29.01.2018       Выпуск 215 (29.01.2018 - 04.02.2018)       Статьи

Масштабируемый Keras + deep learning REST API

Читать>>




Экспериментальная функция:

Ниже вы видите текст статьи по ссылке. По нему можно быстро понять ссылка достойна прочтения или нет

Просим обратить внимание, что текст по ссылке и здесь может не совпадать.

In today’s blog post we are going to create a deep learning REST API that wraps a Keras model in an efficient, scalable manner.

Our Keras + deep learning REST API will be capable of batch processing images, scaling to multiple machines (including multiple web servers and Redis instances), and round-robin scheduling when placed behind a load balancer.

To accomplish this we will be using:

  • Keras
  • Redis (an in-memory data structure store)
  • Flask (a micro web framework for Python)
  • Message queuing and message broker programming paradigms

This blog post is a bit more advanced than other tutorials on PyImageSearch and is intended for readers:

  • Who are familiar with the Keras deep learning library
  • Who have an understanding of web frameworks and web services (and ideally coded a simple website/web service before)
  • Who understand basic data structures, such as hash tables/dictionaries, lists, along with their associated asymptotic complexities

For a more simple Keras + deep learning REST API, please refer to this guest post I did on the official Keras.io blog.

To learn how to create your own scalable Keras + deep learning REST API, just keep reading!

A scalable Keras + deep learning REST API

Today’s tutorial is broken into multiple parts.

We’ll start with a brief discussion of the Redis data store and how it can be used to facilitate message queuing and message brokering.

From there, we’ll configure our Python development environment by installing the required Python packages to build our Keras deep learning REST API.

Once we have our development environment configured we can implement our actual Keras deep learning REST API using the Flask web framework. After implementing, we’ll start the Redis and Flask servers, follow by submitting inference requests to our deep learning API endpoint using both cURL and Python.

Finally, we’ll end with a short discussion on the considerations you should keep in mind when building your own deep learning REST API.

A short introduction to Redis as a REST API message broker/message queue

Figure 1: Redis can be used as a message broker/message queue for our deep learning REST API

Redis is an in-memory data store. It is different than a simple key/value store (such as memcached) as it can can store actual data structures.

Today we’re going to utilize Redis as a message broker/message queue. This involves:

  • Running Redis on our machine
  • Queuing up data (images) to our Redis store to be processed by our REST API
  • Polling Redis for new batches of input images
  • Classifying the images and returning the results to the client

To read more about Redis, I encourage you to review this short introduction.

Configuring and installing Redis for our Keras REST API

Redis is very easy to install. Below you’ll find the commands to download, extract, and install Redis on your system:

$wgethttp://download.redis.io/redis-stable.tar.gz

$tarxvzfredis-stable.tar.gz

$cdredis-stable

$make

$sudomakeinstall

To start the Redis server, use the following command:

Leave this terminal open to keep the Redis data store running.

In another terminal, you can validate Redis is up and running:

Provided that you get a PONG  back from Redis, you’re ready to go.

Configuring your Python development environment to build a Keras REST API

I recommend that you work on this project inside of a Python virtual environment so that it does not impact system level Python and projects.

To do this, you’ll need to install pip, virtualenv, and virtualenvwrapper (provided you haven’t already):

$cd~

$wgethttps://bootstrap.pypa.io/get-pip.py

$sudopythonget-pip.py

$sudopipinstallvirtualenvvirtualenvwrapper

You’ll also need to edit your ~/.bashrc  (or ~/.bash_profile  on macOS) to include the following lines:

# virtualenv and virtualenvwrapper

exportWORKON_HOME=$HOME/.virtualenvs

source/usr/local/bin/virtualenvwrapper.sh

Then, simply source the file in the terminal depending on your OS:

Ubuntu

macOS

From there, you can create a Python virtual environment specifically for this project:

And once your environment is ready and activated, let’s install the necessary packages for our Keras REST API into the environment:

$pipinstallnumpy

$pipinstallscipyh5py

$pipinstalltensorflow# tensorflow-gpu for GPU machines

$pipinstallkeras

$pipinstallflaskgevent

$pipinstallimutilsrequests

$pipinstallredis

$pipinstallPillow

That’s it — and notice that we don’t actually need OpenCV for this project because we’ll be making use of PIL/Pillow.

