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<li class="toctree-l1 current"><a class="reference internal" href="../../index.html">Python Tutorials</a><ul class="current">
<li class="toctree-l2 current"><a class="reference internal" href="../index.html">Getting Started</a><ul class="current">
<li class="toctree-l3 current"><a class="reference internal" href="index.html">Crash Course</a><ul class="current">
<li class="toctree-l4"><a class="reference internal" href="1-ndarray.html">Manipulate data with <code class="docutils literal notranslate"><span class="pre">ndarray</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="2-nn.html">Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="3-autograd.html">Automatic differentiation with <code class="docutils literal notranslate"><span class="pre">autograd</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="4-train.html">Train the neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="5-predict.html">Predict with a pre-trained model</a></li>
<li class="toctree-l4 current"><a class="current reference internal" href="#">Use GPUs</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../gluon_from_experiment_to_deployment.html">Gluon: from experiment to deployment</a></li>
<li class="toctree-l3"><a class="reference internal" href="../logistic_regression_explained.html">Logistic regression explained</a></li>
<li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html">MNIST</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../../packages/index.html">Packages</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../../packages/autograd/index.html">Automatic Differentiation</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../packages/gluon/index.html">Gluon</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../../packages/gluon/blocks/index.html">Blocks</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/custom-layer.html">Custom Layers</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/custom_layer_beginners.html">Customer Layers (Beginners)</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/hybridize.html">Hybridize</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/init.html">Initialization</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/naming.html">Parameter and Block Naming</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/nn.html">Layers and Blocks</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/parameters.html">Parameter Management</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/activations/activations.html">Activation Blocks</a></li>
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<li class="toctree-l4"><a class="reference internal" href="../../packages/gluon/data/index.html">Data Tutorials</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/data/data_augmentation.html">Image Augmentation</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/data/data_augmentation.html#Spatial-Augmentation">Spatial Augmentation</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/data/data_augmentation.html#Color-Augmentation">Color Augmentation</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/data/data_augmentation.html#Composed-Augmentations">Composed Augmentations</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/data/datasets.html">Gluon <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/data/datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/data/datasets.html#Using-own-data-with-custom-Datasets">Using own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/data/datasets.html#Appendix:-Upgrading-from-Module-DataIter-to-Gluon-DataLoader">Appendix: Upgrading from Module <code class="docutils literal notranslate"><span class="pre">DataIter</span></code> to Gluon <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
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<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/image/info_gan.html">Image similarity search with InfoGAN</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/image/mnist.html">Handwritten Digit Recognition</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/image/pretrained_models.html">Using pre-trained models in MXNet</a></li>
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<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/training/fit_api_tutorial.html">MXNet Gluon Fit API</a></li>
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<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/training/learning_rates/index.html">Learning Rates</a><ul>
<li class="toctree-l6"><a class="reference internal" href="../../packages/gluon/training/learning_rates/learning_rate_finder.html">Learning Rate Finder</a></li>
<li class="toctree-l6"><a class="reference internal" href="../../packages/gluon/training/learning_rates/learning_rate_schedules.html">Learning Rate Schedules</a></li>
<li class="toctree-l6"><a class="reference internal" href="../../packages/gluon/training/learning_rates/learning_rate_schedules_advanced.html">Advanced Learning Rate Schedules</a></li>
