Welcome to the world of graph deep learning research with DIG: Dive Into Graphs! This guide aims to make the jump into advanced graph deep learning both smooth and user-friendly.
What is DIG?
DIG is your trusty toolkit in graph deep learning. While other libraries may offer basic functionalities, DIG is designed to be a complete research environment. Think of it as a well-equipped kitchen for chefs (which in this case are researchers), allowing them to create complex dishes (or algorithms) with ease and precision.
With DIG, you can easily navigate advanced tasks such as:
- Graph Generation
- Self-supervised Learning
- Explainability of Graph Neural Networks
- Deep Learning on 3D Graphs
- Graph Out-of-Distribution Detection
- Graph Augmentation
- Fair Graph Learning
How to Get Started
Installation
To dive into the world of graphs, you need to install the DIG library. Let’s walk through the installation process!
Install from Pip
The essential dependencies you’ll need are:
- PyTorch (=1.10.0)
- PyTorch Geometric (=2.0.0)
- RDKit
Follow these steps:
# Check PyTorch version
python -c "import torch; print(torch.__version__)"
# Install PyG
python -c "import torch_geometric; print(torch_geometric.__version__)"
# Install DIG
pip install dive-into-graphs
# Verify Installation
python -c "from dig.version import __version__; print(__version__)"
Install from Source
If you’re curious about the latest features, try the source code installation:
# Clone the repository and install
git clone https://github.com/divelab/DIG.git
cd DIG
pip install .
Usage Example: Running SphereNet
Let’s illustrate how you can run a popular method called SphereNet. Picture preparing a dish, where each line of code is akin to adding an ingredient to your recipe. Each step builds upon the last, leading to a final product that’s delicious (or in our case, effective).
from dig.threedgraph.dataset import QM93D
from dig.threedgraph.method import SphereNet
from dig.threedgraph.evaluation import ThreeDEvaluator
from dig.threedgraph.method import run
# Load dataset
dataset = QM93D(root=dataset)
target = U0dataset.data.y = dataset.data[target]
split_idx = dataset.get_idx_split(len(dataset.data.y), train_size=110000, valid_size=10000, seed=42)
# Split into training, valid, and test datasets
train_dataset, valid_dataset, test_dataset = dataset[split_idx[train]], dataset[split_idx[valid]], dataset[split_idx[test]]
# Define model
model = SphereNet(energy_and_force=False, cutoff=5.0, num_layers=4, hidden_channels=128, out_channels=1)
loss_func = torch.nn.L1Loss()
evaluation = ThreeDEvaluator()
# Train and evaluate
run3d = run()
run3d.run(device, train_dataset, valid_dataset, test_dataset, model, loss_func, evaluation, epochs=20, batch_size=32, lr=0.0005)
Troubleshooting
Having trouble? Here are some common issues and solutions:
- Problem: Errors during installation.
- Solution: Ensure your environment meets all dependency requirements. Verify that Python version compatibility is maintained.
- Problem: Code not running as expected.
- Solution: Carefully read error messages; they often point to the issue. Ensure you’re using the correct dataset and parameters.
- Problem: Performance issues.
- Solution: Try reducing batch sizes or adjusting learning rates.
For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.
Next Steps
Once you’ve gotten your feet wet, explore the full potential of DIG by diving into various tutorials, examples, and benchmarks:
At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.
Concluding Thoughts
Getting started in graph deep learning research has never been easier with DIG. This guide is just a shadow of its full potential—frankly, speaking, there’s a world to explore!