M1

Running Tensor flow on MacBook Pro M1 Metal.

https://youtu.be/_1CaUOHhI6U

TensorFlow-serving

TensorFlow Docker Serving

• https://www.tensorflow.org/serving/

TensorFlow-serving

TensorFlow Docker Serving

• https://www.tensorflow.org/serving/

Install Anaconda Python environment with Jupyter Notebook

This post was updated on September 29, 2022

Check if you have Conda installed

% conda update --all -y
>> command not found: conda

Install Anaconda Python

I prefer an installer on my laptop: 

https://www.anaconda.com/

After installation, make sure you restart the Terminal (control N).

Note, Anaconda includes the following and much more:

• curl

• numpy

• matplotlib

• jupyter_core
  

• protobuf

• sqlite

• ipython

• jupyterlab

• jupyter

• notebook

• matplotlib

• pip

• pandas

• pillow

• scikit-learn

• python-3.9

Update Conda

$ conda activate base

$ conda update -n base -c defaults conda

$ conda update --all -y

How to start Jupyter Notebook

cd $REPO // the directory you want as a base of your project (e.g. in GitHub directory)  
jupyter-lab

This will start in the browser: http://localhost:8888/lab

That is all you need to start working.
Alternatively, you may want to create ENVIRONMENT specific installation. This is useful if you work on multiple projects, especially over a long time when LIBRARIES change and code becomes outdated.

Check Python Version that came with Conda

% python --version
Python 3.9.13

 

Create a Conda TensorFlow environment

$ conda create -n py_39_tf python=3.9 tensorflow -y

This installs tensorflow         pkgs/main/osx-64::tensorflow-2.9.1

List conda environments you already created

$ conda info --envs

 

% conda info --envs

# conda environments:

#

base                  *  /Users/uki/opt/anaconda3

py_39_tf                 /Users/uki/opt/anaconda3/envs/py_39_tf

Activate the Conda Environment you want Jupyter in.

% conda activate py_39_tf

(py_39_tf) uki ~ %

Install new Jupyther kernel

$ python -m ipykernel install --user --name py_36_tf --display-name "Python 3.6 (tensorflow)"

 

List currently installed Jupyther kernels 

$ ls -alt ~/Library/Jupyter/kernels/

total 0
drwx------ 7 uki staff 224 Nov 22 11:50 ..
drwxr-xr-x 9 uki staff 288 Nov 22 11:43 .
drwxr-xr-x 5 uki staff 160 Nov 4 06:35 julia-1.4
drwxr-xr-x 5 uki staff 160 Apr 1 2020 python361064bitpy36condaa60168e76a7b4349b469299762ee4c30
drwxr-xr-x 5 uki staff 160 Apr 1 2020 python38264bitpytorchcondaaf5a833263b448b8b2738bb5a7355c8a
drwxr-xr-x 5 uki staff 160 Apr 1 2020 python361064bitturiconda565ecc262d0845fbb235ae21ac24296f

Installed kernelspec py_36_tf in /Users/uki/Library/Jupyter/kernels/py_36_tf

Refresh a page with Jupyther Lab notebook and change to the new kernel.

Delete kernels that you want to replace

$ rm -r ~/Library/Jupyter/kernels/my_old_kernel_name

 

Ubuntu: installing TensorFlow for NVidia (CUDA) GPU

I am setting TensorFlow on:

• Ubuntu 16.04 LTS 64-bit
• 16 GiB RAM
• AMD Athlon(tm) II X4 640 Processor × 4 
• GeForce GTX 1050 Ti/PCIe/SSE2

Check if you have NVidia CUDA GPU

uki@uki-p6710f:~$  lspci | grep -i nvidia
01:00.0 VGA compatible controller: NVIDIA Corporation Device 1c82 (rev a1)
01:00.1 Audio device: NVIDIA Corporation Device 0fb9 (rev a1)

Check the name of your OS

uki@uki-p6710f:~$ uname -m && cat /etc/*release
x86_64
DISTRIB_ID=Ubuntu
DISTRIB_RELEASE=16.04
DISTRIB_CODENAME=xenial
DISTRIB_DESCRIPTION="Ubuntu 16.04.1 LTS"
NAME="Ubuntu"
VERSION="16.04.1 LTS (Xenial Xerus)"
ID=ubuntu
ID_LIKE=debian
PRETTY_NAME="Ubuntu 16.04.1 LTS"
VERSION_ID="16.04"
HOME_URL="http://www.ubuntu.com/"
SUPPORT_URL="http://help.ubuntu.com/"
BUG_REPORT_URL="http://bugs.launchpad.net/ubuntu/"
VERSION_CODENAME=xenial
UBUNTU_CODENAME=xenial

Check C compiler

uki@uki-p6710f:~$ gcc --version
gcc (Ubuntu 5.4.0-6ubuntu1~16.04.4) 5.4.0 20160609
Copyright (C) 2015 Free Software Foundation, Inc.
This is free software; see the source for copying conditions.  There is NO
warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.

