> For the complete documentation index, see [llms.txt](https://deeplearning4j.konduit.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://deeplearning4j.konduit.ai/en-1.0.0-beta7/model-import/overview.md).

# Overview

### Model Import Overview

Eclipse Deeplearning4j provides multiple paths for importing pre-trained models from other frameworks. Whether your model was trained in Keras, TensorFlow, or exported to ONNX, there is a supported import or inference path that brings it into the JVM ecosystem for production deployment.

This page maps out all available import paths, provides a decision tree to help you pick the right one, and gives a comparison across available approaches.

***

### Import Paths at a Glance

DL4J supports the following import and inference strategies:

| Strategy             | Input Format               | Output                               | Use Case                                           |
| -------------------- | -------------------------- | ------------------------------------ | -------------------------------------------------- |
| Keras model import   | Keras H5 (.h5)             | MultiLayerNetwork / ComputationGraph | Import and run Keras models in DL4J                |
| SameDiff TF import   | TF frozen .pb / SavedModel | SameDiff graph                       | Import TF graphs for inference or further training |
| SameDiff ONNX import | ONNX .onnx                 | SameDiff graph                       | Import ONNX models via SameDiff                    |
| ONNX Runtime         | ONNX .onnx                 | OrtSession inference                 | Direct ONNX inference without conversion           |
| TF Direct (JavaCPP)  | TF frozen .pb              | TF Session inference                 | Run TF graphs via JavaCPP TF bindings              |
| TensorFlow Lite      | .tflite                    | TFLite interpreter                   | Edge/mobile inference with TFLite 2.8              |
| Apache TVM           | Compiled TVM module        | TVM runtime inference                | Compiler-optimized inference via TVM 0.8           |

***

### Decision Tree: Which Import Path to Use?

Work through the questions below to identify the right approach for your situation.

#### Step 1: What is your model source?

**Keras model (.h5)?** Go to [Keras Import](#keras-model-import).

**TensorFlow frozen graph (.pb) or SavedModel?** Continue to Step 2.

**ONNX model (.onnx)?** Continue to Step 3.

**Already compiled TVM module?** Go to [Apache TVM](#apache-tvm-integration).

**TensorFlow Lite model (.tflite)?** Go to [TensorFlow Lite](#tensorflow-lite).

***

#### Step 2: TensorFlow models

Do you need to **further train** the model inside DL4J/SameDiff, or access intermediate activations?

* Yes: use [SameDiff TF import](https://github.com/KonduitAI/deeplearning4j-docs/blob/master/docs/m2.1/model-import/samediff-import/tensorflow/README.md). SameDiff converts the frozen graph into a SameDiff computation graph that supports autograd and further training.
* No (inference only): consider [TF Direct inference](https://github.com/KonduitAI/deeplearning4j-docs/blob/master/docs/m2.1/model-import/tensorflow/overview/README.md) via `nd4j-tensorflow` (JavaCPP bindings). This runs the graph natively without conversion overhead.

***

#### Step 3: ONNX models

Do you need a **Java-native graph representation** (e.g., to inspect ops, modify the graph, or train)?

* Yes: use [SameDiff ONNX import](https://github.com/KonduitAI/deeplearning4j-docs/blob/master/docs/m2.1/model-import/samediff-import/onnx/README.md).
* No (pure inference): use [ONNX Runtime](https://github.com/KonduitAI/deeplearning4j-docs/blob/master/docs/m2.1/model-import/onnx-runtime/overview/README.md) via `nd4j-onnxruntime`. This delegates execution to the ONNX Runtime 1.10 C++ library for optimal throughput without any conversion.

***

### Import Path Details

#### Keras Model Import

The `deeplearning4j-modelimport` module reads Keras H5 files (produced by `model.save()`) and maps:

* `Sequential` models to `MultiLayerNetwork`
* Functional API models to `ComputationGraph`

Weights, layer configurations, and (optionally) training configurations are all preserved. After import, the model participates fully in the DL4J ecosystem for inference, transfer learning, or continued training.

