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| <span class="mdl-layout-title toc">Table Of Contents</span> |
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| <li class="toctree-l1 current"><a class="reference internal" href="../../index.html">Python Tutorials</a><ul class="current"> |
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| <li class="toctree-l4"><a class="reference internal" href="0-introduction.html">Introduction</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="2-create-nn.html">Step 2: Create a neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="3-autograd.html">Step 3: Automatic differentiation with autograd</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="4-components.html">Step 4: Necessary components that are not in the network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="5-datasets.html">Step 5: <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-l4"><a class="reference internal" href="5-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-l4"><a class="reference internal" href="5-datasets.html#Using-your-own-data-with-custom-Datasets">Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="5-datasets.html#New-in-MXNet-2.0:-faster-C++-backend-dataloaders">New in MXNet 2.0: faster C++ backend dataloaders</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="6-train-nn.html">Step 6: Train a Neural Network</a></li> |
| <li class="toctree-l4 current"><a class="current reference internal" href="#">Step 7: Load and Run a NN using GPU</a></li> |
| </ul> |
| </li> |
| <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="../gluon_migration_guide.html">Gluon2.0: Migration Guide</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-l3"><a class="reference internal" href="../../packages/autograd/index.html">Automatic Differentiation</a></li> |
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| <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/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> |
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| <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> |
| </ul> |
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| <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/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> |
| </ul> |
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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> |
| </ul> |
| </li> |
| <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> |
| </ul> |
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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> |
| </ul> |
| </li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/training/normalization/index.html">Normalization Blocks</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <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> |
| </ul> |
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| <li class="toctree-l3"><a class="reference internal" href="../../packages/legacy/index.html">Legacy</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../packages/legacy/ndarray/index.html">NDArray</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/legacy/ndarray/01-ndarray-intro.html">An Intro: Manipulate Data the MXNet Way with NDArray</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/legacy/ndarray/02-ndarray-operations.html">NDArray Operations</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/legacy/ndarray/03-ndarray-contexts.html">NDArray Contexts</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/legacy/ndarray/gotchas_numpy_in_mxnet.html">Gotchas using NumPy in Apache MXNet</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/legacy/ndarray/sparse/index.html">Tutorials</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../../packages/legacy/ndarray/sparse/csr.html">CSRNDArray - NDArray in Compressed Sparse Row Storage Format</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../packages/legacy/ndarray/sparse/row_sparse.html">RowSparseNDArray - NDArray for Sparse Gradient Updates</a></li> |
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| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../packages/np/index.html">What is NP on MXNet</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../packages/np/cheat-sheet.html">The NP on MXNet cheat sheet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../packages/np/np-vs-numpy.html">Differences between NP on MXNet and NumPy</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 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-l3"><a class="reference internal" href="../../packages/optimizer/index.html">Optimizers</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-l5"><a class="reference internal" href="../../performance/backend/dnnl/dnnl_readme.html">Install MXNet with oneDNN</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../performance/backend/dnnl/dnnl_quantization_inc.html">Improving accuracy with Intel® Neural Compressor</a></li> |
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| <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-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arange.html">mxnet.np.arange</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.array-manipulation.html">Array manipulation routines</a><ul> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ravel.html">mxnet.np.ravel</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.flatten.html">mxnet.np.ndarray.flatten</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.swapaxes.html">mxnet.np.swapaxes</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.T.html">mxnet.np.ndarray.T</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.moveaxis.html">mxnet.np.moveaxis</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.squeeze.html">mxnet.np.squeeze</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.broadcast_arrays.html">mxnet.np.broadcast_arrays</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.dstack.html">mxnet.np.dstack</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.split.html">mxnet.np.split</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.hsplit.html">mxnet.np.hsplit</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.vsplit.html">mxnet.np.vsplit</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.array_split.html">mxnet.np.array_split</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.repeat.html">mxnet.np.repeat</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.unique.html">mxnet.np.unique</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.delete.html">mxnet.np.delete</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.append.html">mxnet.np.append</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.resize.html">mxnet.np.resize</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.trim_zeros.html">mxnet.np.trim_zeros</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.roll.html">mxnet.np.roll</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.rot90.html">mxnet.np.rot90</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.fliplr.html">mxnet.np.fliplr</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.flipud.html">mxnet.np.flipud</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.io.html">Input and output</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.genfromtxt.html">mxnet.np.genfromtxt</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.tolist.html">mxnet.np.ndarray.tolist</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.set_printoptions.html">mxnet.np.set_printoptions</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.linalg.html">Linear algebra (<code class="xref py py-mod docutils literal notranslate"><span class="pre">numpy.linalg</span></code>)</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.dot.html">mxnet.np.dot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.vdot.html">mxnet.np.vdot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.inner.html">mxnet.np.inner</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.outer.html">mxnet.np.outer</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.tensordot.html">mxnet.np.tensordot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.einsum.html">mxnet.np.einsum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.multi_dot.html">mxnet.np.linalg.multi_dot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.matmul.html">mxnet.np.matmul</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.matrix_power.html">mxnet.np.linalg.matrix_power</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.kron.html">mxnet.np.kron</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.svd.html">mxnet.np.linalg.svd</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.cholesky.html">mxnet.np.linalg.cholesky</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.qr.html">mxnet.np.linalg.qr</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.eig.html">mxnet.np.linalg.eig</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.eigh.html">mxnet.np.linalg.eigh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.eigvals.html">mxnet.np.linalg.eigvals</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.eigvalsh.html">mxnet.np.linalg.eigvalsh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.norm.html">mxnet.np.linalg.norm</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.trace.html">mxnet.np.trace</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.cond.html">mxnet.np.linalg.cond</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.det.html">mxnet.np.linalg.det</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.matrix_rank.html">mxnet.np.linalg.matrix_rank</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.slogdet.html">mxnet.np.linalg.slogdet</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.solve.html">mxnet.np.linalg.solve</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.tensorsolve.html">mxnet.np.linalg.tensorsolve</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.lstsq.html">mxnet.np.linalg.lstsq</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.inv.html">mxnet.np.linalg.inv</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.pinv.html">mxnet.np.linalg.pinv</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.tensorinv.html">mxnet.np.linalg.tensorinv</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.math.html">Mathematical