Skip to content

Latest commit

 

History

History
95 lines (71 loc) · 3.1 KB

README.md

File metadata and controls

95 lines (71 loc) · 3.1 KB
Raw

ResNet101 Inference

Description

This document has instructions for running ResNet101 inference using Intel-optimized PyTorch.

Bare Metal

General setup

Follow link to install Conda and build Pytorch, IPEX, TorchVison and Jemalloc.

Model Specific Setup

  • Set Jemalloc Preload for better performance

    The jemalloc should be built from the General setup section.

    export LD_PRELOAD="path/lib/libjemalloc.so":$LD_PRELOAD
export MALLOC_CONF="oversize_threshold:1,background_thread:true,metadata_thp:auto,dirty_decay_ms:9000000000,muzzy_decay_ms:9000000000"

  • Set IOMP preload for better performance

    IOMP should be installed in your conda env from the General setup section.

    export LD_PRELOAD=path/lib/libiomp5.so:$LD_PRELOAD

  • Set ENV to use AMX if you are using SPR

    export DNNL_MAX_CPU_ISA=AVX512_CORE_AMX

Datasets

ImageNet

The ImageNet validation dataset is used to run ResNet101 accuracy tests.

Download and extract the ImageNet2012 dataset from http://www.image-net.org/, then move validation images to labeled subfolders, using the valprep.sh shell script

A after running the data prep script, your folder structure should look something like this:

imagenet
└── val
    ├── ILSVRC2012_img_val.tar
    ├── n01440764
    │   ├── ILSVRC2012_val_00000293.JPEG
    │   ├── ILSVRC2012_val_00002138.JPEG
    │   ├── ILSVRC2012_val_00003014.JPEG
    │   ├── ILSVRC2012_val_00006697.JPEG
    │   └── ...
    └── ...

The folder that contains the val directory should be set as the DATASET_DIR (for example: export DATASET_DIR=/home/<user>/imagenet).

Quick Start Scripts

DataType Throughput Latency Accuracy
FP32 bash batch_inference_baremetal.sh fp32 bash online_inference_baremetal.sh fp32 bash accuracy_baremetal.sh fp32
BF16 bash batch_inference_baremetal.sh bf16 bash online_inference_baremetal.sh bf16 bash accuracy_baremetal.sh bf16

Run the model

Follow the instructions above to setup your bare metal environment, download and preprocess the dataset, and do the model specific setup. Once all the setup is done, the Model Zoo can be used to run a quickstart script. Ensure that you have enviornment variables set to point to the dataset directory and an output directory.

# Clone the model zoo repo and set the MODEL_DIR
git clone https://github.com/IntelAI/models.git
cd models
export MODEL_DIR=$(pwd)

# Env vars
export DATASET_DIR=#path_to_Imagenet_Dataset
export OUTPUT_DIR=#Where_to_save_log

# Run a quickstart script (for example, FP32 batch inference)
cd ${MODEL_DIR}/quickstart/image_recognition/pytorch/resnet101/inference/cpu
bash batch_inference_baremetal.sh fp32

License

LICENSE