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The following commit(s) were added to refs/heads/master by this push:
new 0fd2969 [MXNET-303] Update Jetpack to version 3.2 (#10482)
0fd2969 is described below
commit 0fd29698a11c0c77180aefdcf8264e063158a2c0
Author: Kellen Sunderland <kellen.sunderl...@gmail.com>
AuthorDate: Tue Apr 10 13:22:54 2018 +0000
[MXNET-303] Update Jetpack to version 3.2 (#10482)
---
Makefile | 6 +-
ci/docker/Dockerfile.build.jetson | 44 ++++-----
ci/docker/runtime_functions.sh | 24 +----
make/crosscompile.jetson.mk | 196 ++++++++++++++++++++++++++++++++++++++
4 files changed, 224 insertions(+), 46 deletions(-)
diff --git a/Makefile b/Makefile
index ae57114..b0758e9 100644
--- a/Makefile
+++ b/Makefile
@@ -254,8 +254,10 @@ ifneq ($(ADD_LDFLAGS), NONE)
LDFLAGS += $(ADD_LDFLAGS)
endif
-ifneq ($(USE_CUDA_PATH), NONE)
- NVCC=$(USE_CUDA_PATH)/bin/nvcc
+ifeq ($(NVCC), NONE)
+ ifneq ($(USE_CUDA_PATH), NONE)
+ NVCC=$(USE_CUDA_PATH)/bin/nvcc
+ endif
endif
# Sets 'CUDA_ARCH', which determines the GPU architectures supported
diff --git a/ci/docker/Dockerfile.build.jetson
b/ci/docker/Dockerfile.build.jetson
index e49b48e..9fa50f4 100755
--- a/ci/docker/Dockerfile.build.jetson
+++ b/ci/docker/Dockerfile.build.jetson
@@ -20,53 +20,47 @@
# This script assumes /work/mxnet exists and contains the mxnet code you wish
to compile and
# that /work/build exists and is the target for your output.
-FROM nvidia/cuda:8.0-cudnn6-devel as cudabuilder
+FROM nvidia/cuda:9.0-cudnn7-devel as cudabuilder
FROM dockcross/linux-arm64
ENV ARCH aarch64
-ENV NVCCFLAGS "-m64"
-ENV CC /usr/bin/aarch64-linux-gnu-gcc
-ENV CXX /usr/bin/aarch64-linux-gnu-g++
-ENV FC /usr/bin/aarch64-linux-gnu-gfortran-4.9
+ENV FC /usr/bin/${CROSS_TRIPLE}-gfortran
ENV HOSTCC gcc
+ENV TARGET ARMV8
WORKDIR /work
# Build OpenBLAS
-ADD https://api.github.com/repos/xianyi/OpenBLAS/git/refs/heads/master
/tmp/openblas_version.json
-RUN git clone https://github.com/xianyi/OpenBLAS.git && \
+ADD https://api.github.com/repos/xianyi/OpenBLAS/git/refs/tags/v0.2.20
openblas_version.json
+RUN git clone --recursive -b v0.2.20 https://github.com/xianyi/OpenBLAS.git &&
\
cd OpenBLAS && \
- make -j$(nproc) TARGET=ARMV8 && \
- PREFIX=/usr make install
+ make -j$(nproc) && \
+ PREFIX=${CROSS_ROOT} make install
# Setup CUDA build env (including configuring and copying nvcc)
COPY --from=cudabuilder /usr/local/cuda /usr/local/cuda
-ENV PATH $PATH:/usr/local/cuda/bin
ENV TARGET_ARCH aarch64
ENV TARGET_OS linux
-# Install ARM depedencies based on Jetpack 3.1
-RUN
JETPACK_DOWNLOAD_PREFIX=http://developer.download.nvidia.com/devzone/devcenter/mobile/jetpack_l4t/013/linux-x64
&& \
- ARM_CUDA_INSTALLER_PACKAGE=cuda-repo-l4t-8-0-local_8.0.84-1_arm64.deb && \
- ARM_CUDNN_INSTALLER_PACKAGE=libcudnn6_6.0.21-1+cuda8.0_arm64.deb && \
- ARM_CUDNN_DEV_INSTALLER_PACKAGE=libcudnn6-dev_6.0.21-1+cuda8.0_arm64.deb
&& \
+# Install ARM depedencies based on Jetpack 3.2
+RUN
JETPACK_DOWNLOAD_PREFIX=http://developer.download.nvidia.com/devzone/devcenter/mobile/jetpack_l4t/3.2GA/m892ki/JetPackL4T_32_b196/
&& \
+ ARM_CUDA_INSTALLER_PACKAGE=cuda-repo-l4t-9-0-local_9.0.252-1_arm64.deb && \
+ ARM_CUDNN_INSTALLER_PACKAGE=libcudnn7_7.0.5.13-1+cuda9.0_arm64.deb && \
+ ARM_CUDNN_DEV_INSTALLER_PACKAGE=libcudnn7-dev_7.0.5.13-1+cuda9.0_arm64.deb
&& \
wget -nv $JETPACK_DOWNLOAD_PREFIX/$ARM_CUDA_INSTALLER_PACKAGE && \
