dgl.distributed¶
DGL distributed module contains classes and functions to support distributed graph neural network training and inference in a cluster of machines.
This includes a few submodules:
distributed data structures including distributed graph, distributed tensor and distributed embeddings.
distributed sampling.
distributed workload split at runtime.
graph partition.
Initialization¶

Initialize DGL’s distributed module 
Distributed Graph¶

class
dgl.distributed.
DistGraph
(graph_name, gpb=None, part_config=None)[source]¶ The class for accessing a distributed graph.
This class provides a subset of DGLGraph APIs for accessing partitioned graph data in distributed GNN training and inference. Thus, its main use case is to work with distributed sampling APIs to generate minibatches and perform forward and backward computation on the minibatches.
The class can run in two modes: the standalone mode and the distributed mode.
When a user runs the training script normally,
DistGraph
will be in the standalone mode. In this mode, the input data must be constructed bypartition_graph()
with only one partition. This mode is used for testing and debugging purpose. In this mode, users have to providepart_config
so thatDistGraph
can load the input graph.When a user runs the training script with the distributed launch script,
DistGraph
will be set into the distributed mode. This is used for actual distributed training. All data of partitions are loaded by theDistGraph
servers, which are created by DGL’s launch script.DistGraph
connects with the servers to access the partitioned graph data.
Currently, the
DistGraph
servers and clients run on the same set of machines in the distributed mode.DistGraph
uses sharedmemory to access the partition data in the local machine. This gives the best performance for distributed trainingUsers may want to run
DistGraph
servers and clients on separate sets of machines. In this case, a user may want to disable shared memory by passingdisable_shared_mem=False
when creatingDistGraphServer
. When shared memory is disabled, a user has to pass a partition book. Parameters
graph_name (str) – The name of the graph. This name has to be the same as the one used for partitioning a graph in
dgl.distributed.partition.partition_graph()
.gpb (GraphPartitionBook, optional) – The partition book object. Normally, users do not need to provide the partition book. This argument is necessary only when users want to run server process and trainer processes on different machines.
part_config (str, optional) – The path of partition configuration file generated by
dgl.distributed.partition.partition_graph()
. It’s used in the standalone mode.
Examples
The example shows the creation of
DistGraph
in the standalone mode.>>> dgl.distributed.partition_graph(g, 'graph_name', 1, num_hops=1, part_method='metis', ... out_path='output/', reshuffle=True) >>> g = dgl.distributed.DistGraph('graph_name', part_config='output/graph_name.json')
The example shows the creation of
DistGraph
in the distributed mode.>>> g = dgl.distributed.DistGraph('graphname')
The code below shows the minibatch training using
DistGraph
.>>> def sample(seeds): ... seeds = th.LongTensor(np.asarray(seeds)) ... frontier = dgl.distributed.sample_neighbors(g, seeds, 10) ... return dgl.to_block(frontier, seeds) >>> dataloader = dgl.distributed.DistDataLoader(dataset=nodes, batch_size=1000, ... collate_fn=sample, shuffle=True) >>> for block in dataloader: ... feat = g.ndata['features'][block.srcdata[dgl.NID]] ... labels = g.ndata['labels'][block.dstdata[dgl.NID]] ... pred = model(block, feat)
Note
DGL’s distributed training by default runs server processes and trainer processes on the same set of machines. If users need to run them on different sets of machines, it requires manually setting up servers and trainers. The setup is not fully tested yet.

barrier
()[source]¶ Barrier for all client nodes.
This API blocks the current process untill all the clients invoke this API. Please use this API with caution.

property
device
¶ Get the device context of this graph.
Examples
The following example uses PyTorch backend.
>>> g = dgl.bipartite(([0, 1, 1, 2], [0, 0, 2, 1]), 'user', 'plays', 'game') >>> print(g.device) device(type='cpu') >>> g = g.to('cuda:0') >>> print(g.device) device(type='cuda', index=0)
 Returns
 Return type
Device context object

property
edata
¶ Return the data view of all the edges.
 Returns
The data view in the distributed graph storage.
 Return type
EdgeDataView

edge_attr_schemes
()[source]¶ Return the edge feature schemes.
Each feature scheme is a named tuple that stores the shape and data type of the edge feature.
 Returns
The schemes of edge feature columns.
 Return type
dict of str to schemes
Examples
The following uses PyTorch backend.
>>> g.edge_attr_schemes() {'h': Scheme(shape=(4,), dtype=torch.float32)}
See also

property
edges
¶ Return an edge view

property
etypes
¶ Return the list of edge types of this graph.
 Returns
 Return type
list of str
Examples
>>> g = DistGraph("test") >>> g.etypes ['_E']

find_edges
(edges)[source]¶ Given an edge ID array, return the source and destination node ID array
s
andd
.s[i]
andd[i]
are source and destination node ID for edgeeid[i]
. Parameters
edges (tensor) – The edge ID array.
 Returns
tensor – The source node ID array.
tensor – The destination node ID array.

