is creating this file? I thought you remove it in the uncommitted changes

Hi GiganticTurtle0
you should actually get " file://home/user/local_storage_path "
With "file://" prefix.
We always store the file:// prefix to note that this is a local path

Hi ObnoxiousStork61

Is it possible to report ie. validation scalars but shifted by 1/2 iteration?

No 😞 these are integers

What's the reason for the shift?

I'm also curious 🙂

HelplessCrocodile8 I just tried it, everything seems to work (ubuntu 20.04) 😞
What's the OS your are using? Python version? Is it conda ?

Sure, thing, I'll fix the "create_draft" docstring to suggest it

Just making sure, pip package installed on your Conda env, correct?

Hi @<1691620877822595072:profile|FlutteringMouse14>
Yes, feast has been integrated by at least a couple if I remember correctly.
Basically there are two ways offline and online feature transformation. For offline your pipeline is exactly what would be recommended. The main difference is online transformation where I think feast is a great start

Cab you do:
curl

So clearml-init can be skipped, and I provide the users with a template and ask them to append the credentials at the top, is that right?

Correct

What about the "Credential verification" step in clearml-init command, that won't take place in this pipeline right, will that be a problem?

The verification test is basically making sure the credentials were copy pasted correctly.
You can achieve the same by just running the following in your python console:
` from clearml import Ta...

YEY!

GreasyPenguin14

In the process MyProcess other processes are created via a ProcessPoolExecutor.

Hmm that is interesting, the sub-process has an additional ProcessPoolExecutor inside it ?
GrittyKangaroo27 if you can help with reproducible code that will be great (or any insight on reproducing the issue)

Yey!!!!!

FYI: if you need to query stuff you can always look directly in the RestAPI:
https://github.com/allegroai/clearml/blob/master/clearml/backend_api/services/v2_9/projects.py
https://allegro.ai/clearml/docs/rst/references/clearml_api_ref/index.html

I was hoping that there's a universal flag somewhere. Asking this because I want all the Models and Artifacts to be stored in one place and the users shouldn't have to edit their configuration files.

You mean like make sure all models/artifacts are always uploaded?

If you have a requirements file then you can specify it:
Task.force_requirements_env_freeze(requirements_file='requirements.txt')
If you just want pip freeze output to be shown in your "Installed Packages" section then use:
Task.force_requirements_env_freeze()
Notice that in both cases you should call the function Before you call Task.init()
btw, what do you mean by "Packages will be installed from projects requirements file" ?

So the way it will work, is you will also need to have a Task.init in main process (the one using the launch function) and the same Task.init in the main_func. What it does is it signals the sub processes to use the main process task. This way they all report to the same task. Obviously to test it you will need to wait for the RC (after the weekend :)