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SSD-ResNet34 Training

Description

This document has instructions for running SSD-ResNet34 training using Intel-optimized PyTorch.

Bare Metal

General setup

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

Model Specific Setup

  • Install dependencies

    pip install --no-cache-dir cython
pip install --no-cache-dir https://github.com/mlperf/logging/archive/9ea0afa.zip
pip install matplotlib Pillow pycocotools defusedxml
pip install --no-cache-dir pytz==2018.5

  • Download pretrained model

    cd <path to your clone of the model zoo>/quickstart/object_detection/pytorch/ssd-resnet34/training/cpu
export CHECKPOINT_DIR=<directory where the pretrained model will be saved>
bash download_model.sh

  • 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

  • Set ENV to use multi-node distributed training (no need for single-node multi-sockets)

    In this case, we use data-parallel distributed training and every rank will hold same model replica. The NNODES is the number of ip in the HOSTFILE. To use multi-nodes distributed training you should firstly setup the passwordless login (you can refer to link) between these nodes.

    export NNODES=#your_node_number
export HOSTFILE=your_ip_list_file #one ip per line

Datasets

Download the 2017 COCO dataset using the download_dataset.sh script. Export the DATASET_DIR environment variable to specify the directory where the dataset will be downloaded. This environment variable will be used again when running quickstart scripts.

cd <path to your clone of the model zoo>/quickstart/object_detection/pytorch/ssd-resnet34/training/cpu
export DATASET_DIR=<directory where the dataset will be saved>
bash download_dataset.sh

Quick Start Scripts

DataType Throughput Accuracy
FP32 bash throughput.sh fp32 bash accuracy.sh fp32
BF16 bash throughput.sh bf16 bash accuracy.sh bf16
BF32 bash throughput.sh bf32 bash accuracy.sh bf32

| Distributed Training |

DataType Throughput Accuracy
FP32 bash throughput_dist.sh fp32 bash accuracy_dist.sh fp32
BF16 bash throughput_dist.sh bf16 bash accuracy_dist.sh bf16
BF32 bash throughput_dist.sh bf32 bash accuracy_dist.sh bf32

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 an 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 the COCO dataset>
export CHECKPOINT_DIR=<path to the pretrained model>
export OUTPUT_DIR=<path to an output directory>

# Run a quickstart script (for example, FP32 throughput inference)
cd ${MODEL_DIR}/quickstart/object_detection/pytorch/ssd-resnet34/training/cpu
bash throughput.sh fp32

# Run distributed training script (for example, FP32 distributed training throughput)
cd ${MODEL_DIR}/quickstart/object_detection/pytorch/ssd-resnet34/training/cpu
bash throughput_dist.sh fp32

License

LICENSE