Jetson TX2 tensorflow安装+keras安装
使用官方自带的安装方法
TX2产品官网自带安装方法，个人推荐这个，网址如下：https://docs.nvidia.com/deeplearning/dgx/install-tf-jetsontx2/index.html
在安装TensorFlow之前，请确保安装JetPack 3.3，TensorRT已包含在JetPack包中。
$: pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/jp33 tensorflow-gpu
注意：使用 pip3 如果使用的是Python 3.5版。
nvidia@tegra-ubuntu:~$ sudo pip3 install --extra-index-url https://developer.download.nvidia.com/compute/redist/jp33 tensorflow-gpu
The directory '/home/nvidia/.cache/pip/http' or its parent directory is not owned by the current user and the cache has been disabled. Please check the permissions and owner of that directory. If executing pip with sudo, you may want sudo's -H flag.
The directory '/home/nvidia/.cache/pip' or its parent directory is not owned by the current user and caching wheels has been disabled. check the permissions and owner of that directory. If executing pip with sudo, you may want sudo's -H flag.
Looking in indexes: https://pypi.org/simple, https://developer.download.nvidia.com/compute/redist/jp33
Collecting tensorflow-gpu
Downloading https://developer.download.nvidia.com/compute/redist/jp33/tensorflow-gpu/tensorflow_gpu-1.9.0+nv18.8-cp35-cp35m-linux_aarch64.whl (91.1MB)
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Requirement already satisfied: wheel>=0.26 in /usr/lib/python3/dist-packages (from tensorflow-gpu) (0.29.0)
Requirement already satisfied: setuptools<=39.1.0 in /usr/lib/python3/dist-packages (from tensorflow-gpu) (20.7.0)
Collecting termcolor>=1.1.0 (from tensorflow-gpu)
Downloading https://files.pythonhosted.org/packages/8a/48/a76be51647d0eb9f10e2a4511bf3ffb8cc1e6b14e9e4fab46173aa79f981/termcolor-1.1.0.tar.gz
Collecting absl-py>=0.1.6 (from tensorflow-gpu)
Downloading https://files.pythonhosted.org/packages/0c/63/f505d2d4c21db849cf80bad517f0065a30be6b006b0a5637f1b95584a305/absl-py-0.6.1.tar.gz (94kB)
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Requirement already satisfied: numpy>=1.11.0 in /usr/local/lib/python3.5/dist-packages (from tensorflow-gpu) (1.15.4)
Requirement already satisfied: six>=1.10.0 in /usr/lib/python3/dist-packages (from tensorflow-gpu) (1.10.0)
Collecting astor>=0.6.0 (from tensorflow-gpu)
Downloading https://files.pythonhosted.org/packages/35/6b/11530768cac581a12952a2aad00e1526b89d242d0b9f59534ef6e6a1752f/astor-0.7.1-py2.py3-none-any.whl
Collecting tensorboard<1.10.0,>=1.9.0 (from tensorflow-gpu)
Downloading https://files.pythonhosted.org/packages/9e/1f/3da43860db614e294a034e42d4be5c8f7f0d2c75dc1c428c541116d8cdab/tensorboard-1.9.0-py3-none-any.whl (3.3MB)
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Collecting grpcio>=1.8.6 (from tensorflow-gpu)
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Collecting gast>=0.2.0 (from tensorflow-gpu)
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Installing collected packages: termcolor, absl-py, astor, werkzeug, markdown, protobuf, tensorboard, grpcio, gast, tensorflow-gpu
Running setup.py install for termcolor ... done
Running setup.py install for absl-py ... done
Running setup.py install for grpcio ... done
