There is no platform agnostic way to fetch CUDA/ROCm's versions
since environment variables change and users don't necessarily need
CUDA or ROCm installed to run pytorch (pytorch installs the necessary
libs if they don't exist).
Therefore, prompt the user for their GPU lib and store the result in
a textfile so the user doesn't need to constantly enter a preference.
Signed-off-by: kingbri <bdashore3@proton.me>
This is a shared module which manages the model container and provides
extra utility functions around it to help slim down the API.
Signed-off-by: kingbri <bdashore3@proton.me>
Move common functions into their own folder and refactor the backends
to use their own folder as well.
Also cleanup imports and alphabetize import statments themselves.
Finally, move colab and docker into their own folders as well.
Signed-off-by: kingbri <bdashore3@proton.me>
Add an argparser that casts over to dictionaries of subgroups to
integrate with the config.
This argparser doesn't contain everything in the config due to complexity
issues with CLI args, but will eventually progress to parity. In addition,
it's used to override the config.yml rather than replace it.
A config arg is also provided if the user wants to fully override the
config yaml with another file path.
Signed-off-by: kingbri <bdashore3@proton.me>
Direct python can be used for requirements checking. Remove the ps1
script and create a venv purely in batch.
Signed-off-by: kingbri <bdashore3@proton.me>
