[User Guide] Update distributed training code (#2114)

* check num_workers

* update

* update

* update

* update

* update

* update

* update user guide

docs/source/guide/distributed.rst

@@ -22,17 +22,24 @@ are the same as :ref:`mini-batch training <guide-minibatch>`.
     import dgl
     import torch as th
 
-    dgl.distributed.initialize(ip_config, num_workers=num_workers)
+    dgl.distributed.initialize('ip_config.txt', num_servers, num_workers)
     th.distributed.init_process_group(backend='gloo')
-    g = dgl.distributed.DistGraph('ip_config.txt', 'graph_name')
-    train_nid = dgl.distributed.node_split(g.ndata['train_mask'])
+    g = dgl.distributed.DistGraph('graph_name', 'part_config.json')
+    pb = g.get_partition_book()
+    train_nid = dgl.distributed.node_split(g.ndata['train_mask'], pb, force_even=True)
 
 
     # Create sampler
-    sampler = dgl.dataloading.MultiLayerNeighborSampler([10, 25])
-    train_dataloader = dgl.dataloading.NodeDataLoader(g, train_nid,
-                                                      sampler, batch_size=1024,
-                                                      shuffle=True,drop_last=False)
+    sampler = NeighborSampler(g, [10,25],
+                              dgl.distributed.sample_neighbors, 
+                              device)
+
+    dataloader = DistDataLoader(
+        dataset=train_nid.numpy(),
+        batch_size=batch_size,
+        collate_fn=sampler.sample_blocks,
+        shuffle=True,
+        drop_last=False)
 
     # Define model and optimizer
     model = SAGE(in_feats, num_hidden, n_classes, num_layers, F.relu, dropout)