In the first , we covered the two main aspects of deploying a deep learning model: part Preparing the dataset Training the model using the transfer learning technique. Part 2 will focus on preparing a trained model to be served by TensorFlow Serving and deploying the model to Heroku. TensorFlow Serving Is a flexible, high-performance serving system for machine learning models, designed for environments. I am not going to cover it in details because TensorFlow Serving has many ways to serve models and there are too many concepts to digest for this short tutorial. production I strongly encourage TensorFlow Serving beginners to first read, . However, this tutorial will cover how to serve Keras models and using the new RESTful API, which is not covered in that tutorial. TensorFlow Serving 101 pt. 1 Serve the model. First, let’s load the saved model from Part 1 from keras.models import load_model # model_name = tf_serving_keras_mobilenetv2model = load_model(f"models/{model_name}.h5") Save the Model We define the input and output tensors. The name and can be any string. Then we specify a path where the model will be saved, . is the version of the model; this is very useful when you are going to retrain the model and have a new version. _images_ _scores_ _image_ _score_ models/export/tf_serving_keras_mobilenetv2/1 _1_ Each version will be exported to a different sub-directory under the given path. Lastly, we build the model by passing the TensorFlow session, input, and output tensors. is the TensorFlow session that holds the trained model you are exporting [3]. sess is the set of tags with which to save the meta-graph. In this case, since we intend to use the graph in serving, we use the tag from predefined SavedModel tag constants [3]. tags serve specifies what type of model is being exported, and the input/output tensors to bind to when running inference. signature_def_map Containerize Your Model for Deployment — Docker What is Docker? Why Docker? is a tool designed to make it easier to create, deploy, and run applications by using containers. Containers allow a developer to package up an application with all of the parts it needs, such as libraries and other dependencies, and ship it all out as one package. By doing so, thanks to the container, the developer can rest assured that the application will run on any other Linux machine regardless of any customized settings that machine might have that could differ from the machine used for writing and testing the code.[1] Docker Here are some of the benefits of using Docker: Return on Investment and Cost Savings by dramatically reducing infrastructure resources [2]. Docker containers ensure consistency across multiple developments and release cycles, standardizing your environment [2]. Rapid Deployment since Docker creates a container for every process and does not boot an OS [2]. All major cloud computing providers support docker, including Heroku. Docker makes sure each container has its own resources that are isolated from other containers [2]. If you would like to take a deeper dive into docker, . I recommend this blog . Next: Install docker — for macOS, windows or Ubuntu Once Docker has been install run the following to make sure it is working properly. > docker ps CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES Heroku What is Heroku? Why Heroku? Heroku is a platform as a service (PaaS) that enables developers to build, run, and operate applications entirely in the cloud. I am going to use Heroku because it’s free to use for development and testing, easy to deploy (does not require much work), supports many languages by default, (As I could not find many resources on deploying TensorFlow models to Heroku, please leave comments below if you have trouble and I will help troubleshoot) Signup & Install heroku-cli Signup to Heroku Install heroku-cli After you have signed up to install heroku-cil, run the following commands to confirm the installation. > heroku --versionheroku/7.18.3 darwin-x64 node-v10.12.0

# make sure you have logged in to your heroku account> heroku login# Output should have:   Logged in as xxxxx@xxx.xx Nice work — now we are going to deploy TensorFlow Serving docker image to Heroku; however, since the default TensorFlow Serving docker image is not optimized to be deployed to Heroku, I have created a Dockerfile: Run the following code to build the docker image, making sure you are in the same folder as Dockerfile live. > docker build -t tf-serving-heroku-1.11 . Let’s run it locally to test it > docker run -p 8501:8501 -e PORT=8501 -t tf-serving-heroku-1.11...

2018-10-27 21:17:47.515120: I tensorflow_serving/model_servers/server.cc:301] Exporting HTTP/REST API at:localhost:8501 ... Call the model Now we are ready to call the production-ready model. Before calling the model, let’s understand how TensorFlow Serving RESTful API actually works: POST http://host:port/<URI>:<VERB>

URI: /v1/models/${MODEL_NAME}[/versions/${MODEL_VERSION}]VERB: classify|regress|predict In our case the request will look like: http://localhost:8501/v1/models/tf_serving_keras_mobilenetv2/versions/1:predict Second, the JSON data that is sent to TensorFlow Model Server has to be structured in a very particular way {  // (Optional) Serving signature to use.  // If unspecified default serving signature is used.  "signature_name": <string>,

// Input Tensors in row ("instances") or columnar ("inputs") format.  // A request can have either of them but NOT both.  "instances": <value>|<(nested)list>|<list-of-objects>  "inputs": <value>|<(nested)list>|<object>} In our case: {  "signature_name":'prediction',  "instances": [{"images":IMAGE_AS_LIST}]} You can see the full documentation about RESTful API . Here Now, I will show an example how to call our model from python Image before preprocess: Image size 1000 X 1000 Image after preprocess: Image size 96 X 96 It will print T-shirt/top If you have reached this far , you have overcome many hurdles, congratulations! — we are almost there. Now we are going to deploy the model to Heroku. Before running any of the commands below make sure you are in the same folder where Dockerfile live. Log in to Container Registry: > heroku container:login Create a Heroku app: > heroku create ${YOUR_APP_NAME} Push the docker image to Heroku: > heroku  container:push web -a ${YOUR_APP_NAME} To test your app, change the RESTful API URL to: { }/v1/models/ /versions/1:predict YOUR_APP_NAME tf_serving_keras_mobilenetv2 That is - you have now trained and deployed a deep learning model to production. Welcome to the very small production ready machine learning community :) I have created a , which has all the code for part 1 and 2 of this tutorial. Jupyter notebook Conclusion I hope you have enjoyed this 2 part series on deploying and training a deep learning model. I will be responding to question/suggestions, for whomever leaves them below in the comments section. If you would like to see more tutorials like this please follow, clap and suggest what we should learn next :) A special thanks to for his help and feedback. Shaun Gamboa References What is Docker? Top 10 Benefits of Docker Serving a TensorFlow Model

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