Implementing a scalable Keras REST API

Figure 2: Our deep learning Keras + Redis + Flask REST API data flow diagram

Let’s get started building our server script. For convenience I’ve implemented the server in a single file, however it can be modularized as you see fit.

For best results and to avoid copy/paste errors, I encourage you to use the “Downloads” section of this blog post to grab the associated scripts and images.

Let’s open up run_keras_server.py  and walk through it together:

# import the necessary packages

fromkeras.applicationsimportResNet50

fromkeras.preprocessing.imageimportimg_to_array

fromkeras.applicationsimportimagenet_utils

fromthreadingimportThread

fromPILimportImage

importnumpyasnp

importbase64

importflask

importredis

importuuid

importtime

importjson

importsys

importio

There are quite a few imports listed above, notably ResNet50 , flask , and redis .

For the sake of simplicity, we’ll be using ResNet pre-trained on the ImageNet dataset. I’ll point out where you can swap out ResNet for your own models.

The flask  module contains the Flask library (used to build our web API). The redis  module will enable us to interface with the Redis data store.

From there, let’s initialize constants which will be used throughout run_keras_server.py :

# initialize constants used to control image spatial dimensions and

# data type

IMAGE_WIDTH=224

IMAGE_HEIGHT=224

IMAGE_CHANS=3

IMAGE_DTYPE="float32"

 

# initialize constants used for server queuing

IMAGE_QUEUE="image_queue"

BATCH_SIZE=32

SERVER_SLEEP=0.25

CLIENT_SLEEP=0.25

We’ll be passing float32  images to the server with dimensions of 224 x 224 and containing 3  channels.

Our server can handle a BATCH_SIZE=32 . If you have GPU(s) on your production system, you’ll want to tune your BATCH_SIZE  for optimal  performance.

I’ve found that setting both SERVER_SLEEP  and CLIENT_SLEEP  to 0.25  seconds (the amount of time the server and client will pause before polling Redis again, respectively) will work well on most systems. Definitely adjust these constants if you’re building a production system.

Let’s kick off our Flask app and Redis server:

# initialize our Flask application, Redis server, and Keras model

app=flask.Flask(__name__)

db=redis.StrictRedis(host="localhost",port=6379,db=0)

model=None

Here you can see how easy it is to start Flask.

I’ll assume that before you run this server script that your Redis server is running. Our Python script connect to the Redis store on our localhost  on port 6379  (the default host and port values for Redis).

Don’t forget to initialize a global Keras  model  to None here as well.

From there let’s handle serialization of images:

defbase64_encode_image(a):

# base64 encode the input NumPy array

returnbase64.b64encode(a).decode("utf-8")

 

defbase64_decode_image(a,dtype,shape):

# if this is Python 3, we need the extra step of encoding the

# serialized NumPy string as a byte object

ifsys.version_info.major==3:

a=bytes(a,encoding="utf-8")

 

# convert the string to a NumPy array using the supplied data

# type and target shape

a=np.frombuffer(base64.decodestring(a),dtype=dtype)

a=a.reshape(shape)

 

# return the decoded image

returna

Redis will act as our temporary data store on the server. Images will come in to the server via a variety of methods such as cURL, a Python script, or even a mobile app.

Furthermore, images could come in only every once in awhile (a few every hours or days) or at a very high rate (multiple per second). We need to put the images somewhere as they queue up prior to being processed. Our Redis store will act as the temporary storage.

In order to store our images in Redis, they need to be serialized. Since images are just NumPy arrays, we can utilize base64 encoding to serialize the images. Using base64 encoding also has the added benefit of allowing us to use JSON to store additional attributes with the image.

Our base64_encode_image  function handles the serialization and is defined on Lines 35-37.

Similarly, we need to deserialize our image prior to passing them through our model. This is handled by the  base64_decode_image  function on Lines 39-51.

