ML4GW

Torch utilities for training neural networks in gravitational wave physics applications.

Installation

Pip installation

You can install ml4gw with pip:

pip install ml4gw

To build with a specific version of PyTorch/CUDA, please see the PyTorch installation instructions here to see how to specify the desired torch version and --extra-index-url flag. For example, to install with torch 1.12 and CUDA 11.6 support, you would run

pip install ml4gw torch==1.12.0 --extra-index-url=https://download.pytorch.org/whl/cu116

Poetry installation

ml4gw is also fully compatible with use in Poetry, with your pyproject.toml set up like

[tool.poetry.dependencies]
python = "^3.8"  # python versions 3.8-3.11 are supported
ml4gw = "^0.3.0"

To build against a specific PyTorch/CUDA combination, consult the PyTorch installation documentation above and specify the extra-index-url via the tool.poetry.source table in your pyproject.toml. For example, to build against CUDA 11.6, you would do something like:

[tool.poetry.dependencies]
python = "^3.8"
ml4gw = "^0.3.0"
torch = {version = "^1.12", source = "torch"}

[[tool.poetry.source]]
name = "torch"
url = "https://download.pytorch.org/whl/cu116"
secondary = true
default = false

Note: if you are building against CUDA 11.6 or 11.7, make sure that you are using python 3.8, 3.9, or 3.10. Python 3.11 is incompatible with torchaudio 0.13, and the following torchaudio version is incompatible with CUDA 11.7 and earlier.

Use cases

This library provided utilities for both data iteration and transformation via dataloaders defined in ml4gw/dataloading and transform layers exposed in ml4gw/transforms. Lower level functions and utilies are defined at the top level of the library and in the utils library.

For example, to train a simple autoencoder using a cost function in frequency space, you might do something like:

import numpy as np
import torch
from ml4gw.dataloading import InMemoryDataset
from ml4gw.transforms import SpectralDensity

SAMPLE_RATE = 2048
NUM_IFOS = 2
DATA_LENGTH = 128
KERNEL_LENGTH = 4
DEVICE = "cuda"  # or "cpu", wherever you want to run

BATCH_SIZE = 32
LEARNING_RATE = 1e-3
NUM_EPOCHS = 10

dummy_data = np.random.randn(NUM_IFOS, DATA_LENGTH * SAMPLE_RATE)

# this will create a dataloader that iterates through your
# timeseries data sampling 4s long windows of data randomly
# and non-coincidentally: i.e. the background from each IFO
# will be sampled independently
dataset = InMemoryDataset(
    dummy_data,
    kernel_size=KERNEL_LENGTH * SAMPLE_RATE,
    batch_size=BATCH_SIZE,
    batches_per_epoch=50,
    coincident=False,
    shuffle=True,
    device=DEVICE  # this will move your dataset to GPU up-front if "cuda"
)


nn = torch.nn.Sequential(
    torch.nn.Conv1d(
        in_channels=2,
        out_channels=8,
        kernel_size=7
    ),
    torch.nn.ConvTranspose1d(
        in_channels=8,
        out_channels=2,
        kernel_size=7
    )
).to(DEVICE)

optimizer = torch.optim.Adam(nn.parameters(), lr=LEARNING_RATE)

spectral_density = SpectralDensity(SAMPLE_RATE, fftlength=2).to(DEVICE)

def loss_function(X, y):
    """
    MSE in frequency domain. Obviously this doesn't
    give you much on its own, but you can imagine doing
    something like masking to just the bins you care about.
    """
    X = spectral_density(X)
    y = spectral_density(y)
    return ((X - y)**2).mean()


for i in range(NUM_EPOCHS):
    epoch_loss = 0
    for X in dataset:
        optimizer.zero_grad(set_to_none=True)
        assert X.shape == (32, NUM_IFOS, KERNEL_LENGTH * SAMPLE_RATE)
        y = nn(X)

        loss = loss_function(X, y)
        loss.backward()
        optimizer.step()

        epoch_loss += loss.item()
    epoch_loss /= len(dataset)
    print(f"Epoch {i + 1}/{NUM_EPOCHS} Loss: {epoch_loss:0.3e}")

Development

As this library is still very much a work in progress, we anticipate that novel use cases will encounter errors stemming from a lack of robustness. We encourage users who encounter these difficulties to file issues on GitHub, and we'll be happy to offer support to extend our coverage to new or improved functionality. We also strongly encourage ML users in the GW physics space to try their hand at working on these issues and joining on as collaborators! For more information about how to get involved, feel free to reach out to [email protected] . By bringing in new users with new use cases, we hope to develop this library into a truly general-purpose tool which makes DL more accessible for gravitational wave physicists everywhere.

Funding

We are grateful for the support of the U.S. National Science Foundation (NSF) Harnessing the Data Revolution (HDR) Institute for Accelerating AI Algorithms for Data Driven Discovery (A3D3) under Cooperative Agreement No. PHY-2117997.