Deep Java Library

Since Camel 3.3

Only producer is supported

The Deep Java Library component is used to infer Deep Learning models from message exchanges data. This component uses Deep Java Library as underlying library.

In order to use the DJL component, Maven users will need to add the following dependency to their pom.xml:

pom.xml

<dependency>
    <groupId>org.apache.camel</groupId>
    <artifactId>camel-djl</artifactId>
    <version>x.x.x</version>
    <!-- use the same version as your Camel core version -->
</dependency>

URI format

djl:application

Configuring Options

Camel components are configured on two separate levels:

  • component level

  • endpoint level

Configuring Component Options

The component level is the highest level which holds general and common configurations that are inherited by the endpoints. For example a component may have security settings, credentials for authentication, urls for network connection and so forth.

Some components only have a few options, and others may have many. Because components typically have pre configured defaults that are commonly used, then you may often only need to configure a few options on a component; or none at all.

Configuring components can be done with the Component DSL, in a configuration file (application.properties|yaml), or directly with Java code.

Configuring Endpoint Options

Where you find yourself configuring the most is on endpoints, as endpoints often have many options, which allows you to configure what you need the endpoint to do. The options are also categorized into whether the endpoint is used as consumer (from) or as a producer (to), or used for both.

Configuring endpoints is most often done directly in the endpoint URI as path and query parameters. You can also use the Endpoint DSL and DataFormat DSL as a type safe way of configuring endpoints and data formats in Java.

A good practice when configuring options is to use Property Placeholders, which allows to not hardcode urls, port numbers, sensitive information, and other settings. In other words placeholders allows to externalize the configuration from your code, and gives more flexibility and reuse.

The following two sections lists all the options, firstly for the component followed by the endpoint.

Component Options

The Deep Java Library component supports 2 options, which are listed below.

Name Description Default Type

lazyStartProducer (producer)

Whether the producer should be started lazy (on the first message). By starting lazy you can use this to allow CamelContext and routes to startup in situations where a producer may otherwise fail during starting and cause the route to fail being started. By deferring this startup to be lazy then the startup failure can be handled during routing messages via Camel’s routing error handlers. Beware that when the first message is processed then creating and starting the producer may take a little time and prolong the total processing time of the processing.

false

boolean

autowiredEnabled (advanced)

Whether autowiring is enabled. This is used for automatic autowiring options (the option must be marked as autowired) by looking up in the registry to find if there is a single instance of matching type, which then gets configured on the component. This can be used for automatic configuring JDBC data sources, JMS connection factories, AWS Clients, etc.

true

boolean

Endpoint Options

The Deep Java Library endpoint is configured using URI syntax:

djl:application

with the following path and query parameters:

Path Parameters (1 parameters)

Name Description Default Type

application (producer)

Required Application name.

String

Query Parameters (4 parameters)

Name Description Default Type

artifactId (producer)

Model Artifact.

String

model (producer)

Model.

String

translator (producer)

Translator.

String

lazyStartProducer (producer (advanced))

Whether the producer should be started lazy (on the first message). By starting lazy you can use this to allow CamelContext and routes to startup in situations where a producer may otherwise fail during starting and cause the route to fail being started. By deferring this startup to be lazy then the startup failure can be handled during routing messages via Camel’s routing error handlers. Beware that when the first message is processed then creating and starting the producer may take a little time and prolong the total processing time of the processing.

false

boolean

Model Zoo

The following table contains supported models in the model zoo:

CV Image Classification Resnet image classification cv/image_classification ai.djl.zoo:resnet:0.0.1 {layers=50, flavor=v1, dataset=cifar10}