functions</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.sin.html">mxnet.np.sin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.cos.html">mxnet.np.cos</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.tan.html">mxnet.np.tan</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arcsin.html">mxnet.np.arcsin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arccos.html">mxnet.np.arccos</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arctan.html">mxnet.np.arctan</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.degrees.html">mxnet.np.degrees</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.radians.html">mxnet.np.radians</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.hypot.html">mxnet.np.hypot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arctan2.html">mxnet.np.arctan2</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.deg2rad.html">mxnet.np.deg2rad</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.rad2deg.html">mxnet.np.rad2deg</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.unwrap.html">mxnet.np.unwrap</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.sinh.html">mxnet.np.sinh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.cosh.html">mxnet.np.cosh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.tanh.html">mxnet.np.tanh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arcsinh.html">mxnet.np.arcsinh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arccosh.html">mxnet.np.arccosh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arctanh.html">mxnet.np.arctanh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.rint.html">mxnet.np.rint</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.fix.html">mxnet.np.fix</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.floor.html">mxnet.np.floor</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ceil.html">mxnet.np.ceil</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.trunc.html">mxnet.np.trunc</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.around.html">mxnet.np.around</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.round_.html">mxnet.np.round_</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.sum.html">mxnet.np.sum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.prod.html">mxnet.np.prod</a></li> |
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| <span class="mdl-layout-title toc">Table Of Contents</span> |
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| <ul class="current"> |
| <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="0-introduction.html">Introduction</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="2-create-nn.html">Step 2: Create a neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="3-autograd.html">Step 3: Automatic differentiation with autograd</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="4-components.html">Step 4: Necessary components that are not in the network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="5-datasets.html">Step 5: <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-l4"><a class="reference internal" href="5-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-l4"><a class="reference internal" href="5-datasets.html#Using-your-own-data-with-custom-Datasets">Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="5-datasets.html#New-in-MXNet-2.0:-faster-C++-backend-dataloaders">New in MXNet 2.0: faster C++ backend dataloaders</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="6-train-nn.html">Step 6: Train a Neural Network</a></li> |
| <li class="toctree-l4 current"><a class="current reference internal" href="#">Step 7: Load and Run a NN using GPU</a></li> |
| </ul> |
| </li> |
| <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="../gluon_migration_guide.html">Gluon2.0: Migration Guide</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> |
| </ul> |
| </li> |
| <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/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> |
| </ul> |
| </li> |
| <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/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> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../packages/gluon/image/index.html">Image Tutorials</a><ul> |
| <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> |
| </ul> |
| </li> |
| <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> |
| </ul> |
| </li> |
| <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> |
| </ul> |
| </li> |
| <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> |
| </ul> |
| </li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/training/normalization/index.html">Normalization Blocks</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <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> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../packages/legacy/index.html">Legacy</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../packages/legacy/ndarray/index.html">NDArray</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/legacy/ndarray/01-ndarray-intro.html">An Intro: Manipulate Data the MXNet Way with NDArray</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/legacy/ndarray/02-ndarray-operations.html">NDArray Operations</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/legacy/ndarray/03-ndarray-contexts.html">NDArray Contexts</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/legacy/ndarray/gotchas_numpy_in_mxnet.html">Gotchas using NumPy in Apache MXNet</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/legacy/ndarray/sparse/index.html">Tutorials</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../../packages/legacy/ndarray/sparse/csr.html">CSRNDArray - NDArray in Compressed Sparse Row Storage Format</a></li> |
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| </ul> |
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| <li class="toctree-l3"><a class="reference internal" href="../../packages/np/index.html">What is NP on MXNet</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../packages/np/cheat-sheet.html">The NP on MXNet cheat sheet</a></li> |
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| <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-l5"><a class="reference internal" href="../../performance/backend/dnnl/dnnl_readme.html">Install MXNet with oneDNN</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../performance/backend/dnnl/dnnl_quantization_inc.html">Improving accuracy with Intel® Neural Compressor</a></li> |
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| <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-l4"><a class="reference internal" href="../../../api/np/routines.array-manipulation.html">Array manipulation routines</a><ul> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.flatten.html">mxnet.np.ndarray.flatten</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.hsplit.html">mxnet.np.hsplit</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.vsplit.html">mxnet.np.vsplit</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.array_split.html">mxnet.np.array_split</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.repeat.html">mxnet.np.repeat</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.roll.html">mxnet.np.roll</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.rot90.html">mxnet.np.rot90</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.flipud.html">mxnet.np.flipud</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.io.html">Input and output</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.genfromtxt.html">mxnet.np.genfromtxt</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.tolist.html">mxnet.np.ndarray.tolist</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.set_printoptions.html">mxnet.np.set_printoptions</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.linalg.html">Linear algebra (<code class="xref py py-mod docutils literal notranslate"><span