At first, create an Anaconda environment that you will use for all your experiments. For your convenience, simply follow the next steps:
Login on Eagle with:
ssh eagle
or
ssh <username>@eagle.hpc.nrel.gov
The repo provides the env_example.yml file. Use it to create a new Anaconda environment at a directory of your choosing. There are three main directories on Eagle where you can install the new environment, namely /home, /scratch, and /projects. Please go to NREL HPC resources page to find more information about the various Eagle directories and how to create new Anaconda environments.
Example:
Begin by createing a subdirectory /scratch/$USER/github-repos/, cd there and clone the repo. Assuming you want to install your new environment in your scratch directory, you may want to create a directory that will contain all your Anaconda environments, e.g. /scratch/$USER/conda-envs/:
conda env create --prefix=/scratch/$USER/conda-envs/myenv -f env_example.yml
After installation is complete, make sure everything is working correctly. You can test your installation by running a small example using one of the standard Gym environments (e.g. CartPole-v0).
Activate the enironment and start a Python session
module purge
conda activate /scratch/$USER/conda-envs/myenv
python
Then, run the following:
import gym
env = gym.ens.make("CartPole-v0")
env.reset()
done = False
while not done:
action = env.action_space.sample()
obs, rew, done, _ = env.step(action)
print(action, obs, rew, done)If everything works correctly, you will see an output similar to:
0 [-0.04506794 -0.22440939 -0.00831435 0.26149667] 1.0 False
1 [-0.04955613 -0.02916975 -0.00308441 -0.03379707] 1.0 False
0 [-0.05013952 -0.22424733 -0.00376036 0.2579111 ] 1.0 False
0 [-0.05462447 -0.4193154 0.00139787 0.54940559] 1.0 False
0 [-0.06301078 -0.61445696 0.01238598 0.84252861] 1.0 False
1 [-0.07529992 -0.41950623 0.02923655 0.55376634] 1.0 False
0 [-0.08369004 -0.61502627 0.04031188 0.85551538] 1.0 False
0 [-0.09599057 -0.8106737 0.05742218 1.16059658] 1.0 False
0 [-0.11220404 -1.00649474 0.08063412 1.47071687] 1.0 False
1 [-0.13233393 -0.81244634 0.11004845 1.20427076] 1.0 False
1 [-0.14858286 -0.61890536 0.13413387 0.94800442] 1.0 False
0 [-0.16096097 -0.8155534 0.15309396 1.27964413] 1.0 False
1 [-0.17727204 -0.62267747 0.17868684 1.03854806] 1.0 False
0 [-0.18972559 -0.81966549 0.1994578 1.38158021] 1.0 False
0 [-0.2061189 -1.0166379 0.22708941 1.72943365] 1.0 True
Note that the above process does not involve any training.
Later, when you will start running reinforcement learning examples on Eagle, you will need to install other packages, most important of which the Ray RLlib library. This will enable you to run multiple instances of Gym in parallel over multiple cores per node, or even multiple nodes. You can always install new packages via:
conda install -c conda-forge <package_name>
pip install <package_name>