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| <span class="mdl-layout-title toc">Table Of Contents</span> |
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| <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> |
| </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.gluon.trainer</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="c1"># pylint: disable=line-too-long</span> |
| <span class="sd">"""Parameter optimizer."""</span> |
| <span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'Trainer'</span><span class="p">]</span> |
| |
| <span class="kn">import</span> <span class="nn">warnings</span> |
| <span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">OrderedDict</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">..model</span> <span class="kn">import</span> <span class="n">_create_kvstore</span><span class="p">,</span> <span class="n">_create_sparse_kvstore</span> |
| <span class="kn">from</span> <span class="nn">.parameter</span> <span class="kn">import</span> <span class="n">Parameter</span> |
| <span class="kn">from</span> <span class="nn">..kvstore</span> <span class="kn">import</span> <span class="n">KVStore</span> |
| |
| |
| <div class="viewcode-block" id="Trainer"><a class="viewcode-back" href="../../../api/gluon/trainer.html#mxnet.gluon.Trainer">[docs]</a><span class="k">class</span> <span class="nc">Trainer</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Applies an `Optimizer` on a set of Parameters. Trainer should</span> |
| <span class="sd"> be used together with `autograd`.</span> |
| |
| <span class="sd"> .. note::</span> |
| |
| <span class="sd"> For the following cases, updates will always happen on kvstore,</span> |
| <span class="sd"> i.e., you cannot set update_on_kvstore=False.</span> |
| |
| <span class="sd"> - dist kvstore with sparse weights or sparse gradients</span> |
| <span class="sd"> - dist async kvstore</span> |
| <span class="sd"> - `optimizer.lr_scheduler` is not None</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> params : Dict</span> |
| <span class="sd"> The set of parameters to optimize.</span> |
| <span class="sd"> optimizer : str or Optimizer</span> |
| <span class="sd"> The optimizer to use. See</span> |
| <span class="sd"> `help <https://mxnet.apache.org/api/python/docs/api/optimizer/index.html>`_</span> |
| <span class="sd"> on Optimizer for a list of available optimizers.</span> |
| <span class="sd"> optimizer_params : dict</span> |
| <span class="sd"> Key-word arguments to be passed to optimizer constructor. For example,</span> |
| <span class="sd"> `{'learning_rate': 0.1}`. All optimizers accept learning_rate, wd (weight decay),</span> |
| <span class="sd"> clip_gradient, and lr_scheduler. See each optimizer's</span> |
| <span class="sd"> constructor for a list of additional supported arguments.</span> |
| <span class="sd"> kvstore : str or KVStore</span> |
| <span class="sd"> kvstore type for multi-gpu and distributed training. See help on</span> |
| <span class="sd"> :func:`mxnet.kvstore.create` for more information.</span> |
| <span class="sd"> compression_params : dict</span> |
| <span class="sd"> Specifies type of gradient compression and additional arguments depending</span> |
| <span class="sd"> on the type of compression being used. For example, 2bit compression requires a threshold.</span> |
| <span class="sd"> Arguments would then be {'type':'2bit', 'threshold':0.5}</span> |
| <span class="sd"> See mxnet.KVStore.set_gradient_compression method for more details on gradient compression.</span> |
| <span class="sd"> update_on_kvstore : bool, default None</span> |
| <span class="sd"> Whether to perform parameter updates on kvstore. If None and optimizer.aggregate_num <= 1,</span> |
| <span class="sd"> then trainer will choose the more suitable option depending on the type of kvstore.</span> |
| <span class="sd"> If None and optimizer.aggregate_num > 1, `update_on_kvstore` is set to False.</span> |
| <span class="sd"> If the `update_on_kvstore` argument is provided,</span> |
| <span class="sd"> environment variable `MXNET_UPDATE_ON_KVSTORE` will be ignored.</span> |
| |
| <span class="sd"> Properties</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> learning_rate : float</span> |
| <span class="sd"> The current learning rate of the optimizer. Given an Optimizer object</span> |
| <span class="sd"> optimizer, its learning rate can be accessed as optimizer.learning_rate.</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">params</span><span class="p">,</span> <span class="n">optimizer</span><span class="p">,</span> <span class="n">optimizer_params</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">kvstore</span><span class="o">=</span><span class="s1">'device'</span><span class="p">,</span> |
| <span class="n">compression_params</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">update_on_kvstore</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="n">param_list</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">params</span><span class="p">,</span> <span class="p">(</span><span class="nb">dict</span><span class="p">,</span> <span class="n">OrderedDict</span><span class="p">)):</span> |
| <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">params</span><span class="o">.</span><span class="n">keys</span><span class="p">())):</span> |
| <span class="n">param_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">params</span><span class="p">[</span><span class="n">key</span><span class="p">])</span> |
| <span class="n">params</span> <span class="o">=</span> <span class="n">param_list</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">params</span><span class="p">,</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">)):</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span> |
| <span class="s2">"First argument must be a list or dict of Parameters, "</span> \ |
| <span class="sa">f</span><span class="s2">"got </span><span class="si">{</span><span class="nb">type</span><span class="p">(</span><span class="n">params</span><span class="p">)</span><span class="si">}</span><span class="s2">."</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_params</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="c1"># parameters to initialize on the kvstore</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_contains_sparse_weight</span> <span class="o">=</span> <span class="kc">False</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_contains_sparse_grad</span> <span class="o">=</span> <span class="kc">False</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_param2idx</span> <span class="o">=</span> <span class="p">{}</span> |
| <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">param</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">params</span><span class="p">):</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">param</span><span class="p">,</span> <span class="n">Parameter</span><span class="p">):</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span> |
| <span class="s2">"First argument must be a list or dict of Parameters, "</span> \ |
