automl/dac4rl

DAC4RL track of DAC4AutoML competition at AutoML Conf

DAC4RL

DAC4RL track of the DAC4AutoML competition at AutoML-Conf.

Getting Started

git clone [email protected]:automl/dac4automlcomp.git # when using SSH keys to clone
cd dac4automlcomp
pip install -e .
git clone [email protected]:automl/DAC4RL.git
cd DAC4RL
pip install -e .

Basic Usage

import gym
import rlenv

# Create an (outer) env to perform DAC with 3,000 inner env steps and 2 reconfiguration points:
n_epochs = 2
env = gym.make("dac4carl-v0", total_timesteps = 1e3, n_epochs = n_epochs)
env.seed(123)
obs = env.reset()
done = False

print("Environment of sampled instance set to: ", env.current_instance.env_type)

for i in range(n_epochs):

    # Create an action dict containing the algorithm to apply along with its hyperparameter configuration:
    action = {'algorithm': 'PPO', 'learning_rate': 0.001, 'gamma': 0.98, 'gae_lambda': 0.8, 'ent_coef': 0.0, 'n_steps': 32, 'n_epochs': 10, 'batch_size': 256}

    # Apply the desired hyperparameter configs:
    obs, reward, done, info = env.step(action)

    # obs, reward, done, info = env.step(env.action_space.sample())

Sample Submissions

The directory baselines contains sample submissions with sample code files and an optional requirements.txt which can contain additional Python packages to be installed during an evaluation.

The Bash script prepare_upload.sh may be used to package a submission directory into a .zip file ready for submission to the competition servers.

bash prepare_upload.sh <submission_dir>

To create a submission, please follow the guidelines here.

Evaluating Submissions Locally

evaluate_submission.sh can be used to evaluate submissions locally. For example, you can execute the following commands to evaluate an example baseline (the argument n specifies the number of problem instances):

bash evaluate_submission.sh -s ../DAC4RL/baselines/zoo_hyperparams/ -t dac4rl -n 25

NOTE: Please note that if you want to evaluate the experiments in the same runtime environment as the competition servers, you would additionally need to set up the docker container below.

Docker Container

To run your experiments in the same runtime environment as the competition servers they will be evaluated on, we provide a Docker container. Please see the Docker container definition file to see what packages will be available in the runtime environment.

The Docker container may be run using the following command:

docker run -it -u root raghuspacerajan/dac4automlpy39:latest bash

It is not a requirement to use the Docker container to run your local evaluations, but it will be easier for us to debug any issues that may arise when you try evaluating submissions for the competition if you do so inside the provided Docker container.

Discussion Forum

The discussion forum is hosted at: https://codalab.lisn.upsaclay.fr/forums/3727/