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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"> |
| <li class="toctree-l2 current"><a class="reference internal" href="index.html">Getting Started</a><ul class="current"> |
| <li class="toctree-l3"><a class="reference internal" href="crash-course/index.html">Crash Course</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="crash-course/0-introduction.html">Introduction</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="crash-course/2-create-nn.html">Step 2: Create a neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="crash-course/4-components.html">Step 4: Necessary components that are not in the network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="crash-course/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="crash-course/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="crash-course/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="crash-course/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="crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="crash-course/7-use-gpus.html">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 current"><a class="current reference internal" href="#">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> |
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| <li class="toctree-l2"><a class="reference internal" href="../packages/index.html">Packages</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../packages/autograd/index.html">Automatic Differentiation</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../packages/gluon/index.html">Gluon</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../packages/gluon/blocks/index.html">Blocks</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../packages/gluon/blocks/custom-layer.html">Custom Layers</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../packages/gluon/blocks/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> |
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| <li class="toctree-l4"><a class="reference internal" href="../packages/gluon/data/index.html">Data Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../packages/gluon/data/data_augmentation.html">Image Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../packages/gluon/data/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> |
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| <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> |
| <li class="toctree-l6"><a class="reference internal" href="../packages/legacy/ndarray/sparse/row_sparse.html">RowSparseNDArray - NDArray for Sparse Gradient Updates</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| </ul> |
| </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> |
| </ul> |
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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> |
| </ul> |
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| <li class="toctree-l3"><a class="reference internal" href="../packages/optimizer/index.html">Optimizers</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../packages/viz/index.html">Visualization</a><ul> |
| <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-l2"><a class="reference internal" href="../performance/index.html">Performance</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../performance/compression/index.html">Compression</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../performance/compression/int8.html">Deploy with int-8</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/float16">Float16</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/gradient_compression">Gradient Compression</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://gluon-cv.mxnet.io/build/examples_deployment/int8_inference.html">GluonCV with Quantized Models</a></li> |
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| <li class="toctree-l3"><a class="reference internal" href="../performance/backend/index.html">Accelerated Backend Tools</a><ul> |
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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> |
| <li class="toctree-l5"><a class="reference internal" href="../performance/backend/dnnl/dnnl_quantization.html">oneDNN Quantization</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../performance/backend/dnnl/dnnl_quantization_inc.html">Improving accuracy with Intel® Neural Compressor</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../performance/backend/tvm.html">Use TVM</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../performance/backend/profiler.html">Profiling MXNet Models</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../performance/backend/amp.html">Using AMP: Automatic Mixed Precision</a></li> |
| </ul> |
| </li> |
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| <li class="toctree-l2"><a class="reference internal" href="../deploy/index.html">Deployment</a><ul> |
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| <li class="toctree-l4"><a class="reference internal" href="../deploy/export/onnx.html">Exporting to ONNX format</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://gluon-cv.mxnet.io/build/examples_deployment/export_network.html">Export Gluon CV Models</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/save_load_params.html">Save / Load Parameters</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../deploy/inference/index.html">Inference</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../deploy/inference/cpp.html">Deploy into C++</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../deploy/inference/image_classification_jetson.html">Image Classication using pretrained ResNet-50 model on Jetson module</a></li> |
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| <li class="toctree-l3"><a class="reference internal" href="../deploy/run-on-aws/index.html">Run on AWS</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../deploy/run-on-aws/use_ec2.html">Run on an EC2 Instance</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../deploy/run-on-aws/use_sagemaker.html">Run on Amazon SageMaker</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../deploy/run-on-aws/cloud.html">MXNet on the Cloud</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../extend/index.html">Extend</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../extend/customop.html">Custom Numpy Operators</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/faq/new_op">New Operator Creation</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/faq/add_op_in_backend">New Operator in MXNet Backend</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/faq/using_rtc">Using RTC for CUDA kernels</a></li> |
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| </li> |
| </ul> |
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| <li class="toctree-l1"><a class="reference internal" href="../../api/index.html">Python API</a><ul> |
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| <li class="toctree-l3"><a class="reference internal" href="../../api/np/arrays.html">Array objects</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../api/np/arrays.ndarray.html">The N-dimensional array (<code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code>)</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../api/np/arrays.indexing.html">Indexing</a></li> |
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| <li class="toctree-l3"><a class="reference internal" href="../../api/np/routines.html">Routines</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../api/np/routines.array-creation.html">Array creation routines</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.eye.html">mxnet.np.eye</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.empty.html">mxnet.np.empty</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.full.html">mxnet.np.full</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.identity.html">mxnet.np.identity</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.ones.html">mxnet.np.ones</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.ones_like.html">mxnet.np.ones_like</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.zeros.html">mxnet.np.zeros</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.zeros_like.html">mxnet.np.zeros_like</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.array.html">mxnet.np.array</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.copy.html">mxnet.np.copy</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.arange.html">mxnet.np.arange</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.linspace.html">mxnet.np.linspace</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.logspace.html">mxnet.np.logspace</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.tril.html">mxnet.np.tril</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> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.reshape.html">mxnet.np.reshape</a></li> |
| <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> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.transpose.html">mxnet.np.transpose</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.moveaxis.html">mxnet.np.moveaxis</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.rollaxis.html">mxnet.np.rollaxis</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.expand_dims.html">mxnet.np.expand_dims</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.squeeze.html">mxnet.np.squeeze</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.broadcast_to.html">mxnet.np.broadcast_to</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.broadcast_arrays.html">mxnet.np.broadcast_arrays</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.atleast_1d.html">mxnet.np.atleast_1d</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.atleast_2d.html">mxnet.np.atleast_2d</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.atleast_3d.html">mxnet.np.atleast_3d</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.concatenate.html">mxnet.np.concatenate</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.stack.html">mxnet.np.stack</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.dstack.html">mxnet.np.dstack</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.vstack.html">mxnet.np.vstack</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.column_stack.html">mxnet.np.column_stack</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.hstack.html">mxnet.np.hstack</a></li> |
| <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> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.dsplit.html">mxnet.np.dsplit</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.tile.html">mxnet.np.tile</a></li> |
| <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> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.insert.html">mxnet.np.insert</a></li> |
| <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> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.reshape.html">mxnet.np.reshape</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.flip.html">mxnet.np.flip</a></li> |
| <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> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.cumsum.html">mxnet.np.cumsum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.nanprod.html">mxnet.np.nanprod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.nansum.html">mxnet.np.nansum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.cumprod.html">mxnet.np.cumprod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.nancumprod.html">mxnet.np.nancumprod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.nancumsum.html">mxnet.np.nancumsum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.diff.html">mxnet.np.diff</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.ediff1d.html">mxnet.np.ediff1d</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.cross.html">mxnet.np.cross</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.trapz.html">mxnet.np.trapz</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.exp.html">mxnet.np.exp</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.expm1.html">mxnet.np.expm1</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.log.html">mxnet.np.log</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.log10.html">mxnet.np.log10</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.log2.html">mxnet.np.log2</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.log1p.html">mxnet.np.log1p</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.logaddexp.html">mxnet.np.logaddexp</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.i0.html">mxnet.np.i0</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.ldexp.html">mxnet.np.ldexp</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.signbit.html">mxnet.np.signbit</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.copysign.html">mxnet.np.copysign</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.frexp.html">mxnet.np.frexp</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.spacing.html">mxnet.np.spacing</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.lcm.html">mxnet.np.lcm</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.gcd.html">mxnet.np.gcd</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.add.html">mxnet.np.add</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.reciprocal.html">mxnet.np.reciprocal</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.divide.html">mxnet.np.divide</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.power.html">mxnet.np.power</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.subtract.html">mxnet.np.subtract</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.mod.html">mxnet.np.mod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.multiply.html">mxnet.np.multiply</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.true_divide.html">mxnet.np.true_divide</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.remainder.html">mxnet.np.remainder</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.positive.html">mxnet.np.positive</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.float_power.html">mxnet.np.float_power</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.fmod.html">mxnet.np.fmod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.modf.html">mxnet.np.modf</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.divmod.html">mxnet.np.divmod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.floor_divide.html">mxnet.np.floor_divide</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.clip.html">mxnet.np.clip</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.sqrt.html">mxnet.np.sqrt</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.cbrt.html">mxnet.np.cbrt</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.square.html">mxnet.np.square</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.absolute.html">mxnet.np.absolute</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.sign.html">mxnet.np.sign</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.maximum.html">mxnet.np.maximum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.minimum.html">mxnet.np.minimum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.fabs.html">mxnet.np.fabs</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.heaviside.html">mxnet.np.heaviside</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.fmax.html">mxnet.np.fmax</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.fmin.html">