Gpu-z For Mac
GPU-Z latest version: Get the lowdown on your graphics card for free. Does not support every manufacturer; Not available for Mac or Linux. Windows Mac Linux Disable Rules Prefer free software The majority of users won't care for a paid product, especially one they expect to install on every computer they have. GPU-Z application was designed to be a lightweight tool that will give you all information about your video card and GPU. GPU-Z support NVIDIA and ATI cards, displays adapter, GPU, and display information, overclock, default clocks, 3D clocks (if available), and validation of results.
Build tensorflow on OSX with NVIDIA CUDA support (GPU acceleration) These instructions are based on 's but expanded and updated to work with the. Requirements OS X 10.10 (Yosemite) or newer I tested these intructions on OS X v10.10.5.
They will probably work on OS X v10.11 (El Capitan), too. Xcode Command-Line Tools These instructions assume you have Xcode installed and your machine is already set up to compile c/c code. If not, simply type gcc into a terminal and it will prompt you to download and install the Xcode Command-Line Tools. Homebrew To compile tensorflow on OS X, you need several dependent libraries. The easiest way to get them is to install them with the.
If you don't already have brew installed, you can install it like this: /usr/bin/ruby -e '$(curl -fsSL If you don't want to blindly run a ruby script loaded from the internet, they have. Coreutils, swig, bazel First, make sure you have brew up to date with the latest available packages. $ brew cask info cuda cuda: 7.5.20 Nvidia CUDA NVIDIA's cuDNN library NVIDIA requires you to sign up and be approved before you can download this. First, go sign up here: When you sign up, make sure you provide accurate information.
A human at NVIDIA will review your application. If it's a business day, hopefully you'll get approved quickly. Then go here to download cuDNN: Click 'Download' to fill out their survey and agree to their Terms. Finally, you'll see the download options. However, you'll only see download options for cuDNN v4 and cuDNN v3. You'll want to scroll to the very bottom and click 'Archived cuDNN Releases'.
This will take you to this page where you can download cuDNN v2: On that page, download '. Next, tou need to manually install it by copying over some files: tar zxvf /Downloads/cudnn-6.5-osx-v2.tar.gz sudo cp./cudnn-6.5-osx-v2/cudnn.h /usr/local/cuda/include/ sudo cp./cudnn-6.5-osx-v2/libcudnn. /usr/local/cuda/lib/ Finally, you need to make sure the library is in your library load path.
Edit your /.bashprofile file and add this line at the bottom: export DYLDLIBRARYPATH='/usr/local/cuda/lib':$DYLDLIBRARYPATH After that, close and reopen your terminal window to apply the change. Checkout tensorflow Since OS X CUDA support is still an unmerged pull request , you need to check out that specific branch: git clone -recurse-submodules cd tensorflow git fetch origin pull/664/head:cudaosx git checkout cudaosx Look up your NVIDIA card's Graphics Capability on the CUDA website Before you start, open up System Report in OSX: Apple Menu About this Mac System Report. In System Report, click on 'Graphics/Displays' and find out the exact model NVIDIA card you have: NVIDIA GeForce GT 650M: Chipset Model: NVIDIA GeForce GT 650M Then go to and find that exact model name in the list: CUDA-Enabled GeForce Products GeForce GT 650M There it will list the Compute Capability for your card. For the GeForce GT 650M used in late 2011 Macbook Pro Retinas, it is 3.0.
Write this down as it's critical to have this number for the next step. Configure and Build tensorflow You will first need to configure the tensorflow build options: TFUNOFFICIALSETTING=1./configure During the config process, it will ask you a bunch of questions. You can use the answers below except make sure to use the Compute Capability for your NVIDIA card you looked up in the previous step: WARNING: You are configuring unofficial settings in TensorFlow. Because some external libraries are not backward compatible, these settings are largely untested and unsupported.
