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+.. _guide-minibatch:
+
+Chapter 6: Stochastic Training on Large Graphs
+=======================================================
+
+:ref:`(中文版) <guide_cn-minibatch>`
+
+If we have a massive graph with, say, millions or even billions of nodes
+or edges, usually full-graph training as described in
+:ref:`guide-training`
+would not work. Consider an :math:`L`-layer graph convolutional network
+with hidden state size :math:`H` running on an :math:`N`-node graph.
+Storing the intermediate hidden states requires :math:`O(NLH)` memory,
+easily exceeding one GPU’s capacity with large :math:`N`.
+
+This section provides a way to perform stochastic minibatch training,
+where we do not have to fit the feature of all the nodes into GPU.
+
+Overview of Neighborhood Sampling Approaches
+--------------------------------------------
+
+Neighborhood sampling methods generally work as the following. For each
+gradient descent step, we select a minibatch of nodes whose final
+representations at the :math:`L`-th layer are to be computed. We then
+take all or some of their neighbors at the :math:`L-1` layer. This
+process continues until we reach the input. This iterative process
+builds the dependency graph starting from the output and working
+backwards to the input, as the figure below shows:
+
+.. figure:: https://data.dgl.ai/asset/image/guide_6_0_0.png
+   :alt: Imgur
+
+
+
+With this, one can save the workload and computation resources for
+training a GNN on a large graph.
+
+DGL provides a few neighborhood samplers and a pipeline for training a
+GNN with neighborhood sampling, as well as ways to customize your
+sampling strategies.
+
+Roadmap
+-----------
+
+The chapter starts with sections for training GNNs stochastically under
+different scenarios.
+
+* :ref:`guide-minibatch-node-classification-sampler`
+* :ref:`guide-minibatch-edge-classification-sampler`
+* :ref:`guide-minibatch-link-classification-sampler`
+
+The remaining sections cover more advanced topics, suitable for those who
+wish to develop new sampling algorithms, new GNN modules compatible with
+mini-batch training and understand how evaluation and inference can be
+conducted in mini-batches.
+
+* :ref:`guide-minibatch-customizing-neighborhood-sampler`
+* :ref:`guide-minibatch-sparse`
+* :ref:`guide-minibatch-custom-gnn-module`
+* :ref:`guide-minibatch-inference`
+
+The following are performance tips for implementing and using neighborhood
+sampling:
+
+* :ref:`guide-minibatch-gpu-sampling`
+* :ref:`guide-minibatch-parallelism`
+
+.. toctree::
+    :maxdepth: 1
+    :hidden:
+    :glob:
+
+    minibatch-node
+    minibatch-edge
+    minibatch-link
+    minibatch-custom-sampler
+    minibatch-sparse
+    minibatch-nn
+    minibatch-inference
+    minibatch-gpu-sampling
+    minibatch-parallelism