**Supported Keras versions:** 1.x and 2.x (TensorFlow, Theano, and CNTK backends)

**Relevant pages:**

* [Keras Import Overview](https://github.com/KonduitAI/deeplearning4j-docs/blob/master/docs/m2.1/model-import/keras/overview/README.md)
* [Getting Started](https://github.com/KonduitAI/deeplearning4j-docs/blob/master/docs/m2.1/model-import/keras/getting-started/README.md)
* [Sequential Model](https://github.com/KonduitAI/deeplearning4j-docs/blob/master/docs/m2.1/model-import/keras/sequential-model/README.md)
* [Functional Model](https://github.com/KonduitAI/deeplearning4j-docs/blob/master/docs/m2.1/model-import/keras/functional-model/README.md)
* [Supported Features](https://github.com/KonduitAI/deeplearning4j-docs/blob/master/docs/m2.1/model-import/keras/supported-features/README.md)
* [API Reference](https://github.com/KonduitAI/deeplearning4j-docs/blob/master/docs/m2.1/model-import/keras/model-import-api/README.md)

***

#### SameDiff TF/ONNX Import

The `nd4j-samediff-import` subsystem (written in Kotlin) provides a `FrameworkImporter` abstraction and concrete `ImportGraph` implementations for TensorFlow and ONNX. After import, the model lives as a `SameDiff` graph and inherits all SameDiff capabilities: autograd, custom training loops, op inspection, and export.

**Relevant pages:**

* [SameDiff Import Overview](https://github.com/KonduitAI/deeplearning4j-docs/blob/master/docs/m2.1/model-import/samediff-import/overview/README.md)
* [TensorFlow Import](https://github.com/KonduitAI/deeplearning4j-docs/blob/master/docs/m2.1/model-import/samediff-import/tensorflow/README.md)
* [ONNX Import](https://github.com/KonduitAI/deeplearning4j-docs/blob/master/docs/m2.1/model-import/samediff-import/onnx/README.md)

***

#### ONNX Runtime

The `nd4j-onnxruntime` module wraps ONNX Runtime 1.10. Models run directly in the ORT C++ runtime; there is no conversion to a SameDiff graph. This is the fastest path for ONNX inference when you do not need graph mutation.

**Relevant pages:**

* [ONNX Runtime Overview](https://github.com/KonduitAI/deeplearning4j-docs/blob/master/docs/m2.1/model-import/onnx-runtime/overview/README.md)

***

#### TensorFlow Direct Inference

The `nd4j-tensorflow` module uses JavaCPP TF bindings to execute TensorFlow frozen graphs natively. No conversion occurs. Suitable when you have a stable frozen graph and want minimal overhead inference.

**Relevant pages:**

* [TF Direct Inference Overview](https://github.com/KonduitAI/deeplearning4j-docs/blob/master/docs/m2.1/model-import/tensorflow/overview/README.md)

***

#### TensorFlow Lite

The `nd4j-tensorflow-lite` module integrates TFLite 2.8 for embedded and edge inference. Accepts `.tflite` flatbuffer models. Ideal for Android and constrained-resource deployments.

**Relevant pages:**

* [TensorFlow Lite](https://github.com/KonduitAI/deeplearning4j-docs/blob/master/docs/m2.1/model-import/tensorflow/tensorflow-lite/README.md)

***

#### Apache TVM Integration

The `nd4j-tvm` module integrates Apache TVM 0.8. TVM is a machine learning compiler that produces optimized native binaries tuned for a target hardware backend. You compile your model with TVM's Python toolchain first, then load the resulting module in Java.

