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Resnet50 Training

This document has instructions for running ResNet50 inference using Intel-optimized PyTorch.

Model Information

Use Case Framework Model Repo Branch/Commit/Tag Optional Patch
Inference PyTorch https://github.com/KaimingHe/deep-residual-networks - -

Pre-Requisite

Bare Metal

General setup

Follow link to install Miniforge, Pytorch, IPEX, TorchVison, Torch-CCL and Tcmalloc.

  • Install dependencies
conda install -c conda-forge accimage

Model Specific Setup

  • Set Jemalloc and tcmalloc Preload for better performance

    The jemalloc should be built from the General setup section.

    export LD_PRELOAD="<path to the jemalloc directory>/lib/libjemalloc.so":"path_to/tcmalloc/lib/libtcmalloc.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

  pip install packaging intel-openmp
  export LD_PRELOAD=path/lib/libiomp5.so:$LD_PRELOAD
  • Set ENV to use fp16 AMX if you are using a supported platform
  export DNNL_MAX_CPU_ISA=AVX512_CORE_AMX_FP16
  • Set ENV for model and dataset path, and optionally run with no network support

Prepare Dataset

The ImageNet validation dataset is used when testing accuracy. The inference scripts use synthetic data, so no dataset is needed.

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

The accuracy script looks for a folder named val, so 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
    │   └── ...
    └── ...

Training

  1. git clone https://github.com/IntelAI/models.git
  2. cd models/models_v2/pytorch/resnet50/training/cpu
  3. Create virtual environment venv and activate it: 
    python3 -m venv venv
. ./venv/bin/activate
  4. Install the latest CPU versions of torch, torchvision and intel_extension_for_pytorch
  5. Setup required environment paramaters
Parameter export command
DISTRIBUTED (optional) export DISTRIBUTED=True
DATASET_DIR export DATASET_DIR=<path to ImageNet>
OUTPUT_DIR export OUTPUT_DIR=$PWD
MODEL_DIR export MODEL_DIR=$(pwd)
PRECISION export PRECISION=bf16 (fp32, avx-fp32, bf16, bf32, or fp16)
TRAINING_EPOCHS export TRAINING_EPOCHS=1
LOCAL_BATCH_SIZE (required for DISTRIBUTED) export LOCAL_BATCH_SIZE=#local batch_size(for lars optimizer convergency test, the GLOBAL_BATCH_SIZE should be 3264)
NNODES (required for DISTRIBUTED) export NNODES=#your_node_number
HOSTFILE (required for DISTRIBUTED) export HOSTFILE=#your_ip_list_file #one ip per line
MASTER_ADDR (required for DISTRIBUTED) export MASTER_ADDR=#your_master_addr
BATCH_SIZE (required for single-node) export BATCH_SIZE=102
  1. Run run_model.sh

Output

Inference Throughput output will typically looks like (note that accuracy is measured in throughput and realtime):

Training throughput: 51.200 fps

Final results of the inference run can be found in results.yaml file.

results:
 - key: throughput
   value: 315.657
   unit: fps
 - key: latency
   value: 0.0
   unit: ms
 - key: accuracy
   value: 0.0
   unit: f1