RobustRat47 I think you have to use the latest clearml package for that (1.6.0)

RC should be out later today (I hope), this will already be there, I'll ping here when it is out

GrotesqueDog77 when you say "the second issue" , do you mean the fact that both step 1 and step 2 should have access to the same filesystem?

I think the easiest way is to add another glue instance and connect it with CPU pods and the services queue. I have to admit that it has been a while since I looked at the chart but there should be a way to do that

Hi ShortElephant92
You could get a local copy from the local server, then switch credentials to the hosted server and upload again, would that work?

Yeah I can write a script to transfer it over, I was just wondering if there was a built in feature.

unfortunately no 😞
Maybe if you have a script we can put it somewhere?

You can check the example here, just make sure you add the callback and you are good to go 🙂
https://github.com/allegroai/trains/blob/master/examples/frameworks/keras/keras_tensorboard.py#L107

It said the command --aux-config got invalid input

This seems like an interface bug.. let me see if we can fix that 🙂

BTW: this seems like a triton LSTM configuration issue, we might want to move the discussion to the Triton server issue, wdyt?

Definitely!

Could you start an issue https://github.com/triton-inference-server/server/issues , and I'll jump join the conversation?

. Is there any reference about integrating kafka data streaming directly to clearml-serving...

I would expect that after calling Task.enqueue(exit=True), the local task is closed and no processes related to it is running

Ohh my apologies, I did not understand that.
Are you saying that locally you call task.remote_execute(exit_process=True) and it does not leave the local process ?

yes ...
What's your use case for passing an empty dict ? (meaning how would one use it later)

Hi EnviousStarfish54
Verified with the frontend / backend guys.
Backend allows to search for "all" tags, and frontend will add a toggle button for the UI to select or/all for the selected Tags.
Should be part of the next release

Any chance you can test with the latest RC ? 1.8.4rc2

BoredHedgehog47 can you test this one? Is it close to your code ?

Thanks ShakyJellyfish91 this really helps to narrow it down!
Let me see what I can find

orchestration module
When you previously mention clone the Task I the UI and then run it, how do you actually run it?
regarding the exception stack
It's pointing to a stdout that was closed?! How could that be? Any chance you can provide a toy example for us to debug?

Hmm good point, it should probably return he clearml python version. Is this what you mean?

Yes, I find myself trying to select "points" on the overview tab. And I find myself wanting to see more interesting info in the tooltip.

Yep that's a very good point.

The Overview panel would be extremely well suited for the task of selecting a number of projects for comparing them.

So what you are saying, this could be a way to multi select experiments for detailed comparison (i.e. selecting the "dots" on the overview graph), is this what you had in mind?

Hi StraightDog31

I am having trouble using the 

StorageManager

  to upload files to GCP bucket

Are you using the storagemanager directly ? or are you using task.upload_artifact ?
Did you provide the GS credentials in the clearml.conf file, see example here:
https://github.com/allegroai/clearml/blob/c9121debc2998ec6245fe858781eae11c62abd84/docs/clearml.conf#L110

Yup, I just wanted to mark it completed, honestly. But then when I run it, Colab crashes.

task.close() will do that
BTW what's the exception you are getting ?

Nice guys! Notice that the clearml-task can auto add the Task.init call on the fly, so you can connect any arbitrary Task and control the argparser arguments (again as parameters to the cleaml-task)
BTW: A fix for the --task-type Issue will be pushed later today 😉

Okay here is a standalone code that should be close enough? (if I missed anything let me know)

` import tempfile
from datetime import datetime
from pathlib import Path

import tensorflow as tf
import tensorflow_datasets as tfds
from clearml import Task

task = Task.init(project_name="debug", task_name="test")
(ds_train, ds_test), ds_info = tfds.load(
'mnist',
split=['train', 'test'],
shuffle_files=True,
as_supervised=True,
with_info=True,
)

def normalize_img(image, labe...

My main issue with this approach is that it breaks the workflow into “a-sync” set of tasks:

This is kind of the way you depicted it, meaning, there is an an initial dataset, "offline process" (i.e. external labeling) then, ingest process.

I was wondering if the “waiting” operator can actually be a part of the pipeline.
This way it will look more clear what is the workflow we are executing.

Hmm, so pipeline is "aborted", then the trigger relaunches the pipeline, and the pipeli...

Hi EnviousStarfish54
I remember this feature request, let me check where it stands..

I think the crux of the issue is the subprocess calls I removed.

That kind of makes sense, though if the subprocess function also had Task.init call it should have worked.
Would that be the setup to try to replicate?

Hi @<1600661423610925056:profile|StrongMouse81>

using serving base url and also other endpoint of model we add using:

clearml-serving model add

we get the attached respond:

And other model endpoints are working for you?

Great to hear!

EFS get downloaded to the k8 pod local volume?

EFS is an Amazon service that mounts a persistent FS into ec2 instances, I believe they have support for k8s as a service as well, which would make it kind of like a PV only as a service.
Does that make sense ?

GiganticTurtle0
I think that what you are looking for is:
param_dict = {'key': 1234} task.connect(param_dict, name='general')Notice that when this code runs manually (i.e. not by the agent), the dict is stored on "general" parameter section of the Task.
But when the code is executed by the Agent, the opposite happens and the parameters from the "general" section of the Task or put back into the param_dict , here the casting is done based on the type of the original values.
Generall...

Could it be you have two entries of "console_cr_flush_period" ?

Hi @<1692345677285167104:profile|ThoughtfulKitten41>

Is it possible to trigger a pipeline run via API?

Yes! a pipeline is at the end a Task, you can take the pipeline ID and clone and enqueue it

pipeline_task = Task.clone("pipeline_id_here") 
Task.enqueue(pipeline_task, queue_name="services")

You can also monitor the pipeline with the same Task inyerface.
wdyt?