Ubuntu Headers 

uki@uki-p6710f:~$ sudo apt-get install linux-headers-$(uname -r)
[sudo] password for uki:
Reading package lists... Done
Building dependency tree
Reading state information... Done
linux-headers-4.4.0-62-generic is already the newest version (4.4.0-62.83).
linux-headers-4.4.0-62-generic set to manually installed.
The following packages were automatically installed and are no longer required:
  linux-headers-4.4.0-31 linux-headers-4.4.0-31-generic linux-image-4.4.0-31-generic linux-image-extra-4.4.0-31-generic
Use 'sudo apt autoremove' to remove them.
0 upgraded, 0 newly installed, 0 to remove and 89 not upgraded.

Download newest CUDA installer (1.4GB)

https://developer.nvidia.com/cuda-downloads

https://developer.nvidia.com/compute/cuda/8.0/Prod2/local_installers/cuda_8.0.61_375.26_linux-run

Execute CUDA installer

 cd ~/Downloads/

uki@uki-p6710f:~/Downloads$ ls -alt

Mar  5 14:54 cuda_8.0.61_375.26_linux.run

Feb  2 09:53 NVIDIA-Linux-x86_64-375.10.run

$ sudo sh cuda_8.0.61_375.26_linux.run

Set environment variables

uki@uki-p6710f:~$ nano ~/.bashrc

##### CUDA 
export PATH=/usr/local/cuda-8.0/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda-8.0/lib64

uki@uki-p6710f:~$ echo $LD_LIBRARY_PATH

/usr/local/cuda-8.0/lib64

Install TensorFlow via pip3 (Python 3.5)

uki@uki-p6710f:~$ python --version

Python 3.5.2 :: Anaconda 4.3.0 (64-bit)

$ sudo apt install python3-pip

$  conda info --envs
# conda environments:
#
tensorflow               /home/uki/anaconda3/envs/tensorflow
root                  *  /home/uki/anaconda3

$ conda env create -f /Users/ukilucas/dev/uki.guru/conda_enviroment_GPU.yml

$source activate tensorflow

Setting Jupyter kernel to match Python conda environment

http://ukitech.blogspot.com/2017/02/kernel.html

Ubuntu: installing TensorFlow for NVidia (CUDA) GPU

I am setting TensorFlow on:

• Ubuntu 16.04 LTS 64-bit
• 16 GiB RAM
• AMD Athlon(tm) II X4 640 Processor × 4 
• GeForce GTX 1050 Ti/PCIe/SSE2

Check if you have NVidia CUDA GPU

uki@uki-p6710f:~$  lspci | grep -i nvidia
01:00.0 VGA compatible controller: NVIDIA Corporation Device 1c82 (rev a1)
01:00.1 Audio device: NVIDIA Corporation Device 0fb9 (rev a1)

Check the name of your OS

uki@uki-p6710f:~$ uname -m && cat /etc/*release
x86_64
DISTRIB_ID=Ubuntu
DISTRIB_RELEASE=16.04
DISTRIB_CODENAME=xenial
DISTRIB_DESCRIPTION="Ubuntu 16.04.1 LTS"
NAME="Ubuntu"
VERSION="16.04.1 LTS (Xenial Xerus)"
ID=ubuntu
ID_LIKE=debian
PRETTY_NAME="Ubuntu 16.04.1 LTS"
VERSION_ID="16.04"
HOME_URL="http://www.ubuntu.com/"
SUPPORT_URL="http://help.ubuntu.com/"
BUG_REPORT_URL="http://bugs.launchpad.net/ubuntu/"
VERSION_CODENAME=xenial
UBUNTU_CODENAME=xenial

Check C compiler

uki@uki-p6710f:~$ gcc --version
gcc (Ubuntu 5.4.0-6ubuntu1~16.04.4) 5.4.0 20160609
Copyright (C) 2015 Free Software Foundation, Inc.
This is free software; see the source for copying conditions.  There is NO
warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.

Ubuntu Headers 

uki@uki-p6710f:~$ sudo apt-get install linux-headers-$(uname -r)
[sudo] password for uki:
Reading package lists... Done
Building dependency tree
Reading state information... Done
linux-headers-4.4.0-62-generic is already the newest version (4.4.0-62.83).
linux-headers-4.4.0-62-generic set to manually installed.
The following packages were automatically installed and are no longer required:
  linux-headers-4.4.0-31 linux-headers-4.4.0-31-generic linux-image-4.4.0-31-generic linux-image-extra-4.4.0-31-generic
Use 'sudo apt autoremove' to remove them.
0 upgraded, 0 newly installed, 0 to remove and 89 not upgraded.