class="pre">numpy.linalg</span></code>)</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.dot.html">mxnet.np.dot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.vdot.html">mxnet.np.vdot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.inner.html">mxnet.np.inner</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.outer.html">mxnet.np.outer</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.tensordot.html">mxnet.np.tensordot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.einsum.html">mxnet.np.einsum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.multi_dot.html">mxnet.np.linalg.multi_dot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.matmul.html">mxnet.np.matmul</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.matrix_power.html">mxnet.np.linalg.matrix_power</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.kron.html">mxnet.np.kron</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.svd.html">mxnet.np.linalg.svd</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.cholesky.html">mxnet.np.linalg.cholesky</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.qr.html">mxnet.np.linalg.qr</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.eig.html">mxnet.np.linalg.eig</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.eigh.html">mxnet.np.linalg.eigh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.eigvals.html">mxnet.np.linalg.eigvals</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.eigvalsh.html">mxnet.np.linalg.eigvalsh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.norm.html">mxnet.np.linalg.norm</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.trace.html">mxnet.np.trace</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.cond.html">mxnet.np.linalg.cond</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.det.html">mxnet.np.linalg.det</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.matrix_rank.html">mxnet.np.linalg.matrix_rank</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.slogdet.html">mxnet.np.linalg.slogdet</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.solve.html">mxnet.np.linalg.solve</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.tensorsolve.html">mxnet.np.linalg.tensorsolve</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.lstsq.html">mxnet.np.linalg.lstsq</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.inv.html">mxnet.np.linalg.inv</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.pinv.html">mxnet.np.linalg.pinv</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.tensorinv.html">mxnet.np.linalg.tensorinv</a></li> |
| </ul> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.math.html">Mathematical functions</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.sin.html">mxnet.np.sin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.cos.html">mxnet.np.cos</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.tan.html">mxnet.np.tan</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arcsin.html">mxnet.np.arcsin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arccos.html">mxnet.np.arccos</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arctan.html">mxnet.np.arctan</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.degrees.html">mxnet.np.degrees</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.radians.html">mxnet.np.radians</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.hypot.html">mxnet.np.hypot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arctan2.html">mxnet.np.arctan2</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.deg2rad.html">mxnet.np.deg2rad</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.rad2deg.html">mxnet.np.rad2deg</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="Step-7:-Load-and-Run-a-NN-using-GPU"> |
| <h1>Step 7: Load and Run a NN using GPU<a class="headerlink" href="#Step-7:-Load-and-Run-a-NN-using-GPU" title="Permalink to this headline">¶</a></h1> |
| <p>In this step, you will learn how to use graphics processing units (GPUs) with MXNet. If you use GPUs to train and deploy neural networks, you may be able to train or perform inference quicker than with central processing units (CPUs).</p> |
| <div class="section" id="Prerequisites"> |
| <h2>Prerequisites<a class="headerlink" href="#Prerequisites" title="Permalink to this headline">¶</a></h2> |
| <p>Before you start the steps, make sure you have at least one Nvidia GPU on your machine and make sure that you have CUDA properly installed. GPUs from AMD and Intel are not supported. Additionally, you will need to install the GPU-enabled version of MXNet. You can find information about how to install the GPU version of MXNet for your system <a class="reference external" href="https://mxnet.apache.org/versions/1.4.1/install/ubuntu_setup.html">here</a>.</p> |
| <p>You can use the following command to view the number GPUs that are available to MXNet.</p> |
| <div class="nbinput docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="kn">from</span> <span class="nn">mxnet</span> <span class="kn">import</span> <span class="n">np</span><span class="p">,</span> <span class="n">npx</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">import</span> <span class="nn">time</span> |
| <span class="n">npx</span><span class="o">.</span><span class="n">set_np</span><span class="p">()</span> |
| |
| <span class="n">npx</span><span class="o">.</span><span class="n">num_gpus</span><span class="p">()</span> <span class="c1">#This command provides the number of GPUs MXNet can access</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="nboutput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]: |
| </pre></div> |
| </div> |
| <div class="output_area highlight-none notranslate"><div class="highlight"><pre> |
| <span></span>1 |
| </pre></div> |
| </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>MXNet’s ndarray is very similar to NumPy’s. One major difference is that MXNet’s ndarray has a <code class="docutils literal notranslate"><span class="pre">device</span></code> attribute specifying which device an array is on. By default, arrays are stored on <code class="docutils literal notranslate"><span class="pre">npx.cpu()</span></code>. To change it to the first GPU, you can use the following code, <code class="docutils literal notranslate"><span class="pre">npx.gpu()</span></code> or <code class="docutils literal notranslate"><span class="pre">npx.gpu(0)</span></code> to indicate the first GPU.</p> |
| <div class="nbinput docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="n">gpu</span> <span class="o">=</span> <span class="n">npx</span><span class="o">.</span><span class="n">gpu</span><span class="p">()</span> <span class="k">if</span> <span class="n">npx</span><span class="o">.</span><span class="n">num_gpus</span><span class="p">()</span> <span class="o">></span> <span class="mi">0</span> <span class="k">else</span> <span class="n">npx</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span> |
| <span class="n">x</span> <span class="o">=</span> <span class="n">np</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">device</span><span class="o">=</span><span class="n">gpu</span><span class="p">)</span> |
| <span class="n">x</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="nboutput docutils container"> |
| <div class="prompt empty docutils container"> |
| </div> |
| <div class="output_area stderr docutils container"> |
| <div class="highlight"><pre> |
| [04:21:32] /work/mxnet/src/storage/storage.cc:202: Using Pooled (Naive) StorageManager for GPU |
| </pre></div></div> |
| </div> |
| <div class="nboutput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]: |
| </pre></div> |
| </div> |
| <div class="output_area highlight-none notranslate"><div class="highlight"><pre> |
| <span></span>array([[1., 1., 1., 1.], |
| [1., 1., 1., 1.], |
| [1., 1., 1., 1.]], device=gpu(0)) |
| </pre></div> |
| </div> |
| </div> |
| <p>If you’re using a CPU, MXNet allocates data on the main memory and tries to use as many CPU cores as possible. If there are multiple GPUs, MXNet will tell you which GPUs the ndarray is allocated on.</p> |
| <p>Assuming there is at least two GPUs. You can create another ndarray and assign it to a different GPU. If you only have one GPU, then you will get an error trying to run this code. In the example code here, you will copy <code class="docutils literal notranslate"><span class="pre">x</span></code> to the second GPU, <code class="docutils literal notranslate"><span class="pre">npx.gpu(1)</span></code>:</p> |
| <div class="nbinput docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="n">gpu_1</span> <span class="o">=</span> <span class="n">npx</span><span class="o">.</span><span class="n">gpu</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="k">if</span> <span class="n">npx</span><span class="o">.</span><span class="n">num_gpus</span><span class="p">()</span> <span class="o">></span> <span class="mi">1</span> <span class="k">else</span> <span class="n">npx</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span> |
| <span class="n">x</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="n">gpu_1</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="nboutput docutils container"> |
| <div class="prompt empty docutils container"> |
| </div> |
| <div class="output_area stderr docutils container"> |
| <div class="highlight"><pre> |
| [04:21:32] /work/mxnet/src/storage/storage.cc:202: Using Pooled (Naive) StorageManager for CPU |
| </pre></div></div> |
| </div> |
| <div class="nboutput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]: |
| </pre></div> |
| </div> |
| <div class="output_area highlight-none notranslate"><div class="highlight"><pre> |
