$ echo "my, this is my sentence" | sed 's/my/My/'
My, this is my sentence
$ echo "convert /usr/local/bin to /common/bin" | sed "s/\/usr\/local\/bin/\/common\/bin/"
convert /common/bin to /common/bin
$ echo "Repeat me 5 times." | sed 's/[0-9]/& & & & &/'
Repeat me 5 5 5 5 5 times.
~ uki$ sudo pip install python-can
Collecting python-can
Downloading python-can-1.5.2.tar.gz (77kB)
100% |████████████████████████████████| 81kB 3.3MB/s
Installing collected packages: python-can
Running setup.py install for python-can ... done
Successfully installed python-can-1.5.2
UnicodeDecodeError: 'utf-8' codec can't decode byte(result, consumed) = self._buffer_decode(data, self.errors, final)
def read_rows(CSV_file_path: str):
import sys
print("default encoding", sys.getdefaultencoding())
import csv
rows = []
with open(CSV_file_path, 'rt', encoding="utf-8") as csvfile:
cvs_reader = csv.reader(csvfile, delimiter=',', quotechar='"')
for row in cvs_reader: # row is a list
rows.append(row)
return rows
# *args is the usual notation for optional, non-keyword arguments
# **kwargs is the usual notation for optional, keyword arguments
def test_var_args(regular_argument, *args, **kwargs):
print ("- regular argument:", regular_argument)
for arg in args:
print ("- unnamed arguments arg:", arg)
for key in kwargs:
print ("- named arguments: %s: %s" % (key, kwargs[key]))
test_var_args(1, "two", 3, four=4, five="five")
- regular argument: 1
- unnamed arguments arg: two
- unnamed arguments arg: 3
- named arguments: four: 4
- named arguments: five: five
def bubble_sort(the_list: list, verbose: int=0):
"""
author: @UkiDLcuas
This function changes the provided list.
The list can contain integers, decimal numbers, strings, etc.
The list is sorted in ascending order (first to last).
The function does not return anything.
"""
if verbose > 0:
iteration = 0
# count remining bubbles ( aka step backwards)
start = len(the_list)-1 # end, zero-based list
stop = 0 # beginning
step = -1 # backwards
for remaining_bubbles in range(start, stop, step):
for i in range(remaining_bubbles):
if verbose > 0:
iteration = iteration + 1
print("iteration", iteration, "remaining_bubbles", remaining_bubbles, "index", i)
print(" ", the_list)
print(" comparing if is", the_list[i], "bigger than", the_list[i+1])
if the_list[i] > the_list[i+1]:
# swap
temp = the_list[i+1] # temp placehoder for the value to be moved
the_list[i+1] = the_list[i] # bubble up
the_list[i] = temp # bubble down
if verbose > 0:
print("*** finished", len(the_list), "element list in ", iteration, "iterations")
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)
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
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.
