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| <span class="mdl-layout-title toc">Table Of Contents</span> |
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| <li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/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="../../../tutorials/getting-started/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="../../../tutorials/getting-started/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="../../../tutorials/getting-started/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="../../../tutorials/getting-started/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="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/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="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/gluon_from_experiment_to_deployment.html">Gluon: from experiment to deployment</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/gluon_migration_guide.html">Gluon2.0: Migration Guide</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/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="../../../tutorials/packages/index.html">Packages</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/autograd/index.html">Automatic Differentiation</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/gluon/index.html">Gluon</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/index.html">Blocks</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/custom-layer.html">Custom Layers</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/hybridize.html">Hybridize</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/init.html">Initialization</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/naming.html">Parameter and Block Naming</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/nn.html">Layers and Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/parameters.html">Parameter Management</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/activations/activations.html">Activation Blocks</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/data/index.html">Data Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html">Image Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/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="../../../tutorials/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="../../../tutorials/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="../../../tutorials/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="../../../tutorials/packages/gluon/image/index.html">Image Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/info_gan.html">Image similarity search with InfoGAN</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/mnist.html">Handwritten Digit Recognition</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/index.html">Losses</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/custom-loss.html">Custom Loss Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/kl_divergence.html">Kullback-Leibler (KL) Divergence</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/loss.html">Loss functions</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/text/index.html">Text Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/text/gnmt.html">Google Neural Machine Translation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/text/transformer.html">Machine Translation with Transformer</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/training/index.html">Training</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/fit_api_tutorial.html">MXNet Gluon Fit API</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/trainer.html">Trainer</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/index.html">Learning Rates</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_finder.html">Learning Rate Finder</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_schedules.html">Learning Rate Schedules</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/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="../../../tutorials/packages/gluon/training/normalization/index.html">Normalization Blocks</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/kvstore/index.html">KVStore</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/kvstore/kvstore.html">Distributed Key-Value Store</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/legacy/index.html">Legacy</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/index.html">NDArray</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/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="../../../tutorials/packages/legacy/ndarray/02-ndarray-operations.html">NDArray Operations</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/03-ndarray-contexts.html">NDArray Contexts</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/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="../../../tutorials/packages/legacy/ndarray/sparse/index.html">Tutorials</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/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="../../../tutorials/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="../../../tutorials/packages/np/index.html">What is NP on MXNet</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/np/cheat-sheet.html">The NP on MXNet cheat sheet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/np/np-vs-numpy.html">Differences between NP on MXNet and NumPy</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/onnx/index.html">ONNX</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/onnx/fine_tuning_gluon.html">Fine-tuning an ONNX model</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/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="../../../tutorials/packages/optimizer/index.html">Optimizers</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/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> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/performance/index.html">Performance</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/performance/compression/index.html">Compression</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/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> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/performance/backend/index.html">Accelerated Backend Tools</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/backend/dnnl/index.html">oneDNN</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/performance/backend/dnnl/dnnl_readme.html">Install MXNet with oneDNN</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/performance/backend/dnnl/dnnl_quantization.html">oneDNN Quantization</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/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="../../../tutorials/performance/backend/tvm.html">Use TVM</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/backend/profiler.html">Profiling MXNet Models</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/backend/amp.html">Using AMP: Automatic Mixed Precision</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/deploy/index.html">Deployment</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/deploy/export/index.html">Export</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/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="../../../tutorials/deploy/inference/index.html">Inference</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/deploy/inference/cpp.html">Deploy into C++</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/deploy/inference/image_classification_jetson.html">Image Classication using pretrained ResNet-50 model on Jetson module</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/deploy/run-on-aws/index.html">Run on AWS</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/deploy/run-on-aws/use_ec2.html">Run on an EC2 Instance</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/deploy/run-on-aws/use_sagemaker.html">Run on Amazon SageMaker</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/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="../../../tutorials/extend/index.html">Extend</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/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> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/np/index.html">mxnet.np</a><ul> |
| <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> |
| </ul> |
| </li> |
| <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> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.meshgrid.html">mxnet.np.meshgrid</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.tril.html">mxnet.np.tril</a></li> |
| </ul> |
| </li> |
| <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> |
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| <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> |
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| <span class="mdl-layout-title toc">Table Of Contents</span> |
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| |
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| <nav class="mdl-navigation"> |
| <ul> |