get_edge_partition_policy
(etype)[source]¶ Get the partition policy for an edge type.
When creating a new distributed tensor, we need to provide a partition policy that indicates how to distribute data of the distributed tensor in a cluster of machines. When we load a distributed graph in the cluster, we have predefined partition policies for each node type and each edge type. By providing the edge type, we can reference to the predefined partition policy for the edge type.
 Parameters
etype (str) – The edge type
 Returns
The partition policy for the edge type.
 Return type

get_etype_id
(etype)[source]¶ Return the id of the given edge type.
etype can also be None. If so, there should be only one edge type in the graph.

get_node_partition_policy
(ntype)[source]¶ Get the partition policy for a node type.
When creating a new distributed tensor, we need to provide a partition policy that indicates how to distribute data of the distributed tensor in a cluster of machines. When we load a distributed graph in the cluster, we have predefined partition policies for each node type and each edge type. By providing the node type, we can reference to the predefined partition policy for the node type.
 Parameters
ntype (str) – The node type
 Returns
The partition policy for the node type.
 Return type

get_ntype_id
(ntype)[source]¶ Return the ID of the given node type.
ntype can also be None. If so, there should be only one node type in the graph.

get_partition_book
()[source]¶ Get the partition information.
 Returns
Object that stores all graph partition information.
 Return type

property
idtype
¶ The dtype of graph index
 Returns
th.int32/th.int64 or tf.int32/tf.int64 etc.
 Return type
backend dtype object
See also
long
,int

in_degrees
(v='__ALL__')[source]¶ Return the indegree(s) of the given nodes.
It computes the indegree(s). It does not support heterogeneous graphs yet.
 Parameters
v (node IDs) –
The node IDs. The allowed formats are:
int
: A single node.Int Tensor: Each element is a node ID. The tensor must have the same device type and ID data type as the graph’s.
iterable[int]: Each element is a node ID.
If not given, return the indegrees of all the nodes.
 Returns
The indegree(s) of the node(s) in a Tensor. The ith element is the indegree of the ith input node. If
v
is anint
, return anint
too. Return type
int or Tensor
Examples
The following example uses PyTorch backend.
>>> import dgl >>> import torch
Query for all nodes.
>>> g.in_degrees() tensor([0, 2, 1, 1])
Query for nodes 1 and 2.
>>> g.in_degrees(torch.tensor([1, 2])) tensor([2, 1])
See also

property
local_partition
¶ Return the local partition on the client
DistGraph provides a global view of the distributed graph. Internally, it may contains a partition of the graph if it is colocated with the server. When servers and clients run on separate sets of machines, this returns None.
 Returns
The local partition
 Return type

property
ndata
¶ Return the data view of all the nodes.
 Returns
The data view in the distributed graph storage.
 Return type
NodeDataView

node_attr_schemes
()[source]¶ Return the node feature schemes.
Each feature scheme is a named tuple that stores the shape and data type of the node feature.
 Returns
The schemes of node feature columns.
 Return type
dict of str to schemes
Examples
The following uses PyTorch backend.
>>> g.node_attr_schemes() {'h': Scheme(shape=(4,), dtype=torch.float32)}
See also

property
nodes
¶ Return a node view

property
ntypes
¶ Return the list of node types of this graph.
 Returns
 Return type
list of str
Examples
>>> g = DistGraph("test") >>> g.ntypes ['_U']

num_edges
(etype=None)[source]¶ Return the total number of edges in the distributed graph.
 Parameters
etype (str or (str, str, str), optional) –
The type name of the edges. The allowed type name formats are:
(str, str, str)
for source node type, edge type and destination node type.or one
str
edge type name if the name can uniquely identify a triplet format in the graph.
If not provided, return the total number of edges regardless of the types in the graph.
 Returns
The number of edges
 Return type
Examples
>>> g = dgl.distributed.DistGraph('ogbproduct') >>> print(g.num_edges()) 123718280