Let’s pre-process our image:

defprepare_image(image,target):

# if the image mode is not RGB, convert it

ifimage.mode!="RGB":

image=image.convert("RGB")

 

# resize the input image and preprocess it

image=image.resize(target)

image=img_to_array(image)

image=np.expand_dims(image,axis=0)

image=imagenet_utils.preprocess_input(image)

 

# return the processed image

returnimage

On Line 53, I’ve defined a prepare_image  function which pre-processes our input image for classification using the ResNet50 implementation in Keras.. When utilizing your own models I would suggest modifying this function to perform any required pre-processing, scaling, or normalization.

From there we’ll define our classification method:

defclassify_process():

# load the pre-trained Keras model (here we are using a model

# pre-trained on ImageNet and provided by Keras, but you can

# substitute in your own networks just as easily)

print("* Loading model...")

model=ResNet50(weights="imagenet")

print("* Model loaded")

The classify_process  function will be kicked off in its own thread as we’ll see in __main__  below. This function will poll for image batches from the Redis server, classify the images, and return the results to the client.

Line 72 loads the model . I’ve sandwiched this action with terminal print  messages — depending on the size of your Keras model, loading be instantaneous or it could take a few seconds.

Loading the model happens only once when this thread is launched — it would be terribly slow if we had to load the model each time we wanted to process an image and furthermore it could lead to a server crash due to memory exhaustion.

After loading the model, this thread will continually poll for new images and then classify them:

75

76

77

78

79

80

81

82

83

84

85

86

87

88

89

90

91

92

93

94

95

96

97

98

99

# continually poll for new images to classify

whileTrue:

# attempt to grab a batch of images from the database, then

# initialize the image IDs and batch of images themselves

queue=db.lrange(IMAGE_QUEUE,0,BATCH_SIZE-1)

imageIDs=[]

batch=None

 

# loop over the queue

forqinqueue:

# deserialize the object and obtain the input image

q=json.loads(q.decode("utf-8"))

image=base64_decode_image(q["image"],IMAGE_DTYPE,

(1,IMAGE_HEIGHT,IMAGE_WIDTH,IMAGE_CHANS))

 

# check to see if the batch list is None

ifbatchisNone:

batch=image

 

# otherwise, stack the data

else:

batch=np.vstack([batch,image])

 

# update the list of image IDs

imageIDs.append(q["id"])

Here we’re first using the Redis database’s lrange  function to get, at most, BATCH_SIZE  images from our queue (Line 79).

From there we initialize our imageIDs  and batch  (Lines 80 and 81) and begin looping over the queue  beginning on Line 84.

In the loop, we first decode the object and deserialize it into a NumPy array, image  (Lines 86-88).

Next, on Lines 90-96, we’ll add the image  to the batch  (or if the batch  is currently None  we just set the batch  to the current image ).

We also append the id  of the image to imageIDs  (Line 99).

Let’s finish out the loop and function:

101

102

103

104

105

106

107

108

109

110

111

112

113

114

115

116

117

118

119

120

121

122

123

124

125

126

127

128

# check to see if we need to process the batch

iflen(imageIDs)>0:

# classify the batch

print("* Batch size: {}".format(batch.shape))

preds=model.predict(batch)

results=imagenet_utils.decode_predictions(preds)

 

# loop over the image IDs and their corresponding set of

# results from our model

for(imageID,resultSet)inzip(imageIDs,results):

# initialize the list of output predictions

output=[]

 

# loop over the results and add them to the list of

# output predictions

for(imagenetID,label,prob)inresultSet:

r={"label":label,"probability":float(prob)}

output.append(r)

 

# store the output predictions in the database, using

# the image ID as the key so we can fetch the results

db.set(imageID,json.dumps(output))

 

# remove the set of images from our queue

db.ltrim(IMAGE_QUEUE,len(imageIDs),-1)

 

# sleep for a small amount

time.sleep(SERVER_SLEEP)

In this code block, we check if there are any images in our batch (Line 102).

If we have a batch of images, we make predictions on the entire batch by passing it through the model (Line 105).

From there, we loop over a the imageIDs  and corresponding prediction  results  (Lines 110-122). These lines append labels and probabilities to an output list and then store the output in the Redis database using the imageID  as the key (Lines 116-122).

We remove the set of images that we just classified from our queue using ltrim  on Line 125.