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<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/training/normalization/index.html">Normalization Blocks</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../../packages/kvstore/index.html">KVStore</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../../packages/kvstore/kvstore.html">Distributed Key-Value Store</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../../packages/ndarray/index.html">NDArray</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../../packages/ndarray/01-ndarray-intro.html">An Intro: Manipulate Data the MXNet Way with NDArray</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../packages/ndarray/02-ndarray-operations.html">NDArray Operations</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../packages/ndarray/03-ndarray-contexts.html">NDArray Contexts</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../packages/ndarray/gotchas_numpy_in_mxnet.html">Gotchas using NumPy in Apache MXNet</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../packages/ndarray/sparse/index.html">Tutorials</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../../packages/ndarray/sparse/csr.html">CSRNDArray - NDArray in Compressed Sparse Row Storage Format</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/ndarray/sparse/row_sparse.html">RowSparseNDArray - NDArray for Sparse Gradient Updates</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/ndarray/sparse/train.html">Train a Linear Regression Model with Sparse Symbols</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/ndarray/sparse/train_gluon.html">Sparse NDArrays with Gluon</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../../packages/onnx/index.html">ONNX</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../../packages/onnx/fine_tuning_gluon.html">Fine-tuning an ONNX model</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../packages/onnx/inference_on_onnx_model.html">Running inference on MXNet/Gluon from an ONNX model</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../packages/onnx/super_resolution.html">Importing an ONNX model into MXNet</a></li>
<li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/deploy/export/onnx.html">Export ONNX Models</a></li>
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<li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/visualize_graph">Visualize networks</a></li>
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<li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/gradient_compression">Gradient Compression</a></li>
<li class="toctree-l4"><a class="reference external" href="https://gluon-cv.mxnet.io/build/examples_deployment/int8_inference.html">GluonCV with Quantized Models</a></li>
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<li class="toctree-l4"><a class="reference internal" href="../../performance/backend/mkldnn/index.html">Intel MKL-DNN</a><ul>
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<li class="toctree-l5"><a class="reference internal" href="../../performance/backend/mkldnn/mkldnn_quantization.html#Improving-accuracy-with-Intel®-Neural-Compressor">Improving accuracy with Intel® Neural Compressor</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../performance/backend/mkldnn/mkldnn_readme.html">Install MXNet with MKL-DNN</a></li>
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<li class="toctree-l5"><a class="reference internal" href="../../performance/backend/tensorrt/tensorrt.html">Optimizing Deep Learning Computation Graphs with TensorRT</a></li>
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<li class="toctree-l4"><a class="reference internal" href="../../performance/backend/profiler.html">Profiling MXNet Models</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../performance/backend/amp.html">Using AMP: Automatic Mixed Precision</a></li>
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<li class="toctree-l1 current"><a class="reference internal" href="../../index.html">Python Tutorials</a><ul class="current">
<li class="toctree-l2 current"><a class="reference internal" href="../index.html">Getting Started</a><ul class="current">
<li class="toctree-l3 current"><a class="reference internal" href="index.html">Crash Course</a><ul class="current">
<li class="toctree-l4"><a class="reference internal" href="1-ndarray.html">Manipulate data with <code class="docutils literal notranslate"><span class="pre">ndarray</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="2-nn.html">Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="3-autograd.html">Automatic differentiation with <code class="docutils literal notranslate"><span class="pre">autograd</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="4-train.html">Train the neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="5-predict.html">Predict with a pre-trained model</a></li>
<li class="toctree-l4 current"><a class="current reference internal" href="#">Use GPUs</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../gluon_from_experiment_to_deployment.html">Gluon: from experiment to deployment</a></li>
<li class="toctree-l3"><a class="reference internal" href="../logistic_regression_explained.html">Logistic regression explained</a></li>
<li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html">MNIST</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../../packages/index.html">Packages</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../../packages/autograd/index.html">Automatic Differentiation</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../packages/gluon/index.html">Gluon</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../../packages/gluon/blocks/index.html">Blocks</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/custom-layer.html">Custom Layers</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/custom_layer_beginners.html">Customer Layers (Beginners)</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/hybridize.html">Hybridize</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/init.html">Initialization</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/naming.html">Parameter and Block Naming</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/nn.html">Layers and Blocks</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/parameters.html">Parameter Management</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/activations/activations.html">Activation Blocks</a></li>
