SLM Lab
v4.2.3
v4.2.3
  • SLM Lab
  • 🖥Setup
    • Installation
    • Quick Start
  • 🚀Using SLM Lab
    • Lab Command
    • Lab Organization
    • Train: REINFORCE CartPole
    • Resume and Enjoy: REINFORCE CartPole
    • Agent Spec: DDQN+PER on LunarLander
    • Env Spec: A2C on Pong
    • GPU Usage: PPO on Pong
    • Parallelizing Training: Async SAC on Humanoid
    • Experiment and Search Spec: PPO on Breakout
    • Run Benchmark: A2C on Atari Games
    • Meta Spec: High Level Specifications
    • Post-Hoc Analysis
    • TensorBoard: Visualizing Models and Actions
    • Using SLM Lab In Your Project
  • 📈Analyzing Results
    • Data Locations
    • Graphs and Data
    • Performance Metrics
  • 🥇Benchmark Results
    • Public Benchmark Data
    • Discrete Environment Benchmark
    • Continuous Environment Benchmark
    • Atari Environment Benchmark
    • RL GIFs
  • 🔧Development
    • Modular Design
      • Algorithm Taxonomy
      • Class Inheritance: A2C > PPO
    • Algorithm
      • DQN
      • REINFORCE
      • Actor Critic
    • Memory
      • Replay
      • PrioritizedReplay
      • OnPolicyReplay
      • OnPolicyBatchReplay
    • Net
      • MLP
      • CNN
      • RNN
    • Profiling SLM Lab
  • 📖Publications and Talks
    • Book: Foundations of Deep Reinforcement Learning
    • Talks and Presentations
  • 🤓Resources
    • Deep RL Resources
    • Contributing
    • Motivation
    • Help
    • Contact
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  • Installing SLM Lab
  • Alternative Installations
  • Hardware Requirements

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  1. 🖥Setup

Installation

PreviousSLM LabNextQuick Start

Last updated 11 months ago

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Installing SLM Lab

Clone the repository:

git clone https://github.com/kengz/SLM-Lab.git

Install the dependencies:

cd SLM-Lab/
./bin/setup

This runs a prepared bash script with the necessary setup steps, with Python dependencies managed through Conda. Refer to the page if you encounter issues.

Readers of the bookFoundations of Deep Reinforcement Learning: please see .

Alternative Installations

Bring-your-own-PyTorch/GPU

SLM Lab uses PyTorch version 1.3.1 by default. For newer GPU cards, the versions of PyTorch and CUDA that come with SLM Lab default setup above may not be supported. In this case, you may bring-your-own-PyTorch by using to setup dependencies instead. This installs the same Python modules except for PyTorch and cudatoolkit. Use the following commands:

# first install the system dependencies
sudo apt-get update && \
  apt-get install -y build-essential \
  curl nano git wget zip libstdc++6 \
  python3-dev zlib1g-dev libjpeg-dev cmake swig python-pyglet python3-opengl libboost-all-dev libsdl2-dev libosmesa6-dev patchelf ffmpeg xvfb && \
  rm -rf /var/lib/apt/lists/*

# setup Conda environment and install everything except PyTorch and cudatoolkit
conda create -n lab python=3.7.3 -y
conda env update -f environment-byo.yml
# install your own pytorch from https://pytorch.org/get-started/locally/
conda activate lab
conda install pytorch==1.7.1 cudatoolkit=11.0 -c pytorch

Windows

Google Colab/Jupyter Notebook

Google Colab (updated 2024)

Non-image based environments can run on a laptop. Only image based environments such as the Atari games benefit from a GPU speedup. For these, we recommend 1 GPU and at least 4 CPUs. This can run a single Atari Trial consisting of 4 Sessions.

For desktop, a reference spec is GTX 1080 GPU, 4 CPUs above 3.0 GHz, and 32 GB RAM.

Thanks to for help with testing this on RTX A6000 and RTX A5000 GPUs.

The best way to run SLM Lab on Windows is to use a Bash shell/Linux subsystem. Credit to and for providing a detailed instruction PDF for doing so on Windows:

Although we do not recommend running SLM Lab on Google Colab or Jupyter notebooks (notebooks come with inherent limitations, e.g. no rendering/multi-processing), we have prepared an example notebook for illustration. Credit to for helping with this:

For details on how it works, refer to .

Another user has figured out installation in Colab in 2024 - an especially helpful update given the dependencies/hardware architecture changes in the past years. Please find their detailed instructions with additional examples here:

Hardware Requirements

For cloud computing, start with an affordable instance of with a K80 GPU and 4 CPUs. Use the Deep Learning AMI with Conda when .

🖥️
💽
📖
Help
this custom instruction page
environment-byo.yml
@Karl-Grantham
@vladimirnitu
@steindaian
@piosif97
SLM Lab Colab notebook
this Help section
@isacciobota
SLM Lab Colab notebook (2024)
AWS EC2 p2.xlarge
creating an instance
190KB
SLM_for_Windows.pdf
pdf
SLM Lab for Windows (Instruction PDF)