This document has advanced instructions for running NCF FP32
inference, which provides more control over the individual parameters that
are used. For more information on using /benchmarks/launch_benchmark.py,
see the launch benchmark documentation.
Prior to using these instructions, please follow the setup instructions from
the model's README and/or the
AI Kit documentation to get your environment
setup (if running on bare metal) and download the dataset, pretrained model, etc.
If you are using AI Kit, please exclude the --docker-image flag from the
commands below, since you will be running the the TensorFlow conda environment
instead of docker.
Any of the launch_benchmark.py commands below can be run on bare metal by
removing the --docker-image arg. Ensure that you have all of the
required prerequisites installed in your environment
before running without the docker container.
If you are new to docker and are running into issues with the container, see this document for troubleshooting tips.
Once your environment is setup, navigate to the benchmarks directory of
the model zoo and set environment variables pointing to the directory for the
dataset, pretrained model, and an output directory where log
files will be written.
# cd to the benchmarks directory in the model zoo
cd benchmarks
export DATASET_DIR=<path to the dataset>
export OUTPUT_DIR=<directory where log files will be written>
export PRETRAINED_MODEL=<path to the pretrained model>
export TF_MODELS_DIR=<path to your clone of the TensorFlow models repo>
NCF FP32 inference can be run a few different modes:
- For batch inference,
--batch-size 256,--socket-id 0:python launch_benchmark.py \ --checkpoint $PRETRAINED_MODEL \ --model-source-dir $TF_MODELS_DIR \ --data-location ${DATASET_DIR} \ --model-name ncf \ --socket-id 0 \ --batch-size 256 \ --framework tensorflow \ --precision fp32 \ --mode inference \ --output-dir $OUTPUT_DIR \ --docker-image intel/intel-optimized-tensorflow:1.15.2 - For online inference,
--batch-size 1,--socket-id 0:python launch_benchmark.py \ --checkpoint $PRETRAINED_MODEL \ --model-source-dir $TF_MODELS_DIR \ --data-location ${DATASET_DIR} \ --model-name ncf \ --socket-id 0 \ --batch-size 1 \ --framework tensorflow \ --precision fp32 \ --mode inference \ --output-dir $OUTPUT_DIR \ --docker-image intel/intel-optimized-tensorflow:1.15.2 - For Accuracy,
--batch-size 256,--socket-id 0:python launch_benchmark.py \ --checkpoint $PRETRAINED_MODEL \ --model-source-dir $TF_MODELS_DIR \ --data-location ${DATASET_DIR} \ --model-name ncf \ --socket-id 0 \ --accuracy-only \ --batch-size 256 \ --framework tensorflow \ --precision fp32 \ --mode inference \ --output-dir $OUTPUT_DIR \ --docker-image intel/intel-optimized-tensorflow:1.15.2