This document has instructions for running ResNet50* v1.5 FP32 training using Intel® Optimization for TensorFlow*.
Note that the ImageNet dataset is used in these ResNet50 v1.5 examples. 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.
Quick Start Scripts
||Launches a short run using small batch sizes and a limited number of steps to demonstrate the training flow|
||Launches a test run that trains the model for one epoch and saves checkpoint files to an output directory.|
||Trains the model using the full dataset and runs until convergence (90 epochs) and saves checkpoint files to an output directory. Note that this will take a considerable amount of time.|
||Uses numactl to launch one instance per socket of a short run using small batch sizes and a limited number of steps to demonstrate the training flow|
||Uses numactl to launch one instance per socket for the full training flow. Checkpoint files and logs for each instance are saved to the output directory. Note that this will take a considerable amount of time.|
To run on bare metal, the following prerequisites must be installed in your environment:
- Python* 3
- Intel® Optimization for TensorFlow*
Download and untar the model package and then run a quick start script.
DATASET_DIR=<path to the preprocessed imagenet dataset> OUTPUT_DIR=<directory where checkpoint and log files will be written> wget https://storage.googleapis.com/intel-optimized-tensorflow/models/v2_3_0/resnet50v1-5-fp32-training.tar.gz tar -xvf resnet50v1-5-fp32-training.tar.gz cd resnet50v1-5-fp32-training quickstart/<script name>.sh
Documentation and Sources
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Product and Performance Information
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