No worries, I'll see if I can replicate it anyhow

But this is clearml python package, it is not really related to the server. Could it be you also update the clearml package ?

Hi MuddySquid7
Hmmm what would be the use case ? (I mean how are we using Vertex ?)

Notice that in your example you have

plt.figure()

This actually clears the matplotlib figure, this is why we are getting a first white image then the actual plot,
once I removed it I got a single plot (no need for the manual reporting)

X, y = make_regression(
    n_samples=100,     # Number of samples
    n_features=10,     # Number of features
    noise=0.1,   # Number of informative features
    random_state=42    # For reproducibility
)

# Convert to DataFrame for better f...

I ran the test, but there was no result.

what do you mean by no result, no data after the new query?

Hmm GreasyLeopard35 can you specify the range you are passing to the HPO, as well as the type of optimization class ? (grid/random/optuna etc.)

What's strange is that the remote jobs, as soon as they are launched, if I compare their configs while in state pending, they have the right all different configs, but later, while running,

Wait I think I found it, since usuallyu the case with hydra you configure everything from overrides / config, when launched remotely it looks at it by default. But with the launch plugin it should be overwritten with the Task
` task = Task.init(...)
task.set_parameter(name="Hydra/_allow_omegaconf_ed...

Yes, actually the first step would be a toggle button for regexp in the search, the second will be even more advanced search.
May I suggest you post it on the UI suggestion issue https://github.com/allegroai/trains/issues/81 ?

If that's the case you have two options:

• Create a Dataset from local/nfs and upload it to the S3 compatible NetApp storage (notice this create an immutable copy of the data)
• Create a Dataset and add "external links" to either the S3 storage with None :port/bucket/... or direct file link file:///mnt/nfs/path , notice that in this example the system does not manage the data that means that if someone deletes/moves the data you are unaware of that And of course you can...

Hi ScantChimpanzee51
How are you launching the code ?
Basically the easiest way is to do so with the example you just mentioned,
Can this issue be reproduced ?

Hi ElegantCoyote26
If there is, it will have to be using the docker-mode, but I do not think this is actually possible because this is not a feature of docker. It is possible to do on k8s, but that's a diff level of integration 🙂
EDIT:
FYI we do support k8s integration

JitteryCoyote63

I am setting up a new machine with two rtx 3070 GPU

Nice! you are one of the lucky few who managed to buy them 🙂

Which makes me think that the wrong torch package is installed

I think that torch 1.3.1 is does not support cuda 11 😞

Many thanks!

I mean the caching will work, but it will reinstall this repository on top of the cached copy.
make sense ?

SubstantialElk6 when you say "Triton does not support deployment strategies" what exactly do you mean?
BTW: updated documentation already up here:
https://clear.ml/docs/latest/docs/clearml_serving/clearml_serving

Maybe permissions?!
you can test it manually by installing pynvml
and running:
from pynvml.smi import nvidia_smi nvsmi = nvidia_smi.getInstance() nvsmi.DeviceQuery('memory.free, memory.total')

Just making sure i understand, you are to upload your models with clearml to the Yandex compatible s3 storage?

Also what do you have in the "Configuration" section of the serving inference Task?

ElegantKangaroo44 what do you think?

Hi MammothGoat53
Do you mean working with RestAPI directly?
https://clear.ml/docs/latest/docs/references/api/events

Yes, it recreates the venv (or fetches it from cache) if you need your dataset, use Dataset class (it will cache it persistently, so no need to re-download)

GiganticTurtle0 this is exactly what I did, and ended up with two pipelines, comparing them produced what I expected (different arguments as passed by the script).
What are you getting ?

I see, give me a minute to check what would be the easiest

Hi @<1523703397830627328:profile|CrookedMonkey33>
If you click on the "Task Information" (on the Version Info panel, right hand-side). It will open the Task details page, there you have the "hamburger" menu top right, where you have publish
(Maybe we should add that to the main right click menu?!)

Hi ConvincingSwan15

For the train.py do I need a setup.py file in my repo to work corerctly with the agent ? For now it is just the path to train,py

I'm assuming the train.py is part of the repository, no?
If it is, how come the agent after cloning the repository cannot find it ?
Could it be it was accidentally not added to the git repo ?

BTW: 0.14.3 solved the issue you are referring to, so you can import trains before / parsing the args without an issue. Regrading passing project/name as parameters. A few thoughts: (1) you can always rename / move projects from the UI (2) If you are running it with trains-agent there is no meaning to these arguments, as by definition the Task was already created... Maybe we should give an option to exclude a few arguments from argparser, I think this topic came up a few times... What d...

Correct

Hi ConvincingSwan15
A few background questions:

Where is the code that we want to optimize? Do you already have a Task of that code executed?

"find my learning script"

Could you elaborate ? is this connect to the first question ?

Hi ReassuredTiger98
I think you should have something like:

` @PipelineDecorator.component(task_type=TaskTypes.application, docker='clara_docker_container_if_we_need')
def step_one(param):
print('step_one')
import os
os.system('run me clara')
# I'm assuming we should wait?
return

@PipelineDecorator.component(task_type=TaskTypes.training)
def step_two(param):
print('step_two')
import something
somthing.to_do()
return

@PipelineDecorator.pipeline(name='c...

Hmm. What's the Hydra version you have?