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| <span class="mdl-layout-title toc">Table Of Contents</span> |
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| <li class="toctree-l1"><a class="reference internal" href="../../tutorials/index.html">Python Tutorials</a><ul> |
| <li class="toctree-l2"><a class="reference internal" href="../../tutorials/getting-started/index.html">Getting Started</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/1-ndarray.html">Manipulate data with <code class="docutils literal notranslate"><span class="pre">ndarray</span></code></a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/2-nn.html">Create a neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/3-autograd.html">Automatic differentiation with <code class="docutils literal notranslate"><span class="pre">autograd</span></code></a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/4-train.html">Train the neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-predict.html">Predict with a pre-trained model</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/6-use_gpus.html">Use GPUs</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/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> |
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| <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/custom_layer_beginners.html">Customer Layers (Beginners)</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> |
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| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/gluon/image/index.html">Image Tutorials</a><ul> |
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| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/image/mnist.html">Handwritten Digit Recognition</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/image/pretrained_models.html">Using pre-trained models in MXNet</a></li> |
| </ul> |
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| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/gluon/loss/index.html">Losses</a><ul> |
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| <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/ndarray/index.html">NDArray</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/ndarray/01-ndarray-intro.html">An Intro: Manipulate Data the MXNet Way with NDArray</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/ndarray/02-ndarray-operations.html">NDArray Operations</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/ndarray/03-ndarray-contexts.html">NDArray Contexts</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/ndarray/gotchas_numpy_in_mxnet.html">Gotchas using NumPy in Apache MXNet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/ndarray/sparse/index.html">Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/ndarray/sparse/csr.html">CSRNDArray - NDArray in Compressed Sparse Row Storage Format</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/ndarray/sparse/row_sparse.html">RowSparseNDArray - NDArray for Sparse Gradient Updates</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/ndarray/sparse/train.html">Train a Linear Regression Model with Sparse Symbols</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/ndarray/sparse/train_gluon.html">Sparse NDArrays with Gluon</a></li> |
| </ul> |
| </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 internal" href="../../tutorials/packages/onnx/super_resolution.html">Importing an ONNX model into MXNet</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/deploy/export/onnx.html">Export ONNX Models</a></li> |
| </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/mkldnn/index.html">Intel MKL-DNN</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/performance/backend/mkldnn/mkldnn_quantization.html">Quantize with MKL-DNN backend</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/performance/backend/mkldnn/mkldnn_readme.html">Install MXNet with MKL-DNN</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/performance/backend/tensorrt/index.html">TensorRT</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/performance/backend/tensorrt/tensorrt.html">Optimized GPU Inference</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/performance/backend/tvm.html">Use TVM</a></li> |
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| <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> |
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| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/deploy/inference/wine_detector.html">Real-time Object Detection with MXNet On The Raspberry Pi</a></li> |
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| <li class="toctree-l1"><a class="reference internal" href="../../tutorials/index.html">Python Tutorials</a><ul> |
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| <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/1-ndarray.html">Manipulate data with <code class="docutils literal notranslate"><span class="pre">ndarray</span></code></a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/2-nn.html">Create a neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/3-autograd.html">Automatic differentiation with <code class="docutils literal notranslate"><span class="pre">autograd</span></code></a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/4-train.html">Train the neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-predict.html">Predict with a pre-trained model</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/6-use_gpus.html">Use GPUs</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/logistic_regression_explained.html">Logistic regression explained</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html">MNIST</a></li> |
| </ul> |
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| <li class="toctree-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/custom_layer_beginners.html">Customer Layers (Beginners)</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/image/index.html">Image Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/image/image-augmentation.html">Image Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/image/mnist.html">Handwritten Digit Recognition</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/image/pretrained_models.html">Using pre-trained models in MXNet</a></li> |
| </ul> |
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| <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> |
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| <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> |
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| <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> |