| <span class="sa">f</span><span class="s2">"got list of </span><span class="si">{</span><span class="nb">type</span><span class="p">(</span><span class="n">param</span><span class="p">)</span><span class="si">}</span><span class="s2">."</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">param</span><span class="o">.</span><span class="n">_uuid</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_param2idx</span><span class="p">:</span> |
| <span class="c1"># Shared parameters have same uuid; only need to store one of the shared versions</span> |
| <span class="k">continue</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_param2idx</span><span class="p">[</span><span class="n">param</span><span class="o">.</span><span class="n">_uuid</span><span class="p">]</span> <span class="o">=</span> <span class="n">i</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_params</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">param</span><span class="p">)</span> |
| <span class="n">param</span><span class="o">.</span><span class="n">_set_trainer</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">param</span><span class="o">.</span><span class="n">_stype</span> <span class="o">!=</span> <span class="s1">'default'</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_contains_sparse_weight</span> <span class="o">=</span> <span class="kc">True</span> |
| <span class="k">if</span> <span class="n">param</span><span class="o">.</span><span class="n">_grad_stype</span> <span class="o">!=</span> <span class="s1">'default'</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_contains_sparse_grad</span> <span class="o">=</span> <span class="kc">True</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_compression_params</span> <span class="o">=</span> <span class="n">compression_params</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_devices</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_check_devices</span><span class="p">()</span> |
| <span class="n">optimizer_params</span> <span class="o">=</span> <span class="n">optimizer_params</span> <span class="k">if</span> <span class="n">optimizer_params</span> <span class="k">else</span> <span class="p">{}</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_init_optimizer</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">optimizer_params</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_scale</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_optimizer</span><span class="o">.</span><span class="n">rescale_grad</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_optimizer</span><span class="o">.</span><span class="n">aggregate_num</span> <span class="o">></span> <span class="mi">1</span> <span class="ow">and</span> <span class="n">update_on_kvstore</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="n">update_on_kvstore</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"Cannot set update_on_kvstore=True "</span> |
| <span class="s2">"when optimizer.aggregate_num > 1."</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">update_on_kvstore</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">_optimizer</span><span class="o">.</span><span class="n">aggregate_num</span> <span class="o">></span> <span class="mi">1</span><span class="p">:</span> |
| <span class="n">update_on_kvstore</span> <span class="o">=</span> <span class="kc">False</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_kvstore_params</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'kvstore'</span><span class="p">:</span> <span class="n">kvstore</span><span class="p">,</span> <span class="s1">'update_on_kvstore'</span><span class="p">:</span> <span class="n">update_on_kvstore</span><span class="p">}</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_kv_initialized</span> <span class="o">=</span> <span class="kc">False</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_kvstore</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_update_on_kvstore</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_distributed</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_params_to_init</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_reset_kvstore</span><span class="p">()</span> |
| |
| <span class="k">def</span> <span class="nf">_check_contexts</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""This function has been deprecated. Please refer to ``Trainer._check_devices``."""</span> |
| <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s1">'Trainer._check_contexts has been renamed to'</span> |
| <span class="s1">' Trainer._check_devices'</span><span class="p">,</span> <span class="ne">DeprecationWarning</span><span class="p">)</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_check_devices</span><span class="p">()</span> |
| |
| <span class="k">def</span> <span class="nf">_check_devices</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="n">devices</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="k">for</span> <span class="n">param</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_params</span><span class="p">:</span> |
| <span class="n">device</span> <span class="o">=</span> <span class="n">param</span><span class="o">.</span><span class="n">list_device</span><span class="p">()</span> |
| <span class="k">assert</span> <span class="n">devices</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">devices</span> <span class="o">==</span> <span class="n">device</span><span class="p">,</span> \ |
| <span class="s2">"All Parameters must be initialized on the same set of devices, "</span> \ |
| <span class="sa">f</span><span class="s2">"but Parameter </span><span class="si">{</span><span class="n">param</span><span class="o">.</span><span class="n">name</span><span class="si">}</span><span class="s2"> is initialized on </span><span class="si">{</span><span class="nb">str</span><span class="p">(</span><span class="n">device</span><span class="p">)</span><span class="si">}</span><span class="s2"> while previous Parameters "</span> \ |
| <span class="sa">f</span><span class="s2">"are initialized on </span><span class="si">{</span><span class="nb">str</span><span class="p">(</span><span class="n">devices</span><span class="p">)</span><span class="si">}</span><span class="s2">."</span> |
| <span class="n">devices</span> <span class="o">=</span> <span class="n">device</span> |
| <span class="k">return</span> <span class="n">devices</span> |
| |
| <span class="k">def</span> <span class="nf">_init_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="n">optimizer_params</span><span class="p">):</span> |
| <span class="n">param_dict</span> <span class="o">=</span> <span class="p">{</span><span class="n">i</span><span class="p">:</span> <span class="n">param</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">param</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_params</span><span class="p">)}</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">opt</span><span class="o">.</span><span class="n">Optimizer</span><span class="p">):</span> |
| <span class="k">assert</span> <span class="ow">not</span> <span class="n">optimizer_params</span><span class="p">,</span> \ |
