I was thinking of using the --volume settings in clearml.conf to mount the relevant directories for each user (so it's somewhat customizable). Would that work?

It would be amazing if one can specify specific local dependencies for remote execution, and those would be uploaded to the file server and downloaded before the code starts executing

Thanks for investigating this SweetBadger76 !

Any updates on this? We can't do anything with our K8s since this 404...

Thanks for the reply @<1523701827080556544:profile|JuicyFox94> ! I'll debug more and let you know

And last but not least, for dictionary for example, it would be really cool if one could do:
my_config = task.connect_configuration(my_config, name=name) my_other_config = task.connect_configuration(my_other_config, name=other_name) my_other_config['bar'] = my_config # Creates the link automatically between the dictionaries

Hey @<1523701205467926528:profile|AgitatedDove14> , thanks for the reply!

We would like to avoid dockerizing all our repositories. And for the time being we have not used the decorators, but we can do that too.
The pipeline is instead built dynamically at the moment.

The issue is that the components do not have their dependency. For example:

def step_one(...):
    from internal.repo import private
    # do stuff

When step_one is added as a component to the pipeline, it does ...

Okay trying again without detached

These are per-user. Essentially we log user DB access as well (for various backtracking afterwards), so it's beneficial for us to pass the user DB secrets to the task and not have it configured once on the agent.

I believe that happens natively thanks to pyhocon? No idea why it fails on mac

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

Actually, it appears some elements (scalars, plots, etc) have no migrated by moving mongodb data.
Where are these stored? Any idea @<1523701827080556544:profile|JuicyFox94> ?

Yes, you're correct, I misread the exception.
Maybe it hasn't completed uploading? At least for Datasets one needs to explicitly wait IIRC

How or why is this the issue? I great something is getting lost in translation :D
On the local machine, we have all the packages needed. The code gets sent for remote execution, and all the local packages are frozen correctly with pip.
The pipeline controller task is then generated and executed remotely, and it has all the relevant packages.
Each component it launches, however, is missing the internal packages available earlier :(

I'm trying to build an easy SDK that would fit DS work and fit the concept of clearml pipelines.
In doing so, I'm planning to define various Step classes, that the user can then experiment with, providing Steps as input to other steps, etc.

Then I'd like for the user to be able to run any such step, either locally or remotely. Locally is trivial. Remotely is the issue. I understand I'll need to upload additional data to the remote instance, and pull a specific artifact back to the notebo...

Does it make sense to you to run several such glue instances, to manage multiple resource requirements?

TimelyPenguin76 that would have been nice but I'd like to upload files as artifacts (rather than parameters).

AgitatedDove14 I mean like a grouping in the artifact. If I add e.g. foo/bar to my artifact name, it will be uploaded as foo/bar .

Is there a preferred way to stop the agent?

Answering myself for future interested users (at least GrumpySeaurchin29 I think you were interested):

You can "hide" (explained below) secrets directly in the agent 😁 :
When you start the agent listening to a specific queue (i.e. the services worker), you can specify additional environment variables by prefixing them to the execution, i.e. FOO='bar' clearml-agent daemon .... Modify the example AWS autoscaler script - after the driver = AWSDriver.from_config(conf) , inject ...

The error seems to come from this line:
self._driver = _FileStorageDriver(str(path_driver_uri.root)) (line #353 in clearml/storage/helper.py
Where if the path_driver is a local path, then the _FileStorageDriver starts with a base_path = '/' , and then takes extremely long time at iterating over the entire file system (e.g. in _get_objects , line #1931 in helper.py )

The S3 bucket credentials are defined on the agent, as the bucket is also running locally on the same machine - but I would love for the code to download and apply the file automatically!

I'm also getting the following warning, I guess it's some ClearML dependency?
IPython could not be loaded!

task.upload_artifact(..., is_requirement=True) , task.connect_configuration(..., is_requirement=True)
Just implies these artifacts/configurations must be downloaded prior to running the code itself; then you also don't have to worry about zipping? 🤔

Any leads TimelyPenguin76 ? I've also tried setting up a minio s3 bucket, but I'm not sure if the remote agent has copied the credentials and host 🤔

It's also sufficient to see StorageManager.list("/data/clear") takes a really long time to return no results

On my remote server..?

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...

some absolute path, i.e. /data/clearml

Sounds like incorrect parsing on ClearML side then, doesn't it? At least, it does not fully support MinIO then

I don't imagine AWS users get a new folder named aws-key-region-xyz-bucket-hostname when they download_folder(...) from an AWS S3 bucket, or do they? 🤔

Ubuntu 18.04, latest clearml version

I... did not, ashamed to admit. The documentation says only boolean values.