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| <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/ndarray/index.html">NDArray</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/ndarray/01-ndarray-intro.html">An Intro: Manipulate Data the MXNet Way with NDArray</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/ndarray/02-ndarray-operations.html">NDArray Operations</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/ndarray/03-ndarray-contexts.html">NDArray Contexts</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/ndarray/gotchas_numpy_in_mxnet.html">Gotchas using NumPy in Apache MXNet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/ndarray/sparse/index.html">Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/ndarray/sparse/csr.html">CSRNDArray - NDArray in Compressed Sparse Row Storage Format</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/ndarray/sparse/row_sparse.html">RowSparseNDArray - NDArray for Sparse Gradient Updates</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/ndarray/sparse/train.html">Train a Linear Regression Model with Sparse Symbols</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/ndarray/sparse/train_gluon.html">Sparse NDArrays with Gluon</a></li> |
| </ul> |
| </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 internal" href="../../tutorials/packages/onnx/super_resolution.html">Importing an ONNX model into MXNet</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/deploy/export/onnx.html">Export ONNX Models</a></li> |
| </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> |
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| |
| <div class="section" id="module-mxnet.kvstore"> |
| <span id="mxnet-kvstore"></span><h1>mxnet.kvstore<a class="headerlink" href="#module-mxnet.kvstore" title="Permalink to this headline">¶</a></h1> |
| <p>Key value store interface of MXNet for parameter synchronization.</p> |
| <p><strong>Classes</strong></p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.kvstore.KVStore" title="mxnet.kvstore.KVStore"><code class="xref py py-obj docutils literal notranslate"><span class="pre">KVStore</span></code></a>(handle)</p></td> |
| <td><p>A key-value store for synchronization of values, over multiple devices.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p><strong>Functions</strong></p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.kvstore.create" title="mxnet.kvstore.create"><code class="xref py py-obj docutils literal notranslate"><span class="pre">create</span></code></a>([name])</p></td> |
| <td><p>Creates a new KVStore.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <dl class="class"> |
| <dt id="mxnet.kvstore.KVStore"> |
| <em class="property">class </em><code class="sig-prename descclassname">mxnet.kvstore.</code><code class="sig-name descname">KVStore</code><span class="sig-paren">(</span><em class="sig-param">handle</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/kvstore.html#KVStore"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.kvstore.KVStore" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p> |
| <p>A key-value store for synchronization of values, over multiple devices.</p> |
| <p><strong>Methods</strong></p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.kvstore.KVStore.init" title="mxnet.kvstore.KVStore.init"><code class="xref py py-obj docutils literal notranslate"><span class="pre">init</span></code></a>(key, value)</p></td> |
| <td><p>Initializes a single or a sequence of key-value pairs into the store.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.kvstore.KVStore.load_optimizer_states" title="mxnet.kvstore.KVStore.load_optimizer_states"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_optimizer_states</span></code></a>(fname)</p></td> |
| <td><p>Loads the optimizer (updater) state from the file.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.kvstore.KVStore.pull" title="mxnet.kvstore.KVStore.pull"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pull</span></code></a>(key[, out, priority, ignore_sparse])</p></td> |
| <td><p>Pulls a single value or a sequence of values from the store.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.kvstore.KVStore.push" title="mxnet.kvstore.KVStore.push"><code class="xref py py-obj docutils literal notranslate"><span class="pre">push</span></code></a>(key, value[, priority])</p></td> |
| <td><p>Pushes a single or a sequence of key-value pairs into the store.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.kvstore.KVStore.pushpull" title="mxnet.kvstore.KVStore.pushpull"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pushpull</span></code></a>(key, value[, out, priority])</p></td> |
| <td><p>Performs push and pull a single value or a sequence of values from the store.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.kvstore.KVStore.row_sparse_pull" title="mxnet.kvstore.KVStore.row_sparse_pull"><code class="xref py py-obj docutils literal notranslate"><span class="pre">row_sparse_pull</span></code></a>(key[, out, priority, row_ids])</p></td> |
| <td><p>Pulls a single RowSparseNDArray value or a sequence of RowSparseNDArray values from the store with specified row_ids.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.kvstore.KVStore.save_optimizer_states" title="mxnet.kvstore.KVStore.save_optimizer_states"><code class="xref py py-obj docutils literal notranslate"><span class="pre">save_optimizer_states</span></code></a>(fname[, dump_optimizer])</p></td> |
| <td><p>Saves the optimizer (updater) state to a file.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.kvstore.KVStore.set_gradient_compression" title="mxnet.kvstore.KVStore.set_gradient_compression"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_gradient_compression</span></code></a>(compression_params)</p></td> |
| <td><p>Specifies type of low-bit quantization for gradient compression and additional arguments depending on the type of compression being used.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.kvstore.KVStore.set_optimizer" title="mxnet.kvstore.KVStore.set_optimizer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_optimizer</span></code></a>(optimizer)</p></td> |
| <td><p>Registers an optimizer with the kvstore.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p><strong>Attributes</strong></p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.kvstore.KVStore.num_workers" title="mxnet.kvstore.KVStore.num_workers"><code class="xref py py-obj docutils literal notranslate"><span class="pre">num_workers</span></code></a></p></td> |
| <td><p>Returns the number of worker nodes.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.kvstore.KVStore.rank" title="mxnet.kvstore.KVStore.rank"><code class="xref py py-obj docutils literal notranslate"><span class="pre">rank</span></code></a></p></td> |
| <td><p>Returns the rank of this worker node.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.kvstore.KVStore.type" title="mxnet.kvstore.KVStore.type"><code class="xref py py-obj docutils literal notranslate"><span class="pre">type</span></code></a></p></td> |
| <td><p>Returns the type of this kvstore.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <dl class="method"> |
| <dt id="mxnet.kvstore.KVStore.init"> |
| <code class="sig-name descname">init</code><span class="sig-paren">(</span><em class="sig-param">key</em>, <em class="sig-param">value</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/kvstore.html#KVStore.init"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.kvstore.KVStore.init" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Initializes a single or a sequence of key-value pairs into the store.</p> |