| <span class="s2">"optimizer_params must be None if optimizer is an instance of "</span> \ |
| <span class="s2">"Optimizer instead of str"</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_optimizer</span> <span class="o">=</span> <span class="n">optimizer</span> |
| <span class="c1"># param_dict must not be deep copied, so that if user mutate the lr_mult</span> |
| <span class="c1"># or wd_mult of some parameters, it takes effect.</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_optimizer</span><span class="o">.</span><span class="n">param_dict</span> <span class="o">=</span> <span class="n">param_dict</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_optimizer</span> <span class="o">=</span> <span class="n">opt</span><span class="o">.</span><span class="n">create</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">param_dict</span><span class="o">=</span><span class="n">param_dict</span><span class="p">,</span> |
| <span class="o">**</span><span class="n">optimizer_params</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_updaters</span> <span class="o">=</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="bp">self</span><span class="o">.</span><span class="n">_optimizer</span><span class="p">)</span> \ |
| <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_devices</span><span class="p">]</span> |
| |
| <span class="k">def</span> <span class="nf">_init_params</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Initialize parameters in the KVStore.</span> |
| |
| <span class="sd"> Parameters with incomplete initialization are ignored.</span> |
| |
| <span class="sd"> """</span> |
| <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">_kv_initialized</span><span class="p">,</span> <span class="s2">"Cannot initialize parameters in KVStore "</span> \ |
| <span class="s2">"when KVStore is not initialized."</span> |
| <span class="n">params_to_init</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_kvstore</span><span class="p">:</span> |
| <span class="k">for</span> <span class="n">param</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_params_to_init</span><span class="p">:</span> |
| <span class="k">if</span> <span class="n">param</span><span class="o">.</span><span class="n">_deferred_init</span><span class="p">:</span> |
| <span class="n">params_to_init</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">param</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">param_arrays</span> <span class="o">=</span> <span class="n">param</span><span class="o">.</span><span class="n">_check_and_get</span><span class="p">(</span><span class="n">param</span><span class="o">.</span><span class="n">_data</span><span class="p">,</span> <span class="nb">list</span><span class="p">)</span> |
| <span class="n">idx</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_param2idx</span><span class="p">[</span><span class="n">param</span><span class="o">.</span><span class="n">_uuid</span><span class="p">]</span> |
| <span class="k">if</span> <span class="n">param</span><span class="o">.</span><span class="n">_stype</span> <span class="o">!=</span> <span class="s1">'default'</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_kvstore</span><span class="o">.</span><span class="n">init</span><span class="p">(</span><span class="n">idx</span><span class="p">,</span> <span class="n">param_arrays</span><span class="p">[</span><span class="mi">0</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">_kvstore</span><span class="o">.</span><span class="n">broadcast</span><span class="p">(</span><span class="n">idx</span><span class="p">,</span> <span class="n">param_arrays</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">param_arrays</span><span class="p">)</span> |
| |
| <span class="bp">self</span><span class="o">.</span><span class="n">_params_to_init</span> <span class="o">=</span> <span class="n">params_to_init</span> |
| |
| <span class="k">def</span> <span class="nf">_reset_kvstore</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Reset kvstore."""</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_kvstore</span> <span class="ow">and</span> <span class="s1">'dist'</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_kvstore</span><span class="o">.</span><span class="n">type</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">"Cannot reset distributed KVStore."</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_kv_initialized</span> <span class="o">=</span> <span class="kc">False</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_kvstore</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_distributed</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_update_on_kvstore</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_params_to_init</span> <span class="o">=</span> <span class="p">[</span><span class="n">param</span> <span class="k">for</span> <span class="n">param</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_params</span><span class="p">]</span> |
| |
| <span class="k">def</span> <span class="nf">_init_kvstore</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Create kvstore."""</span> |
| <span class="n">config</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_kvstore_params</span> |
| <span class="c1"># configure kvstore, update_on_kvstore and self._distributed on three cases:</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_contains_sparse_weight</span><span class="p">:</span> |
| <span class="c1"># If weight is sparse, kvstore must be present and the weight must be updated on kvstore.</span> |
| <span class="c1"># The training loop is the following:</span> |
| <span class="c1"># - row_sparse_pull(sparse_weight)</span> |
| <span class="c1"># - forward()</span> |
| <span class="c1"># - backward()</span> |
| <span class="c1"># - push_and_update(grad)</span> |
| <span class="c1"># - pull(weight)</span> |
| <span class="n">kvstore</span><span class="p">,</span> <span class="n">update_on_kvstore</span> <span class="o">=</span> <span class="n">_create_sparse_kvstore</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s1">'kvstore'</span><span class="p">])</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_distributed</span> <span class="o">=</span> <span class="s1">'dist'</span> <span class="ow">in</span> <span class="n">kvstore</span><span class="o">.</span><span class="n">type</span> |
| <span class="c1"># raise err if user provides unsupported configs</span> |
| <span class="k">if</span> <span class="n">config</span><span class="p">[</span><span class="s1">'update_on_kvstore'</span><span class="p">]</span> <span class="ow">is</span> <span class="kc">False</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"Cannot set update_on_kvstore=False when sparse weights "</span> |
| <span class="s2">"are present."</span><span class="p">)</span> |
| |
| <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">_contains_sparse_grad</span><span class="p">:</span> |
| <span class="c1"># For single node training with dense weight and sparse grad,</span> |