CV

Image Classification

MLP image classification

cv/image_classification

ai.djl.zoo:mlp:0.0.2

{dataset=mnist}

CV

Image Classification

MLP image classification

cv/image_classification

ai.djl.mxnet:mlp:0.0.1

{dataset=mnist}

CV

Image Classification

Resnet image classification

cv/image_classification

ai.djl.mxnet:resnet:0.0.1

{layers=18, flavor=v1, dataset=imagenet}

CV

Image Classification

Resnet image classification

cv/image_classification

ai.djl.mxnet:resnet:0.0.1

{layers=50, flavor=v2, dataset=imagenet}

CV

Image Classification

Resnet image classification

cv/image_classification

ai.djl.mxnet:resnet:0.0.1

{layers=152, flavor=v1d, dataset=imagenet}

CV

Image Classification

Resnet image classification

cv/image_classification

ai.djl.mxnet:resnet:0.0.1

{layers=50, flavor=v1, dataset=cifar10}

CV

Image Classification

Resnext image classification

cv/image_classification

ai.djl.mxnet:resnext:0.0.1

{layers=101, flavor=64x4d, dataset=imagenet}

CV

Image Classification

Senet image classification

cv/image_classification

ai.djl.mxnet:senet:0.0.1

{layers=154, dataset=imagenet}

CV

Image Classification

Senet and Resnext image classification

cv/image_classification

ai.djl.mxnet:se_resnext:0.0.1

{layers=101, flavor=32x4d, dataset=imagenet}

CV

Image Classification

Senet and Resnext image classification

cv/image_classification

ai.djl.mxnet:se_resnext:0.0.1

{layers=101, flavor=64x4d, dataset=imagenet}

CV

Image Classification

Squeezenet image classification

cv/image_classification

ai.djl.mxnet:squeezenet:0.0.1

{flavor=1.0, dataset=imagenet}

CV

Object Detection

Single Shot Detection for Object Detection

cv/object_detection

ai.djl.zoo:ssd:0.0.1

{flavor=tiny, dataset=pikachu}

CV

Object Detection

Single-shot object detection

cv/object_detection

ai.djl.mxnet:ssd:0.0.1

{size=512, backbone=resnet50, flavor=v1, dataset=voc}

CV

Object Detection

Single-shot object detection

cv/object_detection

ai.djl.mxnet:ssd:0.0.1

{size=512, backbone=vgg16, flavor=atrous, dataset=coco}

CV

Object Detection

Single-shot object detection

cv/object_detection

ai.djl.mxnet:ssd:0.0.1

{size=512, backbone=mobilenet1.0, dataset=voc}

CV

Object Detection

Single-shot object detection

cv/object_detection

ai.djl.mxnet:ssd:0.0.1

{size=300, backbone=vgg16, flavor=atrous, dataset=voc}

DJL Engine implementation

Because DJL is deep learning framework agnostic, you don’t have to make a choice between frameworks when creating your projects. You can switch frameworks at any point. To ensure the best performance, DJL also provides automatic CPU/GPU choice based on hardware configuration.

MxNet engine

You can pull the MXNet engine from the central Maven repository by including the following dependency:

<dependency>
    <groupId>ai.djl.mxnet</groupId>
    <artifactId>mxnet-engine</artifactId>
    <version>x.x.x</version>
    <scope>runtime</scope>
</dependency>

DJL offers an automatic option that will download the jars the first time you run DJL. It will automatically determine the appropriate jars for your system based on the platform and GPU support.

    <dependency>
      <groupId>ai.djl.mxnet</groupId>
      <artifactId>mxnet-native-auto</artifactId>
      <version>1.7.0-a</version>
      <scope>runtime</scope>
    </dependency>

More information about MxNet engine installation

PyTorch engine

You can pull the PyTorch engine from the central Maven repository by including the following dependency:

<dependency>
    <groupId>ai.djl.pytorch</groupId>
    <artifactId>pytorch-engine</artifactId>
    <version>x.x.x</version>
    <scope>runtime</scope>
</dependency>

DJL offers an automatic option that will download the jars the first time you run DJL. It will automatically determine the appropriate jars for your system based on the platform and GPU support.

    <dependency>
      <groupId>ai.djl.pytorch</groupId>
      <artifactId>pytorch-native-auto</artifactId>
      <version>1.5.0</version>
      <scope>runtime</scope>
    </dependency>

More information about PyTorch engine installation

Tensorflow engine

You can pull the Tensorflow engine from the central Maven repository by including the following dependency:

<dependency>
    <groupId>ai.djl.tensorflow</groupId>
    <artifactId>tensorflow-engine</artifactId>
    <version>x.x.x</version>
    <scope>runtime</scope>
</dependency>

DJL offers an automatic option that will download the jars the first time you run DJL. It will automatically determine the appropriate jars for your system based on the platform and GPU support.

    <dependency>
      <groupId>ai.djl.tensorflow</groupId>
      <artifactId>tensorflow-native-auto</artifactId>
      <version>2.1.0</version>
      <scope>runtime</scope>
    </dependency>

More information about Tensorflow engine installation

Examples

MNIST image classification from file

from("file:/data/mnist/0/10.png")
    .to("djl:cv/image_classification?artifactId=ai.djl.mxnet:mlp:0.0.1");

Object detection

from("file:/data/mnist/0/10.png")
    .to("djl:cv/image_classification?artifactId=ai.djl.mxnet:mlp:0.0.1");

Custom deep learning model

// create deep learning model
Model model = Model.newInstance();
model.setBlock(new Mlp(28 * 28, 10, new int[]{128, 64}));
model.load(Paths.get(MODEL_DIR), MODEL_NAME);

// create translator for pre-processing and postprocessing
ImageClassificationTranslator.Builder builder = ImageClassificationTranslator.builder();
builder.setSynsetArtifactName("synset.txt");
builder.setPipeline(new Pipeline(new ToTensor()));
builder.optApplySoftmax(true);
ImageClassificationTranslator translator = new ImageClassificationTranslator(builder);

// Bind model and translator beans
context.getRegistry().bind("MyModel", model);
context.getRegistry().bind("MyTranslator", translator);

from("file:/data/mnist/0/10.png")
    .to("djl:cv/image_classification?model=MyModel&translator=MyTranslator");