| <p>For each key, one must <cite>init</cite> it before calling <cite>push</cite> or <cite>pull</cite>. |
| When multiple workers invoke <cite>init</cite> for the same key, only |
| the value supplied by worker with rank <cite>0</cite> is used. This function returns |
| after data has been initialized successfully.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>key</strong> (<em>str</em><em>, </em><em>int</em><em>, or </em><em>sequence of str</em><em> or </em><em>int</em>) – The keys.</p></li> |
| <li><p><strong>value</strong> (<a class="reference internal" href="../ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><a class="reference internal" href="../ndarray/sparse/index.html#mxnet.ndarray.sparse.RowSparseNDArray" title="mxnet.ndarray.sparse.RowSparseNDArray"><em>RowSparseNDArray</em></a><em> or </em><em>sequence of NDArray</em><em> or </em><a class="reference internal" href="../ndarray/sparse/index.html#mxnet.ndarray.sparse.RowSparseNDArray" title="mxnet.ndarray.sparse.RowSparseNDArray"><em>RowSparseNDArray</em></a>) – Values corresponding to the keys.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># init a single key-value pair</span> |
| <span class="gp">>>> </span><span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">kv</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">kv</span><span class="o">.</span><span class="n">create</span><span class="p">(</span><span class="s1">'local'</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">kv</span><span class="o">.</span><span class="n">init</span><span class="p">(</span><span class="s1">'3'</span><span class="p">,</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span><span class="o">*</span><span class="mi">2</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">a</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">kv</span><span class="o">.</span><span class="n">pull</span><span class="p">(</span><span class="s1">'3'</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">a</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="nb">print</span> <span class="n">a</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span> |
| <span class="go">[[ 2. 2. 2.]</span> |
| <span class="go">[ 2. 2. 2.]]</span> |
| </pre></div> |
| </div> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># init a list of key-value pairs</span> |
| <span class="gp">>>> </span><span class="n">keys</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'5'</span><span class="p">,</span> <span class="s1">'7'</span><span class="p">,</span> <span class="s1">'9'</span><span class="p">]</span> |
| <span class="gp">>>> </span><span class="n">kv</span><span class="o">.</span><span class="n">init</span><span class="p">(</span><span class="n">keys</span><span class="p">,</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">shape</span><span class="p">)]</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="n">keys</span><span class="p">))</span> |
| </pre></div> |
| </div> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># init a row_sparse value</span> |
| <span class="gp">>>> </span><span class="n">kv</span><span class="o">.</span><span class="n">init</span><span class="p">(</span><span class="s1">'4'</span><span class="p">,</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span><span class="o">.</span><span class="n">tostype</span><span class="p">(</span><span class="s1">'row_sparse'</span><span class="p">))</span> |
| <span class="gp">>>> </span><span class="n">b</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="s1">'row_sparse'</span><span class="p">,</span> <span class="n">shape</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">kv</span><span class="o">.</span><span class="n">row_sparse_pull</span><span class="p">(</span><span class="s1">'4'</span><span class="p">,</span> <span class="n">row_ids</span><span class="o">=</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]),</span> <span class="n">out</span><span class="o">=</span><span class="n">b</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="nb">print</span> <span class="n">b</span> |
| <span class="go"><RowSparseNDArray 2x3 @cpu(0)></span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.kvstore.KVStore.load_optimizer_states"> |
| <code class="sig-name descname">load_optimizer_states</code><span class="sig-paren">(</span><em class="sig-param">fname</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/kvstore.html#KVStore.load_optimizer_states"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.kvstore.KVStore.load_optimizer_states" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Loads the optimizer (updater) state from the file.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>fname</strong> (<em>str</em>) – Path to input states file.</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.kvstore.KVStore.num_workers"> |
| <em class="property">property </em><code class="sig-name descname">num_workers</code><a class="headerlink" href="#mxnet.kvstore.KVStore.num_workers" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns the number of worker nodes.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Returns</dt> |
| <dd class="field-odd"><p><strong>size</strong> – The number of worker nodes.</p> |
| </dd> |
| <dt class="field-even">Return type</dt> |
| <dd class="field-even"><p>int</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.kvstore.KVStore.pull"> |
| <code class="sig-name descname">pull</code><span class="sig-paren">(</span><em class="sig-param">key</em>, <em class="sig-param">out=None</em>, <em class="sig-param">priority=0</em>, <em class="sig-param">ignore_sparse=True</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/kvstore.html#KVStore.pull"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.kvstore.KVStore.pull" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Pulls a single value or a sequence of values from the store.</p> |
| <p>This function returns immediately after adding an operator to the engine. |
| Subsequent attempts to read from the <cite>out</cite> variable will be blocked until the |
| pull operation completes.</p> |
| <p><cite>pull</cite> is executed asynchronously after all previous <cite>pull</cite> calls and only |
| the last <cite>push</cite> call for the same input key(s) are finished.</p> |
| <p>The returned values are guaranteed to be the latest values in the store.</p> |
| <p>pull with <cite>RowSparseNDArray</cite> is not supported for dist kvstore. |
| Please use <code class="docutils literal notranslate"><span class="pre">row_sparse_pull</span></code> instead.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>key</strong> (<em>str</em><em>, </em><em>int</em><em>, or </em><em>sequence of str</em><em> or </em><em>int</em>) – Keys.</p></li> |
| <li><p><strong>out</strong> (<a class="reference internal" href="../ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em> or </em><em>list of NDArray</em><em> or </em><em>list of list of NDArray</em>) – Values corresponding to the keys.</p></li> |
| <li><p><strong>priority</strong> (<em>int</em><em>, </em><em>optional</em>) – The priority of the pull operation. |
