Adding aggregated logs for training run

[MayankChaturvedi]

What this does

This change adds four aggregated profiling metrics in training logs- average policy update time, max policy update time, average data loading time, and max data loading time.
The comments of issue describe the thought process behind the metrics.

Examples:

	Example
New log	smpl:2K ep:3 epch:0.06 loss:3.706 grdn:94.749 lr:1.0e-05 pu_mx_av:1.472|1.159 dl_mx_av:0.022|0.010
Old log	step:200 smpl:2K ep:3 epch:0.06 Loss:2.806 grdn: 14.267 lr: 10e-05 updt_s:1.278 data_s:0.010

How it was tested

Ran the training script

python lerobot/scripts/train.py     policy=act     env=aloha     env.task=AlohaInsertion-v0     dataset_repo_id=lerobot/aloha_sim_insertion_human device=cpu

How to checkout & try? (for the reviewer)

Reviewers can run the above command to validate the output

[alexander-soare]

Looking mostly good to me! Please take a review at my comments, and once done I'll try it out on my local machine.

[alexander-soare]

Thanks @MayankChaturvedi

I tried this out, and I think maybe the pipe symbol is quite jarring on the eyes trying to scan the line quickly. Maybe a , looks better?

# Current version.
INFO 2024-09-06 10:28:34 ts/train.py:197 smpl:3K ep:26 epch:0.13 loss:0.391 grdn:8.145 lr:1.0e-05 updt_max|avg:89|84 data_max|avg:106|28

# Suggestion.
# Here I also add "ms" to make it clear it's milliseconds. To make space for that, I removed the "ep" entry.
INFO 2024-09-06 10:28:34 ts/train.py:197 smpl:3K epch:0.13 loss:0.391 grdn:8.145 lr:1.0e-05 updt_ms_max,avg:89,84 data_ms_max,avg:106,28

Let's also get @Cadene's input

[Cadene]

Hello @MayankChaturvedi ,
Thanks a lot of your contribution on this PR.
We will come back to it when time allows.
Thanks for your understanding.
Best