Skip to content

Latest commit

 

History

History
82 lines (55 loc) · 3.63 KB

README.md

File metadata and controls

82 lines (55 loc) · 3.63 KB
Raw

Bridge examples

Simple! Just add import ngraph_bridge after building it.

The simplest hello-world example can be found in axpy.py. For other real-world examples, see the instructions below to run tf_cnn_benchmarks and models from TensorFlow Hub:

tf_cnn_benchmarks: High-performance benchmarks

tf_cnn_benchmarks contains implementations of several popular convolutional models, and is designed to be as fast as possible. tf_cnn_benchmarks supports running either on a single machine or running in distributed mode across multiple hosts. See the High-Performance Models Guide for more information.

These models utilize many of the strategies in the TensorFlow Performance Guide.

These models are designed for performance. For models that have clean and easy-to-read implementations, see the TensorFlow Official Models.

Running examples from tf_cnn_benchmarks

Use the following instructions

git clone https://github.com/tensorflow/benchmarks.git
git checkout 4c7b09ad87bbfc4b1f89650bcee40b3fc5e7dfed
cd benchmarks/scripts/tf_cnn_benchmarks/

Next, enable nGraph by editing the convnet_builder.py and adding import ngraph_bridge right after the import tensorflow line.

Train for a few iterations:

KMP_BLOCKTIME=0  OMP_NUM_THREADS=56 KMP_AFFINITY=granularity=fine,compact,1,0 \
    python tf_cnn_benchmarks.py --data_format NCHW \
    --num_inter_threads 2 --train_dir=./modelsavepath/ \
    --model=resnet50 --num_batches 10 --batch_size=128

Evaluate the model (Inference pass):

KMP_BLOCKTIME=0  OMP_NUM_THREADS=56 KMP_AFFINITY=granularity=fine,compact,1,0 \
    python tf_cnn_benchmarks.py --data_format NCHW \
    --num_inter_threads 1 --train_dir=$(pwd)/modelsavepath \
    --eval --model=resnet50 --batch_size=128```

Tips

  • Use --data_format NCHW to get better performance. Avoid NHWC if possible.
  • Change the batch_size to 128 for batch inference performance and batch_size=1 for real-time inference
  • Change the --model flag to test for different topologies. The (by no means exhaustive) list of models known to run can be found on the nGraph validated workloads page

Please feel free to run more models and let us know if you run across any issues.

  • A more involved example of the run command

    KMP_BLOCKTIME=0 OMP_NUM_THREADS=28 KMP_AFFINITY=granularity=fine,proclist=[0-27] python tf_cnn_benchmarks.py --model=resnet50 --eval --num_inter_threads=1 --batch_size=128 --train_dir /nfs/fm/disks/aipg_trained_dataset/ngraph_tensorflow/partially_trained/resnet50 --data_format NCHW --num_epochs=1 --data_name=imagenet --data_dir /mnt/data/TF_ImageNet_latest/ --datasets_use_prefetch=False

TensorFlow Hub:

TensorFlow Hub models should also work. For example, you can try out retraining tutorials on, inceptionv3.

Keras models:

Keras (with Tensorflow backend) should also work out-of-the-box with nGraph, once one adds import ngraph_bridge to the script. Example