| Higher priority pull operations are likely to be executed before |
| other pull actions.</p></li> |
| <li><p><strong>ignore_sparse</strong> (<em>bool</em><em>, </em><em>optional</em><em>, </em><em>default True</em>) – Whether to ignore sparse arrays in the request.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># pull a single key-value pair</span> |
| <span class="gp">>>> </span><span class="n">a</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">kv</span><span class="o">.</span><span class="n">pull</span><span class="p">(</span><span class="s1">'3'</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">a</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="nb">print</span> <span class="n">a</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span> |
| <span class="go">[[ 2. 2. 2.]</span> |
| <span class="go">[ 2. 2. 2.]]</span> |
| </pre></div> |
| </div> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># pull into multiple devices</span> |
| <span class="gp">>>> </span><span class="n">b</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="n">gpu</span><span class="p">)</span> <span class="k">for</span> <span class="n">gpu</span> <span class="ow">in</span> <span class="n">gpus</span><span class="p">]</span> |
| <span class="gp">>>> </span><span class="n">kv</span><span class="o">.</span><span class="n">pull</span><span class="p">(</span><span class="s1">'3'</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">b</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="nb">print</span> <span class="n">b</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span> |
| <span class="go">[[ 2. 2. 2.]</span> |
| <span class="go">[ 2. 2. 2.]]</span> |
| </pre></div> |
| </div> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># pull a list of key-value pairs.</span> |
| <span class="gp">>>> </span><span class="c1"># On single device</span> |
| <span class="gp">>>> </span><span class="n">keys</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'5'</span><span class="p">,</span> <span class="s1">'7'</span><span class="p">,</span> <span class="s1">'9'</span><span class="p">]</span> |
| <span class="gp">>>> </span><span class="n">b</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">shape</span><span class="p">)]</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="n">keys</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">kv</span><span class="o">.</span><span class="n">pull</span><span class="p">(</span><span class="n">keys</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">b</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="nb">print</span> <span class="n">b</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span> |
| <span class="go">[[ 2. 2. 2.]</span> |
| <span class="go">[ 2. 2. 2.]]</span> |
| <span class="gp">>>> </span><span class="c1"># On multiple devices</span> |
| <span class="gp">>>> </span><span class="n">keys</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'6'</span><span class="p">,</span> <span class="s1">'8'</span><span class="p">,</span> <span class="s1">'10'</span><span class="p">]</span> |
| <span class="gp">>>> </span><span class="n">b</span> <span class="o">=</span> <span class="p">[[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="n">gpu</span><span class="p">)</span> <span class="k">for</span> <span class="n">gpu</span> <span class="ow">in</span> <span class="n">gpus</span><span class="p">]]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">keys</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">kv</span><span class="o">.</span><span class="n">pull</span><span class="p">(</span><span class="n">keys</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">b</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="nb">print</span> <span class="n">b</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span> |
| <span class="go">[[ 2. 2. 2.]</span> |
| <span class="go">[ 2. 2. 2.]]</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.kvstore.KVStore.push"> |
| <code class="sig-name descname">push</code><span class="sig-paren">(</span><em class="sig-param">key</em>, <em class="sig-param">value</em>, <em class="sig-param">priority=0</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/kvstore.html#KVStore.push"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.kvstore.KVStore.push" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Pushes a single or a sequence of key-value pairs into the store.</p> |
| <p>This function returns immediately after adding an operator to the engine. |
| The actual operation is executed asynchronously. If there are consecutive |
| pushes to the same key, there is no guarantee on the serialization of pushes. |
| The execution of a push does not guarantee that all previous pushes are |
| finished. |
| There is no synchronization between workers. |
| One can use <code class="docutils literal notranslate"><span class="pre">_barrier()</span></code> to sync all workers.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>key</strong> (<em>str</em><em>, </em><em>int</em><em>, or </em><em>sequence of str</em><em> or </em><em>int</em>) – Keys.</p></li> |
| <li><p><strong>value</strong> (<a class="reference internal" href="../ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><a class="reference internal" href="../ndarray/sparse/index.html#mxnet.ndarray.sparse.RowSparseNDArray" title="mxnet.ndarray.sparse.RowSparseNDArray"><em>RowSparseNDArray</em></a><em>, </em><em>list of NDArray</em><em> or </em><a class="reference internal" href="../ndarray/sparse/index.html#mxnet.ndarray.sparse.RowSparseNDArray" title="mxnet.ndarray.sparse.RowSparseNDArray"><em>RowSparseNDArray</em></a><em>,</em>) – or list of list of NDArray or RowSparseNDArray |
| Values corresponding to the keys.</p></li> |
| <li><p><strong>priority</strong> (<em>int</em><em>, </em><em>optional</em>) – The priority of the push operation. |
| Higher priority push operations are likely to be executed before |
| other push actions.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># push a single key-value pair</span> |
| <span class="gp">>>> </span><span class="n">kv</span><span class="o">.</span><span class="n">push</span><span class="p">(</span><span class="s1">'3'</span><span class="p">,</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span><span class="o">*</span><span class="mi">8</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">kv</span><span class="o">.</span><span class="n">pull</span><span class="p">(</span><span class="s1">'3'</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">a</span><span class="p">)</span> <span class="c1"># pull out the value</span> |
| <span class="gp">>>> </span><span class="nb">print</span> <span class="n">a</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span> |
| <span class="go">[[ 8. 8. 8.]</span> |
| <span class="go">[ 8. 8. 8.]]</span> |
| </pre></div> |
| </div> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># aggregate the value and the push</span> |
