tutorials.tvmlang.orgGet Started Tutorials — tvm 0.7.dev1 documentation
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Description:TVM Documentation¶ TVM is an open deep learning compiler stack for CPUs GPUs and specialized accelerators It aims to close the gap between the productivity-focused deep learning frameworks and the performance- or efficiency-oriented hardware backends
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0.7.dev1 How to Installation Contribute to TVM Deploy and Integration Developer How-To Guide Tutorials Get Started Tutorials Quick Start Tutorial for Compiling Deep Learning Models Cross Compilation and RPC Get Started with Tensor Expression Compile Deep Learning Models Compile ONNX Models Deploy Single Shot Multibox Detector(SSD) model Using External Libraries in Relay Compile CoreML Models Compile Keras Models Compile PyTorch Object Detection Models Deploy a Quantized Model on Cuda Compile Caffe2 Models Compile MXNet Models Compile PyTorch Models Deploy the Pretrained Model on Raspberry Pi Deploy a Framework-prequantized Model with TVM Compile TFLite Models Deploy a Framework-prequantized Model with TVM - Part 3 (TFLite) Deploy the Pretrained Model on Android Compile Tensorflow Models Compile YOLO-V2 and YOLO-V3 in DarkNet Models Building a Graph Convolutional Network Deploy a Hugging Face Pruned Model on CPU Tensor Expression and Schedules Use Tensor Expression Debug Display (TEDD) for Visualization Compute and Reduce with Tuple Inputs External Tensor Functions Reduction Scan and Recurrent Kernel Intrinsics and Math Functions Schedule Primitives in TVM Use Tensorize to Leverage Hardware Intrinsics Optimize Tensor Operators How to optimize convolution on GPU How to optimize GEMM on CPU How to optimize convolution using TensorCores How to optimize matmul with Auto TensorCore CodeGen AutoTVM : Template-based Auto Tuning Writing tunable template and Using auto-tuner Tuning High Performance Convolution on NVIDIA GPUs Auto-tuning a convolutional network for NVIDIA GPU Auto-tuning a convolutional network for x86 CPU Auto-tuning a convolutional network for ARM CPU Auto-tuning a convolutional network for Mobile GPU AutoScheduler : Template-free Auto Scheduling Auto-scheduling matrix multiplication for CPU Auto-scheduling a convolution layer for GPU Developer Tutorials Writing a Customized Pass How to Use TVM Pass Infra TOPI: TVM Operator Inventory Introduction to TOPI Micro TVM Micro TVM with TFLite Models References Language Reference Python API Links to Other API References Deep Dive Design and Architecture MISC VTA: Deep Learning Accelerator Stack Frequently Asked Questions Index tvm Docs » Get Started Tutorials View page source Get Started Tutorials ¶ Quick Start Tutorial for Compiling Deep Learning Models ¶ Cross Compilation and RPC ¶ Get Started with Tensor Expression ¶ Compile Deep Learning Models ¶ Compile ONNX Models ¶ Deploy Single Shot Multibox Detector(SSD) model ¶ Using External Libraries in Relay ¶ Compile CoreML Models ¶ Compile Keras Models ¶ Compile PyTorch Object Detection Models ¶ Deploy a Quantized Model on Cuda ¶ Compile Caffe2 Models ¶ Compile MXNet Models ¶ Compile PyTorch Models ¶ Deploy the Pretrained Model on Raspberry Pi ¶ Deploy a Framework-prequantized Model with TVM ¶ Compile TFLite Models ¶ Deploy a Framework-prequantized Model with TVM - Part 3 (TFLite) ¶ Deploy the Pretrained Model on Android ¶ Compile Tensorflow Models ¶ Compile YOLO-V2 and YOLO-V3 in DarkNet Models ¶ Building a Graph Convolutional Network ¶ Deploy a Hugging Face Pruned Model on CPU ¶ Tensor Expression and Schedules ¶ Use Tensor Expression Debug Display (TEDD) for Visualization ¶ Compute and Reduce with Tuple Inputs ¶ External Tensor Functions ¶ Reduction ¶ Scan and Recurrent Kernel ¶ Intrinsics and Math Functions ¶ Schedule Primitives in TVM ¶ Use Tensorize to Leverage Hardware Intrinsics ¶ Optimize Tensor Operators ¶ How to optimize convolution on GPU ¶ How to optimize GEMM on CPU ¶ How to optimize convolution using TensorCores ¶ How to optimize matmul with Auto TensorCore CodeGen ¶ AutoTVM : Template-based Auto Tuning ¶ Writing tunable template and Using auto-tuner ¶ Tuning High Performance Convolution on NVIDIA GPUs ¶ Auto-tuning a convolutional network for NVIDIA GPU ¶ Auto-tuning a convolutional network for x86 CPU ¶ Auto-tuning a convolutional network for ARM CPU ¶ Auto-tuning a convolutional network for Mobile GPU ¶ AutoScheduler : Template-free Auto Scheduling ¶ Auto-scheduling matrix multiplication for CPU ¶ Auto-scheduling a convolution layer for GPU ¶ Developer Tutorials ¶ Writing a Customized Pass ¶ How to Use TVM Pass Infra ¶ TOPI: TVM Operator Inventory ¶ Introduction to TOPI ¶ Micro TVM ¶ Micro TVM with TFLite Models ¶ Gallery generated by Sphinx-Gallery Next Previous © Copyright 2020, Apache Software Foundation Built with Sphinx using a theme provided by Read the Docs ....