mxnet.np.fmin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.nan_to_num.html">mxnet.np.nan_to_num</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.interp.html">mxnet.np.interp</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../api/np/random/index.html">np.random</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/random/generated/mxnet.np.random.choice.html">mxnet.np.random.choice</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/random/generated/mxnet.np.random.shuffle.html">mxnet.np.random.shuffle</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/random/generated/mxnet.np.random.normal.html">mxnet.np.random.normal</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/random/generated/mxnet.np.random.uniform.html">mxnet.np.random.uniform</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/random/generated/mxnet.np.random.rand.html">mxnet.np.random.rand</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/random/generated/mxnet.np.random.randint.html">mxnet.np.random.randint</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/random/generated/mxnet.np.random.beta.html">mxnet.np.random.beta</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/random/generated/mxnet.np.random.chisquare.html">mxnet.np.random.chisquare</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../api/np/random/generated/mxnet.np.random.f.html">mxnet.np.random.f</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/random/generated/mxnet.np.random.gamma.html">mxnet.np.random.gamma</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/random/generated/mxnet.np.random.gumbel.html">mxnet.np.random.gumbel</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/random/generated/mxnet.np.random.laplace.html">mxnet.np.random.laplace</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/random/generated/mxnet.np.random.logistic.html">mxnet.np.random.logistic</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/random/generated/mxnet.np.random.lognormal.html">mxnet.np.random.lognormal</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/random/generated/mxnet.np.random.multinomial.html">mxnet.np.random.multinomial</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/random/generated/mxnet.np.random.multivariate_normal.html">mxnet.np.random.multivariate_normal</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/random/generated/mxnet.np.random.pareto.html">mxnet.np.random.pareto</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/random/generated/mxnet.np.random.power.html">mxnet.np.random.power</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/random/generated/mxnet.np.random.rayleigh.html">mxnet.np.random.rayleigh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/random/generated/mxnet.np.random.weibull.html">mxnet.np.random.weibull</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../api/np/routines.sort.html">Sorting, searching, and counting</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.ndarray.sort.html">mxnet.np.ndarray.sort</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.sort.html">mxnet.np.sort</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.lexsort.html">mxnet.np.lexsort</a></li> |
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| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../api/np/routines.statistics.html">Statistics</a><ul> |
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| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.max.html">mxnet.np.max</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.mean.html">mxnet.np.mean</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.std.html">mxnet.np.std</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.nanmedian.html">mxnet.np.nanmedian</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.nanstd.html">mxnet.np.nanstd</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.corrcoef.html">mxnet.np.corrcoef</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.correlate.html">mxnet.np.correlate</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.cov.html">mxnet.np.cov</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.histogram.html">mxnet.np.histogram</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.histogram2d.html">mxnet.np.histogram2d</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.histogramdd.html">mxnet.np.histogramdd</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.histogram_bin_edges.html">mxnet.np.histogram_bin_edges</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.digitize.html">mxnet.np.digitize</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../api/npx/index.html">NPX: NumPy Neural Network Extension</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../api/npx/generated/mxnet.npx.set_np.html">mxnet.npx.set_np</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../api/npx/generated/mxnet.npx.reset_np.html">mxnet.npx.reset_np</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../api/npx/generated/mxnet.npx.cpu.html">mxnet.npx.cpu</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../api/npx/generated/mxnet.npx.cpu_pinned.html">mxnet.npx.cpu_pinned</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../api/npx/generated/mxnet.npx.gpu.html">mxnet.npx.gpu</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../api/npx/generated/mxnet.npx.gpu_memory_info.html">mxnet.npx.gpu_memory_info</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../api/npx/generated/mxnet.npx.current_device.html">mxnet.npx.current_device</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../api/npx/generated/mxnet.npx.num_gpus.html">mxnet.npx.num_gpus</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../api/npx/generated/mxnet.npx.activation.html">mxnet.npx.activation</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../api/npx/generated/mxnet.npx.batch_norm.html">mxnet.npx.batch_norm</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../api/npx/generated/mxnet.npx.convolution.html">mxnet.npx.convolution</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../api/npx/generated/mxnet.npx.dropout.html">mxnet.npx.dropout</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../api/npx/generated/mxnet.npx.embedding.html">mxnet.npx.embedding</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../api/npx/generated/mxnet.npx.fully_connected.html">mxnet.npx.fully_connected</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../api/npx/generated/mxnet.npx.layer_norm.html">mxnet.npx.layer_norm</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../api/npx/generated/mxnet.npx.pooling.html">mxnet.npx.pooling</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../api/npx/generated/mxnet.npx.rnn.html">mxnet.npx.rnn</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../api/npx/generated/mxnet.npx.leaky_relu.html">mxnet.npx.leaky_relu</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../api/npx/generated/mxnet.npx.multibox_detection.html">mxnet.npx.multibox_detection</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../api/npx/generated/mxnet.npx.multibox_prior.html">mxnet.npx.multibox_prior</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../api/npx/generated/mxnet.npx.multibox_target.html">mxnet.npx.multibox_target</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../api/npx/generated/mxnet.npx.roi_pooling.html">mxnet.npx.roi_pooling</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../api/npx/generated/mxnet.npx.sigmoid.html">mxnet.npx.sigmoid</a></li> |
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| <span class="mdl-layout-title toc">Table Of Contents</span> |
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| <nav class="mdl-navigation"> |
| <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"><a class="reference internal" href="crash-course/index.html">Crash Course</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="crash-course/0-introduction.html">Introduction</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="crash-course/2-create-nn.html">Step 2: Create a neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="crash-course/4-components.html">Step 4: Necessary components that are not in the network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="crash-course/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="crash-course/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="crash-course/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="crash-course/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="crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="crash-course/7-use-gpus.html">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 current"><a class="current reference internal" href="#">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> |
| <li class="toctree-l6"><a class="reference internal" href="../packages/legacy/ndarray/sparse/row_sparse.html">RowSparseNDArray - NDArray for Sparse Gradient Updates</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| </ul> |
| </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> |
| </ul> |
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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> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../packages/optimizer/index.html">Optimizers</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../packages/viz/index.html">Visualization</a><ul> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/visualize_graph">Visualize networks</a></li> |
| </ul> |
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| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../performance/index.html">Performance</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../performance/compression/index.html">Compression</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../performance/compression/int8.html">Deploy with int-8</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/float16">Float16</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/gradient_compression">Gradient Compression</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://gluon-cv.mxnet.io/build/examples_deployment/int8_inference.html">GluonCV with Quantized Models</a></li> |
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| <li class="toctree-l3"><a class="reference internal" href="../performance/backend/index.html">Accelerated Backend Tools</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../performance/backend/dnnl/index.html">oneDNN</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../performance/backend/dnnl/dnnl_readme.html">Install MXNet with oneDNN</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../performance/backend/dnnl/dnnl_quantization.html">oneDNN Quantization</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../performance/backend/dnnl/dnnl_quantization_inc.html">Improving accuracy with Intel® Neural Compressor</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../performance/backend/tvm.html">Use TVM</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../performance/backend/profiler.html">Profiling MXNet Models</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../performance/backend/amp.html">Using AMP: Automatic Mixed Precision</a></li> |
| </ul> |
| </li> |
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| <li class="toctree-l2"><a class="reference internal" href="../deploy/index.html">Deployment</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../deploy/export/index.html">Export</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../deploy/export/onnx.html">Exporting to ONNX format</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://gluon-cv.mxnet.io/build/examples_deployment/export_network.html">Export Gluon CV Models</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/save_load_params.html">Save / Load Parameters</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../deploy/inference/index.html">Inference</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../deploy/inference/cpp.html">Deploy into C++</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../deploy/inference/image_classification_jetson.html">Image Classication using pretrained ResNet-50 model on Jetson module</a></li> |
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| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../deploy/run-on-aws/index.html">Run on AWS</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../deploy/run-on-aws/use_ec2.html">Run on an EC2 Instance</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../deploy/run-on-aws/use_sagemaker.html">Run on Amazon SageMaker</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../deploy/run-on-aws/cloud.html">MXNet on the Cloud</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../extend/index.html">Extend</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../extend/customop.html">Custom Numpy Operators</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/faq/new_op">New Operator Creation</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/faq/add_op_in_backend">New Operator in MXNet Backend</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/faq/using_rtc">Using RTC for CUDA kernels</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l1"><a class="reference internal" href="../../api/index.html">Python API</a><ul> |
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| <li class="toctree-l3"><a class="reference internal" href="../../api/np/arrays.html">Array objects</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../api/np/arrays.ndarray.html">The N-dimensional array (<code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code>)</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../api/np/arrays.indexing.html">Indexing</a></li> |
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| <li class="toctree-l3"><a class="reference internal" href="../../api/np/routines.html">Routines</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../api/np/routines.array-creation.html">Array creation routines</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.eye.html">mxnet.np.eye</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.empty.html">mxnet.np.empty</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.full.html">mxnet.np.full</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.identity.html">mxnet.np.identity</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.ones.html">mxnet.np.ones</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.ones_like.html">mxnet.np.ones_like</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.zeros.html">mxnet.np.zeros</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.zeros_like.html">mxnet.np.zeros_like</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.array.html">mxnet.np.array</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.copy.html">mxnet.np.copy</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.arange.html">mxnet.np.arange</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.linspace.html">mxnet.np.linspace</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.tril.html">mxnet.np.tril</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> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.reshape.html">mxnet.np.reshape</a></li> |
