SLM Lab
v4.2.0
v4.2.0
  • 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
Powered by GitBook
On this page
  • Installing SLM Lab
  • Alternative Installations
  • Hardware Requirements

Was this helpful?

  1. 🖥Setup

Installation

PreviousSLM LabNextQuick Start

Last updated 3 years ago

Was this helpful?

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

Windows

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:

Google Colab/Jupyter Notebook

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.

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 .

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 .

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