Ad1. yes, think this is kind of bug. Using _task to get pipeline input values is a little bit ugly

Good point, let;s fix it 🙂

new pipeline is built from scratch (all steps etc), but by clicking "NEW RUN" in GUI it just reuse existing pipeline. Is it correct?

Oh I think I understand what happens, the way the pipeline logic is built, is that the "DAG" is created the first time the code runs, then when you re-run the pipeline step it serializes the DAG from the Task/backend.
Th...

EnviousStarfish54
plt.show will capture the figure, that if you call it multiple times, it will add a running number to the figure itself (because the figure might change, and you might want the history)
if you call plt.imshow, it's the equivalent of debug image, hence it will be shown in the debug-samples tab, as an image.
Make sense ?

The pipeline itself is also a task, so this line works in a pipeline. Task.current_task is a class method that returns the running task (pipeline in our case), then then the usual interface. BTW what are you having in the conf file ?

i think it can only run on multiple GPU at one node

Okay, the first step is to make sure your code is multi-node enabled, there is no magic for that 🙂

This is done in the background while accessing the cache, so it should not have any slowdown effect

Hi DashingHedgehong5
Is the text the ,labels on the histogram bucket ?

https://allegro.ai/clearml/docs/rst/references/clearml_python_ref/logger_module/logger_logger.html#clearml.logger.Logger.report_histogram

Notice the xlabels arguments, id this what you are looking for ?

JumpyPig73 Do you see all the configurations under the Args section in the "Configuration" Tab ?
(Maybe I'm wrong and the latest RC does Not include the python-fire support)

can i run a random task from a queue? like this 

clearml-agent execute --id <TASK_ID>

  or

ChubbyLouse32 This will just work out of the box 🙂
No need to enqueue the Task, just reset it (in the UI)

Any reason not to do so in the conf file ?

Hi GiganticTurtle0
You can keep clearml following the dictionary auto updating the UI
args = task.connect(args)

Hi FreshKangaroo33
clearml.conf is HOCON format, to parse you can use pyhocon:
https://github.com/chimpler/pyhocon
Or the built in version of clearml:
from clearml.utilities.pyhocon import ConfigFactory config_dict = ConfigFactory.parse_string(text).as_plain_ordered_dict()You can also just get the parsed object
from clearml.config import config_obj

task = Task.init(project_name='debug', task_name='test tqdm cr cl') print('start') for i in tqdm.tqdm(range(100), dynamic_ncols=True,): sleep(1) print('done')This code snippet works as expected (console will show the progress at the flush interval without values in between). What's the difference ?!

Yes (Mine isn't and it is working 🙂 )

Correct (copied == uploaded)

why not let the user start with an empty comparison page and add them from "Add Experiment" button as well?

Apologies, I was not clear. Yes I'm with you, this is a great idea 🙂

sh?

Hi ReassuredTiger98
Agent's queue priory can be translated to the order the agent will pull jobs from.
Now let's assume we have two agents with priorities A,B for one and B,A for the other. If we only push a Task to queue A, and both agents are idle (implying queue B is empty), there is no guarantee which one will pull the job.
Does that make sense ?
What is the use-case you are trying to solve/optimize for ?

So I might be a bit out of sync, but I think there should be Triton serving and OpenVino serving built into it (or at least in progress).

Can't think of a reason it will have such an effect ...

the time taken to upload halved. It is puzzling because as you say it's not that much to upload.

Maybe it was the load on the server? meaning dealing with multiple requests at the same time delayed the requests?!

For now I've whittled down the number of entries to a more select but useful few and that has solved the issue. If it crops up again I will try

connect_configuration

properly.
Thanks for your help!

My pleasure 🙂

Where is the cleamlr-server running? GCP as well?

. Does

Task.connect

send each element of the dictionary as a separate api request? Has anyone else encountered this issue?

Hi SuperiorPanda77
the task.connect ends up as a single call with all the data being sent on a single request.
That said, maybe the connect dict is not the best solution for thousand key dictionary ...
Maybe artifact, or connect_configuration are better suited ?
wdyt?

connect_configuration

seems to take about the same amount of time unfortunately!

I think it is a better solution, that said from your description it sounds the issue is the upload bandwidth (i.e. json-ing the dict itself), could that be it?
(and even 1000 entries seems like something that would end up at 1mb upload, that is not that much)

PunySquid88 RC1 is out with a fix:
pip install trains-agent==0.14.2rc1

Hi PunyGoose16 ,
I think the website is probably the easiest 🙂
https://clear.ml/contact-us/
I think they get back to quite quickly

Hi DeliciousBluewhale87 ,
Yes they do (I think it's ClearML Enterprise or Allegro ClearML). I also know it has extended capabilities in data management , permissions , and security.
More than that you should probably talk to them directly ( https://clear.ml/contact-us/ ) 🙂

DeliciousBluewhale87 out of curiosity , what do you mean by "deployment functionality" ? is it model serving ?

DeliciousBluewhale87 Is it ML or DL serving you are after ?

Maybe different API version...
What's the trains-server version?

SuperiorPanda77 I have to admit, not sure what would cause the slowness only on GCP ... (if anything I would expect the network infrastructure would be faster)