| <span class="gp">>>> </span><span class="n">gpus</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">gpu</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">4</span><span class="p">)]</span> |
| <span class="gp">>>> </span><span class="n">b</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="n">gpu</span><span class="p">)</span> <span class="k">for</span> <span class="n">gpu</span> <span class="ow">in</span> <span class="n">gpus</span><span class="p">]</span> |
| <span class="gp">>>> </span><span class="n">kv</span><span class="o">.</span><span class="n">push</span><span class="p">(</span><span class="s1">'3'</span><span class="p">,</span> <span class="n">b</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">kv</span><span class="o">.</span><span class="n">pull</span><span class="p">(</span><span class="s1">'3'</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">a</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="nb">print</span> <span class="n">a</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span> |
| <span class="go">[[ 4. 4. 4.]</span> |
| <span class="go">[ 4. 4. 4.]]</span> |
| </pre></div> |
| </div> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># push a list of keys.</span> |
| <span class="gp">>>> </span><span class="c1"># single device</span> |
| <span class="gp">>>> </span><span class="n">keys</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'4'</span><span class="p">,</span> <span class="s1">'5'</span><span class="p">,</span> <span class="s1">'6'</span><span class="p">]</span> |
| <span class="gp">>>> </span><span class="n">kv</span><span class="o">.</span><span class="n">push</span><span class="p">(</span><span class="n">keys</span><span class="p">,</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">shape</span><span class="p">)]</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="n">keys</span><span class="p">))</span> |
| <span class="gp">>>> </span><span class="n">b</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">shape</span><span class="p">)]</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="n">keys</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">kv</span><span class="o">.</span><span class="n">pull</span><span class="p">(</span><span class="n">keys</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">b</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="nb">print</span> <span class="n">b</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span> |
| <span class="go">[[ 1. 1. 1.]</span> |
| <span class="go">[ 1. 1. 1.]]</span> |
| </pre></div> |
| </div> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># multiple devices:</span> |
| <span class="gp">>>> </span><span class="n">keys</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'7'</span><span class="p">,</span> <span class="s1">'8'</span><span class="p">,</span> <span class="s1">'9'</span><span class="p">]</span> |
| <span class="gp">>>> </span><span class="n">b</span> <span class="o">=</span> <span class="p">[[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="n">gpu</span><span class="p">)</span> <span class="k">for</span> <span class="n">gpu</span> <span class="ow">in</span> <span class="n">gpus</span><span class="p">]]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">keys</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">kv</span><span class="o">.</span><span class="n">push</span><span class="p">(</span><span class="n">keys</span><span class="p">,</span> <span class="n">b</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">kv</span><span class="o">.</span><span class="n">pull</span><span class="p">(</span><span class="n">keys</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">b</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="nb">print</span> <span class="n">b</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span> |
| <span class="go">[[ 4. 4. 4.]</span> |
| <span class="go">[ 4. 4. 4.]]</span> |
| </pre></div> |
| </div> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># push a row_sparse value</span> |
| <span class="gp">>>> </span><span class="n">b</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="s1">'row_sparse'</span><span class="p">,</span> <span class="n">shape</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">kv</span><span class="o">.</span><span class="n">init</span><span class="p">(</span><span class="s1">'10'</span><span class="p">,</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="s1">'row_sparse'</span><span class="p">,</span> <span class="n">shape</span><span class="p">))</span> |
| <span class="gp">>>> </span><span class="n">kv</span><span class="o">.</span><span class="n">push</span><span class="p">(</span><span class="s1">'10'</span><span class="p">,</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span><span class="o">.</span><span class="n">tostype</span><span class="p">(</span><span class="s1">'row_sparse'</span><span class="p">))</span> |
| <span class="gp">>>> </span><span class="c1"># pull out the value</span> |
| <span class="gp">>>> </span><span class="n">kv</span><span class="o">.</span><span class="n">row_sparse_pull</span><span class="p">(</span><span class="s1">'10'</span><span class="p">,</span> <span class="n">row_ids</span><span class="o">=</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]),</span> <span class="n">out</span><span class="o">=</span><span class="n">b</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="nb">print</span> <span class="n">b</span> |
| <span class="go"><RowSparseNDArray 2x3 @cpu(0)></span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.kvstore.KVStore.pushpull"> |
| <code class="sig-name descname">pushpull</code><span class="sig-paren">(</span><em class="sig-param">key</em>, <em class="sig-param">value</em>, <em class="sig-param">out=None</em>, <em class="sig-param">priority=0</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/kvstore.html#KVStore.pushpull"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.kvstore.KVStore.pushpull" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Performs push and pull a single value or a sequence of values from the store.</p> |
| <p>This function is coalesced form of push and pull operations. This function returns |
| immediately after adding an operator to the engine. Subsequent attempts to read |
| from the <cite>out</cite> variable will be blocked until the pull operation completes.</p> |
| <p><cite>value</cite> is pushed to the kvstore server for the specified keys and the updated |
| values are pulled from the server to <cite>out</cite>. If <cite>out</cite> is not specified the pulled |
| values are written to <cite>value</cite>. The returned values are guaranteed to be the latest |
| values in the store.</p> |
| <p>pushpull with <cite>RowSparseNDArray</cite> is not supported for dist kvstore.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>key</strong> (<em>str</em><em>, </em><em>int</em><em>, or </em><em>sequence of str</em><em> or </em><em>int</em>) – Keys.</p></li> |
