Setup for VAB-OmniGibson

Installation

We have tested on Ubuntu. VAB-OmniGibson requires 11 GB NVIDIA RTX GPU and NVIDIA GPU driver version >= 450.80.02. For more detailed requirements, please refer to Isaac Sim 2022.2.0.
Besides docker, install NVIDIA container toolkit on your machine.

Get pre-built docker image.

If you have access to docker hub:

docker pull tianjiezhang/vab_omnigibson:latest

Or you can download from ModelScope.

Download from ModelScope:

git lfs install

git clone https://www.modelscope.cn/datasets/VisualAgentBench/VAB-OmniGibson-Docker.git

Load the docker image from ModelScope dataset.

docker load -i VAB-OmniGibson-Docker/vab_omnigibson.tar

Download datasets of OmniGibson, VAB-OmniGibson test activities, and related scene files. Note that about 25 GB data will be downloaded to data/omnigibson, and make sure you have access to google drive.
```
python scripts/omnigibson_download.py
```

According to your hardware equipment, fill available_ports and available_devices in the task configuration file configs/tasks/omnigibson.yaml.
- available_ports: Please fill in available ports in your machine. Each concurrent docker container requires 1 port for communication with the task server. Ensure that you provide enough ports to accommodate the expected concurrency.
- available_devices: Please fill in GPU IDs and their corresponding capability of concurrency. Each concurrent docker container occupies about 11 GB memory. Ensure that you provide enough GPU memory to accommodate the expected concurrency.
It's recommended to increase the file change watcher for Linux. See Omniverse guide for more details.
- View the current watcher limit: cat /proc/sys/fs/inotify/max_user_watches.
- Update the watcher limit:
  1. Edit /etc/sysctl.conf and add fs.inotify.max_user_watches=524288 line.
  2. Load the new value: sudo sysctl -p.

Note: If you manually shut down the task server and assigner, please ensure you also stop the OmniGibson containers to free up the ports!