| <span></span>array([[1., 1., 1., 1.], |
| [1., 1., 1., 1.], |
| [1., 1., 1., 1.]]) |
| </pre></div> |
| </div> |
| </div> |
| <p>MXNet requries that users explicitly move data between devices. But several operators such as <code class="docutils literal notranslate"><span class="pre">print</span></code>, and <code class="docutils literal notranslate"><span class="pre">asnumpy</span></code>, will implicitly move data to main memory.</p> |
| </div> |
| <div class="section" id="Choosing-GPU-Ids"> |
| <h2>Choosing GPU Ids<a class="headerlink" href="#Choosing-GPU-Ids" title="Permalink to this headline">¶</a></h2> |
| <p>If you have multiple GPUs on your machine, MXNet can access each of them through 0-indexing with <code class="docutils literal notranslate"><span class="pre">npx</span></code>. As you saw before, the first GPU was accessed using <code class="docutils literal notranslate"><span class="pre">npx.gpu(0)</span></code>, and the second using <code class="docutils literal notranslate"><span class="pre">npx.gpu(1)</span></code>. This extends to however many GPUs your machine has. So if your machine has eight GPUs, the last GPU is accessed using <code class="docutils literal notranslate"><span class="pre">npx.gpu(7)</span></code>. This allows you to select which GPUs to use for operations and training. You might find it particularly useful when you want to leverage multiple GPUs |
| while training neural networks.</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, you only need to guarantee that the input of an operation is already on that GPU. The output is allocated on the same GPU as well. Almost all operators in the <code class="docutils literal notranslate"><span class="pre">np</span></code> and <code class="docutils literal notranslate"><span class="pre">npx</span></code> module support running on a GPU.</p> |
| <div class="nbinput docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="n">y</span> <span class="o">=</span> <span class="n">np</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">size</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">device</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> |
| <div class="nboutput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]: |
| </pre></div> |
| </div> |
| <div class="output_area highlight-none notranslate"><div class="highlight"><pre> |
| <span></span>array([[1.7707204, 1.6089329, 1.3154027, 1.416174 ], |
| [1.2215444, 1.203621 , 1.5886476, 1.3864298], |
| [1.0913873, 1.1142501, 1.8820239, 1.1949301]], device=gpu(0)) |
| </pre></div> |
| </div> |
| </div> |
| <p>Remember that if the inputs are not on the same GPU, you will get 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>To run a neural network on a GPU, you only need to copy and move the input data and parameters to the GPU. To demonstrate this you can reuse the previously defined LeafNetwork in <a class="reference internal" href="6-train-nn.html"><span class="doc">Training Neural Networks</span></a>. The following code example shows this.</p> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="c1"># The convolutional block has a convolution layer, a max pool layer and a batch normalization layer</span> |
| <span class="k">def</span> <span class="nf">conv_block</span><span class="p">(</span><span class="n">filters</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">batch_norm</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> |
| <span class="n">conv_block</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">HybridSequential</span><span class="p">()</span> |
| <span class="n">conv_block</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="n">filters</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="n">kernel_size</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">'relu'</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">4</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="n">stride</span><span class="p">))</span> |
| <span class="k">if</span> <span class="n">batch_norm</span><span class="p">:</span> |
| <span class="n">conv_block</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">BatchNorm</span><span class="p">())</span> |
| <span class="k">return</span> <span class="n">conv_block</span> |
| |
| <span class="c1"># The dense block consists of a dense layer and a dropout layer</span> |
| <span class="k">def</span> <span class="nf">dense_block</span><span class="p">(</span><span class="n">neurons</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">'relu'</span><span class="p">,</span> <span class="n">dropout</span><span class="o">=</span><span class="mf">0.2</span><span class="p">):</span> |
| <span class="n">dense_block</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">HybridSequential</span><span class="p">()</span> |
| <span class="n">dense_block</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">Dense</span><span class="p">(</span><span class="n">neurons</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="n">activation</span><span class="p">))</span> |
| <span class="k">if</span> <span class="n">dropout</span><span class="p">:</span> |
| <span class="n">dense_block</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">Dropout</span><span class="p">(</span><span class="n">dropout</span><span class="p">))</span> |
| <span class="k">return</span> <span class="n">dense_block</span> |
| |
| <span class="c1"># Create neural network blueprint using the blocks</span> |
| <span class="k">class</span> <span class="nc">LeafNetwork</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">HybridBlock</span><span class="p">):</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">LeafNetwork</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span> <span class="o">=</span> <span class="n">conv_block</span><span class="p">(</span><span class="mi">32</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">conv2</span> <span class="o">=</span> <span class="n">conv_block</span><span class="p">(</span><span class="mi">64</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">conv3</span> <span class="o">=</span> <span class="n">conv_block</span><span class="p">(</span><span class="mi">128</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">flatten</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Flatten</span><span class="p">()</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">dense1</span> <span class="o">=</span> <span class="n">dense_block</span><span class="p">(</span><span class="mi">100</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">dense2</span> <span class="o">=</span> <span class="n">dense_block</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">dense3</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch</span><span class="p">):</span> |
| <span class="n">batch</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span><span class="p">(</span><span class="n">batch</span><span class="p">)</span> |
| <span class="n">batch</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv2</span><span class="p">(</span><span class="n">batch</span><span class="p">)</span> |
| <span class="n">batch</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv3</span><span class="p">(</span><span class="n">batch</span><span class="p">)</span> |
| <span class="n">batch</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flatten</span><span class="p">(</span><span class="n">batch</span><span class="p">)</span> |
| <span class="n">batch</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dense1</span><span class="p">(</span><span class="n">batch</span><span class="p">)</span> |
| <span class="n">batch</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dense2</span><span class="p">(</span><span class="n">batch</span><span class="p">)</span> |
| <span class="n">batch</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dense3</span><span class="p">(</span><span class="n">batch</span><span class="p">)</span> |
| |
| <span class="k">return</span> <span class="n">batch</span> |
| </pre></div> |
| </div> |
| </div> |
| <p>Load the saved parameters onto GPU 0 directly as shown below; additionally, you could use <code class="docutils literal notranslate"><span class="pre">net.collect_params().reset_device(gpu)</span></code> to change the device.</p> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="n">net</span> <span class="o">=</span> <span class="n">LeafNetwork</span><span class="p">()</span> |
| <span class="n">net</span><span class="o">.</span><span class="n">load_parameters</span><span class="p">(</span><span class="s1">'leaf_models.params'</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">gpu</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <p>Use the following command to create input data on GPU 0. The forward function will then run on GPU 0.</p> |
| <div class="nbinput docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="n">x</span> <span class="o">=</span> <span class="n">np</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">size</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="mi">128</span><span class="p">),</span> <span class="n">device</span><span class="o">=</span><span class="n">gpu</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 class="nboutput docutils container"> |
