Hmm can you test with the latest RC? or even better from the GitHub (that said the Github will break the interface, as we upgraded the pipeline 🙂 )

👍

No the "original one"

PYTHONPATH is still not working as expected

inside your code if you do :
import os print("PYTHONPATH", os.environ["PYTHONPATH"])what are you getting?

They all want to be ubuntu:gpu0. Any idea how I can randomize it? Setting the CLEARML_WORKER_ID env var somehow does not work

You should not have this entry in the conf file, the "worker_id" should be unique (and is based on the "worker_name" as a prefix. You can control it via env variales:
CLEARML_WORKER_ID

VexedCat68 the remote checkpoints (i.e. Models) represent the local storage, so if you internally overwrite the files, this is exactly what will happen in the backend. so the following should work (and store the last 5 checkpoints):
epochs += 1 torch.save("model_{}.pt",format(epochs % 5))Regrading deleting / getting models:
Model.remove(task.models['output'][-1])

The package is just subdir by the way. So it should not be in installed packages anyways, right?

Correct, also when the agent is spinning the code it will automatically add the root of the git repository to the pythonpath so you should be able to load the package.

Looks good!

Hi @<1697056701116583936:profile|JealousArcticwolf24>
Can you run your pipeline on an agent (i.e. remotely) but launching it from the UI (not the taskscheduler)?

Hi WhimsicalLion91
https://allegro.ai/docs/logger.html#trains.logger.Logger.report_line_plot

https://github.com/allegroai/trains/blob/master/examples/reporting/scatter_hist_confusion_mat_reporting.py

GiddyTurkey39 Hmm I'm assuming that by default it cannot access that IP range.
Are you using virtual-box for the VM?
EDIT:
Can I assume the machine running the VM (a.k.a the host) can access the trains-server ?

Basically it is the same as "report_scatter2d"

BTW: there is a fix to the priority thing:
https://github.com/allegroai/trains-agent/blob/216b3e21790659467007957d26172698fd74e075/docs/trains.conf#L77

GiddyTurkey39 do you mean to delete them from the server?

If this is GitHub/GitLab/Bitbucket what I'm thinking is just a link opening an iframe / tab with the exact entry point script / commit.
What do you think?

Hi MotionlessSeagull22
Hmm I'm not this is possible in the UI.
You can compare multiple experiments and view the images in form of thumbnails one next to the other, But full view will be a single image...
You can however right click on the image and get a direct link, then open a new tab ... :(

Also, is there a way to reproduce this issue of not capturing the model?

BeefyCow3 if you are trying to optimizer a specific metric (i.e. a scalar on a graph). The template Task should report it with the same title/series combination, which should be easy enough to verify in the UI 🙂
You can either report with Tensorboard or with the Trains Logger, either way will work.

btw: both should work fine

Hi @<1523701337353621504:profile|FlutteringSheep58>
are you asking how to convert a worker IP into a dns resolved host name ?

.I am using pipeline from tasks method and not pipeline from decorator.

Wait I'm confused nowm if this is a pipeline from Tasks then the Tasks themselves should have clearml in the "installed packages", no? and if they do not, how were they created?

Is there still an issue? Could it be the browser cannot access the file server directly?

I think that's the easiest 🙂

ShortElephant92 yep, this is definitely enterprise feature 🙂
But you can configure user/pass on the open source, even store as hasedh the passwords if you need.

I have install a python environment by virtualenv tool, let's say

/home/frank/env

and python is

/home/frank/env/bin/python3.

How to reuse the virtualenv by setting clearml agent?

So the agent is already caching the entire venv for you, nothing to worry about, just make sure you have this line in clearml:
https://github.com/allegroai/clearml-agent/blob/249b51a31bee97d63f41c6d5542e657962008b68/docs/clearml.conf#L131
No need to provide it an existing...

Hi SteadySeagull18

However, it seems to be entirely hanging here in the "Running" state.

Did you set a an agent to listen to the "services" queue ?
Someone needs to run the pipeline logic itself, it is sometimes part of the clearml-server deployment but not a mist

As a hack you can try DEFAULT_VERSION
(it's just a flag and should basically do Store)
EDIT: sorry that won't work 😞

worker nodes are bare metal and they are not in k8s yet

By default the agent will use 10022 as an initial starting port for running the sshd that will be mapped into the container. This has nothing to do with the Host machine's sshd. (I'm assuming agent running in docker mode)

Yep 🙂

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