| <li><p><strong>value</strong> (<a class="reference internal" href="../ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em>, </em><a class="reference internal" href="../ndarray/sparse/index.html#mxnet.ndarray.sparse.RowSparseNDArray" title="mxnet.ndarray.sparse.RowSparseNDArray"><em>RowSparseNDArray</em></a><em>, </em><em>list of NDArray</em><em> or </em><a class="reference internal" href="../ndarray/sparse/index.html#mxnet.ndarray.sparse.RowSparseNDArray" title="mxnet.ndarray.sparse.RowSparseNDArray"><em>RowSparseNDArray</em></a><em>,</em>) – or list of list of NDArray or RowSparseNDArray |
| Values corresponding to the keys.</p></li> |
| <li><p><strong>out</strong> (<a class="reference internal" href="../ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em> or </em><em>list of NDArray</em><em> or </em><em>list of list of NDArray</em>) – Values corresponding to the keys.</p></li> |
| <li><p><strong>priority</strong> (<em>int</em><em>, </em><em>optional</em>) – The priority of the pull operation. |
| Higher priority pull operations are likely to be executed before |
| other pull actions.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># push a single key-value pair</span> |
| <span class="gp">>>> </span><span class="n">kv</span><span class="o">.</span><span class="n">pushpull</span><span class="p">(</span><span class="s1">'3'</span><span class="p">,</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span><span class="o">*</span><span class="mi">8</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">a</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="nb">print</span> <span class="n">a</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span> |
| <span class="go">[[ 8. 8. 8.]</span> |
| <span class="go">[ 8. 8. 8.]]</span> |
| </pre></div> |
| </div> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># aggregate the value and the push</span> |
| <span class="gp">>>> </span><span class="n">gpus</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">gpu</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">4</span><span class="p">)]</span> |
| <span class="gp">>>> </span><span class="n">b</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="n">gpu</span><span class="p">)</span> <span class="k">for</span> <span class="n">gpu</span> <span class="ow">in</span> <span class="n">gpus</span><span class="p">]</span> |
| <span class="gp">>>> </span><span class="n">kv</span><span class="o">.</span><span class="n">pushpull</span><span class="p">(</span><span class="s1">'3'</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">a</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="nb">print</span> <span class="n">a</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span> |
| <span class="go">[[ 4. 4. 4.]</span> |
| <span class="go">[ 4. 4. 4.]]</span> |
| </pre></div> |
| </div> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># push a list of keys.</span> |
| <span class="gp">>>> </span><span class="c1"># single device</span> |
| <span class="gp">>>> </span><span class="n">keys</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'4'</span><span class="p">,</span> <span class="s1">'5'</span><span class="p">,</span> <span class="s1">'6'</span><span class="p">]</span> |
| <span class="gp">>>> </span><span class="n">b</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">shape</span><span class="p">)]</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="n">keys</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">kv</span><span class="o">.</span><span class="n">push</span><span class="p">(</span><span class="n">keys</span><span class="p">,</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">shape</span><span class="p">)]</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="n">keys</span><span class="p">),</span> <span class="n">out</span><span class="o">=</span><span class="n">b</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="nb">print</span> <span class="n">b</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span> |
| <span class="go">[[ 1. 1. 1.]</span> |
| <span class="go">[ 1. 1. 1.]]</span> |
| </pre></div> |
| </div> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># multiple devices:</span> |
| <span class="gp">>>> </span><span class="n">keys</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'7'</span><span class="p">,</span> <span class="s1">'8'</span><span class="p">,</span> <span class="s1">'9'</span><span class="p">]</span> |
| <span class="gp">>>> </span><span class="n">b</span> <span class="o">=</span> <span class="p">[[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="n">gpu</span><span class="p">)</span> <span class="k">for</span> <span class="n">gpu</span> <span class="ow">in</span> <span class="n">gpus</span><span class="p">]]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">keys</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">kv</span><span class="o">.</span><span class="n">pushpull</span><span class="p">(</span><span class="n">keys</span><span class="p">,</span> <span class="n">b</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="nb">print</span> <span class="n">b</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span> |
| <span class="go">[[ 4. 4. 4.]</span> |
| <span class="go">[ 4. 4. 4.]]</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.kvstore.KVStore.rank"> |
| <em class="property">property </em><code class="sig-name descname">rank</code><a class="headerlink" href="#mxnet.kvstore.KVStore.rank" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns the rank of this worker node.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Returns</dt> |
| <dd class="field-odd"><p><strong>rank</strong> – The rank of this node, which is in range [0, num_workers())</p> |
| </dd> |
| <dt class="field-even">Return type</dt> |
| <dd class="field-even"><p>int</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.kvstore.KVStore.row_sparse_pull"> |
| <code class="sig-name descname">row_sparse_pull</code><span class="sig-paren">(</span><em class="sig-param">key</em>, <em class="sig-param">out=None</em>, <em class="sig-param">priority=0</em>, <em class="sig-param">row_ids=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/kvstore.html#KVStore.row_sparse_pull"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.kvstore.KVStore.row_sparse_pull" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Pulls a single RowSparseNDArray value or a sequence of RowSparseNDArray values from the store with specified row_ids. When there is only one row_id, KVStoreRowSparsePull is invoked just once and the result is broadcast to all the rest of outputs.</p> |
| <p><cite>row_sparse_pull</cite> is executed asynchronously after all previous |
| <cite>pull</cite>/<cite>row_sparse_pull</cite> calls and the last <cite>push</cite> call for the |
| same input key(s) are finished.</p> |
| <p>The returned values are guaranteed to be the latest values in the store.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>key</strong> (<em>str</em><em>, </em><em>int</em><em>, or </em><em>sequence of str</em><em> or </em><em>int</em>) – Keys.</p></li> |
