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Unverified Commit e57c6e35 authored by Jinjing Zhou's avatar Jinjing Zhou Committed by GitHub
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[Fix] Fix lint resource usage & Fix Docs (#3032) 

* fix

* remove nvidiasmi

* fix

* fix docs

* fix

* fix
parent 55e7796a
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Showing with 426 additions and 363 deletions
benchmarks/benchmarks/utils.py
View file @ e57c6e35
......@@ -506,7 +506,7 @@ def skip_if_not_4gpu():
def _wrapper(func):
if GPU_COUNT != 4:
# skip if not enabled
print("Skip {}".format(func.benchmark_name))
print("Skip {}".format(func.__name__))
func.benchmark_name = "skip_" + func.__name__
return func
return _wrapper
......
benchmarks/run.sh
View file @ e57c6e35
......@@ -12,8 +12,6 @@ pip install --upgrade pip
pip install asv
pip uninstall -y dgl
nvidia-smi
export DGL_BENCH_DEVICE=$DEVICE
echo "DGL_BENCH_DEVICE=$DGL_BENCH_DEVICE"
pushd $ROOT/benchmarks
......
docker/pods/ci-lint.yaml
View file @ e57c6e35
......@@ -10,5 +10,5 @@ spec:
tty: true
resources:
requests:
cpu: 4
cpu: 1
serviceAccountName: dglciuser
\ No newline at end of file
tutorials/dist/1_node_classification.py
View file @ e57c6e35
......@@ -8,363 +8,428 @@ read the tutorial of multi-GPU training first. This tutorial is developed on top
multi-GPU training by providing extra steps for partitioning a graph, modifying the training script
and setting up the environment for distributed training.
'''
######################################################
#Partition a graph
#-----------------
#
#In this tutorial, we will use `OGBN products graph <https://ogb.stanford.edu/docs/nodeprop/#ogbn-products>`_
#as an example to illustrate the graph partitioning. Let's first load the graph into a DGL graph.
#Here we store the node labels as node data in the DGL Graph.
#
import dgl
import torch as th
from ogb.nodeproppred import DglNodePropPredDataset
data = DglNodePropPredDataset(name='ogbn-products')
graph, labels = data[0]
labels = labels[:, 0]
graph.ndata['labels'] = labels
######################################################
#We need to split the data into training/validation/test set during the graph partitioning.
#Because this is a node classification task, the training/validation/test sets contain node IDs.
#We recommend users to convert them as boolean arrays, in which True indicates the existence
#of the node ID in the set. In this way, we can store them as node data. After the partitioning,
#the boolean arrays will be stored with the graph partitions.
#
splitted_idx = data.get_idx_split()
train_nid, val_nid, test_nid = splitted_idx['train'], splitted_idx['valid'], splitted_idx['test']
train_mask = th.zeros((graph.number_of_nodes(),), dtype=th.bool)
train_mask[train_nid] = True
val_mask = th.zeros((graph.number_of_nodes(),), dtype=th.bool)
val_mask[val_nid] = True
test_mask = th.zeros((graph.number_of_nodes(),), dtype=th.bool)
test_mask[test_nid] = True
graph.ndata['train_mask'] = train_mask
graph.ndata['val_mask'] = val_mask
graph.ndata['test_mask'] = test_mask
#####################################################
#Then we call the `partition_graph` function to partition the graph with
#`METIS <http://glaros.dtc.umn.edu/gkhome/metis/metis/overview>`_ and save the partitioned results
#in the specified folder. **Note**: `partition_graph` runs on a single machine with a single thread.
#You can go to `our user guide <https://docs.dgl.ai/en/latest/guide/distributed-preprocessing.html#distributed-partitioning>`_
#to see more information on distributed graph partitioning.
#
#The code below shows an example of invoking the partitioning algorithm and generate four partitions.
