Python 3.10.12 : Few example for CUDA and NVCC - part 044.

NVCC use CUDA C/C++ source code and allows developers to write high-performance GPU-accelerated applications by leveraging the power of NVIDIA GPUs for parallel processing tasks.

Today I test some simple examples with this tool on Google Colab using the nvcc4jupyter python package.

You need to install it with the pip and know how to use the CUDA C/C++ source code, or use the basic example from documentation.

pip install nvcc4jupyter

I change some source code because is need to install this library and I don't have time to learn and test.

But this will allow me to test better, because on my desktop I don't have a good hardware.

This is the source I change and I cut the source code linked on error_handling.h.

This is the changed source code , you can see more on my GitHub repo for Google Colab ! !

#include 
//#include "error_handling.h"

const int DSIZE = 4096;
const int block_size = 256;

// vector add kernel: C = A + B
__global__ void vadd(const float *A, const float *B, float *C, int ds){
    int idx = threadIdx.x + blockIdx.x * blockDim.x;
    if (idx < ds) {
        C[idx] = A[idx] + B[idx];
    }
}

int main(){
    float *h_A, *h_B, *h_C, *d_A, *d_B, *d_C;

    // allocate space for vectors in host memory
    h_A = new float[DSIZE];
    h_B = new float[DSIZE];
    h_C = new float[DSIZE];

    // initialize vectors in host memory to random values (except for the
    // result vector whose values do not matter as they will be overwritten)
    for (int i = 0; i < DSIZE; i++) {
        h_A[i] = rand()/(float)RAND_MAX;
        h_B[i] = rand()/(float)RAND_MAX;
    }

    // allocate space for vectors in device memory
    cudaMalloc(&d_A, DSIZE*sizeof(float));
    cudaMalloc(&d_B, DSIZE*sizeof(float));
    cudaMalloc(&d_C, DSIZE*sizeof(float));
    //cudaCheckErrors("cudaMalloc failure"); // error checking

    // copy vectors A and B from host to device:
    cudaMemcpy(d_A, h_A, DSIZE*sizeof(float), cudaMemcpyHostToDevice);
    cudaMemcpy(d_B, h_B, DSIZE*sizeof(float), cudaMemcpyHostToDevice);
    //cudaCheckErrors("cudaMemcpy H2D failure");

    // launch the vector adding kernel
    vadd<<<(DSIZE+block_size-1)/block_size, block_size>>>(d_A, d_B, d_C, DSIZE);
    //cudaCheckErrors("kernel launch failure");

    // wait for the kernel to finish execution
    cudaDeviceSynchronize();
    //cudaCheckErrors("kernel execution failure");

    cudaMemcpy(h_C, d_C, DSIZE*sizeof(float), cudaMemcpyDeviceToHost);
    //cudaCheckErrors("cudaMemcpy D2H failure");

    printf("A[0] = %f\n", h_A[0]);
    printf("B[0] = %f\n", h_B[0]);
    printf("C[0] = %f\n", h_C[0]);
    return 0;
}

This is result ...

A[0] = 0.840188
B[0] = 0.394383
C[0] = 0.000000

Python 3.12.1 : Simple view for sqlite table with PyQt6.

This is the source code that show you how to use QSqlDatabase, QSqlQuery, QSqlTableModel:

import sys
from PyQt6.QtWidgets import QApplication, QMainWindow, QTableView
from PyQt6.QtSql import QSqlDatabase, QSqlQuery, QSqlTableModel

class MainWindow(QMainWindow):
    def __init__(self):
        super().__init__()

        # Initialize the database
        self.init_db()

        # Set up the GUI
        self.table_view = QTableView(self)
        self.setCentralWidget(self.table_view)

        # Set up the model and connect it to the database
        self.model = QSqlTableModel(self)
        self.model.setTable('files')
        self.model.select()
        self.table_view.setModel(self.model)

    def init_db(self):
        # Connect to the database
        db = QSqlDatabase.addDatabase('QSQLITE')
        db.setDatabaseName('file_paths.db')
        if not db.open():
            print('Could not open database')
            sys.exit(1)

    def create_table(self):
        # Create the 'files' table if it doesn't exist
        query = QSqlQuery()
        query.exec('CREATE TABLE IF NOT EXISTS files (id INTEGER PRIMARY KEY, path TEXT)')

if __name__ == '__main__':
    app = QApplication(sys.argv)
    window = MainWindow()
    window.show()
    sys.exit(app.exec())

Python 3.12.1 : Simple browser with PyQt6-WebEngine.