Download newest CUDA installer (1.4GB)

https://developer.nvidia.com/cuda-downloads

https://developer.nvidia.com/compute/cuda/8.0/Prod2/local_installers/cuda_8.0.61_375.26_linux-run

Execute CUDA installer

 cd ~/Downloads/

uki@uki-p6710f:~/Downloads$ ls -alt

Mar  5 14:54 cuda_8.0.61_375.26_linux.run

Feb  2 09:53 NVIDIA-Linux-x86_64-375.10.run

$ sudo sh cuda_8.0.61_375.26_linux.run

Set environment variables

uki@uki-p6710f:~$ nano ~/.bashrc

##### CUDA 
export PATH=/usr/local/cuda-8.0/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda-8.0/lib64

uki@uki-p6710f:~$ echo $LD_LIBRARY_PATH

/usr/local/cuda-8.0/lib64

Install TensorFlow via pip3 (Python 3.5)

uki@uki-p6710f:~$ python --version

Python 3.5.2 :: Anaconda 4.3.0 (64-bit)

$ sudo apt install python3-pip

$  conda info --envs
# conda environments:
#
tensorflow               /home/uki/anaconda3/envs/tensorflow
root                  *  /home/uki/anaconda3

$ conda env create -f /Users/ukilucas/dev/uki.guru/conda_enviroment_GPU.yml

$source activate tensorflow

Setting Jupyter kernel to match Python conda environment

http://ukitech.blogspot.com/2017/02/kernel.html

Installing TensorFlow 1.0 as conda environment with yml definition file on MacBook Pro with NVidia GPU support

My conda environment yml file on GitHub

https://github.com/UkiDLucas/uki.guru/blob/master/conda_enviroment_GPU.yml

Execute yml file, set environment

$ conda env create --file .../conda_environment.yml
$ conda info --envs  

$ source activate py352_tf_gpu

Update jupyter notebook (iPython) kernel with new environment we created

$ python -m ipykernel install --user --name py352_tf_gpu --display-name "conda env py352_tf_gpu"$ jupyter notebook

Test run in jupyter notebook

You can see that jupyter notebook recognize GPU

You can see execution on MacBook Pro (late 2013 with NVidia). Both CPU and GPU are measured.

PLEASE READ THIS if you get DEAD KERNELS in GPU mode:
https://github.com/aymericdamien/TensorFlow-Examples/issues/38#issuecomment-280468579

Installing TensorFlow 1.0 as conda environment with yml definition file on MacBook Pro with NVidia GPU support

My conda environment yml file on GitHub

https://github.com/UkiDLucas/uki.guru/blob/master/conda_enviroment_GPU.yml

Execute yml file, set environment

$ conda env create --file .../conda_environment.yml
$ conda info --envs  

$ source activate py352_tf_gpu

Update jupyter notebook (iPython) kernel with new environment we created

$ python -m ipykernel install --user --name py352_tf_gpu --display-name "conda env py352_tf_gpu"$ jupyter notebook

Test run in jupyter notebook

You can see that jupyter notebook recognize GPU

You can see execution on MacBook Pro (late 2013 with NVidia). Both CPU and GPU are measured.

PLEASE READ THIS if you get DEAD KERNELS in GPU mode:
https://github.com/aymericdamien/TensorFlow-Examples/issues/38#issuecomment-280468579

Google TensorFlow 1.0 - Mac with Nvidia GPU installation and testing in jupyter notebook

TensorFlow 1.0 is promising more stable GPU build for Mac, let's test it.

Make sure you have CUDA installed

$ echo $DYLD_LIBRARY_PATH/usr/local/cuda/lib:

http://docs.nvidia.com/cuda/cuda-installation-guide-mac-os-x/#axzz4Ynrb42hi

$ echo $CUDA_HOME/Developer/NVIDIA/CUDA-8.0/

https://developer.nvidia.com/cudnn

$ sudo easy_install pip

Set variable with TensorFlow 1.0 binary

export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/gpu/tensorflow_gpu-1.0.0-py3-none-any.whl
$ echo $TF_BINARY_URL https://storage.googleapis.com/tensorflow/mac/gpu/tensorflow_gpu-1.0.0-py3-none-any.whl

