Hi, yes I already have that in my Makefile.configure.
Just to be sure, did a “make clean” and rebuilt Caffe but the same problem persists. I am using Caffe 0.16 by the way, (0.15 did not build correctly) so I hope this is not an issue. As I say, I have had no problem doing classification in the previous jetson-inference examples.
When I start DIGITS server I get this message so I hope this is not a cause of the problem:
“Couldn’t import dot_parser, loading of dot files will not be possible.”
I just did a make clean on caffe and rebuilt pycafe etc.
Still get the same error.
The only reason I can see for this error is because I am using Caffe 0.16 instead of the recommended 0.15 however 0.15 won’t even build so I have no choice.
At the bottom of this message is my Caffe makefile.config in the hope it is of some help.
Ultimately, detecting where objects are in an image is not as important as classifying them in my scenario so not the end of the world if we can’t get this working.
## Refer to http://caffe.berkeleyvision.org/installation.html
# Contributions simplifying and improving our build system are welcome!
# cuDNN acceleration switch (uncomment to build with cuDNN).
# cuDNN version 6 or higher is required.
USE_CUDNN := 1
# NCCL acceleration switch (uncomment to build with NCCL)
# See https://github.com/NVIDIA/nccl
# USE_NCCL := 1
# Builds tests with 16 bit float support in addition to 32 and 64 bit.
# TEST_FP16 := 1
# uncomment to disable IO dependencies and corresponding data layers
# USE_OPENCV := 0
# USE_LEVELDB := 0
# USE_LMDB := 0
# Uncomment if you're using OpenCV 3
OPENCV_VERSION := 3
# To customize your choice of compiler, uncomment and set the following.
# N.B. the default for Linux is g++ and the default for OSX is clang++
# CUSTOM_CXX := g++
# CUDA directory contains bin/ and lib/ directories that we need.
CUDA_DIR := /usr/local/cuda
# On Ubuntu 14.04, if cuda tools are installed via
# "sudo apt-get install nvidia-cuda-toolkit" then use this instead:
# CUDA_DIR := /usr
# CUDA architecture setting: going with all of them.
CUDA_ARCH := -gencode arch=compute_50,code=sm_50 \
-gencode arch=compute_52,code=sm_52 \
-gencode arch=compute_60,code=sm_60 \
-gencode arch=compute_61,code=sm_61 \
-gencode arch=compute_61,code=compute_61
# BLAS choice:
# atlas for ATLAS
# mkl for MKL
# open for OpenBlas - default, see https://github.com/xianyi/OpenBLAS
BLAS := open
# Custom (MKL/ATLAS/OpenBLAS) include and lib directories.
BLAS_INCLUDE := /opt/OpenBLAS/include/
BLAS_LIB := /opt/OpenBLAS/lib/
# Homebrew puts openblas in a directory that is not on the standard search path
# BLAS_INCLUDE := $(shell brew --prefix openblas)/include
# BLAS_LIB := $(shell brew --prefix openblas)/lib
# This is required only if you will compile the matlab interface.
# MATLAB directory should contain the mex binary in /bin.
# MATLAB_DIR := /usr/local
# MATLAB_DIR := /Applications/MATLAB_R2012b.app
# NOTE: this is required only if you will compile the python interface.
# We need to be able to find Python.h and numpy/arrayobject.h.
PYTHON_INCLUDE := /usr/include/python2.7 \
/usr/lib/python2.7/dist-packages/numpy/core/include
# Anaconda Python distribution is quite popular. Include path:
# Verify anaconda location, sometimes it's in root.
# ANACONDA_HOME := $(HOME)/anaconda
# PYTHON_INCLUDE := $(ANACONDA_HOME)/include \
# $(ANACONDA_HOME)/include/python2.7 \
# $(ANACONDA_HOME)/lib/python2.7/site-packages/numpy/core/include \
# Uncomment to use Python 3 (default is Python 2)
# PYTHON_LIBRARIES := boost_python3 python3.5m
# PYTHON_INCLUDE := /usr/include/python3.5m \
# /usr/lib/python3.5/dist-packages/numpy/core/include
# We need to be able to find libpythonX.X.so or .dylib.
PYTHON_LIB := /usr/lib
# PYTHON_LIB := $(ANACONDA_HOME)/lib
# Homebrew installs numpy in a non standard path (keg only)
# PYTHON_INCLUDE += $(dir $(shell python -c 'import numpy.core; print(numpy.core.__file__)'))/include
# PYTHON_LIB += $(shell brew --prefix numpy)/lib
# Uncomment to support layers written in Python (will link against Python libs)
WITH_PYTHON_LAYER := 1
# Whatever else you find you need goes here.
INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include /usr/include/hdf5/serial
LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib /usr/lib/x86_64-linux-gnu/hdf5/serial
# If Homebrew is installed at a non standard location (for example your home directory) and you use it for general dependencies
# INCLUDE_DIRS += $(shell brew --prefix)/include
# LIBRARY_DIRS += $(shell brew --prefix)/lib
# Uncomment to use `pkg-config` to specify OpenCV library paths.
# (Usually not necessary -- OpenCV libraries are normally installed in one of the above $LIBRARY_DIRS.)
# USE_PKG_CONFIG := 1
BUILD_DIR := build
DISTRIBUTE_DIR := distribute
# Uncomment for debugging. Does not work on OSX due to https://github.com/BVLC/caffe/issues/171
# DEBUG := 1
# The ID of the GPU that 'make runtest' will use to run unit tests.
TEST_GPUID := 0
# enable pretty build (comment to see full commands)
Q ?= @
# shared object suffix name to differentiate branches
LIBRARY_NAME_SUFFIX := -nv
Sorry to keep you waiting.
This issue comes from Caffe itself rather than DIGITs.
Please upgrade your protobuf library into v3.1.0.
The training job can work correctly after applying this in our environment.
Hi, I just wanted to say that I see DIGITS is now deprecated so this thread is no longer relevant. I’ll bear in mind your answer for the future though. Thanks.
