Environment variables not clear

I am using the TAO toolkit V5 and I see that there are a lot of environment variables that are used inside the notebooks, but most of them are not clear.

For instance, what is the different uses of these variables:
LOCAL_PROJECT_DIR, LOCAL_EXPERIMENT_DIR, USER_EXPERIMENT_DIR.

I am aware that these variables are used to run cleaner commands, however, I don’t understand the intutition behind having these three different variables that are, for me, if I understood them well, serving the same purpose.

The same goes for: LOCAL_SPECS_DIR and SPECS_DIR. Why should there be two different directories?

Thanks

Because there are local folders and docker’s folders.
We need to differentiate them. Actually you can find the different paths when you set ~/.tao_mounts.json file.
One is the path to your local files.
Another is the mapping path to the docker’s files.
For example,
! tao ssd run ls /workspace/xxx.txt

The /workspace/xxx.txt is a path which is inside the docker. It is not a path locally.

I see. Thanks. Much clearer now.
By the way, when I run some help command, I expect to have an imminent result on how to use it, but seems that a docker image is pulled just for that. For example, running tao model faster_rcnn --help, I get the below processing …

When I run the help command again, I should wait a couple of minutes again to get the result. Is there a workaround to avoid this?

You can docker pull nvcr.io/nvidia/tao/tao-toolkit:5.0.0-tf1.15.5 firstly.

Still getting the same waiting time …

You can also run inside the docker directly.
$ tao model faster_rcnn

Then inside the docker
# faster_rcnn --help

It seems I still have the same waiting time.
I went inside the docker and ran the help command from there, but there is still a waiting time. I am wondering whether this is something that all users are experiencing or only myself.

If it’s ‘natural’ that this happens, this this is ok.

I’m closing this topic due to there is no update from you for a period, assuming this issue was resolved. If still need the support, please open a new topic. Thanks

From the screenshot, it cost about 3 seconds to prompt the result for “faster_rcnn --help” . It is normal.

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