SKIL 1.0.3 uses custom JVM bindings of the TensorFlow C++ API (version 1.4) and imposes no limitations. Future versions of SKIL will use
SameDiff functionality within ND4J to execute imported models.
See the Import Models page.
lookup_embedding function is used, then SKIL should be able to import the model into the model server.
In order to deploy a TensorFlow model the graph and its associated weights have to be stored in a single pb file. TensorFlow models can be "frozen" with the
freeze_graph.py script available under
freeze_graph can be run on the command line or within a Python script. It takes a graph definition and a set of checkpoints and freezes them in a single file that can then be deployed with a few clicks in SKIL.
~/tensorflow/bazel-bin/tensorflow/python/tools/freeze_graph \ --input_graph=graph_definition.pbtxt \ --output_graph=file_for_frozen_model.pb \ --input_checkpoint=saved_ckpt \ --output_nodes_names=output_node_name