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Table Of Contents
  • Python Tutorials
    • Getting Started
      • Crash Course
        • Manipulate data with ndarray
        • Create a neural network
        • Automatic differentiation with autograd
        • Train the neural network
        • Predict with a pre-trained model
        • Use GPUs
      • Moving to MXNet from Other Frameworks
        • PyTorch vs Apache MXNet
      • Gluon: from experiment to deployment
      • Logistic regression explained
      • MNIST
    • Packages
      • Automatic Differentiation
      • Gluon
        • Blocks
          • Custom Layers
          • Customer Layers (Beginners)
          • Hybridize
          • Initialization
          • Parameter and Block Naming
          • Layers and Blocks
          • Parameter Management
          • Saving and Loading Gluon Models
          • Activation Blocks
        • Image Tutorials
          • Image Augmentation
          • Handwritten Digit Recognition
          • Using pre-trained models in MXNet
        • Losses
          • Custom Loss Blocks
          • Kullback-Leibler (KL) Divergence
          • Loss functions
        • Text Tutorials
          • Google Neural Machine Translation
          • Machine Translation with Transformer
        • Training
          • MXNet Gluon Fit API
          • Trainer
          • Learning Rates
            • Learning Rate Finder
            • Learning Rate Schedules
            • Advanced Learning Rate Schedules
          • Normalization Blocks
      • KVStore
        • Distributed Key-Value Store
      • NDArray
        • An Intro: Manipulate Data the MXNet Way with NDArray
        • NDArray Operations
        • NDArray Contexts
        • Gotchas using NumPy in Apache MXNet
        • Tutorials
          • CSRNDArray - NDArray in Compressed Sparse Row Storage Format
          • RowSparseNDArray - NDArray for Sparse Gradient Updates
          • Train a Linear Regression Model with Sparse Symbols
          • Sparse NDArrays with Gluon
      • ONNX
        • Fine-tuning an ONNX model
        • Running inference on MXNet/Gluon from an ONNX model
        • Importing an ONNX model into MXNet
        • Export ONNX Models
      • Optimizers
      • Visualization
        • Visualize networks
    • Performance
      • Compression
        • Deploy with int-8
        • Float16
        • Gradient Compression
        • GluonCV with Quantized Models
      • Accelerated Backend Tools
        • Intel MKL-DNN
          • Quantize with MKL-DNN backend
          • Install MXNet with MKL-DNN
        • TensorRT
          • Optimized GPU Inference
        • Use TVM
        • Profiling MXNet Models
        • Using AMP: Automatic Mixed Precision
    • Deployment
      • Export
        • Exporting to ONNX format
        • Export Gluon CV Models
        • Save / Load Parameters
      • Inference
        • Deploy into C++
        • Deploy into a Java or Scala Environment
        • Real-time Object Detection with MXNet On The Raspberry Pi
      • Run on AWS
        • Run on an EC2 Instance
        • Run on Amazon SageMaker
        • MXNet on the Cloud
    • Extend
      • Custom Layers
      • Custom Numpy Operators
      • New Operator Creation
      • New Operator in MXNet Backend
  • Python API
    • mxnet.ndarray
      • ndarray
      • ndarray.contrib
      • ndarray.image
      • ndarray.linalg
      • ndarray.op
      • ndarray.random
      • ndarray.register
      • ndarray.sparse
      • ndarray.utils
    • mxnet.gluon
      • gluon.Block
      • gluon.HybridBlock
      • gluon.SymbolBlock
      • gluon.Constant
      • gluon.Parameter
      • gluon.ParameterDict
      • gluon.Trainer
      • gluon.contrib
      • gluon.data
        • data.vision
          • vision.datasets
          • vision.transforms
      • gluon.loss
      • gluon.model_zoo.vision
      • gluon.nn
      • gluon.rnn
      • gluon.utils
    • mxnet.autograd
    • mxnet.initializer
    • mxnet.optimizer
    • mxnet.lr_scheduler
    • mxnet.metric
    • mxnet.kvstore
    • mxnet.symbol
      • symbol
      • symbol.contrib
      • symbol.image
      • symbol.linalg
      • symbol.op
      • symbol.random
      • symbol.register
      • symbol.sparse
    • mxnet.module
    • mxnet.contrib
      • contrib.autograd
      • contrib.io
      • contrib.ndarray
      • contrib.onnx
      • contrib.quantization
      • contrib.symbol
      • contrib.tensorboard
      • contrib.tensorrt
      • contrib.text
    • mxnet
      • mxnet.attribute
      • mxnet.base
      • mxnet.callback
      • mxnet.context
      • mxnet.engine
      • mxnet.executor
      • mxnet.executor_manager
      • mxnet.image
      • mxnet.io
      • mxnet.kvstore_server
      • mxnet.libinfo
      • mxnet.log
      • mxnet.model
      • mxnet.monitor
      • mxnet.name
      • mxnet.notebook
      • mxnet.operator
      • mxnet.profiler
      • mxnet.random
      • mxnet.recordio
      • mxnet.registry
      • mxnet.rtc
      • mxnet.test_utils
      • mxnet.torch
      • mxnet.util