#The partitioned results are stored in a folder called `4part_data`. While partitioning a graph,
#we allow users to specify how to balance the partitions. By default, the algorithm balances the number
#of nodes in each partition as much as possible. However, this balancing strategy is not sufficient
#for distributed GNN training because some partitions may have many more training nodes than other partitions
#or some partitions may have more edges than others. As such, `partition_graph` provides two additional arguments
#`balance_ntypes` and `balance_edges` to enforce more balancing criteria. For example, we can use the training mask
#to balance the number of training nodes in each partition, as shown in the example below. We can also turn on
#the `balance_edges` flag to ensure that all partitions have roughly the same number of edges.
#
dgl.distributed.partition_graph(graph, graph_name='ogbn-products', num_parts=4,
out_path='4part_data',
balance_ntypes=graph.ndata['train_mask'],
balance_edges=True)
###################################################
#When partitioning a graph, DGL shuffles node IDs and edge IDs so that nodes/edges assigned to
#a partition have contiguous IDs. This is necessary for DGL to maintain the mappings of global
#node/edge IDs and partition IDs. If a user needs to map the shuffled node/edge IDs to their original IDs,
#they can turn on the `return_mapping` flag of `partition_graph`, which returns a vector for the node ID mapping
#and edge ID mapping. Below shows an example of using the ID mapping to save the node embeddings after
#distributed training. This is a common use case when users want to use the trained node embeddings
#in their downstream task. Below let's assume that the trained node embeddings are stored in the `node_emb` tensor,
#which is indexed by the shuffled node IDs. We shuffle the embeddings again and store them in
#the `orig_node_emb` tensor, which is indexed by the original node IDs.
#
nmap, emap = dgl.distributed.partition_graph(graph, graph_name='ogbn-products',
num_parts=4,
out_path='4part_data',
balance_ntypes=graph.ndata['train_mask'],
balance_edges=True,
return_mapping=True)
orig_node_emb = th.zeros(node_emb.shape, dtype=node_emb.dtype)
orig_node_emb[nmap] = node_emb
#####################################################
#Distributed training script
#---------------------------
#
#The distributed training script is very similar to multi-GPU training script with just a few modifications.
#It also relies on the Pytorch distributed component to exchange gradients and update model parameters.
#The distributed training script only contains the code of the trainers.
#
#Initialize network communication
#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
#
#Distributed GNN training requires to access the partitioned graph structure and node/edge features
#as well as aggregating the gradients of model parameters from multiple trainers. DGL's distributed
#component is responsible for accessing the distributed graph structure and distributed node features
#and edge features while Pytorch distributed is responsible for exchanging the gradients of model parameters.
#As such, we need to initialize both DGL and Pytorch distributed components at the beginning of the training script.
#
#We need to call DGL's initialize function to initialize the trainers' network communication and
#connect with DGL's servers at the very beginning of the distributed training script. This function
#has an argument that accepts the path to the cluster configuration file.
#
import dgl
import torch as th
dgl.distributed.initialize(ip_config='ip_config.txt')
#####################################################################
#The configuration file `ip_config.txt` has the following format:
#
#.. code-block:: shell
#
# ip_addr1 [port1]
# ip_addr2 [port2]
#
#Each row is a machine. The first column is the IP address and the second column is the port for
#connecting to the DGL server on the machine. The port is optional and the default port is 30050.
#
#After initializing DGL's network communication, a user can initialize Pytorch's distributed communication.
#
th.distributed.init_process_group(backend='gloo')
#######################################################################
#Reference to the distributed graph
#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
#
#DGL's servers load the graph partitions automatically. After the servers load the partitions,
#trainers connect to the servers and can start to reference to the distributed graph in the cluster as below.
#
g = dgl.distributed.DistGraph('ogbn-products')
#######################################################################
#As shown in the code, we refer to a distributed graph by its name. This name is basically the one passed
#to the `partition_graph` function as shown in the section above.
#
#Get training and validation node IDs
#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
#
#For distributed training, each trainer can run its own set of training nodes.
#The training nodes of the entire graph are stored in a distributed tensor as the `train_mask` node data,
#which was constructed before we partitioned the graph. Each trainer can invoke `node_split` to its set
#of training nodes. The `node_split` function splits the full training set evenly and returns
#the training nodes, majority of which are stored in the local partition, to ensure good data locality.
#
train_nid = dgl.distributed.node_split(g.ndata['train_mask'])
######################################################################
#We can split the validation nodes in the same way as above. In this case, each trainer gets
#a different set of validation nodes.