num_nodes
(ntype=None)[source]¶ Return the total number of nodes in the distributed graph.
 Parameters
ntype (str, optional) – The node type name. If given, it returns the number of nodes of the type. If not given (default), it returns the total number of nodes of all types.
 Returns
The number of nodes
 Return type
Examples
>>> g = dgl.distributed.DistGraph('ogbproduct') >>> print(g.num_nodes()) 2449029

number_of_edges
(etype=None)[source]¶ Alias of
num_edges()

number_of_nodes
(ntype=None)[source]¶ Alias of
num_nodes()

out_degrees
(u='__ALL__')[source]¶ Return the outdegree(s) of the given nodes.
It computes the outdegree(s). It does not support heterogeneous graphs yet.
 Parameters
u (node IDs) –
The node IDs. The allowed formats are:
int
: A single node.Int Tensor: Each element is a node ID. The tensor must have the same device type and ID data type as the graph’s.
iterable[int]: Each element is a node ID.
If not given, return the indegrees of all the nodes.
 Returns
The outdegree(s) of the node(s) in a Tensor. The ith element is the outdegree of the ith input node. If
v
is anint
, return anint
too. Return type
int or Tensor
Examples
The following example uses PyTorch backend.
>>> import dgl >>> import torch
Query for all nodes.
>>> g.out_degrees() tensor([2, 2, 0, 0])
Query for nodes 1 and 2.
>>> g.out_degrees(torch.tensor([1, 2])) tensor([2, 0])
See also
Distributed Tensor¶

class
dgl.distributed.
DistTensor
(shape, dtype, name=None, init_func=None, part_policy=None, persistent=False, is_gdata=True)[source]¶ Distributed tensor.
DistTensor
references to a distributed tensor sharded and stored in a cluster of machines. It has the same interface as Pytorch Tensor to access its metadata (e.g., shape and data type). To access data in a distributed tensor, it supports slicing rows and writing data to rows. It does not support any operators of a deep learning framework, such as addition and multiplication.Currently, distributed tensors are designed to store node data and edge data of a distributed graph. Therefore, their first dimensions have to be the number of nodes or edges in the graph. The tensors are sharded in the first dimension based on the partition policy of nodes or edges. When a distributed tensor is created, the partition policy is automatically determined based on the first dimension if the partition policy is not provided. If the first dimension matches the number of nodes of a node type,
DistTensor
will use the partition policy for this particular node type; if the first dimension matches the number of edges of an edge type,DistTensor
will use the partition policy for this particular edge type. If DGL cannot determine the partition policy automatically (e.g., multiple node types or edge types have the same number of nodes or edges), users have to explicity provide the partition policy.A distributed tensor can be ether named or anonymous. When a distributed tensor has a name, the tensor can be persistent if
persistent=True
. Normally, DGL destroys the distributed tensor in the system when theDistTensor
object goes away. However, a persistent tensor lives in the system even if theDistTenor
object disappears in the trainer process. The persistent tensor has the same life span as the DGL servers. DGL does not allow an anonymous tensor to be persistent.When a
DistTensor
object is created, it may reference to an existing distributed tensor or create a new one. A distributed tensor is identified by the name passed to the constructor. If the name exists,DistTensor
will reference the existing one. In this case, the shape and the data type must match the existing tensor. If the name doesn’t exist, a new tensor will be created in the kvstore.When a distributed tensor is created, its values are initialized to zero. Users can define an initialization function to control how the values are initialized. The init function has two input arguments: shape and data type and returns a tensor. Below shows an example of an init function:
def init_func(shape, dtype): return torch.ones(shape=shape, dtype=dtype)
 Parameters
shape (tuple) – The shape of the tensor. The first dimension has to be the number of nodes or the number of edges of a distributed graph.
dtype (dtype) – The dtype of the tensor. The data type has to be the one in the deep learning framework.
name (string, optional) – The name of the embeddings. The name can uniquely identify embeddings in a system so that another
DistTensor
object can referent to the distributed tensor.init_func (callable, optional) – The function to initialize data in the tensor. If the init function is not provided, the values of the embeddings are initialized to zero.
part_policy (PartitionPolicy, optional) – The partition policy of the rows of the tensor to different machines in the cluster. Currently, it only supports node partition policy or edge partition policy. The system determines the right partition policy automatically.
persistent (bool) – Whether the created tensor lives after the
DistTensor
object is destroyed.is_gdata (bool) – Whether the created tensor is a ndata/edata or not.
Examples
>>> init = lambda shape, dtype: th.ones(shape, dtype=dtype) >>> arr = dgl.distributed.DistTensor((g.number_of_nodes(), 2), th.int32, init_func=init) >>> print(arr[0:3]) tensor([[1, 1], [1, 1], [1, 1]], dtype=torch.int32) >>> arr[0:3] = th.ones((3, 2), dtype=th.int32) * 2 >>> print(arr[0:3]) tensor([[2, 2], [2, 2], [2, 2]], dtype=torch.int32)
Note
The creation of
DistTensor
is a synchronized operation. When a trainer process tries to create aDistTensor
object, the creation succeeds only when all trainer processes do the same.
property
dtype
¶ Return the data type of the distributed tensor.
 Returns
The data type of the tensor.
 Return type
dtype