Use pip to install TensorFlow

$ sudo pip3 install --upgrade  $TF_BINARY_URL Password:The directory '/Users/ukilucas/Library/Caches/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 '/Users/ukilucas/Library/Caches/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 tensorflow-gpu==1.0.0 from https://storage.googleapis.com/tensorflow/mac/gpu/tensorflow_gpu-1.0.0-py3-none-any.whl  Downloading https://storage.googleapis.com/tensorflow/mac/gpu/tensorflow_gpu-1.0.0-py3-none-any.whl (89.0MB)    100% |████████████████████████████████| 89.0MB 8.8kB/s Requirement already up-to-date: numpy>=1.11.0 in /Users/ukilucas/anaconda3/envs/tensorflow_gpu/lib/python3.5/site-packages (from tensorflow-gpu==1.0.0)Requirement already up-to-date: wheel>=0.26 in /Users/ukilucas/anaconda3/envs/tensorflow_gpu/lib/python3.5/site-packages (from tensorflow-gpu==1.0.0)Requirement already up-to-date: six>=1.10.0 in /Users/ukilucas/anaconda3/envs/tensorflow_gpu/lib/python3.5/site-packages (from tensorflow-gpu==1.0.0)Requirement already up-to-date: protobuf>=3.1.0 in /Users/ukilucas/anaconda3/envs/tensorflow_gpu/lib/python3.5/site-packages (from tensorflow-gpu==1.0.0)Collecting setuptools (from protobuf>=3.1.0->tensorflow-gpu==1.0.0)  Downloading setuptools-34.2.0-py2.py3-none-any.whl (389kB)    100% |████████████████████████████████| 399kB 2.0MB/s Requirement already up-to-date: appdirs>=1.4.0 in /Users/ukilucas/anaconda3/envs/tensorflow_gpu/lib/python3.5/site-packages (from setuptools->protobuf>=3.1.0->tensorflow-gpu==1.0.0)Requirement already up-to-date: packaging>=16.8 in /Users/ukilucas/anaconda3/envs/tensorflow_gpu/lib/python3.5/site-packages (from setuptools->protobuf>=3.1.0->tensorflow-gpu==1.0.0)Requirement already up-to-date: pyparsing in /Users/ukilucas/anaconda3/envs/tensorflow_gpu/lib/python3.5/site-packages (from packaging>=16.8->setuptools->protobuf>=3.1.0->tensorflow-gpu==1.0.0)Installing collected packages: tensorflow-gpu, setuptools  Found existing installation: tensorflow-gpu 0.12.1    Uninstalling tensorflow-gpu-0.12.1:      Successfully uninstalled tensorflow-gpu-0.12.1  Found existing installation: setuptools 34.1.1    Uninstalling setuptools-34.1.1:      Successfully uninstalled setuptools-34.1.1Successfully installed setuptools-34.2.0 tensorflow-gpu-1.0.0(tensorflow_gpu) uki@UkiPEsMcBookPro 192.168.1.24 19:44 dev $ 

Start jupyter notebook in the right (same) environment

(tensorflow_gpu) $ jupyter notebook

Try in jupyter notebook

import tensorflow as tf
import time
from tensorflow.python.client import device_lib
​
def get_available_CPU_GPU():
   devices = device_lib.list_local_devices()
   #return [x.name for x in devices if x.device_type == 'CPU']
   return [x.name for x in devices ]
​
print(get_available_CPU_GPU())
['/cpu:0', '/gpu:0']

start = timeit.timeit()
print ("starting")with tf.device('/gpu:0'):
    # [1.0, 2.0, 3.0, 4.0, 5.0, 6.0]
    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 log_device_placement set to True.
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
# Runs the op.
print (sess.run(c))
end = timeit.timeit()
print ("elapsed", end - start)starting
[[ 22. 28.]
[ 49. 64.]]
elapsed -0.003607149003073573

It is a win so far, time will show if it is usable.

Reference:

https://www.tensorflow.org/install/install_mac

Google TensorFlow 1.0 - Mac with Nvidia GPU installation and testing in jupyter notebook

TensorFlow 1.0 is promising more stable GPU build for Mac, let's test it.

Make sure you have CUDA installed

$ echo $DYLD_LIBRARY_PATH/usr/local/cuda/lib:

http://docs.nvidia.com/cuda/cuda-installation-guide-mac-os-x/#axzz4Ynrb42hi

$ echo $CUDA_HOME/Developer/NVIDIA/CUDA-8.0/

https://developer.nvidia.com/cudnn

$ sudo easy_install pip

Set variable with TensorFlow 1.0 binary

export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/gpu/tensorflow_gpu-1.0.0-py3-none-any.whl
$ echo $TF_BINARY_URL https://storage.googleapis.com/tensorflow/mac/gpu/tensorflow_gpu-1.0.0-py3-none-any.whl

