Release SuperBench v0.2.0
SuperBench v0.2.0 Release Notes
===============================
SuperBench Framework
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* Implemented a CLI to provide a command line interface.
* Implemented Runner for nodes control and management.
* Implemented Executor.
* Implemented Benchmark framework.
Supported Benchmarks
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* Supported Micro-benchmarks
* GEMM FLOPS (GFLOPS, TensorCore, cuBLAS, cuDNN)
* Kernel Launch Time (Kernel_Launch_Event_Time, Kernel_Launch_Wall_Time)
* Operator Performance (MatMul, Sharding_MatMul)
* Supported Model-benchmarks
* CNN models
(Reference: [torchvision models](https://github.com/pytorch/vision/tree/v0.8.0/torchvision/models))
* ResNet (ResNet-18, ResNet-34, ResNet-50, ResNet-101, ResNet-152)
* DenseNet (DenseNet-161, DenseNet-169, DenseNet-201)
* VGG (VGG-11, VGG-13, VGG-16, VGG-19, VGG11_bn, VGG13_bn, VGG16_bn, VGG19_bn)
* MNASNet (mnasnet0_5, mnasnet0_75, mnasnet1_0, mnasnet1_3)
* AlexNet
* GoogLeNet
* Inception_v3
* mobilenet_v2
* ResNeXt (resnext50_32x4d, resnext101_32x8d)
* Wide ResNet (wide_resnet50_2, wide_resnet101_2)
* ShuffleNet (shufflenet_v2_x0_5, shufflenet_v2_x1_0, shufflenet_v2_x1_5, shufflenet_v2_x2_0)
* SqueezeNet (squeezenet1_0, squeezenet1_1)
* LSTM model
* BERT models (BERT-Base, BERT-Large)
* GPT-2 model (specify which config)
Examples and Documents
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* Added examples to run benchmarks respectively.
* Tutorial Documents (introduction, getting-started, developer-guides, APIs, benchmarks).
* Built SuperBench [website](https://aka.ms/superbench/).
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