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Herreshoff

 


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Python: Passing *args and **kwargs into a function

# *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
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Python: Passing *args and **kwargs into a function

# *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
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Python bubble sort

Since I had to learn python for my machine learning and self-driving classes and I had to review the basics. Not to waste my efforts, I am creating a new guide, you can see more in:

https://github.com/UkiDLucas/python_zen_interviews

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")
on No comments:

Python bubble sort

Since I had to learn python for my machine learning and self-driving classes and I had to review the basics. Not to waste my efforts, I am creating a new guide, you can see more in:

https://github.com/UkiDLucas/python_zen_interviews

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")
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Sailing on Amazon river? - No, not really.

This video is not related to sailing on great lakes, but it is amazing and worth seeing...

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AWS

Notes on how I use Amazon AWS EC2 instances for Convolutional Deep Neural Networks Machine Learning, using powerful GPU CUDA configurations.

I have moved this post to:

https://ukidlucas.github.io/posts/AWS.html
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AWS

Notes on how I use Amazon AWS EC2 instances for Convolutional Deep Neural Networks Machine Learning, using powerful GPU CUDA configurations.

I have moved this post to:

https://ukidlucas.github.io/posts/AWS.html
on No comments:

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



















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No comments:
  































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














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