#
valid_nid = dgl.distributed.node_split(g.ndata['val_mask'])
#####################################################################
#Define a GNN model
#^^^^^^^^^^^^^^^^^^
#
#For distributed training, we define a GNN model exactly in the same way as
#`mini-batch training <https://doc.dgl.ai/guide/minibatch.html#>`_ or
#`full-graph training <https://doc.dgl.ai/guide/training-node.html#guide-training-node-classification>`_.
#The code below defines the GraphSage model.
#
import torch.nn as nn
import torch.nn.functional as F
import dgl.nn as dglnn
import torch.optim as optim
class SAGE(nn.Module):
def __init__(self, in_feats, n_hidden, n_classes, n_layers):
super().__init__()
self.n_layers = n_layers
self.n_hidden = n_hidden
self.n_classes = n_classes
self.layers = nn.ModuleList()
self.layers.append(dglnn.SAGEConv(in_feats, n_hidden, 'mean'))
for i in range(1, n_layers - 1):
self.layers.append(dglnn.SAGEConv(n_hidden, n_hidden, 'mean'))
self.layers.append(dglnn.SAGEConv(n_hidden, n_classes, 'mean'))
def forward(self, blocks, x):
for l, (layer, block) in enumerate(zip(self.layers, blocks)):
x = layer(block, x)
if l != self.n_layers - 1:
x = F.relu(x)
return x
num_hidden = 256
num_labels = len(th.unique(g.ndata['labels'][0:g.number_of_nodes()]))
num_layers = 2
lr = 0.001
model = SAGE(g.ndata['feat'].shape[1], num_hidden, num_labels, num_layers)
loss_fcn = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=lr)
###################################################################
#For distributed training, we need to convert the model into a distributed model with
#Pytorch's `DistributedDataParallel`.
#
model = th.nn.parallel.DistributedDataParallel(model)
####################################################################
#Distributed mini-batch sampler
#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
#
#We can use the same `NodeDataLoader` to create a distributed mini-batch sampler for
#node classification.
#
sampler = dgl.dataloading.MultiLayerNeighborSampler([25,10])
train_dataloader = dgl.dataloading.NodeDataLoader(
g, train_nid, sampler, batch_size=1024,
shuffle=True, drop_last=False)
valid_dataloader = dgl.dataloading.NodeDataLoader(
g, valid_nid, sampler, batch_size=1024,
shuffle=False, drop_last=False)
###################################################################
#Training loop
#^^^^^^^^^^^^^
#
#The training loop for distributed training is also exactly the same as the single-process training.
#
import sklearn.metrics
import numpy as np
for epoch in range(10):
# Loop over the dataloader to sample mini-batches.
losses = []
for step, (input_nodes, seeds, blocks) in enumerate(train_dataloader):
# Load the input features as well as output labels
batch_inputs = g.ndata['feat'][input_nodes]
batch_labels = g.ndata['labels'][seeds]
# Compute loss and prediction
batch_pred = model(blocks, batch_inputs)
loss = loss_fcn(batch_pred, batch_labels)
optimizer.zero_grad()
loss.backward()
losses.append(loss.detach().cpu().numpy())
optimizer.step()
# validation
predictions = []
labels = []
with th.no_grad():
for step, (input_nodes, seeds, blocks) in enumerate(valid_dataloader):
inputs = g.ndata['feat'][input_nodes]
labels.append(g.ndata['labels'][seeds].numpy())
predictions.append(model(blocks, inputs).argmax(1).numpy())
predictions = np.concatenate(predictions)
labels = np.concatenate(labels)
accuracy = sklearn.metrics.accuracy_score(labels, predictions)
print('Epoch {}: Validation Accuracy {}'.format(epoch, accuracy))
######################################################################
#Set up distributed training environment
#---------------------------------------
#
#After partitioning a graph and preparing the training script, we now need to set up
#the distributed training environment and launch the training job. Basically, we need to
#create a cluster of machines and upload both the training script and the partitioned data
#to each machine in the cluster. A recommended solution of sharing the training script and
#the partitioned data in the cluster is to use NFS (Network File System).