And finally, we sleep for the set SERVER_SLEEP  time and await the next batch of images to classify.

Let’s handle the /predict  endpoint of our REST API next:

130

131

132

133

134

135

136

137

138

139

140

141

142

143

144

145

146

147

148

149

150

151

152

153

@app.route("/predict",methods=["POST"])

defpredict():

# initialize the data dictionary that will be returned from the

# view

data={"success":False}

 

# ensure an image was properly uploaded to our endpoint

ifflask.request.method=="POST":

ifflask.request.files.get("image"):

# read the image in PIL format and prepare it for

# classification

image=flask.request.files["image"].read()

image=Image.open(io.BytesIO(image))

image=prepare_image(image,(IMAGE_WIDTH,IMAGE_HEIGHT))

 

# ensure our NumPy array is C-contiguous as well,

# otherwise we won't be able to serialize it

image=image.copy(order="C")

 

# generate an ID for the classification then add the

# classification ID + image to the queue

k=str(uuid.uuid4())

d={"id":k,"image":base64_encode_image(image)}

db.rpush(IMAGE_QUEUE,json.dumps(d))

As you’ll see later, when we POST to the REST API, we’ll be using the /predict  endpoint. Our server could, of course, have multiple endpoints.

We use the @app.route  decorator above our function in the format shown on Line 130 to define our endpoint so that Flask knows what function to call. We could easily have another endpoint which uses AlexNet instead of ResNet and we’d define the endpoint with associated function in a similar way. You get the idea, but for our purposes today, we just have one endpoint called /predict .

Our predict  method defined on Line 131 will handle the POST requests to the server. The goal of this function is to build the JSON data  that we’ll send back to the client.

If the POST data contains an image (Lines 137 and 138) we convert the image to PIL/Pillow format and preprocess it (Lines 141-143).

While developing this script, I spent considerable time debugging my serialization and deserialization functions, only to figure out that I needed Line 147 to convert the array to C-contiguous ordering (which is something you can read more about here). Honestly, it was a pretty big pain in the ass to figure out, but I hope it helps you get up and running quickly.

If you were wondering about the id  mentioned back on Line 99, it is actually generated here using uuid , a universally unique identifier, on Line 151. We use a UUID to prevent hash/key conflicts.

Next, we append the id  as well as the base64  encoding of the image  to the d  dictionary. It’s very simple to push this JSON data to the Redis db  using rpush  (Line 153).

Let’s poll the server to return the predictions:

155

156

157

158

159

160

161

162

163

164

165

166

167

168

169

170

171

172

173

174

175

176

177

178

179

180

181

182

# keep looping until our model server returns the output

# predictions

whileTrue:

# attempt to grab the output predictions

output=db.get(k)

 

# check to see if our model has classified the input

# image

ifoutputisnotNone:

# add the output predictions to our data

# dictionary so we can return it to the client

output=output.decode("utf-8")

data["predictions"]=json.loads(output)

 

# delete the result from the database and break

# from the polling loop

db.delete(k)

break

 

# sleep for a small amount to give the model a chance

# to classify the input image

time.sleep(CLIENT_SLEEP)

 

# indicate that the request was a success

data["success"]=True

 

# return the data dictionary as a JSON response

returnflask.jsonify(data)

We’ll loop continuously until the model server returns the output predictions. We start an infinite loop and attempt to get the predictions Lines 157-159.

From there, if the output  contains predictions, we deserialize the results and add them to data  which will be returned to the client.

We also delete  the result from the db  (since we have pulled the results form the database and no longer need to store them in the database) and break  out of the loop (Lines 163-172).

Otherwise, we don’t have any predictions and we need to sleep and continue to poll (Line 176).

If we reach Line 179, we’ve successfully got our predictions. In this case we add a success  value of True  to the client data (Line 179).

Note: For this example script, I didn’t bother adding timeout logic in the above loop which would ideally add a success  value of False  to the data. I’ll leave that up to you to handle and implement.

Lastly we call flask.jsonify  on data  and return it to the client (Line 182). This completes our predict function.