This is a source code for a minimal browser with PyQt6 and PyQt6-WebEngine.

You need to install it with the pip tool:

pip install PyQt6 PyQt6-WebEngine

You can update the pip tool with

python.exe -m pip install --upgrade pip

Create a python script use the source code and run it.

Let's see the source code:

import sys
from PyQt6.QtCore import QUrl
from PyQt6.QtGui import QKeySequence, QAction
from PyQt6.QtWidgets import QApplication, QMainWindow, QLineEdit, QToolBar
from PyQt6.QtWebEngineWidgets import QWebEngineView
 
class MainWindow(QMainWindow):
    def __init__(self):
        super().__init__()
         
        # Create a web view
        self.web_view = QWebEngineView()
        self.web_view.setUrl(QUrl("127.0.0.1"))
        self.setCentralWidget(self.web_view)
 
        # Create a toolbar
        toolbar = QToolBar()
        self.addToolBar(toolbar)
         
        # Add a back action to the toolbar
        back_action = QAction("Back", self)
        back_action.setShortcut(QKeySequence("Back"))
        back_action.triggered.connect(self.web_view.back)
        toolbar.addAction(back_action)
         
        # Add a forward action to the toolbar
        forward_action = QAction("Forward", self)
        forward_action.setShortcut(QKeySequence("Forward"))
        forward_action.triggered.connect(self.web_view.forward)
        toolbar.addAction(forward_action)
         
        # Add a reload action to the toolbar
        reload_action = QAction("Reload", self)
        reload_action.setShortcut(QKeySequence("Refresh"))
        reload_action.triggered.connect(self.web_view.reload)
        toolbar.addAction(reload_action)
         
        # Add a search bar to the toolbar
        self.search_bar = QLineEdit()
        self.search_bar.returnPressed.connect(self.load_url)
        toolbar.addWidget(self.search_bar)
         
         
    def load_url(self):
        url = self.search_bar.text()
        if not url.startswith("http"):
            url = "https://" + url
        self.web_view.load(QUrl(url))
         
if __name__ == "__main__":
    app = QApplication(sys.argv)
    window = MainWindow()
    window.show()
    app.exec()

News : How to use ShellExecuteA with Python programming language.

I just discovery this option to use ShellExecuteA in Python.

Let's see some example:

import ctypes
import sys

def is_admin():
    try:
        return ctypes.windll.shell32.IsUserAnAdmin()
    except:
        return False

if not is_admin():
    ctypes.windll.shell32.ShellExecuteW(None, "runas", sys.executable, __file__, None, 1)

... and another example:

import sys
import ctypes

#fix unicode access
import sys
if sys.version_info[0] >= 3:
    unicode = str

def run_as_admin(argv=None, debug=False):
    shell32 = ctypes.windll.shell32
    if argv is None and shell32.IsUserAnAdmin():
        return True    
    if argv is None:
        argv = sys.argv
    if hasattr(sys, '_MEIPASS'):
        # Support pyinstaller wrapped program.
        arguments = map(unicode, argv[1:])
    else:
        arguments = map(unicode, argv)
    argument_line = u' '.join(arguments)
    executable = unicode(sys.executable)
    if debug:
        print('Command line: ', executable, argument_line)
    ret = shell32.ShellExecuteW(None, u"runas", executable, argument_line, None, 1)
    if int(ret) <= 32:
        return False
    return None  

if __name__ == '__main__':
    ret = run_as_admin()
    if ret is True:
        print ('I have admin privilege.')
        input('Press ENTER to exit.')
    elif ret is None:
        print('I am elevating to admin privilege.')
        input('Press ENTER to exit.')
    else:
        print('Error(ret=%d): cannot elevate privilege.' % (ret, ))

News : NVIDIA Kaolin library provides a PyTorch API.

