Zabir Al Nazi Nabil zabir-nabil

## mlflow_experiments
# Speaker Experiments and MLflow

Speaker experiments take relatively long time to finish. For tracking the experiment progress, we can utilize MLflow.

### What is MLflow?

MLflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models. MLflow offers a set of lightweight APIs that can be used with any existing machine learning application or library (TensorFlow, PyTorch, XGBoost, etc), wherever you currently run ML code (e.g. in notebooks, standalone applications or the cloud). MLflow's current components are:

 * MLflow Tracking: An API to log parameters, code, and results in machine learning experiments and compare them using an interactive UI.
 * MLflow Projects: A code packaging format for reproducible runs using Conda and Docker, so you can share your ML code with others.

## ubuntu_upgrade_lts
do-release-upgrade

## tritonpy_grpc_client.py
# python triton client

import numpy as np
import sys

import tritonclient.grpc as grpcclient


try:
    keepalive_options = grpcclient.KeepAliveOptions(

## pytorch2torchscript.py
from model_ecapatdnn import ECAPAModel
import soundfile as sf
import torch

# load your pytorch model (+ weights) here
model_1 = ECAPAModel.ECAPAModel(lr = 0.001, lr_decay = 0.97, C = 1024, n_class = 18505, m = 0.2, s = 30, test_step = 3, gpu = -1)
model_1.load_parameters("/ecapatdnn/exps/finetuned/model/model_0028.model")

model = model_1.speaker_encoder # module with forward implemented
# if your forward function contains any arguments other than the input tensors, make sure to add default values

## location_country_code_lat_lon.csv
Location,Country,Country_Code_Alpha_2,Lat,Long
A Cañiza,Spain,ES,42.216671,-8.26667
A Coruña,Spain,ES,43.371349,-8.396
A Coruña,Spain,ES,43.371262,-8.4188
A Dos Cunhados,Portugal,PT,39.156479,-9.32206
A Dos Francos,Portugal,PT,39.314468,-9.04547
A Estrada,Spain,ES,42.683331,-8.48333
A Guarda,Spain,ES,41.90131,-8.87423
A Guarda,Spain,ES,41.90403,-8.87225
A Mezquita,Spain,ES,42.013142,-7.04675

## cyberpanel-fastapi-ssl-subdomain
Deploying free email server, web hosting panel with CyberPanel on subdomain
Summary:
Let's say we have a domain "test.com". We want to install an email server for our domain, we want it free.

First make sure the droplet has no app running on port 80, 443 (or nginx, apache websites).

Install CyberPanel,
on DO, https://marketplace.digitalocean.com/apps/cyberpanel
sh <(curl https://cyberpanel.net/install.sh || wget -O - https://cyberpanel.net/install.sh)

## key_url.py
from selenium import webdriver
from bs4 import BeautifulSoup
from selenium.webdriver.chrome.options import Options
chrome_options = Options()
chrome_options.add_argument("--headless")
chrome_options.add_argument('--no-sandbox')
driver = webdriver.Chrome('/usr/bin/chromedriver', options=chrome_options)

ips = open("ip.txt", "r")
keywords = ["login", "denied", "username", "password", "Apache2"]

## silence_remove_segment.py
# author: github.com/zabir-nabil

import librosa
from pydub import AudioSegment
import pydub
import os

def segment_aud_eq(audio_segment, k):
    # k denotes, seconds * 1000
    a_segs = [audio_segment[i*k:min((i+1)*k, len(audio_segment)-1)] for i in range(len(audio_segment)//k)]

## opencv_bytes.py
import cv2
import numpy as np
import sys

img = cv2.imread('1.jpg')
print(sys.getsizeof(img))
img_encoded = cv2.imencode('.jpg', img)[1]
print(sys.getsizeof(img_encoded))

img_bytes = img_encoded.tobytes()  # bytes class

## image_procedure.py
# A procedure which decodes base64 image, runs some machine learning model/ operation(s) (in our case we'll just return the mean of the pixel value)

import numpy as np
import base64
import zlib

def predict(b64img_compressed, w, h):
    b64decoded = base64.b64decode(b64img_compressed)

    decompressed = b64decoded #zlib.decompress(b64decoded)
	# Speaker Experiments and MLflow

	Speaker experiments take relatively long time to finish. For tracking the experiment progress, we can utilize MLflow.