To demo our Keras REST API, we need a __main__  function to actually start the server:

# if this is the main thread of execution first load the model and

# then start the server

if__name__=="__main__":

# load the function used to classify input images in a *separate*

# thread than the one used for main classification

print("* Starting model service...")

t=Thread(target=classify_process,args=())

t.daemon=True

t.start()

 

# start the web server

print("* Starting web service...")

app.run()

Lines 186-196 define the __main__  function which will kick off our classify_process  thread (Lines 190-192) and run the Flask app (Line 196).

To test our Keras deep learning REST API, be sure to download the source code + example images using the “Downloads” section of this blog post.

From there, let’s start the Redis server if it isn’t already running:

Then, in a separate terminal, let’s start our REST API Flask server:

$pythonrun_keras_server.py

UsingTensorFlowbackend.

*LoadingKerasmodelandFlaskstartingserver...pleasewaituntilserverhasfullystarted

...

*Runningonhttp://127.0.0.1:5000

Additionally, I would suggest waiting until your model is loaded completely into memory before submitting requests to the server.

Now we can move on to testing the server with both cURL and Python.

Using cURL to access our Keras REST API

Figure 3: Using cURL to test our Keras REST API server. Pictured is my family beagle, Jemma. She is classified as a beagle with 94.6% confidence by our ResNet model.

The cURL tool is available pre-installed on most (Unix-based) operating systems. We can POST an image file to our deep learning REST API at the /predict  endpoint by using the following command:

$curl-XPOST-Fimage=@jemma.png'http://localhost:5000/predict'

You’ll receive the predictions back in JSON format right in your terminal:

{

  "predictions":[

    {

      "label":"beagle",

      "probability":0.9461546540260315

    },

    {

      "label":"bluetick",

      "probability":0.031958919018507004

    },

    {

      "label":"redbone",

      "probability":0.006617196369916201

    },

    {

      "label":"Walker_hound",

      "probability":0.0033879687543958426

    },

    {

      "label":"Greater_Swiss_Mountain_dog",

      "probability":0.0025766862090677023

    }

  ],

  "success":true

}

Let’s try passing another image, this time a space shuttle:

$curl-XPOST-Fimage=@space_shuttle.png'http://localhost:5000/predict'

{

  "predictions":[

    {

      "label":"space_shuttle",

      "probability":0.9918227791786194

    },

    {

      "label":"missile",

      "probability":0.006030891090631485

    },

    {

      "label":"projectile",

      "probability":0.0021368064917623997

    },

    {

      "label":"warplane",

      "probability":1.980597062356537e-06

    },

    {

      "label":"submarine",

      "probability":1.8291866581421345e-06

    }

  ],

  "success":true

}

The results of which can be seen below:

Figure 4: Submitting an input image to our Keras REST API and obtaining the prediction results.

Once again our Keras REST API has correctly classified the input image.

Using Python to submit requests to the Keras REST API

As you can see, verification using cURL was quite easy. Now let’s build a Python script that will POST an image and parse the returning JSON programmatically.

Let’s review simple_request.py :

# import the necessary packages

importrequests

 

# initialize the Keras REST API endpoint URL along with the input

# image path

KERAS_REST_API_URL="http://localhost:5000/predict"

IMAGE_PATH="jemma.png"

We use Python requests  in this script to handle POSTing data to the server.

Our server is running on the localhost  and can be accessed on port 5000  with the endpoint /predict  as is specified by the KERAS_REST_API_URL  variable (Line 6). If the server is running remotely or on a different machine, be sure to specify the appropriate domain/ip, port, and endpoint.

We also define an IMAGE_PATH (Line 7). In this case, jemma.png  is in the same directory as our script. If you want to test with other images, be sure to specify the full path to your input image.

Let’s load the image and send it off to the server:

# load the input image and construct the payload for the request

image=open(IMAGE_PATH,"rb").read()

payload={"image":image}

 

# submit the request

r=requests.post(KERAS_REST_API_URL,files=payload).json()

 

# ensure the request was sucessful

ifr["success"]:

# loop over the predictions and display them

for(i,result)inenumerate(r["predictions"]):

print("{}. {}: {:.4f}".format(i+1,result["label"],

result["probability"]))

 

# otherwise, the request failed

else:

print("Request failed")

We read the image on Line 10in binary mode and put the it into a payload dictionary.