Use pip to install TensorFlow

$ sudo pip3 install --upgrade  $TF_BINARY_URL Password:The directory '/Users/ukilucas/Library/Caches/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 '/Users/ukilucas/Library/Caches/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 tensorflow-gpu==1.0.0 from https://storage.googleapis.com/tensorflow/mac/gpu/tensorflow_gpu-1.0.0-py3-none-any.whl  Downloading https://storage.googleapis.com/tensorflow/mac/gpu/tensorflow_gpu-1.0.0-py3-none-any.whl (89.0MB)    100% |████████████████████████████████| 89.0MB 8.8kB/s Requirement already up-to-date: numpy>=1.11.0 in /Users/ukilucas/anaconda3/envs/tensorflow_gpu/lib/python3.5/site-packages (from tensorflow-gpu==1.0.0)Requirement already up-to-date: wheel>=0.26 in /Users/ukilucas/anaconda3/envs/tensorflow_gpu/lib/python3.5/site-packages (from tensorflow-gpu==1.0.0)Requirement already up-to-date: six>=1.10.0 in /Users/ukilucas/anaconda3/envs/tensorflow_gpu/lib/python3.5/site-packages (from tensorflow-gpu==1.0.0)Requirement already up-to-date: protobuf>=3.1.0 in /Users/ukilucas/anaconda3/envs/tensorflow_gpu/lib/python3.5/site-packages (from tensorflow-gpu==1.0.0)Collecting setuptools (from protobuf>=3.1.0->tensorflow-gpu==1.0.0)  Downloading setuptools-34.2.0-py2.py3-none-any.whl (389kB)    100% |████████████████████████████████| 399kB 2.0MB/s Requirement already up-to-date: appdirs>=1.4.0 in /Users/ukilucas/anaconda3/envs/tensorflow_gpu/lib/python3.5/site-packages (from setuptools->protobuf>=3.1.0->tensorflow-gpu==1.0.0)Requirement already up-to-date: packaging>=16.8 in /Users/ukilucas/anaconda3/envs/tensorflow_gpu/lib/python3.5/site-packages (from setuptools->protobuf>=3.1.0->tensorflow-gpu==1.0.0)Requirement already up-to-date: pyparsing in /Users/ukilucas/anaconda3/envs/tensorflow_gpu/lib/python3.5/site-packages (from packaging>=16.8->setuptools->protobuf>=3.1.0->tensorflow-gpu==1.0.0)Installing collected packages: tensorflow-gpu, setuptools  Found existing installation: tensorflow-gpu 0.12.1    Uninstalling tensorflow-gpu-0.12.1:      Successfully uninstalled tensorflow-gpu-0.12.1  Found existing installation: setuptools 34.1.1    Uninstalling setuptools-34.1.1:      Successfully uninstalled setuptools-34.1.1Successfully installed setuptools-34.2.0 tensorflow-gpu-1.0.0(tensorflow_gpu) uki@UkiPEsMcBookPro 192.168.1.24 19:44 dev $ 

Start jupyter notebook in the right (same) environment

(tensorflow_gpu) $ jupyter notebook

Try in jupyter notebook

import tensorflow as tf
import time
from tensorflow.python.client import device_lib
​
def get_available_CPU_GPU():
   devices = device_lib.list_local_devices()
   #return [x.name for x in devices if x.device_type == 'CPU']
   return [x.name for x in devices ]
​
print(get_available_CPU_GPU())
['/cpu:0', '/gpu:0']

start = timeit.timeit()
print ("starting")with tf.device('/gpu:0'):
    # [1.0, 2.0, 3.0, 4.0, 5.0, 6.0]
    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 log_device_placement set to True.
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
# Runs the op.
print (sess.run(c))
end = timeit.timeit()
print ("elapsed", end - start)starting
[[ 22. 28.]
[ 49. 64.]]
elapsed -0.003607149003073573

It is a win so far, time will show if it is usable.

Reference:

https://www.tensorflow.org/install/install_mac

Installing Keras (with TensorFlow and Thano) using Python conda

(tensorflow_cpu) $ anaconda search -t conda keras | grep forge

     conda-forge/keras         |    1.0.7 | conda           | linux-64, win-32, win-64, osx-64

(tensorflow_cpu) $ conda install -c conda-forge keras=1.0.7

Installing Keras (with TensorFlow and Thano) using Python conda

(tensorflow_cpu) $ anaconda search -t conda keras | grep forge

     conda-forge/keras         |    1.0.7 | conda           | linux-64, win-32, win-64, osx-64

(tensorflow_cpu) $ conda install -c conda-forge keras=1.0.7

Setting TensorFlow Python on MacBook Pro (yet TBD)

In the "free moment" (2-4AM?) I would like to set up TensorFlow on my Mac.

https://gist.github.com/ageitgey/819a51afa4613649bd18

but that will have to wait until I get to it.

Setting TensorFlow Python on MacBook Pro (yet TBD)

In the "free moment" (2-4AM?) I would like to set up TensorFlow on my Mac.

https://gist.github.com/ageitgey/819a51afa4613649bd18

but that will have to wait until I get to it.

TensorFlow: MacBook Pro: detect which CPU and GPU devices are available

from tensorflow.python.client import device_lib

def get_available_gpus():
    devices = device_lib.list_local_devices()
    #return [x.name for x in devices if x.device_type == 'CPU']
    return [x.name for x in devices ]

print(get_available_gpus())

['/cpu:0']

Currently, I can see and execute only on CPU.

