Elastic Neural Radiance Fields
git clone https://github.com/saeejithnair/elastic-nerf.git
git submodule update --init --recursive
Run the initialization script at the root of the directory. This will generate an .env file in the repo root which will be sourced by docker compose and hold your user specific configurations and secret keys for logging and tracking experiments.
./initialize.sh
Elastic-NeRF can be run either in a docker container or in a conda environment.
By default, the container is built for CUDA Architecture 86 which supports the Nvidia RTX A6000 GPU. The container can be easily customized for other architectures by prefixing any docker compose command with CUDA_ARCHITECTURES=ARCH_ID. For example, to build a container for a 75 series architecture, you can set the environment variable at build time as follows (various examples):
CUDA_ARCHITECTURES=75 docker compose build elastic-nerf
CUDA_ARCHITECTURES=75 docker compose build elastic-nerf-dev
CUDA_ARCHITECTURES=75 docker compose up --build elastic-nerf-dev
The container can be built in one of two modes: developer mode or serving mode. The core idea is that developer mode is intended for building on the codebase and automatically enables things like pre-commit hooks for linting, auto-formatting, type checking, etc but these bog down the container start up time if you only want to run a model. Hence for cases like running experiments (including training and inference after you're finished prototyping) or hyperparameter searches with WandB sweeps, try serving mode as it's slightly more lightweight.
- Spin up development container by running
docker compose up --build elastic-nerf-dev - Once it finishes building (wait for an output message like elastic-nerf-smnair-elastic-nerf-dev-1 | Starting container and waiting forever..), attach to the container using the VS-Code docker extension
- Start coding!
To just build the non-dev service, run
docker compose build elastic-nerf
To build and start the service as a detached instance, and then run a specific command
docker compose run -d --build elastic-nerf {your command here}
A common use case is to start a pre-configured WandB sweep on a specific GPU. This can be done by running:
GPU_IDS=0 CUDA_ARCHITECTURES=75 docker compose run -d --build elastic-nerf wandb agent wandb-entity-name/elastic-nerf/abc0defg
TODO
Nerfstudio hosts a number of NeRF related datasets which can be easily downloaded by running Nerfstudio's ns-download command from within a container or environment. E.g. the following command downloads the Blender Synthetic dataset.
ns-download-data blender --save-dir /home/user/shared/nerfstudio/data/
You can also download the dataset to a filesystem drive that has been mounted to a container running in serving mode.
docker compose run -d --build elastic-nerf ns-download-data blender --save-dir /home/user/shared/nerfstudio/data/