Awesome

TensorFlow ZMQ Op

A convenient way to receive data from other processes. This small library can:

• Send a list of numpy arrays from python; serialization is written in C++ for efficiency.
  • One copy in merging all the buffers; One copy in pybind11 overhead (TODO); One copy in ZMQ send.
• Receive a list of tensors from tensorflow;
  • One copy in ZMQ recv; One copy to split the buffer into tensors.
  • The op is stateful and safe to be evaluated multiple times in one sess.run call.
• Serialization is in a custom protocol for efficiency;

Why:

Sometimes for complicated large-scale tasks you would really want data processing to be separate from TensorFlow. However in TensorFlow there is no good way to receive data from other processes.

Build:

Require gcc>=5.3, tensorflow>=1.4, zeromq>=4.

Require the zmq.hpp header from cppzmq at your compiler's include path, or under the src directory.

Add /path/to/git/clone/zmq_ops to PYTHONPATH to be able to import it. Or use pip install . to install it.

Ops will be compiled the first time it gets imported. Note that it usually requires recompilation after a TensorFlow reinstallation.

Use:

See benchmark.py for usage.

On my machine this script can achieve about 1.3GB/s throughput. Equivalent to about 2.3k float32 (or 9.2k uint8) imagenet images per second.