Alternative ways to work with GPU
Hi all,
I have 2 suggested ways for you:
1. Using google colab
You can copy my colab notebook: https://colab.research.google.com/drive/1JRS6xTvBKGL74mJP6wyj1RlBhU7-4A9O and transfer your code to the new notebook.
It requires adjusting the code and takes some babysitting (it only lasts 12H) but it's free available GPU to get you started quickly.
2. Using virtual environment on another server
Some of you may have access to more storage on the university's machine, this is for you (since it takes about 1.5gb and regular disk quota is 1gb).
Just follow the instructions below:
- ssh savant/rack-gamir-g04/5/6 (from nova)
- bash
- cd <directory for your env>
- # create the virtual environment once
- virtualenv -p /usr/local/lib/anaconda3-5.1.0/bin/python dqn_env
- # NOTE: you need to activate venv each time before running
- source dqn_env/bin/activate
- # install all of the requirements
- pip install "gym[atari]"==0.9.5
- pip install opencv-python
- pip install torch
- pip install matplotlib
- # run project from within the environment
- python main.py
- # sanity check from within the environment
- python
- import gym
- print(gym.version) # make sure it says 0.9.5
- gym.make('PongNoFrameskip-v4') # make sure this command succeeds