Skip to content
Waldorf is an efficient, parallel task execution framework written in Python.
Python JavaScript Shell HTML CSS
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
docs
example
script
waldorf
.gitignore
LICENSE
README.md
requirement.txt
setup.py

README.md

Waldorf

Waldorf is an efficient, parallel task execution framework written in Python. It was developed for research into reinforcement learning algorithms at our startup company in Beijing, China.

Waldorf is based on the Celery distributed task queue, and takes its name from Waldorf salad, which also has celery as an ingredient.

It can speed up algorithms such as Monte Carlo Tree Search (MCTS) by spreading concurrent sub-tasks, written as Python functions, across multiple machines and automating the collection of outputs. Waldorf can also be used to implement MapReduce-style work-flows.

Although Waldorf can be deployed on cloud servers, our emphasis at the moment is on utilizing the spare CPU capacity of a commodity PC cluster (e.g. normal office workstations). Support for GPUs may be included in a future release.

Features

  1. Waldorf uses a master node to pass messages between a client and slave nodes.

  2. A client can create a task as a Python function on his or her local machine. Waldorf sends tasks to a network of slave machines for execution using the Celery task queue. When Celery is used on its own, tasks typically must be defined in advance, but Waldorf allows tasks to be defined dynamically without any slave restarts required.

  3. Multiple clients can run their tasks simultaneously without conflict.

  4. Clients can adjust how many CPU cores are used on slave machines to perform calculations. This can be done dynamically from the Waldorf administration webpage.

Screenshot of Waldorf admin webpage

Basic Usage

You can use Waldorf on any task that requires parallel computing.

One of its many uses is to compute rollouts in an MCTS simulation (for example, in game-playing AIs).

Here is a simple illustration:

def rollout(args):
    # Do one rollout
    ...

def backup(result):
    # Backup and handle result
    ...

def mcts_search():
    for _ in range(iter):
        # Select action
        action = select()
        ...
        # Submit rollout job to waldorf client
        client.submit(rollout, args, callback=backup)
        ...
    # More code
    ...

For a more complex example, check out the gym demo.

Quick Start

Install Waldorf using the installation guide and check out the gym demo.

Disclaimer

Waldorf is still research code, so it may be slightly lacking in terms of documentation and support. Any feedback is welcomed.

You can’t perform that action at this time.