| <li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/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="../../../tutorials/getting-started/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="../../../tutorials/getting-started/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="../../../tutorials/getting-started/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="../../../tutorials/getting-started/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="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/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="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/gluon_from_experiment_to_deployment.html">Gluon: from experiment to deployment</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/gluon_migration_guide.html">Gluon2.0: Migration Guide</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/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="../../../tutorials/packages/index.html">Packages</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/autograd/index.html">Automatic Differentiation</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/gluon/index.html">Gluon</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/index.html">Blocks</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/custom-layer.html">Custom Layers</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/hybridize.html">Hybridize</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/init.html">Initialization</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/naming.html">Parameter and Block Naming</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/nn.html">Layers and Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/parameters.html">Parameter Management</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/activations/activations.html">Activation Blocks</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/data/index.html">Data Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html">Image Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/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="../../../tutorials/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="../../../tutorials/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="../../../tutorials/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="../../../tutorials/packages/gluon/image/index.html">Image Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/info_gan.html">Image similarity search with InfoGAN</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/mnist.html">Handwritten Digit Recognition</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/index.html">Losses</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/custom-loss.html">Custom Loss Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/kl_divergence.html">Kullback-Leibler (KL) Divergence</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/loss.html">Loss functions</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/text/index.html">Text Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/text/gnmt.html">Google Neural Machine Translation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/text/transformer.html">Machine Translation with Transformer</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/training/index.html">Training</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/fit_api_tutorial.html">MXNet Gluon Fit API</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/trainer.html">Trainer</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/index.html">Learning Rates</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_finder.html">Learning Rate Finder</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_schedules.html">Learning Rate Schedules</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/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="../../../tutorials/packages/gluon/training/normalization/index.html">Normalization Blocks</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/kvstore/index.html">KVStore</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/kvstore/kvstore.html">Distributed Key-Value Store</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/legacy/index.html">Legacy</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/index.html">NDArray</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/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="../../../tutorials/packages/legacy/ndarray/02-ndarray-operations.html">NDArray Operations</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/03-ndarray-contexts.html">NDArray Contexts</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/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="../../../tutorials/packages/legacy/ndarray/sparse/index.html">Tutorials</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/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="../../../tutorials/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="../../../tutorials/packages/np/index.html">What is NP on MXNet</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/np/cheat-sheet.html">The NP on MXNet cheat sheet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/np/np-vs-numpy.html">Differences between NP on MXNet and NumPy</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/onnx/index.html">ONNX</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/onnx/fine_tuning_gluon.html">Fine-tuning an ONNX model</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/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="../../../tutorials/packages/optimizer/index.html">Optimizers</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/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> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/performance/index.html">Performance</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/performance/compression/index.html">Compression</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/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> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/performance/backend/index.html">Accelerated Backend Tools</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/backend/dnnl/index.html">oneDNN</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/performance/backend/dnnl/dnnl_readme.html">Install MXNet with oneDNN</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/performance/backend/dnnl/dnnl_quantization.html">oneDNN Quantization</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/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="../../../tutorials/performance/backend/tvm.html">Use TVM</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/backend/profiler.html">Profiling MXNet Models</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/backend/amp.html">Using AMP: Automatic Mixed Precision</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/deploy/index.html">Deployment</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/deploy/export/index.html">Export</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/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="../../../tutorials/deploy/inference/index.html">Inference</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/deploy/inference/cpp.html">Deploy into C++</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/deploy/inference/image_classification_jetson.html">Image Classication using pretrained ResNet-50 model on Jetson module</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/deploy/run-on-aws/index.html">Run on AWS</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/deploy/run-on-aws/use_ec2.html">Run on an EC2 Instance</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/deploy/run-on-aws/use_sagemaker.html">Run on Amazon SageMaker</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/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="../../../tutorials/extend/index.html">Extend</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/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> |
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| <li class="toctree-l1"><a class="reference internal" href="../../../api/index.html">Python API</a><ul> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/np/index.html">mxnet.np</a><ul> |
| <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> |
| <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> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.negative.html">mxnet.np.negative</a></li> |
| <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> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.exponential.html">mxnet.np.random.exponential</a></li> |
| <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> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.argsort.html">mxnet.np.argsort</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.msort.html">mxnet.np.msort</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.partition.html">mxnet.np.partition</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.argpartition.html">mxnet.np.argpartition</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.argmax.html">mxnet.np.argmax</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.argmin.html">mxnet.np.argmin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanargmax.html">mxnet.np.nanargmax</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanargmin.html">mxnet.np.nanargmin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.argwhere.html">mxnet.np.argwhere</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nonzero.html">mxnet.np.nonzero</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.flatnonzero.html">mxnet.np.flatnonzero</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.where.html">mxnet.np.where</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.searchsorted.html">mxnet.np.searchsorted</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.extract.html">mxnet.np.extract</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.count_nonzero.html">mxnet.np.count_nonzero</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.statistics.html">Statistics</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.min.html">mxnet.np.min</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.max.html">mxnet.np.max</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.amin.html">mxnet.np.amin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.amax.html">mxnet.np.amax</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanmin.html">mxnet.np.nanmin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanmax.html">mxnet.np.nanmax</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ptp.html">mxnet.np.ptp</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.percentile.html">mxnet.np.percentile</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanpercentile.html">mxnet.np.nanpercentile</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.quantile.html">mxnet.np.quantile</a></li> |
| <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> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.var.html">mxnet.np.var</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.median.html">mxnet.np.median</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.average.html">mxnet.np.average</a></li> |
| <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> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanvar.html">mxnet.np.nanvar</a></li> |
| <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> |