MacBook Pro i7 Late 2013

Device 0: "GeForce GT 750M" CUDA Driver Version / 

Runtime Version 8.0 / 8.0 CUDA Capability Major/Minor version number: 3.0 

Total amount of global memory: 2048 MBytes (2147024896 bytes) 

( 2) Multiprocessors, 

(192) CUDA Cores/MP: 384 CUDA Cores GPU 

Max Clock rate: 926 MHz (0.93 GHz) 

Memory Clock rate: 2508 Mhz Memory Bus Width: 128-bit L2 

Cache Size: 262144 bytes

http://osxdaily.com/2017/01/08/disable-gpu-switching-macbook-pro/

Still not working with TensorFlow

start = timeit.timeit()
print ("starting")

with tf.device('/cpu:0'):
    # [1.0, 2.0, 3.0, 4.0, 5.0, 6.0]
    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 log_device_placement set to True.
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
# Runs the op.
print (sess.run(c))
end = timeit.timeit()
print ("elapsed", end - start)

starting
[[ 22.  28.]
 [ 49.  64.]]
elapsed -0.0033593010011827573

TensorFlow: MacBook Pro: detect which CPU and GPU devices are available

from tensorflow.python.client import device_lib

def get_available_gpus():
    devices = device_lib.list_local_devices()
    #return [x.name for x in devices if x.device_type == 'CPU']
    return [x.name for x in devices ]

print(get_available_gpus())

['/cpu:0']

Currently, I can see and execute only on CPU.

MacBook Pro i7 Late 2013

Device 0: "GeForce GT 750M" CUDA Driver Version / 

Runtime Version 8.0 / 8.0 CUDA Capability Major/Minor version number: 3.0 

Total amount of global memory: 2048 MBytes (2147024896 bytes) 

( 2) Multiprocessors, 

(192) CUDA Cores/MP: 384 CUDA Cores GPU 

Max Clock rate: 926 MHz (0.93 GHz) 

Memory Clock rate: 2508 Mhz Memory Bus Width: 128-bit L2 

Cache Size: 262144 bytes

http://osxdaily.com/2017/01/08/disable-gpu-switching-macbook-pro/

Still not working with TensorFlow

start = timeit.timeit()
print ("starting")

with tf.device('/cpu:0'):
    # [1.0, 2.0, 3.0, 4.0, 5.0, 6.0]
    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 log_device_placement set to True.
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
# Runs the op.
print (sess.run(c))
end = timeit.timeit()
print ("elapsed", end - start)

starting
[[ 22.  28.]
 [ 49.  64.]]
elapsed -0.0033593010011827573

CUDA: late 2013 MacBook Pro GPU: GeForce GT 750M 384 Cores

Installing CUDA on MacBook Pro


$ brew update
$ brew upgrade


$ id -g
20

$ sudo chown -R uki:20 *
Password:

$ brew link pcre

$ brew install coreutils swig
Warning: coreutils-8.26 already installed
==> Downloading https://homebrew.bintray.com/bottles/swig-3.0.11.sierra.bottle.tar.gz
######################################################################## 100.0%
==> Pouring swig-3.0.11.sierra.bottle.tar.gz
🍺 /usr/local/Cellar/swig/3.0.11: 754 files, 5.5M


$ brew cask install cuda
🍺 cuda was successfully installed!


$ brew cask info cuda

cuda: 8.0.55

https://developer.nvidia.com/cuda-zone


$ kextstat | grep -i cuda

... com.nvidia.CUDA (1.1.0) ..


$ cd /usr/local/cuda/samples/
$ sudo make -C 1_Utilities/deviceQuery




Makefile NsightEclipse.xml deviceQuery deviceQuery.cpp deviceQuery.o readme.txt


$ /usr/local/cuda/samples/1_Utilities/deviceQuery/deviceQuery



Device 0: "GeForce GT 750M"
CUDA Driver Version / Runtime Version 8.0 / 8.0
CUDA Capability Major/Minor version number: 3.0
Total amount of global memory: 2048 MBytes (2147024896 bytes)
( 2) Multiprocessors, (192) CUDA Cores/MP: 384 CUDA Cores
deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 8.0, CUDA Runtime Version = 8.0, NumDevs = 1, Device0 = GeForce GT 750M
Result = PASS

cuDNN Download

Register.

https://developer.nvidia.com/rdp/cudnn-download

Download file for OSX: cudnn-8.0-osx-x64-v5.1.tgz

and copy the file to your favorite place..

cd ~/Dropbox/dev/NVidia_CUDA/
NVidia_CUDA $ tar zxvf cudnn-8.0-osx-x64-v5.1.tgz
cd $ /cuda/include
$ sudo cp cudnn.h /usr/local/cuda/include/
cd ../lib/
$ sudo cp libcudnn* /usr/local/cuda/lib/

Add to your bash_profile

########## CUDA cuDNN ########## created: February 6, 2017

export DYLD_LIBRARY_PATH="/usr/local/cuda/lib":$DYLD_LIBRARY_PATH

$ brew cask install java
$ brew install bazel

(carnd-term1) uki@Uki-PEs-MacBookPro 16:46 tensorflow $ TF_UNOFFICIAL_SETTING=1 ./configure


