Add docker-compose support for easier and more portable environment set-up

[apockill]

What this does

This PR enables users to easily build and enter a docker container which has lerobot and it's dependencies installed.

Installing lerobot is now as easy as:

docker compose build
docker compose run lerobot

and that plops you right into a container within a tested lerobot environment.

Features:

• It mounts the current directory at runtime, so development is exactly the same as local development
• It mounts "/dev/" allowing serial communication as though you were running it as host
• It will help linux users who are suffering from environment issues. More on this below.
• It opens up ports that are used within the lerobot scripts (like visualize_dataset and visualize_dataset_html)
• Wandb credentials are mounted into the container

Why:

• I'm using ubuntu 22.04 and have been frustrated several times debugging different dependency related issues. For example, the control_robot record functionality expects that I will want to save my video using libsvtav1 (see video_utils.encode_video_frames). However, for whatever reason my version of ffmpeg doesn't allow that, and I had to manually edit code to get off the ground.
• There are currently incompatibilities with dependencies and their build version. For example, I ran into this issue: Torchvision import leads to OpenCV imshow freeze pytorch/vision#5940
  Which froze my development, since I wanted to run imshow and check out the cameras when debugging policies.

Anyways, I think it'd be exciting to give users another way to install dependencies.

I don't want to get too far into this without some buy-in. Please let me know if this is interesting, and would be wanted. Cheers!

How it was tested

If folks agree this is desired, I'll add a CI pipeline that validates the build.

Personally, I'm still working on testing this. I'll report back after feedback from reviewers.

Here's what I've personally tested, so far:

• visualize_dataset: Works, but you need the rerun server locally. I forward the ports in the docker-compose.
• visualize_dataset_html.py: Works great
• control_robot, train, and replay
• Run unit tests
• Validate cv2.imshow works
• Wandb credential passthrough works
• Huggingface credential passthrough works
• Anything else I'm missing? Let me know! 😁

How to checkout & try? (for the reviewer)

Just go ahead and check out my fork, and run the commands I documented in the README. I'm most interested in tests done in different hardware. I was thinking of supporting GPU and CPU cases, but is it worth supporting ARM images (a la Jetson) as well?

TODO:

• Fix cv2.imshow crashing if torchvision has been installed
• Record, train, test a policy as a sanity test

For a future PR

• Split docker-compose into multiple 'services', to allow users to docker run lerobot or 'docker run lerobot-arm'. It would be nice to have easy set-up on jetson devices!

[apockill]

In future PR's we could split up this service to be lerobot-x86 and lerobot-arm to support multiple platforms (like jetson).

For now, I think let's see how people use this, improve the experience, then tackle multi-arch support in the future.

[apockill]

@Cadene I think this is ready for review.

I'd love to merge it, I'd be happy take responsibility and be the code owner for docker issues, and improve the as people bring in feedback.

I've spent the last two weeks developing in this branch, using docker exclusively. I'm pretty happy with the experience. Everything works great, including integration with huggingface, and wandb. I've recorded, trained, teleoped, and visualized without issues. Display-forwarding works great. That annoying bug with opencv/ffmpeg/pyav dependencies conflicting is also fixed!

Let me know what you think!