From 4cdfc96038e5b82a3e847c23f006a10d290a0655 Mon Sep 17 00:00:00 2001
From: Vadim Markovtsev <gmarkhor@gmail.com>
Date: Tue, 28 Feb 2017 15:46:37 +0100
Subject: [PATCH] Simplify and improve fastprep.mk

There is no need in building Tensorflow from scratch, fastprep successfully links with the Python wrapper from pip and system protobuf v3. Tested this on Ubuntu.
---
 swivel/fastprep.mk | 79 +++++++++++++++-------------------------------
 1 file changed, 26 insertions(+), 53 deletions(-)

diff --git a/swivel/fastprep.mk b/swivel/fastprep.mk
index bbc4a1736a7..b1798d0b68a 100644
--- a/swivel/fastprep.mk
+++ b/swivel/fastprep.mk
@@ -16,50 +16,16 @@
 # limitations under the License.
 
 
-
 # This makefile builds "fastprep", a faster version of prep.py that can be used
-# to build training data for Swivel.  Building "fastprep" is a bit more
-# involved: you'll need to pull and build the Tensorflow source, and then build
-# and install compatible protobuf software.  We've tested this with Tensorflow
-# version 0.7.
-#
-# = Step 1. Pull and Build Tensorflow. =
-#
-# These instructions are somewhat abridged; for pre-requisites and the most
-# up-to-date instructions, refer to:
-#
-#   <https://www.tensorflow.org/versions/r0.7/get_started/os_setup.html#installing-from-sources>
-#
-# To build the Tensorflow components required for "fastpret", you'll need to
-# install Bazel, Numpy, Swig, and Python development headers as described in at
-# the above URL.  Run the "configure" script as appropriate for your
-# environment and then build the "build_pip_package" target:
-#
-#   bazel build -c opt [--config=cuda] //tensorflow/tools/pip_package:build_pip_package
-#
-# This will generate the Tensorflow headers and libraries necessary for
-# "fastprep".
-#
+# to build training data for Swivel.
 #
-# = Step 2. Build and Install Compatible Protobuf Libraries =
+# = Step 1. Install protobuf v3 =
 #
-# "fastprep" also needs compatible protocol buffer libraries, which you can
-# build from the protobuf implementation included with the Tensorflow
-# distribution:
+#   Ubuntu 16.10+: sudo apt install libprotobuf-dev
+#   Ubuntu 16.04: https://launchpad.net/~maarten-fonville/+archive/ubuntu/ppa + replace xenial with yakkety in /etc/apt/sources.list.d/maarten-fonville-ubuntu-ppa-xenial.list
+#   macOS: brew install protobuf
 #
-#   cd ${TENSORFLOW_SRCDIR}/google/protobuf
-#   ./autogen.sh
-#   ./configure --prefix=${HOME}  # ...or whatever
-#   make
-#   make install  # ...or maybe "sudo make install"
-#
-# This will install the headers and libraries appropriately.
-#
-#
-# = Step 3. Build "fastprep". =
-#
-# Finally modify this file (if necessary) to update PB_DIR and TF_DIR to refer
-# to appropriate locations, and:
+# = Step 2. Build "fastprep". =
 #
 #   make -f fastprep.mk
 #
@@ -68,20 +34,27 @@
 # matrices and other files necessary to train a Swivel matrix.
 
 
-# The root directory where the Google Protobuf software is installed.
-# Alternative locations might be "/usr" or "/usr/local".
-PB_DIR=$(HOME)
+CXXFLAGS=-std=c++11 -march=native -g -O2 -flto -Wall -I.
+LDLIBS=-lprotobuf -pthread -lm
+
+FETCHER=curl -L -o
+TF_URL=https://github.com/tensorflow/tensorflow/raw/master
+PROTOC=protoc
+
+
+%.proto: tensorflow/core/example
+	$(FETCHER) $@ $(TF_URL)/$@
 
-# Assuming you've got the Tensorflow source unpacked and built in ${HOME}/src:
-TF_DIR=$(HOME)/src/tensorflow
+%.pb.cc: %.proto
+	$(PROTOC) --cpp_out=. $<
 
-PB_INCLUDE=$(PB_DIR)/include
-TF_INCLUDE=$(TF_DIR)/bazel-genfiles
-CXXFLAGS=-std=c++11 -m64 -mavx -g -Ofast -Wall -I$(TF_INCLUDE) -I$(PB_INCLUDE)
+fastprep: fastprep.cc tensorflow/core/example/feature.pb.cc tensorflow/core/example/example.pb.cc
 
-PB_LIBDIR=$(PB_DIR)/lib
-TF_LIBDIR=$(TF_DIR)/bazel-bin/tensorflow/core
-LDFLAGS=-L$(TF_LIBDIR) -L$(PB_LIBDIR)
-LDLIBS=-lprotos_all_cc -lprotobuf -lpthread -lm
+tensorflow/core/example:
+	@mkdir -p tensorflow/core/example
 
-fastprep: fastprep.cc
+clean:
+	@rm -f fastprep
+	
+mrproper: clean
+	@rm -rf tensorflow