property
name
¶ Return the name of the distributed tensor
 Returns
The name of the tensor.
 Return type

property
part_policy
¶ Return the partition policy
 Returns
The partition policy of the distributed tensor.
 Return type
Distributed Node Embedding¶

class
dgl.distributed.
DistEmbedding
(num_embeddings, embedding_dim, name=None, init_func=None, part_policy=None)[source]¶ Distributed node embeddings.
DGL provides a distributed embedding to support models that require learnable embeddings. DGL’s distributed embeddings are mainly used for learning node embeddings of graph models. Because distributed embeddings are part of a model, they are updated by minibatches. The distributed embeddings have to be updated by DGL’s optimizers instead of the optimizers provided by the deep learning frameworks (e.g., Pytorch and MXNet).
To support efficient training on a graph with many nodes, the embeddings support sparse updates. That is, only the embeddings involved in a minibatch computation are updated. Currently, DGL provides only one optimizer: SparseAdagrad. DGL will provide more optimizers in the future.
Distributed embeddings are sharded and stored in a cluster of machines in the same way as py:meth:dgl.distributed.DistTensor, except that distributed embeddings are trainable. Because distributed embeddings are sharded in the same way as nodes and edges of a distributed graph, it is usually much more efficient to access than the sparse embeddings provided by the deep learning frameworks.
 Parameters
num_embeddings (int) – The number of embeddings. Currently, the number of embeddings has to be the same as the number of nodes or the number of edges.
embedding_dim (int) – The dimension size of embeddings.
name (str, optional) – The name of the embeddings. The name can uniquely identify embeddings in a system so that another DistEmbedding object can referent to the same embeddings.
init_func (callable, optional) – The function to create the initial data. If the init function is not provided, the values of the embeddings are initialized to zero.
part_policy (PartitionPolicy, optional) – The partition policy that assigns embeddings to different machines in the cluster. Currently, it only supports node partition policy or edge partition policy. The system determines the right partition policy automatically.
Examples
>>> def initializer(shape, dtype): arr = th.zeros(shape, dtype=dtype) arr.uniform_(1, 1) return arr >>> emb = dgl.distributed.DistEmbedding(g.number_of_nodes(), 10, init_func=initializer) >>> optimizer = dgl.distributed.optim.SparseAdagrad([emb], lr=0.001) >>> for blocks in dataloader: ... feats = emb(nids) ... loss = F.sum(feats + 1, 0) ... loss.backward() ... optimizer.step()
Note
When a
DistEmbedding
object is used when the deep learning framework is recording the forward computation, users have to invoke py:meth:~dgl.distributed.optim.SparseAdagrad.step afterwards. Otherwise, there will be some memory leak.
Distributed embedding optimizer¶

class
dgl.distributed.optim.pytorch.
SparseAdagrad
(params, lr, eps=1e10)[source]¶ Distributed Node embedding optimizer using the Adagrad algorithm.
This optimizer implements a distributed sparse version of Adagrad algorithm for optimizing
dgl.distributed.DistEmbedding
. Being sparse means it only updates the embeddings whose gradients have updates, which are usually a very small portion of the total embeddings.Adagrad maintains a \(G_{t,i,j}\) for every parameter in the embeddings, where \(G_{t,i,j}=G_{t1,i,j} + g_{t,i,j}^2\) and \(g_{t,i,j}\) is the gradient of the dimension \(j\) of embedding \(i\) at step \(t\).
NOTE: The support of sparse Adagrad optimizer is experimental.
 Parameters
params (list[dgl.distributed.DistEmbedding]) – The list of dgl.distributed.DistEmbedding.
lr (float) – The learning rate.
eps (float, Optional) – The term added to the denominator to improve numerical stability Default: 1e10

step
()¶ The step function.
The step function is invoked at the end of every batch to push the gradients of the embeddings involved in a minibatch to DGL’s servers and update the embeddings.