| <li><p><strong>out</strong> (<a class="reference internal" href="../ndarray/sparse/index.html#mxnet.ndarray.sparse.RowSparseNDArray" title="mxnet.ndarray.sparse.RowSparseNDArray"><em>RowSparseNDArray</em></a><em> or </em><em>list of RowSparseNDArray</em><em> or </em><em>list of list of RowSparseNDArray</em>) – Values corresponding to the keys. The stype is expected to be row_sparse</p></li> |
| <li><p><strong>priority</strong> (<em>int</em><em>, </em><em>optional</em>) – The priority of the pull operation. |
| Higher priority pull operations are likely to be executed before |
| other pull actions.</p></li> |
| <li><p><strong>row_ids</strong> (<a class="reference internal" href="../ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a><em> or </em><em>list of NDArray</em>) – The row_ids for which to pull for each value. Each row_id is an 1-D NDArray whose values don’t have to be unique nor sorted.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">kv</span><span class="o">.</span><span class="n">init</span><span class="p">(</span><span class="s1">'3'</span><span class="p">,</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span><span class="o">.</span><span class="n">tostype</span><span class="p">(</span><span class="s1">'row_sparse'</span><span class="p">))</span> |
| <span class="gp">>>> </span><span class="n">a</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="s1">'row_sparse'</span><span class="p">,</span> <span class="n">shape</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">row_ids</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">'int64'</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">kv</span><span class="o">.</span><span class="n">row_sparse_pull</span><span class="p">(</span><span class="s1">'3'</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">a</span><span class="p">,</span> <span class="n">row_ids</span><span class="o">=</span><span class="n">row_ids</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="nb">print</span> <span class="n">a</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span> |
| <span class="go">[[ 1. 1. 1.]</span> |
| <span class="go">[ 0. 0. 0.]</span> |
| <span class="go">[ 1. 1. 1.]]</span> |
| <span class="gp">>>> </span><span class="n">duplicate_row_ids</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">'int64'</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">kv</span><span class="o">.</span><span class="n">row_sparse_pull</span><span class="p">(</span><span class="s1">'3'</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">a</span><span class="p">,</span> <span class="n">row_ids</span><span class="o">=</span><span class="n">duplicate_row_ids</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="nb">print</span> <span class="n">a</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span> |
| <span class="go">[[ 0. 0. 0.]</span> |
| <span class="go">[ 0. 0. 0.]</span> |
| <span class="go">[ 1. 1. 1.]]</span> |
| <span class="gp">>>> </span><span class="n">unsorted_row_ids</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">'int64'</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">kv</span><span class="o">.</span><span class="n">row_sparse_pull</span><span class="p">(</span><span class="s1">'3'</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">a</span><span class="p">,</span> <span class="n">row_ids</span><span class="o">=</span><span class="n">unsorted_row_ids</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="nb">print</span> <span class="n">a</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span> |
| <span class="go">[[ 1. 1. 1.]</span> |
| <span class="go">[ 1. 1. 1.]</span> |
| <span class="go">[ 0. 0. 0.]]</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.kvstore.KVStore.save_optimizer_states"> |
| <code class="sig-name descname">save_optimizer_states</code><span class="sig-paren">(</span><em class="sig-param">fname</em>, <em class="sig-param">dump_optimizer=False</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/kvstore.html#KVStore.save_optimizer_states"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.kvstore.KVStore.save_optimizer_states" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Saves the optimizer (updater) state to a file. This is often used when checkpointing |
| the model during training.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>fname</strong> (<em>str</em>) – Path to the output states file.</p></li> |
| <li><p><strong>dump_optimizer</strong> (<em>bool</em><em>, </em><em>default False</em>) – Whether to also save the optimizer itself. This would also save optimizer |
| information such as learning rate and weight decay schedules.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.kvstore.KVStore.set_gradient_compression"> |
| <code class="sig-name descname">set_gradient_compression</code><span class="sig-paren">(</span><em class="sig-param">compression_params</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/kvstore.html#KVStore.set_gradient_compression"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.kvstore.KVStore.set_gradient_compression" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Specifies type of low-bit quantization for gradient compression and additional arguments depending on the type of compression being used.</p> |
| <p>2bit Gradient Compression takes a positive float <cite>threshold</cite>. |
| The technique works by thresholding values such that positive values in the |
| gradient above threshold will be set to threshold. Negative values whose absolute |
| values are higher than threshold, will be set to the negative of threshold. |
| Values whose absolute values are less than threshold will be set to 0. |
| By doing so, each value in the gradient is in one of three states. 2bits are |
| used to represent these states, and every 16 float values in the original |
| gradient can be represented using one float. This compressed representation |
| can reduce communication costs. The difference between these thresholded values and |
| original values is stored at the sender’s end as residual and added to the |
| gradient in the next iteration.</p> |
| <p>When kvstore is ‘local’, gradient compression is used to reduce communication |
| between multiple devices (gpus). Gradient is quantized on each GPU which |
| computed the gradients, then sent to the GPU which merges the gradients. This |
| receiving GPU dequantizes the gradients and merges them. Note that this |
| increases memory usage on each GPU because of the residual array stored.</p> |
| <p>When kvstore is ‘dist’, gradient compression is used to reduce communication |
| from worker to sender. Gradient is quantized on each worker which |
| computed the gradients, then sent to the server which dequantizes |
| this data and merges the gradients from each worker. Note that this |
| increases CPU memory usage on each worker because of the residual array stored. |
| Only worker to server communication is compressed in this setting. |
| If each machine has multiple GPUs, currently this GPU to GPU or GPU to CPU communication |
| is not compressed. Server to worker communication (in the case of pull) |
| is also not compressed.</p> |
| <p>To use 2bit compression, we need to specify <cite>type</cite> as <cite>2bit</cite>. |
