The part where I'm lost is why would you need the path to the temp venv the agent creates/uses ?
let's say my task is calling a bash script, and that bash script is calling another python program, I want that last python program to be executed with the environment that was created by the agent for this specific task
Do you mean what's visible in the UI, projects -> Execution: Installed Packages?
If you're just looking to reuse virtual environments, have a look here: https://clear.ml/docs/latest/docs/clearml_agent/#environment-caching
I want that last python program to be executed with the environment that was created by the agent for this specific task
Well basically they all inherit the Python environment that points to the venv they started from, so at least in theory it should be transparent when the agent is spinning the initial process.
I eventually found a different way of achieving what I needed
Now I'm curious, what did you end up doing ?
AgitatedDove14 I eventually found a different way of achieving what I needed
But that was too complicated, I found an easier approach
JitteryCoyote63 you mean in runtime where the agent is installing? I'm not sure I fully understand the use case?!
in my repo I maintain a bash script to setup a separate python env.
Hmm interesting, now I have to wonder what is the difference ? meaning why doesn't the agent build a similar one based on the requirements ?
so that one app I am using inside the Task can use the python packages installed by the agent and I can control the packages using clearml easily
That's the missing part for me, You have all the requiremnts on the Task (that you can fully control), the agent is setting a brand new venv for each Task inside a container (the venv is cahced, and you can also make the agent just use the default python without installing anything). The part where I'm lost is why would you need the path to the temp venv the agent creates/uses ?
Yea again I am trying to understand what I can do with what I have 😄 I would like to be able to export as an environment variable the runtime where the agent is installing, so that one app I am using inside the Task can use the python packages installed by the agent and I can control the packages using clearml easily
Now I'm curious, what did you end up doing ?
in my repo I maintain a bash script to setup a separate python env. then in my task I spawn a subprocess and I don't pass the env variables, so that the subprocess properly picks up the separate python env