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TensorFlow for Java using Maven

The TensorFlow Java API is available on Maven Central and JCenter through artifacts uploaded to OSS Sonatype and Bintray respectively. This document describes the process of updating the release artifacts. It does not describe how to use the artifacts, for which the reader is referred to the TensorFlow for Java installation instructions.

Background

TensorFlow source (which is primarily in C++) is built using bazel and not maven. The Java API wraps over this native code and thus depends on platform (OS, architecture) specific native code.

Hence, the process for building and uploading release artifacts is not a single mvn deploy command.

Artifact Structure

There are seven artifacts and thus pom.xmls involved in this release:

  1. tensorflow: The single dependency for projects requiring TensorFlow for Java. This convenience package depends on libtensorflow and libtensorflow_jni. Typically, this is the single dependency that should be used by client programs (unless GPU support is required).

  2. libtensorflow: Java-only code for the TensorFlow Java API. The .jar itself has no native code, but requires the native code be either already installed on the system or made available through libtensorflow_jni.

  3. libtensorflow_jni: The native libraries required by libtensorflow. Native code for all supported platforms is packaged into a single .jar.

  4. libtensorflow_jni_gpu: The native libraries required by libtensorflow with GPU (CUDA) support enabled. Programs requiring GPU-enabled TensorFlow should add a dependency on libtensorflow and libtensorflow_jni_gpu. As of January 2018, this artifact is Linux only.

  5. proto: Generated Java code for TensorFlow protocol buffers (e.g., MetaGraphDef, ConfigProto etc.)

  6. tensorflow-android: A package geared towards supporting TensorFlow on Android, and is a self-contained Android AAR library containing all necessary native and Java code.

  7. parentpom: Common settings shared by all of the above.

  8. hadoop: The TensorFlow TFRecord InputFormat/OutputFormat for Apache Hadoop. The source code for this package is available in the TensorFlow Ecosystem

  9. spark-connector: A Scala library for loading and storing TensorFlow TFRecord using Apache Spark DataFrames. The source code for this package is available in the TensorFlow Ecosystem

Updating the release

The Maven artifacts are created from files built as part of the TensorFlow release process (which uses bazel). The author's lack of familiarity with Maven best practices combined with the use of a different build system means that this process is possibly not ideal, but it's what we've got. Suggestions are welcome.

In order to isolate the environment used for building, all release processes are conducted in a Docker container.

Pre-requisites

  • docker
  • An account at oss.sonatype.org, that has permissions to update artifacts in the org.tensorflow group. If your account does not have permissions, then you'll need to ask someone who does to file a ticket to add to the permissions (sample ticket).
  • An account at bintray.com that has permissions to update the tensorflow repository. If your account does not have permissions, then you'll need to ask one of the organization administrators to give you permissions to update the tensorflow repository. Please keep the repository option to "GPG sign uploaded files using Bintray's public/private key pair" unchecked, otherwise it will conflict with locally signed artifacts.
  • A GPG signing key, required to sign the release artifacts.

Deploying to Sonatype and Bintray

  1. Create a file with your OSSRH credentials and Bintray API key (or perhaps you use mvn and have it in ~/.m2/settings.xml):

    SONATYPE_USERNAME="your_sonatype.org_username_here"
SONATYPE_PASSWORD="your_sonatype.org_password_here"
BINTRAY_USERNAME="your_bintray_username_here"
BINTRAY_API_KEY="your_bintray_api_key_here"
GPG_PASSPHRASE="your_gpg_passphrase_here"
cat >/tmp/settings.xml <<EOF
<settings>
  <servers>
    <server>
      <id>ossrh</id>
      <username>${SONATYPE_USERNAME}</username>
      <password>${SONATYPE_PASSWORD}</password>
    </server>
    <server>
      <id>bintray</id>
      <username>${BINTRAY_USERNAME}</username>
      <password>${BINTRAY_API_KEY}</password>
    </server>
  </servers>
  <properties>
    <gpg.executable>gpg2</gpg.executable>
    <gpg.passphrase>${GPG_PASSPHRASE}</gpg.passphrase>
  </properties>
</settings>
EOF

  2. Run the release.sh script.

  3. If the script above succeeds then the artifacts would have been uploaded to the private staging repository in Sonatype, and as unpublished artifacts in Bintray. After verifying the release, you should finalize or abort the release on both sites.

  4. Visit https://oss.sonatype.org/#stagingRepositories, find the org.tensorflow release and click on either Release to finalize the release, or Drop to abort.

  5. Visit https://bintray.com/google/tensorflow/tensorflow, and select the version you just uploaded. Notice there's a message about unpublished artifacts. Click on either Publish to finalize the release, or Discard to abort.

  6. Some things of note:

  7. Upon successful release, commit changes to all the pom.xml files (which should have the updated version number).

Skip deploying to a repository

Should you need, setting environment variables DEPLOY_OSSRH=0 or DEPLOY_BINTRAY=0 when calling release.sh will skip deploying to OSSRH or Bintray respectively. Note that snapshots are only uploaded to OSSRH, so you cannot skip deploying to OSSRH for a -SNAPSHOT version.

The overall flow

This section provides some pointers around how artifacts are currently assembled.

All native and java code is first built and tested by the release process which run various scripts under the tools/ci_build directory. Of particular interest may be tools/ci_build/builds/libtensorflow.sh which bundles Java-related build sources and outputs into archives, and tools/ci_build/builds/android_full.sh which produces an Android AAR package.

Maven artifacts however are not created in Jenkins. Instead, artifacts are created and deployed externally on-demand, when a maintainer runs the release.sh script.

This script spins up a Docker instance which downloads the archives created by successful runs of various tools/ci_build scripts on the Tensorflow Jenkins server.

It organizes these archives locally into a maven-friendly layout, and runs mvn deploy to create maven artifacts within the container. Native libraries built in Jenkins are used as-is, but srcjars for java code are used to compile class files and generate javadocs.) It also downloads the Android AAR from the Jenkins server and directly deploys it via mvn gpg:sign-and-deploy-file.

release.sh then stages these artifacts to OSSRH and Bintray, and if all goes well a maintainer can log into both sites to promote them as a new release.

There is a small change to the flow for a standard (rather than a -SNAPSHOT) release. Rather than downloading archives directly from jobs on the Jenkins server, the script uses a static repository of QA-blessed archives.

References