| <div class="prompt empty docutils container"> |
| </div> |
| <div class="output_area stderr docutils container"> |
| <div class="highlight"><pre> |
| [04:21:32] /work/mxnet/src/operator/cudnn_ops.cc:421: Auto-tuning cuDNN op, set MXNET_CUDNN_AUTOTUNE_DEFAULT to 0 to disable |
| [04:21:33] /work/mxnet/src/operator/cudnn_ops.cc:421: Auto-tuning cuDNN op, set MXNET_CUDNN_AUTOTUNE_DEFAULT to 0 to disable |
| </pre></div></div> |
| </div> |
| <div class="nboutput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]: |
| </pre></div> |
| </div> |
| <div class="output_area highlight-none notranslate"><div class="highlight"><pre> |
| <span></span>array([[ 5.52604 , -0.286441]], device=gpu(0)) |
| </pre></div> |
| </div> |
| </div> |
| </div> |
| <div class="section" id="Training-with-multiple-GPUs"> |
| <h2>Training with multiple GPUs<a class="headerlink" href="#Training-with-multiple-GPUs" title="Permalink to this headline">¶</a></h2> |
| <p>Finally, you will see how you can use multiple GPUs to jointly train a neural network through data parallelism. To elaborate on what data parallelism is, assume there are <em>n</em> GPUs, then you can split each data batch into <em>n</em> parts, and use a GPU on each of these parts to run the forward and backward passes on the seperate chunks of the data.</p> |
| <p>First copy the data definitions with the following commands, and the transform functions from the tutorial <a class="reference internal" href="6-train-nn.html"><span class="doc">Training Neural Networks</span></a>.</p> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="c1"># Import transforms as compose a series of transformations to the images</span> |
| <span class="kn">from</span> <span class="nn">mxnet.gluon.data.vision</span> <span class="kn">import</span> <span class="n">transforms</span> |
| |
| <span class="n">jitter_param</span> <span class="o">=</span> <span class="mf">0.05</span> |
| |
| <span class="c1"># mean and std for normalizing image value in range (0,1)</span> |
| <span class="n">mean</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.485</span><span class="p">,</span> <span class="mf">0.456</span><span class="p">,</span> <span class="mf">0.406</span><span class="p">]</span> |
| <span class="n">std</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.229</span><span class="p">,</span> <span class="mf">0.224</span><span class="p">,</span> <span class="mf">0.225</span><span class="p">]</span> |
| |
| <span class="n">training_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">Resize</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="mi">224</span><span class="p">,</span> <span class="n">keep_ratio</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span> |
| <span class="n">transforms</span><span class="o">.</span><span class="n">CenterCrop</span><span class="p">(</span><span class="mi">128</span><span class="p">),</span> |
| <span class="n">transforms</span><span class="o">.</span><span class="n">RandomFlipLeftRight</span><span class="p">(),</span> |
| <span class="n">transforms</span><span class="o">.</span><span class="n">RandomColorJitter</span><span class="p">(</span><span class="n">contrast</span><span class="o">=</span><span class="n">jitter_param</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="n">mean</span><span class="p">,</span> <span class="n">std</span><span class="p">)</span> |
| <span class="p">])</span> |
| |
| <span class="n">validation_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">Resize</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="mi">224</span><span class="p">,</span> <span class="n">keep_ratio</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span> |
| <span class="n">transforms</span><span class="o">.</span><span class="n">CenterCrop</span><span class="p">(</span><span class="mi">128</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="n">mean</span><span class="p">,</span> <span class="n">std</span><span class="p">)</span> |
| <span class="p">])</span> |
| |
| <span class="c1"># Use ImageFolderDataset to create a Dataset object from directory structure</span> |
| <span class="n">train_dataset</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">vision</span><span class="o">.</span><span class="n">ImageFolderDataset</span><span class="p">(</span><span class="s1">'./datasets/train'</span><span class="p">)</span> |
| <span class="n">val_dataset</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">vision</span><span class="o">.</span><span class="n">ImageFolderDataset</span><span class="p">(</span><span class="s1">'./datasets/validation'</span><span class="p">)</span> |
| <span class="n">test_dataset</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">vision</span><span class="o">.</span><span class="n">ImageFolderDataset</span><span class="p">(</span><span class="s1">'./datasets/test'</span><span class="p">)</span> |
| |
| <span class="c1"># Create data loaders</span> |
| <span class="n">batch_size</span> <span class="o">=</span> <span class="mi">4</span> |
| <span class="n">train_loader</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">train_dataset</span><span class="o">.</span><span class="n">transform_first</span><span class="p">(</span><span class="n">training_transformer</span><span class="p">),</span><span class="n">batch_size</span><span class="o">=</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">try_nopython</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| <span class="n">validation_loader</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">val_dataset</span><span class="o">.</span><span class="n">transform_first</span><span class="p">(</span><span class="n">validation_transformer</span><span class="p">),</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">try_nopython</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| <span class="n">test_loader</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">test_dataset</span><span class="o">.</span><span class="n">transform_first</span><span class="p">(</span><span class="n">validation_transformer</span><span class="p">),</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">try_nopython</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="Define-a-helper-function"> |
| <h3>Define a helper function<a class="headerlink" href="#Define-a-helper-function" title="Permalink to this headline">¶</a></h3> |
| <p>This is the same test function defined previously in the <strong>Step 6</strong>.</p> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="c1"># Function to return the accuracy for the validation and test set</span> |
| <span class="k">def</span> <span class="nf">test</span><span class="p">(</span><span class="n">val_data</span><span class="p">,</span> <span class="n">devices</span><span class="p">):</span> |
| <span class="n">acc</span> <span class="o">=</span> <span class="n">gluon</span><span class="o">.</span><span class="n">metric</span><span class="o">.</span><span class="n">Accuracy</span><span class="p">()</span> |
| <span class="k">for</span> <span class="n">batch</span> <span class="ow">in</span> <span class="n">val_data</span><span class="p">:</span> |
| <span class="n">data</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">batch</span><span class="p">[</span><span class="mi">1</span><span class="p">]</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="n">outputs</span> <span class="o">=</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="k">for</span> <span class="n">X</span> <span class="ow">in</span> <span class="n">data_list</span><span class="p">]</span> |
| <span class="n">acc</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">label_list</span><span class="p">,</span> <span class="n">outputs</span><span class="p">)</span> |
| |
| <span class="n">_</span><span class="p">,</span> <span class="n">accuracy</span> <span class="o">=</span> <span class="n">acc</span><span class="o">.</span><span class="n">get</span><span class="p">()</span> |
| <span class="k">return</span> <span class="n">accuracy</span> |
| </pre></div> |
| </div> |
| </div> |
| <p>The training loop is quite similar to that shown earlier. The major differences are highlighted in the following code.</p> |
| <div class="nbinput docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="c1"># Diff 1: Use two GPUs for training.</span> |
| <span class="n">available_gpus</span> <span class="o">=</span> <span class="p">[</span><span class="n">npx</span><span class="o">.</span><span class="n">gpu</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">npx</span><span class="o">.</span><span class="n">num_gpus</span><span class="p">())]</span> |
| <span class="n">num_gpus</span> <span class="o">=</span> <span class="mi">2</span> |
| <span class="n">devices</span> <span class="o">=</span> <span class="n">available_gpus</span><span class="p">[:</span><span class="n">num_gpus</span><span class="p">]</span> |
| <span class="nb">print</span><span class="p">(</span><span class="s1">'Using </span><span class="si">{}</span><span class="s1"> GPUs'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">devices</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">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">device</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">loss_fn</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">optimizer</span> <span class="o">=</span> <span class="s1">'sgd'</span> |