      • mxnet.visualization
Table Of Contents
  • Python Tutorials
    • Getting Started
      • Crash Course
        • Manipulate data with ndarray
        • Create a neural network
        • Automatic differentiation with autograd
        • Train the neural network
        • Predict with a pre-trained model
        • Use GPUs
      • Moving to MXNet from Other Frameworks
        • PyTorch vs Apache MXNet
      • Gluon: from experiment to deployment
      • Logistic regression explained
      • MNIST
    • Packages
      • Automatic Differentiation
      • Gluon
        • Blocks
          • Custom Layers
          • Customer Layers (Beginners)
          • Hybridize
          • Initialization
          • Parameter and Block Naming
          • Layers and Blocks
          • Parameter Management
          • Saving and Loading Gluon Models
          • Activation Blocks
        • Image Tutorials
          • Image Augmentation
          • Handwritten Digit Recognition
          • Using pre-trained models in MXNet
        • Losses
          • Custom Loss Blocks
          • Kullback-Leibler (KL) Divergence
          • Loss functions
        • Text Tutorials
          • Google Neural Machine Translation
          • Machine Translation with Transformer
        • Training
          • MXNet Gluon Fit API
          • Trainer
          • Learning Rates
            • Learning Rate Finder
            • Learning Rate Schedules
            • Advanced Learning Rate Schedules
          • Normalization Blocks
      • KVStore
        • Distributed Key-Value Store
      • NDArray
        • An Intro: Manipulate Data the MXNet Way with NDArray
        • NDArray Operations
        • NDArray Contexts
        • Gotchas using NumPy in Apache MXNet
        • Tutorials
          • CSRNDArray - NDArray in Compressed Sparse Row Storage Format
          • RowSparseNDArray - NDArray for Sparse Gradient Updates
          • Train a Linear Regression Model with Sparse Symbols
          • Sparse NDArrays with Gluon
      • ONNX
        • Fine-tuning an ONNX model
        • Running inference on MXNet/Gluon from an ONNX model
        • Importing an ONNX model into MXNet
        • Export ONNX Models
      • Optimizers
      • Visualization
        • Visualize networks
    • Performance
      • Compression
        • Deploy with int-8
        • Float16
        • Gradient Compression
        • GluonCV with Quantized Models
      • Accelerated Backend Tools
        • Intel MKL-DNN
          • Quantize with MKL-DNN backend
          • Install MXNet with MKL-DNN
        • TensorRT
          • Optimized GPU Inference
        • Use TVM
        • Profiling MXNet Models
        • Using AMP: Automatic Mixed Precision
    • Deployment
      • Export
        • Exporting to ONNX format
        • Export Gluon CV Models
        • Save / Load Parameters
      • Inference
        • Deploy into C++
        • Deploy into a Java or Scala Environment
        • Real-time Object Detection with MXNet On The Raspberry Pi
      • Run on AWS
        • Run on an EC2 Instance
        • Run on Amazon SageMaker
        • MXNet on the Cloud
    • Extend
      • Custom Layers
      • Custom Numpy Operators
      • New Operator Creation
      • New Operator in MXNet Backend
  • Python API
    • mxnet.ndarray
      • ndarray
      • ndarray.contrib
      • ndarray.image
      • ndarray.linalg
      • ndarray.op
      • ndarray.random
      • ndarray.register
      • ndarray.sparse
      • ndarray.utils
    • mxnet.gluon
      • gluon.Block
      • gluon.HybridBlock
      • gluon.SymbolBlock
      • gluon.Constant
      • gluon.Parameter
      • gluon.ParameterDict
      • gluon.Trainer
      • gluon.contrib
      • gluon.data
        • data.vision
          • vision.datasets
          • vision.transforms
      • gluon.loss
      • gluon.model_zoo.vision
      • gluon.nn
      • gluon.rnn
      • gluon.utils
    • mxnet.autograd
    • mxnet.initializer
    • mxnet.optimizer
    • mxnet.lr_scheduler
    • mxnet.metric
    • mxnet.kvstore
    • mxnet.symbol
      • symbol
      • symbol.contrib
      • symbol.image
      • symbol.linalg
      • symbol.op
      • symbol.random
      • symbol.register
      • symbol.sparse
    • mxnet.module
    • mxnet.contrib
      • contrib.autograd
      • contrib.io
      • contrib.ndarray
      • contrib.onnx
      • contrib.quantization
      • contrib.symbol
      • contrib.tensorboard
      • contrib.tensorrt
      • contrib.text
    • mxnet
      • mxnet.attribute
      • mxnet.base
      • mxnet.callback
      • mxnet.context
      • mxnet.engine
      • mxnet.executor
      • mxnet.executor_manager
      • mxnet.image
      • mxnet.io
      • mxnet.kvstore_server
      • mxnet.libinfo
      • mxnet.log
      • mxnet.model
      • mxnet.monitor
      • mxnet.name
      • mxnet.notebook
      • mxnet.operator
      • mxnet.profiler
      • mxnet.random
      • mxnet.recordio
      • mxnet.registry
      • mxnet.rtc
      • mxnet.test_utils
      • mxnet.torch
      • mxnet.util
      • mxnet.visualization

