Maybe create_function_task ? 🤔

I tried that, unfortunately it does not help 😞

Last but not least - can I cancel the offline zip creation if I'm not interested in it 🤔
EDIT: I see not, guess one has to patch ZipFile ...

Hey AgitatedDove14 🙂
Finally managed; you keep saying "all projects" but you meant the "All Experiments" project instead. That's a good start 👍 Thanks!

Couple of thoughts from this experience:
Could we add a comparison feature directly from the search results (Dashboard view -> search -> highlight some experiments for comparison)? Could we add a filter on the project name in the "All Experiments" project? Could we add the project for each of the search results? (see above pictur...

Note that it would succeed if e.g. run with pytest -s

If I set the following:
"extra_clearml_conf": "sdk.aws.s3.credentials = [\n{\nhost: 'ip:9000'\nkey: 'xxx'\nsecret: 'xxx'\nmultipart: false\nsecure: false\n},\n{\nhost: 'ip2:9000'\nkey: 'xxx'\nsecret: 'xxx'\nmultipart: false\nsecure: false\n}\n]"I run into a weird furl error:
ValueError: Invalid port '9000''.

That's up and running and is perfectly fine.

I'm trying, let's see; our infra person is away on holidays :X Thanks! Uh, which configuration exactly would you like to see? We're running using the helm charts on K8s, so I don't think I have direct access to the agent configuration/update it separately?

Also I can't select any tasks from the dashboard search results 😞

I'm running tests with pytest , it consumes/owns the stream

The last kind when launching the agents.
The former on task execution.

Thanks!

There's not much (or anything) in the log to provide...

` (.venv) 15:42 [0:user@server$~] CLEARML_CONFIG_FILE=~/agent_clearml.conf clearml-agent daemon --queue default on_prem --detached --order-fairness
Environment variables set from configuration: ['AWS_ACCESS_KEY_ID', 'AWS_SECRET_ACCESS_KEY', 'AWS_DEFAULT_REGION']
...

Thanks SuccessfulKoala55 and AgitatedDove14 ! We'll go through the hoops of setting up mongo on AWS then.
We're working to decouple the data from the helm chart, seems like a dangerous idea to store long term data on k8s in case of failure 😅

I'm not sure about the intended use of connect_configuration now.
I was under the assumption that in connect_configuration(configuration, name=None, description=None) , the configuration is only used in local execution.
But when I run config = task.connect_configuration({}, name='General') (in remote execution), the configuration is set to the empty dictionary

True, and we plan to migrate to pipelines once we have some time for it :) but anyway that condition is flawed I believe

A couple of minutes or so

Yes, thanks AgitatedDove14 ! It's just that the configuration object passed onwards was a bit confusing.
Is there a planned documentation overhaul? 🤔

I can also do this via Mongo directly, but I was hoping to skip the K8S interaction there.

Not necessarily on the same branch, no

Yeah that works too. So one can override the queue ID but not the worker 🤔

Ah. In the extra_vm_bash_script of the AWS autoscaler.

Could also be that the use of ./ is the issue? I'm not sure what else I can provide you with, SweetBadger76

Another example - trying to validate dataset interactions ends with

` else:
self._created_task = True
dataset_project, parent_project = self._build_hidden_project_name(dataset_project, dataset_name)
task = Task.create(
project_name=dataset_project, task_name=dataset_name, task_type=Task.TaskTypes.data_processing)
if bool(Session.check_min_api_server_version(Dataset.__min_api_version)):
get_or_create_proje...

JitteryCoyote63 yes exactly, sorry, I forgot to add the Task.get_task in my response. That's exactly what we do 😅

Haha, I've opened so many issues these past few days... Sure, np!

Most of these are configurations (specific for an execution, but one such configuration defines multiple tasks). Some models might be uploaded if the user does not use our built-in link to ClearML model fetching 😄

Honestly, this is all related to issue #340. The only reason we have this to begin with is because we need one separate "initializer" task that downloads the remote cache and prepares the agent environment for execution (downloading the configuration files, etc).
Otherwise it fits perfectly with pipelines, but we're not there yet.

In the local execution we don't have this initializer task, so we use Task.init() before starting to work on a model, and task.close() when we're done....

This also appears in the error log:

` StorageManager.download_folder(cache_dir.as_posix(), local_folder=".")
File "/home/idan/.clearml/venvs-builds/3.7/lib/python3.7/site-packages/clearml/storage/manager.py", line 278, in download_folder
for path in helper.list(prefix=remote_url):
File "/home/idan/.clearml/venvs-builds/3.7/lib/python3.7/site-packages/clearml/storage/helper.py", line 596, in list
res = self._driver.list_container_objects(self._container, ex_prefix=prefix)
Fi...

I'm working on the config object references 😉