| <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> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.transpose.html">mxnet.np.transpose</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.moveaxis.html">mxnet.np.moveaxis</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.rollaxis.html">mxnet.np.rollaxis</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.expand_dims.html">mxnet.np.expand_dims</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.squeeze.html">mxnet.np.squeeze</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.broadcast_to.html">mxnet.np.broadcast_to</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.broadcast_arrays.html">mxnet.np.broadcast_arrays</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.atleast_1d.html">mxnet.np.atleast_1d</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.atleast_2d.html">mxnet.np.atleast_2d</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.atleast_3d.html">mxnet.np.atleast_3d</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.concatenate.html">mxnet.np.concatenate</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.stack.html">mxnet.np.stack</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.dstack.html">mxnet.np.dstack</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.vstack.html">mxnet.np.vstack</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.column_stack.html">mxnet.np.column_stack</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.hstack.html">mxnet.np.hstack</a></li> |
| <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> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.dsplit.html">mxnet.np.dsplit</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.tile.html">mxnet.np.tile</a></li> |
| <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> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.insert.html">mxnet.np.insert</a></li> |
| <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> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.reshape.html">mxnet.np.reshape</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.flip.html">mxnet.np.flip</a></li> |
| <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> |
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| <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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| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.nansum.html">mxnet.np.nansum</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.nancumsum.html">mxnet.np.nancumsum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.diff.html">mxnet.np.diff</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.trapz.html">mxnet.np.trapz</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.expm1.html">mxnet.np.expm1</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.frexp.html">mxnet.np.frexp</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.spacing.html">mxnet.np.spacing</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.float_power.html">mxnet.np.float_power</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.modf.html">mxnet.np.modf</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.sqrt.html">mxnet.np.sqrt</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.cbrt.html">mxnet.np.cbrt</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.maximum.html">mxnet.np.maximum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.minimum.html">mxnet.np.minimum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.fabs.html">mxnet.np.fabs</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="../../api/np/random/index.html">np.random</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/random/generated/mxnet.np.random.choice.html">mxnet.np.random.choice</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/random/generated/mxnet.np.random.shuffle.html">mxnet.np.random.shuffle</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/random/generated/mxnet.np.random.normal.html">mxnet.np.random.normal</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/random/generated/mxnet.np.random.uniform.html">mxnet.np.random.uniform</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/random/generated/mxnet.np.random.rand.html">mxnet.np.random.rand</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../api/np/random/generated/mxnet.np.random.chisquare.html">mxnet.np.random.chisquare</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../api/np/random/generated/mxnet.np.random.f.html">mxnet.np.random.f</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/random/generated/mxnet.np.random.gamma.html">mxnet.np.random.gamma</a></li> |
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| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../api/np/routines.sort.html">Sorting, searching, and counting</a><ul> |
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| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../api/np/routines.statistics.html">Statistics</a><ul> |
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| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.nanquantile.html">mxnet.np.nanquantile</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.mean.html">mxnet.np.mean</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.std.html">mxnet.np.std</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.nanmedian.html">mxnet.np.nanmedian</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.nanstd.html">mxnet.np.nanstd</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.corrcoef.html">mxnet.np.corrcoef</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.correlate.html">mxnet.np.correlate</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.cov.html">mxnet.np.cov</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.histogram.html">mxnet.np.histogram</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.histogramdd.html">mxnet.np.histogramdd</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.bincount.html">mxnet.np.bincount</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.histogram_bin_edges.html">mxnet.np.histogram_bin_edges</a></li> |
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| </li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../api/npx/index.html">NPX: NumPy Neural Network Extension</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../api/npx/generated/mxnet.npx.set_np.html">mxnet.npx.set_np</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../api/npx/generated/mxnet.npx.reset_np.html">mxnet.npx.reset_np</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../api/npx/generated/mxnet.npx.cpu.html">mxnet.npx.cpu</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../api/npx/generated/mxnet.npx.cpu_pinned.html">mxnet.npx.cpu_pinned</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../api/npx/generated/mxnet.npx.gpu.html">mxnet.npx.gpu</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../api/npx/generated/mxnet.npx.gpu_memory_info.html">mxnet.npx.gpu_memory_info</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../api/npx/generated/mxnet.npx.current_device.html">mxnet.npx.current_device</a></li> |
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| <div class="page-content" role="main"> |
| |
| <!--- 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="Gluon2.0:-Migration-Guide"> |
| <h1>Gluon2.0: Migration Guide<a class="headerlink" href="#Gluon2.0:-Migration-Guide" title="Permalink to this headline">¶</a></h1> |
| <div class="section" id="Overview"> |
| <h2>Overview<a class="headerlink" href="#Overview" title="Permalink to this headline">¶</a></h2> |
| <p>Since the introduction of the Gluon API in MXNet 1.x, it has superseded commonly used symbolic, module and model APIs for model development. In fact, Gluon was the first in the deep learning community to unify the flexibility of imperative programming with the performance benefits of symbolic programming, through just-in-time compilation.</p> |
| <p>In Gluon2.0, we extend the support to MXNet NumPy and NumPy extension with simplified interface and new functionalities:</p> |
| <ul class="simple"> |
| <li><p><strong>Simplified hybridization with deferred compute and tracing</strong>: Deferred compute allows the imperative execution to be used for graph construction, which allows us to unify the historic divergence of NDArray and Symbol. Hybridization now works in a simplified hybrid forward interface. Users only need to specify the computation through imperative programming. Hybridization also works through tracing, i.e. tracing the data flow of the first input data to create a graph.</p></li> |
| <li><p><strong>Data 2.0</strong>: The new design for data loading in Gluon allows hybridizing and deploying data processing pipeline in the same way as model hybridization. The new C++ data loader improves data loading efficiency on CIFAR 10 by 50%.</p></li> |
| <li><p><strong>Distributed 2.0</strong>: The new distributed-training design in Gluon 2.0 provides a unified distributed data parallel interface across native Parameter Server, BytePS, and Horovod, and is extensible for supporting custom distributed training libraries.</p></li> |
| <li><p><strong>Gluon Probability</strong>: parameterizable probability distributions and sampling functions to facilitate more areas of research such as Baysian methods and AutoML.</p></li> |
| <li><p><strong>Gluon Metrics</strong> and <strong>Optimizers</strong>: refactored with MXNet NumPy interface and addressed legacy issues.</p></li> |
| </ul> |
| <p>Adopting these new functionalities may or may not require modifications on your models. But don’t worry, this migration guide will go through a high-level mapping from old functionality to new APIs and make Gluon2.0 migration a hassle-free experience.</p> |
| </div> |
| <div class="section" id="Data-Pipeline"> |
| <h2>Data Pipeline<a class="headerlink" href="#Data-Pipeline" title="Permalink to this headline">¶</a></h2> |
| <p><strong>What’s new</strong>: In Gluon2.0, <code class="docutils literal notranslate"><span class="pre">MultithreadingDataLoader</span></code> is introduced to speed up the data loading pipeline. It will use the pure MXNet C++ implementation of dataloader, datasets and batchify functions. So, you can use either MXNet internal multithreading mode dataloader or python multiprocessing mode dataloader in Gluon2.0.</p> |
| <p><strong>Migration Guide</strong>: Users can continue with the traditional gluon.data.Dataloader and the C++ backend will be applied automatically.</p> |
| <p><a class="reference internal" href="../../api/gluon/data/index.html#mxnet.gluon.data.DataLoader"><span class="std std-ref">Gluon2.0 dataloader</span></a> will provide a new parameter called <code class="docutils literal notranslate"><span class="pre">try_nopython</span></code>. This parameter takes a default value of None; when set to <code class="docutils literal notranslate"><span class="pre">True</span></code> the dataloader will compile the python dataloading pipeline into pure MXNet C++ implementation. The compilation is not guaranteed to support all use cases, but it will fallback to python in case of failure:</p> |
| <ul class="simple"> |
| <li><p>The dataset is not fully <a class="reference internal" href="../../api/gluon/data/index.html#mxnet.gluon.data.Dataset"><span class="std std-ref">supported by the backend</span></a> (e.g., there are custom python datasets).</p></li> |
| <li><p>Transform is not fully hybridizable.</p></li> |
| <li><p>Bachify is not fully <a class="reference external" href="https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/gluon/data/batchify.py">supported by the backend</a>.</p></li> |
| </ul> |
| <p>You can refer to <a class="reference external" href="https://mxnet.apache.org/versions/master/api/python/docs/tutorials/getting-started/crash-course/5-datasets.html#New-in-MXNet-2.0:-faster-C++-backend-dataloaders">Step 5 in Crash Course</a> for a detailed performance increase with C++ backend.</p> |
| </div> |
| <div class="section" id="Modeling"> |
| <h2>Modeling<a class="headerlink" href="#Modeling" title="Permalink to this headline">¶</a></h2> |
| <p>In Gluon2.0, users will have a brand new modeling experience with NumPy-compatible APIs and the deferred compute mechanism.</p> |
| <ul class="simple"> |
| <li><p><strong>NumPy-compatible programing experience</strong>: users can build their models with MXNet implementation with NumPy array library, NumPy-compatible math operators and some neural network extension operators.</p></li> |
| <li><p><strong>Imperative-only coding experience</strong>: with the deferred compute and tracing being introduced, users only need to specify the computation through imperative coding but can still make hybridization work. Users will no longer need to interact with symbol APIs.</p></li> |
| </ul> |
| <p>To help users migrate smoothly to use these simplified interfaces, we will provide the following guidance on how to replace legacy operators with NumPy-compatible operators, how to build models with <code class="docutils literal notranslate"><span class="pre">forward</span></code> instead of <code class="docutils literal notranslate"><span class="pre">hybrid_forward</span></code> and how to use <code class="docutils literal notranslate"><span class="pre">Parameter</span></code> class to register your parameters.</p> |
| <div class="section" id="NumPy-compatible-Programming-Experience"> |
| <h3>NumPy-compatible Programming Experience<a class="headerlink" href="#NumPy-compatible-Programming-Experience" title="Permalink to this headline">¶</a></h3> |
| <div class="section" id="NumPy-Arrays"> |
| <h4>NumPy Arrays<a class="headerlink" href="#NumPy-Arrays" title="Permalink to this headline">¶</a></h4> |
| <p>MXNet <a class="reference internal" href="../../api/np/arrays.ndarray.html"><span class="doc">NumPy ndarray (i.e. mx.np.ndarray)</span></a> is a multidimensional container of items of the same type and size. Most of its properties and attributes are the same as legacy NDArrays (i.e. <code class="docutils literal notranslate"><span class="pre">mx.nd.ndarray</span></code>), so users can use the NumPy array library just as they did with legacy NDArrays. But, there are still some changes and deprecations that need attention, as mentioned below.</p> |
| <p><strong>Migration Guide</strong>:</p> |
| <ol class="arabic simple"> |
| <li><p>Currently, NumPy ndarray only supports <code class="docutils literal notranslate"><span class="pre">default</span></code> storage type, other storage types, like <code class="docutils literal notranslate"><span class="pre">row_sparse</span></code>, <code class="docutils literal notranslate"><span class="pre">csr</span></code> are not supported. Also, <code class="docutils literal notranslate"><span class="pre">tostype()</span></code> attribute is deprecated.</p></li> |
| <li><p>Users can use <code class="docutils literal notranslate"><span class="pre">as_np_ndarray</span></code> attribute to switch from a legacy NDArray to NumPy ndarray just like this:</p></li> |
| </ol> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">mxnet</span> <span class="k">as</span> <span class="nn">mx</span> |
| <span class="n">nd_array</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">5</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span> |
| <span class="n">np_array</span> <span class="o">=</span> <span class="n">nd_array</span><span class="o">.</span><span class="n">as_np_ndarray</span><span class="p">()</span> |
| </pre></div> |
| </div> |
| <ol class="arabic simple" start="3"> |
