Update spark-deep-learning to depend on Tensorframes 0.2.9 (#47)

* Update DLP to use new RC of tensorframes (v0.2.9-rc0)

* Update tensorflow dependency from 1.1 -> 1.3, add pandas dependency

* Update test_tf_consistency to only compare graph nodes (exclude 'version', 'library' fields from comparison)

* Add cython dependency

* Move cython up in requirements file, hopefully pip installs it first?

* fix typo

* Update pandas to version that has cython dependency

* Remove cython dependency

* Update numpy version from 1.11.2 --> 1.13.1

* Update python dependencies to minimum version requirements instead
of exact requirements

* Add upper version bound on pillow

* Temporarily set keras version to 2.0.4 to see if build succeeds

* Debugging, do not merge

* Try tests with keras >= 2.0.4

* Fix keras version to 2.0.4, add explanatory comment

* Update tensorframes dep to 0.2.9-rc1

* Remove print statements

* Update tensorframes version to 0.2.9-rc1

* Upgrade to tensorframes RC2

* Update to tensorframes 0.2.9-rc3

* Bump tensorframes dependency to 0.2.9

Author: smurching

build.sbt

@@ -34,8 +34,7 @@ sparkComponents ++= Seq("mllib-local", "mllib", "sql")
 
 // add any Spark Package dependencies using spDependencies.
 // e.g. spDependencies += "databricks/spark-avro:0.1"
-spDependencies += s"databricks/tensorframes:0.2.9-rc3-s_${scalaMajorVersion}"
-
+spDependencies += s"databricks/tensorframes:0.2.9-s_${scalaMajorVersion}"
 
 // These versions are ancient, but they cross-compile around scala 2.10 and 2.11.
 // Update them when dropping support for scala 2.10

python/spark-package-deps.txt

@@ -1,3 +1,3 @@
 # This file should list any spark package dependencies as:
 # :package_name==:version   e.g. databricks/spark-csv==0.1
-databricks/tensorframes==0.2.9-rc3
+databricks/tensorframes==0.2.9