#
#For any users who are not familiar with NFS, below is a small tutorial of setting up NFS
#in an existing cluster.
#
#NFS server side setup (ubuntu only)
#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
#
#First, install essential libs on the storage server
#
#.. code-block:: shell
#
# sudo apt-get install nfs-kernel-server
#
#Below we assume the user account is ubuntu and we create a directory of workspace in the home directory.
#
#.. code-block:: shell
#
# mkdir -p /home/ubuntu/workspace
#
#We assume that the all servers are under a subnet with ip range 192.168.0.0 to 192.168.255.255.
#We need to add the following line to `/etc/exports`
#
#.. code-block:: shell
#
# /home/ubuntu/workspace 192.168.0.0/16(rw,sync,no_subtree_check)
#
#Then restart NFS, the setup on server side is finished.
#
#.. code-block:: shell
#
# sudo systemctl restart nfs-kernel-server
#
#For configuration details, please refer to NFS ArchWiki (https://wiki.archlinux.org/index.php/NFS).
#
#NFS client side setup (ubuntu only)
#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
#
#To use NFS, clients also require to install essential packages
#
#.. code-block:: shell
#
# sudo apt-get install nfs-common
#
#You can either mount the NFS manually
#
#.. code-block:: shell
#
# mkdir -p /home/ubuntu/workspace
# sudo mount -t nfs <nfs-server-ip>:/home/ubuntu/workspace /home/ubuntu/workspace
#
#or add the following line to `/etc/fstab` so the folder will be mounted automatically
#
#.. code-block:: shell
#
# <nfs-server-ip>:/home/ubuntu/workspace /home/ubuntu/workspace nfs defaults 0 0
#
#Then run
#
#.. code-block:: shell
#
# mount -a
#
#Now go to `/home/ubuntu/workspace` and save the training script and the partitioned data in the folder.
#
#SSH Access
#^^^^^^^^^^
#
#The launch script accesses the machines in the cluster via SSH. Users should follow the instruction
#in `this document <https://linuxize.com/post/how-to-setup-passwordless-ssh-login/>`_ to set up
#the passwordless SSH login on every machine in the cluster. After setting up the passwordless SSH,
#users need to authenticate the connection to each machine and add their key fingerprints to `~/.ssh/known_hosts`.
#This can be done automatically when we ssh to a machine for the first time.
#
#Launch the distributed training job
#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
#
#After everything is ready, we can now use the launch script provided by DGL to launch the distributed
#training job in the cluster. We can run the launch script on any machine in the cluster.
#
#.. code-block:: shell
#
# python3 ~/workspace/dgl/tools/launch.py \
# --workspace ~/workspace/ \
# --num_trainers 1 \
# --num_samplers 0 \
# --num_servers 1 \
# --part_config 4part_data/ogbn-products.json \
# --ip_config ip_config.txt \
# "python3 train_dist.py"
#
#If we split the graph into four partitions as demonstrated at the beginning of the tutorial, the cluster has to have four machines. The command above launches one trainer and one server on each machine in the cluster. `ip_config.txt` lists the IP addresses of all machines in the cluster as follows:
#
#.. code-block:: shell
#
# ip_addr1
# ip_addr2
# ip_addr3
# ip_addr4
Partition a graph
-----------------
In this tutorial, we will use `OGBN products graph <https://ogb.stanford.edu/docs/nodeprop/#ogbn-products>`_
as an example to illustrate the graph partitioning. Let's first load the graph into a DGL graph.
Here we store the node labels as node data in the DGL Graph.
.. code-block:: python
import dgl
import torch as th
from ogb.nodeproppred import DglNodePropPredDataset
data = DglNodePropPredDataset(name='ogbn-products')
graph, labels = data[0]
labels = labels[:, 0]
graph.ndata['labels'] = labels
We need to split the data into training/validation/test set during the graph partitioning.
Because this is a node classification task, the training/validation/test sets contain node IDs.
We recommend users to convert them as boolean arrays, in which True indicates the existence
of the node ID in the set. In this way, we can store them as node data. After the partitioning,
the boolean arrays will be stored with the graph partitions.