Tutorials¶

  • CSRNDArray - NDArray in Compressed Sparse Row Storage Format
    • Advantages of Compressed Sparse Row NDArray (CSRNDArray)
    • Prerequisites
    • Compressed Sparse Row Matrix
      • Example Matrix Compression
    • Array Creation
    • Inspecting Arrays
    • Storage Type Conversion
    • Copies
    • Indexing and Slicing
    • Sparse Operators and Storage Type Inference
    • Data Loading
    • Advanced Topics
      • GPU Support
    • Next
  • RowSparseNDArray - NDArray for Sparse Gradient Updates
    • Motivation
    • Prerequisites
    • Row Sparse Format
    • Array Creation
    • Function Overview
    • Setting Type
    • Inspecting Arrays
    • Storage Type Conversion
    • Copies
    • Retain Row Slices
    • Sparse Operators and Storage Type Inference
    • Sparse Optimizers
    • Advanced Topics
      • GPU Support
    • Next
  • Train a Linear Regression Model with Sparse Symbols
    • Prerequisites
    • Variables
      • Variable Storage Types
      • Bind with Sparse Arrays
    • Symbol Composition and Storage Type Inference
      • Basic Symbol Composition
      • Storage Type Inference
      • Storage Type Fallback
      • Inspecting Storage Types of the Symbol Graph
    • Training with Module APIs
      • Preparing the Data
      • Defining the Model
      • Training the model
      • Training the model with multiple machines or multiple devices
  • Sparse NDArrays with Gluon
    • Generating Sparse Data
    • Writing Sparse Data
    • Reading Sparse Data
      • Gluon Models for Sparse Data
    • Benchmark: FullyConnected
    • Benchmark: FullyConnectedSparse
    • Benchmark: FullyConnectedSparse with grad_stype=row_sparse
    • Advanced: Sparse weight
      • Conclusion
      • Recommended Next Steps
Previous
Gotchas using NumPy in Apache MXNet
Next
CSRNDArray - NDArray in Compressed Sparse Row Storage Format

Resources

  • Dev list
  • User mailing list
  • Developer Wiki
  • Jira Tracker
  • Github Roadmap
  • MXNet Discuss forum
  • Contribute To MXNet
  • apache/incubator-mxnet
  • apachemxnet
  • apachemxnet

A flexible and efficient library for deep learning.

Apache MXNet is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator. Incubation is required of all newly accepted projects until a further review indicates that the infrastructure, communications, and decision making process have stabilized in a manner consistent with other successful ASF projects. While incubation status is not necessarily a reflection of the completeness or stability of the code, it does indicate that the project has yet to be fully endorsed by the ASF.

"Copyright © 2017-2018, The Apache Software Foundation Apache MXNet, MXNet, Apache, the Apache feather, and the Apache MXNet project logo are either registered trademarks or trademarks of the Apache Software Foundation."