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<li class="toctree-l1 current"><a class="reference internal" href="../../index.html">Python Tutorials</a><ul class="current">
<li class="toctree-l2"><a class="reference internal" href="../../getting-started/index.html">Getting Started</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../../getting-started/crash-course/index.html">Crash Course</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../../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="../../getting-started/crash-course/2-nn.html">Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../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="../../getting-started/crash-course/4-train.html">Train the neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../getting-started/crash-course/5-predict.html">Predict with a pre-trained model</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../getting-started/crash-course/6-use_gpus.html">Use GPUs</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../../getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../../getting-started/to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li>
</ul>
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<li class="toctree-l3"><a class="reference internal" href="../../getting-started/gluon_from_experiment_to_deployment.html">Gluon: from experiment to deployment</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../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>
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<li class="toctree-l3"><a class="reference internal" href="../autograd/index.html">Automatic Differentiation</a></li>
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<li class="toctree-l4"><a class="reference internal" href="../gluon/blocks/index.html">Blocks</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../gluon/blocks/custom-layer.html">Custom Layers</a></li>
<li class="toctree-l5"><a class="reference internal" href="../gluon/blocks/custom_layer_beginners.html">Customer Layers (Beginners)</a></li>
<li class="toctree-l5"><a class="reference internal" href="../gluon/blocks/hybridize.html">Hybridize</a></li>
<li class="toctree-l5"><a class="reference internal" href="../gluon/blocks/init.html">Initialization</a></li>
<li class="toctree-l5"><a class="reference internal" href="../gluon/blocks/naming.html">Parameter and Block Naming</a></li>
<li class="toctree-l5"><a class="reference internal" href="../gluon/blocks/nn.html">Layers and Blocks</a></li>
<li class="toctree-l5"><a class="reference internal" href="../gluon/blocks/parameters.html">Parameter Management</a></li>
<li class="toctree-l5"><a class="reference internal" href="../gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></li>
<li class="toctree-l5"><a class="reference internal" href="../gluon/blocks/activations/activations.html">Activation Blocks</a></li>
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<li class="toctree-l5"><a class="reference internal" href="../gluon/data/data_augmentation.html">Image Augmentation</a></li>
<li class="toctree-l5"><a class="reference internal" href="../gluon/data/data_augmentation.html#Spatial-Augmentation">Spatial Augmentation</a></li>
<li class="toctree-l5"><a class="reference internal" href="../gluon/data/data_augmentation.html#Color-Augmentation">Color Augmentation</a></li>
<li class="toctree-l5"><a class="reference internal" href="../gluon/data/data_augmentation.html#Composed-Augmentations">Composed Augmentations</a></li>
<li class="toctree-l5"><a class="reference internal" href="../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="../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="../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="../gluon/data/datasets.html#Appendix:-Upgrading-from-Module-DataIter-to-Gluon-DataLoader">Appendix: Upgrading from Module <code class="docutils literal notranslate"><span class="pre">DataIter</span></code> to Gluon <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
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<li class="toctree-l5"><a class="reference internal" href="../gluon/image/mnist.html">Handwritten Digit Recognition</a></li>
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<li class="toctree-l5"><a class="reference internal" href="../gluon/text/transformer.html">Machine Translation with Transformer</a></li>
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<li class="toctree-l5"><a class="reference internal" href="../gluon/training/fit_api_tutorial.html">MXNet Gluon Fit API</a></li>
<li class="toctree-l5"><a class="reference internal" href="../gluon/training/trainer.html">Trainer</a></li>
<li class="toctree-l5"><a class="reference internal" href="../gluon/training/learning_rates/index.html">Learning Rates</a><ul>
<li class="toctree-l6"><a class="reference internal" href="../gluon/training/learning_rates/learning_rate_finder.html">Learning Rate Finder</a></li>
<li class="toctree-l6"><a class="reference internal" href="../gluon/training/learning_rates/learning_rate_schedules.html">Learning Rate Schedules</a></li>
<li class="toctree-l6"><a class="reference internal" href="../gluon/training/learning_rates/learning_rate_schedules_advanced.html">Advanced Learning Rate Schedules</a></li>