.. code-block:: python
splitted_idx = data.get_idx_split()
train_nid, val_nid, test_nid = splitted_idx['train'], splitted_idx['valid'], splitted_idx['test']
train_mask = th.zeros((graph.number_of_nodes(),), dtype=th.bool)
train_mask[train_nid] = True
val_mask = th.zeros((graph.number_of_nodes(),), dtype=th.bool)
val_mask[val_nid] = True
test_mask = th.zeros((graph.number_of_nodes(),), dtype=th.bool)
test_mask[test_nid] = True
graph.ndata['train_mask'] = train_mask
graph.ndata['val_mask'] = val_mask
graph.ndata['test_mask'] = test_mask
Then we call the `partition_graph` function to partition the graph with
`METIS <http://glaros.dtc.umn.edu/gkhome/metis/metis/overview>`_ and save the partitioned results
in the specified folder. **Note**: `partition_graph` runs on a single machine with a single thread.
You can go to `our user guide <https://docs.dgl.ai/en/latest/guide/distributed-preprocessing.html#distributed-partitioning>`_
to see more information on distributed graph partitioning.
The code below shows an example of invoking the partitioning algorithm and generate four partitions.
The partitioned results are stored in a folder called `4part_data`. While partitioning a graph,
we allow users to specify how to balance the partitions. By default, the algorithm balances the number
of nodes in each partition as much as possible. However, this balancing strategy is not sufficient
for distributed GNN training because some partitions may have many more training nodes than other partitions
or some partitions may have more edges than others. As such, `partition_graph` provides two additional arguments
`balance_ntypes` and `balance_edges` to enforce more balancing criteria. For example, we can use the training mask
to balance the number of training nodes in each partition, as shown in the example below. We can also turn on
the `balance_edges` flag to ensure that all partitions have roughly the same number of edges.
.. code-block:: python
dgl.distributed.partition_graph(graph, graph_name='ogbn-products', num_parts=4,
out_path='4part_data',
balance_ntypes=graph.ndata['train_mask'],
balance_edges=True)
When partitioning a graph, DGL shuffles node IDs and edge IDs so that nodes/edges assigned to
a partition have contiguous IDs. This is necessary for DGL to maintain the mappings of global
node/edge IDs and partition IDs. If a user needs to map the shuffled node/edge IDs to their original IDs,
they can turn on the `return_mapping` flag of `partition_graph`, which returns a vector for the node ID mapping
and edge ID mapping. Below shows an example of using the ID mapping to save the node embeddings after
distributed training. This is a common use case when users want to use the trained node embeddings
in their downstream task. Below let's assume that the trained node embeddings are stored in the `node_emb` tensor,
which is indexed by the shuffled node IDs. We shuffle the embeddings again and store them in
the `orig_node_emb` tensor, which is indexed by the original node IDs.
.. code-block:: python
nmap, emap = dgl.distributed.partition_graph(graph, graph_name='ogbn-products',
num_parts=4,
out_path='4part_data',
balance_ntypes=graph.ndata['train_mask'],
balance_edges=True,
return_mapping=True)
orig_node_emb = th.zeros(node_emb.shape, dtype=node_emb.dtype)
orig_node_emb[nmap] = node_emb
Distributed training script
---------------------------
The distributed training script is very similar to multi-GPU training script with just a few modifications.
It also relies on the Pytorch distributed component to exchange gradients and update model parameters.
The distributed training script only contains the code of the trainers.
Initialize network communication
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Distributed GNN training requires to access the partitioned graph structure and node/edge features
as well as aggregating the gradients of model parameters from multiple trainers. DGL's distributed
component is responsible for accessing the distributed graph structure and distributed node features
and edge features while Pytorch distributed is responsible for exchanging the gradients of model parameters.
As such, we need to initialize both DGL and Pytorch distributed components at the beginning of the training script.
We need to call DGL's initialize function to initialize the trainers' network communication and
connect with DGL's servers at the very beginning of the distributed training script. This function
has an argument that accepts the path to the cluster configuration file.
