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Setup Guide on MIT Supercloud

Setting up http proxy for outbound logging requests

  1. first pip install proxy.py

    pip install --user proxy.py

  2. Create a script called ~/proxy.sh contianing the following

    # manually add the local path
export PATH=$HOME/.local/bin:$PATH

export PROXY_PORT=5090
export http_proxy=http://$HOSTNAME:$PROXY_PORT
export https_proxy=http://$HOSTNAME:$PROXY_PORT
# This line runs the proxy in a screen session in the background
screen -dm proxy --hostname 0.0.0.0 --port $PROXY_PORT --timeout 3600 --client-recvbuf-size 131072 --server-recvbuf-size 131072

    Note: make sure you use a different port from 5000, since others might be using it.

    How to Debug: to make sure that the proxy runs and the port is unoccupied, try the following with different ports

    proxy --hostname 0.0.0.0 --port $PROXY_PORT --timeout 3600 --client-recvbuf-size 131072 --server-recvbuf-size 131072

    Important: save these in a separate ~/proxy.sh file and reference that file in your .jaynes.yml config. If you put these into your ~/.bashrc file, pip installing from the worker node would fail.

    Important: pip install uses the $https_proxy variable, where the ml-logger instrumentation server is using http at the moment.

These environment parameters should setup the requests module to automatically use your login node as the proxy server. ml_logger uses the requests-future module, which inherets from requests module for async http calls.

In case you want to run pip install from within the worker node and the https_proxy variable is not set, you need to use the following syntax:

pip install --user --proxy <your http_proxy value> <your-package>

A common mistake is setting the http_proxy variable which pip does not use because pyPI servers are all https already.

Setting up MuJoCo-py (for gym)

Summary: First install mujoco (step 1 below), and then add the following to your ~/.bashrc

export MJKEY_PATH=$HOME/.mujoco/mujoco200/mjkey.txt
# These are required for mujoco-py installation
export MUJOCO_PY_MJPRO_PATH=$HOME/.mujoco/mujoco200/
export MUJOCO_PY_MJKEY_PATH=$HOME/.mujoco/mujoco200/mjkey.txt
# These are required for GPU-accelerated rendering
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$HOME/.mujoco/mujoco200/bin
export MUJOCO_GL=egl
# this is required my dm_control but Ge does not rememeber.
export MJLIB_PATH=$HOME/.mujoco/mujoco200/bin/libmujoco200.so
# the reason for this see the detailed setup below
export PYTHONPATH=$PYTHONPATH:/state/partition1/user/$USER/mujoco-py

Detailed setup steps see below:

  1. install mujoco

    # Download mujoco
mkdir $HOME/.mujoco
cd $HOME/.mujoco
curl -O https://www.roboti.us/download/mujoco200_linux.zip
unzip mujoco200_linux.zip

    And then in your ~/.bashrc file you need to have the following

    export LD_LIBRARY_PATH $LD_LIBRARY_PATH:$HOME/.mujoco/mujoco200_linux/bin
export MUJOCO_PY_MJPRO_PATH $HOME/.mujoco/mujoco200_linux
export MJLIB_PATH $HOME/.mujoco/mujoco200_linux/bin/libmujoco200.so
export MJKEY_PATH $HOME/.mujoco/mjkey.txt

  2. Installing mujoco-py (specific to the MIT Supercloud cluster):

    If you run

    pip install --user mujoco-py

    it will raise an error that says:

    • add error
    • add link to github issue.

    What’s going on is mujoco uses file locking, which can cause a lot of performance issues, particularly on shared file systems. For that reason it’s disabled on the Lustre Shared Filesystem that we have the home directories. Luckily each node has its own local filesystem where file locking is not disabled, so there is a workaround. I’ve had someone run into this before, here is what I sent her as a workaround (along with some handy scripts to get you started). Let us know if you run into any problems or you have any questions.To do this you’ll need a copy of the source code (I’m grabbing the latest release from the github repo, you can pick a different release here if you want https://github.com/openai/mujoco-py/releases, right click on a “Source Code (tar.gz)” link and select “Copy link address” to get the URL for a different release):

    wget https://github.com/openai/mujoco-py/archive/v2.0.2.5.tar.gz
tar -xzf v2.0.2.5.tar.gz
mv mujoco-py-2.0.2.5 mujoco-py

    (this creates a directory mujoco-py-2.0.2.5 and renames it for simplicity later)Then you’ll need to do the build in the local filesystem on the login node. This will make sure all the paths are set properly.Make a directory under /state/partition1/user/ with your username:

    mkdir /state/partition1/user/$USER

    Then copy the mujoco-py folder there and go to that directory:

    cp -r /home/gridsan/$USER/mujoco-py /state/partition1/user/$USER
cd /state/partition1/user/$USER/mujoco-py

    Build/install the package (use whichever anaconda module you’d like):

    module load anaconda/2021a
python setup.py install --user

    Then check if the installation succeeds by

    pip show mujoco_py

    Important this shall not fail. If this fails, there is something wrong.``

    Now copy this directory back to your home directory:

    Important: the '/' at the end is important -- it means putting the mujoco-py folder under the $USER

    cp -r /state/partition1/user/$USER/mujoco-py /home/gridsan/$USER/

    Since each node on the system has its own local filesystem, compute nodes won’t be able to see what’s on the login node local filesystem. So what we’ll do is copy the mujoco install to the local filesystem at the start of the job. I’ve written a setup.sh script that will check to see if it’s already been copied, and copy it over if not. I also have the start of a submission script, you’ll have to put in the name of your python script and any other scheduler options you need. (edited)

  3. In your .jaynes.yml file, add the following segment to the startup entry. This is ran inside the the worker machine. setup happens in the login node instead.

    starup: >-
    mkdir -p /state/partition1/user/$USER;
    echo "copying mujoco-py";
    cp -r /home/gridsan/$USER/mujoco-py /state/partition1/user/$USER/;
    echo "finished";

To Render with dmc domains

Add the following to your setup config

startup: | 
    export MUJOCO_GL=egl;
    export CUDA_VISIBLE_DEVICES=0;

Archived Setup Instructions

These steps are no-longer needed because of the availability of default packages through the anaconda/2021a module. However, we keep these here in case you need a custom environment.

Setting up GPU Accelerated Rendering with DeepMind Control suite domains

Update: this is no longer needed because the supercloud admins made libglew  available directly. You will still need to add the egl setting to get accelerated rendering. See the steps above.

  1. You need to install glew with the following script

    mkdir ~/vendor
cd ~/vendor
wget https://github.com/nigels-com/glew/releases/download/glew-2.2.0/glew-2.2.0.zip     
unzip glew-2.2.0.zip
mv glew-2.2.0 glew
cd glew
make

  2. Then add the following into your setup config

startup: | 
    export MUJOCO_GL=egl;
    export CUDA_VISIBLE_DEVICES=0;
    export LD_PRELOAD=$HOME/vendor/glew/lib/libGLEW.so.2.2.0:/usr/lib/libGL.so.1;