What Is Apache MXNet ? ● A deep learning framework ● Open source Apache 2.0 license ● Supports distributed gpu cluster training/deployment ● Of deep neural networks ● It supports a variety of language bindings ● Supports hybridize for increased speed/optimization ● Supports near linear scaling on gpu / host clusters ● Provides support for the Horovod framework 

MXNet Language Bindings ● MXNet has a Python based API ● MXNet also supports the following language bindings – Scala – Julia – Clojure – Java – C++ – R – Perl 

MXNet Related Terms Horovod MMS DGL ONNX Hyperparameter D2l.ai KVStore DMLC A distributed deep learning framework from Uber MXNet Model Server Deep Graph Library Open Neural Network Exchange A parameter whose value is used to control the learning process A jupyter notebook based deep learning book for Mxnet ++ Key-value store interface used by MXNet Distributed (Deep) Machine Learning Community - GitHub 

MXNet Eco System Coach RL Deep Graph GluonFR InsightFace Keras-MXNet MXBoard MXFusion MXNet Model Optuna Sockeye A Python reinforcement learning framework DGL is a Python pkg for deep learning on graphs A community driven toolkit for face detection and recognition A face detection and recognition repository A back end of high level API Keras Logging API's for TensorBoard visualisation A modular deep probabilistic programming library A flexible tool for serving models exported from Mxnet A hyperparameter optimization framework A sequence to sequence framework for neural translation 

MXNet Eco System TensorLY TVM Xfer GluonCV GluonNLP GluonTS A high level API for tensor methods An open deep learning stack for GPU's, CPU's etc A library for the transfer of knowledge in deep nets A computer vision toolkit with a rich model zoo Deep learning models for natural language processing A toolkit for probabilistic time series modelling 

MXNet User Community 

MXNet Architecture 

MXNet Architecture ● Runtime Dependency Engine – Schedules and executes the operations – According to their read/write dependency ● Storage Allocator – Efficiently allocates and recycles memory blocks – On host (CPU) and devices (GPUs) ● Resource Manager – Manages global resources, such as – The random number generator and temporal space ● NDArray – Dynamic, asynchronous n-dimensional arrays 

MXNet Architecture ● Symbolic Execution – Static symbolic graph executor, which provides – Efficient symbolic graph execution and optimization ● Operator – Operators that define static forward/gradient calc (backprop) ● SimpleOp – Operators that extend NDArray operators and – Symbolic operators in a unified fashion ● Symbol Construction – Symbolic construction, which provides a way to construct – A computation graph (net configuration) 

MXNet Architecture ● KVStore – Key-value store interface for efficient parameter synchronization ● Data Loading(IO) – Efficient distributed data loading and augmentation 

MXNet Data Loading ● For large data sets data loading is optimized in MXNet ● Data format – Uses dmlc-core’s binary recordIO implementation ● Data Loading – Reduced IO cost by utilizing the threaded iterator – Provided by dmlc-core ● Interface design – Write MXNet data iterators in just a few lines of Python 

MXNet Dependency Engine ● Helps to parallelize computation across devices ● Helps to synchronize computation when – We introduce multi-threading ● A run time dependency schedule graph is created ● The graph is then used to – Optimize processing – Optimize memory use – Aid parallelism when using ● GPU / CPU clusters ● For deep learning memory use – Usage during training > during prediction 

MXNet Forward Vs Backward Graph 

Available Books ● See “Big Data Made Easy” – Apress Jan 2015 ● See “Mastering Apache Spark” – Packt Oct 2015 ● See “Complete Guide to Open Source Big Data Stack – “Apress Jan 2018” ● Find the author on Amazon – www.amazon.com/Michael-Frampton/e/B00NIQDOOM/ ● Connect on LinkedIn – www.linkedin.com/in/mike-frampton-38563020 

Connect ● Feel free to connect on LinkedIn – www.linkedin.com/in/mike-frampton-38563020 ● See my open source blog at – open-source-systems.blogspot.com/ ● I am always interested in – New technology – Opportunities – Technology based issues – Big data integration