**Relevant pages:**

* [Apache TVM Overview](https://github.com/KonduitAI/deeplearning4j-docs/blob/master/docs/m2.1/model-import/tvm/overview/README.md)

***

### Comparison Table

| Feature                | Keras Import               | SameDiff TF                     | SameDiff ONNX             | ORT              | TF Direct       | TFLite               | TVM      |
| ---------------------- | -------------------------- | ------------------------------- | ------------------------- | ---------------- | --------------- | -------------------- | -------- |
| Java-native graph      | Yes (DL4J)                 | Yes (SameDiff)                  | Yes (SameDiff)            | No               | No              | No                   | No       |
| Further training       | Yes                        | Yes                             | Partial                   | No               | No              | No                   | No       |
| No conversion overhead | No                         | No                              | No                        | Yes              | Yes             | Yes                  | Yes      |
| Op coverage            | Keras ops                  | TF op set                       | ONNX op set               | ONNX op set      | TF op set       | TFLite op set        | Wide     |
| Edge/mobile            | No                         | No                              | No                        | Limited          | No              | Yes                  | Yes      |
| Compiler optimization  | No                         | No                              | No                        | Limited          | No              | No                   | Yes      |
| Maven module           | deeplearning4j-modelimport | nd4j-samediff-import-tensorflow | nd4j-samediff-import-onnx | nd4j-onnxruntime | nd4j-tensorflow | nd4j-tensorflow-lite | nd4j-tvm |

***

### Maven Coordinates

All modules share the same version. Replace `${dl4j.version}` with your project version (e.g., `1.0.0-M2.1`).

```xml
<!-- Keras model import -->
<dependency>
    <groupId>org.deeplearning4j</groupId>
    <artifactId>deeplearning4j-modelimport</artifactId>
    <version>${dl4j.version}</version>
</dependency>

<!-- SameDiff TF import -->
<dependency>
    <groupId>org.nd4j</groupId>
    <artifactId>nd4j-samediff-import-tensorflow</artifactId>
    <version>${dl4j.version}</version>
</dependency>

<!-- SameDiff ONNX import -->
<dependency>
    <groupId>org.nd4j</groupId>
    <artifactId>nd4j-samediff-import-onnx</artifactId>
    <version>${dl4j.version}</version>
</dependency>

<!-- ONNX Runtime direct inference -->
<dependency>
    <groupId>org.nd4j</groupId>
    <artifactId>nd4j-onnxruntime</artifactId>
    <version>${dl4j.version}</version>
</dependency>

<!-- TF direct inference -->
<dependency>
    <groupId>org.nd4j</groupId>
    <artifactId>nd4j-tensorflow</artifactId>
    <version>${dl4j.version}</version>
</dependency>

<!-- TFLite -->
<dependency>
    <groupId>org.nd4j</groupId>
    <artifactId>nd4j-tensorflow-lite</artifactId>
    <version>${dl4j.version}</version>
</dependency>

<!-- Apache TVM -->
<dependency>
    <groupId>org.nd4j</groupId>
    <artifactId>nd4j-tvm</artifactId>
    <version>${dl4j.version}</version>
</dependency>
```

***

### Troubleshooting

**`IncompatibleKerasConfigurationException`**: the Keras model uses a layer or feature not yet supported by the importer. Check [supported features](https://github.com/KonduitAI/deeplearning4j-docs/blob/master/docs/m2.1/model-import/keras/supported-features/README.md) and file a GitHub issue if needed.

**`UnsupportedKerasConfigurationException`**: a specific parameter value or combination is not handled. Usually workable by adjusting the Keras model before export (e.g., removing unsupported regularizers).

**Op not found during SameDiff import**: the TF or ONNX graph contains an op with no registered SameDiff mapping. Check the op coverage tables in the SameDiff import pages and consider filing an issue or contributing a mapping.

**ORT session creation fails**: verify that the `nd4j-onnxruntime` native binaries match your OS/architecture. ORT ships CPU binaries by default; GPU requires additional configuration.

***

### Further Reading

* [DL4J examples repository](https://github.com/eclipse/deeplearning4j-examples)
* [Model import source code](https://github.com/eclipse/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport)
* [SameDiff import source code](https://github.com/eclipse/deeplearning4j/tree/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/transform)