docker swarm simple command

*install virtualbox

-+-

#install virtualbox on terminal

#osx

brew install virtualbox

#linux

sudo apt install virtualbox

-+-

 

* install docker-machine

...

#osx

curl -L https://github.com/docker/machine/releases/download/v0.16.2/docker-machine-`uname -s`-`uname -m` >/usr/local/bin/docker-machine && chmod +x /usr/local/bin/docker-machine

#linux

curl -L https://github.com/docker/machine/releases/download/v0.16.2/docker-machine-`uname -s`-`uname -m` >/tmp/docker-machine && chmod +x /tmp/docker-machine && sudo cp /tmp/docker-machine /usr/local/bin/docker-machine

...

*docker-machine

...

#docker-machine basic cmd

docker-machine ls

docker-machine rm name

docker-machine kill name

docker-machine create name

...

python save & load argparse

 

refer to code:

..

from argparse import ArgumentParser

import json

parser = ArgumentParser()

parser.add_argument('--seed', type=int, default=8)

parser.add_argument('--resume', type=str, default='a/b/c.ckpt')

parser.add_argument('--surgery', type=str, default='190', choices=['190', '417'])

args = parser.parse_args()

with open('commandline_args.txt', 'w') as f:

json.dump(args.__dict__, f, indent=2)

parser = ArgumentParser()

args = parser.parse_args()

with open('commandline_args.txt', 'r') as f:

args.__dict__ = json.load(f)

print(args)

..

Thank you!

🙇🏻‍♂️

1D sequence (signal) data resampling (python)

 import numpy as np

import matplotlib.pyplot as plt

from scipy.signal import resample

from scipy.interpolate import interp1d

def ResampleLinear1D(original, targetLen):

original = np.array(original, dtype=np.float)

index_arr = np.linspace(0, len(original)-1, num=targetLen, dtype=np.float)

index_floor = np.array(index_arr, dtype=np.int) #Round down

index_ceil = index_floor + 1

index_rem = index_arr - index_floor #Remain

val1 = original[index_floor]

val2 = original[index_ceil % len(original)]

interp = val1 * (1.0-index_rem) + val2 * index_rem

assert(len(interp) == targetLen)

return interp

if __name__=="__main__":

original = np.sin(np.arange(256)/10.0)

targetLen = 100

# Method 1: Use scipy interp1d (linear interpolation)

# This is the simplest conceptually as it just uses linear interpolation. Scipy

# also offers a range of other interpolation methods.

f = interp1d(np.arange(256), original, 'linear')

plt.plot(np.apply_along_axis(f, 0, np.linspace(0, 255, num=targetLen)))

# Method 2: Use numpy to do linear interpolation

# If you don't have scipy, you can do it in numpy with the above function

plt.plot(ResampleLinear1D(original, targetLen))

# Method 3: Use scipy's resample

# Converts the signal to frequency space (Fourier method), then back. This

# works efficiently on periodic functions but poorly on non-periodic functions.

plt.plot(resample(original, targetLen))

plt.show()

jupyter notebook to python py

 

run this command

>jupyter nbconvert your_notebook_name.ipynb --to python

thank you.

pytorch cuda definition

Their syntax varies slightly, but they are equivalent:

⠀	.to(name)	.to(device)	.cuda()
CPU	to('cpu')	to(torch.device('cpu'))	cpu()
Current GPU	to('cuda')	to(torch.device('cuda'))	cuda()
Specific GPU	to('cuda:1')	to(torch.device('cuda:1'))	cuda(device=1)

Note: the current cuda device is 0 by default, but this can be set with torch.cuda.set_device().

write classification_report to txt file

Refer to code ^^ 

..

from sklearn.metrics import classification_report

report = classification_report(y_test_true_list, y_test_pred_list, labels=labels, target_names=target_names)

fp = open('report.txt', 'w')

fp.write(report)

fp.close()

..

Thank you.

www.marearts.com

python pil change dpi without save file

 

look at the code! ^^

..

#load image

tif_path = './input_img.tif'

#open image

pil_image = Image.open(tif_path)

#change dpi in memory

temp_mem_file = BytesIO()

page.save(temp_mem_file, format='png', dpi=(100,100))

#save it to pil

temp_mem_file.seek(0)

pil_image = Image.open(temp_mem_file)

#do it someting

#..

#save file

pil_image.save('./output.png')

#www.marearts.com

#study.marearts.com

..

Thank you.

convert numpy.ndarray object to float

*Here is object dtype numpy.ndarray

print(oneD_pt.dtype, type(oneD_pt), oneD_pt)
> object <class 'numpy.ndarray'> [3 2 1 2 2] 

* convert float dtype numpy.ndarray

oneD_pt=oneD_pt.astype('float')

print(oneD_pt.dtype, type(oneD_pt), oneD_pt)

> float64 <class 'numpy.ndarray'> [3. 2. 1. 2. 2.]

thank you.

python argparse simple example

 

It's useful to save hyper parameter for machine learning

import argparse

paser = argparse.ArgumentParser()

args = paser.parse_args("")

args.batch_size=10

args.epoch=10

Thank you.

vs code server failed to start

try to kill the server which you want access and try to access again

1 .ctrl-shift-p and choose:
2. Remote-SSH: kill VS Code Server on Host...

3. access remote again

Thank you.

nginx.conf, set to accept last path and poxy it.

 

ex)

www.xxx.yy/aaa/ping

www.xxx.yy/bbb/ping

Those urls will go to /ping proxy.

location ~ ^/.*/(ping|invocations) {

rewrite ^/.*/(.*)$ /$1 last;

proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;

proxy_set_header Host $http_host;

proxy_redirect off;

proxy_pass http://gunicorn/$1;

}

TensorFlow cuda version compatible

 

AWS dynamoDB safe searching way, search item by scan and get it by paginator.

 refer to follow code

--

import boto3

dynamo_client = boto3.client('dynamodb')

def Search_Item(TABLE_NAME, uid):

paginator = dynamo_client.get_paginator('scan')

operation_parameters = {

'TableName': TABLE_NAME,

'FilterExpression': 'UID = :search',

'ExpressionAttributeValues': {

':search': {'S': uid},

}

}

page_iterator = paginator.paginate(**operation_parameters)

return page_iterator

UID_list = []

TABLE_NAME ='example_Table'

UID ='example_uid'

page_iterator = Search_Item(TABLE_NAME, UID)

for page in page_iterator:

for item in page['Items']:

UID_list.append(item['UID']['S'])

--

Thank you.

www.marearts.com