| <span class="c1"># we prefer update_on_kvstore=False because this is usually faster.</span> |
| <span class="c1"># This means we push and pull sparse gradients, and we do not store weight in kvstore.</span> |
| <span class="c1"># The training loop is the following:</span> |
| <span class="c1"># - forward()</span> |
| <span class="c1"># - backward()</span> |
| <span class="c1"># - push(grad)</span> |
| <span class="c1"># - pull(grad)</span> |
| <span class="c1"># - update(grad, weight)</span> |
| <span class="c1">#</span> |
| <span class="c1"># For multi-node training with dense weight and sparse grad,</span> |
| <span class="c1"># only update_on_kvstore=True is supported, due to the fact that</span> |
| <span class="c1"># kv.row_sparse_pull(grad) is not implemented.</span> |
| <span class="c1"># Therefore, we push sparse gradients and pull dense weights.</span> |
| <span class="c1"># The training loop contains:</span> |
| <span class="c1"># - forward()</span> |
| <span class="c1"># - backward()</span> |
| <span class="c1"># - push_and_update(grad)</span> |
| <span class="c1"># - pull(weight)</span> |
| <span class="n">arg_arrays</span> <span class="o">=</span> <span class="p">{</span><span class="n">param</span><span class="o">.</span><span class="n">_uuid</span><span class="p">:</span> <span class="n">param</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_devices</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="k">for</span> <span class="n">param</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_params</span><span class="p">}</span> |
| <span class="n">kvstore</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">_create_kvstore</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s1">'kvstore'</span><span class="p">],</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_devices</span><span class="p">),</span> <span class="n">arg_arrays</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_distributed</span> <span class="o">=</span> <span class="s1">'dist'</span> <span class="ow">in</span> <span class="n">kvstore</span><span class="o">.</span><span class="n">type</span> <span class="k">if</span> <span class="n">kvstore</span> <span class="k">else</span> <span class="kc">False</span> |
| <span class="n">update_on_kvstore</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_distributed</span> |
| <span class="c1"># raise err if user provides unsupported configs</span> |
| <span class="k">if</span> <span class="n">config</span><span class="p">[</span><span class="s1">'update_on_kvstore'</span><span class="p">]</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="n">config</span><span class="p">[</span><span class="s1">'update_on_kvstore'</span><span class="p">]</span> <span class="ow">is</span> <span class="kc">False</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">_distributed</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"Cannot set update_on_kvstore=False on dist kvstore "</span> |
| <span class="s2">"when sparse gradients are present."</span><span class="p">)</span> |
| <span class="n">update_on_kvstore</span> <span class="o">=</span> <span class="n">config</span><span class="p">[</span><span class="s1">'update_on_kvstore'</span><span class="p">]</span> |
| <span class="c1"># raise err if a custom kvstore is used for sparse training</span> |
| <span class="k">if</span> <span class="n">kvstore</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">kvstore</span><span class="p">,</span> <span class="n">KVStore</span><span class="p">):</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"Cannot use </span><span class="si">{}</span><span class="s2"> for multi-device training with sparse gradients"</span> |
| <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">kvstore</span><span class="p">)))</span> |
| |
| <span class="k">else</span><span class="p">:</span> |
| <span class="c1"># Training with dense weight and dense gradients.</span> |
| <span class="c1"># The only unsupported mode is async with update_on_kvstore=False</span> |
| <span class="n">arg_arrays</span> <span class="o">=</span> <span class="p">{</span><span class="n">param</span><span class="o">.</span><span class="n">_uuid</span><span class="p">:</span> <span class="n">param</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_devices</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="k">for</span> <span class="n">param</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_params</span><span class="p">}</span> |
| <span class="n">kvstore</span><span class="p">,</span> <span class="n">update_on_kvstore</span> <span class="o">=</span> <span class="n">_create_kvstore</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s1">'kvstore'</span><span class="p">],</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_devices</span><span class="p">),</span> |
| <span class="n">arg_arrays</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_distributed</span> <span class="o">=</span> <span class="s1">'dist'</span> <span class="ow">in</span> <span class="n">kvstore</span><span class="o">.</span><span class="n">type</span> <span class="k">if</span> <span class="n">kvstore</span> <span class="k">else</span> <span class="kc">False</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_distributed</span> <span class="ow">and</span> <span class="s1">'async'</span> <span class="ow">in</span> <span class="n">kvstore</span><span class="o">.</span><span class="n">type</span><span class="p">:</span> |
| <span class="n">update_on_kvstore</span> <span class="o">=</span> <span class="kc">True</span> |
| <span class="c1"># raise err if user provides unsupported configs</span> |
| <span class="k">if</span> <span class="n">config</span><span class="p">[</span><span class="s1">'update_on_kvstore'</span><span class="p">]</span> <span class="ow">is</span> <span class="kc">False</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"Please set update_on_kvstore=True "</span> |
| <span class="s2">"when training in async mode."</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">config</span><span class="p">[</span><span class="s1">'update_on_kvstore'</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">update_on_kvstore</span> <span class="o">=</span> <span class="n">config</span><span class="p">[</span><span class="s1">'update_on_kvstore'</span><span class="p">]</span> |
| <span class="c1"># raise err if update_on_kvstore is set to True with kvstores that do not support optimizers</span> |
| <span class="k">if</span> <span class="n">update_on_kvstore</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">kvstore</span><span class="o">.</span><span class="n">is_capable</span><span class="p">(</span><span class="s1">'optimizer'</span><span class="p">):</span> |
| <span class="k">if</span> <span class="n">config</span><span class="p">[</span><span class="s1">'update_on_kvstore'</span><span class="p">]:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"Please set update_on_kvstore=False "</span> |
| <span class="s2">"when training with </span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">kvstore</span><span class="p">)))</span> |