| <span class="n">optimizer_params</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'learning_rate'</span><span class="p">:</span> <span class="mf">0.001</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="n">optimizer</span><span class="p">,</span> <span class="n">optimizer_params</span><span class="p">)</span> |
| |
| <span class="n">epochs</span> <span class="o">=</span> <span class="mi">2</span> |
| <span class="n">accuracy</span> <span class="o">=</span> <span class="n">gluon</span><span class="o">.</span><span class="n">metric</span><span class="o">.</span><span class="n">Accuracy</span><span class="p">()</span> |
| <span class="n">log_interval</span> <span class="o">=</span> <span class="mi">5</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="n">epochs</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="n">btic</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="n">accuracy</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span> |
| <span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">batch</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">train_loader</span><span class="p">):</span> |
| <span class="n">data</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">batch</span><span class="p">[</span><span class="mi">1</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">outputs</span> <span class="o">=</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="k">for</span> <span class="n">X</span> <span class="ow">in</span> <span class="n">data_list</span><span class="p">]</span> |
| <span class="n">losses</span> <span class="o">=</span> <span class="p">[</span><span class="n">loss_fn</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="n">label</span><span class="p">)</span> |
| <span class="k">for</span> <span class="n">output</span><span class="p">,</span> <span class="n">label</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">outputs</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. Here, the float</span> |
| <span class="c1"># function will copy data into CPU.</span> |
| <span class="n">train_loss</span> <span class="o">+=</span> <span class="nb">sum</span><span class="p">([</span><span class="nb">float</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="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">accuracy</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">label_list</span><span class="p">,</span> <span class="n">outputs</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">log_interval</span> <span class="ow">and</span> <span class="p">(</span><span class="n">idx</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">%</span> <span class="n">log_interval</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> |
| <span class="n">_</span><span class="p">,</span> <span class="n">acc</span> <span class="o">=</span> <span class="n">accuracy</span><span class="o">.</span><span class="n">get</span><span class="p">()</span> |
| |
| <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"""Epoch[</span><span class="si">{</span><span class="n">epoch</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="mi">1</span><span class="si">}</span><span class="s2">] Batch[</span><span class="si">{</span><span class="n">idx</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="mi">1</span><span class="si">}</span><span class="s2">] Speed: </span><span class="si">{</span><span class="n">batch_size</span><span class="w"> </span><span class="o">/</span><span class="w"> </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="w"> </span><span class="o">-</span><span class="w"> </span><span class="n">btic</span><span class="p">)</span><span class="si">}</span><span class="s2"> samples/sec </span><span class="se">\</span> |
| <span class="s2"> batch loss = </span><span class="si">{</span><span class="n">train_loss</span><span class="si">}</span><span class="s2"> | accuracy = </span><span class="si">{</span><span class="n">acc</span><span class="si">}</span><span class="s2">"""</span><span class="p">)</span> |
| <span class="n">btic</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="n">_</span><span class="p">,</span> <span class="n">acc</span> <span class="o">=</span> <span class="n">accuracy</span><span class="o">.</span><span class="n">get</span><span class="p">()</span> |
| |
| <span class="n">acc_val</span> <span class="o">=</span> <span class="n">test</span><span class="p">(</span><span class="n">validation_loader</span><span class="p">,</span> <span class="n">devices</span><span class="p">)</span> |
| <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"[Epoch </span><span class="si">{</span><span class="n">epoch</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="mi">1</span><span class="si">}</span><span class="s2">] training: accuracy=</span><span class="si">{</span><span class="n">acc</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span> |
| <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"[Epoch </span><span class="si">{</span><span class="n">epoch</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="mi">1</span><span class="si">}</span><span class="s2">] time cost: </span><span class="si">{</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="n">tic</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span> |
| <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"[Epoch </span><span class="si">{</span><span class="n">epoch</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="mi">1</span><span class="si">}</span><span class="s2">] validation: validation accuracy=</span><span class="si">{</span><span class="n">acc_val</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="nboutput nblast docutils container"> |
| <div class="prompt empty docutils container"> |
| </div> |
| <div class="output_area docutils container"> |
| <div class="highlight"><pre> |
| Using 1 GPUs |
| Epoch[1] Batch[5] Speed: 0.7173089899203501 samples/sec batch loss = 14.109387874603271 | accuracy = 0.4 |
| Epoch[1] Batch[10] Speed: 1.2488635911932617 samples/sec batch loss = 27.856971979141235 | accuracy = 0.475 |
| Epoch[1] Batch[15] Speed: 1.2547331219178495 samples/sec batch loss = 41.62733340263367 | accuracy = 0.5166666666666667 |
| Epoch[1] Batch[20] Speed: 1.2574016512018573 samples/sec batch loss = 55.84801149368286 | accuracy = 0.4875 |
| Epoch[1] Batch[25] Speed: 1.249793820569105 samples/sec batch loss = 69.66716718673706 | accuracy = 0.49 |
| Epoch[1] Batch[30] Speed: 1.2504747636094988 samples/sec batch loss = 83.63934445381165 | accuracy = 0.48333333333333334 |
| Epoch[1] Batch[35] Speed: 1.2540013241700803 samples/sec batch loss = 98.12923693656921 | accuracy = 0.4785714285714286 |
| Epoch[1] Batch[40] Speed: 1.2433366882315402 samples/sec batch loss = 111.83499693870544 | accuracy = 0.48125 |
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| Epoch[1] Batch[760] Speed: 1.2522337384312432 samples/sec batch loss = 1971.902447938919 | accuracy = 0.6236842105263158 |
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| Epoch[1] Batch[775] Speed: 1.249143379517365 samples/sec batch loss = 2005.0542666912079 | accuracy = 0.6274193548387097 |
| Epoch[1] Batch[780] Speed: 1.2507209612379153 samples/sec batch loss = 2017.8336730003357 | accuracy = 0.6278846153846154 |
| Epoch[1] Batch[785] Speed: 1.2525516014827356 samples/sec batch loss = 2031.7250916957855 | accuracy = 0.6270700636942675 |
| [Epoch 1] training: accuracy=0.6272208121827412 |
| [Epoch 1] time cost: 650.7169613838196 |
| [Epoch 1] validation: validation accuracy=0.7366666666666667 |
| Epoch[2] Batch[5] Speed: 1.2340196984579255 samples/sec batch loss = 13.191937804222107 | accuracy = 0.6 |
| Epoch[2] Batch[10] Speed: 1.2310370853126993 samples/sec batch loss = 26.464403986930847 | accuracy = 0.6 |
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| Epoch[2] Batch[30] Speed: 1.2322364535608838 samples/sec batch loss = 75.13800632953644 | accuracy = 0.7 |
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| Epoch[2] Batch[60] Speed: 1.2333847524966035 samples/sec batch loss = 143.2937877178192 | accuracy = 0.7125 |
| Epoch[2] Batch[65] Speed: 1.236500870669069 samples/sec batch loss = 153.73799800872803 | accuracy = 0.7192307692307692 |
| Epoch[2] Batch[70] Speed: 1.23287393197801 samples/sec batch loss = 165.10248506069183 | accuracy = 0.7142857142857143 |
| Epoch[2] Batch[75] Speed: 1.231276952872063 samples/sec batch loss = 179.1421970129013 | accuracy = 0.71 |
| Epoch[2] Batch[80] Speed: 1.230124636409622 samples/sec batch loss = 189.26601195335388 | accuracy = 0.709375 |
| Epoch[2] Batch[85] Speed: 1.2319504365338176 samples/sec batch loss = 198.50052571296692 | accuracy = 0.7147058823529412 |
| Epoch[2] Batch[90] Speed: 1.2315103148763311 samples/sec batch loss = 210.53342199325562 | accuracy = 0.7111111111111111 |
| Epoch[2] Batch[95] Speed: 1.235229981223303 samples/sec batch loss = 222.21577048301697 | accuracy = 0.7105263157894737 |
| Epoch[2] Batch[100] Speed: 1.232857986979065 samples/sec batch loss = 235.56106781959534 | accuracy = 0.705 |
| Epoch[2] Batch[105] Speed: 1.228153899896607 samples/sec batch loss = 246.29658603668213 | accuracy = 0.7047619047619048 |
