hyperfetch 1.0.0

HyperFetch. A tool to optimize and fetch hyperparameters for your reinforcement learning application.

HyperFetch

HyperFetch is a tool consisting of:

• Website for fetching hyperparameters that are tuned by others
• Pip-module for tuning hyperparameters

The intention of HyperFetch is to:

• Make recreation of existing projects easier within the reinforcement learning research community.
• Allow beginners to train and implement their own reinforcement learning models easier due to abstracting away the advanced tuning-step.

The tool is expected to aid in decreasing CO2-emissions related to tuning hyperparameters when training RL models.

This is expected to be done by posting tuned algorithm x environment combinations to the websitesuch that:

• Developers/Students can access hyperparameters that have been optimially tuned before instead of having to tune them themselves.
• Researchers can filter by project on the website and access hyperparameters they wish to recreate/replicate for their own research.

The persistance endpoints opens up to the user through this package. To access/fetch hyperparameters optimized by other RL-practicioners, have a look at the HyperFetch website.

Content

• Links
• 1.0 Using the pip module
  • 1.1 Examples
• 2.0 Using the Website
  • 2.1 Installation backend
  • 2.2 Setup backend
  • 2.3 Setup frontend
  • 2.4 Installation frontend

Links

Repository: HyperFetch Github
Documentation: HyperFetch Website

Using the pip module

To use the pip model please do the following:

1. Create a virtual environment in your favorite IDE. The virtual environment must be of the type virtualenv.

Install virtualenv if you haven't

    pip install virtualenv

Create a virtual environment

    virtualenv [some_name]

Activate virtualenv this way if using windows:

   # In cmd.exe
   venv\Scripts\activate.bat
   # In PowerShell
   venv\Scripts\Activate.ps1

Activate virtualenv this way if using Linux/MacOS:

    $ source myvenv/bin/activate

2. Install the pip-module!

Example 1: tuning + posting using HyperFetch

Here is a quick example of how to tune and run PPO in the LunarLander-v2 environment inside your new or existing project:

Just a reminder:

The pip package must be installed before this can be done. To install the pip-package, the steps to get the front -or backend started/running do not need to be done.
For details, see using the pip module.

1. Create configuartion YAML file (minimal example)

# Required (example values)
alg: ppo
env: LunarLander-v2
project_name: some_project
git_link: github.com/user/some_project

# Some other useful parameters
sampler: tpe
tuner: median
n_trials: 20
log_folder: logs

2. Tune using python file or command line

from hyperfetch import tuning

# Path to your YAML config file 
config_path = "../some_folder/config_name.yml"

# Writes each trial's best hyperparameters to log folder
tuning.tune(config_path)

Command line:

If in the same directory as the config file and the config file is called "config.yml"

  tune config.yml

Enjoy your hyperparameters!

Example 2: Posting hyperparameters that are not tuned by Hyperfetch

Just a reminder:

The pip package must be installed before this can be done. To install the pip-package, the steps to get the front -or backend started/running do not need to be done.
For details, see using the pip module.

1. Create configuartion YAML file

# Required (example values)
alg: dqn
env: LunarLander-v2
project_name: some_project
git_link: github.com/user/some_project
hyperparameters: # These depend on the choice of algorithm
  batch_size: 256
  buffer_size: 50000
  exploration_final_eps: 0.10717928118310233
  exploration_fraction: 0.3318973226098944
  gamma: 0.9
  learning_rate: 0.0002126832542803243
  learning_starts: 10000
  net_arch: medium
  subsample_steps: 4
  target_update_interval: 1000
  train_freq: 8
  
# Not required (but appreciated)
CO2_emissions: 0.78 #kgs
energy_consumed: 3.27 #kWh
cpu_model: 12th Gen Intel(R) Core(TM) i5-12500H
gpu_model: NVIDIA GeForce RTX 3070
total_time: 0:04:16.842800 # H:M:S:MS

2. Save/post using python file or command line

Python file:

from hyperfetch import tuning

# Path to your YAML config file 
config_path = "../some_folder/config_name.yml"

# Writes each trial's best hyperparameters to log folder
tuning.save(config_path)

Command line:

If in the same directory as the config file and the config file is called "config.yml"

  save config.yml

Getting the website up and running

Installation backend

Make sure you have

• Pip version 23.0.1 or higher
• Python 3.10
• virtualenv (not venv) Clone this repository by either:

1. Open git bash

2. Change the current working directory to the location where you want the cloned directory.

3. Paste this snip:

    git clone https://github.com/YOUR-USERNAME/YOUR-REPOSITORY
   

4. Install virtualenv if you haven't

    pip install virtualenv
   

5. Create a virtual environment

    virtualenv [some_name]
   

   Activate virtualenv this way if using windows:

   # In cmd.exe
   venv\Scripts\activate.bat
   # In PowerShell
   venv\Scripts\Activate.ps1
   

   Activate virtualenv this way if using Linux/MacOS:

    $ source myvenv/bin/activate
   

6. Press Enter to create your local clone.

    Cloning into 'hyperFetch'...
    remote: Enumerating objects: 466, done.
    remote: Counting objects: 100% (466/466), done.
    remote: Compressing objects: 100% (238/238), done.
    remote: Total 466 (delta 221), reused 438 (delta 200), pack-reused 0
    Receiving objects: 100% (466/466), 4.17 MiB | 10.29 MiB/s, done.
    Resolving deltas: 100% (221/221), done.
   

7. You may now change directory by writing into the terminal:

    cd hyperfetch
   

8. Then, install the dependencies into your virtual environment

     pip install -r requirements.txt
   

Start up backend

After cloning and installing, you can finally start up the server!

      uvicorn main:app --reload   

Installation frontend

The frontend-branch is inside of the same project. However, because the frontend-branch (frontend) and backend-branch (master) must run at the same time to serve the website, the project must be cloned twice into two different local respositories.

1. Follow stages 3-6 in installation backend This includes:

   • Move into another working directory
   • Clone the project
   • Create a new virtualenv
   • Activate the virtualenv

2. The frontend-branch does not exist locally and must be fetched remotely. In the terminal, type:

   git switch frontend
   

3. Enter the correct folder

   cd src
   

4. Install dependencies. This will creat a node_modules folder in your local repository.

   npm install
   

Start up frontend

1. To serve the website (dev mode), run:

   npm run dev
   

2. Click the link that appears in the terminal, or access your browser of choice and type in:

   http://localhost:5173/
   

3. Good luck!