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<li class="toctree-l5"><a class="reference internal" href="../gluon/training/normalization/index.html">Normalization Blocks</a></li>
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<li class="toctree-l3 current"><a class="reference internal" href="index.html">KVStore</a><ul class="current">
<li class="toctree-l4 current"><a class="current reference internal" href="#">Distributed Key-Value Store</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../ndarray/index.html">NDArray</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../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="../ndarray/02-ndarray-operations.html">NDArray Operations</a></li>
<li class="toctree-l4"><a class="reference internal" href="../ndarray/03-ndarray-contexts.html">NDArray Contexts</a></li>
<li class="toctree-l4"><a class="reference internal" href="../ndarray/gotchas_numpy_in_mxnet.html">Gotchas using NumPy in Apache MXNet</a></li>
<li class="toctree-l4"><a class="reference internal" href="../ndarray/sparse/index.html">Tutorials</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../ndarray/sparse/csr.html">CSRNDArray - NDArray in Compressed Sparse Row Storage Format</a></li>
<li class="toctree-l5"><a class="reference internal" href="../ndarray/sparse/row_sparse.html">RowSparseNDArray - NDArray for Sparse Gradient Updates</a></li>
<li class="toctree-l5"><a class="reference internal" href="../ndarray/sparse/train.html">Train a Linear Regression Model with Sparse Symbols</a></li>
<li class="toctree-l5"><a class="reference internal" href="../ndarray/sparse/train_gluon.html">Sparse NDArrays with Gluon</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../onnx/index.html">ONNX</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../onnx/fine_tuning_gluon.html">Fine-tuning an ONNX model</a></li>
<li class="toctree-l4"><a class="reference internal" href="../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="../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>
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<li class="toctree-l4"><a class="reference internal" href="../../performance/compression/int8.html">Deploy with int-8</a></li>
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<li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/gradient_compression">Gradient Compression</a></li>
<li class="toctree-l4"><a class="reference external" href="https://gluon-cv.mxnet.io/build/examples_deployment/int8_inference.html">GluonCV with Quantized Models</a></li>
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<li class="toctree-l4"><a class="reference internal" href="../../performance/backend/mkldnn/index.html">Intel MKL-DNN</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../../performance/backend/mkldnn/mkldnn_quantization.html">Quantize with MKL-DNN backend</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../performance/backend/mkldnn/mkldnn_quantization.html#Improving-accuracy-with-Intel®-Neural-Compressor">Improving accuracy with Intel® Neural Compressor</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../performance/backend/mkldnn/mkldnn_readme.html">Install MXNet with MKL-DNN</a></li>
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<li class="toctree-l5"><a class="reference internal" href="../../performance/backend/tensorrt/tensorrt.html">Optimizing Deep Learning Computation Graphs with TensorRT</a></li>
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<li class="toctree-l4"><a class="reference internal" href="../../performance/backend/tvm.html">Use TVM</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../performance/backend/profiler.html">Profiling MXNet Models</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../performance/backend/amp.html">Using AMP: Automatic Mixed Precision</a></li>
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<li class="toctree-l1 current"><a class="reference internal" href="../../index.html">Python Tutorials</a><ul class="current">
<li class="toctree-l2"><a class="reference internal" href="../../getting-started/index.html">Getting Started</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../../getting-started/crash-course/index.html">Crash Course</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../../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="../../getting-started/crash-course/2-nn.html">Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../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="../../getting-started/crash-course/4-train.html">Train the neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../getting-started/crash-course/5-predict.html">Predict with a pre-trained model</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../getting-started/crash-course/6-use_gpus.html">Use GPUs</a></li>
</ul>
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<li class="toctree-l3"><a class="reference internal" href="../../getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../../getting-started/to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../getting-started/gluon_from_experiment_to_deployment.html">Gluon: from experiment to deployment</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../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>