| <span class="n">update_on_kvstore</span> <span class="o">=</span> <span class="kc">False</span> |
| |
| <span class="c1"># set grad compression and optimizers</span> |
| <span class="k">if</span> <span class="n">kvstore</span><span class="p">:</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_compression_params</span><span class="p">:</span> |
| <span class="n">kvstore</span><span class="o">.</span><span class="n">set_gradient_compression</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_compression_params</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">update_on_kvstore</span><span class="p">:</span> |
| <span class="c1"># optimizer preferably needs to be set before init for multiprecision</span> |
| <span class="n">kvstore</span><span class="o">.</span><span class="n">set_optimizer</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_optimizer</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_kvstore</span> <span class="o">=</span> <span class="n">kvstore</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_update_on_kvstore</span> <span class="o">=</span> <span class="n">update_on_kvstore</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_kvstore</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_update_on_kvstore</span> <span class="o">=</span> <span class="kc">None</span> |
| |
| <span class="bp">self</span><span class="o">.</span><span class="n">_kv_initialized</span> <span class="o">=</span> <span class="kc">True</span> |
| |
| <span class="nd">@property</span> |
| <span class="k">def</span> <span class="nf">learning_rate</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_optimizer</span><span class="p">,</span> <span class="n">opt</span><span class="o">.</span><span class="n">Optimizer</span><span class="p">):</span> |
| <span class="k">raise</span> <span class="ne">UserWarning</span><span class="p">(</span><span class="s2">"Optimizer has to be defined before its learning "</span> |
| <span class="s2">"rate can be accessed."</span><span class="p">)</span> |
| |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_optimizer</span><span class="o">.</span><span class="n">learning_rate</span> |
| |
| <span class="nd">@property</span> |
| <span class="k">def</span> <span class="nf">optimizer</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_optimizer</span><span class="p">,</span> <span class="n">opt</span><span class="o">.</span><span class="n">Optimizer</span><span class="p">):</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_optimizer</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">UserWarning</span><span class="p">(</span><span class="s2">"Optimizer has not been initialized yet"</span><span class="p">)</span> |
| |
| <div class="viewcode-block" id="Trainer.set_learning_rate"><a class="viewcode-back" href="../../../api/gluon/trainer.html#mxnet.gluon.Trainer.set_learning_rate">[docs]</a> <span class="k">def</span> <span class="nf">set_learning_rate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">lr</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Sets a new learning rate of the optimizer.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> lr : float</span> |
| <span class="sd"> The new learning rate of the optimizer.</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_optimizer</span><span class="p">,</span> <span class="n">opt</span><span class="o">.</span><span class="n">Optimizer</span><span class="p">):</span> |
| <span class="k">raise</span> <span class="ne">UserWarning</span><span class="p">(</span><span class="s2">"Optimizer has to be defined before its learning "</span> |
| <span class="s2">"rate is mutated."</span><span class="p">)</span> |
| |
| <span class="bp">self</span><span class="o">.</span><span class="n">_optimizer</span><span class="o">.</span><span class="n">set_learning_rate</span><span class="p">(</span><span class="n">lr</span><span class="p">)</span></div> |
| |
| <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">parameter</span><span class="p">,</span> <span class="n">out</span><span class="p">,</span> <span class="n">row_id</span><span class="p">,</span> <span class="n">full_idx</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Internal method to invoke pull operations on KVStore. If `full_idx` is set to True,</span> |
| <span class="sd"> `kv.pull` is preferred instead of `kv.row_sparse_pull`.</span> |
| <span class="sd"> """</span> |
| <span class="c1"># initialize kv and params if not already</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_kv_initialized</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_init_kvstore</span><span class="p">()</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_params_to_init</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_init_params</span><span class="p">()</span> |
| <span class="n">idx</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_param2idx</span><span class="p">[</span><span class="n">parameter</span><span class="o">.</span><span class="n">_uuid</span><span class="p">]</span> |
| <span class="k">if</span> <span class="n">full_idx</span> <span class="ow">and</span> <span class="s1">'dist'</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_kvstore</span><span class="o">.</span><span class="n">type</span><span class="p">:</span> |
| <span class="k">assert</span> <span class="n">row_id</span><span class="o">.</span><span class="n">size</span> <span class="o">==</span> <span class="n">out</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_kvstore</span><span class="o">.</span><span class="n">pull</span><span class="p">(</span><span class="n">idx</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">,</span> <span class="n">priority</span><span class="o">=-</span><span class="n">idx</span><span class="p">,</span> <span class="n">ignore_sparse</span><span class="o">=</span><span class="kc">False</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">_kvstore</span><span class="o">.</span><span class="n">row_sparse_pull</span><span class="p">(</span><span class="n">idx</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">,</span> <span class="n">row_ids</span><span class="o">=</span><span class="n">row_id</span><span class="p">,</span> <span class="n">priority</span><span class="o">=-</span><span class="n">idx</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">_check_and_rescale_grad</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">scale</span><span class="p">):</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_update_on_kvstore</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">_distributed</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">_kv_initialized</span><span class="p">:</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_optimizer</span><span class="o">.</span><span class="n">rescale_grad</span> <span class="o">!=</span> <span class="n">scale</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">UserWarning</span><span class="p">(</span><span class="s1">'Possible change in the `batch_size` from previous '</span> |
| <span class="s1">'`step` detected. Optimizer gradient normalizing '</span> |