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| |
| <h1>Source code for mxnet.kvstore.kvstore</h1><div class="highlight"><pre> |
| <span></span><span class="c1"># Licensed to the Apache Software Foundation (ASF) under one</span> |
| <span class="c1"># or more contributor license agreements. See the NOTICE file</span> |
| <span class="c1"># distributed with this work for additional information</span> |
| <span class="c1"># regarding copyright ownership. The ASF licenses this file</span> |
| <span class="c1"># to you under the Apache License, Version 2.0 (the</span> |
| <span class="c1"># "License"); you may not use this file except in compliance</span> |
| <span class="c1"># with the License. You may obtain a copy of the License at</span> |
| <span class="c1">#</span> |
| <span class="c1"># http://www.apache.org/licenses/LICENSE-2.0</span> |
| <span class="c1">#</span> |
| <span class="c1"># Unless required by applicable law or agreed to in writing,</span> |
| <span class="c1"># software distributed under the License is distributed on an</span> |
| <span class="c1"># "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY</span> |
| <span class="c1"># KIND, either express or implied. See the License for the</span> |
| <span class="c1"># specific language governing permissions and limitations</span> |
| <span class="c1"># under the License.</span> |
| |
| <span class="c1"># coding: utf-8</span> |
| <span class="sd">""" Key value store interface of MXNet for parameter synchronization."""</span> |
| |
| <span class="kn">import</span> <span class="nn">pickle</span> |
| <span class="kn">import</span> <span class="nn">ctypes</span> |
| <span class="kn">import</span> <span class="nn">os</span> |
| <span class="kn">from</span> <span class="nn">..ndarray</span> <span class="kn">import</span> <span class="n">NDArray</span> |
| <span class="kn">from</span> <span class="nn">..ndarray</span> <span class="kn">import</span> <span class="n">_ndarray_cls</span> |
| <span class="kn">from</span> <span class="nn">..base</span> <span class="kn">import</span> <span class="n">_LIB</span><span class="p">,</span> <span class="n">c_str</span> |
| <span class="kn">from</span> <span class="nn">..base</span> <span class="kn">import</span> <span class="n">check_call</span><span class="p">,</span> <span class="n">mx_uint</span><span class="p">,</span> <span class="n">py_str</span> |
| <span class="kn">from</span> <span class="nn">..base</span> <span class="kn">import</span> <span class="n">NDArrayHandle</span><span class="p">,</span> <span class="n">KVStoreHandle</span> |
| <span class="kn">from</span> <span class="nn">..</span> <span class="kn">import</span> <span class="n">optimizer</span> <span class="k">as</span> <span class="n">opt</span> |
| <span class="kn">from</span> <span class="nn">.base</span> <span class="kn">import</span> <span class="n">_ctype_key_value</span><span class="p">,</span> <span class="n">_ctype_dict</span><span class="p">,</span> <span class="n">KVStoreBase</span> |
| |
| <span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'KVStore'</span><span class="p">]</span> |
| |
| <span class="k">def</span> <span class="nf">_updater_wrapper</span><span class="p">(</span><span class="n">updater</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""A wrapper for the user-defined handle."""</span> |
| <span class="k">def</span> <span class="nf">updater_handle</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">lhs_handle</span><span class="p">,</span> <span class="n">rhs_handle</span><span class="p">,</span> <span class="n">_</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">""" ctypes function """</span> |
| <span class="n">lhs</span> <span class="o">=</span> <span class="n">_ndarray_cls</span><span class="p">(</span><span class="n">NDArrayHandle</span><span class="p">(</span><span class="n">lhs_handle</span><span class="p">))</span> |
| <span class="n">rhs</span> <span class="o">=</span> <span class="n">_ndarray_cls</span><span class="p">(</span><span class="n">NDArrayHandle</span><span class="p">(</span><span class="n">rhs_handle</span><span class="p">))</span> |
| <span class="n">updater</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">lhs</span><span class="p">,</span> <span class="n">rhs</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">updater_handle</span> |
| |
| <span class="k">def</span> <span class="nf">_get_kvstore_server_command_type</span><span class="p">(</span><span class="n">command</span><span class="p">):</span> |
| <span class="n">command_types</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'kController'</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span> |
| <span class="s1">'kSetMultiPrecision'</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span> |
| <span class="s1">'kStopServer'</span><span class="p">:</span> <span class="mi">2</span><span class="p">,</span> |
| <span class="s1">'kSyncMode'</span><span class="p">:</span> <span class="mi">3</span><span class="p">,</span> |
| <span class="s1">'kSetGradientCompression'</span><span class="p">:</span> <span class="mi">4</span><span class="p">,</span> |
| <span class="s1">'kSetProfilerParams'</span><span class="p">:</span> <span class="mi">5</span><span class="p">}</span> |
| <span class="k">assert</span> <span class="p">(</span><span class="n">command</span> <span class="ow">in</span> <span class="n">command_types</span><span class="p">),</span> <span class="s2">"Unknown command type to send to server"</span> |
| <span class="k">return</span> <span class="n">command_types</span><span class="p">[</span><span class="n">command</span><span class="p">]</span> |
| |
| |
| <div class="viewcode-block" id="KVStore"><a class="viewcode-back" href="../../../api/kvstore/generated/mxnet.kvstore.KVStore.html#mxnet.kvstore.KVStore">[docs]</a><span class="k">class</span> <span class="nc">KVStore</span><span class="p">(</span><span class="n">KVStoreBase</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""A key-value store for synchronization of values, over multiple devices."""</span> |
| |
| <div class="viewcode-block" id="KVStore.__init__"><a class="viewcode-back" href="../../../api/kvstore/generated/mxnet.kvstore.KVStore.html#mxnet.kvstore.KVStore.__init__">[docs]</a> <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">handle</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Initializes a new KVStore.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> handle : KVStoreHandle</span> |
| <span class="sd"> `KVStore` handle of C API.</span> |
| <span class="sd"> """</span> |
| <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">handle</span><span class="p">,</span> <span class="n">KVStoreHandle</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">handle</span> <span class="o">=</span> <span class="n">handle</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_updater</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_updater_func</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_str_updater_func</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_is_p3</span> <span class="o">=</span> <span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">getenv</span><span class="p">(</span><span class="s1">'DMLC_PS_VAN_TYPE'</span><span class="p">,</span> <span class="s1">''</span><span class="p">)</span> <span class="o">==</span> <span class="s1">'p3'</span><span class="p">)</span></div> |
| |
| <span class="k">def</span> <span class="fm">__del__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXKVStoreFree</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">handle</span><span class="p">))</span> |
| |
| <div class="viewcode-block" id="KVStore.broadcast"><a class="viewcode-back" href="../../../api/kvstore/generated/mxnet.kvstore.KVStore.html#mxnet.kvstore.KVStore.broadcast">[docs]</a> <span class="k">def</span> <span class="nf">broadcast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span><span class="p">,</span> <span class="n">out</span><span class="p">,</span> <span class="n">priority</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">""" Broadcast the `value` NDArray at rank 0 to all ranks,</span> |
| <span class="sd"> and store the result in `out`.</span> |
| |
| <span class="sd"> Note that the native KVStore does not support broadcasting the same key more than once.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> key : str, or int</span> |
| <span class="sd"> The key.</span> |
| |
| <span class="sd"> value : NDArray, list of NDArray, or list of list of NDArray</span> |
| <span class="sd"> Values corresponding to the keys.</span> |
| |
| <span class="sd"> out: NDArray or list of NDArray or list of list of NDArray</span> |
| <span class="sd"> Outputs corresponding to the keys.</span> |
| |
| <span class="sd"> priority : int, optional</span> |
| <span class="sd"> The priority of the operation.</span> |
| <span class="sd"> Higher priority operations are likely to be executed before other actions.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> # broadcast a single key-value pair</span> |
| <span class="sd"> >>> shape = (2,3)</span> |
| <span class="sd"> >>> kv = mx.kv.create('local')</span> |
| <span class="sd"> >>> a = mx.nd.zeros(shape)</span> |
| <span class="sd"> >>> kv.broadcast('3', mx.nd.ones(shape)*2, out=a)</span> |
| <span class="sd"> >>> print a.asnumpy()</span> |
| <span class="sd"> [[ 2. 2. 2.]</span> |
| <span class="sd"> [ 2. 2. 2.]]</span> |
| |
| <span class="sd"> """</span> |
| <span class="n">cvkeys</span><span class="p">,</span> <span class="n">cvals</span><span class="p">,</span> <span class="n">use_str_keys</span> <span class="o">=</span> <span class="n">_ctype_key_value</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">value</span><span class="p">)</span> |
| <span class="n">cokeys</span><span class="p">,</span> <span class="n">couts</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">_ctype_key_value</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">out</span><span class="p">)</span> |
| |