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<li class="toctree-l4"><a class="reference internal" href="../../packages/gluon/data/index.html">Data Tutorials</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/data/data_augmentation.html">Image Augmentation</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/data/data_augmentation.html#Spatial-Augmentation">Spatial Augmentation</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/data/data_augmentation.html#Color-Augmentation">Color Augmentation</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/data/data_augmentation.html#Composed-Augmentations">Composed Augmentations</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/data/datasets.html">Gluon <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/data/datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/data/datasets.html#Using-own-data-with-custom-Datasets">Using own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/data/datasets.html#Appendix:-Upgrading-from-Module-DataIter-to-Gluon-DataLoader">Appendix: Upgrading from Module <code class="docutils literal notranslate"><span class="pre">DataIter</span></code> to Gluon <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
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<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/image/image-augmentation.html">Image Augmentation</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/image/info_gan.html">Image similarity search with InfoGAN</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/image/mnist.html">Handwritten Digit Recognition</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/image/pretrained_models.html">Using pre-trained models in MXNet</a></li>
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<li class="toctree-l4"><a class="reference internal" href="../../packages/gluon/loss/index.html">Losses</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/loss/custom-loss.html">Custom Loss Blocks</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/loss/kl_divergence.html">Kullback-Leibler (KL) Divergence</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/loss/loss.html">Loss functions</a></li>
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<li class="toctree-l4"><a class="reference internal" href="../../packages/gluon/text/index.html">Text Tutorials</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/text/gnmt.html">Google Neural Machine Translation</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/text/transformer.html">Machine Translation with Transformer</a></li>
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<li class="toctree-l4"><a class="reference internal" href="../../packages/gluon/training/index.html">Training</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/training/fit_api_tutorial.html">MXNet Gluon Fit API</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/training/trainer.html">Trainer</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/training/learning_rates/index.html">Learning Rates</a><ul>
<li class="toctree-l6"><a class="reference internal" href="../../packages/gluon/training/learning_rates/learning_rate_finder.html">Learning Rate Finder</a></li>
<li class="toctree-l6"><a class="reference internal" href="../../packages/gluon/training/learning_rates/learning_rate_schedules.html">Learning Rate Schedules</a></li>
<li class="toctree-l6"><a class="reference internal" href="../../packages/gluon/training/learning_rates/learning_rate_schedules_advanced.html">Advanced Learning Rate Schedules</a></li>
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<li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/training/normalization/index.html">Normalization Blocks</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../../packages/kvstore/index.html">KVStore</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../../packages/kvstore/kvstore.html">Distributed Key-Value Store</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../../packages/ndarray/index.html">NDArray</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../../packages/ndarray/01-ndarray-intro.html">An Intro: Manipulate Data the MXNet Way with NDArray</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../packages/ndarray/02-ndarray-operations.html">NDArray Operations</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../packages/ndarray/03-ndarray-contexts.html">NDArray Contexts</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../packages/ndarray/gotchas_numpy_in_mxnet.html">Gotchas using NumPy in Apache MXNet</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../packages/ndarray/sparse/index.html">Tutorials</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../../packages/ndarray/sparse/csr.html">CSRNDArray - NDArray in Compressed Sparse Row Storage Format</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/ndarray/sparse/row_sparse.html">RowSparseNDArray - NDArray for Sparse Gradient Updates</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/ndarray/sparse/train.html">Train a Linear Regression Model with Sparse Symbols</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../packages/ndarray/sparse/train_gluon.html">Sparse NDArrays with Gluon</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../../packages/onnx/index.html">ONNX</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../../packages/onnx/fine_tuning_gluon.html">Fine-tuning an ONNX model</a></li>