NVIDIA Kaolin library provides a PyTorch API for working with a variety of 3D representations and includes a growing collection of GPU-optimized operations such as modular differentiable rendering, fast conversions between representations, data loading, 3D checkpoints, differentiable camera API, differentiable lighting with spherical harmonics and spherical gaussians, powerful quadtree acceleration structure called Structured Point Clouds, interactive 3D visualizer for jupyter notebooks, convenient batched mesh container and more ... from GitHub repo - kaolin.

See this example from NVIDIA:

NVIDIA Kaolin library has introduced the SurfaceMesh class to make it easier to track attributes such as faces, vertices, normals, face_normals, and others associated with surface meshes.

Python 3.12.1 : Create a simple color palette.

Today I tested a simple source code for a color palette created with PIL python package.

This is the result:

Let's see the source code:

from PIL import Image, ImageDraw

# 640x640 pixeli with 10x10 squares 64x64 
img = Image.new('RGB', (640, 640))
draw = ImageDraw.Draw(img)

# color list
colors = []
# for 100 colors you need to set steps with 62,62,64 
# or you can change 64,62,62 some little changes 
for r in range(0, 256, 62):
    for g in range(0, 256, 62):
        for b in range(0, 256, 64):
            colors.append((r, g, b))

# show result of colors and size up 100 items 
print(colors)
print(len(colors))

# create 10x10 colors and fill the image 
for i in range(10):
    for j in range(10):
        x0 = j * 64
        y0 = i * 64
        x1 = x0 + 64
        y1 = y0 + 64
        color = colors[i*10 + j]  # Selectarea culorii din lista
        draw.rectangle([x0, y0, x1, y1], fill=color)

# save teh image
img.save('rgb_color_matrix.png')

Python 3.10.12 : LaTeX on colab notebook - part 043.

Today I tested with a simple LaTeX examples in the Colab notebook.

If you open my notebook colab then you will see some issues and how can be fixed.

You can find this on my notebook - catafest_056.ipynb on colab repo.

Python 3.12.1 : How to read all CLSID with winreg.

The winreg python module can be found default into python instalation.

This is the source code I used to list all of CLSID:

import winreg

# Open the CLSID key under HKEY_CLASSES_ROOT
clsid_key = winreg.OpenKey(winreg.HKEY_CLASSES_ROOT, "CLSID")

# Iterate through all the subkeys
for i in range(winreg.QueryInfoKey(clsid_key)[0]):
    # Get the name of the subkey
    subkey_name = winreg.EnumKey(clsid_key, i)
    # Open the subkey
    subkey = winreg.OpenKey(clsid_key, subkey_name, 0, winreg.KEY_READ)
    try:
        # Read the default value of the subkey
        value, type = winreg.QueryValueEx(subkey, "")
        # Print the subkey name and the value
        print(subkey_name, value)
    except:
        # Skip the subkeys that cannot be read
        pass
    # Close the subkey
    winreg.CloseKey(subkey)

# Close the CLSID key
winreg.CloseKey(clsid_key)

... and this source code comes with an text file output:

import winreg

# Open the CLSID key under HKEY_CLASSES_ROOT
clsid_key = winreg.OpenKey(winreg.HKEY_CLASSES_ROOT, "CLSID")

# Open the file for writing
with open("clsid_info.txt", "w") as f:
    # Iterate through all the subkeys
    for i in range(winreg.QueryInfoKey(clsid_key)[0]):
        # Get the name of the subkey
        subkey_name = winreg.EnumKey(clsid_key, i)
        # Open the subkey
        subkey = winreg.OpenKey(clsid_key, subkey_name, 0, winreg.KEY_READ)
        try:
            # Read the default value of the subkey
            value, type = winreg.QueryValueEx(subkey, "")
            # Write the subkey name and the value to the file
            f.write(subkey_name + " " + value + "\n")
        except:
            # Skip the subkeys that cannot be read
            pass
        # Close the subkey
        winreg.CloseKey(subkey)

# Close the CLSID key
winreg.CloseKey(clsid_key)

Python : MLOps with neptune.ai .

I just started testing with neptune.ai.

