You can also use Task.force_requirements_env_freeze to freeze an exact copy of your environment.
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In general I would suggest running in docker mode 🙂
Hi @<1540867420321746944:profile|DespicableSeaturtle77> , what didn't work? What showed up in the experiment? What was logged in the installed packages?
incompatibility issue because the agent was trying to setup a version of numpy not supported.
I have a question regarding docker mode use here, I want to run my task remotely on docker, when I execute the script containing task.execute_remotely(), it analyzes the packages in the current env which are different from the ones needed.
I also tried using the task.set_packages('./requirements.txt')
but it didn't work
Solved my issue by adding this before the Task.init()
Task.force_requirements_env_freeze(requirements_file='./requirements.txt')
Hi @<1548839979558375424:profile|DelightfulFrog42> , you can use tasks.set_requirements to provide specific packages or a requirements.txt:
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