| <span class="k">if</span> <span class="n">use_str_keys</span><span class="p">:</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXKVStoreBroadcastEx</span><span class="p">(</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">handle</span><span class="p">,</span> <span class="n">mx_uint</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">cvkeys</span><span class="p">)),</span> <span class="n">cvkeys</span><span class="p">,</span> <span class="n">mx_uint</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">cokeys</span><span class="p">)),</span> <span class="n">cokeys</span><span class="p">,</span> |
| <span class="n">cvals</span><span class="p">,</span> <span class="n">couts</span><span class="p">,</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">(</span><span class="n">priority</span><span class="p">)))</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXKVStoreBroadcast</span><span class="p">(</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">handle</span><span class="p">,</span> <span class="n">mx_uint</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">cvkeys</span><span class="p">)),</span> <span class="n">cvkeys</span><span class="p">,</span> <span class="n">mx_uint</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">cokeys</span><span class="p">)),</span> <span class="n">cokeys</span><span class="p">,</span> |
| <span class="n">cvals</span><span class="p">,</span> <span class="n">couts</span><span class="p">,</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">(</span><span class="n">priority</span><span class="p">)))</span></div> |
| |
| |
| <div class="viewcode-block" id="KVStore.is_capable"><a class="viewcode-back" href="../../../api/kvstore/generated/mxnet.kvstore.KVStore.html#mxnet.kvstore.KVStore.is_capable">[docs]</a> <span class="k">def</span> <span class="nf">is_capable</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">capability</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Queries if the KVStore type supports certain capability, such as optimizer algorithm,</span> |
| <span class="sd"> gradient compression, sparsity, etc.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> capability: str</span> |
| <span class="sd"> The capability to query</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> result : bool</span> |
| <span class="sd"> Whether the capability is supported or not.</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="n">capability</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="o">==</span> <span class="n">KVStoreBase</span><span class="o">.</span><span class="n">OPTIMIZER</span><span class="p">:</span> |
| <span class="k">return</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_is_p3</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'Unknown capability: </span><span class="si">{}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">capability</span><span class="p">))</span></div> |
| |
| <div class="viewcode-block" id="KVStore.init"><a class="viewcode-back" href="../../../api/kvstore/generated/mxnet.kvstore.KVStore.html#mxnet.kvstore.KVStore.init">[docs]</a> <span class="k">def</span> <span class="nf">init</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">""" Initializes a single or a sequence of key-value pairs into the store.</span> |
| |
| <span class="sd"> For each key, one must `init` it before calling `push` or `pull`.</span> |
| <span class="sd"> When multiple workers invoke `init` for the same key, only</span> |
| <span class="sd"> the value supplied by worker with rank `0` is used. This function returns</span> |
| <span class="sd"> after data has been initialized successfully.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> key : str, int, or sequence of str or int</span> |
| <span class="sd"> The keys.</span> |
| <span class="sd"> value : NDArray, RowSparseNDArray or sequence of NDArray or RowSparseNDArray</span> |
| <span class="sd"> Values corresponding to the keys.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> # init a single key-value pair</span> |
| <span class="sd"> >>> shape = (2,3)</span> |
| <span class="sd"> >>> kv = mx.kv.create('local')</span> |
| <span class="sd"> >>> kv.init('3', mx.nd.ones(shape)*2)</span> |
| <span class="sd"> >>> a = mx.nd.zeros(shape)</span> |
| <span class="sd"> >>> kv.pull('3', out=a)</span> |
| <span class="sd"> >>> print a.asnumpy()</span> |
| <span class="sd"> [[ 2. 2. 2.]</span> |
| <span class="sd"> [ 2. 2. 2.]]</span> |
| |
| <span class="sd"> >>> # init a list of key-value pairs</span> |
| <span class="sd"> >>> keys = ['5', '7', '9']</span> |
| <span class="sd"> >>> kv.init(keys, [mx.nd.ones(shape)]*len(keys))</span> |
| |
| <span class="sd"> >>> # init a row_sparse value</span> |
| <span class="sd"> >>> kv.init('4', mx.nd.ones(shape).tostype('row_sparse'))</span> |
| <span class="sd"> >>> b = mx.nd.sparse.zeros('row_sparse', shape)</span> |
| <span class="sd"> >>> kv.row_sparse_pull('4', row_ids=mx.nd.array([0, 1]), out=b)</span> |
| <span class="sd"> >>> print b</span> |
| <span class="sd"> <RowSparseNDArray 2x3 @cpu(0)></span> |
| <span class="sd"> """</span> |
| <span class="n">ckeys</span><span class="p">,</span> <span class="n">cvals</span><span class="p">,</span> <span class="n">use_str_keys</span> <span class="o">=</span> <span class="n">_ctype_key_value</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">value</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">use_str_keys</span><span class="p">:</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXKVStoreInitEx</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">handle</span><span class="p">,</span> <span class="n">mx_uint</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">ckeys</span><span class="p">)),</span> <span class="n">ckeys</span><span class="p">,</span> <span class="n">cvals</span><span class="p">))</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXKVStoreInit</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">handle</span><span class="p">,</span> <span class="n">mx_uint</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">ckeys</span><span class="p">)),</span> <span class="n">ckeys</span><span class="p">,</span> <span class="n">cvals</span><span class="p">))</span></div> |
| |
| <div class="viewcode-block" id="KVStore.push"><a class="viewcode-back" href="../../../api/kvstore/generated/mxnet.kvstore.KVStore.html#mxnet.kvstore.KVStore.push">[docs]</a> <span class="k">def</span> <span class="nf">push</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span><span class="p">,</span> <span class="n">priority</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">""" Pushes a single or a sequence of key-value pairs into the store.</span> |
| |
| <span class="sd"> This function returns immediately after adding an operator to the engine.</span> |
| <span class="sd"> The actual operation is executed asynchronously. If there are consecutive</span> |
| <span class="sd"> pushes to the same key, there is no guarantee on the serialization of pushes.</span> |
| <span class="sd"> The execution of a push does not guarantee that all previous pushes are</span> |
| <span class="sd"> finished.</span> |
| <span class="sd"> There is no synchronization between workers.</span> |
| <span class="sd"> One can use ``_barrier()`` to sync all workers.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> key : str, int, or sequence of str or int</span> |
| <span class="sd"> Keys.</span> |
| |
| <span class="sd"> value : NDArray, RowSparseNDArray, list of NDArray or RowSparseNDArray,</span> |
| <span class="sd"> or list of list of NDArray or RowSparseNDArray</span> |
| <span class="sd"> Values corresponding to the keys.</span> |
| |
| <span class="sd"> priority : int, optional</span> |
| <span class="sd"> The priority of the push operation.</span> |
| <span class="sd"> Higher priority push operations are likely to be executed before</span> |
| <span class="sd"> other push actions.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> # push a single key-value pair</span> |
| <span class="sd"> >>> shape = (2,3)</span> |
| <span class="sd"> >>> kv.push('3', mx.nd.ones(shape)*8)</span> |
| <span class="sd"> >>> kv.pull('3', out=a) # pull out the value</span> |
| <span class="sd"> >>> print a.asnumpy()</span> |
| <span class="sd"> [[ 8. 8. 8.]</span> |
| <span class="sd"> [ 8. 8. 8.]]</span> |
| |
| <span class="sd"> >>> # aggregate the value and the push</span> |
| <span class="sd"> >>> gpus = [mx.gpu(i) for i in range(4)]</span> |
| <span class="sd"> >>> b = [mx.nd.ones(shape, gpu) for gpu in gpus]</span> |
| <span class="sd"> >>> kv.push('3', b)</span> |
| <span class="sd"> >>> kv.pull('3', out=a)</span> |
| <span class="sd"> >>> print a.asnumpy()</span> |
| <span class="sd"> [[ 4. 4. 4.]</span> |
| <span class="sd"> [ 4. 4. 4.]]</span> |
| |
| <span class="sd"> >>> # push a list of keys.</span> |
| <span class="sd"> >>> # single device</span> |
| <span class="sd"> >>> keys = ['4', '5', '6']</span> |
| <span class="sd"> >>> kv.push(keys, [mx.nd.ones(shape)]*len(keys))</span> |
| <span class="sd"> >>> b = [mx.nd.zeros(shape)]*len(keys)</span> |
| <span class="sd"> >>> kv.pull(keys, out=b)</span> |
| <span class="sd"> >>> print b[1].asnumpy()</span> |
| <span class="sd"> [[ 1. 1. 1.]</span> |
| <span class="sd"> [ 1. 1. 1.]]</span> |
| |
| <span class="sd"> >>> # multiple devices:</span> |
| <span class="sd"> >>> keys = ['7', '8', '9']</span> |
| <span class="sd"> >>> b = [[mx.nd.ones(shape, gpu) for gpu in gpus]] * len(keys)</span> |
| <span class="sd"> >>> kv.push(keys, b)</span> |
| <span class="sd"> >>> kv.pull(keys, out=b)</span> |
| <span class="sd"> >>> print b[1][1].asnumpy()</span> |
| <span class="sd"> [[ 4. 4. 4.]</span> |
| <span class="sd"> [ 4. 4. 4.]]</span> |
| |
| <span class="sd"> >>> # push a row_sparse value</span> |
| <span class="sd"> >>> b = mx.nd.sparse.zeros('row_sparse', shape)</span> |
| <span class="sd"> >>> kv.init('10', mx.nd.sparse.zeros('row_sparse', shape))</span> |
| <span class="sd"> >>> kv.push('10', mx.nd.ones(shape).tostype('row_sparse'))</span> |
| <span class="sd"> >>> # pull out the value</span> |
| <span class="sd"> >>> kv.row_sparse_pull('10', row_ids=mx.nd.array([0, 1]), out=b)</span> |
| <span class="sd"> >>> print b</span> |
| <span class="sd"> <RowSparseNDArray 2x3 @cpu(0)></span> |
| <span class="sd"> """</span> |