Neptune is the MLOps stack component for experiment tracking. It offers a single place to log, compare, store, and collaborate on experiments and models.

MLOps or ML Ops is a paradigm that aims to deploy and maintain machine learning models in production reliably and efficiently.

MLOps is practiced between Data Scientists, DevOps, and Machine Learning engineers to transition the algorithm to production systems.

MLOps aims to facilitate the creation of machine learning products by leveraging these principles: CI/CD automation, workflow orchestration, reproducibility; versioning of data, model, and code; collaboration; continuous ML training and evaluation; ML metadata tracking and logging; continuous monitoring; and feedback loops.

See Wikipedia.

You will understand these features of MLOps if you look at these practical examples.

Neptune uses a token:

Your Neptune API token is like a password to the application. By saving your token as an environment variable, you avoid putting it in your source code, which is more convenient and secure.

When I started I tested with this default project:

example-project-tensorflow-keras

Another good feature is the working team, by adding collaborators in the People section of your workspace settings.

• For a free account you can have these options:
• 5 users
• 1 active project
• Unlimited archived projects
• Unlimited experiments
• Unlimited model versions
• Unlimited logging hours
• Artifacts tracking
• Service accounts for CI/CD pipelines
• 200 GB storage

Let's see the default source code shared by neptune.ai for that default project:

import glob
import hashlib

import matplotlib.pyplot as plt
import neptune.new as neptune
import numpy as np
import pandas as pd
import tensorflow as tf
from neptune.new.integrations.tensorflow_keras import NeptuneCallback
from scikitplot.metrics import plot_roc, plot_precision_recall

# Select project
run = neptune.init(project='common/example-project-tensorflow-keras',
                   tags=['keras', 'fashion-mnist'],
                   name='keras-training')

# Prepare params
parameters = {'dense_units': 128,
              'activation': 'relu',
              'dropout': 0.23,
              'learning_rate': 0.15,
              'batch_size': 64,
              'n_epochs': 30}

run['model/params'] = parameters

# Prepare dataset
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.fashion_mnist.load_data()
x_train = x_train / 255.0
x_test = x_test / 255.0

class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']

# Log data version
run['data/version/x_train'] = hashlib.md5(x_train).hexdigest()
run['data/version/y_train'] = hashlib.md5(y_train).hexdigest()
run['data/version/x_test'] = hashlib.md5(x_test).hexdigest()
run['data/version/y_test'] = hashlib.md5(y_test).hexdigest()
run['data/class_names'] = class_names

# Log example images
for j, class_name in enumerate(class_names):
    plt.figure(figsize=(10, 10))
    label_ = np.where(y_train == j)
    for i in range(9):
        plt.subplot(3, 3, i + 1)
        plt.xticks([])
        plt.yticks([])
        plt.grid(False)
        plt.imshow(x_train[label_[0][i]], cmap=plt.cm.binary)
        plt.xlabel(class_names[j])
    run['data/train_sample'].log(neptune.types.File.as_image(plt.gcf()))
    plt.close('all')

# Prepare model
model = tf.keras.Sequential([
    tf.keras.layers.Flatten(input_shape=(28, 28)),
    tf.keras.layers.Dense(parameters['dense_units'], activation=parameters['activation']),
    tf.keras.layers.Dropout(parameters['dropout']),
    tf.keras.layers.Dense(parameters['dense_units'], activation=parameters['activation']),
    tf.keras.layers.Dropout(parameters['dropout']),
    tf.keras.layers.Dense(10, activation='softmax')
])
optimizer = tf.keras.optimizers.SGD(learning_rate=parameters['learning_rate'])
model.compile(optimizer=optimizer,
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

# Log model summary
model.summary(print_fn=lambda x: run['model/summary'].log(x))

# Train model
neptune_cbk = NeptuneCallback(run=run, base_namespace='metrics')

model.fit(x_train, y_train,
          batch_size=parameters['batch_size'],
          epochs=parameters['n_epochs'],
          validation_split=0.2,
          callbacks=[neptune_cbk])

# Log model weights
model.save('trained_model')
run['model/weights/saved_model'].upload('trained_model/saved_model.pb')
for name in glob.glob('trained_model/variables/*'):
    run[name].upload(name)