| <span class="s1">'factor will not change w.r.t new batch_size when '</span> |
| <span class="s1">'update_on_kvstore=True and when distributed kvstore '</span> |
| <span class="s1">'is used.'</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_optimizer</span><span class="o">.</span><span class="n">rescale_grad</span> <span class="o">=</span> <span class="n">scale</span> |
| |
| <div class="viewcode-block" id="Trainer.step"><a class="viewcode-back" href="../../../api/gluon/trainer.html#mxnet.gluon.Trainer.step">[docs]</a> <span class="k">def</span> <span class="nf">step</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">,</span> <span class="n">ignore_stale_grad</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Makes one step of parameter update. Should be called after</span> |
| <span class="sd"> `autograd.backward()` and outside of `record()` scope.</span> |
| |
| <span class="sd"> For normal parameter updates, `step()` should be used, which internally calls</span> |
| <span class="sd"> `allreduce_grads()` and then `update()`. However, if you need to get the reduced</span> |
| <span class="sd"> gradients to perform certain transformation, such as in gradient clipping, then</span> |
| <span class="sd"> you may want to manually call `allreduce_grads()` and `update()` separately.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> batch_size : int</span> |
| <span class="sd"> Batch size of data processed. Gradient will be normalized by `1/batch_size`.</span> |
| <span class="sd"> Set this to 1 if you normalized loss manually with `loss = mean(loss)`.</span> |
| <span class="sd"> ignore_stale_grad : bool, optional, default=False</span> |
| <span class="sd"> If true, ignores Parameters with stale gradient (gradient that has not</span> |
| <span class="sd"> been updated by `backward` after last step) and skip update.</span> |
| <span class="sd"> """</span> |
| <span class="n">rescale_grad</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_scale</span> <span class="o">/</span> <span class="n">batch_size</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_check_and_rescale_grad</span><span class="p">(</span><span class="n">rescale_grad</span><span class="p">)</span> |
| |
| <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_kv_initialized</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_init_kvstore</span><span class="p">()</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_params_to_init</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_init_params</span><span class="p">()</span> |
| |
| <span class="bp">self</span><span class="o">.</span><span class="n">_allreduce_grads</span><span class="p">()</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_update</span><span class="p">(</span><span class="n">ignore_stale_grad</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="Trainer.allreduce_grads"><a class="viewcode-back" href="../../../api/gluon/trainer.html#mxnet.gluon.Trainer.allreduce_grads">[docs]</a> <span class="k">def</span> <span class="nf">allreduce_grads</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""For each parameter, reduce the gradients from different devices.</span> |
| |
| <span class="sd"> Should be called after `autograd.backward()`, outside of `record()` scope,</span> |
| <span class="sd"> and before `trainer.update()`.</span> |
| |
| <span class="sd"> For normal parameter updates, `step()` should be used, which internally calls</span> |
| <span class="sd"> `allreduce_grads()` and then `update()`. However, if you need to get the reduced</span> |
| <span class="sd"> gradients to perform certain transformation, such as in gradient clipping, then</span> |
| <span class="sd"> you may want to manually call `allreduce_grads()` and `update()` separately.</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_kv_initialized</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_init_kvstore</span><span class="p">()</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_params_to_init</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_init_params</span><span class="p">()</span> |
| <span class="k">assert</span> <span class="ow">not</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_kvstore</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">_update_on_kvstore</span><span class="p">),</span> \ |
| <span class="s1">'allreduce_grads() when parameters are updated on kvstore '</span> \ |
| <span class="s1">'is not supported. Try setting `update_on_kvstore` '</span> \ |
| <span class="s1">'to False when creating trainer.'</span> |
| |
| <span class="bp">self</span><span class="o">.</span><span class="n">_allreduce_grads</span><span class="p">()</span></div> |
| |
| <span class="k">def</span> <span class="nf">_allreduce_grads</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="c1"># nothing to reduce</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_kvstore</span><span class="p">:</span> |
| <span class="k">return</span> |
| <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">param</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_params</span><span class="p">):</span> |
| <span class="k">if</span> <span class="n">param</span><span class="o">.</span><span class="n">grad_req</span> <span class="o">!=</span> <span class="s1">'null'</span><span class="p">:</span> |
| <span class="n">idx</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_param2idx</span><span class="p">[</span><span class="n">param</span><span class="o">.</span><span class="n">_uuid</span><span class="p">]</span> |
| <span class="n">grad_list</span> <span class="o">=</span> <span class="n">param</span><span class="o">.</span><span class="n">list_grad</span><span class="p">()</span> |
| <span class="c1"># sparse gradients, call push and pull separately</span> |
| <span class="k">if</span> <span class="n">grad_list</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">stype</span> <span class="o">!=</span> <span class="s1">'default'</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_kvstore</span><span class="o">.</span><span class="n">push</span><span class="p">(</span><span class="n">idx</span><span class="p">,</span> <span class="n">grad_list</span><span class="p">,</span> <span class="n">priority</span><span class="o">=-</span><span class="n">i</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">param</span><span class="o">.</span><span class="n">_stype</span> <span class="o">==</span> <span class="s1">'default'</span><span class="p">:</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_update_on_kvstore</span><span class="p">:</span> |
| <span class="n">pull_list</span> <span class="o">=</span> <span class="n">param</span><span class="o">.</span><span class="n">list_data</span><span class="p">()</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">pull_list</span> <span class="o">=</span> <span class="n">param</span><span class="o">.</span><span class="n">list_grad</span><span class="p">()</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_kvstore</span><span class="o">.</span><span class="n">pull</span><span class="p">(</span><span class="n">idx</span><span class="p">,</span> <span class="n">pull_list</span><span class="p">,</span> <span class="n">priority</span><span class="o">=-</span><span class="n">i</span><span class="p">,</span> |