Running setup.py install for gast ... done
Successfully installed absl-py-0.6.1 astor-0.7.1 gast-0.2.0 grpcio-1.17.1 markdown-3.0.1 protobuf-3.6.1 tensorboard-1.9.0 tensorflow-gpu-1.9.0+nv18.8 termcolor-1.1.0 werkzeug-0.14.1
编译安装TensorFlow
也可尝试源码编译的方式进行安装，谨慎其间还是不要尝试，因为各种报错。。。以下为我自己编译的过程。
安装cudnn
安装tensorflow，刷机后发现import tensorflow 并没有，于是自己编译tensorflow，查看CUDA版本为CUDA9，但是没有cudnn，在https://developer.nvidia.com/cudnn 上下载了cudnn-9.0-linux-x64-v7.4.2.24.solitairetheme8，修改其后缀为tgz格式cudnn-9.0-linux-x64-v7.4.2.24.tgz，将其拷贝进ubuntu指定的文件夹中，比如我在/usr/local/下建立了一个文件夹。
$ sudo mkdir cuda_cudnn
此时解压文件
$ sudo tar -xvf cudnn-9.0-linux-x64-v7.4.2.24.tgz
发现有cuda和include两个文件夹，记得路径为：/usr/local/cuda_cudnn/
将解压后的文件中的lib64文件夹关联到环境变量中
$ cd ~
$ sudo gedit .bashrc
在文件最后一行加入，一定是你自己解压的路径
export LD_LIBRARY_PATH=/usr/local/cuda_cudnn/cudnn/lib64:$LD_LIBRARY_PATH
之后
$ source .bashrc
重启终端，该步骤可以成功的配置cuDNN的Lib文件。
将解压后的cuDNN文件夹中的include文件夹下的cudnn.h 文件拷贝至/usr/local/cuda/include中，由于进入了系统路径，因此执行该操作时需要获取管理员权限。
$ sudo chmod a+r /usr/local/cuda/include/cudnn.h
cuDNN的配置就全部安装完成了。运行 cat /usr/local/cuda/include/cudnn.h | grep CUDNN_MAJOR -A 2 查看其版本信息，如下：
$ cat /usr/local/cuda/include/cudnn.h | grep CUDNN_MAJOR -A 2
#define CUDNN_MAJOR 7
#define CUDNN_MINOR 4
#define CUDNN_PATCHLEVEL 2
--
#define CUDNN_VERSION (CUDNN_MAJOR * 1000 + CUDNN_MINOR * 100 + CUDNN_PATCHLEVEL)
安装JAVA
$ sudo add-apt-repository ppa:webupd8team/java
$ sudo apt-get update
$ sudo apt-get install oracle-java8-installer
其间会有提示，选择ok即可。安装其他库
sudo apt-get install zip unzip autoconf automake libtool curl zlib1g-dev maven -y
sudo apt-get install python-numpy swig python-dev python-pip python-wheel -y
下载 Bazel，此时耗时较长
$ bazel_version=0.10.0 wget https://github.com/bazelbuild/bazel/releases/download/$bazel_version/bazel-$bazel_version-dist.zip
$ unzip bazel-$bazel_version-dist.zip -d bazel-dist
$ sudo chmod -R ug+rwx bazel-dist
$ cd bazel-dist
$ ./compile.sh
创建交换文件
由于编译 TensorFlow 需要大约 8GB 空间，我们将创建一个交换文件。 这里 ~/data 挂载了 硬盘，将交换文件建立在该目录下。 (挂载方式可以查看博文，我这里用的旧的笔记本硬盘)
nvidia@tegra-ubuntu:~/data$ sudo mkdir swap
nvidia@tegra-ubuntu:~/data$ sudo chmod 777 swap/
nvidia@tegra-ubuntu:~/data$ sudo fallocate -l 8G swapfile
nvidia@tegra-ubuntu:~/data$ sudo chmod 600 swapfile
nvidia@tegra-ubuntu:~/data$ ls
swap swapfile
nvidia@tegra-ubuntu:~/data$ sudo mkswap swapfile
Setting up swapspace version 1, size = 8 GiB (8589930496 bytes)
no label, UUID=40bbd9d8-131f-45c4-8d65-1f7054ccc4f3
nvidia@tegra-ubuntu:~/data$ sudo swapon swapfile
nvidia@tegra-ubuntu:~/data$ free -m
total used free shared buff/cache available
Mem: 7846 2302 4610 49 933 5402
Swap: 8191 0 8191
nvidia@tegra-ubuntu:~/data$ swapon -s
Filename Type Size Used Priority
/home/nvidia/data/swapfile file 8388604 0 -1
编译tensorflow源码
git clone https://github.com/tensorflow/tensorflow,首先git config --global http.postBuffer 524288000，否则会报错error: RPC failed; curl 56 GnuTLS recv error (-54): Error ina the pull functi
nvidia@tegra-ubuntu:~$ git config --global http.postBuffer 524288000
nvidia@tegra-ubuntu:~$ sudo git clone https://github.com/tensorflow/tensorflow
Cloning into 'tensorflow'...
