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OmniGibson
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update download instructions

This commit is contained in:
fxia22 2018-05-13 19:59:57 -07:00
parent 807b50ee78
commit 9a4e1f0415
5 changed files with 23 additions and 17 deletions
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2
.dockerignore
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@ -1 +1 @@
gibson/assets
gibson/assets/dataset

4
Dockerfile
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@ -28,8 +28,8 @@ RUN conda create -y -n py35 python=3.5
ENV PATH /miniconda/envs/py35/bin:$PATH
RUN pip install http://download.pytorch.org/whl/cu90/torch-0.3.0.post4-cp35-cp35m-linux_x86_64.whl
RUN pip install torchvision
RUN pip install http://download.pytorch.org/whl/cu90/torch-0.3.1-cp35-cp35m-linux_x86_64.whl
RUN pip install torchvision==0.2.0
RUN pip install tensorflow==1.3
WORKDIR /root

4
Dockerfile_server
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@ -84,8 +84,8 @@ RUN conda create -y -n py35 python=3.5
ENV PATH /miniconda/envs/py35/bin:$PATH
RUN pip install http://download.pytorch.org/whl/cu90/torch-0.3.0.post4-cp35-cp35m-linux_x86_64.whl
RUN pip install torchvision
RUN pip install http://download.pytorch.org/whl/cu90/torch-0.3.1-cp35-cp35m-linux_x86_64.whl
RUN pip install torchvision==0.2.0
RUN pip install tensorflow==1.3
WORKDIR /root

28
README.md
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@ -99,13 +99,16 @@ You can either 1. build your own docker image or 2. pull from our docker image.
1. Build your own docker image (recommended)
```bash
git clone https://github.com/StanfordVL/GibsonEnv.git
cd GibsonEnv
wget https://storage.googleapis.com/gibsonassets/assets.tar.gz -P gibson
./build.sh decompress_data
cd GibsonEnv/gibson
wget https://storage.googleapis.com/gibsonassets/assets_core.tar.gz
tar -zxf assets_core.tar.gz
cd assets
wget https://storage.googleapis.com/gibsonassets/dataset.tar.gz
tar -zxf dataset.tar.gz
### the commands above downloads assets data file and decpmpress it into gibson/assets folder
docker build . -t gibson ### finish building inside docker
```
If the installation is successful, you should be able to run `docker run --runtime=nvidia -ti --rm -e DISPLAY -v /tmp/.X11-unix:/tmp/.X11-unix -v <host path to assets folder>:/root/mount/gibson/gibson/assets gibson` to create a container.
If the installation is successful, you should be able to run `docker run --runtime=nvidia -ti --rm -e DISPLAY -v /tmp/.X11-unix:/tmp/.X11-unix -v <host path to dataset folder>:/root/mount/gibson/gibson/assets/dataset gibson` to create a container.
2. Or pull from our docker image
@ -121,7 +124,7 @@ Instructions to run gibson on a headless server:
1. Install nvidia-docker2 dependencies following the starter guide.
2. Use `openssl req -new -x509 -days 365 -nodes -out self.pem -keyout self.pem` create `self.pem` file
3. `docker build -f Dockerfile_server -t gibson_server .` use the `Dockerfile_server` to build a new docker image that support virtualgl and turbovnc
4. `docker run --runtime=nvidia -ti --rm -e DISPLAY -v /tmp/.X11-unix/X0:/tmp/.X11-unix/X0 -v <host path to assets folder>:/root/mount/gibson/gibson/assets -p 5901:5901 gibson_server`
4. `docker run --runtime=nvidia -ti --rm -e DISPLAY -v /tmp/.X11-unix/X0:/tmp/.X11-unix/X0 -v <host path to dataset folder>:/root/mount/gibson/gibson/assets/dataset -p 5901:5901 gibson_server`
in docker terminal, start `/opt/websockify/run 5901 --web=/opt/noVNC --wrap-mode=ignore -- vncserver :1 -securitytypes otp -otp -noxstartup` in background, potentially with `tmux`
5. Run gibson with `DISPLAY=:1 vglrun python <gibson example or training>`
6. Visit your `host:5901` and type in one time password to see the GUI.
@ -146,17 +149,20 @@ Install required deep learning libraries: Using python3.5 is recommended. You ca
conda create -n py35 python=3.5 anaconda
source activate py35 # the rest of the steps needs to be performed in the conda environment
conda install -c conda-forge opencv
pip install http://download.pytorch.org/whl/cu90/torch-0.3.0.post4-cp35-cp35m-linux_x86_64.whl
pip install torchvision==0.1.9
pip install http://download.pytorch.org/whl/cu90/torch-0.3.1-cp35-cp35m-linux_x86_64.whl
pip install torchvision==0.2.0
pip install tensorflow==1.3
```
Clone the repository, download data and build
```bash
git clone https://github.com/StanfordVL/GibsonEnv.git
cd GibsonEnv
wget https://storage.googleapis.com/gibsonassets/assets.tar.gz -P gibson
./build.sh decompress_data ### decompress data
#the commands above downloads assets data file and decpmpress it into gibson/assets folder
cd GibsonEnv/gibson
wget https://storage.googleapis.com/gibsonassets/assets_core.tar.gz
tar -zxf assets_core.tar.gz
cd assets
wget https://storage.googleapis.com/gibsonassets/dataset.tar.gz
tar -zxf dataset.tar.gz
#### the commands above downloads assets data file and decpmpress it into gibson/assets folder
./build.sh build_local ### build C++ and CUDA files
pip install -e . ### Install python libraries
```

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gibson/.gitignore vendored
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assets.tar.gz
*.tar.gz