| <span class="n">ignore_sparse</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_distributed</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="c1"># allreduce dense gradients if not update_on_kvstore,</span> |
| <span class="c1"># otherwise push dense gradients, pull dense weights</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_update_on_kvstore</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_kvstore</span><span class="o">.</span><span class="n">pushpull</span><span class="p">(</span><span class="n">idx</span><span class="p">,</span> <span class="n">grad_list</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">param</span><span class="o">.</span><span class="n">list_data</span><span class="p">(),</span> <span class="n">priority</span><span class="o">=-</span><span class="n">i</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">_kvstore</span><span class="o">.</span><span class="n">pushpull</span><span class="p">(</span><span class="n">idx</span><span class="p">,</span> <span class="n">grad_list</span><span class="p">,</span> <span class="n">priority</span><span class="o">=-</span><span class="n">i</span><span class="p">)</span> |
| |
| <div class="viewcode-block" id="Trainer.update"><a class="viewcode-back" href="../../../api/gluon/trainer.html#mxnet.gluon.Trainer.update">[docs]</a> <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">,</span> <span class="n">ignore_stale_grad</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Makes one step of parameter update.</span> |
| |
| <span class="sd"> Should be called after `autograd.backward()` and outside of `record()` scope,</span> |
| <span class="sd"> and after `trainer.update()`.</span> |
| |
| |
| <span class="sd"> For normal parameter updates, `step()` should be used, which internally calls</span> |
| <span class="sd"> `allreduce_grads()` and then `update()`. However, if you need to get the reduced</span> |
| <span class="sd"> gradients to perform certain transformation, such as in gradient clipping, then</span> |
| <span class="sd"> you may want to manually call `allreduce_grads()` and `update()` separately.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> batch_size : int</span> |
| <span class="sd"> Batch size of data processed. Gradient will be normalized by `1/batch_size`.</span> |
| <span class="sd"> Set this to 1 if you normalized loss manually with `loss = mean(loss)`.</span> |
| <span class="sd"> ignore_stale_grad : bool, optional, default=False</span> |
| <span class="sd"> If true, ignores Parameters with stale gradient (gradient that has not</span> |
| <span class="sd"> been updated by `backward` after last step) and skip update.</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_kv_initialized</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_init_kvstore</span><span class="p">()</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_params_to_init</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_init_params</span><span class="p">()</span> |
| <span class="k">assert</span> <span class="ow">not</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_kvstore</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">_update_on_kvstore</span><span class="p">),</span> \ |
| <span class="s1">'update() when parameters are updated on kvstore '</span> \ |
| <span class="s1">'is not supported. Try setting `update_on_kvstore` '</span> \ |
| <span class="s1">'to False when creating trainer.'</span> |
| |
| <span class="bp">self</span><span class="o">.</span><span class="n">_check_and_rescale_grad</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_scale</span> <span class="o">/</span> <span class="n">batch_size</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_update</span><span class="p">(</span><span class="n">ignore_stale_grad</span><span class="p">)</span></div> |
| |
| <span class="k">def</span> <span class="nf">_update</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ignore_stale_grad</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> |
| <span class="n">loss_scaler</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s1">'_amp_loss_scaler'</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">loss_scaler</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="n">loss_scaler</span><span class="o">.</span><span class="n">has_overflow</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_params</span><span class="p">):</span> |
| <span class="k">return</span> <span class="c1"># skip on overflow</span> |
| |
| <span class="n">updates</span> <span class="o">=</span> <span class="p">[[]</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_updaters</span><span class="p">]</span> |
| |
| <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">param</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_params</span><span class="p">):</span> |
| <span class="k">if</span> <span class="n">param</span><span class="o">.</span><span class="n">grad_req</span> <span class="o">==</span> <span class="s1">'null'</span><span class="p">:</span> |
| <span class="k">continue</span> |
| |
| <span class="k">if</span> <span class="ow">not</span> <span class="n">ignore_stale_grad</span><span class="p">:</span> |
| <span class="k">for</span> <span class="n">data</span> <span class="ow">in</span> <span class="n">param</span><span class="o">.</span><span class="n">_check_and_get</span><span class="p">(</span><span class="n">param</span><span class="o">.</span><span class="n">_data</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="n">data</span><span class="o">.</span><span class="n">_fresh_grad</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">UserWarning</span><span class="p">(</span> |
| <span class="sa">f</span><span class="s2">"Gradient of Parameter `</span><span class="si">{</span><span class="n">param</span><span class="o">.</span><span class="n">name</span><span class="si">}</span><span class="s2">` on device </span><span class="si">{</span><span class="nb">str</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">device</span><span class="p">)</span><span class="si">}</span><span class="s2"> has not been updated "</span> |
| <span class="s2">"by backward since last `step`. This could mean a bug in your "</span> |
| <span class="s2">"model that made it only use a subset of the Parameters (Blocks) "</span> |
| <span class="s2">"for this iteration. If you are intentionally only using a subset, "</span> |
| <span class="s2">"call step with ignore_stale_grad=True to suppress this "</span> |
| <span class="s2">"warning and skip updating of Parameters with stale gradient"</span><span class="p">)</span> |
| |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_kvstore</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">_update_on_kvstore</span><span class="p">:</span> |
| <span class="k">continue</span> |
| |
