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‎README.md

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* [DJL Website](https://djl.ai/)
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* [Documentation](https://docs.djl.ai/)
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* [DJL Demos](https://docs.djl.ai/docs/demos/index.html)
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* [DJL Demos](https://docs.djl.ai/master/docs/demos/index.html)
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* [Dive into Deep Learning Book Java version](https://d2l.djl.ai/)
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## License

‎api/build.gradle.kts

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"Notice" to "DJL will collect telemetry to help us better understand our users'" +
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" needs, diagnose issues, and deliver additional features. If you would" +
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" like to learn more or opt-out please go to: " +
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"https://docs.djl.ai/docs/telemetry.html for more information."
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"https://docs.djl.ai/master/docs/telemetry.html for more information."
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)
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}
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}

‎api/src/main/java/ai/djl/engine/Engine.java

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* contain methods to detect information about the usable machine hardware and to create a new
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* {@link NDManager} and {@link Model}.
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*
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* @see <a href="http://docs.djl.ai/docs/engine.html">Engine Guide</a>
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* @see <a href="https://docs.djl.ai/master/docs/engine.html">Engine Guide</a>
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* @see EngineProvider
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* @see <a href="http://docs.djl.ai/docs/development/cache_management.html">The guide on resource
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* and engine caching</a>
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* @see <a href="https://docs.djl.ai/master/docs/development/cache_management.html">The guide on
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* resource and engine caching</a>
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*/
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public abstract class Engine {
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‎api/src/main/java/ai/djl/inference/Predictor.java

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*
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* <ul>
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* <li><a
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* href="http://docs.djl.ai/docs/demos/jupyter/tutorial/03_image_classification_with_your_model.html">Inference
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* href="https://docs.djl.ai/master/docs/demos/jupyter/tutorial/03_image_classification_with_your_model.html">Inference
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* with a custom trained model</a>
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* <li><a
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* href="http://docs.djl.ai/docs/demos/jupyter/object_detection_with_model_zoo.html">Inference
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* href="https://docs.djl.ai/master/docs/demos/jupyter/object_detection_with_model_zoo.html">Inference
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* with a model zoo model</a>
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* <li><a href="http://docs.djl.ai/docs/demos/jupyter/load_mxnet_model.html">Inference with an
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* MXNet model</a>
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* <li><a href="https://docs.djl.ai/master/docs/demos/jupyter/load_mxnet_model.html">Inference
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* with an MXNet model</a>
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* </ul>
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*
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* @param <I> the input type
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* @param <O> the output type
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* @see Model
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* @see Translator
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* @see <a href="http://docs.djl.ai/docs/development/memory_management.html">The guide on memory
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* management</a>
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* @see <a href="https://docs.djl.ai/master/docs/development/memory_management.html">The guide on
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* memory management</a>
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* @see <a
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* href="https://github.com/deepjavalibrary/djl/blob/master/examples/docs/multithread_inference.md">The
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* guide on running multi-threaded inference</a>
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* @see <a href="http://docs.djl.ai/docs/development/inference_performance_optimization.html">The
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* @see <a
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* href="https://docs.djl.ai/master/docs/development/inference_performance_optimization.html">The
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* guide on inference performance optimization</a>
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*/
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public class Predictor<I, O> implements AutoCloseable {

‎api/src/main/java/ai/djl/nn/Block.java

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* further refine these elements, use {@link Block#freezeParameters(boolean)} to unfreeze them.
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*
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* @see <a
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* href="http://docs.djl.ai/docs/demos/jupyter/tutorial/01_create_your_first_network.html">this
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* href="https://docs.djl.ai/master/docs/demos/jupyter/tutorial/01_create_your_first_network.html">this
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* tutorial on creating your first network</a>
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* @see <a href="https://d2l.djl.ai/chapter_deep-learning-computation/model-construction.html">The
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* D2L chapter on blocks</a> and <a

‎api/src/main/java/ai/djl/repository/zoo/Criteria.java

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* .build();
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* </pre>
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*
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* <p>See <a href="http://docs.djl.ai/docs/load_model.html#criteria-class">Model loading</a> for
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* more detail.
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* <p>See <a href="https://docs.djl.ai/master/docs/load_model.html#criteria-class">Model loading</a>
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* for more detail.
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*
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* @param <I> the model input type
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* @param <O> the model output type

