|
|
@@ -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
|
Chris Waterson