# Evaluate model
eval_metrics = model.evaluate(x_test, y_test, verbose=0)
for j, metric in enumerate(eval_metrics):
    run['test/scores/{}'.format(model.metrics_names[j])] = metric

# Log predictions as table
y_pred_proba = model.predict(x_test)
y_pred = np.argmax(y_pred_proba, axis=1)
y_pred = y_pred
df = pd.DataFrame(data={'y_test': y_test, 'y_pred': y_pred, 'y_pred_probability': y_pred_proba.max(axis=1)})
run['test/predictions'] = neptune.types.File.as_html(df)

# Log model performance visualizations
fig, ax = plt.subplots()
plot_roc(y_test, y_pred_proba, ax=ax)
run['charts/ROC'] = neptune.types.File.as_image(fig)

fig, ax = plt.subplots()
plot_precision_recall(y_test, y_pred_proba, ax=ax)
run['charts/precision-recall'] = neptune.types.File.as_image(fig)
plt.close('all')

run.wait()

This screenshot shows the web interface for neptune.ai:

Python 3.10.12 : Simple examples with some Python modules - part 042.

I added some simple examples in my repo on GitHub where I have notebooks from Google Colab.

Python usage is limited to this type of interface with Python version stability rules.

You can see the Python version that Colab is using with this command in the code area:

!python
Python 3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0] on linux
Type "help", "copyright", "credits" or "license" for more information.

These are the Python packages I used: cartopy, matplotlib, numpy, geemap, earthengine-api, rasterio.

Of these examples, some require some Google configuration, others require knowledge of topography ... or are very simple to use with a dedicated module as we have visualized more special types of image files.

See there an example with the cartopy python package :

import matplotlib.pyplot as plt

import cartopy.crs as ccrs
from cartopy.io import shapereader
from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER

import cartopy.io.img_tiles as cimgt

extent = [15, 25, 55, 35]

request = cimgt.OSM()

fig = plt.figure(figsize=(9, 13))
ax = plt.axes(projection=request.crs)
gl = ax.gridlines(draw_labels=True, alpha=0.2)
gl.top_labels = gl.right_labels = False
gl.xformatter = LONGITUDE_FORMATTER
gl.yformatter = LATITUDE_FORMATTER

ax.set_extent(extent)

ax.add_image(request, 11)

plt.show()

Python 3.13.0a1 : Testing with scapy - part 001.

Scapy is a powerful interactive packet manipulation library written in Python. Scapy is able to forge or decode packets of a wide number of protocols, send them on the wire, capture them, match requests and replies, and much more. see the official website.

You need to install NPCap.

Beacon frames are transmitted periodically, they serve to announce the presence of a wireless LAN and to synchronise the members of the service set.

In IBSS network beacon generation is distributed among the stations.

Beacon frames are transmitted by the access point (AP) in an infrastructure basic service set (BSS).

Beacon frames include information about the access point and supported data rates and what encryption is being used.

These are received by your device’s wireless network interface and interpreted by your operating system to build the list of available networks.

The beacon variable indicates the capabilities of our access point.

Let's see the source code:

C:\PythonProjects\scapy_001>pip install scapy
Collecting scapy
  Downloading scapy-2.5.0.tar.gz (1.3 MB)
     ---------------------------------------- 1.3/1.3 MB 3.5 MB/s eta 0:00:00
  Installing build dependencies ... done
...
Successfully built scapy
Installing collected packages: scapy
Successfully installed scapy-2.5.0

