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Merge doc changes from r1.0
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‎MIGRATING.md

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TensorFlow Java is still in an alpha stage, therefore is subject to contain breaking changes between the different releases. This guide explain in detail
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how to migrate your code from a previous version to a new one that includes some changes that are not backward compatible.
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## Migrating to 1.0.0
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TensorFlow-Java 1.0.0 requires Java 11 or later.
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### Native Artifact Renaming
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The native artifacts, that used to be distributed as `tensorflow-core-api`, are now distributed under `tensorflow-core-native`. If you still add
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`tensorflow-core-platform` in your project, that won't affect you. But if you were adding dependencies to specific native runtimes, you need to update
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them to reflect the new artifact name.
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For example,
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```xml
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<dependency>
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<groupId>org.tensorflow</groupId>
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<artifactId>tensorflow-core-api</artifactId>
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<version>0.5.0</version>
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</dependency>
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<dependency>
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<groupId>org.tensorflow</groupId>
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<artifactId>tensorflow-core-api</artifactId>
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<version>0.5.0</version>
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<classifier>linux-x86_64</classifier>
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</dependency>
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```
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will now be
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```xml
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<dependency>
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<groupId>org.tensorflow</groupId>
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<artifactId>tensorflow-core-api</artifactId>
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<version>1.0.0</version>
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</dependency>
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<dependency>
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<groupId>org.tensorflow</groupId>
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<artifactId>tensorflow-core-native</artifactId>
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<version>1.0.0</version>
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<classifier>linux-x86_64</classifier>
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</dependency>
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```
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### Session Run Result
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In versions before 0.4.0 `Session.Runner.run` and `TensorFunction.call` returned a `List<Tensor>`. In newer versions
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they return a `Result` class which is `AutoCloseable` to make management of the tensor lifetime simpler. To migrate
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users should wrap the `run` invocation in a try-with-resources statement rather than closing the output tensors
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individually.
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### Proto Definitions Moved
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Some proto definitions under `org.tensorflow.proto` have been moved to a different location under the same package. You will need to reimport these
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proto bindings to match the new location. Your IDE should easily be able to do this for you.
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## Migrating to 0.3.0
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### Non-parameterized Typed Tensors

‎README.md

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While TensorFlow Java can be compiled for [multiple platforms](https://github.com/tensorflow/java/blob/master/tensorflow-core/pom.xml#L54),
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only binaries for the followings are being **supported and distributed** by this project:
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- `linux-x86_64`: Linux platforms on Intel chips
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- `linux-x86_64-gpu`: Linux platforms on Intel chips with Cuda GPU support
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- `macosx-x86_64`: MacOS X platforms on Intel chips
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- `linux-x86_64`: Linux platforms on Intel/AMD chips
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- `linux-x86_64-gpu`: Linux platforms on Intel/AMD chips with Cuda GPU support
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- `macosx-x86_64`: MacOS X platforms on Intel/AMD chips
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- `macosx-arm64`: MacOS X platforms on Apple Silicon chips
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- `windows-x86_64`: Windows platforms on Intel chips
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- `windows-x86_64`: Windows platforms on Intel/AMD chips
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For example, for building a JAR that uses TensorFlow and is targeted to be deployed only on Linux
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systems with no GPU support, you should add the following dependencies:
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```xml
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<dependency>
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<groupId>org.tensorflow</groupId>
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<artifactId>tensorflow-core-api</artifactId>
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<version>0.5.0</version>
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<version>1.0.0-rc.1</version>
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</dependency>
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<dependency>
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<groupId>org.tensorflow</groupId>
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<artifactId>tensorflow-core-native</artifactId>
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<version>0.5.0</version>
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<version>1.0.0-rc.1</version>
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<classifier>linux-x86_64</classifier>
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</dependency>
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```
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<dependency>
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<groupId>org.tensorflow</groupId>
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<artifactId>tensorflow-core-api</artifactId>
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<version>0.5.0</version>
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<version>1.0.0-rc.1</version>
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</dependency>
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<dependency>
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<groupId>org.tensorflow</groupId>
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<artifactId>tensorflow-core-native</artifactId>
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<version>0.5.0</version>
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<version>1.0.0-rc.1</version>
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<classifier>linux-x86_64-gpu</classifier>
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</dependency>
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<dependency>
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<groupId>org.tensorflow</groupId>
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<artifactId>tensorflow-core-native</artifactId>
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<version>0.5.0</version>
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<version>1.0.0-rc.1</version>
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<classifier>macosx-arm64</classifier>
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</dependency>
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<dependency>
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<groupId>org.tensorflow</groupId>
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<artifactId>tensorflow-core-native</artifactId>
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<version>0.5.0</version>
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<version>1.0.0-rc.1</version>
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<classifier>windows-x86_64</classifier>
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</dependency>
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```
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- `tensorflow-core-platform`: Includes `tensorflow-core-api`, plus native artifacts for `linux-x86_64`, `macosx-arm64`, `macosx-x86_64` and `windows-x86_64`
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For example, to run TensorFlow Java on any platform for which a binary is being distributed by this project, you can
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For example, to run TensorFlow Java on any CPU platform for which a binary is being distributed by this project, you can
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simply add this dependency to your application:
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```xml
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<dependency>
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<groupId>org.tensorflow</groupId>
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<artifactId>tensorflow-core-platform</artifactId>
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<version>0.5.0</version>
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<version>1.0.0-rc.1</version>
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</dependency>
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```
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| 0.4.1 | 2.7.1 | 8 |
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| 0.4.2 | 2.7.4 | 8 |
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| 0.5.0 | 2.10.1 | 11 |
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| 0.6.0-SNAPSHOT | 2.10.1 | 11 |
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| 1.0.0-rc.1 | 2.16.1 | 11 |
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| 1.0.0-SNAPSHOT | 2.16.1 | 11 |
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## How to Contribute?

‎docs/install.md

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## Versions
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TensorFlow Java has its own release cycle, independent from the
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TensorFlow Java has its own release cycle, independent of the
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[TensorFlow runtime](https://github.com/tensorflow/tensorflow). Consequently,
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its version does not match the version of TensorFlow runtime it runs on. Consult
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the TensorFlow Java
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<dependency>
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<groupId>org.tensorflow</groupId>
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<artifactId>tensorflow-core-platform</artifactId>
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<version>0.5.0</version>
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<version>1.0.0-rc.1</version>
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</dependency>
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```
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}
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dependencies {
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compile group: 'org.tensorflow', name: 'tensorflow-core-platform', version: '0.5.0'
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compile group: 'org.tensorflow', name: 'tensorflow-core-platform', version: '1.0.0-rc.1'
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}
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```
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<dependency>
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<groupId>org.tensorflow</groupId>
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<artifactId>tensorflow-core-platform</artifactId>
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<version>0.5.0</version>
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<version>1.0.0-rc.1</version>
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</dependency>
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</dependencies>
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</project>

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