| <li><p>Compared with legacy NDArray, some attributes are deprecated in NumPy ndarray. Listed below are some of the deprecated APIs and their corresponding replacements in NumPy ndarray, others can be found in <a class="reference external" href="#NumPy-Array-Deprecated-Attributes">Appendix/NumPy Array Deprecated Attributes</a>.</p></li> |
| </ol> |
| <table class="docutils align-default"> |
| <colgroup> |
| <col style="width: 47%" /> |
| <col style="width: 53%" /> |
| </colgroup> |
| <thead> |
| <tr class="row-odd"><th class="head"><p>Deprecated Attributes</p></th> |
| <th class="head"><p>NumPy ndarray Equivalent</p></th> |
| </tr> |
| </thead> |
| <tbody> |
| <tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">a.asscalar()</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">a.item()</span></code></p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">a.as_in_context()</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">a.to_device()</span></code></p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">a.context</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">a.device</span></code></p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">a.reshape_like(b)</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">a.reshape(b.shape)</span></code></p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">a.zeros_like(b)</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.np.zeros_like(b)</span></code></p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">a.ones_like(b)</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.np.ones_like(b)</span></code></p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p><strong>NOTE</strong></p> |
| <p><code class="docutils literal notranslate"><span class="pre">Context</span></code> class has also been deprecated in MXNet2.0, it is renamed to <code class="docutils literal notranslate"><span class="pre">Device</span></code> and some related methods and attributes are also renamed as above. All the creation functions inside MXNet NumPy package will take <code class="docutils literal notranslate"><span class="pre">device</span></code> as keyword instead of <code class="docutils literal notranslate"><span class="pre">ctx</span></code>.</p> |
| <ol class="arabic simple" start="4"> |
| <li><p>Compared with legacy NDArray, some attributes will have different behaviors and take different inputs.</p></li> |
| </ol> |
| <table class="docutils align-default"> |
| <colgroup> |
| <col style="width: 28%" /> |
| <col style="width: 35%" /> |
| <col style="width: 37%" /> |
| </colgroup> |
| <thead> |
| <tr class="row-odd"><th class="head"><p>Attribute</p></th> |
| <th class="head"><p>Legacy Inputs</p></th> |
| <th class="head"><p>NumPy Inputs</p></th> |
| </tr> |
| </thead> |
| <tbody> |
| <tr class="row-even"><td><div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">a</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </td> |
| <td><p><strong>shape</strong>: Some dimensions of the shape can take special values from the set {0, -1, -2, -3, -4}. The significance of each is explained below: 0 copy this dimension from |
| the input to the output shape. -1 infers the dimension of the output shape by using the remainder of the input dimensions. -2 copy all/remainder of the input dimensions to |
| the output shape. -3 use the product of two consecutive dimensions of the input shape as the output dimension. -4 split one dimension of the input into two dimensions |
| passed subsequent to -4 in shape (can contain -1). <strong>reverse</strong>: If set to 1, then the special values are inferred from right to left</p></td> |
| <td><p><strong>shape</strong>: shape parameter will be <strong>positional argument</strong> rather than key-word argument. Some dimensions of the shape can take special values from the set {-1, -2, -3, -4, -5, -6}. |
| The significance of each is explained below: -1 infers the dimension of the output shape by using the remainder of the input dimensions. -2 copy this dimension from the input to the |
| output shape. -3 skip the current dimension if and only if the current dim size is one. -4 copy all the remaining the input dimensions to the output shape. -5 use the product of two |
| consecutive dimensions of the input shape as the output. -6 split one dimension of the input into two dimensions passed subsequent to -6 in the new shape. <strong>reverse</strong>: No <strong>reverse</strong> |
| parameter for <code class="docutils literal notranslate"><span class="pre">np.reshape</span></code> but for <code class="docutils literal notranslate"><span class="pre">npx.reshape</span></code>. <strong>order</strong>: Read the elements of <code class="docutils literal notranslate"><span class="pre">a</span></code> using this index order, and place the elements into the reshaped array using this index |
| order.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| </div> |
| <div class="section" id="NumPy-and-NumPy-extension-Operators"> |
| <h4>NumPy and NumPy-extension Operators<a class="headerlink" href="#NumPy-and-NumPy-extension-Operators" title="Permalink to this headline">¶</a></h4> |
| <p>Most of the legacy NDArray operators (<code class="docutils literal notranslate"><span class="pre">mx.nd.op</span></code>) have the equivalent ones in np/npx namespace. Users can just replace them with <code class="docutils literal notranslate"><span class="pre">mx.np.op</span></code> or <code class="docutils literal notranslate"><span class="pre">mx.npx.op</span></code> to migrate. Some of the operators will have different inputs and behaviors as listed in the table below.</p> |
| <p><strong>Migration Guide</strong>:</p> |
| <ol class="arabic simple"> |
| <li><p>Operators migration with name/inputs changes</p></li> |
| </ol> |
| <table class="docutils align-default"> |
| <colgroup> |
| <col style="width: 28%" /> |
| <col style="width: 32%" /> |
| <col style="width: 40%" /> |
| </colgroup> |
| <thead> |
| <tr class="row-odd"><th class="head"><p>Legacy Operators</p></th> |
| <th class="head"><p>NumPy Operators Equivalent</p></th> |
| <th class="head"><p>Changes</p></th> |
| </tr> |
| </thead> |
| <tbody> |
| <tr class="row-even"><td><div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">flatten</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </td> |
| <td><div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">mx</span><span class="o">.</span><span class="n">npx</span><span class="o">.</span><span class="n">batch_flatten</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </td> |
| <td><div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">moved</span> <span class="n">to</span> <span class="n">npx</span> <span class="n">namespace</span> <span class="k">with</span> <span class="n">new</span> <span class="n">name</span> <span class="n">batch_flatten</span> |
| </pre></div> |
| </div> |
| </td> |
| </tr> |
| <tr class="row-odd"><td><div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">concat</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </td> |
| <td><div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">mx</span><span class="o">.</span><span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">])</span> |
| </pre></div> |
| </div> |
| </td> |
| <td><div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="o">-</span> <span class="n">moved</span> <span class="n">to</span> <span class="n">np</span> <span class="n">namespace</span> <span class="k">with</span> <span class="n">new</span> <span class="n">name</span> <span class="n">concatenate</span><span class="o">.</span> |
| <span class="o">-</span> <span class="n">use</span> <span class="nb">list</span> <span class="n">of</span> <span class="n">ndarrays</span> <span class="k">as</span> <span class="nb">input</span> <span class="n">rather</span> <span class="n">than</span> <span class="n">positional</span> <span class="n">ndarrays</span> |
| </pre></div> |
| </div> |
| </td> |
| </tr> |
| <tr class="row-even"><td><div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </td> |
| <td><div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">mx</span><span class="o">.</span><span class="n">np</span><span class="o">.</span><span class="n">stack</span><span class="p">([</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">])</span> |
| </pre></div> |
| </div> |
| </td> |
| <td><div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="o">-</span> <span class="n">moved</span> <span class="n">to</span> <span class="n">np</span> <span class="n">namespace</span><span class="o">.</span> |
| <span class="o">-</span> <span class="n">use</span> <span class="nb">list</span> <span class="n">of</span> <span class="n">ndarrays</span> <span class="k">as</span> <span class="nb">input</span> <span class="n">rather</span> <span class="n">than</span> <span class="n">positional</span> <span class="n">ndarrays</span> |
| </pre></div> |
| </div> |
| </td> |
| </tr> |
| <tr class="row-odd"><td><div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">SliceChannel</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </td> |
| <td><div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">mx</span><span class="o">.</span><span class="n">npx</span><span class="o">.</span><span class="n">slice_channel</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </td> |
| <td><div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">moved</span> <span class="n">to</span> <span class="n">npx</span> <span class="n">namespace</span> <span class="k">with</span> <span class="n">new</span> <span class="n">name</span> <span class="n">slice_channel</span><span class="o">.</span> |
| </pre></div> |
| </div> |
| </td> |
| </tr> |
| <tr class="row-even"><td><div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">FullyConnected</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </td> |
| <td><div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">mx</span><span class="o">.</span><span class="n">npx</span><span class="o">.</span><span class="n">fully_connected</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </td> |
| <td><div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">moved</span> <span class="n">to</span> <span class="n">npx</span> <span class="n">namespace</span> <span class="k">with</span> <span class="n">new</span> <span class="n">name</span> <span class="n">fully_connected</span><span class="o">.</span> |
| </pre></div> |
| </div> |
| </td> |
| </tr> |
| <tr class="row-odd"><td><div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">Activation</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </td> |
| <td><div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">mx</span><span class="o">.</span><span class="n">npx</span><span class="o">.</span><span class="n">activation</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </td> |
| <td><div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">moved</span> <span class="n">to</span> <span class="n">npx</span> <span class="n">namespace</span> <span class="k">with</span> <span class="n">new</span> <span class="n">name</span> <span class="n">activation</span><span class="o">.</span> |
| </pre></div> |
| </div> |
| </td> |
| </tr> |
| <tr class="row-even"><td><div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">elemwise_add</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </td> |
| <td><div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">a</span> <span class="o">+</span> <span class="n">b</span> |
| </pre></div> |
| </div> |
| </td> |
| <td><div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Just</span> <span class="n">use</span> <span class="n">ndarray</span> <span class="n">python</span> <span class="n">operator</span><span class="o">.</span> |
| </pre></div> |
| </div> |
| </td> |
| </tr> |
| <tr class="row-odd"><td><div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">elemwise_mul</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </td> |
| <td><div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">mx</span><span class="o">.</span><span class="n">np</span><span class="o">.</span><span class="n">multiply</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </td> |
| <td><div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Use</span> <span class="n">multiply</span> <span class="n">operator</span> <span class="ow">in</span> <span class="n">np</span> <span class="n">namespace</span><span class="o">.</span> |
| </pre></div> |
| </div> |
| </td> |
| </tr> |
| </tbody> |
| </table> |
| <ol class="arabic simple" start="2"> |
| <li><p>Operators migration with multiple steps: <code class="docutils literal notranslate"><span class="pre">mx.nd.mean</span></code> -> <code class="docutils literal notranslate"><span class="pre">mx.np.mean</span></code>:</p></li> |
| </ol> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">mxnet</span> <span class="k">as</span> <span class="nn">mx</span> |
| <span class="c1"># Legacy: calculate mean value with reduction on axis 1</span> |
| <span class="c1"># with `exclude` option on</span> |
| <span class="n">nd_mean</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">exclude</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> |
| |
| <span class="c1"># Numpy: no exclude option to users, but user can perform steps as follow</span> |
| <span class="n">axes</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">ndim</span><span class="p">))</span> |
| <span class="k">del</span> <span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> |
| <span class="n">np_mean</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">axes</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <ol class="arabic simple" start="3"> |
| <li><p>Random Operators</p></li> |
| </ol> |
| <table class="docutils align-default"> |
| <colgroup> |
| <col style="width: 28%" /> |
| <col style="width: 32%" /> |
| <col style="width: 40%" /> |
| </colgroup> |
| <thead> |
| <tr class="row-odd"><th class="head"><p>Legacy Operators</p></th> |
| <th class="head"><p>NumPy Operators Equivalent</p></th> |
| <th class="head"><p>Changes</p></th> |
| </tr> |
| </thead> |
| <tbody> |
| <tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">mx.random.uniform(-1.0,</span> <span class="pre">1.0,</span> <span class="pre">shape=(2,</span> <span class="pre">3))</span></code> <code class="docutils literal notranslate"><span class="pre">mx.nd.random.uniform(-1.0,</span> <span class="pre">1.0,</span> <span class="pre">shape=(2,</span> <span class="pre">3))</span></code></p></td> |
| <td><div class="highlight-default notranslate"><div class="highlight"><pre><span></span>`mx.np.random.uniform(-1.0, 1.0, size=(2, 3))` |
| </pre></div> |
| </div> |
| </td> |
| <td><p>For all the NumPy random operators, use <strong>size</strong> keyword instead of <strong>shape</strong></p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">mx.nd.random.multinomial(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><div class="highlight-default notranslate"><div class="highlight"><pre><span></span>`mx.npx.random.categorical(*args, **kwargs)` |
| </pre></div> |
| </div> |
| </td> |
| <td><p>use <code class="docutils literal notranslate"><span class="pre">npx.random.categorical</span></code> to have the behavior of drawing 1 sample from multiple distributions.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <ol class="arabic simple" start="4"> |
| <li><p>Control Flow Operators</p></li> |
| </ol> |
| <table class="docutils align-default"> |
| <colgroup> |
| <col style="width: 29%" /> |
| <col style="width: 29%" /> |
| <col style="width: 42%" /> |
| </colgroup> |
| <thead> |
