Getting Started with MXNet (ResNet50)

Neuron supports Python module, Symbol APIs and the C predict API. The following quick start example uses the Symbol API.

Steps Overview:

  1. Launch an EC2 instance for compilation and inference

  2. Install MXNet-Neuron and Neuron Compiler on a compilation instance

  3. Compile on compilation server

  4. Install MXNet-Neuron and Neuron Runtime on inference instance

  5. Execute inference on Inf1

Step 1: Launch EC2 Instances

A typical workflow with the Neuron SDK will be to compile trained ML models on a compilation server and then distribute the artifacts to a fleet of Inf1 instances for execution. Neuron enables MXNet to be used for all of these steps.

1.1. Select an AMI of your choice. Refer to the Setup Guide for details.

1.2. Select and launch an EC2 instance of your choice to compile. Launch an instance by following EC2 instructions.

  • It is recommended to use c5.4xlarge or larger. For this example we will use a c5.4xlarge.

  • If you would like to compile and infer on the same machine, please select inf1.6xlarge.

1.3. Select and launch an Inf1 instance of your choice to run the compiled model. Launch an instance by following EC2 instructions.

Step 2: Install MXNet-Neuron and Neuron Compiler On Compilation Instance

If using Conda DLAMI version 26 and up, activate pre-installed MXNet-Neuron environment (using source activate aws_neuron_mxnet_p36 command). Please update Neuron by following update steps in DLAMI with Neuron Release Notes.

To install in your own AMI, please see Setup Guide to setup virtual environment and install MXNet-Neuron (mxnet-neuron) and Neuron Compiler (neuron-cc) packages.

Step 3: Compile on Compilation Server

A trained model must be compiled to Inferentia target before it can run on Inferentia. In this step we compile a pre-trained ResNet50 and export it as a compiled MXNet checkpoint.

3.1. Create a file with the content below and run it using python Compilation will take a few minutes on c5.4xlarge. At the end of compilation, the files resnet-50_compiled-0000.params and resnet-50_compiled-symbol.json will be created in local directory.

import mxnet as mx
import numpy as np

sym, args, aux = mx.model.load_checkpoint('resnet-50', 0)

# Compile for Inferentia using Neuron
inputs = { "data" : mx.nd.ones([1,3,224,224], name='data', dtype='float32') }
sym, args, aux = mx.contrib.neuron.compile(sym, args, aux, inputs)

#save compiled model
mx.model.save_checkpoint("resnet-50_compiled", 0, sym, args, aux)

3.2. If not compiling and inferring on the same instance, copy the artifact to the inference server (use ec2-user as user for AML2):

scp -i <PEM key file>  resnet-50_compiled-0000.params ubuntu@<instance DNS>:~/  # Ubuntu
scp -i <PEM key file>  resnet-50_compiled-symbol.json ubuntu@<instance DNS>:~/  # Ubuntu

3.3. To check the supported operations for the uncompiled model or information on Neuron subgraphs for the compiled model, please see Neuron Check Model.

Step 4: Install MXNet-Neuron and Neuron Runtime on Inference Instance

If using DLAMI, activate pre-installed MXNet-Neuron environment (using ``source activate aws_neuron_mxnet_p36`` command) and skip this step.

On the instance you are going to use for inference, install TensorFlow-Neuron and Neuron Runtime.

4.1. Follow Step 2 above to install MXNet-Neuron.

  • Install neuron-cc if compilation on inference instance is desired (see notes above on recommended Inf1 sizes for compilation)

  • Skip neuron-cc if compilation is not done on inference instance

4.2. To install Neuron Runtime, see Getting started: Installing and Configuring Neuron-RTD.

Step 5: Execute inference on Inf1

In this step we run inference on Inf1 using the model compiled in Step 3.

5.1. On the Inf1, create a inference Python script named with the following content:

import mxnet as mx
import numpy as np


fname ='')
img = mx.image.imread(fname)# convert into format (batch, RGB, width, height)
img = mx.image.imresize(img, 224, 224) # resize
img = img.transpose((2, 0, 1)) # Channel first
img = img.expand_dims(axis=0) # batchify
img = img.astype(dtype='float32')

sym, args, aux = mx.model.load_checkpoint('resnet-50_compiled', 0)
softmax = mx.nd.random_normal(shape=(1,))
args['softmax_label'] = softmax
args['data'] = img

# Inferentia context
ctx = mx.neuron()

exe = sym.bind(ctx=ctx, args=args, aux_states=aux, grad_req='null')

with open('synset.txt', 'r') as f:
     labels = [l.rstrip() for l in f]

prob = exe.outputs[0].asnumpy()# print the top-5
prob = np.squeeze(prob)
a = np.argsort(prob)[::-1]
for i in a[0:5]:
     print('probability=%f, class=%s' %(prob[i], labels[i]))

5.2. Run the script to see inference results:

probability=0.642454, class=n02123045 tabby, tabby cat
probability=0.189407, class=n02123159 tiger cat
probability=0.100798, class=n02124075 Egyptian cat
probability=0.030649, class=n02127052 lynx, catamount
probability=0.016278, class=n02129604 tiger, Panthera tigris