not the most intuitive approach but I’ll give it a go

RoughTiger69 , you can also use Task.add_requirements  for a specific package through the script

Example: Task.add_requirements('tensorflow', '2.4.0') Example: Task.add_requirements('tensorflow', '>=2.4') Example: Task.add_requirements('tensorflow') -> use the installed tensorflow version Example: Task.add_requirements('tensorflow', '') -> no version limit

RoughTiger69 Hi!

Regarding your questions:
You can use the following: Task.force_requirements_env_freeze(requirements_file='repo/some_folder/requirements.txt') before your task=Task.init(...) You can configure sdk.development.detect_with_pip_freeze=true in your ~/clearml.conf file for full env detection from the environment you're running from

Hi RoughTiger69

seems to not take the pacakges that are in the requirements.txt

The reason for not taking the entire python packages, it will most likely break when trying to run inside the agent.
The directly imported packages aill essentially pull their required packages, and thus create a stable env on the remote machine. The agent then will store the Entire env, as it assumes it will be able to fully replicate it the next time it runs.
If the "Installed Packages" section is empty (notice on hover you have a button to clear it), the agent will revert to using the requirement.txt inside the repo.
Make sense?