‎api/src/main/java/ai/djl/training/Trainer.java

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*
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* <ul>
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* <li><a
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* href="http://docs.djl.ai/docs/demos/jupyter/tutorial/02_train_your_first_model.html">Training
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* href="https://docs.djl.ai/master/docs/demos/jupyter/tutorial/02_train_your_first_model.html">Training
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* your first model</a>
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* <li><a href="http://docs.djl.ai/docs/demos/jupyter/transfer_learning_on_cifar10.html">Training
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* <li><a
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* href="https://docs.djl.ai/master/docs/demos/jupyter/transfer_learning_on_cifar10.html">Training
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* using transfer learning</a>
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* <li><a href="http://docs.djl.ai/docs/demos/jupyter/load_mxnet_model.html">Inference with an
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* MXNet model</a>
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* <li><a href="https://docs.djl.ai/master/docs/demos/jupyter/load_mxnet_model.html">Inference
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* with an MXNet model</a>
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* </ul>
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*
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* @see <a href="http://docs.djl.ai/docs/development/memory_management.html">The guide on memory
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* management</a>
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* @see <a href="https://docs.djl.ai/master/docs/development/memory_management.html">The guide on
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* memory management</a>
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*/
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public class Trainer implements AutoCloseable {
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‎api/src/main/java/ai/djl/training/dataset/Dataset.java

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/**
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* An interface to represent a set of sample data/label pairs to train a model.
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*
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* @see <a href="http://docs.djl.ai/docs/dataset.html">The guide to datasets</a>
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* @see <a href="http://docs.djl.ai/docs/development/how_to_use_dataset.html">The guide to
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* @see <a href="https://docs.djl.ai/master/docs/dataset.html">The guide to datasets</a>
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* @see <a href="https://docs.djl.ai/master/docs/development/how_to_use_dataset.html">The guide to
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* implementing a custom dataset</a>
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*/
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public interface Dataset {

‎api/src/main/java/ai/djl/training/dataset/RandomAccessDataset.java

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*
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* <p>Almost all datasets in DJL extend, either directly or indirectly, {@link RandomAccessDataset}.
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*
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* @see <a href="http://docs.djl.ai/docs/development/how_to_use_dataset.html">The guide to
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* @see <a href="https://docs.djl.ai/master/docs/development/how_to_use_dataset.html">The guide to
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* implementing a custom dataset</a>
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*/
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public abstract class RandomAccessDataset implements Dataset {

‎api/src/main/java/ai/djl/training/listener/LoggingTrainingListener.java

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/**
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* {@link TrainingListener} that outputs the progress of training each batch and epoch into logs.
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*
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* @see <a href="http://docs.djl.ai/docs/development/configure_logging.html">The guide on DJL
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* logging</a>
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* @see <a href="https://docs.djl.ai/master/docs/development/configure_logging.html">The guide on
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* DJL logging</a>
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*/
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public class LoggingTrainingListener implements TrainingListener {
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‎api/src/main/java/ai/djl/util/Ec2Utils.java

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"DJL will collect telemetry to help us better understand our users' needs,"
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+ " diagnose issues, and deliver additional features. If you would like"
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+ " to learn more or opt-out please go to:"
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+ " https://docs.djl.ai/docs/telemetry.html for more information.");
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+ " https://docs.djl.ai/master/docs/telemetry.html for more"
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+ " information.");
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}
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} catch (IOException e) {
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logger.debug("Failed call home.");

‎basicdataset/README.md

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This module contains a number of basic and standard datasets in the Deep Java Library's (DJL). These datasets are used to train deep learning models.
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You can find the datasets provided by this module on our [docs](http://docs.djl.ai/docs/dataset.html).
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You can find the datasets provided by this module on our [docs](https://docs.djl.ai/master/docs/dataset.html).
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## Documentation
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‎djl-zero/src/main/java/ai/djl/zero/RequireZoo.java

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if (!ModelZoo.hasModelZoo("ai.djl.zoo")) {
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throw new IllegalStateException(
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"The basic model zoo is required, but not found.Please install it by following"
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+ " http://docs.djl.ai/model-zoo/index.html#installation");
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+ " https://docs.djl.ai/model-zoo/index.html#installation");
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}
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}
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throw new IllegalStateException(
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"The MXNet model zoo is required, but not found.Please install it by following"
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+ " http://docs.djl.ai/engines/mxnet/mxnet-model-zoo/index.html#installation");
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+ " https://docs.djl.ai/master/engines/mxnet/mxnet-model-zoo/index.html#installation");
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}
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if (!Engine.hasEngine("MXNet")) {
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throw new IllegalStateException(
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"The MXNet engine is required, but not found.Please install it by following"
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+ " http://docs.djl.ai/engines/mxnet/mxnet-engine/index.html#installation");
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+ " https://docs.djl.ai/master/engines/mxnet/mxnet-engine/index.html#installation");
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}
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}
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throw new IllegalStateException(
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"The PyTorch model zoo is required, but not found.Please install it by"
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+ " https://docs.djl.ai/master/pytorch/pytorch-model-zoo/index.html#installation");
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}
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throw new IllegalStateException(
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"The PyTorch engine is required, but not found.Please install it by following"
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+ " https://docs.djl.ai/master/pytorch/pytorch-engine/index.html#installation");
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}
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"The TensorFlow model zoo is required, but not found.Please install it by"
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+ " following"
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+ " http://docs.djl.ai/engines/tensorflow/tensorflow-model-zoo/index.html#installation");
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+ " https://docs.djl.ai/master/engines/tensorflow/tensorflow-model-zoo/index.html#installation");
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}
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if (!Engine.hasEngine("TensorFlow")) {
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"The TensorFlow engine is required, but not found.Please install it by"
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+ " following"
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+ " http://docs.djl.ai/engines/tensorflow/tensorflow-engine/index.html#installation");
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+ " https://docs.djl.ai/master/engines/tensorflow/tensorflow-engine/index.html#installation");
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‎djl-zero/src/main/java/ai/djl/zero/package-info.java