wget -nv $JETPACK_DOWNLOAD_PREFIX/$ARM_CUDNN_INSTALLER_PACKAGE && \
wget -nv $JETPACK_DOWNLOAD_PREFIX/$ARM_CUDNN_DEV_INSTALLER_PACKAGE && \
dpkg -i $ARM_CUDA_INSTALLER_PACKAGE && \
+ apt-key add /var/cuda-repo-9-0-local/7fa2af80.pub && \
dpkg -i $ARM_CUDNN_INSTALLER_PACKAGE && \
dpkg -i $ARM_CUDNN_DEV_INSTALLER_PACKAGE && \
- apt update -y && \
- apt install -y unzip cuda-cudart-cross-aarch64-8-0
cuda-cublas-cross-aarch64-8-0 \
- cuda-nvml-cross-aarch64-8-0 cuda-nvrtc-cross-aarch64-8-0
cuda-cufft-cross-aarch64-8-0 \
- cuda-curand-cross-aarch64-8-0 cuda-cusolver-cross-aarch64-8-0
cuda-cusparse-cross-aarch64-8-0 \
- cuda-misc-headers-cross-aarch64-8-0 cuda-npp-cross-aarch64-8-0 libcudnn6
&& \
- cp /usr/local/cuda-8.0/targets/aarch64-linux/lib/*.so
/usr/local/cuda/lib64/ && \
- cp /usr/local/cuda-8.0/targets/aarch64-linux/lib/stubs/*.so
/usr/local/cuda/lib64/stubs/ && \
- cp -r /usr/local/cuda-8.0/targets/aarch64-linux/include/
/usr/local/cuda/include/ && \
- rm $ARM_CUDA_INSTALLER_PACKAGE $ARM_CUDNN_INSTALLER_PACKAGE
$ARM_CUDNN_DEV_INSTALLER_PACKAGE
+ apt update -y && apt install -y unzip cuda-libraries-dev-9-0 libcudnn7-dev
-WORKDIR /work/mxnet
+ENV PATH $PATH:/usr/local/cuda/bin
+ENV NVCCFLAGS "-m64"
+ENV CUDA_ARCH "-gencode arch=compute_53,code=sm_53 -gencode
arch=compute_62,code=sm_62"
+ENV NVCC /usr/local/cuda/bin/nvcc
COPY runtime_functions.sh /work/
+WORKDIR /work/mxnet
diff --git a/ci/docker/runtime_functions.sh b/ci/docker/runtime_functions.sh
index d5a1883..484b49c 100755
--- a/ci/docker/runtime_functions.sh
+++ b/ci/docker/runtime_functions.sh
@@ -38,36 +38,22 @@ clean_repo() {
build_jetson() {
set -ex
pushd .
+ mv make/crosscompile.jetson.mk make/config.mk
+ make -j$(nproc)
- #cd /work/mxnet
- #make -j$(nproc) USE_OPENCV=0 USE_BLAS=openblas USE_SSE=0 USE_CUDA=1
USE_CUDNN=1 ENABLE_CUDA_RTC=0 USE_NCCL=0 USE_CUDA_PATH=/usr/local/cuda/
- cd /work/build
- cmake\
- -DUSE_CUDA=OFF\
- -DUSE_OPENCV=OFF\
- -DUSE_OPENMP=ON\
- -DUSE_SIGNAL_HANDLER=ON\
- -DUSE_MKL_IF_AVAILABLE=OFF\
- -DUSE_LAPACK=OFF\
- -DCMAKE_BUILD_TYPE=RelWithDebInfo\
- -G Ninja /work/mxnet
- ninja
export MXNET_LIBRARY_PATH=`pwd`/libmxnet.so
cd /work/mxnet/python
python setup.py bdist_wheel --universal
-
- # Fix pathing issues in the wheel. We need to move libmxnet.so from the
data folder to the root
- # of the wheel, then repackage the wheel.
- # Create a temp dir to do the work.
- # TODO: move apt call to install
+ # Fix pathing issues in the wheel. We need to move libmxnet.so from the
data folder to the
+ # mxnet folder, then repackage the wheel.
WHEEL=`readlink -f dist/*.whl`
TMPDIR=`mktemp -d`
unzip -d $TMPDIR $WHEEL
rm $WHEEL
cd $TMPDIR
mv *.data/data/mxnet/libmxnet.so mxnet
- zip -r $WHEEL $TMPDIR
+ zip -r $WHEEL .
cp $WHEEL /work/build
rm -rf $TMPDIR
popd
diff --git a/make/crosscompile.jetson.mk b/make/crosscompile.jetson.mk
new file mode 100644
index 0000000..9ca4109
--- /dev/null
+++ b/make/crosscompile.jetson.mk
@@ -0,0 +1,196 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+#-------------------------------------------------------------------------------
+# Template configuration for compiling mxnet
+#
+# If you want to change the configuration, please use the following
+# steps. Assume you are on the root directory of mxnet. First copy the this
+# file so that any local changes will be ignored by git
+#
+# $ cp make/config.mk .