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<li class="toctree-l2 current"><a class="reference internal" href="../index.html">Packages</a><ul class="current">
<li class="toctree-l3"><a class="reference internal" href="../autograd/index.html">Automatic Differentiation</a></li>
<li class="toctree-l3"><a class="reference internal" href="../gluon/index.html">Gluon</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../gluon/blocks/index.html">Blocks</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../gluon/blocks/custom-layer.html">Custom Layers</a></li>
<li class="toctree-l5"><a class="reference internal" href="../gluon/blocks/custom_layer_beginners.html">Customer Layers (Beginners)</a></li>
<li class="toctree-l5"><a class="reference internal" href="../gluon/blocks/hybridize.html">Hybridize</a></li>
<li class="toctree-l5"><a class="reference internal" href="../gluon/blocks/init.html">Initialization</a></li>
<li class="toctree-l5"><a class="reference internal" href="../gluon/blocks/naming.html">Parameter and Block Naming</a></li>
<li class="toctree-l5"><a class="reference internal" href="../gluon/blocks/nn.html">Layers and Blocks</a></li>
<li class="toctree-l5"><a class="reference internal" href="../gluon/blocks/parameters.html">Parameter Management</a></li>
<li class="toctree-l5"><a class="reference internal" href="../gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></li>
<li class="toctree-l5"><a class="reference internal" href="../gluon/blocks/activations/activations.html">Activation Blocks</a></li>
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<li class="toctree-l4"><a class="reference internal" href="../gluon/data/index.html">Data Tutorials</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../gluon/data/data_augmentation.html">Image Augmentation</a></li>
<li class="toctree-l5"><a class="reference internal" href="../gluon/data/data_augmentation.html#Spatial-Augmentation">Spatial Augmentation</a></li>
<li class="toctree-l5"><a class="reference internal" href="../gluon/data/data_augmentation.html#Color-Augmentation">Color Augmentation</a></li>
<li class="toctree-l5"><a class="reference internal" href="../gluon/data/data_augmentation.html#Composed-Augmentations">Composed Augmentations</a></li>
<li class="toctree-l5"><a class="reference internal" href="../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="../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="../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="../gluon/data/datasets.html#Appendix:-Upgrading-from-Module-DataIter-to-Gluon-DataLoader">Appendix: Upgrading from Module <code class="docutils literal notranslate"><span class="pre">DataIter</span></code> to Gluon <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
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<li class="toctree-l4"><a class="reference internal" href="../gluon/image/index.html">Image Tutorials</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../gluon/image/image-augmentation.html">Image Augmentation</a></li>
<li class="toctree-l5"><a class="reference internal" href="../gluon/image/info_gan.html">Image similarity search with InfoGAN</a></li>
<li class="toctree-l5"><a class="reference internal" href="../gluon/image/mnist.html">Handwritten Digit Recognition</a></li>
<li class="toctree-l5"><a class="reference internal" href="../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="../gluon/loss/index.html">Losses</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../gluon/loss/custom-loss.html">Custom Loss Blocks</a></li>
<li class="toctree-l5"><a class="reference internal" href="../gluon/loss/kl_divergence.html">Kullback-Leibler (KL) Divergence</a></li>
<li class="toctree-l5"><a class="reference internal" href="../gluon/loss/loss.html">Loss functions</a></li>
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<li class="toctree-l4"><a class="reference internal" href="../gluon/text/index.html">Text Tutorials</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../gluon/text/gnmt.html">Google Neural Machine Translation</a></li>
<li class="toctree-l5"><a class="reference internal" href="../gluon/text/transformer.html">Machine Translation with Transformer</a></li>
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<li class="toctree-l4"><a class="reference internal" href="../gluon/training/index.html">Training</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../gluon/training/fit_api_tutorial.html">MXNet Gluon Fit API</a></li>
<li class="toctree-l5"><a class="reference internal" href="../gluon/training/trainer.html">Trainer</a></li>
<li class="toctree-l5"><a class="reference internal" href="../gluon/training/learning_rates/index.html">Learning Rates</a><ul>
<li class="toctree-l6"><a class="reference internal" href="../gluon/training/learning_rates/learning_rate_finder.html">Learning Rate Finder</a></li>
<li class="toctree-l6"><a class="reference internal" href="../gluon/training/learning_rates/learning_rate_schedules.html">Learning Rate Schedules</a></li>