| <tr class="row-odd"><th class="head"><p>Legacy Operators</p></th> |
| <th class="head"><p>NumPy Operators Equivalent</p></th> |
| <th class="head"><p>Changes</p></th> |
| </tr> |
| </thead> |
| <tbody> |
| <tr class="row-even"><td><div class="highlight-default notranslate"><div class="highlight"><pre><span></span>`mx.nd.contrib.foreach(body, data, init_states, name)` |
| </pre></div> |
| </div> |
| </td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.npx.foreach(body,</span> <span class="pre">data,</span> <span class="pre">init_states,</span> <span class="pre">name)</span></code></p></td> |
| <td><ul class="simple"> |
| <li><p>moved to <code class="docutils literal notranslate"><span class="pre">npx</span></code> namespace.</p></li> |
| <li><p>Will not support global variables as body’s inputs(body’s inputs must be either data or states or both)</p></li> |
| </ul> |
| </td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">mx.nd.contrib.while_loop(cond,</span> <span class="pre">func,</span> <span class="pre">loop_vars,</span> <span class="pre">max_iterations,</span> <span class="pre">name)</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.npx.while_loop(cond,</span> <span class="pre">func,</span> <span class="pre">loop_vars,</span> <span class="pre">max_iterations,</span> <span class="pre">name)</span></code></p></td> |
| <td><ul class="simple"> |
| <li><p>moved to <code class="docutils literal notranslate"><span class="pre">npx</span></code> namespace.</p></li> |
| <li><p>Will not support global variables as cond or func’s inputs(cond or func’s inputs must be in loop_vars)</p></li> |
| </ul> |
| </td> |
| </tr> |
| <tr class="row-even"><td><div class="highlight-default notranslate"><div class="highlight"><pre><span></span>`mx.nd.contrib.cond(pred, then_func, else_func, inputs, name)` |
| </pre></div> |
| </div> |
| </td> |
| <td><div class="highlight-default notranslate"><div class="highlight"><pre><span></span>`mx.npx.cond(pred, then_func, else_func, name)` |
| </pre></div> |
| </div> |
| </td> |
| <td><ul class="simple"> |
| <li><p>moved to <code class="docutils literal notranslate"><span class="pre">npx</span></code> namespace.</p></li> |
| <li><p>users needs to provide the inputs of pred, then_func and else_func as inputs</p></li> |
| <li><p>Will not support global variables as pred, then_func or else_func’s inputs(pred, then_func or else_func’s inputs must be in inputs)</p></li> |
| </ul> |
| </td> |
| </tr> |
| </tbody> |
| </table> |
| <ol class="arabic simple" start="5"> |
| <li><p>Functionalities</p></li> |
| </ol> |
| <table class="docutils align-default"> |
| <colgroup> |
| <col style="width: 28%" /> |
| <col style="width: 32%" /> |
| <col style="width: 40%" /> |
| </colgroup> |
| <thead> |
| <tr class="row-odd"><th class="head"><p>Legacy Operators</p></th> |
| <th class="head"><p>NumPy Operators Equivalent</p></th> |
| <th class="head"><p>Changes</p></th> |
| </tr> |
| </thead> |
| <tbody> |
| <tr class="row-even"><td><div class="highlight-default notranslate"><div class="highlight"><pre><span></span>`mx.nd.save(*args, **kwargs)` |
| </pre></div> |
| </div> |
| </td> |
| <td><div class="highlight-default notranslate"><div class="highlight"><pre><span></span>`mx.npx.savez(*args, **kwargs)` |
| </pre></div> |
| </div> |
| </td> |
| <td><ul class="simple"> |
| <li><p>moved to <code class="docutils literal notranslate"><span class="pre">npx</span></code> namespace.</p></li> |
| <li><p>Only accept positional arguments, try to flatten the list/dict before feed in</p></li> |
| </ul> |
| </td> |
| </tr> |
| <tr class="row-odd"><td><div class="highlight-default notranslate"><div class="highlight"><pre><span></span>`mx.nd.load(*args, **kwargs)` |
| </pre></div> |
| </div> |
| </td> |
| <td><div class="highlight-default notranslate"><div class="highlight"><pre><span></span>`mx.npx.load(*args, **kwargs)` |
| </pre></div> |
| </div> |
| </td> |
| <td><ul class="simple"> |
| <li><p>moved to <code class="docutils literal notranslate"><span class="pre">npx</span></code> namespace.</p></li> |
| </ul> |
| </td> |
| </tr> |
| <tr class="row-even"><td><div class="highlight-default notranslate"><div class="highlight"><pre><span></span>`mx.nd.waitall()` |
| </pre></div> |
| </div> |
| </td> |
| <td><div class="highlight-default notranslate"><div class="highlight"><pre><span></span>`mx.npx.waitall()` |
| </pre></div> |
| </div> |
| </td> |
| <td><ul class="simple"> |
| <li><p>moved to <code class="docutils literal notranslate"><span class="pre">npx</span></code> namespace.</p></li> |
| </ul> |
| </td> |
| </tr> |
| </tbody> |
| </table> |
| <p>Other operator changes are included in <a class="reference external" href="#NumPy-and-NumPy-extension-Operators1">Appendix/NumPy and NumPy-extension Operators</a></p> |
| </div> |
| </div> |
| <div class="section" id="Layers-and-Blocks"> |
| <h3>Layers and Blocks<a class="headerlink" href="#Layers-and-Blocks" title="Permalink to this headline">¶</a></h3> |
| <p>With the deferred compute and tracing being introduced in Gluon2.0, users do not need to interact with symbols any more. There are a lot of changes in building a model with Gluon API, including parameter management and naming, forward pass computing and parameter shape inferencing. We provide step-by-step migration guidance on how to build a model with new APIs.</p> |
| <div class="section" id="Parameter-Management-and-Block-Naming"> |
| <h4>Parameter Management and Block Naming<a class="headerlink" href="#Parameter-Management-and-Block-Naming" title="Permalink to this headline">¶</a></h4> |
| <p>In Gluon, each Parameter or Block has a name (and prefix). Parameter names are specified by users and Block names can be either specified by users or automatically created. In Gluon 1.x, parameters are accessed via the <code class="docutils literal notranslate"><span class="pre">params</span></code> variable of the <code class="docutils literal notranslate"><span class="pre">ParameterDict</span></code> in <code class="docutils literal notranslate"><span class="pre">Block</span></code>. Users will need to manually use <code class="docutils literal notranslate"><span class="pre">with</span> <span class="pre">self.name_scope():</span></code> for children blocks and specify prefix for the top level block. Otherwise, it will lead to wrong name scopes and can return parameters of children blocks that are |
| not in the current name scope. An example for initializing the Block and Parameter in Gluon 1.x:</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">mxnet.gluon</span> <span class="kn">import</span> <span class="n">Parameter</span><span class="p">,</span> <span class="n">Constant</span><span class="p">,</span> <span class="n">HybridBlock</span> |
| <span class="k">class</span> <span class="nc">SampleBlock</span><span class="p">(</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">SampleBlock</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="k">with</span> <span class="bp">self</span><span class="o">.</span><span class="n">name_scope</span><span class="p">():</span> |
| <span class="c1"># Access parameters, which are iterated during training</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">weight</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">params</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'weight'</span><span class="p">)</span> |
| <span class="c1"># Access constant parameters, which are not iterated during training</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">weight</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">params</span><span class="o">.</span><span class="n">get_constant</span><span class="p">(</span><span class="s1">'const'</span><span class="p">,</span> <span class="n">const_arr</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>Now in Gluon 2.0, Block/HybridBlock objects will not maintain the parameter dictionary (<code class="docutils literal notranslate"><span class="pre">ParameterDict</span></code>). Instead, users can access these parameters via <code class="docutils literal notranslate"><span class="pre">Parameter</span></code> class and <code class="docutils literal notranslate"><span class="pre">Constant</span></code> class. These parameters will be registered automatically as part of the Block. Users will no longer need to manage the name scope for children blocks and hence can remove <code class="docutils literal notranslate"><span class="pre">with</span> <span class="pre">self.name_scope():</span></code> this statement. For example:</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">SampleBlock</span><span class="p">(</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">SampleBlock</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="c1"># Access parameters, which are iterated during training</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">weight</span> <span class="o">=</span> <span class="n">Parameter</span><span class="p">(</span><span class="s1">'weight'</span><span class="p">)</span> |
| <span class="c1"># Access constant parameters, which are not iterated during training</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">weight</span> <span class="o">=</span> <span class="n">Constant</span><span class="p">(</span><span class="s1">'const'</span><span class="p">,</span> <span class="n">const_arr</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>Also, there will be new mechanisms for parameter loading, sharing and setting device.</p> |
| <ol class="arabic simple"> |
| <li><p>Parameter loading in Gluon 1.x vs Gluon 2.0:</p></li> |
| </ol> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># in Gluon 1.x</span> |
| <span class="n">net</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">8</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">net</span><span class="o">.</span><span class="n">collect_params</span><span class="p">()</span><span class="o">.</span><span class="n">load_dict</span><span class="p">(</span><span class="n">arg_dict</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">ctx</span><span class="p">)</span> |
| <span class="c1"># in Gluon 2.0</span> |
| <span class="n">net</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">8</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">net</span><span class="o">.</span><span class="n">load_dict</span><span class="p">(</span><span class="n">arg_dict</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <ol class="arabic simple" start="2"> |
| <li><p>Parameter sharing in Gluon 1.x vs Gluon 2.0:</p></li> |
| </ol> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># in Gluon 1.x</span> |
| <span class="n">shared</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">8</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">net</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">8</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">params</span><span class="o">=</span><span class="n">shared</span><span class="o">.</span><span class="n">params</span><span class="p">)</span> |
| <span class="c1"># in Gluon 2.0</span> |
| <span class="n">shared</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">8</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">net</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">8</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="o">.</span><span class="n">share_parameters</span><span class="p">(</span><span class="n">shared</span><span class="o">.</span><span class="n">params</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <ol class="arabic simple" start="3"> |
| <li><p>Parameter setting device in Gluon 1.x vs Gluon 2.0:</p></li> |
| </ol> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># in Gluon 1.x</span> |
| <span class="n">net</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">8</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">net</span><span class="o">.</span><span class="n">collect_params</span><span class="p">()</span><span class="o">.</span><span class="n">reset_ctx</span><span class="p">(</span><span class="n">devices</span><span class="p">)</span> |
| <span class="c1"># in Gluon 2.0</span> |
| <span class="n">net</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">8</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">net</span><span class="o">.</span><span class="n">reset_device</span><span class="p">(</span><span class="n">devices</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="Forward-Interface"> |
| <h4>Forward Interface<a class="headerlink" href="#Forward-Interface" title="Permalink to this headline">¶</a></h4> |
| <p><code class="docutils literal notranslate"><span class="pre">hybrid_forward</span></code> interface in Gluon1.x provides the user with a unified imperative and symbolic programming interface to do graph construction and imperative execution. For the inputs of <code class="docutils literal notranslate"><span class="pre">hybrid_forward</span></code>, <code class="docutils literal notranslate"><span class="pre">F</span></code> can be either mx.symbol or mx.ndarray depending on the running mode(symbolic or imperative) of variable recording. Apart from <code class="docutils literal notranslate"><span class="pre">F</span></code> and input arrays, the parameters registered when Block is initialized are also required as part of the inputs. Take <code class="docutils literal notranslate"><span class="pre">nn.Dense</span></code> as an example:</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># hybrid_forward interface, F can be either symbol or ndarray, weights</span> |
| <span class="c1"># and bias are part of inputs</span> |
| <span class="k">def</span> <span class="nf">hybrid_forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">F</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="n">fc</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">npx</span><span class="o">.</span><span class="n">fully_connected</span> <span class="k">if</span> <span class="n">is_np_array</span><span class="p">()</span> <span class="k">else</span> <span class="n">F</span><span class="o">.</span><span class="n">FullyConnected</span> |
| <span class="n">act</span> <span class="o">=</span> <span class="n">fc</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">bias</span><span class="p">,</span> <span class="n">no_bias</span><span class="o">=</span><span class="n">bias</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">,</span> <span class="n">num_hidden</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_units</span><span class="p">,</span> |
| <span class="n">flatten</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">name</span><span class="o">=</span><span class="s1">'fwd'</span><span class="p">)</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">act</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">act</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">act</span><span class="p">(</span><span class="n">act</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">act</span> |
| </pre></div> |
| </div> |