+#
+# Next modify the according entries, and then compile by
+#
+# $ make
+#
+# or build in parallel with 8 threads
+#
+# $ make -j8
+#-------------------------------------------------------------------------------
+
+#---------------------
+# For cross compilation we only explictily set a compiler when one is not
already present.
+#--------------------
+
+ifndef CC
+export CC = gcc
+endif
+ifndef CXX
+export CXX = g++
+endif
+ifndef NVCC
+export NVCC = nvcc
+endif
+
+# whether compile with options for MXNet developer
+DEV = 0
+
+# whether compile with debug
+DEBUG = 0
+
+# whether to turn on segfault signal handler to log the stack trace
+USE_SIGNAL_HANDLER = 1
+
+# the additional link flags you want to add
+ADD_LDFLAGS =
+
+# the additional compile flags you want to add
+ADD_CFLAGS =
+
+#---------------------------------------------
+# matrix computation libraries for CPU/GPU
+#---------------------------------------------
+
+# whether use CUDA during compile
+USE_CUDA = 1
+
+# add the path to CUDA library to link and compile flag
+# if you have already add them to environment variable, leave it as NONE
+# USE_CUDA_PATH = /usr/local/cuda
+USE_CUDA_PATH = /usr/local/cuda-9.0/targets/aarch64-linux
+
+# whether to enable CUDA runtime compilation
+ENABLE_CUDA_RTC = 0
+
+# whether use CuDNN R3 library
+USE_CUDNN = 1
+
+#whether to use NCCL library
+USE_NCCL = 0
+#add the path to NCCL library
+USE_NCCL_PATH = NONE
+
+# whether use opencv during compilation
+# you can disable it, however, you will not able to use
+# imbin iterator
+USE_OPENCV = 0
+
+#whether use libjpeg-turbo for image decode without OpenCV wrapper
+USE_LIBJPEG_TURBO = 0
+#add the path to libjpeg-turbo library
+USE_LIBJPEG_TURBO_PATH = NONE
+
+# use openmp for parallelization
+USE_OPENMP = 1
+
+# whether use MKL-DNN library
+USE_MKLDNN = 0
+
+# whether use NNPACK library
+USE_NNPACK = 0
+
+# choose the version of blas you want to use
+# can be: mkl, blas, atlas, openblas
+# in default use atlas for linux while apple for osx
+UNAME_S := $(shell uname -s)
+USE_BLAS = openblas
+
+# whether use lapack during compilation
+# only effective when compiled with blas versions openblas/apple/atlas/mkl
+USE_LAPACK = 1
+
+# path to lapack library in case of a non-standard installation
+USE_LAPACK_PATH =
+
+# add path to intel library, you may need it for MKL, if you did not add the
path
+# to environment variable
+USE_INTEL_PATH = NONE
+
+# If use MKL only for BLAS, choose static link automatically to allow python
wrapper
+ifeq ($(USE_BLAS), mkl)
+USE_STATIC_MKL = 1
+else
+USE_STATIC_MKL = NONE
+endif
+
+#----------------------------
+# Settings for power and arm arch
+#----------------------------
+USE_SSE=0
+
+#----------------------------
+# distributed computing
+#----------------------------
+
+# whether or not to enable multi-machine supporting
+USE_DIST_KVSTORE = 0
+
+# whether or not allow to read and write HDFS directly. If yes, then hadoop is
+# required
+USE_HDFS = 0
+
+# path to libjvm.so. required if USE_HDFS=1
+LIBJVM=$(JAVA_HOME)/jre/lib/amd64/server
+
+# whether or not allow to read and write AWS S3 directly. If yes, then
+# libcurl4-openssl-dev is required, it can be installed on Ubuntu by
+# sudo apt-get install -y libcurl4-openssl-dev
+USE_S3 = 0
+
+#----------------------------
+# performance settings
+#----------------------------
+# Use operator tuning
+USE_OPERATOR_TUNING = 1
+
+# Use gperftools if found
+USE_GPERFTOOLS = 1
+
+# Use JEMalloc if found, and not using gperftools
+USE_JEMALLOC = 1
+
+#----------------------------
+# additional operators
+#----------------------------
+
+# path to folders containing projects specific operators that you don't want
to put in src/operators
+EXTRA_OPERATORS =
+
+#----------------------------
+# other features
+#----------------------------
+
+# Create C++ interface package
+USE_CPP_PACKAGE = 0
+
+#----------------------------
+# plugins
+#----------------------------
+
+# whether to use caffe integration. This requires installing caffe.
+# You also need to add CAFFE_PATH/build/lib to your LD_LIBRARY_PATH
+# CAFFE_PATH = $(HOME)/caffe
+# MXNET_PLUGINS += plugin/caffe/caffe.mk
+
+# WARPCTC_PATH = $(HOME)/warp-ctc
+# MXNET_PLUGINS += plugin/warpctc/warpctc.mk
+
+# whether to use sframe integration. This requires build sframe
+# g...@github.com:dato-code/SFrame.git
+# SFRAME_PATH = $(HOME)/SFrame
+# MXNET_PLUGINS += plugin/sframe/plugin.mk
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