| <span class="n">ckeys</span><span class="p">,</span> <span class="n">cvals</span><span class="p">,</span> <span class="n">use_str_keys</span> <span class="o">=</span> <span class="n">_ctype_key_value</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">value</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">use_str_keys</span><span class="p">:</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXKVStorePushEx</span><span class="p">(</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">handle</span><span class="p">,</span> <span class="n">mx_uint</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">ckeys</span><span class="p">)),</span> <span class="n">ckeys</span><span class="p">,</span> <span class="n">cvals</span><span class="p">,</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">(</span><span class="n">priority</span><span class="p">)))</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXKVStorePush</span><span class="p">(</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">handle</span><span class="p">,</span> <span class="n">mx_uint</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">ckeys</span><span class="p">)),</span> <span class="n">ckeys</span><span class="p">,</span> <span class="n">cvals</span><span class="p">,</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">(</span><span class="n">priority</span><span class="p">)))</span></div> |
| |
| |
| <div class="viewcode-block" id="KVStore.pull"><a class="viewcode-back" href="../../../api/kvstore/generated/mxnet.kvstore.KVStore.html#mxnet.kvstore.KVStore.pull">[docs]</a> <span class="k">def</span> <span class="nf">pull</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">key</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">priority</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">ignore_sparse</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">""" Pulls a single value or a sequence of values from the store.</span> |
| |
| <span class="sd"> This function returns immediately after adding an operator to the engine.</span> |
| <span class="sd"> Subsequent attempts to read from the `out` variable will be blocked until the</span> |
| <span class="sd"> pull operation completes.</span> |
| |
| <span class="sd"> `pull` is executed asynchronously after all previous `pull` calls and only</span> |
| <span class="sd"> the last `push` call for the same input key(s) are finished.</span> |
| |
| <span class="sd"> The returned values are guaranteed to be the latest values in the store.</span> |
| |
| <span class="sd"> pull with `RowSparseNDArray` is not supported for dist kvstore.</span> |
| <span class="sd"> Please use ``row_sparse_pull`` instead.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> key : str, int, or sequence of str or int</span> |
| <span class="sd"> Keys.</span> |
| |
| <span class="sd"> out: NDArray or list of NDArray or list of list of NDArray</span> |
| <span class="sd"> Values corresponding to the keys.</span> |
| |
| <span class="sd"> priority : int, optional</span> |
| <span class="sd"> The priority of the pull operation.</span> |
| <span class="sd"> Higher priority pull operations are likely to be executed before</span> |
| <span class="sd"> other pull actions.</span> |
| |
| <span class="sd"> ignore_sparse: bool, optional, default True</span> |
| <span class="sd"> Whether to ignore sparse arrays in the request.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> # pull a single key-value pair</span> |
| <span class="sd"> >>> shape = (2,3)</span> |
| <span class="sd"> >>> a = mx.nd.zeros(shape)</span> |
| <span class="sd"> >>> kv.pull('3', out=a)</span> |
| <span class="sd"> >>> print a.asnumpy()</span> |
| <span class="sd"> [[ 2. 2. 2.]</span> |
| <span class="sd"> [ 2. 2. 2.]]</span> |
| |
| <span class="sd"> >>> # pull into multiple devices</span> |
| <span class="sd"> >>> b = [mx.nd.ones(shape, gpu) for gpu in gpus]</span> |
| <span class="sd"> >>> kv.pull('3', out=b)</span> |
| <span class="sd"> >>> print b[1].asnumpy()</span> |
| <span class="sd"> [[ 2. 2. 2.]</span> |
| <span class="sd"> [ 2. 2. 2.]]</span> |
| |
| <span class="sd"> >>> # pull a list of key-value pairs.</span> |
| <span class="sd"> >>> # On single device</span> |
| <span class="sd"> >>> keys = ['5', '7', '9']</span> |
| <span class="sd"> >>> b = [mx.nd.zeros(shape)]*len(keys)</span> |
| <span class="sd"> >>> kv.pull(keys, out=b)</span> |
| <span class="sd"> >>> print b[1].asnumpy()</span> |
| <span class="sd"> [[ 2. 2. 2.]</span> |
| <span class="sd"> [ 2. 2. 2.]]</span> |
| <span class="sd"> >>> # On multiple devices</span> |
| <span class="sd"> >>> keys = ['6', '8', '10']</span> |
| <span class="sd"> >>> b = [[mx.nd.ones(shape, gpu) for gpu in gpus]] * len(keys)</span> |
| <span class="sd"> >>> kv.pull(keys, out=b)</span> |
| <span class="sd"> >>> print b[1][1].asnumpy()</span> |
| <span class="sd"> [[ 2. 2. 2.]</span> |
| <span class="sd"> [ 2. 2. 2.]]</span> |
| <span class="sd"> """</span> |
| <span class="k">assert</span><span class="p">(</span><span class="n">out</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">)</span> |
| <span class="n">ckeys</span><span class="p">,</span> <span class="n">cvals</span><span class="p">,</span> <span class="n">use_str_keys</span> <span class="o">=</span> <span class="n">_ctype_key_value</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">out</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">use_str_keys</span><span class="p">:</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXKVStorePullWithSparseEx</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">handle</span><span class="p">,</span> <span class="n">mx_uint</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">ckeys</span><span class="p">)),</span> <span class="n">ckeys</span><span class="p">,</span> |
| <span class="n">cvals</span><span class="p">,</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">(</span><span class="n">priority</span><span class="p">),</span> |
| <span class="n">ctypes</span><span class="o">.</span><span class="n">c_bool</span><span class="p">(</span><span class="n">ignore_sparse</span><span class="p">)))</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXKVStorePullWithSparse</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">handle</span><span class="p">,</span> <span class="n">mx_uint</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">ckeys</span><span class="p">)),</span> <span class="n">ckeys</span><span class="p">,</span> |
| <span class="n">cvals</span><span class="p">,</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">(</span><span class="n">priority</span><span class="p">),</span> |
| <span class="n">ctypes</span><span class="o">.</span><span class="n">c_bool</span><span class="p">(</span><span class="n">ignore_sparse</span><span class="p">)))</span></div> |
| |
| <div class="viewcode-block" id="KVStore.pushpull"><a class="viewcode-back" href="../../../api/kvstore/generated/mxnet.kvstore.KVStore.html#mxnet.kvstore.KVStore.pushpull">[docs]</a> <span class="k">def</span> <span class="nf">pushpull</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">priority</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">""" Performs push and pull a single value or a sequence of values from the store.</span> |
| |
| <span class="sd"> This function is coalesced form of push and pull operations. This function returns</span> |
| <span class="sd"> immediately after adding an operator to the engine. Subsequent attempts to read</span> |
| <span class="sd"> from the `out` variable will be blocked until the pull operation completes.</span> |
| |
| <span class="sd"> `value` is pushed to the kvstore server for the specified keys and the updated</span> |
| <span class="sd"> values are pulled from the server to `out`. If `out` is not specified the pulled</span> |
| <span class="sd"> values are written to `value`. The returned values are guaranteed to be the latest</span> |
| <span class="sd"> values in the store.</span> |
| |
| <span class="sd"> pushpull with `RowSparseNDArray` is not supported for dist kvstore.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> key : str, int, or sequence of str or int</span> |
| <span class="sd"> Keys.</span> |
| |
| <span class="sd"> value : NDArray, list of NDArray, or list of list of NDArray</span> |
| <span class="sd"> Values corresponding to the keys.</span> |
| |
| <span class="sd"> out: NDArray or list of NDArray or list of list of NDArray, optional</span> |
| <span class="sd"> Outputs corresponding to the keys.</span> |
| |
| <span class="sd"> priority : int, optional</span> |
| <span class="sd"> The priority of the operation.</span> |
| <span class="sd"> Higher priority operations are likely to be executed before other actions.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> # pushpull a single key-value pair</span> |
| <span class="sd"> >>> shape = (2,3)</span> |
| <span class="sd"> >>> kv.pushpull('3', mx.nd.ones(shape)*8, out=a)</span> |
| <span class="sd"> >>> print a.asnumpy()</span> |
| <span class="sd"> [[ 8. 8. 8.]</span> |
| <span class="sd"> [ 8. 8. 8.]]</span> |
| |
| <span class="sd"> >>> # aggregate the value and then pushpull</span> |
| <span class="sd"> >>> gpus = [mx.gpu(i) for i in range(4)]</span> |
| <span class="sd"> >>> b = [mx.nd.ones(shape, gpu) for gpu in gpus]</span> |
| <span class="sd"> >>> kv.pushpull('3', b, out=a)</span> |
| <span class="sd"> >>> print a.asnumpy()</span> |
| <span class="sd"> [[ 4. 4. 4.]</span> |
| <span class="sd"> [ 4. 4. 4.]]</span> |
| |
| <span class="sd"> >>> # pushpull a list of keys.</span> |
| <span class="sd"> >>> # single device</span> |
| <span class="sd"> >>> keys = ['4', '5', '6']</span> |
| <span class="sd"> >>> b = [mx.nd.zeros(shape)]*len(keys)</span> |
| <span class="sd"> >>> kv.pushpull(keys, [mx.nd.ones(shape)]*len(keys), out=b)</span> |
| <span class="sd"> >>> print b[1].asnumpy()</span> |
| <span class="sd"> [[ 1. 1. 1.]</span> |
| <span class="sd"> [ 1. 1. 1.]]</span> |
| |
| <span class="sd"> >>> # multiple devices:</span> |
| <span class="sd"> >>> keys = ['7', '8', '9']</span> |
| <span class="sd"> >>> b = [[mx.nd.ones(shape, gpu) for gpu in gpus]] * len(keys)</span> |
| <span class="sd"> >>> kv.pushpull(keys, b)</span> |