| <p>Now, in deferred computation mode of Gluon2.0, the divergence of NDArray and Symbol is unified, which means users no longer need to define <code class="docutils literal notranslate"><span class="pre">F</span></code> with specific running mode. One can easily specify the computation through imperative programming, hybridization will work through the tracing mechanism(data flow of the first input batch). What’s more, users can implement the forward interface with <code class="docutils literal notranslate"><span class="pre">npx/npx</span></code> operators instead of <code class="docutils literal notranslate"><span class="pre">nd</span></code> and <code class="docutils literal notranslate"><span class="pre">symbol</span></code>.</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># forward interface, no F any more</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">x</span><span class="p">):</span> |
| <span class="c1"># get the device information of input array and make parameters run on the same device</span> |
| <span class="n">device</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">device</span> |
| <span class="c1"># use np/npx interfaces instead of F</span> |
| <span class="n">act</span> <span class="o">=</span> <span class="n">npx</span><span class="o">.</span><span class="n">fully_connected</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="n">device</span><span class="p">),</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">bias</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="n">device</span><span class="p">)</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">bias</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">no_bias</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">bias</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">num_hidden</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_units</span><span class="p">,</span> <span class="n">flatten</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">name</span><span class="o">=</span><span class="s1">'fwd'</span><span class="p">)</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">act</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">act</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">act</span><span class="p">(</span><span class="n">act</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">act</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="Implement-Infer-Shape"> |
| <h4>Implement Infer Shape<a class="headerlink" href="#Implement-Infer-Shape" title="Permalink to this headline">¶</a></h4> |
| <p>In Gluon1.x, parameter shape inference happens in MXNet backend. Now in Gluon2.0, shape inference is disabled in the case of deferred parameter initialization. So, users should now always implement <code class="docutils literal notranslate"><span class="pre">infer_shape</span></code> method to set the parameter shapes if the parameter shape was not set during HybridBlock initialization.</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">infer_shape</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">):</span> |
| <span class="c1"># if true, self.weight.shape[1] will be flattened of input's shape</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_flatten</span><span class="p">:</span> |
| <span class="n">num_input</span> <span class="o">=</span> <span class="mi">1</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="mi">1</span><span class="p">,</span> <span class="n">x</span><span class="o">.</span><span class="n">ndim</span><span class="p">):</span> |
| <span class="n">num_input</span> <span class="o">*=</span> <span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">num_input</span><span class="p">)</span> |
| <span class="c1"># if false, self.weight.shape[1] = x.shape[-1]</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="n">x</span><span class="o">.</span><span class="n">ndim</span> <span class="o">-</span> <span class="mi">1</span><span class="p">])</span> |
| </pre></div> |
| </div> |
| <p>Now, in Gluon2.0, users can implement a Dense Block like this:</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">Dense</span><span class="p">(</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="n">units</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">use_bias</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">flatten</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> |
| <span class="n">dtype</span><span class="o">=</span><span class="s1">'float32'</span><span class="p">,</span> <span class="n">weight_initializer</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">bias_initializer</span><span class="o">=</span><span class="s1">'zeros'</span><span class="p">,</span> |
| <span class="n">in_units</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">Dense</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="o">**</span><span class="n">kwargs</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">flatten</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_units</span> <span class="o">=</span> <span class="n">units</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_in_units</span> <span class="o">=</span> <span class="n">in_units</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">weight</span> <span class="o">=</span> <span class="n">Parameter</span><span class="p">(</span><span class="s1">'weight'</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="n">units</span><span class="p">,</span> <span class="n">in_units</span><span class="p">),</span> |
| <span class="n">init</span><span class="o">=</span><span class="n">weight_initializer</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">,</span> |
| <span class="n">allow_deferred_init</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">use_bias</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">bias</span> <span class="o">=</span> <span class="n">Parameter</span><span class="p">(</span><span class="s1">'bias'</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="n">units</span><span class="p">,),</span> |
| <span class="n">init</span><span class="o">=</span><span class="n">bias_initializer</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">,</span> |
| <span class="n">allow_deferred_init</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">bias</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="k">if</span> <span class="n">activation</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">act</span> <span class="o">=</span> <span class="n">Activation</span><span class="p">(</span><span class="n">activation</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">act</span> <span class="o">=</span> <span class="kc">None</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">x</span><span class="p">):</span> |
| <span class="n">device</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">device</span> |
| <span class="n">act</span> <span class="o">=</span> <span class="n">npx</span><span class="o">.</span><span class="n">fully_connected</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="n">device</span><span class="p">),</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">bias</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="n">device</span><span class="p">)</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">bias</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">no_bias</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">bias</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">num_hidden</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_units</span><span class="p">,</span> <span class="n">flatten</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">name</span><span class="o">=</span><span class="s1">'fwd'</span><span class="p">)</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">act</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">act</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">act</span><span class="p">(</span><span class="n">act</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">act</span> |
| |
| <span class="k">def</span> <span class="nf">infer_shape</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">):</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_flatten</span><span class="p">:</span> |
| <span class="n">num_input</span> <span class="o">=</span> <span class="mi">1</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="mi">1</span><span class="p">,</span> <span class="n">x</span><span class="o">.</span><span class="n">ndim</span><span class="p">):</span> |
| <span class="n">num_input</span> <span class="o">*=</span> <span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">num_input</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="n">x</span><span class="o">.</span><span class="n">ndim</span> <span class="o">-</span> <span class="mi">1</span><span class="p">])</span> |
| </pre></div> |
| </div> |
| </div> |
| </div> |
| </div> |
| <div class="section" id="Optimizers"> |
| <h2>Optimizers<a class="headerlink" href="#Optimizers" title="Permalink to this headline">¶</a></h2> |
| <p>Optimizer module in MXNet provides a lot of optimization algorithms to reduce the training error. In Gluon 2.0, optimizers will also switch to use MXNet NumPy-compatible interface. Some important changes that needs attention are:</p> |
| <ol class="arabic simple"> |
| <li><p>AdaGrad:</p> |
| <ul class="simple"> |
| <li><p>use <code class="docutils literal notranslate"><span class="pre">epsilon</span></code> instead of <code class="docutils literal notranslate"><span class="pre">eps</span></code></p></li> |
| <li><p>e.g. <code class="docutils literal notranslate"><span class="pre">adagrad_optimizer</span> <span class="pre">=</span> <span class="pre">optimizer.AdaGrad(learning_rate=0.1,</span> <span class="pre">epsilon=1e-07)</span></code></p></li> |
| </ul> |
| </li> |
| <li><p>RMSProp:</p> |
| <ul class="simple"> |
| <li><p>use <code class="docutils literal notranslate"><span class="pre">rho</span></code> instead of <code class="docutils literal notranslate"><span class="pre">gamma1</span></code> and use <code class="docutils literal notranslate"><span class="pre">momentum</span></code> instead of <code class="docutils literal notranslate"><span class="pre">gamma2</span></code></p></li> |
| <li><p>e.g. <code class="docutils literal notranslate"><span class="pre">rmsprop_optimizer</span> <span class="pre">=</span> <span class="pre">optimizer.RMSProp(learning_rate=0.001,</span> <span class="pre">rho=0.9,</span> <span class="pre">momentum=0.9,</span> <span class="pre">epsilon=1e-07,</span> <span class="pre">centered=False)</span></code></p></li> |
| </ul> |
| </li> |
| <li><p><code class="docutils literal notranslate"><span class="pre">optimizer.ccSGD</span></code> and <code class="docutils literal notranslate"><span class="pre">optimizer.LBSGD</span></code> are deprecated.</p></li> |
| </ol> |
| </div> |
| <div class="section" id="Metrics"> |
| <h2>Metrics<a class="headerlink" href="#Metrics" title="Permalink to this headline">¶</a></h2> |
| <p>Metrics module in MXNet provides different methods for users to judge the performance of models. In Gluon 2.0, metrics will use MXNet NumPy-compatible interface and also introduce a lot of new evaluation metrics. <strong>Changes</strong>: 1. metric module has been moved to gluon namespace - <code class="docutils literal notranslate"><span class="pre">mxnet.metric</span></code> -> <code class="docutils literal notranslate"><span class="pre">mxnet.gluon.metric</span></code></p> |
| <ol class="arabic simple" start="2"> |
| <li><p>Add new evaluation metrics:</p> |
| <ul class="simple"> |
| <li><p><code class="docutils literal notranslate"><span class="pre">Class</span> <span class="pre">BinaryAccuracy(threshold=0.5)</span></code></p></li> |
| <li><p><code class="docutils literal notranslate"><span class="pre">Class</span> <span class="pre">MeanCosineSimilarity(axis=-1,</span> <span class="pre">eps=1e-12)</span></code></p></li> |
| <li><p><code class="docutils literal notranslate"><span class="pre">Class</span> <span class="pre">MeanPairwiseDistance(p=2)</span></code></p></li> |
| <li><p><code class="docutils literal notranslate"><span class="pre">Class</span> <span class="pre">Fbeta(class_type="binary",</span> <span class="pre">beta=1,</span> <span class="pre">threshold=0.5,</span> <span class="pre">average="micro")</span></code></p></li> |
| </ul> |
| </li> |
| <li><p>Improve Class F1</p> |
| <ul class="simple"> |
| <li><p><code class="docutils literal notranslate"><span class="pre">Class</span> <span class="pre">F1(name='f1',output_names=None,</span> <span class="pre">label_names=None,</span> <span class="pre">average="macro")</span></code> to <code class="docutils literal notranslate"><span class="pre">Class</span> <span class="pre">F1(name='f1',output_names=None,</span> <span class="pre">label_names=None,</span> <span class="pre">class_type="binary",</span> <span class="pre">threshold=0.5,</span> <span class="pre">average="micro")</span></code></p></li> |
| <li><p><strong>average</strong>: Strategy to be used for aggregating across mini-batches.</p> |
| <ul> |
| <li><p>“macro”: Calculate metrics for each label and return unweighted mean of f1.</p></li> |
| <li><p>“micro”: Calculate metrics globally by counting the total TP, FN and FP.</p></li> |
| <li><p>None: Return f1 scores for each class (numpy.ndarray).</p></li> |
| </ul> |
| </li> |
| <li><p><strong>class_type</strong>:</p> |
| <ul> |
| <li><p>“binary”: f1 for binary classification.</p></li> |
| <li><p>“multiclass”: f1 for multiclassification problem.</p></li> |
| <li><p>“multilabel”: f1 for multilabel classification.</p></li> |
| </ul> |
| </li> |
| <li><p><strong>threshold</strong>: threshold for postive confidence value.</p></li> |
| </ul> |
| </li> |
| </ol> |
| </div> |
| <div class="section" id="Key-Value-Store"> |
| <h2>Key-Value Store<a class="headerlink" href="#Key-Value-Store" title="Permalink to this headline">¶</a></h2> |
| <p>Gluon 2.0 will provide a new and unified low level API for data parallel training. These unified APIs can support different communication backends, including native Parameter Server, Horovod and BytePS. Example:</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">mxnet</span> <span class="k">as</span> <span class="nn">mx</span> |
| <span class="c1"># create key-value store with horovod backend</span> |
| <span class="n">kv</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">kv</span><span class="o">.</span><span class="n">create</span><span class="p">(</span><span class="s1">'horovod'</span><span class="p">)</span> <span class="c1"># or choose 'kvstore', 'byteps' as backend</span> |
| <span class="n">device</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">gpu</span><span class="p">(</span><span class="n">kv</span><span class="o">.</span><span class="n">local_rank</span><span class="p">)</span> <span class="k">if</span> <span class="n">mx</span><span class="o">.</span><span class="n">device</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">mx</span><span class="o">.</span><span class="n">cpu</span><span class="p">(</span><span class="n">kv</span><span class="o">.</span><span class="n">local_rank</span><span class="p">)</span> |
| <span class="n">val</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">)</span> |
| <span class="c1"># broadcast the value at rank 0 to all ranks</span> |
| <span class="n">kv</span><span class="o">.</span><span class="n">broadcast</span><span class="p">(</span><span class="s1">'0'</span><span class="p">,</span> <span class="n">mx</span><span class="o">.</span><span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">),</span> <span class="n">out</span><span class="o">=</span><span class="n">val</span><span class="p">)</span> |
