This repository contains the code that produces the numeric section in On the Use of TensorFlow Computation Graphs in combination with Distributed Optimization to Solve Large-Scale Convex Problems
=== Dependencies ===
Python 3.5
mpi4py (Version == 3.0)(Windows system in this example):
The mpi4py documentation and installation instructions can be found at:
TensorFlow-gpu (Windows version in this example):
The TensorFlow-gpu documentation and installation instructions can be found at:
https://www.tensorflow.org/install/install_windows
Some other libraries/packages needed are: NumPy, scipy
=== How to generate multiple processes on a single (multi-core/cpu) host ===
Run it with
mpirun -np N ./some-program
where the number after "-np " is the numer of parallel MPI processes to be started.
=== Set up VM (Virtual Machine) instance on Google Cloud Engine ===
Step 1: Visit Google Cloud Platform and click Compute Engine with a Google account;
Step 2: Click CREATE INSTANCE button (every new user will have $ 300 free trial for one year at present);
Step 3: Customize what kind of machine you need (Name, Machine Zone, #cores/cpus, memory, gpus, Boot disk (Linux, Centos, Windows) and storage size);
Step 4: Connect to your VM instance via SSH/RDP with your dynamic External IP, User ID, password;
Step 5: Set up the libraries/packages/applications you need;
Step 5: Test your code in prompt command window;
PS: You can edit your instance size (#cpu/memory/gpu) whnever you need