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<!--- Licensed to the Apache Software Foundation (ASF) under one --><!--- or more contributor license agreements. See the NOTICE file --><!--- distributed with this work for additional information --><!--- regarding copyright ownership. The ASF licenses this file --><!--- to you under the Apache License, Version 2.0 (the --><!--- "License"); you may not use this file except in compliance --><!--- with the License. You may obtain a copy of the License at --><!--- http://www.apache.org/licenses/LICENSE-2.0 --><!--- Unless required by applicable law or agreed to in writing, --><!--- software distributed under the License is distributed on an --><!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY --><!--- KIND, either express or implied. See the License for the --><!--- specific language governing permissions and limitations --><!--- under the License. --><div class="section" id="Use-GPUs">
<h1>Use GPUs<a class="headerlink" href="#Use-GPUs" title="Permalink to this headline"></a></h1>
<p>We often use GPUs to train and deploy neural networks, because it offers significant more computation power compared to CPUs. In this tutorial we will introduce how to use GPUs with MXNet.</p>
<p>First, make sure you have at least one Nvidia GPU in your machine and CUDA properly installed. Other GPUs such as AMD and Intel GPUs are not supported yet. Then be sure you have installed the GPU-enabled version of MXNet.</p>
<div class="nbinput nblast docutils container">
<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[15]:
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<div class="input_area highlight-python notranslate"><div class="highlight"><pre>
<span></span><span class="c1"># If you pip installed the plain `mxnet` before, uncomment the</span>
<span class="c1"># following two lines to install the GPU version. You may need to</span>
<span class="c1"># replace `cu92` according to your CUDA version.</span>
<span class="c1"># !pip uninstall mxnet</span>
<span class="c1"># !pip install mxnet-cu92</span>
<span class="kn">from</span> <span class="nn">mxnet</span> <span class="kn">import</span> <span class="n">nd</span><span class="p">,</span> <span class="n">gpu</span><span class="p">,</span> <span class="n">gluon</span><span class="p">,</span> <span class="n">autograd</span>
<span class="kn">from</span> <span class="nn">mxnet.gluon</span> <span class="kn">import</span> <span class="n">nn</span>
<span class="kn">from</span> <span class="nn">mxnet.gluon.data.vision</span> <span class="kn">import</span> <span class="n">datasets</span><span class="p">,</span> <span class="n">transforms</span>
<span class="kn">import</span> <span class="nn">time</span>
</pre></div>
</div>
</div>
<div class="section" id="Allocate-data-to-a-GPU">
<h2>Allocate data to a GPU<a class="headerlink" href="#Allocate-data-to-a-GPU" title="Permalink to this headline"></a></h2>
<p>You may notice that MXNet’s NDArray is very similar to Numpy. One major difference is NDArray has a <code class="docutils literal notranslate"><span class="pre">context</span></code> attribute that specifies which device this array is on. By default, it is <code class="docutils literal notranslate"><span class="pre">cpu()</span></code>. Now we will change it to the first GPU. You can use <code class="docutils literal notranslate"><span class="pre">gpu()</span></code> or <code class="docutils literal notranslate"><span class="pre">gpu(0)</span></code> to indicate the first GPU.</p>
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<span></span><span class="n">x</span> <span class="o">=</span> <span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">ctx</span><span class="o">=</span><span class="n">gpu</span><span class="p">())</span>
<span class="n">x</span>
</pre></div>
</div>
</div>
<p>For a CPU, MXNet will allocate data on main memory, and try to use all CPU cores as possible, even if there is more than one CPU socket. While if there are multiple GPUs, MXNet needs to specify which GPUs the NDArray will be allocated.</p>
<p>Let’s assume there is a least one more GPU. We can create another NDArray and assign it there. (If you only have one GPU, then you will see an error). Here we copy <code class="docutils literal notranslate"><span class="pre">x</span></code> to the second GPU, <code class="docutils literal notranslate"><span class="pre">gpu(1)</span></code>:</p>
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<span></span><span class="n">x</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="n">gpu</span><span class="p">(</span><span class="mi">1</span><span class="p">))</span>
</pre></div>
</div>
</div>
<p>MXNet needs users to explicitly move data between devices. But several operators such as <code class="docutils literal notranslate"><span class="pre">print</span></code>, <code class="docutils literal notranslate"><span class="pre">asnumpy</span></code> and <code class="docutils literal notranslate"><span class="pre">asscalar</span></code>, will implicitly move data to main memory.</p>
</div>
<div class="section" id="Run-an-operation-on-a-GPU">
<h2>Run an operation on a GPU<a class="headerlink" href="#Run-an-operation-on-a-GPU" title="Permalink to this headline"></a></h2>
<p>To perform an operation on a particular GPU, we only need to guarantee that the inputs of this operation are already on that GPU. The output will be allocated on the same GPU as well. Almost all operators in the <code class="docutils literal notranslate"><span class="pre">nd</span></code> module support running on a GPU.</p>
