ThickDove42 sorry, it took some time 🙂
import json from trains.backend_api.session.client import APIClient client = APIClient() events = client.events.get_task_plots(task='task_id_here') table = json.loads(events.plots[0]['plot_str']) print('column order', table['data'][0]['cells']['values'])Not the most comfortable way, but at least it is there

ThickDove42 If you need the name itself :
events.plots[0]['metric'] events.plots[0]['variant']

Let me check...

Give me a moment 🙂

Hi ShallowArcticwolf27

from the command line to a remote machine while loading a local 

.env

 file as a configuration object?

Where would the ".env" go to ? Are we trying to pass it to the remote machine somehow ?

Sounds good to me. DepressedChimpanzee34 any chance you can add a github feature request, so we do not forget to add it?

Also, can the image not be pulled from dockerhub but used from the local build instead?

If you have your docker configured to pull from local artifactory, then the agent will do the same 🙂 (it is calling the docker command just like you do)

agent.default_docker.arguments: "--mount type=bind,source=$DATA_DIR,target=/data"

Notice that you are use default docker arguments in the example
If you want the mount to always be there use extra_docker_arguments :
https://github.com/...

Hmm the agent's venv caching is the next thing on the to do list for the agent (post clearml release).
Currently the easiest thing is to build a new docker image with the entire "Installed packages" section and use that as the base docker image.
(The installed packages format is "requirement" compatible, so you can just use it as is when building the dockerfile)
The second option is to wait for the next clearml-agent release (probably in a couple of weeks)

Thanks StrongHorse8
Where do you think would be a good place to put a more advanced setup? Maybe we should add an entry for DevOps? Wdyt?

Basically it is the same as "report_scatter2d"

Thanks JuicyFox94 for letting us know.
I'm checking what's the status with it

SmarmyDolphin68 is it the same code as in the snippet ?

Let me check

Will such an docker image need a trains configuration file?

If you need to configure things other than credentials (see above) than yes you might need to map trains.conf into the pod.
Specifically, if you need, map your trains.conf to /root/.trains inside the pod/container

but this would be still part of the clearml.conf right?

You can pass it per Task , also you can configure the agent to always pass it add this env.
https://github.com/allegroai/clearml-agent/blob/5a080798cb4292e198948fbe16cba70136cb6bdf/docs/clearml.conf#L137

but is there any other way to get env vars / any value or secret from the host to the docker of a task?

if this is docker -e/--env as argument would do the same
-e VAR=somevalue

I like this approach more but it still requires resolved environment variables inside the clearml.conf

Yes 😞 maybe this is a feature request ?

the issue was related to task.connect being called multiple times I guess.

This is odd?! how would that effect the crash?
Do notice that when you connect objects, each time you call connect you are basically deserializing the configuration from the backend into the code, maybe this somehow effected the object?

I don't know whether you have access to the backend,

Creepy , no I do not 🙂

I can't make anything appear in the console part of the ui

clearml_task.logger.report_text("some text") should work

So it is the automagic that is not working.
Can you print the following before calling Both Task.debug_simulate_remote_task and Task.init , Notice you have to call Task.init
print(os.environ)

DepressedChimpanzee34 something along the lines of:
from multiprocessing.pool import ThreadPool p = ThreadPool() def get_last_metric(t): return t.get_last_scalar_metrics() task_scalars_list = p.map(get_last_metric, top_tasks) p.close()We parallelized network connection as I'm assuming the delay is fetching

With 

pipe.start(queue='services')

, it still tries to run some docker for some reason

The services agent is always running with --docker:
https://github.com/allegroai/clearml-agent/blob/e416ab526ba9fe05daa977b34c9e46b50fb214a0/docker/services/entrypoint.sh#L16
Actually I think we should have it as an argument, so it is easier to control from docker-compose

I'll be waiting for the full log to check the "git clone" issue

or creating a dedicated function I would suggest also including the actual sampled point in the HP space.

Could you expand ?

This would be the most common use case, and essentially the reason for running the HPO understanding the sensitivity of metrics with respect to hyper-parameters

Does this relates to:
https://github.com/allegroai/clearml/issues/430

manually" filtering the keys I've put in for the HP space. I find it a bit strange that they are not saved as part of t...

from clearml import TaskTypes

That will only work if you are using the latest from the GitHub, I guess the example code was modified before a stable release ...

Hi MelancholyBeetle72
You mean the venv creation takes the bulk of the time, or it something else ?

Docker cmd is basically docker image name but you can add parameters as well.
For example "Nvidia/cuda" or "Nvidia/cuda -v /mnt/data:/mnt/data"

Maybe this one?
https://github.com/allegroai/clearml/issues/448
I think it is already there (i.e. 1.1.1)

WickedGoat98 did you setup a machine with trains-agent pulling from the "default" queue ?

SmarmyDolphin68 , All looks okay to me...
Could you verify you still get the plot on debug samples as image with the latest trains RC
pip install trains==0.16.4rc0

trains-server 3.6

you mean 0.16?