Do note that the needed module is just a local folder with scripts.

Oh that is the issue, is it in the git repo ?

I guess it’s on me to check whether this slowdown is negligible or not

Usually performance is negligible, especially with GPU
But if you really want the best:
Add --security-opt seccomp=unconfined to the extra_docker_arguments
See detials:
https://betterprogramming.pub/faster-python-in-docker-d1a71a9b9917

1724924574994 g-s:gpu1 DEBUG WARNING:root:Could not lock cache folder /root/.clearml/venvs-cache: [Errno 9] Bad file descriptor

You have an issue with your OS / mount, specifically "/mnt/clearml/" is the base folder for all the cached stuff and it fails to create the lock files there either use a Local folder or try to understand what is the issue with the Host machine /mnt/ mounts (because it looks like a network mount)

Oh that is odd. Is this reproducible? @<1533620191232004096:profile|NuttyLobster9> what was the flow that required another task.init?

What if I have multiple files that are not in the same folder? (That is the current use-case)

I think you can do weights_filenames= ['a_folder/firstfile.bin', 'b_folder/secondfile.bin']
(it will look for a common file path for both so it retains the folder structure)

Our workaround now for using a

Dataset

as we do, is to store the dataset ID as a configuration parameter, so it's always included too

Exactly, so with Input Model it's the same only kind of ...

it does

not

include the “internal.repo” as a package dependency, so it crashes.

understood

And for the time being we have not used the decorators,

So how are you building the pipeline component ?

😞 CooperativeFox72 please see if you can send a code snippet to reproduce the issue. I'd be happy to solve the it ...

hm ReassuredTiger98 can you send the full log? I think it should have worked (but as you mentioned it might be conda/pip mix?!)

Right, DepressedChimpanzee34 what's the clearml version you are using ?

agree, but setting the agent’s env variable TMPDIR

I think this needs to be passed to the docker with -e TMPDIR=/new/tmp as additional container args:
see example
None
wdyt?

oh sorry my bad, then you probably need to define all OS environment variable for python temp folder for the agent (the Task process itself is a child process so it will inherit it)

TMPDIR/new/tmp TMP=/new/tmp TEMP=/new/tmp clearml-agent daemon ...

where people can do @'s for experiments/projects/tasks and even comparisons ...

ohhh I like that! for me this throws me directly to Slack integration .
I think my main question is, "is the discussion ephemeral?" in other words, is this an on going discussion that later no one will care about, or are we creating some "knowledge base" that we want to later share?

Also, by "address bar at the top", i assume you mean address url right?

yes... apologies for the phrasing, it was w...

MysteriousBee56 I see...
So yes, you can with the APIClient you have full RESTful access to the backend.
I think there was a similar discussion https://allegroai-trains.slack.com/archives/CTK20V944/p1593524144116300
HandsomeCrow5 how did you end up solving it? I think you had a similar use case?!

MysteriousBee56 I would do Task.create()
you can get the full Task internal representation with task.data
Then call task._edit(script={'repo': ...}) to edit/update all the Task entries.
You can check the dull details of the task object here: https://github.com/allegroai/trains/blob/master/trains/backend_api/services/v2_8/tasks.py#L954
BTW: when you have a sample script working, consider PR-ing it, I'm sure it will be useful for others 🙂 (also a great way to get us involved with debuggin...

programmatically or from the UI?

Hi JealousParrot68

do tasks that are created through create_function_task run the entry_script again instead of just the pure function

Basically they will run the code until the "create_function_task" call, but never after. We are working on adding a decorator to a function, making it a "standalone" script, is this what you actually need ?

just want to be very precise an concise about them

Always appreciated 🙂

DefeatedCrab47 link is fixed :)

It will automatically switch to docker mode

It's the safest way to run multiple processes and make sure they are cleaned afterwards ...

@<1734020162731905024:profile|RattyBluewhale45> could you attach the full Task log? Also what do you have under "installed packages" in the original manual execution that works for you?

because it should have detected it...
Did you see "Repository and package analysis timed out ..."

Hi @<1590152178218045440:profile|HarebrainedToad56>
Yes you are correct all TB logs are stored into the ELK in the clearml backend. This really scales well and rarely has issues, as long of course that the clearml-server is running on strong enough machine. How many RAM / HD you have on the clearml-server ?

How much free RAM / disk do you have there now? How's the CPU utilization ? how many Tasks are working with this machine at the same time

Hi FreshKangaroo33
clearml.conf is HOCON format, to parse you can use pyhocon:
https://github.com/chimpler/pyhocon
Or the built in version of clearml:
from clearml.utilities.pyhocon import ConfigFactory config_dict = ConfigFactory.parse_string(text).as_plain_ordered_dict()You can also just get the parsed object
from clearml.config import config_obj

Is it possible to make a checkbox in the profile settings. which would answer az the maximum limit for comparison?

This feature is becoming more and more relevant.

So we are working on a better UI for it, so that this is not limited (it's actually the UI that is the limit here)
specifically you can add custom columns to the experiment table (like accuracy loss etc), and sort based on those (multiple values are also supported, just hold the Shift-Key). This way you can quickly explore ...

I also found that you should have a deterministic ordering

before

you apply a fixed seed

Not sure I follow ?

Hi @<1523706645840924672:profile|VirtuousFish83>
could it be you have some permission issues ?

: Forbidden: updates to statefulset spec for fields other than 'replicas', 

It might be that you will need to take it down and restart it. not while it is running.
(do make sure you backup your server 🙂 )

But my previous ques and other query are still not figured out.

What do you mean by "previous ques and other query" ?

Notice the error:

Cannot install albucore==0.0.13 and numpy==1.23.5 because these package versions have conflicting dependencies

what is the pip version you have configured in the clearml.conf? also can you provide the full Task log (i.e. click on Download in the web UI console tab)