Please specify the location of python. Default is /usr/bin/python: Do you wish to build TensorFlow with GPU support? y/N y GPU support will be enabled for TensorFlow Please specify the Cuda SDK version you want to use.
Default is 7.0: 7.5 Please specify the location where CUDA 7.5 toolkit is installed. Refer to README.md for more details. Default is /usr/local/cuda: Please specify the Cudnn version you want to use. Default is 6.5: Please specify the location where cuDNN 6.5 library is installed. Refer to README.md for more details. Default is /usr/local/cuda: 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: Please note that each additional compute capability significantly increases your build time and binary size. Default is: '3.5,5.2': 3.0 Setting up Cuda include Setting up Cuda lib Setting up Cuda bin Setting up Cuda nvvm Configuration finished Now you can actually build and install tensorflow! Bazel build -c opt -config=cuda //tensorflow/cc:tutorialsexampletrainer bazel build -c opt -config=cuda //tensorflow/tools/pippackage:buildpippackage bazel-bin/tensorflow/tools/pippackage/buildpippackage /tmp/tensorflowpkg pip install /tmp/tensorflowpkg/tensorflow-0.6.0-py2-none-any.whl Verify Installaion You need to exit the tensorflow build folder to test your installation.
Cd Now, run python and paste in this test script. Import tensorflow as tf # Creates a graph. A = tf.constant( 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, shape = 2, 3, name = 'a ') b = tf.constant( 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, shape = 3, 2, name = 'b ') c = tf.matmul(a, b) # Creates a session with logdeviceplacement set to True. Sess = tf.Session( config =tf.ConfigProto( logdeviceplacement = True)) # Runs the op. It worked for me! I used python 3.5.1 for my installation and ran into a couple hiccups along the way, but nothing that wasn't fixable. In particular, I ran into issues with permissions and versionings of my python software.
So, I ran the following: sudo -H pip3 freeze -local sudo -H grep -v '^ -e' sudo -H cut -d = -f 1 xargs sudo -H pip3 install -U sudo -H pip freeze -local sudo -H grep -v '^ -e' sudo -H cut -d = -f 1 xargs sudo -H pip install -U sudo -H pip3 install -upgrade pip sudo -H pip install -upgrade pip and Finally, sudo -H pip3 install /private/tmp/tensorflowpkg/tensorflow-0.8.0rc0-py3-none-any.whl Then, in the little test function, I needed to use print(sess.run(c)) otherwise, python3 gives you an error because of the differences in how it treats the print function. Hi Adam, I am also trying to install tensor flow on MAC OSX (version 10.11.5 ) with nvidia GPU support. I am following the instructions given in the page and received following errors: when i run the python code I get the following output: I tensorflow/streamexecutor/dsoloader.cc:108 successfully opened CUDA library libcublas.7.5.dylib locally I tensorflow/streamexecutor/dsoloader.cc:108 successfully opened CUDA library libcudnn.5.dylib locally I tensorflow/streamexecutor/dsoloader.cc:108 successfully opened CUDA library libcufft.7.5.dylib locally I tensorflow/streamexecutor/dsoloader.cc:102 Couldn't open CUDA library libcuda.1.dylib. LDLIBRARYPATH::/usr/local/cuda/lib I tensorflow/streamexecutor/cuda/cudadiagnostics.cc:160 hostname: sfocfjgn32.ads.autodesk.com I tensorflow/streamexecutor/cuda/cudadiagnostics.cc:185 libcuda reported version is: Not found: was unable to find libcuda.so DSO loaded into this program Segmentation fault: 11 Hi esd, Since I am using python 3.5.1, I used the suggestions provided by you. I tried running: sudo -H pip3 freeze -local sudo -H grep -v '^-e' sudo -H cut -d = -f 1 xargs sudo -H pip3 install -U I received the following error: Collecting conda-build Using cached conda-build-1.20.1.tar.gz Complete output from command python setup.py egginfo: Traceback (most recent call last): File ', line 1, in File '/private/tmp/pip-build-7tya1d46/conda-build/setup.py', line 10, in import versioneer ImportError: No module named 'versioneer' Can you suggest something that I might be doing wrong? Thanks in advance!!
