Getting Started with Adept: An Essential Guide

Jan 30, 2021 | Data Science

Adept is a powerful reinforcement learning framework designed to help researchers accelerate their work by removing engineering hurdles typically associated with deep reinforcement learning. With features like multi-GPU training, a modular interface, and baseline reinforcement learning algorithms implemented in PyTorch, Adept is an invaluable tool for AI enthusiasts. In this article, we’ll walk you through the installation process, quickstart instructions, and some troubleshooting ideas.

Installation

Getting Adept up and running is a breeze! Just follow the instructions below:

bash
git clone https://github.com/heronsystems/adeptRLcd
cd adeptRL
pip install -e .[all]

If you prefer to use Docker, you can follow the docker instructions.

Quickstart: Training an Agent

To get started with training an agent, Adept provides easy commands to use both local and distributed modes. Here’s how:

  • Local Mode (A2C): We recommend using a system with at least 4GB GPU memory, 8GB RAM, and 4 CPU cores. Run:
  • bash
    python -m adept.app local --env BeamRiderNoFrameskip-v4
    
  • Distributed Mode (A2C): For enhanced performance, use 2+ GPUs and run the following command:
  • bash
    python -m adept.app distrib --env BeamRiderNoFrameskip-v4
    
  • IMPALA: If you’re up for it, try the IMPALA mode, which requires more resources:
  • bash
    python -m adept.app actorlearner --env BeamRiderNoFrameskip-v4
    
  • To see all options, type:
  • bash
    python -m adept.app -h
    

Creating Custom Implementations

If you want to use your own Agent, Network, Environment, or SubModule, the structure is straightforward:

python
from adept.scripts.local import parse_args, main
from adept.network import NetworkModule, SubModule1D
from adept.agent import AgentModule
from adept.env import EnvModule

class MyAgent(AgentModule):
    pass  # Implement

class MyEnv(EnvModule):
    pass  # Implement

class MyNet(NetworkModule):
    pass  # Implement

class MySubModule1D(SubModule1D):
    pass  # Implement

if __name__ == '__main__':
    import adept
    adept.register_agent(MyAgent)
    adept.register_env(MyEnv)
    adept.register_network(MyNet)
    adept.register_submodule(MySubModule1D)
    main(parse_args())

Run your script by specifying the agent and environment:

bash
python my_script.py --agent MyAgent --env env-id-1 --custom-network MyNet

Features of Adept

Adept offers a myriad of features to enhance your deep reinforcement learning journey, including:

  • Multi-GPU support with NCCL backend for efficient training
  • Modular Network Interface to support different shapes with stateful networks
  • OpenAI Gym environments for benchmarking
  • Baseline algorithms like Advantage Actor Critic (A2C) and IMPALA

Performance Insights

Adept showcases remarkable performance, achieving up to 12,000 frames per second on Atari with a GeForce 2080 Ti. Our experiments demonstrated:

  • Training over 200 million frames in under an hour
  • Efficiency with NVLINK and InfiniBand support for multi-GPU environments

Troubleshooting Guide

Encountering issues? Here are some common troubleshooting ideas to assist you:

  • Ensure your GPU is set up correctly and has enough memory.
  • Check dependencies and ensure you have the required packages installed.
  • Consult the logs located in tmp/adept_logs for any error messages.
  • If you encounter any performance issues, consider optimizing your hyperparameters based on the provided defaults.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Final Thoughts

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.

Now that you know how to get started with Adept, dive into your deep reinforcement learning projects and push the boundaries of what’s possible!

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