| <span class="sd"> >>> print b[1][1].asnumpy()</span> |
| <span class="sd"> [[ 4. 4. 4.]</span> |
| <span class="sd"> [ 4. 4. 4.]]</span> |
| |
| <span class="sd"> """</span> |
| <span class="n">cvkeys</span><span class="p">,</span> <span class="n">cvals</span><span class="p">,</span> <span class="n">use_str_keys</span> <span class="o">=</span> <span class="n">_ctype_key_value</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">value</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">cokeys</span><span class="p">,</span> <span class="n">couts</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">_ctype_key_value</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">out</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">cokeys</span> <span class="o">=</span> <span class="n">cvkeys</span> |
| <span class="n">couts</span> <span class="o">=</span> <span class="n">cvals</span> |
| |
| <span class="k">if</span> <span class="n">use_str_keys</span><span class="p">:</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXKVStorePushPullEx</span><span class="p">(</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">handle</span><span class="p">,</span> <span class="n">mx_uint</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">cvkeys</span><span class="p">)),</span> <span class="n">cvkeys</span><span class="p">,</span> <span class="n">mx_uint</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">cokeys</span><span class="p">)),</span> <span class="n">cokeys</span><span class="p">,</span> |
| <span class="n">cvals</span><span class="p">,</span> <span class="n">couts</span><span class="p">,</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">(</span><span class="n">priority</span><span class="p">)))</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXKVStorePushPull</span><span class="p">(</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">handle</span><span class="p">,</span> <span class="n">mx_uint</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">cvkeys</span><span class="p">)),</span> <span class="n">cvkeys</span><span class="p">,</span> <span class="n">mx_uint</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">cokeys</span><span class="p">)),</span> <span class="n">cokeys</span><span class="p">,</span> |
| <span class="n">cvals</span><span class="p">,</span> <span class="n">couts</span><span class="p">,</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">(</span><span class="n">priority</span><span class="p">)))</span></div> |
| |
| <div class="viewcode-block" id="KVStore.row_sparse_pull"><a class="viewcode-back" href="../../../api/kvstore/generated/mxnet.kvstore.KVStore.html#mxnet.kvstore.KVStore.row_sparse_pull">[docs]</a> <span class="k">def</span> <span class="nf">row_sparse_pull</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">key</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">priority</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">row_ids</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">""" Pulls a single RowSparseNDArray value or a sequence of RowSparseNDArray values \</span> |
| <span class="sd"> from the store with specified row_ids. When there is only one row_id, KVStoreRowSparsePull \</span> |
| <span class="sd"> is invoked just once and the result is broadcast to all the rest of outputs.</span> |
| |
| <span class="sd"> `row_sparse_pull` is executed asynchronously after all previous</span> |
| <span class="sd"> `pull`/`row_sparse_pull` calls and the last `push` call for the</span> |
| <span class="sd"> same input key(s) are finished.</span> |
| |
| <span class="sd"> The returned values are guaranteed to be the latest values in the store.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> key : str, int, or sequence of str or int</span> |
| <span class="sd"> Keys.</span> |
| |
| <span class="sd"> out: RowSparseNDArray or list of RowSparseNDArray or list of list of RowSparseNDArray</span> |
| <span class="sd"> Values corresponding to the keys. The stype is expected to be row_sparse</span> |
| |
| <span class="sd"> priority : int, optional</span> |
| <span class="sd"> The priority of the pull operation.</span> |
| <span class="sd"> Higher priority pull operations are likely to be executed before</span> |
| <span class="sd"> other pull actions.</span> |
| |
| <span class="sd"> row_ids : NDArray or list of NDArray</span> |
| <span class="sd"> The row_ids for which to pull for each value. Each row_id is an 1-D NDArray \</span> |
| <span class="sd"> whose values don't have to be unique nor sorted.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> shape = (3, 3)</span> |
| <span class="sd"> >>> kv.init('3', mx.nd.ones(shape).tostype('row_sparse'))</span> |
| <span class="sd"> >>> a = mx.nd.sparse.zeros('row_sparse', shape)</span> |
| <span class="sd"> >>> row_ids = mx.nd.array([0, 2], dtype='int64')</span> |
| <span class="sd"> >>> kv.row_sparse_pull('3', out=a, row_ids=row_ids)</span> |
| <span class="sd"> >>> print a.asnumpy()</span> |
| <span class="sd"> [[ 1. 1. 1.]</span> |
| <span class="sd"> [ 0. 0. 0.]</span> |
| <span class="sd"> [ 1. 1. 1.]]</span> |
| <span class="sd"> >>> duplicate_row_ids = mx.nd.array([2, 2], dtype='int64')</span> |
| <span class="sd"> >>> kv.row_sparse_pull('3', out=a, row_ids=duplicate_row_ids)</span> |
| <span class="sd"> >>> print a.asnumpy()</span> |
| <span class="sd"> [[ 0. 0. 0.]</span> |
| <span class="sd"> [ 0. 0. 0.]</span> |
| <span class="sd"> [ 1. 1. 1.]]</span> |
| <span class="sd"> >>> unsorted_row_ids = mx.nd.array([1, 0], dtype='int64')</span> |
| <span class="sd"> >>> kv.row_sparse_pull('3', out=a, row_ids=unsorted_row_ids)</span> |
| <span class="sd"> >>> print a.asnumpy()</span> |
| <span class="sd"> [[ 1. 1. 1.]</span> |
| <span class="sd"> [ 1. 1. 1.]</span> |
| <span class="sd"> [ 0. 0. 0.]]</span> |
| <span class="sd"> """</span> |
| <span class="k">assert</span><span class="p">(</span><span class="n">out</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">)</span> |
| <span class="k">assert</span><span class="p">(</span><span class="n">row_ids</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">)</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">row_ids</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">):</span> |
| <span class="n">row_ids</span> <span class="o">=</span> <span class="p">[</span><span class="n">row_ids</span><span class="p">]</span> |
| <span class="k">assert</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">row_ids</span><span class="p">,</span> <span class="nb">list</span><span class="p">)),</span> \ |
| <span class="s2">"row_ids should be NDArray or list of NDArray"</span> |
| <span class="n">first_out</span> <span class="o">=</span> <span class="n">out</span> |
| <span class="c1"># whether row_ids are the same</span> |
| <span class="n">single_rowid</span> <span class="o">=</span> <span class="kc">False</span> |
| <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">row_ids</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">out</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span> |
| <span class="n">single_rowid</span> <span class="o">=</span> <span class="kc">True</span> |
| <span class="n">first_out</span> <span class="o">=</span> <span class="p">[</span><span class="n">out</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span> |
| <span class="n">ckeys</span><span class="p">,</span> <span class="n">cvals</span><span class="p">,</span> <span class="n">use_str_keys</span> <span class="o">=</span> <span class="n">_ctype_key_value</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">first_out</span><span class="p">)</span> |
| <span class="n">_</span><span class="p">,</span> <span class="n">crow_ids</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">_ctype_key_value</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">row_ids</span><span class="p">)</span> |
| <span class="k">assert</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">crow_ids</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">cvals</span><span class="p">)),</span> \ |
| <span class="s2">"the number of row_ids doesn't match the number of values"</span> |
| <span class="k">if</span> <span class="n">use_str_keys</span><span class="p">:</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXKVStorePullRowSparseEx</span><span class="p">(</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">handle</span><span class="p">,</span> <span class="n">mx_uint</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">ckeys</span><span class="p">)),</span> <span class="n">ckeys</span><span class="p">,</span> <span class="n">cvals</span><span class="p">,</span> <span class="n">crow_ids</span><span class="p">,</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">(</span><span class="n">priority</span><span class="p">)))</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXKVStorePullRowSparse</span><span class="p">(</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">handle</span><span class="p">,</span> <span class="n">mx_uint</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">ckeys</span><span class="p">)),</span> <span class="n">ckeys</span><span class="p">,</span> <span class="n">cvals</span><span class="p">,</span> <span class="n">crow_ids</span><span class="p">,</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">(</span><span class="n">priority</span><span class="p">)))</span> |
| <span class="c1"># the result can be copied to other devices without invoking row_sparse_pull</span> |
| <span class="c1"># if the indices are the same</span> |
| <span class="k">if</span> <span class="n">single_rowid</span><span class="p">:</span> |
| <span class="k">for</span> <span class="n">out_i</span> <span class="ow">in</span> <span class="n">out</span><span class="p">[</span><span class="mi">1</span><span class="p">:]:</span> |
| <span class="n">out</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="n">out_i</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="KVStore.set_gradient_compression"><a class="viewcode-back" href="../../../api/kvstore/generated/mxnet.kvstore.KVStore.html#mxnet.kvstore.KVStore.set_gradient_compression">[docs]</a> <span class="k">def</span> <span class="nf">set_gradient_compression</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">compression_params</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">""" Specifies type of low-bit quantization for gradient compression \</span> |
