Deep Learning with Spring Boot and DJL is a May 2020 tutorial by David Kiss showing how to connect a Spring Boot REST API to DJL and TensorFlow to classify chest X-ray images. Its code is a historical example, not a current setup recipe: it uses Java 8 and DJL 0.5.0, while current DJL setup guidance recommends JDK 11 or later. The demo is not for medical diagnosis.
What the tutorial builds
The tutorial takes an image URL through a web application to a REST endpoint, where DJL runs a TensorFlow-backed classification model and returns a prediction. The application demonstrates how to place model inference inside a Java/Spring Boot service; it does not establish that the model is clinically accurate or appropriate for care.
The tutorial’s stated disclaimer is explicit: the COVID-19 X-ray demo uses a public dataset and “SHOULD NOT be used for actual medical diagnosis.” Treat its output as a software demonstration only.
Read David Kiss’s original tutorial for its original code and walkthrough.
How DJL fits into a Spring Boot application
DJL is an open-source, high-level, framework-agnostic Java API for deep learning. It gives Java applications a common API for model inference, while the selected engine supplies the underlying framework implementation. AWS’s Spring Boot example describes a starter that bundles dependencies and auto-configuration, allowing DJL components to be wired into the Spring application context.
In practical terms, a Spring Boot service can accept a request, prepare or load the input, invoke a DJL model, and return a response through an HTTP endpoint. The model format and engine must be compatible, and the selected backend and its native libraries remain part of the application’s build and deployment design.
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AWS’s Spring Boot microservice example illustrates in-process inference for object detection and classification.
Why the 2020 dependency versions should not be copied blindly
The tutorial’s dependency set includes Spring Boot Web, DJL API, TensorFlow API and engine, TensorFlow native-auto, and JNA. Its version properties specify Java 8, DJL 0.5.0, JNA 5.3.0, and TensorFlow native-auto 2.1.0. These are the article’s 2020 choices, not a recommendation for a new project.
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DJL development setup documentation provides current setup guidance. The engine overview lists MXNet, PyTorch, TensorFlow, ONNX Runtime, XGBoost, and LightGBM, with different support levels and engine-specific instructions.
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How to adapt the example for a current project
- Choose an engine and model together. Confirm that the engine supports the model and its format, and that both are supported for your target operating system and deployment environment. Add the intended engine to the classpath and follow its current setup instructions.
- Use current dependency guidance. Start with the live DJL setup and quick-start documentation; do not treat the 2020 Java 8 and DJL 0.5.0 values as current defaults.
- Decide where the model will come from. DJL’s ModelZoo API supports model loading from local paths, archives, URLs, and supported remote-storage extensions. Choose a source that fits your deployment and availability requirements.
- Plan native-library behavior. DJL may download native engine libraries automatically. If a production environment cannot access the network, account for distributing the appropriate offline native packages with the application.
- Define the API’s request and response contract. The original example accepts an image URL. A real service should specify accepted inputs, validation, error handling, timeouts, and the response format rather than assuming every URL or image can be processed.
- Test the full runtime path. Verify model loading and inference in the same JDK, operating system, architecture, and network conditions used for deployment. A dependency compiling successfully does not by itself establish that native libraries or model retrieval will work at runtime.
The live DJL model-loading guide explains model loading, and the ModelZoo documentation includes a Maven example at version 0.38.0. Since versions move, use the documentation’s current instructions when implementing a new service.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.In-process inference or a separate model service?
The tutorial and AWS example illustrate in-process inference: the Spring Boot application loads and calls the model itself. That can keep the application path direct, but it also makes the service responsible for model and engine dependencies, native runtime setup, and inference resource use.
Best Value
A separate inference service is another architecture option when model deployment or scaling should be managed independently of the web application. The sources here do not establish that one architecture is universally faster or better; the choice depends on operational boundaries, deployment constraints, and how inference demand should be managed.
Blocking requests and higher-volume APIs
AWS notes that its sample controller is blocking and suggests considering a reactive API such as Spring WebFlux for high-volume production use. This is architectural guidance, not a performance guarantee: reactive request handling does not by itself make model inference non-blocking or increase inference capacity. Measure the service under its expected workload and account for where inference executes.
DJL’s quick start links to beginner tutorials and training and inference examples for readers who want current framework examples beyond the older tutorial.
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