remote: Enumerating objects: 12, done.
remote: Counting objects: 100% (12/12), done.
remote: Compressing objects: 100% (12/12), done.
remote: Total 492309 (delta 2), reused 7 (delta 0), pack-reused 492297
Receiving objects: 100% (492309/492309), 286.82 MiB | 209.00 KiB/s, done.
Resolving deltas: 100% (394055/394055), done.
Checking connectivity... done.
Checking out files: 100% (15123/15123), done.
git r1.7版本
nvidia@tegra-ubuntu:~$ cd tensorflow/
nvidia@tegra-ubuntu:~/tensorflow$ ls
ACKNOWLEDGMENTS CODE_OF_CONDUCT.md ISSUES.md RELEASE.md WORKSPACE
ADOPTERS.md CODEOWNERS ISSUE_TEMPLATE.md SECURITY.md
arm_compiler.BUILD configure LICENSE tensorflow
AUTHORS configure.py models.BUILD third_party
BUILD CONTRIBUTING.md README.md tools
nvidia@tegra-ubuntu:~/tensorflow$ sudo git checkout r1.7
[sudo] password for nvidia:
Branch r1.7 set up to track remote branch r1.7 from origin.
Switched to a new branch 'r1.7'
查看/usr/lib/aarch64-linux-gnu路径下有没有include文件夹，没有则添加修改权限。
nvidia@tegra-ubuntu:/usr/lib/aarch64-linux-gnu$ cd include
bash: cd: include: No such file or directory
nvidia@tegra-ubuntu:/usr/lib/aarch64-linux-gnu$ sudo mkdir include
接着复制 cudnn.h，便于 TensorFlow 找到：
nvidia@tegra-ubuntu:sudo cp /usr/include/cudnn.h /usr/lib/aarch64-linux-gnu/include/cudnn.h
配置
nvidia@tegra-ubuntu:~/tensorflow$ sudo ./configure
Extracting Bazel installation...
You have bazel 0.10.0- (@non-git) installed.
Please specify the location of python. [Default is /usr/bin/python]:
Found possible Python library paths:
/usr/local/lib/python2.7/dist-packages
/usr/lib/python2.7/dist-packages
Please input the desired Python library path to use. Default is [/usr/local/lib/python2.7/dist-packages]
Do you wish to build TensorFlow with jemalloc as malloc support? [Y/n]:
jemalloc as malloc support will be enabled for TensorFlow.
Do you wish to build TensorFlow with Google Cloud Platform support? [Y/n]: n
No Google Cloud Platform support will be enabled for TensorFlow.
Do you wish to build TensorFlow with Hadoop File System support? [Y/n]: n
No Hadoop File System support will be enabled for TensorFlow.
Do you wish to build TensorFlow with Amazon S3 File System support? [Y/n]: n
No Amazon S3 File System support will be enabled for TensorFlow.
Do you wish to build TensorFlow with Apache Kafka Platform support? [y/N]: n
No Apache Kafka Platform support will be enabled for TensorFlow.
