Hmm I wonder, can you try with this line before?
Task._report_subprocess_enabled = False frameworks = { 'tensorboard': True, 'pytorch': False } Task.init(...)

Correct :)

Hi ShallowCat10
What's the TB your are using?
Is this example working correctly for you?
https://github.com/allegroai/clearml/blob/master/examples/frameworks/tensorflow/tensorboard_pr_curve.py

According to you the VPN shouldn't be a problem right?

Correct as long as all parties are on the same VPN it should work, all the connections are always http so basically trivial communication

Correct 🙂

SmugDog62 so on plain vanilla Jupyter/lab everything seems to work.
What do you think is different in your setup ?

Nice! 🙂

Oh :)
task.get_parameters_as_dict()

ShinyWhale52 any time 🙂
Feel free to followup with more questions

Hi SubstantialElk6

but in terms of data provenance, its not clear how i can associate the data versions with the processes that created it.

I think DeliciousBluewhale87 ’s approach is what we are aiming for, but with code.
So using clearml-data from CLI is basically storing/versioning of files (with differentiable based storage etc, but still).
What ou are after (I think) is in your preprocessing code using the programtic Dataset class, to create the Dataset from code, this a...

Yep... some went wrong with the elastic container, I think it lost it's indexes (or they got screwed somehow)
Do you have a backup of the persistence volume attached to the container? Can you try restoring it?

I would restart the entire clearml-server (docker-compose), then can you post here the startup logs? It should provide some info on what's wrong

Hi VexedCat68
Are we talking youtubes ? docs? courses ?

I look forward to your response on Github.

Great, I would like to make this discussion a bit more open and accessible so GitHub is probably better

I'd like to start contributing to the project...

That will be awesome!

(2) yes weekdays with specific hour should do exactly that:)
(3) yes I see your point, maybe we should add boolean allowing you to run immediately?
Back to (1) , let me see if I can reproduce, anything specific I need to add to the schedule call?

BTW: the same hold for tagging multiple experiments at once

So the "packages" are the packages you need in the steps themselves ?

Hi NonchalantDeer14
In multi-gpu, can you still see the logs on the local Tensorboard ?
Are you running manually or with an agent ?

AdventurousRabbit79 you are correct, caching was introduced in v1.0 , also notice the default is no caching, you have to specify that you want caching per step.

DepressedChimpanzee34 I cannot find cfg.py here
https://github.com/allegroai/clearml/tree/master/examples/frameworks/hydra/config_files
(or anywhere else)

I see... In the triton pod, when you run it, it should print the combined pbtxt. Can you print both before/after ones? so that we could compare ?

Hi AgitatedTurtle16

My question is how to use it to manage my experiments (docker containers). Simply put, let's say:

So basically once you see an experiment in the UI, it means you can launch it on an agent.
There is No need to containerize your experiment (actually that's kind of the idea, removing the need to always containerize everything).
The agent will clone the code, apply uncommitted changes & install the packages in the base-container-image at runtime.
This allows you to u...

VivaciousWalrus99 any chance the original Task was executed with python2 ?
what do you have for:
ls -la /cs/usr/gal.hyams/.trains/venvs-builds/3.7/bin/

Hi CharmingShrimp37
Go to Github to your newly forked repo, you should have a green button suggesting to take your branch and making it a PR. It is that simple 🙂

Is it not possible to say just look at my requirements.txt file and the imports in the script?

I think there is a GitHub Issue for this feature
(basically the issue is, requirements.txt are very often not updated, and have no real version lock, so replicating a working env is always safer)

` from time import sleep
from clearml import Task
import tqdm

task = Task.init(project_name='debug', task_name='test tqdm cr cl')
print('start')
for i in tqdm.tqdm(range(100)):
sleep(1)
print('done') `The above example code will output a line every 10 seconds (with the default console_cr_flush_period=10) , can you verify it works for you?

TrickyRaccoon92 actually Click is on the to do list as well ...

ClearML does

Ohh sorry. task_log_buffer_capacity is actually internal buffer for the console output, on how many lines it will store before flushing it to the server.
To be honest, I can't think of a reason to expose / modify it...

` param = {'arg': value}
task.connect(param, section='new section')

create pipeline here

pipeline `

Does that help ?