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TensorFlow ResNet50 v1.5 Training

Description

This document has instructions for running ResNet50 v1.5 training using Intel-optimized TensorFlow.

Enviromnment setup

  • Create a virtual environment venv-tf:
python -m venv venv-tf
source venv-tf/bin/activate

# Install Intel Optimized TensorFlow
pip install intel-tensorflow

Note: For kernel version 5.16, AVX512_CORE_AMX is turned on by default. If the kernel version < 5.16 , please set the following environment variable for AMX environment: DNNL_MAX_CPU_ISA=AVX512_CORE_AMX. To run VNNI, please set DNNL_MAX_CPU_ISA=AVX512_CORE_BF16.

Quick Start Scripts

Script name Description
multi_instance_training.sh Uses mpirun to execute 1 processes with 1 process per socket with a batch size of 1024 for the specified precision (fp32 or bfloat16 or bfloat32 or fp16). Checkpoint files and logs for each instance are saved to the output directory.

Datasets

Download and preprocess the ImageNet dataset using the instructions here. After running the conversion script you should have a directory with the ImageNet dataset in the TF records format.

Set the DATASET_DIR to point to the TF records directory when running ResNet50 v1.5 (if needed).

Run the model

After finishing the setup above, set environment variables for the path to your DATASET_DIR for ImageNet and an OUTPUT_DIR where log files and checkpoints will be written. Navigate to your AI Reference Models directory and then run a quickstart script.

cd to your AI Reference Models directory

# Set the required environment vars
export PRECISION=<specify the precision to run: fp32 or bfloat16 or bfloat32 or fp16>
export OUTPUT_DIR=<directory where log files will be written>
export DATASET_DIR=<set path to the dataset directory>

# Optional env vars
export BATCH_SIZE=<set batch size value else it will run with default value>

Navigate to the models directory to run any of the available benchmarks.

cd models

./quickstart/image_recognition/tensorflow/resnet50v1_5/training/cpu/multi_instance_training.sh

License

Licenses can be found in the model package, in the licenses directory.