Do you wish to build TensorFlow with XLA JIT support? [y/N]: n
No XLA JIT support will be enabled for TensorFlow.
Do you wish to build TensorFlow with GDR support? [y/N]: y
GDR support will be enabled for TensorFlow.
Do you wish to build TensorFlow with VERBS support? [y/N]: n
No VERBS support will be enabled for TensorFlow.
Do you wish to build TensorFlow with OpenCL SYCL support? [y/N]: n
No OpenCL SYCL support will be enabled for TensorFlow.
Do you wish to build TensorFlow with CUDA support? [y/N]: y
CUDA support will be enabled for TensorFlow.
Please specify the CUDA SDK version you want to use, e.g. 7.0. [Leave empty to default to CUDA 9.0]:
Please specify the location where CUDA 9.0 toolkit is installed. Refer to README.md for more details. [Default is /usr/local/cuda]:
Please specify the cuDNN version you want to use. [Leave empty to default to cuDNN 7.0]:
Please specify the location where cuDNN 7 library is installed. Refer to README.md for more details. [Default is /usr/local/cuda]:
Do you wish to build TensorFlow with TensorRT support? [y/N]:
No TensorRT support will be enabled for TensorFlow.
Please specify a list of comma-separated Cuda compute capabilities you want to build with.
You can find the compute capability of your device at: https://developer.nvidia.com/cuda-gpus.
Please note that each additional compute capability significantly increases your build time and binary size. [Default is: 3.5,5.2]
Do you want to use clang as CUDA compiler? [y/N]: n
nvcc will be used as CUDA compiler.
Please specify which gcc should be used by nvcc as the host compiler. [Default is /usr/bin/gcc]:
Do you wish to build TensorFlow with MPI support? [y/N]: n
No MPI support will be enabled for TensorFlow.
Please specify optimization flags to use during compilation when bazel option "--config=opt" is specified [Default is -march=native]:
Would you like to interactively configure ./WORKSPACE for Android builds? [y/N]: n
Not configuring the WORKSPACE for Android builds.
Preconfigured Bazel build configs. You can use any of the below by adding "--config=<>" to your build command. See tools/bazel.rc for more details.
--config=mkl # Build with MKL support.
--config=monolithic # Config for mostly static monolithic build.
Configuration finished
开始编译
nvidia@tegra-ubuntu:sudo bazel build -c opt --local_resources 3072,4.0,1.0 --verbose_failures --config=cuda //tensorflow/tools/pip_package:build_pip_package
编译时间较长，大约2-3小时。接着安装编译完成的whl文件。
bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg
cp /tmp/tensorflow_pkg/tensorflow-1.5.0rc0-cp27-cp27mu-linux_aarch64.whl .
pip install ./tensorflow-1.5.0rc0-cp27-cp27mu-linux_aarch64.whl
pip install --upgrade pip
安装keras
先安装libhdf5-dev、python-h5py，之后再安装keras
sudo apt-get install libhdf5-dev
sudo apt-get install python-h5py
sudo pip3 install keras
nvidia@tegra-ubuntu:~$ sudo apt-get install libhdf5-dev
[sudo] password for nvidia:
Reading package lists... Done
Building dependency tree
Reading state information... Done
The following packages were automatically installed and are no longer required:
libjbig-dev libjpeg62 libllvm5.0 liblzma-dev libqmi-glib1 libtiffxx5 ubuntu-core-launcher
Use 'sudo apt autoremove' to remove them.
The following additional packages will be installed:
hdf5-helpers libaec-dev libaec0 libhdf5-10 libhdf5-cpp-11 libjpeg-dev libjpeg-turbo8-dev
libjpeg8-dev libsz2
Suggested packages:
libhdf5-doc
The following packages will be REMOVED:
libjpeg62-dev
The following NEW packages will be installed:
hdf5-helpers libaec-dev libaec0 libhdf5-10 libhdf5-cpp-11 libhdf5-dev libjpeg-dev
libjpeg-turbo8-dev libjpeg8-dev libsz2
0 upgraded, 10 newly installed, 1 to remove and 0 not upgraded.