Please specify the location of python. [Default is /Users/ukilucas/anaconda3/envs/carnd-term1/bin/python]: /Users/ukilucas/anaconda3/envs/carnd-term1/bin/python


Please specify optimization flags to use during compilation when bazel option "--config=opt" is specified [Default is -march=native]:


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 the XLA just-in-time compiler (experimental)? [y/N] y


XLA JIT support will be enabled for TensorFlow


Found possible Python library paths:


/Users/ukilucas/anaconda3/envs/carnd-term1/lib/python3.5/site-packages


Please input the desired Python library path to use. Default is [/Users/ukilucas/anaconda3/envs/carnd-term1/lib/python3.5/site-packages]






Using python library path: /Users/ukilucas/anaconda3/envs/carnd-term1/lib/python3.5/site-packages


Do you wish to build TensorFlow with OpenCL support? [y/N] N


No OpenCL 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 which gcc should be used by nvcc as the host compiler. [Default is /usr/bin/gcc]:


Please specify the CUDA SDK version you want to use, e.g. 7.0. [Leave empty to use system default]:


Please specify the location where CUDA 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 use system default]:


Please specify the location where cuDNN 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: 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"]: 3.0


INFO: Starting clean (this may take a while). Consider using --expunge_async if the clean takes more than several minutes.


............


INFO: All external dependencies fetched successfully.


Configuration finished

bazel build -c opt --config=cuda //tensorflow/cc:tutorials_example_trainer
bazel build -c opt --config=cuda //tensorflow/tools/pip_package:build_pip_package
bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg
pip install /tmp/tensorflow_pkg/tensorflow-0.6.0-py2-none-any.whl

CUDA: late 2013 MacBook Pro GPU: GeForce GT 750M 384 Cores

Installing CUDA on MacBook Pro


$ brew update
$ brew upgrade


$ id -g
20

$ sudo chown -R uki:20 *
Password:

$ brew link pcre

$ brew install coreutils swig
Warning: coreutils-8.26 already installed
==> Downloading https://homebrew.bintray.com/bottles/swig-3.0.11.sierra.bottle.tar.gz
######################################################################## 100.0%
==> Pouring swig-3.0.11.sierra.bottle.tar.gz
🍺 /usr/local/Cellar/swig/3.0.11: 754 files, 5.5M


$ brew cask install cuda
🍺 cuda was successfully installed!


$ brew cask info cuda

cuda: 8.0.55

https://developer.nvidia.com/cuda-zone


$ kextstat | grep -i cuda

... com.nvidia.CUDA (1.1.0) ..


$ cd /usr/local/cuda/samples/
$ sudo make -C 1_Utilities/deviceQuery




Makefile NsightEclipse.xml deviceQuery deviceQuery.cpp deviceQuery.o readme.txt


$ /usr/local/cuda/samples/1_Utilities/deviceQuery/deviceQuery



Device 0: "GeForce GT 750M"
CUDA Driver Version / Runtime Version 8.0 / 8.0
CUDA Capability Major/Minor version number: 3.0
Total amount of global memory: 2048 MBytes (2147024896 bytes)
( 2) Multiprocessors, (192) CUDA Cores/MP: 384 CUDA Cores
deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 8.0, CUDA Runtime Version = 8.0, NumDevs = 1, Device0 = GeForce GT 750M
Result = PASS

cuDNN Download

Register.

https://developer.nvidia.com/rdp/cudnn-download

Download file for OSX: cudnn-8.0-osx-x64-v5.1.tgz

and copy the file to your favorite place..

cd ~/Dropbox/dev/NVidia_CUDA/
NVidia_CUDA $ tar zxvf cudnn-8.0-osx-x64-v5.1.tgz
cd $ /cuda/include
$ sudo cp cudnn.h /usr/local/cuda/include/
cd ../lib/
$ sudo cp libcudnn* /usr/local/cuda/lib/

Add to your bash_profile

########## CUDA cuDNN ########## created: February 6, 2017

export DYLD_LIBRARY_PATH="/usr/local/cuda/lib":$DYLD_LIBRARY_PATH

$ brew cask install java
$ brew install bazel

(carnd-term1) uki@Uki-PEs-MacBookPro 16:46 tensorflow $ TF_UNOFFICIAL_SETTING=1 ./configure


Please specify the location of python. [Default is /Users/ukilucas/anaconda3/envs/carnd-term1/bin/python]: /Users/ukilucas/anaconda3/envs/carnd-term1/bin/python


Please specify optimization flags to use during compilation when bazel option "--config=opt" is specified [Default is -march=native]:


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 the XLA just-in-time compiler (experimental)? [y/N] y


XLA JIT support will be enabled for TensorFlow


Found possible Python library paths:


/Users/ukilucas/anaconda3/envs/carnd-term1/lib/python3.5/site-packages


Please input the desired Python library path to use. Default is [/Users/ukilucas/anaconda3/envs/carnd-term1/lib/python3.5/site-packages]






Using python library path: /Users/ukilucas/anaconda3/envs/carnd-term1/lib/python3.5/site-packages


Do you wish to build TensorFlow with OpenCL support? [y/N] N


No OpenCL 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 which gcc should be used by nvcc as the host compiler. [Default is /usr/bin/gcc]:


Please specify the CUDA SDK version you want to use, e.g. 7.0. [Leave empty to use system default]:


Please specify the location where CUDA 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 use system default]:


Please specify the location where cuDNN 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: 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"]: 3.0


INFO: Starting clean (this may take a while). Consider using --expunge_async if the clean takes more than several minutes.