class
dgl.distributed.optim.pytorch.
SparseAdam
(params, lr, betas=(0.9, 0.999), eps=1e08)[source]¶ Distributed Node embedding optimizer using the Adam algorithm.
This optimizer implements a distributed sparse version of Adam algorithm for optimizing
dgl.distributed.DistEmbedding
. Being sparse means it only updates the embeddings whose gradients have updates, which are usually a very small portion of the total embeddings.Adam maintains a \(Gm_{t,i,j}\) and Gp_{t,i,j} for every parameter in the embeddings, where \(Gm_{t,i,j}=beta1 * Gm_{t1,i,j} + (1beta1) * g_{t,i,j}\), \(Gp_{t,i,j}=beta2 * Gp_{t1,i,j} + (1beta2) * g_{t,i,j}^2\), \(g_{t,i,j} = lr * Gm_{t,i,j} / (1  beta1^t) / \sqrt{Gp_{t,i,j} / (1  beta2^t)}\) and \(g_{t,i,j}\) is the gradient of the dimension \(j\) of embedding \(i\) at step \(t\).
NOTE: The support of sparse Adam optimizer is experimental.
 Parameters
params (list[dgl.distributed.DistEmbedding]) – The list of dgl.distributed.DistEmbedding.
lr (float) – The learning rate.
betas (tuple[float, float], Optional) – Coefficients used for computing running averages of gradient and its square. Default: (0.9, 0.999)
eps (float, Optional) – The term added to the denominator to improve numerical stability Default: 1e8

step
()¶ The step function.
The step function is invoked at the end of every batch to push the gradients of the embeddings involved in a minibatch to DGL’s servers and update the embeddings.
Distributed workload split¶



Distributed Sampling¶
Distributed DataLoader¶

class
dgl.distributed.dist_dataloader.
DistDataLoader
(dataset, batch_size, shuffle=False, collate_fn=None, drop_last=False, queue_size=None)[source]¶ DGL customized multiprocessing dataloader.
DistDataLoader provides a similar interface to Pytorch’s DataLoader to generate minibatches with multiprocessing. It utilizes the worker processes created by
dgl.distributed.initialize()
to parallelize sampling. Parameters
dataset (a tensor) – A tensor of node IDs or edge IDs.
batch_size (int) – The number of samples per batch to load.
shuffle (bool, optional) – Set to
True
to have the data reshuffled at every epoch (default:False
).collate_fn (callable, optional) – The function is typically used to sample neighbors of the nodes in a batch or the endpoint nodes of the edges in a batch.
drop_last (bool, optional) – Set to
True
to drop the last incomplete batch, if the dataset size is not divisible by the batch size. IfFalse
and the size of dataset is not divisible by the batch size, then the last batch will be smaller. (default:False
)queue_size (int, optional) – Size of multiprocessing queue
Examples
>>> g = dgl.distributed.DistGraph('graphname') >>> def sample(seeds): ... seeds = th.LongTensor(np.asarray(seeds)) ... frontier = dgl.distributed.sample_neighbors(g, seeds, 10) ... return dgl.to_block(frontier, seeds) >>> dataloader = dgl.distributed.DistDataLoader(dataset=nodes, batch_size=1000, collate_fn=sample, shuffle=True) >>> for block in dataloader: ... feat = g.ndata['features'][block.srcdata[dgl.NID]] ... labels = g.ndata['labels'][block.dstdata[dgl.NID]] ... pred = model(block, feat)
Note
When performing DGL’s distributed sampling with multiprocessing, users have to use this class instead of Pytorch’s DataLoader because DGL’s RPC requires that all processes establish connections with servers before invoking any DGL’s distributed API. Therefore, this dataloader uses the worker processes created in
dgl.distributed.initialize()
.Note
This dataloader does not guarantee the iteration order. For example, if dataset = [1, 2, 3, 4], batch_size = 2 and shuffle = False, the order of [1, 2] and [3, 4] is not guaranteed.
Distributed Neighbor Sampling¶

Sample from the neighbors of the given nodes from a distributed graph. 