.. code-block:: python
import dgl
import torch as th
dgl.distributed.initialize(ip_config='ip_config.txt')
The configuration file `ip_config.txt` has the following format:
.. code-block:: shell
ip_addr1 [port1]
ip_addr2 [port2]
Each row is a machine. The first column is the IP address and the second column is the port for
connecting to the DGL server on the machine. The port is optional and the default port is 30050.
After initializing DGL's network communication, a user can initialize Pytorch's distributed communication.
.. code-block:: python
th.distributed.init_process_group(backend='gloo')
Reference to the distributed graph
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
DGL's servers load the graph partitions automatically. After the servers load the partitions,
trainers connect to the servers and can start to reference to the distributed graph in the cluster as below.
.. code-block:: python
g = dgl.distributed.DistGraph('ogbn-products')
As shown in the code, we refer to a distributed graph by its name. This name is basically the one passed
to the `partition_graph` function as shown in the section above.
Get training and validation node IDs
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
For distributed training, each trainer can run its own set of training nodes.
The training nodes of the entire graph are stored in a distributed tensor as the `train_mask` node data,
which was constructed before we partitioned the graph. Each trainer can invoke `node_split` to its set
of training nodes. The `node_split` function splits the full training set evenly and returns
the training nodes, majority of which are stored in the local partition, to ensure good data locality.
.. code-block:: python
train_nid = dgl.distributed.node_split(g.ndata['train_mask'])
We can split the validation nodes in the same way as above. In this case, each trainer gets
a different set of validation nodes.
.. code-block:: python
valid_nid = dgl.distributed.node_split(g.ndata['val_mask'])
Define a GNN model
^^^^^^^^^^^^^^^^^^
For distributed training, we define a GNN model exactly in the same way as
`mini-batch training <https://doc.dgl.ai/guide/minibatch.html#>`_ or
`full-graph training <https://doc.dgl.ai/guide/training-node.html#guide-training-node-classification>`_.
The code below defines the GraphSage model.
.. code-block:: python
import torch.nn as nn
import torch.nn.functional as F
import dgl.nn as dglnn
import torch.optim as optim
class SAGE(nn.Module):
def __init__(self, in_feats, n_hidden, n_classes, n_layers):
super().__init__()
self.n_layers = n_layers
self.n_hidden = n_hidden
self.n_classes = n_classes
self.layers = nn.ModuleList()
self.layers.append(dglnn.SAGEConv(in_feats, n_hidden, 'mean'))
for i in range(1, n_layers - 1):
self.layers.append(dglnn.SAGEConv(n_hidden, n_hidden, 'mean'))
self.layers.append(dglnn.SAGEConv(n_hidden, n_classes, 'mean'))
def forward(self, blocks, x):
for l, (layer, block) in enumerate(zip(self.layers, blocks)):
x = layer(block, x)
if l != self.n_layers - 1:
x = F.relu(x)
return x
num_hidden = 256
num_labels = len(th.unique(g.ndata['labels'][0:g.number_of_nodes()]))
num_layers = 2
lr = 0.001
model = SAGE(g.ndata['feat'].shape[1], num_hidden, num_labels, num_layers)
loss_fcn = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=lr)
For distributed training, we need to convert the model into a distributed model with
Pytorch's `DistributedDataParallel`.
.. code-block:: python
model = th.nn.parallel.DistributedDataParallel(model)
Distributed mini-batch sampler
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
We can use the same `NodeDataLoader` to create a distributed mini-batch sampler for
node classification.
.. code-block:: python
sampler = dgl.dataloading.MultiLayerNeighborSampler([25,10])
train_dataloader = dgl.dataloading.NodeDataLoader(
g, train_nid, sampler, batch_size=1024,
shuffle=True, drop_last=False)
valid_dataloader = dgl.dataloading.NodeDataLoader(
g, valid_nid, sampler, batch_size=1024,
shuffle=False, drop_last=False)
Training loop
^^^^^^^^^^^^^
The training loop for distributed training is also exactly the same as the single-process training.