| <span class="k">for</span> <span class="n">upd</span><span class="p">,</span> <span class="n">arr</span><span class="p">,</span> <span class="n">grad</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">updates</span><span class="p">,</span> <span class="n">param</span><span class="o">.</span><span class="n">list_data</span><span class="p">(),</span> <span class="n">param</span><span class="o">.</span><span class="n">list_grad</span><span class="p">()):</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="n">ignore_stale_grad</span> <span class="ow">or</span> <span class="n">arr</span><span class="o">.</span><span class="n">_fresh_grad</span><span class="p">:</span> |
| <span class="n">upd</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="n">grad</span><span class="p">,</span> <span class="n">arr</span><span class="p">))</span> |
| <span class="n">arr</span><span class="o">.</span><span class="n">_fresh_grad</span> <span class="o">=</span> <span class="kc">False</span> |
| |
| <span class="k">if</span> <span class="ow">not</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_kvstore</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">_update_on_kvstore</span><span class="p">):</span> |
| <span class="k">for</span> <span class="n">updater</span><span class="p">,</span> <span class="n">upd</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_updaters</span><span class="p">,</span> <span class="n">updates</span><span class="p">):</span> |
| <span class="k">if</span> <span class="n">upd</span><span class="p">:</span> |
| <span class="n">i</span><span class="p">,</span> <span class="n">g</span><span class="p">,</span> <span class="n">w</span> <span class="o">=</span> <span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">upd</span><span class="p">)</span> |
| <span class="n">updater</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">g</span><span class="p">,</span> <span class="n">w</span><span class="p">)</span> |
| |
| <div class="viewcode-block" id="Trainer.save_states"><a class="viewcode-back" href="../../../api/gluon/trainer.html#mxnet.gluon.Trainer.save_states">[docs]</a> <span class="k">def</span> <span class="nf">save_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">"""Saves trainer states (e.g. optimizer, momentum) to a file.</span> |
| |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> fname : str</span> |
| <span class="sd"> Path to output states file.</span> |
| |
| <span class="sd"> Note</span> |
| <span class="sd"> ----</span> |
| <span class="sd"> `optimizer.param_dict`, which contains Parameter information (such as</span> |
| <span class="sd"> `lr_mult` and `wd_mult`) will not be saved.</span> |
| <span class="sd"> """</span> |
| <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">_optimizer</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> |
| |
| <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_kv_initialized</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_init_kvstore</span><span class="p">()</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_params_to_init</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_init_params</span><span class="p">()</span> |
| |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_update_on_kvstore</span><span class="p">:</span> |
| <span class="k">assert</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_params_to_init</span><span class="p">,</span> <span class="s2">"Cannot save trainer states when some "</span> \ |
| <span class="s2">"parameters are not yet initialized in kvstore."</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_kvstore</span><span class="o">.</span><span class="n">save_optimizer_states</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">True</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</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">_updaters</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">get_states</span><span class="p">(</span><span class="n">dump_optimizer</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span></div> |
| |
| <div class="viewcode-block" id="Trainer.load_states"><a class="viewcode-back" href="../../../api/gluon/trainer.html#mxnet.gluon.Trainer.load_states">[docs]</a> <span class="k">def</span> <span class="nf">load_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 trainer states (e.g. optimizer, momentum) from a 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"> Note</span> |
| <span class="sd"> ----</span> |
| <span class="sd"> `optimizer.param_dict`, which contains Parameter information (such as</span> |
| <span class="sd"> `lr_mult` and `wd_mult`) will not be loaded from the file, but rather set</span> |
| <span class="sd"> based on current Trainer's parameters.</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_kv_initialized</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_init_kvstore</span><span class="p">()</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_params_to_init</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_init_params</span><span class="p">()</span> |
| |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_update_on_kvstore</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_kvstore</span><span class="o">.</span><span class="n">load_optimizer_states</span><span class="p">(</span><span class="n">fname</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_optimizer</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_kvstore</span><span class="o">.</span><span class="n">_updater</span><span class="o">.</span><span class="n">optimizer</span> |
| <span class="k">else</span><span class="p">:</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">'rb'</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span> |
| <span class="n">states</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">read</span><span class="p">()</span> |
| <span class="k">for</span> <span class="n">updater</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_updaters</span><span class="p">:</span> |
| <span class="n">updater</span><span class="o">.</span><span class="n">set_states</span><span class="p">(</span><span class="n">states</span><span class="p">)</span> |
| <span class="n">updater</span><span class="o">.</span><span class="n">optimizer</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_updaters</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">optimizer</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_optimizer</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_updaters</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">optimizer</span> |
| <span class="n">param_dict</span> <span class="o">=</span> <span class="p">{</span><span class="n">i</span><span class="p">:</span> <span class="n">param</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">param</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_params</span><span class="p">)}</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_optimizer</span><span class="o">.</span><span class="n">param_dict</span> <span class="o">=</span> <span class="n">param_dict</span></div></div> |
| </pre></div> |
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