Thanks for this. It worked great with a few tweaks. My setup:. OSX 10.11.5, MacBook Pro (15-inch, Mid 2012), NVIDIA GeForce GT 650M 512 MB. I'm using python Python 2.7.11:: Anaconda 2.4.1 (x8664),.
brew, Homebrew 0.9.9 (git revision 1455a; last commit 2016-07-24). bazel release 0.3.0-homebrew, etc.
(just ran brew update, brew upgrade). cuda: 7.5.27, Nvidia CUDA (from brew) A few additions:. During the Checkout Tensorflow section, even after calling git clone -recurse-submodules I had to manually call git submodule update -init to bring in the submodules. To install the package I had to call sudo -H pip install /private/tmp/tensorflowpkg/tensorflow-0.8.0rc0-py2-none-any.whl (note the version difference).
While implementing this tutorial when i am running the command bazel build -c opt -config=cuda //tensorflow/cc:tutorialsexampletrainer it's giving me following error: WARNING: /Users/asuma2/tensorflow/google/protobuf/BUILD:59:16: in includes attribute of cclibrary rule //google/protobuf:protobuflite: 'src/' resolves to 'google/protobuf/src' not in 'thirdparty'. This will be an error in the future. WARNING: /Users/asuma2/tensorflow/google/protobuf/BUILD:124:16: in includes attribute of cclibrary rule //google/protobuf:protobuf: 'src/' resolves to 'google/protobuf/src' not in 'thirdparty'. This will be an error in the future. WARNING: /Users/asuma2/tensorflow/google/protobuf/BUILD:266:16: in includes attribute of cclibrary rule //google/protobuf:protoclib: 'src/' resolves to 'google/protobuf/src' not in 'thirdparty'.
Gpu-z For Mac
This will be an error in the future. INFO: Found 1 target.
INFO: From Executing genrule //thirdparty/gpus/cuda:cudaconfigcheck for host: /bin/bash: greadlink: command not found INFO: From Executing genrule //thirdparty/gpus/cuda:cudaconfigcheck: /bin/bash: greadlink: command not found ERROR: /Users/asuma2/tensorflow/thirdparty/gpus/cuda/BUILD:196:1: declared output 'thirdparty/gpus/cuda/cuda.config' was not created by genrule. This is probably because the genrule actually didn't create this output, or because the output was a directory and the genrule was run remotely (note that only the contents of declared file outputs are copied from genrules run remotely). ERROR: /Users/asuma2/tensorflow/thirdparty/gpus/cuda/BUILD:196:1: declared output 'thirdparty/gpus/cuda/cuda.config' was not created by genrule.
This is probably because the genrule actually didn't create this output, or because the output was a directory and the genrule was run remotely (note that only the contents of declared file outputs are copied from genrules run remotely). ERROR: /Users/asuma2/tensorflow/thirdparty/gpus/cuda/BUILD:196:1: not all outputs were created. Target //tensorflow/cc:tutorialsexampletrainer failed to build Use -verbosefailures to see the command lines of failed build steps. INFO: Elapsed time: 22.253s, Critical Path: 0.25s Can someone suggest me what is wrong i am doing here.
Thanks in advance!!! Sorry, the complete error message whild running $ bazel build -c opt -config=cuda //tensorflow/cc:tutorialsexampletrainer WARNING: The following rc files are no longer being read, please transfer their contents or import their path into one of the standard rc files: /Users/peisungtsai/tensorflow/tools/bazel.rc INFO: Options provided by the client: Inherited 'common' options: -isatty=1 -terminalcolumns=80 ERROR: Config value cuda is not defined in any.rc file INFO: Invocation ID: 80a35643-a47b-4d78-95db-dc799800258b.