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<span></span><span class="n">y</span> <span class="o">=</span> <span class="n">nd</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">ctx</span><span class="o">=</span><span class="n">gpu</span><span class="p">())</span>
<span class="n">x</span> <span class="o">+</span> <span class="n">y</span>
</pre></div>
</div>
</div>
<p>Remember that if the inputs are not on the same GPU, you will see an error.</p>
</div>
<div class="section" id="Run-a-neural-network-on-a-GPU">
<h2>Run a neural network on a GPU<a class="headerlink" href="#Run-a-neural-network-on-a-GPU" title="Permalink to this headline"></a></h2>
<p>Similarly, to run a neural network on a GPU, we only need to copy/move the input data and parameters to the GPU. Let’s reuse the previously defined LeNet.</p>
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<span></span><span class="n">net</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">()</span>
<span class="n">net</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Conv2D</span><span class="p">(</span><span class="n">channels</span><span class="o">=</span><span class="mi">6</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">),</span>
<span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="mi">2</span><span class="p">),</span>
<span class="n">nn</span><span class="o">.</span><span class="n">Conv2D</span><span class="p">(</span><span class="n">channels</span><span class="o">=</span><span class="mi">16</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">),</span>
<span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="mi">2</span><span class="p">),</span>
<span class="n">nn</span><span class="o">.</span><span class="n">Flatten</span><span class="p">(),</span>
<span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">120</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s2">&quot;relu&quot;</span><span class="p">),</span>
<span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">84</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s2">&quot;relu&quot;</span><span class="p">),</span>
<span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">10</span><span class="p">))</span>
</pre></div>
</div>
</div>
<p>And then load the saved parameters into GPU 0 directly, or use <code class="docutils literal notranslate"><span class="pre">net.collect_params().reset_ctx</span></code> to change the device.</p>
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<span></span><span class="n">net</span><span class="o">.</span><span class="n">load_parameters</span><span class="p">(</span><span class="s1">&#39;net.params&#39;</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">gpu</span><span class="p">(</span><span class="mi">0</span><span class="p">))</span>
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<p>Now create input data on GPU 0. The forward function will then run on GPU 0.</p>
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<span></span><span class="n">x</span> <span class="o">=</span> <span class="n">nd</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">28</span><span class="p">,</span><span class="mi">28</span><span class="p">),</span> <span class="n">ctx</span><span class="o">=</span><span class="n">gpu</span><span class="p">(</span><span class="mi">0</span><span class="p">))</span>
<span class="n">net</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="section" id="[Advanced]-Multi-GPU-training">
<h2>[Advanced] Multi-GPU training<a class="headerlink" href="#[Advanced]-Multi-GPU-training" title="Permalink to this headline"></a></h2>
<p>Finally, we show how to use multiple GPUs to jointly train a neural network through data parallelism. Let’s assume there are <em>n</em> GPUs. We split each data batch into <em>n</em> parts, and then each GPU will run the forward and backward passes using one part of the data.</p>
<p>Let’s first copy the data definitions and the transform function from the <a class="reference external" href="5-predict.html">previous tutorial</a>.</p>
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<span></span><span class="n">batch_size</span> <span class="o">=</span> <span class="mi">256</span>
<span class="n">transformer</span> <span class="o">=</span> <span class="n">transforms</span><span class="o">.</span><span class="n">Compose</span><span class="p">([</span>
<span class="n">transforms</span><span class="o">.</span><span class="n">ToTensor</span><span class="p">(),</span>
<span class="n">transforms</span><span class="o">.</span><span class="n">Normalize</span><span class="p">(</span><span class="mf">0.13</span><span class="p">,</span> <span class="mf">0.31</span><span class="p">)])</span>
<span class="n">train_data</span> <span class="o">=</span> <span class="n">gluon</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span>
<span class="n">datasets</span><span class="o">.</span><span class="n">FashionMNIST</span><span class="p">(</span><span class="n">train</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">transform_first</span><span class="p">(</span><span class="n">transformer</span><span class="p">),</span>
<span class="n">batch_size</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>
<span class="n">valid_data</span> <span class="o">=</span> <span class="n">gluon</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span>
<span class="n">datasets</span><span class="o">.</span><span class="n">FashionMNIST</span><span class="p">(</span><span class="n">train</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">transform_first</span><span class="p">(</span><span class="n">transformer</span><span class="p">),</span>
<span class="n">batch_size</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>
