Bring velocity to deep-learning research, by providing tried and tested large pool of prebuilt components that are known to be working well together.
Having conducted a few research projects, I've gathered a small collection of repositories lying around with various model implementations suited to a particular usecase. Usually, starting a new project involved copying pieces of code from one or multiple of these past experiments, gluing, tweaking and debugging them until the code started working in a new setting.
After repeating that pattern multiple times, I've decided that this is the time to bite the bullet and start organising deep learning models into a structure that is designed to be reused rather than copied over.
As a goal, it should be enough to write a config file that wires existing components together and defines their hyperparameters for most common applications. If that's not the case few bits of custom glue code should do the job.
This repository is still in an early stage of that journey but it will grow as I'll be putting work into it.
This project requires Python 3.6 and PyTorch 0.4.1. Default project configuration writes
metrics to MongoDB instance open on localhost port 27017 and Visom instance
on localhost port 8097. These can be changed in project-wide config file
- Models should be runnable from the configuration files that are easy to store in version control, generate automatically and diff. Codebase should be generic and not contain any of the model hyperparameters. Unless user intervenes, it should be obvious which model was run with which hyperparameters and what output it gave.
- The amount of "magic" in the framework should be limited and it should be easy to understand what exactly the model is doing for newcomers already comfortable with PyTorch.
- All state-of-the-art models should be implemented in the framework with accuracy matching published results. Currently I'm focusing on computer vision and reinforcement learning models.
- All common deep learning workflows should be fast to implement, while uncommon ones should be possible. At least as far as PyTorch allows.
Implemented models - Computer Vision
Several models are already implemented in the framework and have example config files that are ready to run and easy to modify for other similar usecases:
- State-of-the art results on Cifar10 dataset using residual networks
- Cats vs dogs classification using transfer learning from a resnet34 model pretrained on ImageNet
Implemented models - Reinforcement learning
- Continuous and discrete action spaces
- Basic support for LSTM policies for A2C and PPO
- Advantage Actor-Critic (A2C), Proximal Policy Optimization (PPO), Trust Region Policy Optimization (TRPO), Deep Deterministic Policy Gradient (DDPG), and Actor-Critic with Experience Replay (ACER) policy gradient reinforcement learning algorithms.
- Deep Q-Learning (DQN) as described by DeepMind in their Nature publication with following improvements: Double DQN, Dueling DQN, Prioritized experience replay.
Most of the examples for this framework are defined using config files in the
examples-configs directory with sane default hyperparameters already selected.
For example, to run the A2C algorithm on a Breakout atari environment, simply invoke:
python -m vel.launcher examples-configs/rl/atari/a2c/breakout_a2c.yaml train
If you install the library locally, you'll have a special wrapper created that will invoke the launcher for you. Then, above becomes:
vel examples-configs/rl/atari/a2c/breakout_a2c.yaml train
General command line interface of the launcher is:
python -m vel.launcher CONFIGFILE COMMAND --device PYTORCH_DEVICE -r RUN_NUMBER -s SEED
PYTORCH_DEVICE is a valid name of pytorch device, most likely
Run number is a sequential number you wish to record your results with.
If you prefer to use the library from inside your scripts, take a look at the
examples-scripts directory. From time to time I'll be putting some examples in there as
well. Scripts generally don't require any MongoDB or Visdom setup, so they can be run straight
away in any setup, but their output will be less rich and less informative.
Here is an example script running the same setup as a config file from above:
import torch import torch.optim as optim from vel.rl.metrics import EpisodeRewardMetric from vel.storage.streaming.stdout import StdoutStreaming from vel.util.random import set_seed from vel.rl.env.classic_atari import ClassicAtariEnv from vel.rl.vecenv.subproc import SubprocVecEnvWrapper from vel.rl.models.policy_gradient_model import PolicyGradientModelFactory from vel.rl.models.backbone.nature_cnn import NatureCnnFactory from vel.rl.reinforcers.on_policy_iteration_reinforcer import ( OnPolicyIterationReinforcer, OnPolicyIterationReinforcerSettings ) from vel.rl.algo.policy_gradient.a2c import A2CPolicyGradient from vel.rl.env_roller.vec.step_env_roller import StepEnvRoller from vel.api.info import TrainingInfo, EpochInfo def breakout_a2c(): device = torch.device('cuda:0') seed = 1001 # Set random seed in python std lib, numpy and pytorch set_seed(seed) # Create 16 environments evaluated in parallel in sub processess with all usual DeepMind wrappers # These are just helper functions for that vec_env = SubprocVecEnvWrapper( ClassicAtariEnv('BreakoutNoFrameskip-v4'), frame_history=4 ).instantiate(parallel_envs=16, seed=seed) # Again, use a helper to create a model # But because model is owned by the reinforcer, model should not be accessed using this variable # but from reinforcer.model property model = PolicyGradientModelFactory( backbone=NatureCnnFactory(input_width=84, input_height=84, input_channels=4) ).instantiate(action_space=vec_env.action_space) # Reinforcer - an object managing the learning process reinforcer = OnPolicyIterationReinforcer( device=device, settings=OnPolicyIterationReinforcerSettings( discount_factor=0.99, batch_size=256 ), model=model, algo=A2CPolicyGradient( entropy_coefficient=0.01, value_coefficient=0.5, max_grad_norm=0.5 ), env_roller=StepEnvRoller( environment=vec_env, device=device, number_of_steps=5, discount_factor=0.99, ) ) # Model optimizer optimizer = optim.RMSprop(reinforcer.model.parameters(), lr=7.0e-4, eps=1e-3) # Overall information store for training information training_info = TrainingInfo( metrics=[ EpisodeRewardMetric('episode_rewards'), # Calculate average reward from episode ], callbacks=[StdoutStreaming()] # Print live metrics every epoch to standard output ) # A bit of training initialization bookkeeping... training_info.initialize() reinforcer.initialize_training(training_info) training_info.on_train_begin() # Let's make 100 batches per epoch to average metrics nicely num_epochs = int(1.1e7 / (5 * 16) / 100) # Normal handrolled training loop for i in range(1, num_epochs+1): epoch_info = EpochInfo( training_info=training_info, global_epoch_idx=i, batches_per_epoch=100, optimizer=optimizer ) reinforcer.train_epoch(epoch_info) training_info.on_train_end() if __name__ == '__main__': breakout_a2c()
Dockerized version of this library is available in from the Docker Hub as
millionintegrals/vel. Link: https://hub.docker.com/r/millionintegrals/vel/
pip install vel
pip install vel[gym,mongo,visdom]
For a glossary of terms used in the library please refer to Glossary. If there is anything you'd like to see there, feel free to open an issue or make a pull request.
For a more or less exhaustive bibliography please refer to Bibliography.