The payload is POST’ed to the server with requests.post  on Line 14.

If we get a success  message, we can loop over the predictions and print them to the terminal. I made this script simple, but you could also draw the highest prediction text on the image using OpenCV if you want to get fancy.

Running the simple request script

Putting the script to work is easy. Open up a terminal and execute the following command (provided both our Flask server and Redis server are running, of course).

$pythonsimple_request.py

1.beagle:0.9462

2.bluetick:0.0320

3.redbone:0.0066

4.Walker_hound:0.0034

5.Greater_Swiss_Mountain_dog:0.0026

Figure 5: Using Python to programmatically consume the results of our Keras deep learning REST API.

For the space_shuttle.png , simply modify the IMAGE_PATH  variable:

And from there, run the script again:

$pythonsimple_request.py

1.space_shuttle:0.9918

2.missile:0.0060

3.projectile:0.0021

4.warplane:0.0000

5.submarine:0.0000

Figure 6: A second example of programmatically consuming our Keras deep learning REST API. Here a space shuttle is classified with 99% confidence by ResNet + Keras REST API.

Considerations when scaling your deep learning REST API

If you anticipate heavy load for extended periods of time on your deep learning REST API you may want to consider a load balancing algorithm such as round-robin scheduling to help evenly distribute requests across multiple GPU machines and Redis servers.

Keep in mind that Redis is an in-memory data store so we can only store as many images in the queue we have available memory.

A single 224 x 224 x 3 image with a float32  data type will consume 60,2112 bytes of memory.

Assuming a server with a modest 16GB of RAM, this implies that we can hold approximately 26,500 images in our queue, but at that point we likely would want to add more GPU servers to burn through the queue faster.

However, there is a subtle problem…

Depending on how you deploy your deep learning REST API, there is a subtle problem with keeping the classify_process  function in the same file as the rest of our web API code.

Most web servers, including Apache and nginx, allow for multiple client threads.

If you keep classify_process  in the same file as your predict  view, then you may load multiple models if your server software deems it necessary to create a new thread to serve the incoming client requests — for every new thread, a new view will be created, and therefore a new model will be loaded.

The solution is to move classify_process  to an entirely separate process and then start it along with your Flask web server and Redis server.

In next week’s blog post I’ll build on today’s solution, show how to resolve this problem, and demonstrate:

  • How to configure the Apache web server to serve our deep learning REST API
  • How to run classify_process  as an entirely separate Python script, avoiding “multiple model syndrome”
  • Provide stress test results, confirming and verifying that our deep learning REST API can scale under heavy load

What now?

If you’re interested in taking a deeper dive into deep learning and discovering how to:

  • Train Convolutional Neural Networks on your own custom datasets
  • Study advanced deep learning techniques, including object detection, multi-GPU training, transfer learning, and Generative Adversarial Networks (GANs)
  • Replicate the results of state-of-the-art papers, including ResNet, SqueezeNet, VGGNet, and others

…then be sure to take a look at my new book,Deep Learning for Computer Vision with Python!

My complete, self-study deep learning book is trusted by members of top machine learning schools, companies, and organizations, including Microsoft, Google, Stanford, MIT, CMU, and more!

Be sure to take a look  — and while you’re at it, don’t forget to grab your (free) table of contents + sample chapters.

Summary

In today’s blog post we learned how to build a scalable Keras + deep learning REST API.

To accomplish this, we:

  • Built a simple Flask app to load our Keras model into memory and accept incoming requests.
  • Utilized Redis to act as an in-memory message queue/message broker.
  • Utilized threading to batch process input images, write them back to the message queue and then return the results to the client.

This method can scale to multiple machines, including multiple web servers and Redis instances.

I hope you enjoyed today’s blog post!

Be sure to enter your email address in the form below to be notified when future tutorials are published here on PyImageSearch!

Downloads:



Лучшая Python рассылка




Разместим вашу рекламу

Пиши: mail@pythondigest.ru

Нашли опечатку?

Выделите фрагмент и отправьте нажатием Ctrl+Enter.

Система Orphus