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<p>The training loop is quite similar to what we introduced before. The major differences are highlighted in the following code.</p>
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<span></span><span class="c1"># Diff 1: Use two GPUs for training.</span>
<span class="n">devices</span> <span class="o">=</span> <span class="p">[</span><span class="n">gpu</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="n">gpu</span><span class="p">(</span><span class="mi">1</span><span class="p">)]</span>
<span class="c1"># Diff 2: reinitialize the parameters and place them on multiple GPUs</span>
<span class="n">net</span><span class="o">.</span><span class="n">collect_params</span><span class="p">()</span><span class="o">.</span><span class="n">initialize</span><span class="p">(</span><span class="n">force_reinit</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">devices</span><span class="p">)</span>
<span class="c1"># Loss and trainer are the same as before</span>
<span class="n">softmax_cross_entropy</span> <span class="o">=</span> <span class="n">gluon</span><span class="o">.</span><span class="n">loss</span><span class="o">.</span><span class="n">SoftmaxCrossEntropyLoss</span><span class="p">()</span>
<span class="n">trainer</span> <span class="o">=</span> <span class="n">gluon</span><span class="o">.</span><span class="n">Trainer</span><span class="p">(</span><span class="n">net</span><span class="o">.</span><span class="n">collect_params</span><span class="p">(),</span> <span class="s1">&#39;sgd&#39;</span><span class="p">,</span> <span class="p">{</span><span class="s1">&#39;learning_rate&#39;</span><span class="p">:</span> <span class="mf">0.1</span><span class="p">})</span>
<span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">):</span>
<span class="n">train_loss</span> <span class="o">=</span> <span class="mf">0.</span>
<span class="n">tic</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="k">for</span> <span class="n">data</span><span class="p">,</span> <span class="n">label</span> <span class="ow">in</span> <span class="n">train_data</span><span class="p">:</span>
<span class="c1"># Diff 3: split batch and load into corresponding devices</span>
<span class="n">data_list</span> <span class="o">=</span> <span class="n">gluon</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">split_and_load</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">devices</span><span class="p">)</span>
<span class="n">label_list</span> <span class="o">=</span> <span class="n">gluon</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">split_and_load</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">devices</span><span class="p">)</span>
<span class="c1"># Diff 4: run forward and backward on each devices.</span>
<span class="c1"># MXNet will automatically run them in parallel</span>
<span class="k">with</span> <span class="n">autograd</span><span class="o">.</span><span class="n">record</span><span class="p">():</span>
<span class="n">losses</span> <span class="o">=</span> <span class="p">[</span><span class="n">softmax_cross_entropy</span><span class="p">(</span><span class="n">net</span><span class="p">(</span><span class="n">X</span><span class="p">),</span> <span class="n">y</span><span class="p">)</span>
<span class="k">for</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">data_list</span><span class="p">,</span> <span class="n">label_list</span><span class="p">)]</span>
<span class="k">for</span> <span class="n">l</span> <span class="ow">in</span> <span class="n">losses</span><span class="p">:</span>
<span class="n">l</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
<span class="n">trainer</span><span class="o">.</span><span class="n">step</span><span class="p">(</span><span class="n">batch_size</span><span class="p">)</span>
<span class="c1"># Diff 5: sum losses over all devices</span>
<span class="n">train_loss</span> <span class="o">+=</span> <span class="nb">sum</span><span class="p">([</span><span class="n">l</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">.</span><span class="n">asscalar</span><span class="p">()</span> <span class="k">for</span> <span class="n">l</span> <span class="ow">in</span> <span class="n">losses</span><span class="p">])</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Epoch </span><span class="si">%d</span><span class="s2">: loss </span><span class="si">%.3f</span><span class="s2">, in </span><span class="si">%.1f</span><span class="s2"> sec&quot;</span> <span class="o">%</span> <span class="p">(</span>
<span class="n">epoch</span><span class="p">,</span> <span class="n">train_loss</span><span class="o">/</span><span class="nb">len</span><span class="p">(</span><span class="n">train_data</span><span class="p">)</span><span class="o">/</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span><span class="o">-</span><span class="n">tic</span><span class="p">))</span>
</pre></div>
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<span class="caption-text">Table Of Contents</span>
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<ul>
<li><a class="reference internal" href="#">Use GPUs</a><ul>
<li><a class="reference internal" href="#Allocate-data-to-a-GPU">Allocate data to a GPU</a></li>
<li><a class="reference internal" href="#Run-an-operation-on-a-GPU">Run an operation on a GPU</a></li>
<li><a class="reference internal" href="#Run-a-neural-network-on-a-GPU">Run a neural network on a GPU</a></li>
<li><a class="reference internal" href="#[Advanced]-Multi-GPU-training">[Advanced] Multi-GPU training</a></li>
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