Merge pull request #7 from ori-drs/et-dev

Update Docker Build

Author: YifuTao

.docker/.env

@@ -2,8 +2,6 @@ UID=1000
 GID=1000
 USERNAME=docker_dev # Set your username for the container
 
-HOST_DATA_DIR=~/data # folder on the host machine which you can attach to the container
 
 DOCKER_HOME_DIR=/home/${USERNAME} # Home directory inside the container
 DOCKER_WORK_DIR=${DOCKER_HOME_DIR}/silvr # Path to the main codebase inside the docker container
-DOCKER_DATA_DIR=${DOCKER_HOME_DIR}/data # folder inside the docker container of the attached folder HOST_DATA_DIR

.docker/docker_compose.yaml

@@ -17,5 +17,8 @@ services:
     network_mode: "host"
     tty: true
     volumes:
-        - ${HOST_DATA_DIR}:${DOCKER_DATA_DIR}
+        - ../scripts:${DOCKER_WORK_DIR}/scripts
+        - ../configs:${DOCKER_WORK_DIR}/configs
+        - ../outputs:${DOCKER_WORK_DIR}/outputs
+        - ../data:${DOCKER_WORK_DIR}/data
 

README.md

@@ -15,10 +15,17 @@
 ## Setup
 ### Docker
 Build a docker image to install dependencies and run SiLVR. 
+
 ```
+mkdir data outputs
 docker compose -f .docker/docker_compose.yaml run --build silvr
 ```
-Note: You can use your own `CUDA_ARCHITECTURES` in the Dockerfile to make the `tinycudann` build quicker.
+
+Tip 1: Update `UID` and `GID` in the `.docker/.env` to be the same as your system user ID (check by running `id` in the terminal) such that the host machine has proper permission to the `data` and `outputs` folders generated inside the docker.
+
+Tip 2: You can use your own `CUDA_ARCHITECTURES` in the Dockerfile to make the `tinycudann` build quicker.
+
+
 ### Manual Installation
 You can also install SiLVR to your system manually.
 ```
@@ -40,9 +47,9 @@ pip install -e .
 ```
 python scripts/data_downloader.py
 
-python scripts/main.py --config config/2024-03-13-roq-01-unc.yaml
+python scripts/main.py --config configs/2024-03-13-roq-01-unc.yaml
 
-python scripts/main.py --config config/2024-bodleian-01+02-unc.yaml
+python scripts/main.py --config configs/2024-bodleian-01+02-unc.yaml
 ```
 ### ICRA 24 Results
 Download sample data from [Hugging face](https://huggingface.co/datasets/ori-drs/silvr_data/tree/main), setup the [config file](./scripts/config_train.yaml), and then run the training script.