	### What is MLflow?

	MLflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models. MLflow offers a set of lightweight APIs that can be used with any existing machine learning application or library (TensorFlow, PyTorch, XGBoost, etc), wherever you currently run ML code (e.g. in notebooks, standalone applications or the cloud). MLflow's current components are:

	* MLflow Tracking: An API to log parameters, code, and results in machine learning experiments and compare them using an interactive UI.
	* MLflow Projects: A code packaging format for reproducible runs using Conda and Docker, so you can share your ML code with others.
	# python triton client

	import numpy as np
	import sys

	import tritonclient.grpc as grpcclient


	try:
	keepalive_options = grpcclient.KeepAliveOptions(
	from model_ecapatdnn import ECAPAModel
	import soundfile as sf
	import torch

	# load your pytorch model (+ weights) here
	model_1 = ECAPAModel.ECAPAModel(lr = 0.001, lr_decay = 0.97, C = 1024, n_class = 18505, m = 0.2, s = 30, test_step = 3, gpu = -1)
	model_1.load_parameters("/ecapatdnn/exps/finetuned/model/model_0028.model")

	model = model_1.speaker_encoder # module with forward implemented
	# if your forward function contains any arguments other than the input tensors, make sure to add default values
	Location,Country,Country_Code_Alpha_2,Lat,Long
	A Cañiza,Spain,ES,42.216671,-8.26667
	A Coruña,Spain,ES,43.371349,-8.396
	A Coruña,Spain,ES,43.371262,-8.4188
	A Dos Cunhados,Portugal,PT,39.156479,-9.32206
	A Dos Francos,Portugal,PT,39.314468,-9.04547
	A Estrada,Spain,ES,42.683331,-8.48333
	A Guarda,Spain,ES,41.90131,-8.87423
	A Guarda,Spain,ES,41.90403,-8.87225
	A Mezquita,Spain,ES,42.013142,-7.04675
	Deploying free email server, web hosting panel with CyberPanel on subdomain
	Summary:
	Let's say we have a domain "test.com". We want to install an email server for our domain, we want it free.

	First make sure the droplet has no app running on port 80, 443 (or nginx, apache websites).

	Install CyberPanel,
	on DO, https://marketplace.digitalocean.com/apps/cyberpanel
	sh <(curl https://cyberpanel.net/install.sh \|\| wget -O - https://cyberpanel.net/install.sh)
	from selenium import webdriver
	from bs4 import BeautifulSoup
	from selenium.webdriver.chrome.options import Options
	chrome_options = Options()
	chrome_options.add_argument("--headless")
	chrome_options.add_argument('--no-sandbox')
	driver = webdriver.Chrome('/usr/bin/chromedriver', options=chrome_options)

	ips = open("ip.txt", "r")
	keywords = ["login", "denied", "username", "password", "Apache2"]
	# author: github.com/zabir-nabil

	import librosa
	from pydub import AudioSegment
	import pydub
	import os

	def segment_aud_eq(audio_segment, k):
	# k denotes, seconds * 1000
	a_segs = [audio_segment[ik:min((i+1)k, len(audio_segment)-1)] for i in range(len(audio_segment)//k)]
	import cv2
	import numpy as np
	import sys

	img = cv2.imread('1.jpg')
	print(sys.getsizeof(img))
	img_encoded = cv2.imencode('.jpg', img)[1]
	print(sys.getsizeof(img_encoded))

	img_bytes = img_encoded.tobytes() # bytes class
	# A procedure which decodes base64 image, runs some machine learning model/ operation(s) (in our case we'll just return the mean of the pixel value)

	import numpy as np
	import base64
	import zlib

	def predict(b64img_compressed, w, h):
	b64decoded = base64.b64decode(b64img_compressed)

	decompressed = b64decoded #zlib.decompress(b64decoded)