This document is relevant for: Inf1, Inf2, Trn1, Trn2

Using NEURON_RT_VISIBLE_CORES with TensorFlow Serving#

TensorFlow serving allows customers to scale-up inference workloads across a network. TensorFlow Neuron Serving uses the same API as normal TensorFlow Serving with two differences: (a) the saved model must be compiled for Inferentia and (b) the entry point is a different binary named tensorflow_model_server_neuron. The binary is found at /usr/local/bin/tensorflow_model_server_neuron and is pre-installed in the DLAMI or installed with APT/YUM tensorflow-model-server-neuron package.

Install TensorFlow Model Server and Serving API#

Follow the steps in the Install TensorFlow Neuron.

Then ensure you install using either apt-get or yum. If using TF 1.x, install the appropriate version (see above).:

sudo apt-get install tensorflow-model-server-neuron

sudo yum install tensorflow-model-server-neuron

Also, you would need TensorFlow Serving API (use –no-deps to prevent installation of regular tensorflow). Depending on the version of Tensorflow you wish to use:

For Tensorflow 1.x:

pip install --no-deps tensorflow_serving_api==1.15

For Tensorflow 2.x:

pip install --no-deps tensorflow_serving_api

For the example image preprocessing using Keras preprocessing, the Python Imaging Library Pillow is required:

pip install pillow

To workaround h5py issue aws/aws-neuron-sdk#220:

pip install "h5py<3.0.0"

Export and Compile Saved Model#

The following example shows graph construction followed by the addition of Neuron compilation step before exporting to saved model.

For Tensorflow 1.x:

import tensorflow as tf
import tensorflow.neuron

tf.keras.backend.set_learning_phase(0)
tf.keras.backend.set_image_data_format('channels_last')
model = tf.keras.applications.ResNet50(weights='imagenet')
sess = tf.keras.backend.get_session()
inputs = {'input': model.inputs[0]}
outputs = {'output': model.outputs[0]}

# save the model using tf.saved_model.simple_save
modeldir = "./resnet50/1"
tf.saved_model.simple_save(sess, modeldir, inputs, outputs)

# compile the model for Inferentia
neuron_modeldir = "./resnet50_inf1/1"
tf.neuron.saved_model.compile(modeldir, neuron_modeldir, batch_size=1)

For Tensorflow 2.x:

import tensorflow as tf
import tensorflow.neuron as tfn
import numpy as np

tf.keras.backend.set_learning_phase(0)
tf.keras.backend.set_image_data_format('channels_last')
image_sizes = [224, 224]
model = tf.keras.applications.ResNet50(weights='imagenet')
example_inputs = tf.random.uniform([1, *image_sizes, 3], dtype=tf.float32)

# run the model once to define the forward pass and allow for saving
model_neuron(example_inputs)
model_neuron = tfn.trace(model, example_inputs)
tf.keras.models.save_model(model_neuron, './resnet50_inf1/1')

Serving Saved Model#

User can now serve the saved model with the tensorflow_model_server_neuron binary. To utilize multiple NeuronCores, it is recommended to launch multiple tensorflow model servers that listen to the same gRPC port:

export NEURON_RT_VISIBLE_CORES=0  # important to set this environment variable before launching model servers
tensorflow_model_server_neuron --model_name=resnet50_inf1 \
     --model_base_path=$(pwd)/resnet50_inf1/ --port=8500

#then to run another server on a different neuron core open another
#window and run this, except this time set NEURON_RT_VISIBLE_CORES=1
#you can keep doing this up to the number of Neuron Cores on your machine

export NEURON_RT_VISIBLE_CORES=1
tensorflow_model_server_neuron --model_name=resnet50_inf1 \
     --model_base_path=$(pwd)/resnet50_inf1/ --port=8500

The compiled model is staged in Inferentia DRAM by the server to prepare for inference.

Generate inference requests to the model server#

Now run inferences via GRPC as shown in the following sample client code:

For Tensorflow 1.x:

import numpy as np
import grpc
import tensorflow as tf
from tensorflow.keras.preprocessing import image
from tensorflow.keras.applications.resnet50 import preprocess_input
from tensorflow.keras.applications.resnet50 import decode_predictions
from tensorflow_serving.apis import predict_pb2
from tensorflow_serving.apis import prediction_service_pb2_grpc

if __name__ == '__main__':
    channel = grpc.insecure_channel('localhost:8500')
    stub = prediction_service_pb2_grpc.PredictionServiceStub(channel)
    img_file = tf.keras.utils.get_file(
        "./kitten_small.jpg",
        "https://raw.githubusercontent.com/awslabs/mxnet-model-server/master/docs/images/kitten_small.jpg")
    img = image.load_img(img_file, target_size=(224, 224))
    img_array = preprocess_input(image.img_to_array(img)[None, ...])
    request = predict_pb2.PredictRequest()
    request.model_spec.name = 'resnet50_inf1'
    request.inputs['input'].CopyFrom(
        tf.contrib.util.make_tensor_proto(img_array, shape=img_array.shape))
    result = stub.Predict(request)
    prediction = tf.make_ndarray(result.outputs['output'])
    print(decode_predictions(prediction))

For Tensorflow 2.x:

import numpy as np
import grpc
import tensorflow as tf
from tensorflow.keras.preprocessing import image
from tensorflow.keras.applications.resnet50 import preprocess_input
from tensorflow_serving.apis import predict_pb2
from tensorflow_serving.apis import prediction_service_pb2_grpc
from tensorflow.keras.applications.resnet50 import decode_predictions

tf.keras.backend.set_image_data_format('channels_last')

if __name__ == '__main__':
    channel = grpc.insecure_channel('localhost:8500')
    stub = prediction_service_pb2_grpc.PredictionServiceStub(channel)
    img_file = tf.keras.utils.get_file(
        "./kitten_small.jpg",
        "https://raw.githubusercontent.com/awslabs/mxnet-model-server/master/docs/images/kitten_small.jpg")
    img = image.load_img(img_file, target_size=(224, 224))
    img_array = preprocess_input(image.img_to_array(img)[None, ...])
    request = predict_pb2.PredictRequest()
    request.model_spec.name = 'resnet50_inf1'
    request.inputs['input_1'].CopyFrom(
        tf.make_tensor_proto(img_array, shape=img_array.shape))
    result = stub.Predict(request)
    prediction = tf.make_ndarray(result.outputs['output_1'])
    print(decode_predictions(prediction))

This document is relevant for: Inf1, Inf2, Trn1, Trn2

Using NEURON_RT_VISIBLE_CORES with TensorFlow Serving

Contents

Using NEURON_RT_VISIBLE_CORES with TensorFlow Serving#

Install TensorFlow Model Server and Serving API#

Export and Compile Saved Model#

Serving Saved Model#

Generate inference requests to the model server#