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Manoj Rao

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A typical Deep Learning Workflow from a software engineering design perspective is shown in the figure below. This is a very high level overview of how a typical process flow works. There are exceptions to this when using Unsupervised Learning Techniques, but we will ignore them for sake of simplicity.

Typical Deep Learning Workflow

In production systems, the most amount of time and compute resources are spent in the iteration of Input -> Model -> Output cycle. It’s the bottom row in the figure. If you think about relatively mature models that perform off-the-shelf techniques, ex: Object Detection, Image Classification, Text Classification etc this is truer than other cases. Common Sense follows that it is imperative that this control path be the focus of all optimizations. It’s the reason behind AWS launching a completely separate service called SageMaker Neo. To be fair, optimal inference is just one of the many aspects/features of this service. Check it out.

TVM is an open source project that aims to standardize the representation of output from the Training phase of the several Deep Learning Frameworks and optimize for the supported target hardwares (plenty of them). TVM does this by optimizing the implementation of the individual operators in the standardized representation for each supported hardware architecture.

So far it’s all sunshine and roses, correct? The trouble is when you smell them. They stink! A harsh reality is that most of the efforts were (rightly) spent into achieving a stage where requests can be sent continuously to make inferences. But these inferences were simply put behind a Flask App which is the equivalent of having a single Raspberry Pi powered server to handle all the search requests of Google (exaggerated but true).

So many big players have put out significant efforts to solve this issue using their know-how. As a result, there are several acceptable solutions being used in production by several players:

To give you a bird’s view here’s the MXNet Solution from the AWS Blog MXNet Model Server

Start the Server:

Looking at the documentation, it is pretty easy to get started.

mxnet-model-server --models squeezenet=https://s3.amazonaws.com/model-server/models/squeezenet_v1.1/squeezenet_v1.1.model

Request from Client:

Now the client can send HTTP requests to obtain inferences

curl -O https://s3.amazonaws.com/model-server/inputs/kitten.jpg
curl http://127.0.0.1:8080/squeezenet/predict -F "input0=@kitten.jpg"

Get a Result:

{
  "prediction": [
    [
      {
        "class": "n02124075 Egyptian cat",
        "probability": 0.8515276312828064
      },
… (other lower probability predictions)}

The reason I chose Apache’s Solution here is because after my cursory browsing research that seemed like the most mature option with decent amount of features.

Model Archive:

It offers a feature to archive the model into a common format/representation which allows it to support Models from various Deep Learning Frameworks. Model Export and Archive

Source:


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