Python 3.11.11 : Colab simple tests with Protocol Buffers - part 054.

Today, something simple into colab_google GitHub project, see catafest_071.ipynb - simple example with altair and vega-datasets python packages.

The result comes with some charts example.

Python 3.12.12 : Google colab example - satellite sentinel-2-l2a.

Tested today, location my home, this is the result of satellite sentinel-2-l2a - octomber 2025.

Python 3.12.12 : Google colab example of satellite detecting edges .

Simple colab project with google satellite detecting ...

I used these python packages:

%pip install segment-geospatial
%pip install leafmap
%pip install samgeo
%pip install localtileserver
%pip install fiona

This is the result:

Python 3.11.11 : Colab simple tests with Protocol Buffers - part 053.

The .proto file definition serves as a blueprint or schema for defining the structure of your data in Protocol Buffers. It plays a crucial role in ensuring data consistency and enabling efficient serialization and deserialization across different programming languages and systems.

You can find all steps on my colab_google - the GitHub repo.

Python 3.11.11 : Colab simple image to video with stabilityai - part 052.

Today, a simple test with artificial intelligence and stabilityai/stable-video-diffusion-img2vid-xt.

The result is not very good, but I believe the source code can be improved ...

pipe = StableVideoDiffusionPipeline.from_pretrained(
    'stabilityai/stable-video-diffusion-img2vid-xt',
    torch_dtype=torch.float16,
    variant='fp16'
)

pipe.enable_model_cpu_offload()

You can find the source code on my colab GitHUb projects - catafest_069.

Python 3.11.11 : Colab fixed catafest_0059 with NVIDIA models - part 051.

Fixed the colab notebook for test models from NVIDIA for waveglow and tacotron2.

Python 3.11.11 : Colab simple test with CogVideoX-5B model and default example - part 050.

I tested this GitHub project from THUDM user on my colab google account and works well with CogVideoX-5B model.

You can find the default implementation on my colab GitHub repo.

The default example comes with thjs prompt:

prompt = ( "A panda, dressed in a small, red jacket and a tiny hat, sits on a wooden stool in a serene bamboo forest. " "The panda's fluffy paws strum a miniature acoustic guitar, producing soft, melodic tunes. Nearby, a few other " "pandas gather, watching curiously and some clapping in rhythm. Sunlight filters through the tall bamboo, " "casting a gentle glow on the scene. The panda's face is expressive, showing concentration and joy as it plays. " "The background includes a small, flowing stream and vibrant green foliage, enhancing the peaceful and magical " "atmosphere of this unique musical performance." )

The speed of rendering starts from :

38% ... 19/50 [30:54<50:57, 98.61s/it] using T4 GPU

... then run at :

100% ... 50/50 [1:26:09<00:00, 109.87s/it]

when the video render was 100% somehow google give this error, but the source code run well:

OutOfMemoryError: CUDA out of memory. Tried to allocate 1.32 GiB. GPU 0 has a total capacity of 14.74 GiB of which 654.12 MiB is free. Process 26970 has 14.10 GiB memory in use. Of the allocated memory 12.97 GiB is allocated by PyTorch, and 1.00 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)

I think is need to set some extra memory on CUDA but this require to parse some documentation and is not a task for me now.

Python 3.11.11 : Colab simple test with VGG16 - part 049.

VGG16 is a deep convolutional neural network (CNN) trained on a massive image dataset called ImageNet. This architecture is known for its remarkable performance in image classification tasks and is widely used in various computer vision applications.

You can find one simple example on my GitHub project.

Python 3.11.11 : Colab simple test with mistralai - part 048.

Two notebook on my colab repo project

One with xterm colab festure and another with hull-convex known as lattrice with OllamaFunctions.

Python 3.10.12 : Simple test with diffusers to create texture.

Today I tested on colab a simple python script to generate a texture using: diffusers, torch and gradio.

I upload the notebook on my colab repo from GitHub.

The script is simple and works good:

import gradio as gr
from diffusers import StableDiffusionPipeline
import torch

model_id = "dream-textures/texture-diffusion"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
pipe = pipe.to("cuda")

def generate_image(prompt):
    image = pipe(prompt).images[0]
    image.save("result.png")
    return image

iface = gr.Interface(
    fn=generate_image,
    inputs="text",
    outputs="image",
    title="Stable Diffusion Image Generator",
    description="Introduceți un prompt pentru a genera o imagine folosind Stable Diffusion."
)

iface.launch()

The result for the pbr winter terrain is this image:

Python 3.10.12 : Simple tests with gradio python package.

This is a simple example with gradio python package on colab notebook.

I test some basic UI graphics interface edit text , selection , slider and upload feature for some files : image, audio, video, any file.

You can find more on my Github - colab notebook repo.

Python 3.10.12 : Colab simple test with mistralai - part 047.

This source code can be found on my github repo - catafest_062 colab notebook.

Python 3.10.12 : Colab quantum circuits with qiskit - part 046.

I've added another introductory example to my GitHub repository with Google Colab notebooks on how to use quantum circuits with the Python package called qiskit and the IBM Quantum Platform.

I used the IBM Quantum Platform and it provides an A.P.I symbol so that it can be used with the source code.

You can find this notebook at this catafest_061.ipynb repo file.

Python 3.10.12 : YOLOv9 with GELAN-C model on colab notebook - part 045.

Today I tested YOLOv9 with GELAN-C model. Find the notebook on my GitHub repo.

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 3.10.12 : 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 3.10.12 : 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 3.10.12 : 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.

Python 3.10.12 : My colab get images from imdb.com by the name - part 039.

You can use gspread and google-auth to get data from a spreadsheet from google drive and use it.

The colab notebook can be found on my colab repo on Github.

from google.colab import auth
auth.authenticate_user()

import gspread
from google.auth import default
creds, _ = default()

gc = gspread.authorize(creds)

# Open the spreadsheet by name
spreadsheet = gc.open('fiecare_saptamana')

# Choose a worksheet from the spreadsheet
worksheet = spreadsheet.get_worksheet(0)  # 0 represents the index of the first worksheet

# Get and print the data
data = worksheet.get_all_values()

#uncoment this line to print data sau add more source code to parse data
#print(data)

#the result will be 
#[['noreply@projects.blender.org', '437', 'notifications@github.com',  ...