............


INFO: All external dependencies fetched successfully.


Configuration finished

bazel build -c opt --config=cuda //tensorflow/cc:tutorials_example_trainer
bazel build -c opt --config=cuda //tensorflow/tools/pip_package:build_pip_package
bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg
pip install /tmp/tensorflow_pkg/tensorflow-0.6.0-py2-none-any.whl

Updating Anaconda TensorFlow on Mac for use with jupyter notebook

I have been running TensorFlow, but it has been acting up and needed an update.

# Initializing the variables
# init = tf.initialize_all_variables() # older TF 0.11.0-py35_0 conda-forge
init = tf.global_variables_initializer() # newer TF 0.12.1-py35_1 conda-forge

Check what environment you are running, especially if you switch often, or just restarted the computer.

$ conda info --envs
# conda environments:
#
CarND-TensorFlow-Lab /Users/ukilucas/anaconda3/envs/CarND-TensorFlow-Lab
IntroToTensorFlow /Users/ukilucas/anaconda3/envs/IntroToTensorFlow
py3 /Users/ukilucas/anaconda3/envs/py3
py35 /Users/ukilucas/anaconda3/envs/py35
root * /Users/ukilucas/anaconda3

I have been running "nohup jupyter notebook &" command and my Jupyter Notebook did not see my newest TensorFlow environments. I wanted to update my root anyway.

I execute the following:

$ conda install -c conda-forge tensorflow
Fetching package metadata .........
Solving package specifications: ..........
Package plan for installation in environment /Users/ukilucas/anaconda3:

The following packages will be UPDATED:

protobuf: 3.0.0-py35_0 conda-forge --> 3.1.0-py35_0 conda-forge
tensorflow: 0.11.0-py35_0 conda-forge --> 0.12.1-py35_1 conda-forge
Proceed ([y]/n)? y
Unlinking packages ...
[ COMPLETE ]|#####################| 100%
Linking packages ...
[ COMPLETE ]|#####################| 100%
$

Update:

I decided to create my TensorFlow in a new environment because I have Python 3.6:

$ python --version 

Python 3.6.0 :: Continuum Analytics, Inc.

$  conda create -n tensorflow python=3.5 

Another option is DOWNGRADE Python 3.6 to Python 3.5

$ conda install python=3.5

Updating Anaconda TensorFlow on Mac for use with jupyter notebook

I have been running TensorFlow, but it has been acting up and needed an update.

# Initializing the variables
# init = tf.initialize_all_variables() # older TF 0.11.0-py35_0 conda-forge
init = tf.global_variables_initializer() # newer TF 0.12.1-py35_1 conda-forge

Check what environment you are running, especially if you switch often, or just restarted the computer.

$ conda info --envs
# conda environments:
#
CarND-TensorFlow-Lab /Users/ukilucas/anaconda3/envs/CarND-TensorFlow-Lab
IntroToTensorFlow /Users/ukilucas/anaconda3/envs/IntroToTensorFlow
py3 /Users/ukilucas/anaconda3/envs/py3
py35 /Users/ukilucas/anaconda3/envs/py35
root * /Users/ukilucas/anaconda3

I have been running "nohup jupyter notebook &" command and my Jupyter Notebook did not see my newest TensorFlow environments. I wanted to update my root anyway.

I execute the following:

$ conda install -c conda-forge tensorflow
Fetching package metadata .........
Solving package specifications: ..........
Package plan for installation in environment /Users/ukilucas/anaconda3:

The following packages will be UPDATED:

protobuf: 3.0.0-py35_0 conda-forge --> 3.1.0-py35_0 conda-forge
tensorflow: 0.11.0-py35_0 conda-forge --> 0.12.1-py35_1 conda-forge
Proceed ([y]/n)? y
Unlinking packages ...
[ COMPLETE ]|#####################| 100%
Linking packages ...
[ COMPLETE ]|#####################| 100%
$

Update:

I decided to create my TensorFlow in a new environment because I have Python 3.6:

$ python --version 

Python 3.6.0 :: Continuum Analytics, Inc.

$  conda create -n tensorflow python=3.5 

Another option is DOWNGRADE Python 3.6 to Python 3.5

$ conda install python=3.5