i'm also not sure what this is
-H "Content-Type: application/octet-stream" -H' NV-InferRequest:batch_size: 1 input { name: "dense_input" dims: [-1, 784] } output { name: "activation_2" cls { count: 1 } }'

logger.report_media( title=name_title, series="Nan", iteration=0, local_path=fig_nan, delete_after_upload=delete_after_upload, ) clearml_task.upload_artifact( name=name_title, artifact_object=fig_nan, wait_on_upload=True, )

can you elaborate a bit on the token side? i'm not sure exactly what would be a bad practice here

does this make more sense? SuccessfulKoala55

so it tries to find it under /usr/bin/python/ I assume?

sheesh, weird. I don't get the same.

Hey SuccessfulKoala55 thanks for the answer.
any ideas how I can try to fix this?

Hi SuccessfulKoala55 I am having some issues with this. I have put a concurrency limit of 2 and I can see 3 workers running

right, and why can't a particular version be found? how it does it try to find python versions?

And got the same error

Is this a possible future feature? I have used cometML before and they have this. I'm not sure how they do it though...

I am curious about the updates on version 1.0.0, where can I see some info regarding this?
Passing state information from pre to postprocessing and the dynamic preprocessing code thing, for example

my server isn't the latest, though..

But where do you manually set the name of each task in this code? the .component has a name argument you can provide

yeah, that's fair enough. is it possible to assign cpu cores? I wasn't aware

using clearML agent

I'm on Ubuntu 18.04.3

i can't get some sort of response from curl

Yeah, that would be nice!

um, this line is not doing anything for me 🤔
controller_clearml_task = Task.current_task() controller_clearml_task.set_resource_monitor_iteration_timeout( seconds_from_start=10 )

yes, I just ran steps 6-12 again from https://allegro.ai/docs/deploying_trains/trains_server_linux_mac/

in particular, I ran the agent with clearml-agent daemon --queue test-concurrency --create-queue --services-mode 2 --docker "ubuntu:20.04" --detached and enqueued 4 tasks to it that sleep 15 minutes.
I can see all 4 tasks running, see

yes

This is a minimal comet example. I'm afraid I don't know what it does under the hood.. There are no callbacks on the metrics tracked in model.fit and yet if you check out your project in the website, your training and validation losses are tracked automatically, live.

https://www.comet.ml/docs/python-sdk/keras/

Ok, going to ask the server admins, will keep you posted, thanks!

Absolutely, I could try but I'm not sure what it entails...

"this means the elasticsearch feature set remains the same. and JDK versions are usually drop-in replacements when on the same feature level (ex. 1.13.0_2 can be replaced by 1.13.2)"

yes, in the corresponding task

yes

AgitatedDove14 can you please help me with this?