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if: ${{ github.event_name == 'push' && (github.ref == 'refs/heads/master' || github.ref == 'refs/heads/staging') }} # DEPLOY_SNAPSHOT (releases should be signed and deployed manually from local machine)
Copy file name to clipboardExpand all lines: CONTRIBUTING.md
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@@ -82,7 +82,7 @@ To upgrade the version of TensorFlow that is embedded within TensorFlow Java, pl
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3. Update `urls`, `sha256` and `strip_prefix` fields of the `org_tensorflow` archive rule in Bazel [workspace](https://github.com/tensorflow/java/blob/master/tensorflow-core/tensorflow-core-native/WORKSPACE#L19)
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4. Extract the archive in a temporary folder
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5. Copy the content of `tensorflow-x.x.x/.bazelrc` file to `tensorflow-core/tensorflow-core-native/tensorflow.bazelrc` under TensorFlow Java source tree
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6. Copy the content of `tensorflow-x.x.x/WORKSPACE` after the "###### Copy content of..." notice if`tensorflow-core/tensorflow-core-native/WORKSPACE`, read notice for more details
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6. Copy the content of `tensorflow-x.x.x/WORKSPACE` after the "###### Copy content of..." notice to`tensorflow-core/tensorflow-core-native/WORKSPACE`, read notice for more details
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7. Copy the content of `tensorflow-x.x.x/.bazelversion` file to `tensorflow-core/tensorflow-core-native/.bazelversion`
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8. Validate that options in `tensorflow-core/tensorflow-core-native/.bazelrc` are still accurate or update them accordingly
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9. Update URLs of existing TensorFlow binaries in the `tensorflow-core/tensorflow-core-native/scripts/dist_download` script
Copy file name to clipboardExpand all lines: README.md
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@@ -59,22 +59,24 @@ only binaries for the followings are being **supported and distributed** by this
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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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-`linux-arm64`: Linux platforms on Arm chips
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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/AMD chips
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Binaries for `macosx-x86_64` are available for TF-Java 1.0 series releases and earlier, they were dropped from
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TF-Java 1.1 and newer as they are no longer supported or released by Google.
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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>1.0.0-rc.2</version>
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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-rc.2</version>
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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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<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-rc.2</version>
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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-rc.2</version>
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<version>1.0.0</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>1.0.0-rc.2</version>
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<version>1.0.0</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>1.0.0-rc.2</version>
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<version>1.0.0</version>
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<classifier>windows-x86_64</classifier>
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</dependency>
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```
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In some cases, it might be preferable to add a single dependency that includes transitively all the artifacts
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required to run TensorFlow Java on any [supported platforms](README.md#individual-dependencies)
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-`tensorflow-core-platform`: Includes `tensorflow-core-api`, plus native artifacts for `linux-x86_64`, `linux-x86_64-arm64`, `macosx-arm64`, `macosx-x86_64` and `windows-x86_64`
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-`tensorflow-core-platform`: Includes `tensorflow-core-api`, plus native artifacts for `linux-x86_64`, `linux-x86_64-arm64`, `macosx-arm64` and `windows-x86_64`
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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>1.0.0-rc.2</version>
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<version>1.0.0</version>
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</dependency>
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```
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@@ -157,7 +159,7 @@ to add Sonatype OSS repository in your pom.xml, like the following
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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>1.0.0-SNAPSHOT</version>
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<version>1.1.0-SNAPSHOT</version>
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</dependency>
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</dependencies>
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```
@@ -179,7 +181,8 @@ This table shows the mapping between TensorFlow, TensorFlow Java and minimum sup
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