| <span class="n">scale</span> <span class="o">=</span> <span class="n">kv</span><span class="o">.</span><span class="n">rank</span> <span class="o">+</span> <span class="mi">1</span> |
| <span class="c1"># performs allreduce on a single array</span> |
| <span class="n">kv</span><span class="o">.</span><span class="n">pushpull</span><span class="p">(</span><span class="s1">'3'</span><span class="p">,</span> <span class="n">val</span> <span class="o">*</span> <span class="n">scale</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="Probability"> |
| <h2>Probability<a class="headerlink" href="#Probability" title="Permalink to this headline">¶</a></h2> |
| <p>A new module called <code class="docutils literal notranslate"><span class="pre">mxnet.gluon.probability</span></code> has been introduced in Gluon 2.0. It is analogous to pytorch distribution and the main difference is that <code class="docutils literal notranslate"><span class="pre">mxnet.gluon.probability</span></code> will use MXNet NumPy compatible operators and will allow hybridization. It has three parts:</p> |
| <ol class="arabic simple"> |
| <li><p><a class="reference external" href="https://github.com/apache/incubator-mxnet/tree/master/python/mxnet/gluon/probability/distributions">Distribution Objects</a>: <code class="docutils literal notranslate"><span class="pre">gluon.probability.Bernoulli</span></code>, <code class="docutils literal notranslate"><span class="pre">gluon.probability.Beta</span></code> …</p></li> |
| <li><p><a class="reference external" href="https://github.com/apache/incubator-mxnet/tree/master/python/mxnet/gluon/probability/block">StochasticBlock</a>: support accumulating loss in the forward phase, which is useful in building Bayesian Neural Network.</p></li> |
| <li><p><a class="reference external" href="https://github.com/apache/incubator-mxnet/tree/master/python/mxnet/gluon/probability/transformation">Transformation</a>: implement invertible transformation with computable log det jacobians.</p></li> |
| </ol> |
| </div> |
| <div class="section" id="oneDNN-Integration"> |
| <h2>oneDNN Integration<a class="headerlink" href="#oneDNN-Integration" title="Permalink to this headline">¶</a></h2> |
| <div class="section" id="Operator-Fusion"> |
| <h3>Operator Fusion<a class="headerlink" href="#Operator-Fusion" title="Permalink to this headline">¶</a></h3> |
| <p>In versions 1.x of MXNet pattern fusion in execution graph was enabled by default when using MXNet built with oneDNN library support and could have been disabled by setting ‘MXNET_SUBGRAPH_BACKEND’ environment flag to <code class="docutils literal notranslate"><span class="pre">None</span></code>. MXNet 2.0 introduced changes in forward inference flow which led to refactor of fusion mechanism. To fuse model in MXNet 2.0 there are two requirements:</p> |
| <ul class="simple"> |
| <li><p>the model must be defined as a subclass of HybridBlock or Symbol,</p></li> |
| <li><p>the model must have specific operator patterns which can be fused.</p></li> |
| </ul> |
| <p>Both HybridBlock and Symbol classes provide API to easily run fusion of operators. Adding only one line of code is needed to run fusion passes on model:</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># on HybridBlock</span> |
| <span class="n">net</span><span class="o">.</span><span class="n">optimize_for</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">backend</span><span class="o">=</span><span class="s1">'ONEDNN'</span><span class="p">)</span> |
| <span class="c1"># on Symbol</span> |
| <span class="n">optimized_symbol</span> <span class="o">=</span> <span class="n">sym</span><span class="o">.</span><span class="n">optimize_for</span><span class="p">(</span><span class="n">backend</span><span class="o">=</span><span class="s1">'ONEDNN'</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>Controling which patterns should be fused still can be done by setting proper environment variables. See <a class="reference external" href="#oneDNN-Environment-Variables">oneDNN Environment Variables</a></p> |
| </div> |
| <div class="section" id="INT8-Quantization-/-Precision-reduction"> |
| <h3>INT8 Quantization / Precision reduction<a class="headerlink" href="#INT8-Quantization-/-Precision-reduction" title="Permalink to this headline">¶</a></h3> |
| <p>Quantization API was also refactored to be consistent with other new features and mechanisms. In comparison to MXNet 1.x releases, in MXNet 2.0 <code class="docutils literal notranslate"><span class="pre">quantize_net_v2</span></code> function has been removed and development focused mainly on <code class="docutils literal notranslate"><span class="pre">quantize_net</span></code> function to make it easier to use for end user and ultimately give him more flexibility. Quantization can be performed on either subclass of HybridBlock with <code class="docutils literal notranslate"><span class="pre">quantize_net</span></code> or Symbol with deprecated <code class="docutils literal notranslate"><span class="pre">quantize_model</span></code> (<code class="docutils literal notranslate"><span class="pre">quantize_model</span></code> is left only to |
| provide backward compatibility and its usage is strongly discouraged).</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">mxnet</span> <span class="k">as</span> <span class="nn">mx</span> |
| <span class="kn">from</span> <span class="nn">mxnet.contrib.quantization</span> <span class="kn">import</span> <span class="n">quantize_net</span> |
| <span class="kn">from</span> <span class="nn">mxnet.gluon.model_zoo.vision</span> <span class="kn">import</span> <span class="n">resnet50_v1</span> |
| |
| <span class="c1"># load model</span> |
| <span class="n">net</span> <span class="o">=</span> <span class="n">resnet50_v1</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| |
| <span class="c1"># prepare calibration data</span> |
| <span class="n">dummy_data</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="o">-</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="p">(</span><span class="n">batch_size</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">))</span> |
| <span class="n">calib_data_loader</span> <span class="o">=</span> <span class="n">mx</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">dummy_data</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="c1"># quantization</span> |
| <span class="n">qnet</span> <span class="o">=</span> <span class="n">quantize_net</span><span class="p">(</span><span class="n">net</span><span class="p">,</span> <span class="n">calib_mode</span><span class="o">=</span><span class="s1">'naive'</span><span class="p">,</span> <span class="n">calib_data</span><span class="o">=</span><span class="n">calib_data_loader</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p><code class="docutils literal notranslate"><span class="pre">quantize_net</span></code> can be much more complex - all function attributes can be found in the <a class="reference internal" href="../../api/contrib/quantization/index.html"><span class="doc">API</span></a>.</p> |
| </div> |
| <div class="section" id="oneDNN-Environment-Variables"> |
| <h3>oneDNN Environment Variables<a class="headerlink" href="#oneDNN-Environment-Variables" title="Permalink to this headline">¶</a></h3> |
| <p>In version 2.0 of MXNet all references to MKLDNN (former name of oneDNN) were replaced by ONEDNN. Below table lists all environment variables:</p> |
| <table class="docutils align-default"> |
| <colgroup> |
| <col style="width: 50%" /> |
| <col style="width: 50%" /> |
| </colgroup> |
| <thead> |
| <tr class="row-odd"><th class="head"><p>MXNet 1.x</p></th> |
| <th class="head"><p>MXNet 2.0</p></th> |
| </tr> |
| </thead> |
| <tbody> |
| <tr class="row-even"><td><p>MXNET_MKLDNN_ENABLED</p></td> |
| <td><p>MXNET_ONEDNN_ENABLED</p></td> |
| </tr> |
| <tr class="row-odd"><td><p>MXNET_MKLDNN_CACHE_NUM</p></td> |
| <td><p>MXNET_ONEDNN_CACHE_NUM</p></td> |
| </tr> |
| <tr class="row-even"><td><p>MXNET_MKLDNN_FORCE_FC_AB_FORMAT</p></td> |
| <td><p>MXNET_ONEDNN_FORCE_FC_AB_FORMAT</p></td> |
| </tr> |
| <tr class="row-odd"><td><p>MXNET_MKLDNN_ENABLED</p></td> |
| <td><p>MXNET_ONEDNN_ENABLED</p></td> |
| </tr> |
| <tr class="row-even"><td><p>MXNET_MKLDNN_DEBUG</p></td> |
| <td><p>MXNET_ONEDNN_DEBUG</p></td> |
| </tr> |
| <tr class="row-odd"><td><p>MXNET_USE_MKLDNN_RNN</p></td> |
| <td><p>MXNET_USE_ONEDNN_RNN</p></td> |
| </tr> |
| <tr class="row-even"><td><p>MXNET_DISABLE_MKLDNN_CONV_OPT</p></td> |
| <td><p>MXNET_DISABLE_ONEDNN_CONV_OPT</p></td> |
| </tr> |
| <tr class="row-odd"><td><p>MXNET_DISABLE_MKLDNN_FUSE_CONV_BN</p></td> |
| <td><p>MXNET_DISABLE_ONEDNN_FUSE_CONV_BN</p></td> |
| </tr> |
| <tr class="row-even"><td><p>MXNET_DISABLE_MKLDNN_FUSE_CONV_RELU</p></td> |
| <td><p>MXNET_DISABLE_ONEDNN_FUSE_CONV_RELU</p></td> |
| </tr> |
| <tr class="row-odd"><td><p>MXNET_DISABLE_MKLDNN_FUSE_CONV_SUM</p></td> |
| <td><p>MXNET_DISABLE_ONEDNN_FUSE_CONV_SUM</p></td> |
| </tr> |
| <tr class="row-even"><td><p>MXNET_DISABLE_MKLDNN_FC_OPT</p></td> |
| <td><p>MXNET_DISABLE_ONEDNN_FC_OPT</p></td> |
| </tr> |
| <tr class="row-odd"><td><p>MXNET_DISABLE_MKLDNN_FUSE_FC_ELTWISE</p></td> |
| <td><p>MXNET_DISABLE_ONEDNN_FUSE_FC_ELTWISE</p></td> |
| </tr> |
| <tr class="row-even"><td><p>MXNET_DISABLE_MKLDNN_TRANSFORMER_OPT</p></td> |
| <td><p>MXNET_DISABLE_ONEDNN_TRANSFORMER_OPT</p></td> |
| </tr> |
| <tr class="row-odd"><td><p>n/a</p></td> |
| <td><p>MXNET_DISABLE_ONEDNN_BATCH_DOT_FUSE</p></td> |
| </tr> |
| <tr class="row-even"><td><p>n/a</p></td> |
| <td><p>MXNET_ONEDNN_FUSE_REQUANTIZE</p></td> |
| </tr> |
| <tr class="row-odd"><td><p>n/a</p></td> |
| <td><p>MXNET_ONEDNN_FUSE_DEQUANTIZE</p></td> |
| </tr> |
| </tbody> |
| </table> |
| </div> |
| </div> |
| <div class="section" id="Appendix"> |
| <h2>Appendix<a class="headerlink" href="#Appendix" title="Permalink to this headline">¶</a></h2> |
| <div class="section" id="NumPy-Array-Deprecated-Attributes"> |
| <h3>NumPy Array Deprecated Attributes<a class="headerlink" href="#NumPy-Array-Deprecated-Attributes" title="Permalink to this headline">¶</a></h3> |
| <table class="docutils align-default"> |
| <colgroup> |
| <col style="width: 64%" /> |
| <col style="width: 36%" /> |
| </colgroup> |
| <thead> |
| <tr class="row-odd"><th class="head"><p>Deprecated Attributes</p></th> |
| <th class="head"><p>NumPy ndarray Equivalent</p></th> |
| </tr> |
| </thead> |
| <tbody> |
| <tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">a.abs()</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.np.abs(a)</span></code></p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">a.sign()</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.np.sign(a)</span></code></p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">a.split_v2(2,</span> <span class="pre">axis=1)</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.np.split(a,</span> <span class="pre">2,</span> <span class="pre">axis=1)</span></code></p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">a.flip(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.np.flip(a,</span> <span class="pre">*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">a.diag(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.np.diag(a,</span> <span class="pre">*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">a.nansum(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.np.nan_to_num(a,</span> <span class="pre">*args,</span> <span class="pre">**kwargs).sum()</span></code></p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">a.nanprod(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.np.nan_to_num(a,</span> <span class="pre">*args,</span> <span class="pre">**kwargs).prod()</span></code></p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">a.diag(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.np.diag(a,</span> <span class="pre">*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">a.norm()</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.npx.norm(a)</span></code></p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">a.rint(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.np.rint(a,</span> <span class="pre">*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">a.fix(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.np.fix(a,</span> <span class="pre">*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">a.floor(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.np.floor(a,</span> <span class="pre">*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">a.ceil(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.np.ceil(a,</span> <span class="pre">*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">a.trunc(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.np.trunc(a,</span> <span class="pre">*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">a.sin(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.np.sin(a,</span> <span class="pre">*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">a.cos(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.np.cos(a,</span> <span class="pre">*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">a.tan(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.np.tan(a,</span> <span class="pre">*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">a.arcsin(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.np.arcsin(a,</span> <span class="pre">*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">a.arccos(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.np.arccos(a,</span> <span class="pre">*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">a.arctan(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.np.arctan(a,</span> <span class="pre">*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">a.degrees(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.np.degrees(a,</span> <span class="pre">*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">a.radians(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.np.radians(a,</span> <span class="pre">*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">a.sinh(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.np.sinh(a,</span> <span class="pre">*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">a.cosh(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.np.cosh(a,</span> <span class="pre">*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">a.tanh(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.np.tanh(a,</span> <span class="pre">*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">a.arcsinh(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.np.arcsinh(a,</span> <span class="pre">*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">a.arccosh(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.np.arccosh(a,</span> <span class="pre">*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">a.arctanh(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.np.arctanh(a,</span> <span class="pre">*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">a.exp(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.np.exp(a,</span> <span class="pre">*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">a.expm1(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.np.expm1(a,</span> <span class="pre">*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">a.log(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.np.log(a,</span> <span class="pre">*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">a.log10(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.np.log10(a,</span> <span