Skip to main content

Launchpad is a library that simplifies writing distributed programs and seamlessly launching them on a range of supported platforms.

Project description

Launchpad

PyPI - Python Version PyPI version

Launchpad is a library that simplifies writing distributed programs by seamlessly launching them on a variety of different platforms. Switching between local and distributed execution requires only a flag change.

Launchpad introduces a programming model that represents a distributed system as a graph data structure (a Program) describing the system’s topology. Each node in the program graph represents a service in the distributed system, i.e. the fundamental unit of computation that we are interested in running. As nodes are added to this graph, Launchpad constructs a handle for each of them. A handle ultimately represents a client to the yet-to-be-constructed service. A directed edge in the program graph, representing communication between two services, is created when the handle associated with one node is given to another at construction time. This edge originates from the receiving node, indicating that the receiving node will be the one initiating communication. This process allows Launchpad to define cross-service communication simply by passing handles to nodes. Launchpad provides a number of node types, including:

  • PyNode - a simple node executing provided Python code upon entry. It is similar to a main function, but with the distinction that each node may be running in separate processes and on different machines.
  • CourierNode - it enables cross-node communication. CourierNodes can communicate by calling public methods on each other either synchronously or asynchronously via futures. The underlying remote procedure calls are handled transparently by Launchpad.
  • ReverbNode - it exposes functionality of Reverb, an easy-to-use data storage and transport system primarily used by RL algorithms as an experience replay. You can read more about Reverb here.
  • MultiThreadingColocation - allows to colocate multiple other nodes in a single process.
  • MultiProcessingColocation - allows to colocate multiple other nodes as sub processes.

Using Launchpad involves writing nodes and defining the topology of your distributed program by passing to each node references of the other nodes that it can communicate with. The core data structure dealing with this is called a Launchpad program, which can then be executed seamlessly with a number of supported runtimes.

Supported launch types

Launchpad supports a number of launch types, both for running programs on a single machine, in a distributed manner, or in a form of a test. Launch type can be controlled by the launch_type argument passed to lp.launch method, or specified through the --lp_launch_type command line flag. Please refer to the documentation of the LaunchType for details.

Table of Contents

Installation

Please keep in mind that Launchpad is not hardened for production use, and while we do our best to keep things in working order, things may break or segfault.

:warning: Launchpad currently only supports Linux based OSes.

The recommended way to install Launchpad is with pip. We also provide instructions to build from source using the same docker images we use for releases.

TensorFlow can be installed separately or as part of the pip install. Installing TensorFlow as part of the install ensures compatibility.

$ pip install dm-launchpad[tensorflow]

# Without Tensorflow install and version dependency check.
$ pip install dm-launchpad

Nightly builds

PyPI version

$ pip install dm-launchpad-nightly[tensorflow]

# Without Tensorflow install and version dependency check.
$ pip install dm-launchpad-nightly

Similarily, Reverb can be installed ensuring compatibility:

$ pip install dm-launchpad[reverb]

Develop Launchpad inside a docker container

The most convenient way to develop Launchpad is with Docker. This way you can compile and test Launchpad inside a container without having to install anything on your host machine, while you can still use your editor of choice for making code changes. The steps are as follows.

Checkout Launchpad's source code from GitHub.

$ git checkout https://github.com/deepmind/launchpad.git
$ cd launchpad

Build the Docker container to be used for compiling and testing Launchpad. You can specify tensorflow_pip parameter to set the version of Tensorflow to build against. You can also specify which version(s) of Python container should support. The command below enables support for Python 3.7, 3.8, 3.9 and 3.10.

$ docker build --tag launchpad:devel \
  --build-arg tensorflow_pip=tensorflow==2.3.0 \
  --build-arg python_version="3.7 3.8 3.9 3.10" - < docker/build.dockerfile

The next step is to enter the built Docker image, binding checked out Launchpad's sources to /tmp/launchpad within the container.

$ docker run --rm --mount "type=bind,src=$PWD,dst=/tmp/launchpad" \
  -it launchpad:devel bash

At this point you can build and install Launchpad within the container by executing:

$ /tmp/launchpad/oss_build.sh

By default it builds Python 3.8 version, you can change that with --python flag.

$ /tmp/launchpad/oss_build.sh --python 3.8

To make sure installation was successful and Launchpad works as expected, you can run some examples provided:

$ python3.8 -m launchpad.examples.hello_world.launch
$ python3.8 -m launchpad.examples.consumer_producers.launch --lp_launch_type=local_mp

To make changes to Launchpad codebase, edit sources checked out from GitHub directly on your host machine (outside of the Docker container). All changes are visible inside the Docker container. To recompile just run the oss_build.sh script again from the Docker container. In order to reduce compilation time of the consecutive runs, make sure to not exit the Docker container.