<li class="toctree-l6"><a class="reference internal" href="../gluon/training/learning_rates/learning_rate_schedules_advanced.html">Advanced Learning Rate Schedules</a></li>
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<li class="toctree-l5"><a class="reference internal" href="../gluon/training/normalization/index.html">Normalization Blocks</a></li>
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<li class="toctree-l3 current"><a class="reference internal" href="index.html">KVStore</a><ul class="current">
<li class="toctree-l4 current"><a class="current reference internal" href="#">Distributed Key-Value Store</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../ndarray/index.html">NDArray</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../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="../ndarray/02-ndarray-operations.html">NDArray Operations</a></li>
<li class="toctree-l4"><a class="reference internal" href="../ndarray/03-ndarray-contexts.html">NDArray Contexts</a></li>
<li class="toctree-l4"><a class="reference internal" href="../ndarray/gotchas_numpy_in_mxnet.html">Gotchas using NumPy in Apache MXNet</a></li>
<li class="toctree-l4"><a class="reference internal" href="../ndarray/sparse/index.html">Tutorials</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../ndarray/sparse/csr.html">CSRNDArray - NDArray in Compressed Sparse Row Storage Format</a></li>
<li class="toctree-l5"><a class="reference internal" href="../ndarray/sparse/row_sparse.html">RowSparseNDArray - NDArray for Sparse Gradient Updates</a></li>
<li class="toctree-l5"><a class="reference internal" href="../ndarray/sparse/train.html">Train a Linear Regression Model with Sparse Symbols</a></li>
<li class="toctree-l5"><a class="reference internal" href="../ndarray/sparse/train_gluon.html">Sparse NDArrays with Gluon</a></li>
</ul>
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<li class="toctree-l3"><a class="reference internal" href="../onnx/index.html">ONNX</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../onnx/fine_tuning_gluon.html">Fine-tuning an ONNX model</a></li>
<li class="toctree-l4"><a class="reference internal" href="../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="../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>
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<!--- Licensed to the Apache Software Foundation (ASF) under one --><!--- or more contributor license agreements. See the NOTICE file --><!--- distributed with this work for additional information --><!--- regarding copyright ownership. The ASF licenses this file --><!--- to you under the Apache License, Version 2.0 (the --><!--- "License"); you may not use this file except in compliance --><!--- with the License. You may obtain a copy of the License at --><!--- http://www.apache.org/licenses/LICENSE-2.0 --><!--- Unless required by applicable law or agreed to in writing, --><!--- software distributed under the License is distributed on an --><!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY --><!--- KIND, either express or implied. See the License for the --><!--- specific language governing permissions and limitations --><!--- under the License. --><div class="section" id="Distributed-Key-Value-Store">
<h1>Distributed Key-Value Store<a class="headerlink" href="#Distributed-Key-Value-Store" title="Permalink to this headline"></a></h1>
<p>KVStore is a place for data sharing. Think of it as a single object shared across different devices (GPUs and computers), where each device can push data in and pull data out.</p>
<div class="section" id="Initialization">
<h2>Initialization<a class="headerlink" href="#Initialization" title="Permalink to this headline"></a></h2>
<p>Let’s consider a simple example: initializing a (<code class="docutils literal notranslate"><span class="pre">int</span></code>, <code class="docutils literal notranslate"><span class="pre">NDArray</span></code>) pair into the store, and then pulling the value out:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">mxnet</span> <span class="k">as</span> <span class="nn">mx</span>
<span class="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">&#39;local&#39;</span><span class="p">)</span> <span class="c1"># create a local kv store.</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="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">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="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="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">a</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">a</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">())</span>
</pre></div>
</div>
<p><code class="docutils literal notranslate"><span class="pre">[[</span> <span class="pre">2.</span>&#160; <span class="pre">2.</span>&#160; <span class="pre">2.],[</span> <span class="pre">2.</span>&#160; <span class="pre">2.</span>&#160; <span class="pre">2.]]</span></code></p>
</div>
<div class="section" id="Push,-Aggregate,-and-Update">
<h2>Push, Aggregate, and Update<a class="headerlink" href="#Push,-Aggregate,-and-Update" title="Permalink to this headline"></a></h2>
