Raspberry Pi 3 - TensorFlow v0.8.0 RC0

Updates to this Project

• Latest binaries are now available in the bin directory
• Old binaries will now be kept in the archive directory
• Figured out how to build the latest version of Bazel natively on RPi3- the Bazel portion of GUIDE.md has been updated
• We should be able to release new binaries to coincide with the official release now that we have Bazel version>0.2 running on RPi3.
  • In the future, the goal will be to release within a week of the official release binaries.
  • Future binaries will always be built at the exact commit of the official release. You probably didn't notice, but the 0.7.1 release was built at HEAD somewhere in the middle :)

TensorFlow Updates

See the official pre-release notes for details on latest supported features, updates, and changes to TensorFlow itself.

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Notable quirks:

tf.train.ClusterSpec throws AttributeError when used as a parameter to tf.train.Server

For whatever reason, it seems that the internal _cluster_spec parameter wants to hide itself when used as an input to the constructor of tf.train.Server. Hopefully we'll be able to figure out what the deal is, but in the meantime, it is possible to start distributed servers by just passing in the ClusterSpec dictionary directly. For example, instead of declaring a server like this:

cluster = tf.train.ClusterSpec({"local": ["localhost:2222", "localhost:2223"]})
server = tf.train.Server(cluster, job_name="local", task_index=0)

You can declare it directly like this:

server = tf.train.Server({"local": ["localhost:2222", "localhost:2223"]}, job_name="local", task_index=0)