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DGL v0.3 Release

V0.3 release includes many crucial updates:

  • Fused message passing kernels that greatly boost the training speed of GNNs on large graphs. Please refer to our blogpost for more details.
  • Add components to enable distributed training of GNNs on giant graphs with graph sampling. Please see our blogpost for more details.
  • New models and NN modules.
  • Many other bugfixes and other enhancement.

As a result, please be aware of the following changes:

Installation

Previous installation methods with pip and conda, i.e.:

pip install dgl
conda install -c dglteam dgl

now only install CPU builds (works for Linux/MacOS/Windows).

Installing CUDA builds with pip

Pip users could install the DGL CUDA builds with the following:

pip install <package-url>

where <package-url> is one of the following:

  CUDA 9.0 CUDA 10.0
Linux + Py35 pip install https://s3.us-east-2.amazonaws.com/dgl.ai/wheels/cuda9.0/dgl-0.3-cp35-cp35m-manylinux1_x86_64.whl pip install https://s3.us-east-2.amazonaws.com/dgl.ai/wheels/cuda10.0/dgl-0.3-cp35-cp35m-manylinux1_x86_64.whl
Linux + Py36 pip install https://s3.us-east-2.amazonaws.com/dgl.ai/wheels/cuda9.0/dgl-0.3-cp36-cp36m-manylinux1_x86_64.whl pip install https://s3.us-east-2.amazonaws.com/dgl.ai/wheels/cuda10.0/dgl-0.3-cp36-cp36m-manylinux1_x86_64.whl
Linux + Py37 pip install https://s3.us-east-2.amazonaws.com/dgl.ai/wheels/cuda9.0/dgl-0.3-cp37-cp37m-manylinux1_x86_64.whl pip install https://s3.us-east-2.amazonaws.com/dgl.ai/wheels/cuda10.0/dgl-0.3-cp37-cp37m-manylinux1_x86_64.whl
Win + Py35 pip install https://s3.us-east-2.amazonaws.com/dgl.ai/wheels/cuda9.0/dgl-0.3-cp35-cp35m-win_amd64.whl pip install https://s3.us-east-2.amazonaws.com/dgl.ai/wheels/cuda10.0/dgl-0.3-cp35-cp35m-win_amd64.whl
Win + Py36 pip install https://s3.us-east-2.amazonaws.com/dgl.ai/wheels/cuda9.0/dgl-0.3-cp36-cp36m-win_amd64.whl pip install https://s3.us-east-2.amazonaws.com/dgl.ai/wheels/cuda10.0/dgl-0.3-cp36-cp36m-win_amd64.whl
Win + Py37 pip install https://s3.us-east-2.amazonaws.com/dgl.ai/wheels/cuda9.0/dgl-0.3-cp37-cp37m-win_amd64.whl pip install https://s3.us-east-2.amazonaws.com/dgl.ai/wheels/cuda10.0/dgl-0.3-cp37-cp37m-win_amd64.whl
MacOS N/A N/A

Installing CUDA builds with conda

Conda users could install the CUDA builds with

conda install -c dglteam dgl-cuda9.0   # For CUDA 9.0
conda install -c dglteam dgl-cuda10.0  # For CUDA 10.0

DGL currently support CUDA 9.0 (dgl-cuda9.0) and 10.0 (dgl-cuda10.0). To find your CUDA version, use nvcc --version. To install from source, checkout our installation guide.

New built-in message and reduce functions

We have expanded the list of built-in message and reduce functions to cover more use cases. Previously, DGL only has copy_src, copy_edge, src_mul_edge. With the v0.3 release, we support more combinations. Here is a demonstration of some of the new builtin functions.

import dgl
import dgl.function as fn
import torch as th
g = ... # create a DGLGraph
g.ndata['h'] = th.randn((g.number_of_nodes(), 10)) # each node has feature size 10
g.edata['w'] = th.randn((g.number_of_edges(), 1))  # each edge has feature size 1
# collect features from source nodes and aggregate them in destination nodes
g.update_all(fn.copy_u('h', 'm'), fn.sum('m', 'h_sum'))
# multiply source node features with edge weights and aggregate them in destination nodes
g.update_all(fn.u_mul_e('h', 'w', 'm'), fn.max('m', 'h_max'))
# compute edge embedding by multiplying source and destination node embeddings
g.apply_edges(fn.u_mul_v('h', 'h', 'w_new'))

As you can see, the syntax is quite straight-forward. u_mul_e means multiplying the source node data with the edge data; u_mul_v means multiplying the source node data with the destination node data, and so on and so forth. Each builtin combination will be mapped to a CPU/CUDA kernel and broadcasting and gradient computation are also supported. Checkout our document for more details.

Training giant graphs

We added new components shared-memory DGLGraph and distributed samplers to support distributed and multi-processing training of graph neural networks.

Two new tutorials are now live:

  • Train GNNs by neighbor sampling and its variants (link).
  • Scale the sampler-trainer architecture to giant graphs using distributed graph store (link).

We also provide scripts on how to setup such distributed setting (link).

Enhancement and bugfix

  • NN modules
    • dgl.nn.[mxnet|pytorch].edge_softmax now directly returns the normalized scores on edges.
    • Fix a memory leak bug when graph is passed as the input.
  • Graph
    • DGLGraph now supports direct conversion from scipy csr matrix rather than conversion to coo matrix first.
    • Readonly graph can now be batched via dgl.batch.
    • DGLGraph now supports node/edge removal via DGLGraph.remove_nodes and DGLGraph.remove_edges (doc).
    • A new API DGLGraph.to(device) that can move all node/edge data to the given device.
    • A new API dgl.to_simple that can convert a graph to a simple graph with no multi-edges.
    • A new API dgl.to_bidirected that can convert a graph to a bidirectional graph.
    • A new API dgl.contrib.sampling.random_walk that can generate random walks from a graph.
    • Allow DGLGraph to be constructed from another DGLGraph.
  • New model examples
    • APPNP
    • GIN
    • PinSage (slow version)
    • DGI
  • Bugfix
    • Fix a bug where numpy integer is passed in as the argument.
    • Fix a bug when constructing from a networkx graph that has no edge.
    • Fix a bug in nodeflow where id is not correctly converted sometimes.
    • Fix a bug in MiniGC dataset where the number of nodes is not consistent.
    • Fix a bug in RGCN example when bfs_level=0.
    • Fix a bug where DLContext is not correctly exposed in CFFI.
    • Fix a crash during Cython build.
    • Fix a bug in send when the given message function is a builtin.