Given an edge ID array, return the source and destination node ID array 

Return the subgraph induced on the inbound edges of the given nodes. 
Partition¶
Graph partition book¶

class
dgl.distributed.graph_partition_book.
GraphPartitionBook
[source]¶ The base class of the graph partition book.
For distributed training, a graph is partitioned into multiple parts and is loaded in multiple machines. The partition book contains all necessary information to locate nodes and edges in the cluster.
The partition book contains various partition information, including
the number of partitions,
the partition ID that a node or edge belongs to,
the node IDs and the edge IDs that a partition has.
the local IDs of nodes and edges in a partition.
Currently, there are two classes that implement
GraphPartitionBook
:BasicGraphPartitionBook
andRangePartitionBook
.BasicGraphPartitionBook
stores the mappings between every individual node/edge ID and partition ID on every machine, which usually consumes a lot of memory, whileRangePartitionBook
calculates the mapping between node/edge IDs and partition IDs based on some small metadata because nodes/edges have been relabeled to have IDs in the same partition fall in a contiguous ID range.RangePartitionBook
is usually a preferred way to provide mappings between node/edge IDs and partition IDs.A graph partition book is constructed automatically when a graph is partitioned. When a graph partition is loaded, a graph partition book is loaded as well. Please see
partition_graph()
,load_partition()
andload_partition_book()
for more details.
eid2partid
(eids, etype)[source]¶ From global edge IDs to partition IDs
 Parameters
eids (tensor) – global edge IDs
etype (str) – The edge type
 Returns
partition IDs
 Return type
tensor

map_to_homo_eid
(ids, etype)[source]¶ Map typewise edge IDs and type IDs to homogeneous edge IDs.
 Parameters
ids (tensor) – Typewise edge Ids
etype (str) – edge type
 Returns
Homogeneous edge IDs.
 Return type
Tensor

map_to_homo_nid
(ids, ntype)[source]¶ Map typewise node IDs and type IDs to homogeneous node IDs.
 Parameters
ids (tensor) – Typewise node Ids
ntype (str) – node type
 Returns
Homogeneous node IDs.
 Return type
Tensor

map_to_per_etype
(ids)[source]¶ Map homogeneous edge IDs to typewise IDs and edge types.
 Parameters
ids (tensor) – Homogeneous edge IDs.
 Returns
edge type IDs and typewise edge IDs.
 Return type
(tensor, tensor)

map_to_per_ntype
(ids)[source]¶ Map homogeneous node IDs to typewise IDs and node types.
 Parameters
ids (tensor) – Homogeneous node IDs.
 Returns
node type IDs and typewise node IDs.
 Return type
(tensor, tensor)

metadata
()[source]¶ Return the partition meta data.
The meta data includes:
The machine ID.
Number of nodes and edges of each partition.
Examples
>>> print(g.get_partition_book().metadata()) >>> [{'machine_id' : 0, 'num_nodes' : 3000, 'num_edges' : 5000}, ... {'machine_id' : 1, 'num_nodes' : 2000, 'num_edges' : 4888}, ... ...]

nid2partid
(nids, ntype)[source]¶ From global node IDs to partition IDs
 Parameters
nids (tensor) – global node IDs
ntype (str) – The node type
 Returns
partition IDs
 Return type
tensor

property
partid
¶ Get the current partition ID
 Returns
The partition ID of current machine
 Return type
Move the partition book to shared memory.
 Parameters
graph_name (str) – The graph name. This name will be used to read the partition book from shared memory in another process.

class
dgl.distributed.graph_partition_book.
PartitionPolicy
(policy_str, partition_book)[source]¶ This defines a partition policy for a distributed tensor or distributed embedding.
When DGL shards tensors and stores them in a cluster of machines, it requires partition policies that map rows of the tensors to machines in the cluster.
Although an arbitrary partition policy can be defined, DGL currently supports two partition policies for mapping nodes and edges to machines. To define a partition policy from a graph partition book, users need to specify the policy name (‘node’ or ‘edge’).
 Parameters
policy_str (str) – Partition policy name, e.g., ‘edge:_E’ or ‘node:_N’.
partition_book (GraphPartitionBook) – A graph partition book

property
partition_book
¶ Get partition book
 Returns
The graph partition book
 Return type
Split and Load Graphs¶

Load data of a partition from the data path. 

Load a graph partition book from the partition config file. 

Partition a graph for distributed training and store the partitions on files. 