class="pre">*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">a.log2(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.np.log2(a,</span> <span class="pre">*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">a.log1p(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.np.log1p(a,</span> <span class="pre">*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">a.sqrt(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.np.sqrt(a,</span> <span class="pre">*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">a.rsqrt(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">1</span> <span class="pre">/</span> <span class="pre">mx.np.sqrt(a,</span> <span class="pre">*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">a.cbrt(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.np.cbrt(a,</span> <span class="pre">*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">a.rcbrt(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">1</span> <span class="pre">/</span> <span class="pre">mx.np.cbrt(a,</span> <span class="pre">*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">a.square(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.np.square(a,</span> <span class="pre">*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">a.pad(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.npx.pad(a,</span> <span class="pre">*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">a.split(axis=1,</span> <span class="pre">num_outputs=2)</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.np.split(a,</span> <span class="pre">2,</span> <span class="pre">axis=1)</span></code></p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">a.slice(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.npx.slice(a,</span> <span class="pre">*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">a.one_hot(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.npx.one_hot(a,</span> <span class="pre">*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">a.pick(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.npx.pick(a,</span> <span class="pre">*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">a.topk(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.npx.topk(a,</span> <span class="pre">*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">a.shape_array()</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.np.array(a.shape)</span></code></p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">a.size_array()</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.np.array(a.size)</span></code></p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">a.expand_dims(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.np.expand_dims(a,</span> <span class="pre">*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">a.relu(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.npx.relu(a,</span> <span class="pre">*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">a.sigmoid(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.npx.sigmoid(a,</span> <span class="pre">*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">a.softmax(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.npx.softmax(a,</span> <span class="pre">*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">a.log_softmax(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.npx.log_softmax(a,</span> <span class="pre">*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">a.broadcast_like(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.npx.broadcast_like(a,</span> <span class="pre">*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">a.reciprocal(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.np.reciprocal(a,</span> <span class="pre">*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| </tr> |
| </tbody> |
| </table> |
| </div> |
| <div class="section" id="NumPy-and-NumPy-extension-Operators"> |
| <h3>NumPy and NumPy-extension Operators<a class="headerlink" href="#NumPy-and-NumPy-extension-Operators" title="Permalink to this headline">¶</a></h3> |
| <table class="docutils align-default"> |
| <colgroup> |
| <col style="width: 52%" /> |
| <col style="width: 29%" /> |
| <col style="width: 19%" /> |
| </colgroup> |
| <thead> |
| <tr class="row-odd"><th class="head"><p>Legacy Operators</p></th> |
| <th class="head"><p>NumPy Operators Equivalent</p></th> |
| <th class="head"><p>Changes</p></th> |
| </tr> |
| </thead> |
| <tbody> |
| <tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">mx.nd.softmax(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.npx.softmax(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p>moved to <code class="docutils literal notranslate"><span class="pre">npx</span></code> namespace</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">mx.nd.log_softmax(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.npx.log_softmax(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p>moved to <code class="docutils literal notranslate"><span class="pre">npx</span></code> namespace</p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">mx.nd.masked_softmax(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.npx.masked_softmax(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p>moved to <code class="docutils literal notranslate"><span class="pre">npx</span></code> namespace</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">mx.nd.masked_log_softmax(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.npx.masked_log_softmax(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p>moved to <code class="docutils literal notranslate"><span class="pre">npx</span></code> namespace</p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">mx.nd.pick(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.npx.pick(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p>moved to <code class="docutils literal notranslate"><span class="pre">npx</span></code> namespace</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">mx.nd.topk(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.npx.topk(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p>moved to <code class="docutils literal notranslate"><span class="pre">npx</span></code> namespace</p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">mx.nd.batch_dot(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.npx.batch_dot(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p>moved to <code class="docutils literal notranslate"><span class="pre">npx</span></code> namespace</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">mx.nd.broadcast_like(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.npx.broadcast_like(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p>moved to <code class="docutils literal notranslate"><span class="pre">npx</span></code> namespace</p></td> |
| </tr> |
| <tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">mx.nd.arange_like(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.npx.arange_like(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p>moved to <code class="docutils literal notranslate"><span class="pre">npx</span></code> namespace</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">mx.nd.BatchNorm(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.npx.batch_norm(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><ul class="simple"> |
| <li><p>moved to <code class="docutils literal notranslate"><span class="pre">npx</span></code> namespace with new name <code class="docutils literal notranslate"><span class="pre">batch_norm</span></code>.</p></li> |
| </ul> |
| </td> |
| </tr> |
| <tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">mx.nd.Convolution(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.npx.convolution(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><ul class="simple"> |
| <li><p>moved to <code class="docutils literal notranslate"><span class="pre">npx</span></code> namespace with new name <code class="docutils literal notranslate"><span class="pre">convolution</span></code>.</p></li> |
| </ul> |
| </td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">mx.nd.Deconvolution(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.npx.deconvolution(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><ul class="simple"> |
| <li><p>moved to <code class="docutils literal notranslate"><span class="pre">npx</span></code> namespace with new name <code class="docutils literal notranslate"><span class="pre">deconvolution</span></code>.</p></li> |
| </ul> |
| </td> |
| </tr> |
| <tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">mx.nd.Pooling(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.npx.pooling(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><ul class="simple"> |
| <li><p>moved to <code class="docutils literal notranslate"><span class="pre">npx</span></code> namespace with new name <code class="docutils literal notranslate"><span class="pre">pooling</span></code>.</p></li> |
| </ul> |
| </td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">mx.nd.Dropout(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.npx.dropout(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><ul class="simple"> |
| <li><p>moved to <code class="docutils literal notranslate"><span class="pre">npx</span></code> namespace with new name <code class="docutils literal notranslate"><span class="pre">dropout</span></code>.</p></li> |
| </ul> |
| </td> |
| </tr> |
| <tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">mx.nd.RNN(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.npx.rnn(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><ul class="simple"> |
| <li><p>moved to <code class="docutils literal notranslate"><span class="pre">npx</span></code> namespace with new name <code class="docutils literal notranslate"><span class="pre">rnn</span></code>.</p></li> |
| </ul> |
| </td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">mx.nd.Embedding(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.npx.embedding(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><ul class="simple"> |
| <li><p>moved to <code class="docutils literal notranslate"><span class="pre">npx</span></code> namespace with new name <code class="docutils literal notranslate"><span class="pre">embedding</span></code>.</p></li> |
| </ul> |
| </td> |
| </tr> |
| <tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">mx.nd.LayerNorm(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.npx.layer_norm(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><ul class="simple"> |
| <li><p>moved to <code class="docutils literal notranslate"><span class="pre">npx</span></code> namespace with new name <code class="docutils literal notranslate"><span class="pre">layer_norm</span></code>.</p></li> |
| </ul> |
| </td> |
| </tr> |
| <tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">mx.nd.LeakyReLU(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.npx.leaky_relu(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><ul class="simple"> |
| <li><p>moved to <code class="docutils literal notranslate"><span class="pre">npx</span></code> namespace with new name <code class="docutils literal notranslate"><span class="pre">leaky_relu</span></code>.</p></li> |
| </ul> |
| </td> |
| </tr> |
| <tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">mx.nd.GroupNorm(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><p><code class="docutils literal notranslate"><span class="pre">mx.npx.group_norm(*args,</span> <span class="pre">**kwargs)</span></code></p></td> |
| <td><ul class="simple"> |
| <li><p>moved to <code class="docutils literal notranslate"><span class="pre">npx</span></code> namespace with new name <code class="docutils literal notranslate"><span class="pre">group_norm</span></code>.</p></li> |
| </ul> |
| </td> |
| </tr> |
| </tbody> |
| </table> |
| </div> |
| </div> |
| <div class="section" id="Reference"> |
| <h2>Reference<a class="headerlink" href="#Reference" title="Permalink to this headline">¶</a></h2> |
| <ol class="arabic simple"> |
| <li><p><a class="reference external" href="https://github.com/dmlc/gluon-nlp/tree/master">Next Generation of GluonNLP</a></p></li> |
| <li><p><a class="reference external" href="https://github.com/apache/incubator-mxnet/issues/14253">MXNet NumPy-compatible coding experience</a></p></li> |
| <li><p><a class="reference external" href="https://github.com/apache/incubator-mxnet/issues/17269">Gluon Data API Extension</a></p></li> |
| <li><p><a class="reference external" href="https://github.com/apache/incubator-mxnet/issues/18412">Simplifying MXNet Gluon APIs</a></p></li> |
| <li><p><a class="reference external" href="https://github.com/apache/incubator-mxnet/issues/16376">Deferred Compute and Tracing</a></p></li> |
| <li><p><a class="reference external" href="https://github.com/apache/incubator-mxnet/issues/18046">MXNet Metrics Improvements</a></p></li> |
| <li><p><a class="reference external" href="https://github.com/apache/incubator-mxnet/issues/17240">Gluon Distribution Module</a></p></li> |
| </ol> |
| </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="#">Gluon2.0: Migration Guide</a><ul> |
| <li><a class="reference internal" href="#Overview">Overview</a></li> |
| <li><a class="reference internal" href="#Data-Pipeline">Data Pipeline</a></li> |
| <li><a class="reference internal" href="#Modeling">Modeling</a><ul> |
| <li><a class="reference internal" href="#NumPy-compatible-Programming-Experience">NumPy-compatible Programming Experience</a><ul> |
| <li><a class="reference internal" href="#NumPy-Arrays">NumPy Arrays</a></li> |
| <li><a class="reference internal" href="#NumPy-and-NumPy-extension-Operators">NumPy and NumPy-extension Operators</a></li> |
| </ul> |
| </li> |
| <li><a class="reference internal" href="#Layers-and-Blocks">Layers and Blocks</a><ul> |
| <li><a class="reference internal" href="#Parameter-Management-and-Block-Naming">Parameter Management and Block Naming</a></li> |
| <li><a class="reference internal" href="#Forward-Interface">Forward Interface</a></li> |
| <li><a class="reference internal" href="#Implement-Infer-Shape">Implement Infer Shape</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li><a class="reference internal" href="#Optimizers">Optimizers</a></li> |
| <li><a class="reference internal" href="#Metrics">Metrics</a></li> |
| <li><a class="reference internal" href="#Key-Value-Store">Key-Value Store</a></li> |
| <li><a class="reference internal" href="#Probability">Probability</a></li> |
| <li><a class="reference internal" href="#oneDNN-Integration">oneDNN Integration</a><ul> |
| <li><a class="reference internal" href="#Operator-Fusion">Operator Fusion</a></li> |
| <li><a class="reference internal" href="#INT8-Quantization-/-Precision-reduction">INT8 Quantization / Precision reduction</a></li> |
| <li><a class="reference internal" href="#oneDNN-Environment-Variables">oneDNN Environment Variables</a></li> |
| </ul> |
| </li> |
| <li><a class="reference internal" href="#Appendix">Appendix</a><ul> |
| <li><a class="reference internal" href="#NumPy-Array-Deprecated-Attributes">NumPy Array Deprecated Attributes</a></li> |
| <li><a class="reference internal" href="#NumPy-and-NumPy-extension-Operators">NumPy and NumPy-extension Operators</a></li> |
| </ul> |
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| <li><a class="reference internal" href="#Reference">Reference</a></li> |
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