$ pip install flask-ask
$ jupyter notebook
$ python skill_001.py * Running on http://127.0.0.1:5000/ (Press CTRL+C to quit) * Restarting with stat * Debugger is active! * Debugger pin code: 275-052-815
$ /Applications/ngrok http 5000
ngrok by @inconshreveable (Ctrl+C to quit) Session Status online Version 2.1.18 Region United States (us) Web Interface http://127.0.0.1:4040 Forwarding http://908cd95a.ngrok.io -> localhost:5000 Forwarding https://908cd95a.ngrok.io -> localhost:5000 Connections ttl opn rt1 rt5 p50 p90 0 0 0.00 0.00 0.00 0.00
$ conda env create --file .../conda_environment.yml
$ conda info --envs
$ source activate py352_tf_gpu
$ python -m ipykernel install --user --name py352_tf_gpu --display-name "conda env py352_tf_gpu"$ jupyter notebook
$ echo $DYLD_LIBRARY_PATH/usr/local/cuda/lib:
$ echo $CUDA_HOME/Developer/NVIDIA/CUDA-8.0/
$ sudo easy_install pip
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
$ 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 $
(tensorflow_gpu) $ 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
import PIL
import numpy
from PIL import Image
def resize_image(numpy_array_image, new_height):
# convert nympy array image to PIL.Image
image = Image.fromarray(numpy.uint8(numpy_array_image))
old_width = float(image.size[0])
old_height = float(image.size[1])
ratio = float( new_height / old_height)
new_width = int(old_width * ratio)
image = image.resize((new_width, new_height), PIL.Image.ANTIALIAS)
# convert PIL.Image into nympy array back again
return array(image)
$ conda info --envs# conda environments:#/Users/uki/.julia/conda/3/Users/uki/.julia/packages/ORCA/uEiWT/depsbase * /anaconda3py2 /anaconda3/envs/py2py37 /anaconda3/envs/py37
$ source activate tensorflow
$ sudo pip install ipykernel
$ python -m ipykernel install --user --name tensorflow --display-name "conda env tensorflow"
import tensorflow as tf
print("OUT: tensorflow imported")
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())
print(get_available_CPU_GPU())
---------------------------------------------------------------------------
ImportError Traceback (most recent call last)in ()
----> 1 from tensorflow.python.client import device_lib
2
3 def get_available_CPU_GPU():
4 devices = device_lib.list_local_devices()
5 #return [x.name for x in devices if x.device_type == 'CPU']
ImportError: No module named 'tensorflow.python'
ImportError: No module named 'sklearn.model_selection'(tensorflow_gpu) $ conda list | grep scikit
$ conda install scikit-learn
Fetching package metadata .........
Solving package specifications: .
Package plan for installation in environment /Users/ukilucas/anaconda3/envs/tensorflow_gpu:
The following NEW packages will be INSTALLED:
mkl: 2017.0.1-0 numpy: 1.11.3-py35_0 scikit-learn: 0.18.1-np111py35_1 scipy: 0.18.1-np111py35_1
(tensorflow_gpu) $ conda list | grep scikit
scikit-learn 0.18.1 np111py35_1
import sklearn0.18.1
print (sklearn.__version__)
from sklearn.model_selection import train_test_split
import sklearn
print (sklearn.__version__)
0.17.1
$ conda install scikit-learn
$ source activate tensorflow
$ conda install scikit-learn
(tensorflow) uki@Uki ~ $ jupyter notebook ~/dev/
$ conda listscikit-learn 0.18.1
sklearn 0.0
(py35) ~ $ conda remove scikit-learn
(py35) ~ $ pip uninstall scikit-learn
(py35) ~ $ pip uninstall sklearn
(py35) ~ $ conda install scikit-learn
(py35) ~ $ jupyter notebook ~/dev/
'/Users/ukilucas/anaconda3/bin/python'
$ conda list
# packages in environment at /Users/ukilucas/anaconda3/envs/py35:
Deleting Old and Reinstalling Jupyter Notebook
from jupyter_core.paths import jupyter_data_dir
print(jupyter_data_dir())
/Users/ukilucas/Library/Jupyter
Deleting :
- /Users/ukilucas/Library/Jupyter
- ~/.ipython
- ~/.jupyter
$ rm -r ~/Library/Jupyter
$ rm -r ~/.ipython
$ rm -r ~/.jupyter
$ rm -r ~/anaconda3/
Downloads $ wget https://repo.continuum.io/archive/Anaconda3-4.3.0-MacOSX-x86_64.sh
bash Anaconda3-4.3.0-MacOSX-x86_64.sh
TensorFlow Installation instructions# Python 3.5
$ conda create -n tensorflow python=3.5# Mac OS X, GPU enabled, Python 3.4 or 3.5:
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/gpu/tensorflow_gpu-0.12.1-py3-none-any.whl
$ sudo pip3 install --ignore-installed --upgrade $TF_BINARY_URL