| <span class="sd"> and additional arguments depending on the type of compression being used.</span> |
| |
| <span class="sd"> The 1bit compression works as follows: values which is above the threshold in the</span> |
| <span class="sd"> gradient will be set to +1, whereas values below threshold will be set to -1.</span> |
| |
| <span class="sd"> 2bit Gradient Compression takes a positive float `threshold`.</span> |
| <span class="sd"> The technique works by thresholding values such that positive values in the</span> |
| <span class="sd"> gradient above threshold will be set to threshold. Negative values whose absolute</span> |
| <span class="sd"> values are higher than threshold, will be set to the negative of threshold.</span> |
| <span class="sd"> Values whose absolute values are less than threshold will be set to 0.</span> |
| <span class="sd"> By doing so, each value in the gradient is in one of three states. 2bits are</span> |
| <span class="sd"> used to represent these states, and every 16 float values in the original</span> |
| <span class="sd"> gradient can be represented using one float. This compressed representation</span> |
| <span class="sd"> can reduce communication costs. The difference between these thresholded values and</span> |
| <span class="sd"> original values is stored at the sender's end as residual and added to the</span> |
| <span class="sd"> gradient in the next iteration.</span> |
| |
| <span class="sd"> When kvstore is 'local', gradient compression is used to reduce communication</span> |
| <span class="sd"> between multiple devices (gpus). Gradient is quantized on each GPU which</span> |
| <span class="sd"> computed the gradients, then sent to the GPU which merges the gradients. This</span> |
| <span class="sd"> receiving GPU dequantizes the gradients and merges them. Note that this</span> |
| <span class="sd"> increases memory usage on each GPU because of the residual array stored.</span> |
| |
| <span class="sd"> When kvstore is 'dist', gradient compression is used to reduce communication</span> |
| <span class="sd"> from worker to sender. Gradient is quantized on each worker which</span> |
| <span class="sd"> computed the gradients, then sent to the server which dequantizes</span> |
| <span class="sd"> this data and merges the gradients from each worker. Note that this</span> |
| <span class="sd"> increases CPU memory usage on each worker because of the residual array stored.</span> |
| <span class="sd"> Only worker to server communication is compressed in this setting.</span> |
| <span class="sd"> If each machine has multiple GPUs, currently this GPU to GPU or GPU to CPU communication</span> |
| <span class="sd"> is not compressed. Server to worker communication (in the case of pull)</span> |
| <span class="sd"> is also not compressed.</span> |
| |
| <span class="sd"> To use 2bit compression, we need to specify `type` as `2bit`.</span> |
| <span class="sd"> Only specifying `type` would use default value for the threshold.</span> |
| <span class="sd"> To completely specify the arguments for 2bit compression, we would need to pass</span> |
| <span class="sd"> a dictionary which includes `threshold` like:</span> |
| <span class="sd"> {'type': '2bit', 'threshold': 0.5}</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> compression_params : dict</span> |
| <span class="sd"> A dictionary specifying the type and parameters for gradient compression.</span> |
| <span class="sd"> The key `type` in this dictionary is a</span> |
| <span class="sd"> required string argument and specifies the type of gradient compression.</span> |
| <span class="sd"> Currently `type` can be only `1bit` and `2bit`</span> |
| <span class="sd"> Other keys in this dictionary are optional and specific to the type</span> |
| <span class="sd"> of gradient compression.</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="p">(</span><span class="s1">'device'</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">type</span><span class="p">)</span> <span class="ow">or</span> <span class="p">(</span><span class="s1">'dist'</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">type</span><span class="p">):</span> <span class="c1"># pylint: disable=unsupported-membership-test</span> |
| <span class="n">ckeys</span><span class="p">,</span> <span class="n">cvals</span> <span class="o">=</span> <span class="n">_ctype_dict</span><span class="p">(</span><span class="n">compression_params</span><span class="p">)</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXKVStoreSetGradientCompression</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">handle</span><span class="p">,</span> |
| <span class="n">mx_uint</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">compression_params</span><span class="p">)),</span> |
| <span class="n">ckeys</span><span class="p">,</span> <span class="n">cvals</span><span class="p">))</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s1">'Gradient compression is not supported for this type of kvstore'</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="KVStore.set_optimizer"><a class="viewcode-back" href="../../../api/kvstore/generated/mxnet.kvstore.KVStore.html#mxnet.kvstore.KVStore.set_optimizer">[docs]</a> <span class="k">def</span> <span class="nf">set_optimizer</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">optimizer</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">""" Registers an optimizer with the kvstore.</span> |
| |
| <span class="sd"> When using a single machine, this function updates the local optimizer.</span> |
| <span class="sd"> If using multiple machines and this operation is invoked from a worker node,</span> |
| <span class="sd"> it will serialized the optimizer with pickle and send it to all servers.</span> |
| <span class="sd"> The function returns after all servers have been updated.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> optimizer : Optimizer</span> |
| <span class="sd"> The new optimizer for the store</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| |
| <span class="sd"> >>> kv = mx.kv.create()</span> |
| <span class="sd"> >>> shape = (2, 2)</span> |
| <span class="sd"> >>> weight = mx.nd.zeros(shape)</span> |
| <span class="sd"> >>> kv.init(3, weight)</span> |
| <span class="sd"> >>> # set the optimizer for kvstore as the default SGD optimizer</span> |
| <span class="sd"> >>> kv.set_optimizer(mx.optimizer.SGD())</span> |
| <span class="sd"> >>> grad = mx.nd.ones(shape)</span> |
| <span class="sd"> >>> kv.push(3, grad)</span> |
| <span class="sd"> >>> kv.pull(3, out = weight)</span> |
| <span class="sd"> >>> # weight is updated via gradient descent</span> |
| <span class="sd"> >>> weight.asnumpy()</span> |
| <span class="sd"> array([[-0.01, -0.01],</span> |
| <span class="sd"> [-0.01, -0.01]], dtype=float32)</span> |
| <span class="sd"> """</span> |
| <span class="n">is_worker</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">()</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXKVStoreIsWorkerNode</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">byref</span><span class="p">(</span><span class="n">is_worker</span><span class="p">)))</span> |
| |
| <span class="c1"># pylint: disable=invalid-name</span> |
| <span class="k">if</span> <span class="s1">'dist'</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">type</span> <span class="ow">and</span> <span class="n">is_worker</span><span class="o">.</span><span class="n">value</span><span class="p">:</span> <span class="c1"># pylint: disable=unsupported-membership-test</span> |
| <span class="c1"># send the optimizer to server</span> |
| <span class="k">try</span><span class="p">:</span> |
| <span class="c1"># use ASCII protocol 0, might be slower, but not a big ideal</span> |
| <span class="n">optim_str</span> <span class="o">=</span> <span class="n">py_str</span><span class="p">(</span><span class="n">pickle</span><span class="o">.</span><span class="n">dumps</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span> |
| <span class="k">except</span><span class="p">:</span> |
| <span class="k">raise</span> |
| <span class="n">cmd</span> <span class="o">=</span> <span class="n">_get_kvstore_server_command_type</span><span class="p">(</span><span class="s1">'kController'</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_send_command_to_servers</span><span class="p">(</span><span class="n">cmd</span><span class="p">,</span> <span class="n">optim_str</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">optimizer</span><span class="o">.</span><span class="n">multi_precision</span><span class="p">:</span> |
| <span class="n">cmd</span> <span class="o">=</span> <span class="n">_get_kvstore_server_command_type</span><span class="p">(</span><span class="s1">'kSetMultiPrecision'</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_send_command_to_servers</span><span class="p">(</span><span class="n">cmd</span><span class="p">,</span> <span class="s1">''</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">_set_updater</span><span class="p">(</span><span class="n">opt</span><span class="o">.</span><span class="n">get_updater</span><span class="p">(</span><span class="n">optimizer</span><span class="p">))</span></div> |
| |
| <span class="nd">@property</span> |
| <span class="k">def</span> <span class="nf">type</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">""" Returns the type of this kvstore.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> type : str</span> |
| <span class="sd"> the string type</span> |
| <span class="sd"> """</span> |
| <span class="n">kv_type</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_char_p</span><span class="p">()</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXKVStoreGetType</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">handle</span><span class="p">,</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">byref</span><span class="p">(</span><span class="n">kv_type</span><span class="p">)))</span> |