| Epoch[2] Batch[110] Speed: 1.2345419159369972 samples/sec batch loss = 256.8897407054901 | accuracy = 0.7045454545454546 |
| Epoch[2] Batch[115] Speed: 1.230914703045376 samples/sec batch loss = 268.92195451259613 | accuracy = 0.7065217391304348 |
| Epoch[2] Batch[120] Speed: 1.2312387304247892 samples/sec batch loss = 281.6716022491455 | accuracy = 0.70625 |
| Epoch[2] Batch[125] Speed: 1.230405474878182 samples/sec batch loss = 290.8304713964462 | accuracy = 0.71 |
| Epoch[2] Batch[130] Speed: 1.2320699485360205 samples/sec batch loss = 301.62862718105316 | accuracy = 0.7115384615384616 |
| Epoch[2] Batch[135] Speed: 1.2331189569992693 samples/sec batch loss = 312.6072155237198 | accuracy = 0.7074074074074074 |
| Epoch[2] Batch[140] Speed: 1.230335817146746 samples/sec batch loss = 323.0715608596802 | accuracy = 0.7107142857142857 |
| Epoch[2] Batch[145] Speed: 1.2294742297399517 samples/sec batch loss = 333.3850485086441 | accuracy = 0.7120689655172414 |
| Epoch[2] Batch[150] Speed: 1.228943416736621 samples/sec batch loss = 344.21285820007324 | accuracy = 0.7133333333333334 |
| Epoch[2] Batch[155] Speed: 1.230386615983145 samples/sec batch loss = 351.91108351945877 | accuracy = 0.7161290322580646 |
| Epoch[2] Batch[160] Speed: 1.2290947606893128 samples/sec batch loss = 363.82177871465683 | accuracy = 0.7140625 |
| Epoch[2] Batch[165] Speed: 1.2334957460494567 samples/sec batch loss = 375.11759382486343 | accuracy = 0.7151515151515152 |
| Epoch[2] Batch[170] Speed: 1.2350278450031669 samples/sec batch loss = 383.88711553812027 | accuracy = 0.7176470588235294 |
| Epoch[2] Batch[175] Speed: 1.2359369322991975 samples/sec batch loss = 397.7323417067528 | accuracy = 0.7157142857142857 |
| Epoch[2] Batch[180] Speed: 1.2326006591320082 samples/sec batch loss = 408.222943007946 | accuracy = 0.7180555555555556 |
| Epoch[2] Batch[185] Speed: 1.2286690039663009 samples/sec batch loss = 419.9541248679161 | accuracy = 0.7162162162162162 |
| Epoch[2] Batch[190] Speed: 1.2348820352113132 samples/sec batch loss = 436.4125308394432 | accuracy = 0.7118421052631579 |
| Epoch[2] Batch[195] Speed: 1.229741250438049 samples/sec batch loss = 445.3180300593376 | accuracy = 0.7153846153846154 |
| Epoch[2] Batch[200] Speed: 1.2276386885677262 samples/sec batch loss = 458.6773495078087 | accuracy = 0.71375 |
| Epoch[2] Batch[205] Speed: 1.2322305708230772 samples/sec batch loss = 474.16221684217453 | accuracy = 0.7060975609756097 |
| Epoch[2] Batch[210] Speed: 1.236188641564287 samples/sec batch loss = 486.45931726694107 | accuracy = 0.705952380952381 |
| Epoch[2] Batch[215] Speed: 1.2269664998651428 samples/sec batch loss = 497.142437517643 | accuracy = 0.7069767441860465 |
| Epoch[2] Batch[220] Speed: 1.227698877596039 samples/sec batch loss = 511.4827534556389 | accuracy = 0.7034090909090909 |
| Epoch[2] Batch[225] Speed: 1.2295424383087572 samples/sec batch loss = 521.8850936293602 | accuracy = 0.7044444444444444 |
| Epoch[2] Batch[230] Speed: 1.2296271464053932 samples/sec batch loss = 534.2236570715904 | accuracy = 0.7043478260869566 |
| Epoch[2] Batch[235] Speed: 1.228783470222361 samples/sec batch loss = 547.6792865395546 | accuracy = 0.7031914893617022 |
| Epoch[2] Batch[240] Speed: 1.226652788615943 samples/sec batch loss = 560.1972559094429 | accuracy = 0.7020833333333333 |
| Epoch[2] Batch[245] Speed: 1.2333460364060655 samples/sec batch loss = 570.8941144943237 | accuracy = 0.7010204081632653 |
| Epoch[2] Batch[250] Speed: 1.2323818202773085 samples/sec batch loss = 581.0161567926407 | accuracy = 0.702 |
| Epoch[2] Batch[255] Speed: 1.2315246882396889 samples/sec batch loss = 590.5841120481491 | accuracy = 0.703921568627451 |
| Epoch[2] Batch[260] Speed: 1.2313567487492096 samples/sec batch loss = 603.2001624107361 | accuracy = 0.7038461538461539 |
| Epoch[2] Batch[265] Speed: 1.2319485368330394 samples/sec batch loss = 614.1992572546005 | accuracy = 0.7037735849056603 |
| Epoch[2] Batch[270] Speed: 1.228155608101228 samples/sec batch loss = 626.8009022474289 | accuracy = 0.7027777777777777 |
| Epoch[2] Batch[275] Speed: 1.2277637446187692 samples/sec batch loss = 637.8115049004555 | accuracy = 0.7027272727272728 |
| Epoch[2] Batch[280] Speed: 1.2313637980328085 samples/sec batch loss = 653.7936922907829 | accuracy = 0.7 |
| Epoch[2] Batch[285] Speed: 1.2298324767914899 samples/sec batch loss = 665.3686112761497 | accuracy = 0.7008771929824561 |
| Epoch[2] Batch[290] Speed: 1.2271632231151068 samples/sec batch loss = 674.8924590945244 | accuracy = 0.7043103448275863 |
| Epoch[2] Batch[295] Speed: 1.2271321668035655 samples/sec batch loss = 685.4287278056145 | accuracy = 0.7025423728813559 |
| Epoch[2] Batch[300] Speed: 1.229344050771688 samples/sec batch loss = 696.8948901295662 | accuracy = 0.7033333333333334 |
| Epoch[2] Batch[305] Speed: 1.2305269436237125 samples/sec batch loss = 709.539255797863 | accuracy = 0.7032786885245902 |
| Epoch[2] Batch[310] Speed: 1.2277334664989021 samples/sec batch loss = 720.9653010964394 | accuracy = 0.7024193548387097 |
| Epoch[2] Batch[315] Speed: 1.2320834301489585 samples/sec batch loss = 735.4295864701271 | accuracy = 0.7 |
| Epoch[2] Batch[320] Speed: 1.2337472783931034 samples/sec batch loss = 747.0269188284874 | accuracy = 0.69921875 |
| Epoch[2] Batch[325] Speed: 1.2303642387141367 samples/sec batch loss = 758.9655331969261 | accuracy = 0.6976923076923077 |
| Epoch[2] Batch[330] Speed: 1.2318759907398658 samples/sec batch loss = 770.8926784396172 | accuracy = 0.696969696969697 |
| Epoch[2] Batch[335] Speed: 1.2302050046415316 samples/sec batch loss = 781.7621143460274 | accuracy = 0.6977611940298507 |
| Epoch[2] Batch[340] Speed: 1.2321256865118086 samples/sec batch loss = 793.5896945595741 | accuracy = 0.6970588235294117 |
| Epoch[2] Batch[345] Speed: 1.232044162380111 samples/sec batch loss = 804.6628307700157 | accuracy = 0.696376811594203 |
| Epoch[2] Batch[350] Speed: 1.2317640223959818 samples/sec batch loss = 817.6521719098091 | accuracy = 0.6957142857142857 |
| Epoch[2] Batch[355] Speed: 1.2342185993178596 samples/sec batch loss = 828.8623519539833 | accuracy = 0.6957746478873239 |
| Epoch[2] Batch[360] Speed: 1.2368537417348167 samples/sec batch loss = 838.1571943163872 | accuracy = 0.6979166666666666 |
| Epoch[2] Batch[365] Speed: 1.229889545215764 samples/sec batch loss = 850.3256699442863 | accuracy = 0.6972602739726027 |
| Epoch[2] Batch[370] Speed: 1.22365342601773 samples/sec batch loss = 862.8044571280479 | accuracy = 0.6959459459459459 |
| Epoch[2] Batch[375] Speed: 1.2250326060241592 samples/sec batch loss = 874.7381972670555 | accuracy = 0.696 |
| Epoch[2] Batch[380] Speed: 1.2292805478547912 samples/sec batch loss = 885.6196042895317 | accuracy = 0.6953947368421053 |
| Epoch[2] Batch[385] Speed: 1.2262928060364084 samples/sec batch loss = 897.9612233042717 | accuracy = 0.6948051948051948 |
| Epoch[2] Batch[390] Speed: 1.2257007963847661 samples/sec batch loss = 910.6947954297066 | accuracy = 0.6935897435897436 |
| Epoch[2] Batch[395] Speed: 1.2271841376441512 samples/sec batch loss = 920.7800042033195 | accuracy = 0.6955696202531646 |
| Epoch[2] Batch[400] Speed: 1.228623565336638 samples/sec batch loss = 932.4678912758827 | accuracy = 0.694375 |
| Epoch[2] Batch[405] Speed: 1.228689159998872 samples/sec batch loss = 944.510540664196 | accuracy = 0.6938271604938272 |
| Epoch[2] Batch[410] Speed: 1.2343303786888913 samples/sec batch loss = 956.4577099680901 | accuracy = 0.6932926829268292 |
| Epoch[2] Batch[415] Speed: 1.2211435847516017 samples/sec batch loss = 968.2406663298607 | accuracy = 0.6933734939759036 |
| Epoch[2] Batch[420] Speed: 1.223634684290708 samples/sec batch loss = 979.0851874947548 | accuracy = 0.694047619047619 |
| Epoch[2] Batch[425] Speed: 1.2231536646748846 samples/sec batch loss = 990.9242896437645 | accuracy = 0.6935294117647058 |
| Epoch[2] Batch[430] Speed: 1.222433289278115 samples/sec batch loss = 1001.2824978232384 | accuracy = 0.6947674418604651 |
| Epoch[2] Batch[435] Speed: 1.2288869761558563 samples/sec batch loss = 1013.8018770813942 | accuracy = 0.6948275862068966 |
| Epoch[2] Batch[440] Speed: 1.2258045001065998 samples/sec batch loss = 1025.6744262576103 | accuracy = 0.6943181818181818 |
| Epoch[2] Batch[445] Speed: 1.2294223350267197 samples/sec batch loss = 1039.92163926363 | accuracy = 0.6932584269662921 |
| Epoch[2] Batch[450] Speed: 1.2305351567149998 samples/sec batch loss = 1052.8086115717888 | accuracy = 0.6933333333333334 |
| Epoch[2] Batch[455] Speed: 1.226263227823574 samples/sec batch loss = 1063.562524497509 | accuracy = 0.6928571428571428 |
| Epoch[2] Batch[460] Speed: 1.2290582942821577 samples/sec batch loss = 1074.8319408297539 | accuracy = 0.6929347826086957 |
| Epoch[2] Batch[465] Speed: 1.2308020064374732 samples/sec batch loss = 1086.642512023449 | accuracy = 0.6924731182795699 |
| Epoch[2] Batch[470] Speed: 1.2269165213879796 samples/sec batch loss = 1096.9285895228386 | accuracy = 0.6925531914893617 |
| Epoch[2] Batch[475] Speed: 1.2326932159469668 samples/sec batch loss = 1112.122159421444 | accuracy = 0.6915789473684211 |
| Epoch[2] Batch[480] Speed: 1.2319340631147153 samples/sec batch loss = 1125.8898395895958 | accuracy = 0.6916666666666667 |
| Epoch[2] Batch[485] Speed: 1.227795192318318 samples/sec batch loss = 1135.0219516158104 | accuracy = 0.6938144329896907 |
