Hey TimelyPenguin76 , I am actually trying to perform hyper param optimization. In the parent task, the libs appear installed. The training task (child), there are only a few libraries installed. Incidentally, it is in training task that fails. The parent task runs to completion initially. Perhaps I am doing something incorrect with the hyper param set up.

I suspect that the agent may be pulling in the requirements from the base env and not the Research env. How is that controlled and can I specify that explicitly?

I think my mistake AgitatedDove14 was to run the experiment task in the same script.py as the hyper parameter task. I was creating and closing individual tasks which I assumed would work. It seems that it did not. However when I split the experiment task out completely it seems to have built the cloned task correctly. Perhaps it is the imports at the start of the script only being assigned to the first task that is created?

The console output for the cloned task also does not appear to install any of the private packages. Seems like a minimal install, possibly from the conda base env.

Thanks guys, working well now. Cheers for the assistance 🙂

Thanks TimelyPenguin76 this is the output from execution

Hey that worked. It is reproducing the same results as the base task now. Interesting, in that I didn't specify the :wq. Perhaps it got added when the agent initialized. Thanks for your help TimelyPenguin76

Yes, that is right, the cloned task. In the INSTALLED PACKAGES section it only shows 5 libs, which can't be right.

azure_storage_blob == 12.5.0
clearml == 1.1.4
pandas == 1.1.4
plotly == 4.14.2
pytz == 2021.3