Hi DeliciousBluewhale87
I think we had a docker that does exactly that, and then you would spin the docker as a k8s service , is this what you are referring to?

I can't seem to find a difference between the two, why would matplotlib get listed and pandas does not... Any other package that is missing?
BTW: as an immediate "hack" , before your Task.init call add the following:
Task.add_requirements("pandas")

Hi VexedCat68

The scheduler is set to run once per hour but even now I've got around 40+ anonymous running tasks.

Based on the screenshots these are the Datasets (which are also a Task with specific type etc).
I would actually name the Datasets you are creating You need to specify the parent version (i.e. how would it know it is a child dataset changeset) I'm assuming they are all uploading everything, hence still running?BTW: you can use the argument single_instance=True maki...

 are models technically 

Task

s and can they be treated as such? If not, how to delete a model permanently (both from the server and from AWS storage)?

When you call Task.delete() it actually goes over a;; the models/artifacts and deletes them from the storage

Ohh okay something seems to half work in terms of configuration, the agent has enough configuration to register itself, but fails to pass it to the task.
Can you test with the latest agent RC:
0.17.2rc4

Okay let me check if I can test on this git version.

JitteryCoyote63
Could you copy paste the "installed packages" section? the answer might be there

Hi EmbarrassedSpider34
clearml-init will try to create ~/clearml.conf I'm assuming that when you execute under root it is resolved to /root/clearml.conf That said you might be able to override it with:
CLEARML_CONFIG_FILE=$HOME/clearml.con sudo clearml-init

we also provide a custom

aux-config

file. We also had to make sure to update the name inside

config.pbtxt

so that Triton is happy:

Good point, what would be the logic of the auto "config.pbtxt" patching we should employ ?

What's the trains-server version?

I did change the

instead of 8080?

So this is the issue

Almost forgot, pipeline screenshot 🙂

Hi ElegantCoyote26

is there a way to get a Task's docker container id/name?

you mean like Task.get_task("task_id_here").get_base_docker() ?

ow a Task's results page also has a plot for this, but I guess it's at the machine level and not the task level?

This is actually on the container level, meaning checked from inside the container. It should be what you are looking for

Hi LazyTurkey38
Documentation for applications is currently worked on, generally speaking this is a way to package features available in ClearML with a UI interface. First these are going to be applications built by the ClearML team and later expanded for the community to be able to contribute to them. Finally users will be able to add their own applications (i.e. package Tasks with UI wizard and dashboard) in their hosted solutions. wdyt?

Okay, I think I understand, but missing something. It seems you call get_parameters from old API , is your code actually calling get_parameters ? The trains-agent runs the code externally, whatever happens inside the agent should have now effect on the code. So who exactly is calling the task.get_parameters, and well, why ? :)

Hi @<1547028116780617728:profile|TimelyRabbit96>
It should process the new request A (this is a multi threading / async implementation)
Is this consistent with what you are seeing ?

GiganticTurtle0 is there any git redundancy on your network ? maybe you could configure a fallback server ?

😞 DilapidatedDucks58 how exactly are you "relaunching/continue" the execution? And what exactly are you setting?

Yep

Hi DangerousDragonfly8

, is it possible to somehow extract the information about the experiment/task of which status has changed?

From the docstring of add_task_trigger
```py def schedule_function(task_id): pass ```This means you are getting the Task ID that caused the trigger, now you can get all the info that you need with Task.get_task(task_id)
` def schedule_function(task_id):
the_task = Task.get_task(task_id)
# now we have all the info on the Task tha...

Hi DisturbedElk70
I think in a few hours 🙂

BTW: CloudyHamster42 I think this issue was discussed on GitHub, and the final "verdict" was we should have an option to split/combine graphs on the UI side (i.e. similar to the "smoothing" or wall-time axis etc.)

In your trains.conf, change the value
files_server: ' s3://ip :port/bucket'

Could you please add it, I really do not want to miss it 🙂

RobustGoldfish9
I think you need to set the trains-agent docker to be aware of the host, so it knows how to mount data/cache/configurations into the sibling docker

It should look something like:
TRAINS_AGENT_DOCKER_HOST_MOUNT="/mnt/host/data:/root/.trains"So if running a docker:
docker run -e TRAINS_AGENT_DOCKER_HOST_MOUNT="/mnt/host/data:/root/.trains" ...

Are you trying to upload an artifact post execution ?

it fails but with COMPLETED status

Which Task is marked "completed" the pipeline Task or the Step ?

DefeatedCrab47 If I remember correctly v1+ has their arguments coming from argparse .
Are you using this feature ? 2. How do you set the TB HParam ? Currently Trains does not support TB HParams, the reason is the set of HParams needs to match a single experiment. Is that your case?

Mmm well, I can think of a pipeline that could save its state in the instant before the error occurred.

This is already the case, if you clone the pipeline Task change the Args/_continue_pipeline_ to True and enqueue

:param list(str) xlabels: Labels per entry in each bucket in the histogram (vector), creating a set of labels for each histogram bar on the x-axis. (Optional)