<p>For any key that has been initialized, you can push a new value with the same shape to the key:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></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">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">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">a</span><span class="p">)</span> <span class="c1"># pull out the value</span>
<span class="nb">print</span><span class="p">(</span><span class="n">a</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">())</span>
</pre></div>
</div>
<p><code class="docutils literal notranslate"><span class="pre">[[</span> <span class="pre">8.</span>&#160; <span class="pre">8.</span>&#160; <span class="pre">8.],[</span> <span class="pre">8.</span>&#160; <span class="pre">8.</span>&#160; <span class="pre">8.]]</span></code></p>
<p>The data for pushing can be stored on any device. Furthermore, you can push multiple values into the same key, where KVStore will first sum all of these values and then push the aggregated value. Here we will just demonstrate pushing a list of values on CPU. Please note summation only happens if the value list is longer than one</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">contexts</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">cpu</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="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">ctx</span><span class="p">)</span> <span class="k">for</span> <span class="n">ctx</span> <span class="ow">in</span> <span class="n">contexts</span><span class="p">]</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">b</span><span class="p">)</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">a</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">a</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">())</span>
</pre></div>
</div>
<p><code class="docutils literal notranslate"><span class="pre">[[</span> <span class="pre">4.</span>&#160; <span class="pre">4.</span>&#160; <span class="pre">4.],[</span> <span class="pre">4.</span>&#160; <span class="pre">4.</span>&#160; <span class="pre">4.]]</span></code></p>
<p>For each push, KVStore combines the pushed value with the value stored using an <code class="docutils literal notranslate"><span class="pre">updater</span></code>. The default updater is <code class="docutils literal notranslate"><span class="pre">ASSIGN</span></code>. You can replace the default to control how data is merged:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="nb">input</span><span class="p">,</span> <span class="n">stored</span><span class="p">):</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;update on key: </span><span class="si">%d</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">key</span><span class="p">)</span>
<span class="n">stored</span> <span class="o">+=</span> <span class="nb">input</span> <span class="o">*</span> <span class="mi">2</span>
<span class="n">kv</span><span class="o">.</span><span class="n">_set_updater</span><span class="p">(</span><span class="n">update</span><span class="p">)</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">a</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">a</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">())</span>
</pre></div>
</div>
<p><code class="docutils literal notranslate"><span class="pre">[[</span> <span class="pre">4.</span>&#160; <span class="pre">4.</span>&#160; <span class="pre">4.],[</span> <span class="pre">4.</span>&#160; <span class="pre">4.</span>&#160; <span class="pre">4.]]</span></code></p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></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">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">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">a</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">a</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">())</span>
</pre></div>
</div>
<p><code class="docutils literal notranslate"><span class="pre">update</span> <span class="pre">on</span> <span class="pre">key:</span> <span class="pre">3</span></code></p>
<p><code class="docutils literal notranslate"><span class="pre">[[</span> <span class="pre">6.</span>&#160; <span class="pre">6.</span>&#160; <span class="pre">6.],[</span> <span class="pre">6.</span>&#160; <span class="pre">6.</span>&#160; <span class="pre">6.]]</span></code></p>
</div>
<div class="section" id="Pull">
<h2>Pull<a class="headerlink" href="#Pull" title="Permalink to this headline"></a></h2>
<p>You’ve already seen how to pull a single key-value pair. Similarly, to push, you can pull the value onto several devices with a single call:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></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">ctx</span><span class="p">)</span> <span class="k">for</span> <span class="n">ctx</span> <span class="ow">in</span> <span class="n">contexts</span><span class="p">]</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">b</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</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>
</pre></div>
</div>
<p><code class="docutils literal notranslate"><span class="pre">[</span> <span class="pre">6.</span>&#160; <span class="pre">6.</span>&#160; <span class="pre">6.]],[[</span> <span class="pre">6.</span>&#160; <span class="pre">6.</span>&#160; <span class="pre">6.]</span></code></p>