.. code-block:: python
import sklearn.metrics
import numpy as np
for epoch in range(10):
# Loop over the dataloader to sample mini-batches.
losses = []
for step, (input_nodes, seeds, blocks) in enumerate(train_dataloader):
# Load the input features as well as output labels
batch_inputs = g.ndata['feat'][input_nodes]
batch_labels = g.ndata['labels'][seeds]
# Compute loss and prediction
batch_pred = model(blocks, batch_inputs)
loss = loss_fcn(batch_pred, batch_labels)
optimizer.zero_grad()
loss.backward()
losses.append(loss.detach().cpu().numpy())
optimizer.step()
# validation
predictions = []
labels = []
with th.no_grad():
for step, (input_nodes, seeds, blocks) in enumerate(valid_dataloader):
inputs = g.ndata['feat'][input_nodes]
labels.append(g.ndata['labels'][seeds].numpy())
predictions.append(model(blocks, inputs).argmax(1).numpy())
predictions = np.concatenate(predictions)
labels = np.concatenate(labels)
accuracy = sklearn.metrics.accuracy_score(labels, predictions)
print('Epoch {}: Validation Accuracy {}'.format(epoch, accuracy))
Set up distributed training environment
---------------------------------------
After partitioning a graph and preparing the training script, we now need to set up
the distributed training environment and launch the training job. Basically, we need to
create a cluster of machines and upload both the training script and the partitioned data
to each machine in the cluster. A recommended solution of sharing the training script and
the partitioned data in the cluster is to use NFS (Network File System).
For any users who are not familiar with NFS, below is a small tutorial of setting up NFS
in an existing cluster.
NFS server side setup (ubuntu only)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
First, install essential libs on the storage server
.. code-block:: shell
sudo apt-get install nfs-kernel-server
Below we assume the user account is ubuntu and we create a directory of workspace in the home directory.
.. code-block:: shell
mkdir -p /home/ubuntu/workspace
We assume that the all servers are under a subnet with ip range 192.168.0.0 to 192.168.255.255.
We need to add the following line to `/etc/exports`
.. code-block:: shell
/home/ubuntu/workspace 192.168.0.0/16(rw,sync,no_subtree_check)
Then restart NFS, the setup on server side is finished.
.. code-block:: shell
sudo systemctl restart nfs-kernel-server
For configuration details, please refer to NFS ArchWiki (https://wiki.archlinux.org/index.php/NFS).
NFS client side setup (ubuntu only)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
To use NFS, clients also require to install essential packages
.. code-block:: shell
sudo apt-get install nfs-common
You can either mount the NFS manually
.. code-block:: shell
mkdir -p /home/ubuntu/workspace
sudo mount -t nfs <nfs-server-ip>:/home/ubuntu/workspace /home/ubuntu/workspace
or add the following line to `/etc/fstab` so the folder will be mounted automatically
.. code-block:: shell
<nfs-server-ip>:/home/ubuntu/workspace /home/ubuntu/workspace nfs defaults 0 0
Then run
.. code-block:: shell
mount -a
Now go to `/home/ubuntu/workspace` and save the training script and the partitioned data in the folder.
SSH Access
^^^^^^^^^^
The launch script accesses the machines in the cluster via SSH. Users should follow the instruction
in `this document <https://linuxize.com/post/how-to-setup-passwordless-ssh-login/>`_ to set up
the passwordless SSH login on every machine in the cluster. After setting up the passwordless SSH,
users need to authenticate the connection to each machine and add their key fingerprints to `~/.ssh/known_hosts`.
This can be done automatically when we ssh to a machine for the first time.
Launch the distributed training job
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
After everything is ready, we can now use the launch script provided by DGL to launch the distributed
training job in the cluster. We can run the launch script on any machine in the cluster.
.. code-block:: shell
python3 ~/workspace/dgl/tools/launch.py \
--workspace ~/workspace/ \
--num_trainers 1 \
--num_samplers 0 \
--num_servers 1 \
--part_config 4part_data/ogbn-products.json \
--ip_config ip_config.txt \
"python3 train_dist.py"
If we split the graph into four partitions as demonstrated at the beginning of the tutorial, the cluster has to have four machines. The command above launches one trainer and one server on each machine in the cluster. `ip_config.txt` lists the IP addresses of all machines in the cluster as follows:
.. code-block:: shell
ip_addr1
ip_addr2
ip_addr3
ip_addr4
'''
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