Multithreading

Tensorflow.NET's is thread-safe, our multithreading model is thread-wide Session and Graph; meaning tf.get_default_graph/session() are unique to the thread they are executed in.

We chose this model because a dominant portion of our api does not accept Graph as an argument, instead it accesses tf.get_default_graph() to initialize an Operation in it.
This allows cleaner and similar code to Python and still having complete isolation between threads.

Capabilities

• Initialize sessions and graphs.
• Call your_session.run(...) parallely and in separate threads regardless to the defaults in the current thread.

Limitations

• When writing a model, it has to be done in the same thread unless yourgraph.as_default() is called in a different thread.
  For example : You can't start a model and then continue it in a Task unless you call graph.as_default() and session.as_default() before doing so.
• Tensorflow's c_api for the most part is thread-safe, calls of status.Check() sometimes to be inside a process-wide lock, for example:

  lock (Locks.ProcessWide) { var status = new Status(); c_api.someapicall(); status.Check(true); }