The source code is simple:

from scapy.all import Dot11,Dot11Beacon,Dot11Elt,RadioTap,sendp,hexdump

netSSID = 'testSSID'       #Network name here
iface = 'Realtek PCIe GbE Family Controller'         #Interface name here

dot11 = Dot11(type=0, subtype=8, addr1='ff:ff:ff:ff:ff:ff',
addr2='22:22:22:22:22:22', addr3='33:33:33:33:33:33')
beacon = Dot11Beacon(cap='ESS+privacy')
essid = Dot11Elt(ID='SSID',info=netSSID, len=len(netSSID))
rsn = Dot11Elt(ID='RSNinfo', info=(
'\x01\x00'                 #RSN Version 1
'\x00\x0f\xac\x02'         #Group Cipher Suite : 00-0f-ac TKIP
'\x02\x00'                 #2 Pairwise Cipher Suites (next two lines)
'\x00\x0f\xac\x04'         #AES Cipher
'\x00\x0f\xac\x02'         #TKIP Cipher
'\x01\x00'                 #1 Authentication Key Managment Suite (line below)
'\x00\x0f\xac\x02'         #Pre-Shared Key
'\x00\x00'))               #RSN Capabilities (no extra capabilities)

frame = RadioTap()/dot11/beacon/essid/rsn

frame.show()
print("\nHexdump of frame:")
hexdump(frame)
input("\nPress enter to start\n")

sendp(frame, iface=iface, inter=0.100, loop=1)

Let's run this source code:

python scapy_network_001.py
###[ RadioTap ]###
  version   = 0
  pad       = 0
  len       = None
  present   = None
  notdecoded= ''
###[ 802.11 ]###
     subtype   = Beacon
     type      = Management
     proto     = 0
     FCfield   =
     ID        = 0
     addr1     = ff:ff:ff:ff:ff:ff (RA=DA)
     addr2     = 22:22:22:22:22:22 (TA=SA)
     addr3     = 33:33:33:33:33:33 (BSSID/STA)
     SC        = 0
###[ 802.11 Beacon ]###
        timestamp = 0
        beacon_interval= 100
        cap       = ESS+privpython scapy_network_001.py
###[ RadioTap ]### tion Element ]###
  version   = 0      = SSID
  pad       = 0      = 8
  len       = None   = 'testSSID'
  present   = Noneation Element ]###
  notdecoded= ''     = RSN
###[ 802.11 ]###     = None
     subtype   = Beacon'\x01\x00\x00\x0f¬\x02\x02\x00\x00\x0f¬\x04\x00\x0f¬\x02\x01\x00\x00\x
     type      = Management
     proto     = 0
     FCfield   =
     ID        = 0
     addr1     = ff:ff:ff:ff:ff:ff (RA=DA)FF FF FF FF  ................
     addr2     = 22:22:22:22:22:22 (TA=SA)33 33 00 00  ..""""""333333..
     addr3     = 33:33:33:33:33:33 (BSSID/STA)8 74 65  ........d.....te
     SC        = 049 44 30 1C 01 00 00 0F C2 AC 02 02  stSSID0.........
###[ 802.11 Beacon ]### 00 0F C2 AC 02 01 00 00 0F C2  ................
        timestamp = 0                                  ....
        beacon_interval= 100
        cap       = ESS+privacy
###[ 802.11 Information Element ]###
           ID        = SSID..................................................................
           len       = 8.....................................................................
           info      = 'testSSID'
###[ 802.11 Information Element ]###
           ID        = RSN
           len       = None>
           info      = '\x01\x00\x00\x0f¬\x02\x02\x00\x00\x0f¬\x04\x00\x0f¬\x02\x01\x00\x00\x0f¬\x02\x00\x00'


Hexdump of frame:
0000  00 00 08 00 00 00 00 00 80 00 00 00 FF FF FF FF  ................
0010  FF FF 22 22 22 22 22 22 33 33 33 33 33 33 00 00  ..""""""333333..
0020  00 00 00 00 00 00 00 00 64 00 11 00 00 08 74 65  ........d.....te
0030  73 74 53 53 49 44 30 1C 01 00 00 0F C2 AC 02 02  stSSID0.........
0040  00 00 0F C2 AC 04 00 0F C2 AC 02 01 00 00 0F C2  ................
0050  AC 02 00 00                                      ....

Press enter to start

.................................................................
Sent 130 packets.

Python 3.10.12 : Simple example with SymPy - part 041.

SymPy is a Python library for symbolic mathematics. If you are new to SymPy, start with the introductory tutorial.

You can find a simple example with a partial differentiation on my GitHub colab repo.

Python 3.10.12 : My colab get images from imdb.com by the name - part 040.

Transformers provides thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio.

You find more on my Colab Github repo.