| <span class="k">return</span> <span class="n">py_str</span><span class="p">(</span><span class="n">kv_type</span><span class="o">.</span><span class="n">value</span><span class="p">)</span> |
| |
| <span class="nd">@property</span> |
| <span class="k">def</span> <span class="nf">rank</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">""" Returns the rank of this worker node.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> rank : int</span> |
| <span class="sd"> The rank of this node, which is in range [0, num_workers())</span> |
| <span class="sd"> """</span> |
| <span class="n">rank</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">()</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXKVStoreGetRank</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">handle</span><span class="p">,</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">byref</span><span class="p">(</span><span class="n">rank</span><span class="p">)))</span> |
| <span class="k">return</span> <span class="n">rank</span><span class="o">.</span><span class="n">value</span> |
| |
| <span class="nd">@property</span> |
| <span class="k">def</span> <span class="nf">num_workers</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Returns the number of worker nodes.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> size :int</span> |
| <span class="sd"> The number of worker nodes.</span> |
| <span class="sd"> """</span> |
| <span class="n">size</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">()</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXKVStoreGetGroupSize</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">handle</span><span class="p">,</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">byref</span><span class="p">(</span><span class="n">size</span><span class="p">)))</span> |
| <span class="k">return</span> <span class="n">size</span><span class="o">.</span><span class="n">value</span> |
| |
| <div class="viewcode-block" id="KVStore.save_optimizer_states"><a class="viewcode-back" href="../../../api/kvstore/generated/mxnet.kvstore.KVStore.html#mxnet.kvstore.KVStore.save_optimizer_states">[docs]</a> <span class="k">def</span> <span class="nf">save_optimizer_states</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">fname</span><span class="p">,</span> <span class="n">dump_optimizer</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Saves the optimizer (updater) state to a file. This is often used when checkpointing</span> |
| <span class="sd"> the model during training.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> fname : str</span> |
| <span class="sd"> Path to the output states file.</span> |
| <span class="sd"> dump_optimizer : bool, default False</span> |
| <span class="sd"> Whether to also save the optimizer itself. This would also save optimizer</span> |
| <span class="sd"> information such as learning rate and weight decay schedules.</span> |
| <span class="sd"> """</span> |
| <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">_updater</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">,</span> <span class="s2">"Cannot save states for distributed training"</span> |
| <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">fname</span><span class="p">,</span> <span class="s1">'wb'</span><span class="p">)</span> <span class="k">as</span> <span class="n">fout</span><span class="p">:</span> |
| <span class="n">fout</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_updater</span><span class="o">.</span><span class="n">get_states</span><span class="p">(</span><span class="n">dump_optimizer</span><span class="p">))</span></div> |
| |
| <div class="viewcode-block" id="KVStore.load_optimizer_states"><a class="viewcode-back" href="../../../api/kvstore/generated/mxnet.kvstore.KVStore.html#mxnet.kvstore.KVStore.load_optimizer_states">[docs]</a> <span class="k">def</span> <span class="nf">load_optimizer_states</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">fname</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Loads the optimizer (updater) state from the file.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> fname : str</span> |
| <span class="sd"> Path to input states file.</span> |
| <span class="sd"> """</span> |
| <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">_updater</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">,</span> <span class="s2">"Cannot load states for distributed training"</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_updater</span><span class="o">.</span><span class="n">set_states</span><span class="p">(</span><span class="nb">open</span><span class="p">(</span><span class="n">fname</span><span class="p">,</span> <span class="s1">'rb'</span><span class="p">)</span><span class="o">.</span><span class="n">read</span><span class="p">())</span></div> |
| |
| <span class="k">def</span> <span class="nf">_set_updater</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">updater</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Sets a push updater into the store.</span> |
| |
| <span class="sd"> This function only changes the local store. When running on multiple machines one must</span> |
| <span class="sd"> use `set_optimizer`.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> updater : function</span> |
| <span class="sd"> The updater function.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> def update(key, input, stored):</span> |
| <span class="sd"> ... print "update on key: %d" % key</span> |
| <span class="sd"> ... stored += input * 2</span> |
| <span class="sd"> >>> kv._set_updater(update)</span> |
| <span class="sd"> >>> kv.pull('3', out=a)</span> |
| <span class="sd"> >>> print a.asnumpy()</span> |
| <span class="sd"> [[ 4. 4. 4.]</span> |
| <span class="sd"> [ 4. 4. 4.]]</span> |
| <span class="sd"> >>> kv.push('3', mx.nd.ones(shape))</span> |
| <span class="sd"> update on key: 3</span> |
| <span class="sd"> >>> kv.pull('3', out=a)</span> |
| <span class="sd"> >>> print a.asnumpy()</span> |
| <span class="sd"> [[ 6. 6. 6.]</span> |
| <span class="sd"> [ 6. 6. 6.]]</span> |
| <span class="sd"> """</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_updater</span> <span class="o">=</span> <span class="n">updater</span> |
| <span class="c1"># set updater with int keys</span> |
| <span class="n">_updater_proto</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">CFUNCTYPE</span><span class="p">(</span> |
| <span class="kc">None</span><span class="p">,</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">,</span> <span class="n">NDArrayHandle</span><span class="p">,</span> <span class="n">NDArrayHandle</span><span class="p">,</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_void_p</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_updater_func</span> <span class="o">=</span> <span class="n">_updater_proto</span><span class="p">(</span><span class="n">_updater_wrapper</span><span class="p">(</span><span class="n">updater</span><span class="p">))</span> |
| <span class="c1"># set updater with str keys</span> |
| <span class="n">_str_updater_proto</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">CFUNCTYPE</span><span class="p">(</span> |
| <span class="kc">None</span><span class="p">,</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_char_p</span><span class="p">,</span> <span class="n">NDArrayHandle</span><span class="p">,</span> <span class="n">NDArrayHandle</span><span class="p">,</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_void_p</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_str_updater_func</span> <span class="o">=</span> <span class="n">_str_updater_proto</span><span class="p">(</span><span class="n">_updater_wrapper</span><span class="p">(</span><span class="n">updater</span><span class="p">))</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXKVStoreSetUpdaterEx</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">handle</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_updater_func</span><span class="p">,</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_str_updater_func</span><span class="p">,</span> <span class="kc">None</span><span class="p">))</span> |
| |
| |
| <span class="k">def</span> <span class="nf">_barrier</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Invokes global barrier among all worker nodes.</span> |
| |
| <span class="sd"> For example, assume there are `n` machines. We would like machine `0` to first</span> |
| <span class="sd"> `init` the values and then have all the workers `pull` the initialized value.</span> |
| <span class="sd"> Before pulling, we can place invoke `_barrier()` to guarantee that the</span> |
| <span class="sd"> initialization is finished.</span> |
| <span class="sd"> """</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXKVStoreBarrier</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">handle</span><span class="p">))</span> |
| |
| <span class="k">def</span> <span class="nf">_send_command_to_servers</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">head</span><span class="p">,</span> <span class="n">body</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Sends a command to all server nodes.</span> |
| |
| <span class="sd"> Sending command to a server node will cause that server node to invoke</span> |
| <span class="sd"> ``KVStoreServer.controller`` to execute the command.</span> |
| |
| <span class="sd"> This function returns after the command has been executed on all server</span> |
| <span class="sd"> nodes.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> head : int</span> |
| <span class="sd"> the head of the command.</span> |
| <span class="sd"> body : str</span> |
| <span class="sd"> the body of the command.</span> |
| <span class="sd"> """</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXKVStoreSendCommmandToServers</span><span class="p">(</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">handle</span><span class="p">,</span> <span class="n">mx_uint</span><span class="p">(</span><span class="n">head</span><span class="p">),</span> <span class="n">c_str</span><span class="p">(</span><span class="n">body</span><span class="p">)))</span></div> |
| </pre></div> |
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