Skip to content

Latest commit

 

History

History
85 lines (72 loc) · 3.41 KB

Advanced.md

File metadata and controls

85 lines (72 loc) · 3.41 KB

WaveNet FP32 inference - Advanced Instructions

This document has advanced instructions for running WaveNet 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 OUTPUT_DIR=<directory where log files will be written>
export PRETRAINED_MODEL=<path to the pretrained model checkpoints>
export TF_WAVENET_DIR=<path to your clone of the TensorFlow WaveNet repo>

Start a model run by executing the launch script and passing args specifying that we are running wavenet fp32 inference using TensorFlow, along with a docker image that includes Intel Optimizations for TensorFlow and the path to your clone of the WaveNet repo and the checkpoint files that were downloaded. We are also passing a couple of extra model args for wavenet: the name of the checkpoint to use and the sample number.

python launch_benchmark.py \
    --precision fp32 \
    --model-name wavenet \
    --mode inference \
    --framework tensorflow \
    --socket-id 0 \
    --num-cores 1 \
    --docker-image intel/intel-optimized-tensorflow:1.15.2 \
    --model-source-dir ${TF_WAVENET_DIR} \
    --checkpoint ${PRETRAINED_MODEL} \
    --output-dir ${OUTPUT_DIR} \
    -- checkpoint_name=model.ckpt-99 sample=8510

The logs are displayed in the console output as well as saved to a file in the value of ${OUTPUT_DIR}.

The tail of the log should look something like this:

Time per 500 Samples: ... sec
Samples / sec: ...
msec / sample: ...
Sample: 8000
Time per 500 Samples: ... sec
Samples / sec: ...
msec / sample: ...
Sample: 8500

Average Throughput of whole run: Samples / sec: ...
Average Latency of whole run: msec / sample: ...
Finished generating. The result can be viewed in TensorBoard.
Log file location: ${OUTPUT_DIR}/benchmark_wavenet_inference_fp32_20210601_143852.log