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/**
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* Contains a zero deep learning knowledge required wrapper over DJL.
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*
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* <p><a href="https://docs.djl.ai/zero/index.html">See more details</a>.
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* <p><a href="https://docs.djl.ai/master/zero/index.html">See more details</a>.
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*/
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package ai.djl.zero;

‎docs/README.md

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- [Troubleshooting](development/troubleshooting.md)
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- [Inference Optimization](development/inference_performance_optimization.md)
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## [Jupyter notebook tutorials](http://docs.djl.ai/docs/demos/jupyter/index.html)
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- **[Beginner Jupyter Tutorial](http://docs.djl.ai/docs/demos/jupyter/tutorial/index.html)**
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- [Run object detection with model zoo](http://docs.djl.ai/docs/demos/jupyter/object_detection_with_model_zoo.html)
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- [Load pre-trained PyTorch model](http://docs.djl.ai/docs/demos/jupyter/load_pytorch_model.html)
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- [Load pre-trained Apache MXNet model](http://docs.djl.ai/docs/demos/jupyter/load_mxnet_model.html)
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- [Transfer learning example](http://docs.djl.ai/docs/demos/jupyter/transfer_learning_on_cifar10.html)
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- [Question answering example](http://docs.djl.ai/docs/demos/jupyter/BERTQA.html)
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## [Jupyter notebook tutorials](https://docs.djl.ai/master/docs/demos/jupyter/index.html)
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- **[Beginner Jupyter Tutorial](https://docs.djl.ai/master/docs/demos/jupyter/tutorial/index.html)**
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- [Run object detection with model zoo](https://docs.djl.ai/master/docs/demos/jupyter/object_detection_with_model_zoo.html)
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- [Load pre-trained PyTorch model](https://docs.djl.ai/master/docs/demos/jupyter/load_pytorch_model.html)
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- [Load pre-trained Apache MXNet model](https://docs.djl.ai/master/docs/demos/jupyter/load_mxnet_model.html)
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- [Transfer learning example](https://docs.djl.ai/master/docs/demos/jupyter/transfer_learning_on_cifar10.html)
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- [Question answering example](https://docs.djl.ai/master/docs/demos/jupyter/BERTQA.html)
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## [API Examples](../examples/README.md)
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‎docs/cv_utils.md

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```
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## Useful Information
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If you want to learn more about loading models, click [here](http://docs.djl.ai/docs/load_model.html).
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If you want to learn more about loading models, click [here](https://docs.djl.ai/master/docs/load_model.html).
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If you want to learn more about the model zoo, click [here](http://docs.djl.ai/docs/model-zoo.html).
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If you want to learn more about the model zoo, click [here](https://docs.djl.ai/master/docs/model-zoo.html).

‎docs/development/inference_performance_optimization.md

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Therefore, we recommend creating a new [Predictor](https://javadoc.io/doc/ai.djl/api/latest/ai/djl/inference/Predictor.html) for each thread.
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That Predictor should be reused if the thread does multiple predictions.
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Alternatively, you can also use a pool of Predictors or you can leverage the [DJL Serving WorkLoadManager](http://docs.djl.ai/docs/serving/wlm/index.html).
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Alternatively, you can also use a pool of Predictors or you can leverage the [DJL Serving WorkLoadManager](https://docs.djl.ai/master/docs/serving/wlm/index.html).
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For a reference implementation, see [Multi-threaded Benchmark](https://github.com/deepjavalibrary/djl-serving/blob/master/benchmark/src/main/java/ai/djl/benchmark/MultithreadedBenchmark.java).
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In addition, you may need to set engine-specific configurations as well.
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Engine-specific details are given below.
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You can also reference the [list of all DJL system configurations](http://docs.djl.ai/docs/serving/serving/docs/configurations.html).
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You can also reference the [list of all DJL system configurations](https://docs.djl.ai/master/docs/serving/serving/docs/configurations.html).
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### Apache MXNet
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‎docs/development/troubleshooting.md

