You can disable it if you want to. Sometimes it's useful to retrace your steps but it works without :)

I'm afraid what you're trying to do isn't a supported implementation.
You'll have to choose between using docker mode to have one virtual environment for everything or using the pip mode where you can used the cached virtual environments but you can't reuse the one you currently have.

Just checking, are you just trying to use a different docker image in a task? Because then you might want to use this: https://clear.ml/docs/latest/docs/apps/clearml_task/#docker
https://clear.ml/docs/latest/docs/clearml_agent#docker-mode

And in the logs?

Or you can just load a config file or object: https://clear.ml/docs/latest/docs/references/sdk/task/#connect_configuration

That's interesting, I don't know enough to answer though. :/ AnxiousSeal95 do you happen to know if this is expected behavior? Or how to have the desired outcome?

Could you test the following:
Without reusing the virtual environment you made manually:
Can you run a task twice and see if the second run is at least reusing the virtual environment of the first run?

As far as I know, you can start a docker container with the same version and the same volumes and you should be able to just continue.

There seem to be a similar error here: https://github.com/aws/aws-cli/issues/4758

Could it be minio isn't on the same API version as S3?

I don't see SB3 here so PyTorch would be best: https://clear.ml/docs/latest/docs/integrations/libraries

You can configure what to log and what not in the task init: https://clear.ml/docs/latest/docs/clearml_sdk/task_sdk/#automatic-logging

You can turn it all off by setting auto_connect_frameworks to false but you can do a finer grained control of logged frameworks with framework-boolean pairs

You can add them in env files:
https://clear.ml/docs/latest/docs/clearml_serving/clearml_serving_setup/#advanced-setup---s3gsazure-access-optional

So could you just setup your virtual environment with a task?

PIP can install from git repositories!
So you can point to your own repository or even a specific commit hash.

https://pip.pypa.io/en/stable/topics/vcs-support/

You can fix this by using a requirements.txt or the --packages parameter
https://clear.ml/docs/latest/docs/apps/clearml_task/#package-dependencies

Could you try to see if it does work when you log those manually?
https://clear.ml/docs/latest/docs/clearml_sdk/model_sdk#manually-logging-models

Both server and agent can be configured with different ports. Which is it you`re looking for?

https://clear.ml/docs/latest/docs/webapp/webapp_exp_track_visual/#installed-packages

Happy to help :)

Have you tried
logger = Logger.current_logger()in your code?
Logger is a singleton so you should get the same object from your previously created task
https://clear.ml/docs/latest/docs/references/sdk/logger/#loggercurrent_logger

I think if you use explicit logging it only logs things you've selected but I'm not entirely sure
https://clear.ml/docs/latest/docs/guides/reporting/clearml_logging_example/

AnxiousSeal95 Do you know this one?

Do you mean in the WebUI or via the API?

Is this after you've started the clearML server that you can't find the experiments?

ThoughtfulBadger56 Have you uncommented the existing venvs_cache section in the config file?
https://clear.ml/docs/latest/docs/clearml_agent#virtual-environment-reuse

For pipelines there's currently no way to use different compressions. You can still use it when explicitly uploading https://clear.ml/docs/latest/docs/references/sdk/dataset/#upload

You can also use https://clear.ml/docs/latest/docs/references/sdk/task/#taskget_task since task.clone also accepts a task object

ExasperatedCrab78 do you know how this could be?

You can use https://clear.ml/docs/latest/docs/references/sdk/task/#taskget_project_id to get the id of the last updated project with that name

Could you test again with
https://clear.ml/docs/latest/docs/references/sdk/task/#mark_completed ?