Okay verified, it won't work with the demo server. give me a minute 🙂

That said, it might be different backend, I'll test with the demoserver

That makes total sense, this is exactly an OS scenario for signal 9 🙂

and trains version

If that's the case check the free space in the monitoring of the experiment, you will find the free space in GB logged

Hmm pseudo stack:
https://github.com/huggingface/transformers/blob/040283170cd559b59b8eb37fe9fe8e99ff7edcbc/src/transformers/trainer_tf.py#L779

https://github.com/huggingface/transformers/blob/040283170cd559b59b8eb37fe9fe8e99ff7edcbc/src/transformers/feature_extraction_utils.py#L285

https://github.com/huggingface/transformers/blob/040283170cd559b59b8eb37fe9fe8e99ff7edcbc/src/transformers/feature_extraction_utils.py#L470

Is there a way to force clearml not to upload these models?

DistressedGoat23 is it uploading models or registering them? to disable both set auto_connect_frameworks https://clear.ml/docs/latest/docs/clearml_sdk/task_sdk#automatic-logging

Their name only contain the task name and some unique id so how can i know to which exact training

You mean the models or the experiments being created ?

Hi @<1547028116780617728:profile|TimelyRabbit96>

Trying to do model inference on a video, so first step in

Preprocess

class is to extract frames.

Basically this depends on the RestAPI, usually would will be sending a link to data to be processed and returned Synchronously
What you should have a custom endpoint doing the extraction, send Raw data into another endpoint doing the model inference, basically think "pipeline" end points:
[None](https://github.com/allegro...

Could you verify you have 8 subfolders named 'venv.X' in the cache folder ~/. trains ?

Hmm, so what is the difference ?

GrotesqueDog77 one issue with this design, in order to run a sub-component, the call must be done from the parent component, does that make sense?

` def step_one(data):
return data

def step_two(path):
return model

def both_steps()
path = step_one("stuff")
return step_two(path)

def pipeline():
both_steps() Which would make both_steps ` a component and step_one and step_two sub-components
wdyt?

because step can be constructed with multiple

sub-components

but not all of them might be added to the UI graph

Just to make sure I fully understand when we decorate with @sub_node we want that to also appear in the UI graph (and have it's own Task / metrics etc)
correct?

Yes, but I'm not sure that they need to have separate task

Hmm okay I need to check if this can be easily done
(BTW, the downside of that, you can only cache a component, not a sub-component)

Sounds good to me, adding it to the to do list, probably should not be very complicated to add 🙂

but I'd prefer to have a new instance deployed for each new experiment and that it also terminates when no new experiments are queued

I'm not objecting, just wondered on the rational behind the decision 🙂
Back to the AWS autoscaler:
Basically if you have the services-agent running on your cluster, it will just run the aws-autoscaler for you 🙂
The idea of the service-agent is to run logic/monitoring Tasks suck as the aws autoscaler. Notice that service-mode means multiple job per...

LudicrousParrot69 there is already
Task.add_tags
https://github.com/allegroai/clearml/blob/2d561bf4b3598b61525511a1a5f72a9dba74953e/clearml/task.py#L964

SmarmySeaurchin8
Something like this one:
vector_series = np.random.randint(10, size=10).reshape(2,5) logger.report_vector(title='vector example', series='vector series', values=vector_series, iteration=0, labels=['A','B'], xaxis='X axis label', yaxis='Y axis label')

DefeatedCrab47 no idea, but you are more then welcome to join the thread here, and point it out:
https://github.com/PyTorchLightning/pytorch-lightning-bolts/issues/249

Hi GreasyPenguin66
Is this for the client side ? If it is why not set them in the clearml.conf ?

Hi SmugLizard24

The question is what is the reason of the issue?

That is a good question, could it be out of memory? (trying to compress or send the file in one chunk?)

Hi ScaryBluewhale66

TaskScheduler I created. The status is still

running

. Any idea?

The TaskScheduler needs to actually run in order to trigger the jobs (think cron daemon)
Usually it will be executed on the clearml-agent services queue/mahine.
Make sense ?

It’s the correct way to do it, right?

Yep 🙂 that said this is not running as a service you will need to spin it on your machine. that said you can definitely connect it with the free SaaS server, and spin the serving on your machine with docker-compose

Hi CheekyAnt38

However now I would like to evaluate directly my machine learning model via api requests, directly over clearml. It’s possible?

This basically means serving the model, is this what you mean?

I see TrickyFox41 try the following:
--args overrides="param=value"Notice this will change the Args/overrides argument that will be parsed by hydra to override it's params

@<1577468638728818688:profile|DelightfulArcticwolf22>

How can I tell clearml-agent not to run pip install unless my requierments.txt file was changed.

the agent has built in cache, it will reuse the previous venv if nothing changed (cache local on the agent's machine).
Make sure this is line is not commented :
None

Hi EagerOtter28
I think the replacement should happen here:
https://github.com/allegroai/clearml-agent/blob/42606d9247afbbd510dc93eeee966ddf34bb0312/clearml_agent/helper/repo.py#L277

yup, i updated this in my local clearml.conf... Or should be updating this elsewhere as well

On the agent's machine, you should update the default_output_uri. Make sense ?

Could that be it ?

Hmm, Notice that it does store sym links to parent data versions (to save on multiple copies of the same file). If you call get_mutable_local_copy() you will get a standalone copy

Can you see the repo itself ? the commit id ?