| Epoch[2] Batch[490] Speed: 1.2330217146314293 samples/sec batch loss = 1143.4660302996635 | accuracy = 0.6948979591836735 |
| Epoch[2] Batch[495] Speed: 1.2283184487878263 samples/sec batch loss = 1153.8384173512459 | accuracy = 0.6944444444444444 |
| Epoch[2] Batch[500] Speed: 1.231757058962938 samples/sec batch loss = 1166.0878913998604 | accuracy = 0.6945 |
| Epoch[2] Batch[505] Speed: 1.2287922900483754 samples/sec batch loss = 1178.929158270359 | accuracy = 0.694059405940594 |
| Epoch[2] Batch[510] Speed: 1.228906599246756 samples/sec batch loss = 1190.3485252261162 | accuracy = 0.6936274509803921 |
| Epoch[2] Batch[515] Speed: 1.2278663599174895 samples/sec batch loss = 1202.1567012667656 | accuracy = 0.6941747572815534 |
| Epoch[2] Batch[520] Speed: 1.2290040038628482 samples/sec batch loss = 1212.99599558115 | accuracy = 0.6942307692307692 |
| Epoch[2] Batch[525] Speed: 1.2229375431525127 samples/sec batch loss = 1221.1800859570503 | accuracy = 0.6957142857142857 |
| Epoch[2] Batch[530] Speed: 1.2294278306135973 samples/sec batch loss = 1234.8584129214287 | accuracy = 0.6943396226415094 |
| Epoch[2] Batch[535] Speed: 1.2295529811274073 samples/sec batch loss = 1245.7880756258965 | accuracy = 0.6939252336448598 |
| Epoch[2] Batch[540] Speed: 1.2256907672490176 samples/sec batch loss = 1255.1600200533867 | accuracy = 0.6949074074074074 |
| Epoch[2] Batch[545] Speed: 1.2301580091479405 samples/sec batch loss = 1262.4292892813683 | accuracy = 0.6972477064220184 |
| Epoch[2] Batch[550] Speed: 1.2303730812473594 samples/sec batch loss = 1272.4460882544518 | accuracy = 0.6981818181818182 |
| Epoch[2] Batch[555] Speed: 1.2239685514824734 samples/sec batch loss = 1285.0484514832497 | accuracy = 0.6981981981981982 |
| Epoch[2] Batch[560] Speed: 1.2348070529339519 samples/sec batch loss = 1294.4679728150368 | accuracy = 0.7 |
| Epoch[2] Batch[565] Speed: 1.2309475767769544 samples/sec batch loss = 1303.1254943013191 | accuracy = 0.7013274336283186 |
| Epoch[2] Batch[570] Speed: 1.230075482565809 samples/sec batch loss = 1316.4713049530983 | accuracy = 0.7017543859649122 |
| Epoch[2] Batch[575] Speed: 1.227909316072026 samples/sec batch loss = 1324.9961784482002 | accuracy = 0.7030434782608695 |
| Epoch[2] Batch[580] Speed: 1.2274785426314483 samples/sec batch loss = 1336.1549338698387 | accuracy = 0.7030172413793103 |
| Epoch[2] Batch[585] Speed: 1.226800159993097 samples/sec batch loss = 1348.2013884186745 | accuracy = 0.7029914529914529 |
| Epoch[2] Batch[590] Speed: 1.2298689891540981 samples/sec batch loss = 1358.4156247973442 | accuracy = 0.7038135593220339 |
| Epoch[2] Batch[595] Speed: 1.2273475288875058 samples/sec batch loss = 1370.7123524546623 | accuracy = 0.7033613445378152 |
| Epoch[2] Batch[600] Speed: 1.2289090296731893 samples/sec batch loss = 1381.9451199173927 | accuracy = 0.7041666666666667 |
| Epoch[2] Batch[605] Speed: 1.23099020686782 samples/sec batch loss = 1394.8828805088997 | accuracy = 0.702892561983471 |
| Epoch[2] Batch[610] Speed: 1.22789475737333 samples/sec batch loss = 1405.62037473917 | accuracy = 0.7024590163934427 |
| Epoch[2] Batch[615] Speed: 1.2283783447381098 samples/sec batch loss = 1414.0183553099632 | accuracy = 0.7028455284552846 |
| Epoch[2] Batch[620] Speed: 1.2275000965774134 samples/sec batch loss = 1424.5221813321114 | accuracy = 0.7032258064516129 |
| Epoch[2] Batch[625] Speed: 1.2284641518170887 samples/sec batch loss = 1438.8212504982948 | accuracy = 0.7028 |
| Epoch[2] Batch[630] Speed: 1.2223265039260094 samples/sec batch loss = 1452.665615260601 | accuracy = 0.7027777777777777 |
| Epoch[2] Batch[635] Speed: 1.2281521017916135 samples/sec batch loss = 1463.2709195017815 | accuracy = 0.7031496062992126 |
| Epoch[2] Batch[640] Speed: 1.2262747900459958 samples/sec batch loss = 1474.8328509926796 | accuracy = 0.703515625 |
| Epoch[2] Batch[645] Speed: 1.2305037490018833 samples/sec batch loss = 1482.5473827123642 | accuracy = 0.7046511627906977 |
| Epoch[2] Batch[650] Speed: 1.2268855672815617 samples/sec batch loss = 1496.133915424347 | accuracy = 0.7038461538461539 |
| Epoch[2] Batch[655] Speed: 1.228560586655208 samples/sec batch loss = 1508.042652606964 | accuracy = 0.7034351145038168 |
| Epoch[2] Batch[660] Speed: 1.2305761335556562 samples/sec batch loss = 1518.958142399788 | accuracy = 0.703030303030303 |
| Epoch[2] Batch[665] Speed: 1.23144324361659 samples/sec batch loss = 1528.3985501527786 | accuracy = 0.7041353383458646 |
| Epoch[2] Batch[670] Speed: 1.2258400571491157 samples/sec batch loss = 1539.1386450529099 | accuracy = 0.7044776119402985 |
| Epoch[2] Batch[675] Speed: 1.2295986687642204 samples/sec batch loss = 1549.0479488372803 | accuracy = 0.7051851851851851 |
| Epoch[2] Batch[680] Speed: 1.2276081471389826 samples/sec batch loss = 1557.7756605148315 | accuracy = 0.7066176470588236 |
| Epoch[2] Batch[685] Speed: 1.2255831434630442 samples/sec batch loss = 1568.49676913023 | accuracy = 0.7065693430656934 |
| Epoch[2] Batch[690] Speed: 1.2269875871585485 samples/sec batch loss = 1576.7246295809746 | accuracy = 0.7076086956521739 |
| Epoch[2] Batch[695] Speed: 1.2281175791967158 samples/sec batch loss = 1586.2875626683235 | accuracy = 0.7079136690647482 |
| Epoch[2] Batch[700] Speed: 1.2299273232787562 samples/sec batch loss = 1603.1909262537956 | accuracy = 0.7075 |
| Epoch[2] Batch[705] Speed: 1.2270075983242643 samples/sec batch loss = 1615.0498873591423 | accuracy = 0.7074468085106383 |
| Epoch[2] Batch[710] Speed: 1.2273998771518544 samples/sec batch loss = 1626.8721486926079 | accuracy = 0.7073943661971831 |
| Epoch[2] Batch[715] Speed: 1.2253525591257681 samples/sec batch loss = 1638.3502686619759 | accuracy = 0.7076923076923077 |
| Epoch[2] Batch[720] Speed: 1.2282458799898357 samples/sec batch loss = 1647.9793664813042 | accuracy = 0.7086805555555555 |
| Epoch[2] Batch[725] Speed: 1.2295865030854698 samples/sec batch loss = 1657.6263877749443 | accuracy = 0.7089655172413794 |
| Epoch[2] Batch[730] Speed: 1.2310131488591025 samples/sec batch loss = 1668.8062285780907 | accuracy = 0.7095890410958904 |
| Epoch[2] Batch[735] Speed: 1.2307631814565543 samples/sec batch loss = 1676.3949342370033 | accuracy = 0.7105442176870749 |
| Epoch[2] Batch[740] Speed: 1.2271674418682899 samples/sec batch loss = 1688.0925043225288 | accuracy = 0.7097972972972973 |
| Epoch[2] Batch[745] Speed: 1.223377445182976 samples/sec batch loss = 1696.91601729393 | accuracy = 0.710738255033557 |
| Epoch[2] Batch[750] Speed: 1.225483147285371 samples/sec batch loss = 1709.0300940275192 | accuracy = 0.7103333333333334 |
| Epoch[2] Batch[755] Speed: 1.2295042333696065 samples/sec batch loss = 1716.6224944591522 | accuracy = 0.7112582781456953 |
| Epoch[2] Batch[760] Speed: 1.2277638344670445 samples/sec batch loss = 1727.1613991260529 | accuracy = 0.7115131578947368 |
| Epoch[2] Batch[765] Speed: 1.2299555456373832 samples/sec batch loss = 1739.5258980989456 | accuracy = 0.711437908496732 |
| Epoch[2] Batch[770] Speed: 1.2265002521395307 samples/sec batch loss = 1748.1015099287033 | accuracy = 0.7123376623376624 |
| Epoch[2] Batch[775] Speed: 1.225936886655823 samples/sec batch loss = 1758.5067769289017 | accuracy = 0.7129032258064516 |
| Epoch[2] Batch[780] Speed: 1.2259969985127022 samples/sec batch loss = 1768.2216209173203 | accuracy = 0.7141025641025641 |
| Epoch[2] Batch[785] Speed: 1.2287310938641942 samples/sec batch loss = 1779.2798628807068 | accuracy = 0.7136942675159236 |
| [Epoch 2] training: accuracy=0.7131979695431472 |
| [Epoch 2] time cost: 657.6945281028748 |
| [Epoch 2] validation: validation accuracy=0.7822222222222223 |
| </pre></div></div> |
| </div> |
| </div> |
| </div> |
| <div class="section" id="Next-steps"> |
| <h2>Next steps<a class="headerlink" href="#Next-steps" title="Permalink to this headline">¶</a></h2> |
| <p>Now that you have completed training and predicting with a neural network on GPUs, you reached the conclusion of the crash course. Congratulations. If you are keen on studying more, checkout <a class="reference external" href="https://d2l.ai">D2L.ai</a>, <a class="reference external" href="https://cv.gluon.ai/tutorials/index.html">GluonCV</a>, <a class="reference external" href="https://nlp.gluon.ai">GluonNLP</a>, <a class="reference external" href="https://ts.gluon.ai/">GluonTS</a>, <a class="reference external" href="https://auto.gluon.ai">AutoGluon</a>.</p> |
| </div> |
| </div> |
| |
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| <div class="side-doc-outline"> |
| <div class="side-doc-outline--content"> |
| <div class="localtoc"> |
| <p class="caption"> |
| <span class="caption-text">Table Of Contents</span> |
| </p> |
| <ul> |
| <li><a class="reference internal" href="#">Step 7: Load and Run a NN using GPU</a><ul> |
| <li><a class="reference internal" href="#Prerequisites">Prerequisites</a></li> |
| <li><a class="reference internal" href="#Allocate-data-to-a-GPU">Allocate data to a GPU</a></li> |
| <li><a class="reference internal" href="#Choosing-GPU-Ids">Choosing GPU Ids</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="#Training-with-multiple-GPUs">Training with multiple GPUs</a><ul> |
| <li><a class="reference internal" href="#Define-a-helper-function">Define a helper function</a></li> |
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