JitteryCoyote63 sure, this is how it was designed to work 🙂

repeat it until they are all dead 🙂

Wait, let me double check

Hmm I assume it is not running from the code directory...
(I'm still amazed it worked the first time)
Are you actually using "." ?

No -- that section is blank,

This is the main issue, it should be filled with the requirement being auto detected.
The entire script was executed from within vscode, and the Task was created but it was not prefilled with anything ?
Just making sure, you called Task.init inside your code ?

Notice that if you pass string it will split it based on spaces

Can you share the modified help/yaml ?
Did you run any specific migration script after the upgrade ?
How many apiserver instances do you have ?
How did you configure the elastic container? is it booting?

While if I just download the right packages from the requirements.txt than I don't need to think about that

I see you point, the only question how come these packages are not automatically detected ?

If I have access to the logs, python env and git commits, is there an API to log those to the experiments too?

Sure:
task.update_task see here:
https://clear.ml/docs/latest/docs/references/sdk/task#update_task
example:
task.update_task(task_data={'script': {'branch': 'new_branch', 'repository': 'new_repo'}})The easiest way to get all the different sections (they should be relatively self explanatory) is calling task.export_task() which returns a dict with all the fields yo...

why are there indefinitely growing anonymous tasks, even after i've closed the main schedulers.

The anonymous Tasks are The Dataset you are creating (a Dataset version is also a Task of a certain type with artifacts, the idea is usually Datasets are created from code, hence the need to combine the two).
Make sense ?

😄

Hi all! Does anyone know a solution to my issue with deploying models saved on azure on the clearml-serving docker container?

Hi NuttyCamel41
The easiest is to map the clearml.conf to both the serving and triton containers in your docker-compose.yaml (or k8s secrets) and make sure the conf file has the credentials to access the azure blob. wdyt ?

Hi GrotesqueOctopus42

creates a graph of the neural network and would be nice to have it on the experiment logs aswell

I think the main issue is displaying later in the UI, thoughts?
BTW: is this useful for you outside f very local TF debugging ?

Hi OddAlligator72

for instance - remove all the metrics from some step onward? 

(I think that as long as the Task is not published you could do such a thing directly with the RestAPI (aka APIClient from python)
What's the use case?

Thanks!
Hmm from here : None
Could it be you do not have privileges to the resource, or that you did not provide credentials ?
Did that autoscaler work before ?

in my repo I maintain a bash script to setup a separate python env.

Hmm interesting, now I have to wonder what is the difference ? meaning why doesn't the agent build a similar one based on the requirements ?

https://stackoverflow.com/questions/37743683/why-is-an-empty-mongodb-database-so-big

Hi CluelessElephant89

hey guys, I believe 

clearml-agent-services

 isn't necessary right?

Generally speaking, yes you are corrected 🙂
Specifically, this is the "services" queue agent, running your pipeline logic, services etc.
But it is not a must to get the server to work, and you can also spin it on a different host

Task.add_requirements('.')Should work

but this is not different from not using clearml-data,

ReassuredTiger98 just making sure we are on the same page. clearml-data immutability is fixed, the user cannot change the content of the dataset (it is actually compressed and uploaded). If you want to change it, you create a new child version

Since my deps are listed in the dependencies of my setup.py, I don't want clearml to list the dependencies of the current environment

Make sense 🙂
Okay let me check regrading the "." in the venv cache.

Hi PanickyMoth78
` torch.save(net.state_dict(), PATH) # auto-uploads to GCS

get all the models from the Task

output_models = Task.current_task().models["output"]

get the last one

last_model = output_models[-1]

set meta-data

last_model.set_metadata(key="my key", value="my value", type="str") `

Actually doesn't matter (systemd and init.d are diff ways to spin services on diff linux distros) you can pick whatever seems more continent for you, and whichever is supported by the linux you are running (in most cases both are) 🙂

Hi SubstantialElk6
where exactly in the log do you see the credentials ?

/tmp/.clearml_agent.234234e24s.cfg

What's the exact setup ? (I mean are you using the glue? if that's the case I think the temp config file is only created inside the pod/docker so upon completion it will be deleted along side the pod.

GiganticTurtle0 found it, fix will be pushed tomorrow 🙂

We use an empty queue to enqueue our tasks in, just to trigger the scheduler

it's only importance is that the experiment is not enqueued anywhere else, but the trigger then enqueues it

👍

It's just that the trigger is never triggered
(Except when a new task is created - this was not the case)

Is the trigger controller running on the services queue ?

The configuration tab -> configuration objects -> pipeline is empty

That's the reason it is doing nothing 😞
How come it is empty if you Cloned the local one?

Hmmm are you saying the Dataset Tasks do not have the "dataset" system_tag as well as the type ?

How can the first process corrupt the second

I think that something went wrong and both Agents are using the same "temp" folder to setup the experiment.

why doesn't this occur if I run pipeline from command line?

The services queue is creating new dockers with everything in them so they cannot step on each others toes (so to speak)

I run all the processes as administrator. However, I've tested running the pipeline from command line in non-administrator mode, it works fine....

Thanks!
I think this one will cover both case (the issue is with files on the root of the dataset)
if not (fnmatch(k, path) and fnmatch(k if '/' in k else '/{}'.format(k), '*/' + wildcard))}