Citing Launchpad

If you use Launchpad in your work, please cite the accompanying technical report:

@article{yang2021launchpad,
    title={Launchpad: A Programming Model for Distributed Machine Learning
           Research},
    author={Fan Yang and Gabriel Barth-Maron and Piotr Stańczyk and Matthew
            Hoffman and Siqi Liu and Manuel Kroiss and Aedan Pope and Alban
            Rrustemi},
    year={2021},
    journal={arXiv preprint arXiv:2106.04516},
    url={https://arxiv.org/abs/2106.04516},
}

Acknowledgements

We greatly appreciate all the help from Reverb and TF-Agents teams in setting up building and testing setup for Launchpad.

Other resources

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distributions

File details

Details for the file dm_launchpad_nightly-0.5.1.dev20220422-cp310-cp310-manylinux2014_x86_64.whl.

File metadata

  • Download URL: dm_launchpad_nightly-0.5.1.dev20220422-cp310-cp310-manylinux2014_x86_64.whl
  • Upload date:
  • Size: 12.6 MB
  • Tags: CPython 3.10
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.8.0 pkginfo/1.8.2 readme-renderer/34.0 requests/2.27.1 requests-toolbelt/0.9.1 urllib3/1.26.9 tqdm/4.64.0 importlib-metadata/4.8.3 keyring/23.4.1 rfc3986/1.5.0 colorama/0.4.4 CPython/3.6.9

File hashes

Hashes for dm_launchpad_nightly-0.5.1.dev20220422-cp310-cp310-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 93bdade0487b6e70b80f875ca47218a51d17fcdbcf64965c0dad77003e09a190
MD5 b2ca4c720e15cf477f1f72e16483279c
BLAKE2b-256 e69a50b241e2487b99a021868b61c3780a1e961239c4c79f25d2446c5438d4b0

See more details on using hashes here.

Provenance

File details

Details for the file dm_launchpad_nightly-0.5.1.dev20220422-cp39-cp39-manylinux2014_x86_64.whl.

File metadata

  • Download URL: dm_launchpad_nightly-0.5.1.dev20220422-cp39-cp39-manylinux2014_x86_64.whl
  • Upload date:
  • Size: 12.6 MB
  • Tags: CPython 3.9
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.8.0 pkginfo/1.8.2 readme-renderer/34.0 requests/2.27.1 requests-toolbelt/0.9.1 urllib3/1.26.9 tqdm/4.64.0 importlib-metadata/4.8.3 keyring/23.4.1 rfc3986/1.5.0 colorama/0.4.4 CPython/3.6.9

File hashes

Hashes for dm_launchpad_nightly-0.5.1.dev20220422-cp39-cp39-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 f457f8aeb2852b379ae7cb46a70aa034b98bcd535238acb893373d789dc84a4e
MD5 0c5c0c75026adc973585a08ef13afcd0
BLAKE2b-256 31c2f868c4a6d8e215345f93f49f902f59a2ef8865a4df9af48bcb98316f3bca

See more details on using hashes here.

Provenance

File details

Details for the file dm_launchpad_nightly-0.5.1.dev20220422-cp38-cp38-manylinux2014_x86_64.whl.

File metadata

  • Download URL: dm_launchpad_nightly-0.5.1.dev20220422-cp38-cp38-manylinux2014_x86_64.whl
  • Upload date:
  • Size: 12.6 MB
  • Tags: CPython 3.8
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.8.0 pkginfo/1.8.2 readme-renderer/34.0 requests/2.27.1 requests-toolbelt/0.9.1 urllib3/1.26.9 tqdm/4.64.0 importlib-metadata/4.8.3 keyring/23.4.1 rfc3986/1.5.0 colorama/0.4.4 CPython/3.6.9

File hashes

Hashes for dm_launchpad_nightly-0.5.1.dev20220422-cp38-cp38-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 f9ee2eb2dca121651d4890d8d791be39a560cf270b5238c91f17b2d06ca685e9
MD5 120febd0d3310acb2bfe2049ae36997b
BLAKE2b-256 9bfb231d4a0c9536c2170685b2e0737286dfdc9801e223502bb6988b702c3dba

See more details on using hashes here.

Provenance

File details

Details for the file dm_launchpad_nightly-0.5.1.dev20220422-cp37-cp37m-manylinux2014_x86_64.whl.

File metadata

  • Download URL: dm_launchpad_nightly-0.5.1.dev20220422-cp37-cp37m-manylinux2014_x86_64.whl
  • Upload date:
  • Size: 12.6 MB
  • Tags: CPython 3.7m
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.8.0 pkginfo/1.8.2 readme-renderer/34.0 requests/2.27.1 requests-toolbelt/0.9.1 urllib3/1.26.9 tqdm/4.64.0 importlib-metadata/4.8.3 keyring/23.4.1 rfc3986/1.5.0 colorama/0.4.4 CPython/3.6.9

File hashes

Hashes for dm_launchpad_nightly-0.5.1.dev20220422-cp37-cp37m-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 c3e3c0ed8f5cf3db9cc6c97a38f1ecd53103429e097685989983576de2fa91c8
MD5 578bd1cd779dee87e224aa575913c98c
BLAKE2b-256 6c54c30c8eaa1522d3933ed9ca9282b2c03c48b531030b95de22a57450f66a02

See more details on using hashes here.

Provenance

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page