Deploy Deep Learning Models

TVM is capable of deploying models to a variety of different platforms. These how-tos describe how to prepapre and deploy models to many of the supported backends.

Deploy the Pretrained Model on Adreno™

Deploy the Pretrained Model on Adreno™

Deploy the Pretrained Model on Adreno™
Deploy the Pretrained Model on Adreno™ with tvmc Interface

Deploy the Pretrained Model on Adreno™ with tvmc Interface

Deploy the Pretrained Model on Adreno™ with tvmc Interface
Deploy the Pretrained Model on Android

Deploy the Pretrained Model on Android

Deploy the Pretrained Model on Android
Deploy the Pretrained Model on Jetson Nano

Deploy the Pretrained Model on Jetson Nano

Deploy the Pretrained Model on Jetson Nano
Deploy the Pretrained Model on Raspberry Pi

Deploy the Pretrained Model on Raspberry Pi

Deploy the Pretrained Model on Raspberry Pi
Compile PyTorch Object Detection Models

Compile PyTorch Object Detection Models

Compile PyTorch Object Detection Models
Deploy a Framework-prequantized Model with TVM

Deploy a Framework-prequantized Model with TVM

Deploy a Framework-prequantized Model with TVM
Deploy a Framework-prequantized Model with TVM - Part 3 (TFLite)

Deploy a Framework-prequantized Model with TVM - Part 3 (TFLite)

Deploy a Framework-prequantized Model with TVM - Part 3 (TFLite)
Deploy a Quantized Model on Cuda

Deploy a Quantized Model on Cuda

Deploy a Quantized Model on Cuda
Deploy a Hugging Face Pruned Model on CPU

Deploy a Hugging Face Pruned Model on CPU

Deploy a Hugging Face Pruned Model on CPU

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