| Only specifying <cite>type</cite> would use default value for the threshold. |
| To completely specify the arguments for 2bit compression, we would need to pass |
| a dictionary which includes <cite>threshold</cite> like: |
| {‘type’: ‘2bit’, ‘threshold’: 0.5}</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>compression_params</strong> (<em>dict</em>) – A dictionary specifying the type and parameters for gradient compression. |
| The key <cite>type</cite> in this dictionary is a |
| required string argument and specifies the type of gradient compression. |
| Currently <cite>type</cite> can be only <cite>2bit</cite> |
| Other keys in this dictionary are optional and specific to the type |
| of gradient compression.</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.kvstore.KVStore.set_optimizer"> |
| <code class="sig-name descname">set_optimizer</code><span class="sig-paren">(</span><em class="sig-param">optimizer</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/kvstore.html#KVStore.set_optimizer"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.kvstore.KVStore.set_optimizer" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Registers an optimizer with the kvstore.</p> |
| <p>When using a single machine, this function updates the local optimizer. |
| If using multiple machines and this operation is invoked from a worker node, |
| it will serialized the optimizer with pickle and send it to all servers. |
| The function returns after all servers have been updated.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>optimizer</strong> (<a class="reference internal" href="../optimizer/index.html#mxnet.optimizer.Optimizer" title="mxnet.optimizer.Optimizer"><em>Optimizer</em></a>) – The new optimizer for the store</p> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">kv</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">kv</span><span class="o">.</span><span class="n">create</span><span class="p">()</span> |
| <span class="gp">>>> </span><span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">weight</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">kv</span><span class="o">.</span><span class="n">init</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="n">weight</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="c1"># set the optimizer for kvstore as the default SGD optimizer</span> |
| <span class="gp">>>> </span><span class="n">kv</span><span class="o">.</span><span class="n">set_optimizer</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">SGD</span><span class="p">())</span> |
| <span class="gp">>>> </span><span class="n">grad</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">kv</span><span class="o">.</span><span class="n">push</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="n">grad</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">kv</span><span class="o">.</span><span class="n">pull</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="n">out</span> <span class="o">=</span> <span class="n">weight</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="c1"># weight is updated via gradient descent</span> |
| <span class="gp">>>> </span><span class="n">weight</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span> |
| <span class="go">array([[-0.01, -0.01],</span> |
| <span class="go"> [-0.01, -0.01]], dtype=float32)</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.kvstore.KVStore.type"> |
| <em class="property">property </em><code class="sig-name descname">type</code><a class="headerlink" href="#mxnet.kvstore.KVStore.type" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns the type of this kvstore.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Returns</dt> |
| <dd class="field-odd"><p><strong>type</strong> – the string type</p> |
| </dd> |
| <dt class="field-even">Return type</dt> |
| <dd class="field-even"><p>str</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.kvstore.create"> |
| <code class="sig-prename descclassname">mxnet.kvstore.</code><code class="sig-name descname">create</code><span class="sig-paren">(</span><em class="sig-param">name='local'</em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/mxnet/kvstore.html#create"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.kvstore.create" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Creates a new KVStore.</p> |
| <p>For single machine training, there are two commonly used types:</p> |
| <p><code class="docutils literal notranslate"><span class="pre">local</span></code>: Copies all gradients to CPU memory and updates weights there.</p> |
| <p><code class="docutils literal notranslate"><span class="pre">device</span></code>: Aggregates gradients and updates weights on GPUs. With this setting, |
| the KVStore also attempts to use GPU peer-to-peer communication, |
| potentially accelerating the communication.</p> |
| <p>For distributed training, KVStore also supports a number of types:</p> |
| <p><code class="docutils literal notranslate"><span class="pre">dist_sync</span></code>: Behaves similarly to <code class="docutils literal notranslate"><span class="pre">local</span></code> but with one major difference. |
| With <code class="docutils literal notranslate"><span class="pre">dist_sync</span></code>, batch-size now means the batch size used on each machine. |
| So if there are <code class="docutils literal notranslate"><span class="pre">n</span></code> machines and we use batch size <code class="docutils literal notranslate"><span class="pre">b</span></code>, |
| then <code class="docutils literal notranslate"><span class="pre">dist_sync</span></code> behaves like <code class="docutils literal notranslate"><span class="pre">local</span></code> with batch size <code class="docutils literal notranslate"><span class="pre">n</span> <span class="pre">*</span> <span class="pre">b</span></code>.</p> |
| <p><code class="docutils literal notranslate"><span class="pre">dist_device_sync</span></code>: Identical to <code class="docutils literal notranslate"><span class="pre">dist_sync</span></code> with the difference similar |
| to <code class="docutils literal notranslate"><span class="pre">device</span></code> vs <code class="docutils literal notranslate"><span class="pre">local</span></code>.</p> |
| <p><code class="docutils literal notranslate"><span class="pre">dist_async</span></code>: Performs asynchronous updates. |
| The weights are updated whenever gradients are received from any machine. |
| No two updates happen on the same weight at the same time. However, the order is not |
| guaranteed.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>name</strong> (<em>{'local'</em><em>, </em><em>'device'</em><em>, </em><em>'nccl'</em><em>, </em><em>'dist_sync'</em><em>, </em><em>'dist_device_sync'</em><em>, </em><em>'dist_async'}</em>) – The type of KVStore.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><strong>kv</strong> – The created KVStore.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="#mxnet.kvstore.KVStore" title="mxnet.kvstore.KVStore">KVStore</a></p> |
| </dd> |
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