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nsorflow/cache/2.3.1-cu101-linux-x86_64/libtensorflow.so.2)
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```
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Please upgrade gcc version according to the steps on [TensorFlow install page](https://docs.djl.ai/engines/tensorflow/index.html#note)
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Please upgrade gcc version according to the steps on [TensorFlow install page](https://docs.djl.ai/master/engines/tensorflow/index.html#note)
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## 9. X11 error when running object detection notebooks on EC2 instances.
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‎docs/interactive_tool.md

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Finally, you can get the running project setup by clicking `Get Template`. This will bring you a gradle project that can be used in your local machine.
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## [Java Jupyter Notebook](http://docs.djl.ai/docs/demos/jupyter/index.html)
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## [Java Jupyter Notebook](https://docs.djl.ai/master/docs/demos/jupyter/index.html)
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Wait a second, are we talking about hosting Jupyter Notebook in python?
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No, it’s Java 11, only.
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![jupyter](https://djl-ai.s3.amazonaws.com/web-data/images/jupyter.gif)
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Inspired by Spencer Park’s [IJava project](https://github.com/SpencerPark/IJava), we integrated DJL with Jupyter Notebooks.
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For more information on the simple setup, follow the instructions in [DJL Jupyter notebooks](http://docs.djl.ai/docs/demos/jupyter/index.html#setup).
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For more information on the simple setup, follow the instructions in [DJL Jupyter notebooks](https://docs.djl.ai/master/docs/demos/jupyter/index.html#setup).
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After that, use the Jupyter Notebook freely in your hosted server. You can do all kinds of work, like block building and plotting a graph.
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There are [tutorials and instructions](http://docs.djl.ai/docs/demos/jupyter/index.html#djl---jupyter-notebooks) to guide you how you can run training and/or inference with Java.
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There are [tutorials and instructions](https://docs.djl.ai/master/docs/demos/jupyter/index.html#djl---jupyter-notebooks) to guide you how you can run training and/or inference with Java.

‎docs/pytorch/pytorch-djl-ndarray-cheatsheet.md

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In the following examples, we assume
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- `manager` is an instance of `ai.djl.ndarray.NDManager`
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- it is recommended to look through https://docs.djl.ai/docs/development/memory_management.html
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- it is recommended to look through https://docs.djl.ai/master/docs/development/memory_management.html
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in advance so that you can get better insight of `NDManager`
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- `$tensor` is a placeholder for an instance of `torch.tensor`
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- `$ndarray` is a placeholder for an instance of `ai.djl.ndarray.NDArray`

‎docs/quick_start.md

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# Quick start
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Deep Java Library (DJL) is designed to be easy to get started with and simple to use.
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The easiest way to learn DJL is to read the [beginner tutorial](http://docs.djl.ai/docs/demos/jupyter/tutorial/README.md) or
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The easiest way to learn DJL is to read the [beginner tutorial](https://docs.djl.ai/master/docs/demos/jupyter/tutorial/README.md) or
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our [examples](../examples/README.md).
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You can also view our 1.5 hour long (in 8 x ~10 minute segments) DJL 101 tutorial video series:
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## Beginner tutorial
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To get started, we recommend that you follow our short [beginner tutorial](http://docs.djl.ai/docs/demos/jupyter/tutorial/index.html). It takes you through some of the basics of deep learning to create a model, train your model, and run inference using your trained model.
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To get started, we recommend that you follow our short [beginner tutorial](https://docs.djl.ai/master/docs/demos/jupyter/tutorial/index.html). It takes you through some of the basics of deep learning to create a model, train your model, and run inference using your trained model.
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## Run examples
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- [Train your first model](../examples/docs/train_mnist_mlp.md)
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- [Single-shot Object Detection inference example](../examples/docs/object_detection.md)
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- [More examples](https://github.com/deepjavalibrary/djl/tree/master/examples)
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- [Jupyter examples](http://docs.djl.ai/docs/demos/jupyter/index.html)
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- [Jupyter examples](https://docs.djl.ai/master/docs/demos/jupyter/index.html)
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## Other resources
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‎docs/tensorflow/how_to_import_tensorflow_models_in_DJL.md

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## How to load DJL TensorFlow model zoo models
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The steps are the same as loading any other DJL model zoo models, you can use the `Criteria` API as documented [here](https://docs.djl.ai/docs/load_model.html#criteria-class).
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The steps are the same as loading any other DJL model zoo models, you can use the `Criteria` API as documented [here](https://docs.djl.ai/master/docs/load_model.html#criteria-class).
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Note for TensorFlow image classification models, you need to manually specify the translator instead of using the built-in one because
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TensorFlow requires channels last ("NHWC") image formats while DJL use channels first ("NCHW") image formats. By default, DJL will add

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