OutrageousGiraffe8 so basically replacing to:
self.d1 = ReLU()

Okay that makes sense.
best_diabetes_detection is different from your example curl -X POST " None " notice best_mage_diabetes_detection` ?

Hmm whats the OS and python version?
Is this simple example working for you?
None

Notice that the StorageManager has default configuration here:
https://github.com/allegroai/trains/blob/f27aed767cb3aa3ea83d8f273e48460dd79a90df/docs/trains.conf#L76
Then a per bucket credentials list, with detials:
https://github.com/allegroai/trains/blob/f27aed767cb3aa3ea83d8f273e48460dd79a90df/docs/trains.conf#L81

Are you saying you have a single line in the console output of the component Task?

And when retrieve just this file? is it working ?
(Maybe for some reason the file is corrupted) ?

OutrageousGiraffe8 this sounds like a bug, how can we reproduce it?
Maybe a add another layer here?
https://github.com/allegroai/clearml/blob/a47f127679ebf5912690f7c3e60791a2daa5c984/examples/frameworks/tensorflow/tensorflow_mnist.py#L40

Exactly!
Regarding adding feature store, probably not in the near future, a scalable feature store is quite the project, probably more realistic to somehow have a recipe to deploy with Feast

Unfortunately not yet in venv mode. What would you have put there?

Check the log usually ~/.trains/venv/...

That somehow the PV never worked and it was all local inside the pod

@<1523710674990010368:profile|GreasyPenguin14> If I understand correctly you can use tokens as user/pass (it's basically the same interface from the git client perspective, meaning from ClearML

git_user = gitlab-ci-token
git_pass = <the_actual_toke>

WDYT?

Ohh that's why you don't have it 🙂

The way I understand it is that K8s glue agent is enabled by default (and I do see a Deployment for

clearml-k8sagent

SarcasticSquirrel56
Good start, when you say you see the Task in ""k8s_scheduler" queue, originally did you enqueue it to "default" ?

My current experience is there is only print out in the console but no training graph

Yes Nvidia TLT needs to actually use tensorboard for clearml to catch it and display it.
I think that in the latest version they added that. TimelyPenguin76 might know more

I am running clearml-agent in docker mode btw.

Try -e PYTHONOPTIMIZE=1 in the docker args section, should do the same 🙂
https://docs.python.org/3/using/cmdline.html#envvar-PYTHONOPTIMIZE

Yep 🙂

I assume it is reported into TB, right ?

Right, I think the naming is a by-product of Hydra / TB

GloriousPanda26 Are you getting multiple Tasks or is it a single Task ?

Thanks GloriousPanda26 !

I think it would make sense to have one task per run to make the comparison on hyper-parameters easier

I agree. Could you maybe open a GitHub issue on it, I want to make sure we solve this issue 🙂

It's a running number because PL is creating the same TB file for every run

GloriousPanda26 wouldn't it make more sense that multi run would create multiple experiments ?

Hi @<1739455989154844672:profile|SmarmyHamster62>
Yeah the docs needs to be fixed there, this is the equivalent of:
None
Specifically task_filter

🤔

DeliciousBluewhale87 you can try:
` import sqlite3
import pandas as pd

conn = sqlite3.connect('test_database')

sql_query = pd.read_sql_query ('''
SELECT
*
FROM products
''', conn)

sql_query.to_csv(...) `

PanickyMoth78 RC is out
pip install clearml==1.6.3rc1🤞

Hi @<1578193378640662528:profile|MoodySeaurchin4>

but is it possible to log some metrics too, like rmse or the likes? If so, how would you do it?

Sure, I'm assuming this is part of the output ? if not, this means this is part of your code, and if this is the case then yes you should use collect_custom_statistics_fn
None
`collect_custom_statistics_fn({'rmse'...

Hi ObedientDolphin41

However, all of the pipelines tasks are ran on the same queue. Could I be missing something?

The pipeline Task itself is running on a dedicated queue (meaning agent/s) usually because the pipeline logic is mostly idling, where as the components themselves are doing the actual compute.
Specifically you can control the pipeline logic queue with pipeline_execution_queue
https://github.com/allegroai/clearml/blob/7016138c849a4f8d0b4d296b319e0b23a1b7bd9e/clearm...