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update-alternatives: using /usr/lib/aarch64-linux-gnu/pkgconfig/hdf5-serial.pc to provide /usr/lib/aarch64-linux-gnu/pkgconfig/hdf5.pc (hdf5.pc) in auto mode
Processing triggers for libc-bin (2.23-0ubuntu10) ...
nvidia@tegra-ubuntu:~$ sudo apt-get install python-h5py
Reading package lists... Done
Building dependency tree
Reading state information... Done
The following packages were automatically installed and are no longer required:
libjbig-dev libjpeg62 libllvm5.0 liblzma-dev libqmi-glib1 libtiffxx5 ubuntu-core-launcher
Use 'sudo apt autoremove' to remove them.
The following additional packages will be installed:
python-six
The following NEW packages will be installed:
python-h5py python-six
0 upgraded, 2 newly installed, 0 to remove and 0 not upgraded.
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nvidia@tegra-ubuntu:~$ sudo pip3 install keras
The directory '/home/nvidia/.cache/pip/http' or its parent directory is not owned by the current user and the cache has been disabled. Please check the permissions and owner of that directory. If executing pip with sudo, you may want sudo's -H flag.
The directory '/home/nvidia/.cache/pip' or its parent directory is not owned by the current user and caching wheels has been disabled. check the permissions and owner of that directory. If executing pip with sudo, you may want sudo's -H flag.
Collecting keras
Downloading https://files.pythonhosted.org/packages/5e/10/aa32dad071ce52b5502266b5c659451cfd6ffcbf14e6c8c4f16c0ff5aaab/Keras-2.2.4-py2.py3-none-any.whl (312kB)
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Collecting scipy>=0.14 (from keras)
Downloading https://files.pythonhosted.org/packages/ea/c8/c296904f2c852c5c129962e6ca4ba467116b08cd5b54b7180b2e77fe06b2/scipy-1.2.0.tar.gz (23.3MB)
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Collecting keras-applications>=1.0.6 (from keras)
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Requirement already satisfied: numpy>=1.9.1 in /usr/local/lib/python3.5/dist-packages (from keras) (1.15.4)
Collecting keras-preprocessing>=1.0.5 (from keras)
Downloading https://files.pythonhosted.org/packages/fc/94/74e0fa783d3fc07e41715973435dd051ca89c550881b3454233c39c73e69/Keras_Preprocessing-1.0.5-py2.py3-none-any.whl
Collecting pyyaml (from keras)
Downloading https://files.pythonhosted.org/packages/9e/a3/1d13970c3f36777c583f136c136f804d70f500168edc1edea6daa7200769/PyYAML-3.13.tar.gz (270kB)
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Requirement already satisfied: six>=1.9.0 in /usr/lib/python3/dist-packages (from keras) (1.10.0)
Collecting h5py (from keras)
Downloading https://files.pythonhosted.org/packages/43/27/a6e7dcb8ae20a4dbf3725321058923fec262b6f7835179d78ccc8d98deec/h5py-2.9.0.tar.gz (287kB)
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Installing collected packages: scipy, h5py, keras-applications, keras-preprocessing, pyyaml, keras
Running setup.py install for scipy ... done
Running setup.py install for h5py ... done
Running setup.py install for pyyaml ... done
Successfully installed h5py-2.9.0 keras-2.2.4 keras-applications-1.0.6 keras-preprocessing-1.0.5 pyyaml-3.13 scipy-1.2.0
nvidia@tegra-ubuntu:~$ python3
Python 3.5.2 (default, Nov 12 2018, 13:43:14)
[GCC 5.4.0 20160609] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import keras
Using TensorFlow backend.
>>>