</div>
<div class="section" id="Handle-a-List-of-Key-Value-Pairs">
<h2>Handle a List of Key-Value Pairs<a class="headerlink" href="#Handle-a-List-of-Key-Value-Pairs" title="Permalink to this headline"></a></h2>
<p>All operations introduced so far involve a single key. KVStore also provides an interface for a list of key-value pairs.</p>
<p>For a single device:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">keys</span> <span class="o">=</span> <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">9</span><span class="p">]</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>
<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">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="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="nb">print</span><span class="p">(</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>
</pre></div>
</div>
<p><code class="docutils literal notranslate"><span class="pre">update</span> <span class="pre">on</span> <span class="pre">key:</span> <span class="pre">5</span></code></p>
<p><code class="docutils literal notranslate"><span class="pre">update</span> <span class="pre">on</span> <span class="pre">key:</span> <span class="pre">7</span></code></p>
<p><code class="docutils literal notranslate"><span class="pre">update</span> <span class="pre">on</span> <span class="pre">key:</span> <span class="pre">9</span></code></p>
<p><code class="docutils literal notranslate"><span class="pre">[[</span> <span class="pre">3.</span>&#160; <span class="pre">3.</span>&#160; <span class="pre">3.],[</span> <span class="pre">3.</span>&#160; <span class="pre">3.</span>&#160; <span class="pre">3.]]</span></code></p>
<p>For multiple devices:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></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">ctx</span><span class="p">)</span> <span class="k">for</span> <span class="n">ctx</span> <span class="ow">in</span> <span class="n">contexts</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">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="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="nb">print</span><span class="p">(</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>
</pre></div>
</div>
<p><code class="docutils literal notranslate"><span class="pre">update</span> <span class="pre">on</span> <span class="pre">key:</span> <span class="pre">5</span></code></p>
<p><code class="docutils literal notranslate"><span class="pre">update</span> <span class="pre">on</span> <span class="pre">key:</span> <span class="pre">7</span></code></p>
<p><code class="docutils literal notranslate"><span class="pre">update</span> <span class="pre">on</span> <span class="pre">key:</span> <span class="pre">9</span></code></p>
<p><code class="docutils literal notranslate"><span class="pre">[[</span> <span class="pre">11.</span>&#160; <span class="pre">11.</span>&#160; <span class="pre">11.],[</span> <span class="pre">11.</span>&#160; <span class="pre">11.</span>&#160; <span class="pre">11.]]</span></code></p>
</div>
<div class="section" id="Run-on-Multiple-Machines">
<h2>Run on Multiple Machines<a class="headerlink" href="#Run-on-Multiple-Machines" title="Permalink to this headline"></a></h2>
<p>Based on parameter server, the <code class="docutils literal notranslate"><span class="pre">updater</span></code> runs on the server nodes. When the distributed version is ready, we will update this section.</p>
<!-- ## How to Choose Between APIs --><!-- You can mix APIs as much as you like. Here are some guidelines --><!-- * Use the Symbolic API and a coarse-grained operator to create an established structure. --><!-- * Use a fine-grained operator to extend parts of a more flexible symbolic graph. --><!-- * Do some dynamic NDArray tricks, which are even more flexible, between the calls of forward and backward executors. --><!-- Different approaches offer you different levels of flexibility and --><!-- efficiency. Normally, you do not need to be flexible in all parts of the --><!-- network, so use the parts optimized for speed, and compose it --><!-- flexibly with a fine-grained operator or a dynamic NDArray. Such a --><!-- mixture allows you to build the deep learning architecture both efficiently and --><!-- flexibly as your choice. --></div>
<div class="section" id="Next-Steps">
<h2>Next Steps<a class="headerlink" href="#Next-Steps" title="Permalink to this headline"></a></h2>
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<ul>
<li><a class="reference internal" href="#">Distributed Key-Value Store</a><ul>
<li><a class="reference internal" href="#Initialization">Initialization</a></li>
<li><a class="reference internal" href="#Push,-Aggregate,-and-Update">Push, Aggregate, and Update</a></li>
<li><a class="reference internal" href="#Pull">Pull</a></li>
<li><a class="reference internal" href="#Handle-a-List-of-Key-Value-Pairs">Handle a List of Key-Value Pairs</a></li>
<li><a class="reference internal" href="#Run-on-Multiple-Machines">Run on Multiple Machines</a></li>
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