Ubuntu Git install

How To Install and Set Up Git on Ubuntu
https://phoenixnap.com/kb/how-to-install-git-on-ubuntu


sudo apt update
sudo apt install git -y
git --version

Managing your personal access tokens
https://docs.github.com/en/enterprise-server@3.9/authentication/keeping-your-account-and-data-secure/managing-your-personal-access-tokens

Create and store your GitHub personal access token
https://www.pragmaticlinux.com/2023/05/create-and-store-your-github-personal-access-token/

nano ~/.git-credentials

Next, enter the following on the first line. Make sure to replace user with your account’s username and pass with your personal access token:

https://user:pass@github.com

git config --global credential.helper store

git config --global user.email "you@example.com"
git config --global user.name "Your Name"

Ubuntu : install Miniconda, Jupyter notebook

,

mkdir -p ~/miniconda3
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda3/miniconda.sh
bash ~/miniconda3/miniconda.sh -b -u -p ~/miniconda3
rm ~/miniconda3/miniconda.sh

Quick command line install
https://docs.anaconda.com/miniconda/install/#quick-command-line-install

$: python –version
Python 3.12.8

/home/zsolt/Python_projects/anaconda:
conda create –prefix /home/zsolt/Python_projects/anaconda/tensorflow1/env
conda activate /home/zsolt/Python_projects/anaconda/tensorflow1/env
conda deactivate
sudo apt install python3-pip
cd /home/zsolt/Python_projects/anaconda/tensorflow1/
install notebook: conda install jupyter
start: jupyter notebook

Install TensorFlow with pip
https://www.tensorflow.org/install/pip

python3 -m pip install 'tensorflow[and-cuda]'
# Verify the installation:
python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"

python -c “import tensorflow as tf; print(tf.config.list_physical_devices(‘GPU’))”

pip install --upgrade pip
pip install tensorflow[and-cuda]
Verify:
python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"

####################################

cd /home/zsolt/Python_projects/anaconda/tensorflow1/

conda activate /home/zsolt/Python_projects/anaconda/tensorflow1/env

jupyter notebook

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uninstall TensorFlow:
(ezzel töröltem /home/zsolt/miniconda3/ a duplán installált TensorFlow-t
pip uninstall tensorflow
pip uninstall tensorflow-gpu

#########################################
NEM így!

pip install tensorflow-hub

A Jupyter Notbook-ban futtatni, mert különben máshova telepíti:
!pip install tensorflow-hub

Motherboard type

Ubuntu:
sudo dmidecode -t baseboard

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Disable Secure boot

some popular deep learning models and their primary use cases

1. ResNet (Residual Network)

  • Purpose: Overcomes vanishing gradient issues in deep networks using residual connections.
  • Applications: Image classification, object detection, feature extraction.

2. EfficientNet

  • Purpose: Balances model size, computation cost, and accuracy using compound scaling.
  • Applications: Image classification, transfer learning for vision tasks.

3. VGG (Visual Geometry Group)

  • Purpose: Simplified convolutional network with a fixed architecture using convolution and pooling layers.
  • Applications: Image classification, feature extraction.

4. Inception (GoogleNet)

  • Purpose: Utilizes multi-scale convolutions within an “Inception module” to improve efficiency and accuracy.
  • Applications: Image classification, object detection, scene recognition.

5. DenseNet (Dense Convolutional Network)

  • Purpose: Improves feature reuse by connecting every layer to all subsequent layers.
  • Applications: Image classification, medical image analysis.

6. MobileNet

  • Purpose: Designed for mobile and embedded devices with lightweight and efficient architecture.
  • Applications: Real-time image processing on mobile, object detection.

7. Xception

  • Purpose: Employs depthwise separable convolutions for a more efficient network.
  • Applications: Image classification, object detection.

8. Vision Transformer (ViT)

  • Purpose: Applies transformer architectures to image classification by modeling global relationships.
  • Applications: Image classification, visual-language tasks.

9. Swin Transformer

  • Purpose: Hierarchical transformer for capturing both local and global features in images.
  • Applications: Image classification, object detection, image segmentation.

10. NASNet (Neural Architecture Search Network)

  • Purpose: Auto-generated architecture optimized for accuracy and computational efficiency.
  • Applications: Image classification, object detection.

11. YOLO (You Only Look Once)

  • Purpose: Real-time object detection by processing the entire image in a single pass.
  • Applications: Object detection, real-time video analysis.

12. ConvNeXt

  • Purpose: A modernized convolutional network with features inspired by transformers.
  • Applications: Image classification, object detection.

Google Colab – Cannot connect to GPU backend – Best Alternatives to Google Colab

You cannot currently connect to a GPU due to usage limits in Colab. – Connect without GPU

What are the usage limits of Colab?link
Colab is able to provide resources free of charge in part by having dynamic usage limits that sometimes fluctuate, and by not providing guaranteed or unlimited resources. This means that overall usage limits as well as idle timeout periods, maximum VM lifetime, GPU types available, and other factors vary over time. Colab does not publish these limits, in part because they can vary over time.

https://research.google.com/colaboratory/faq.html#usage-limits

www.reddit.com : Can’t connect to GPU backend
https://www.reddit.com/r/GoogleColab/comments/1awijw4/cant_connect_to_gpu_backend/

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alternative:
Digitalocean > Paperspace
https://console.paperspace.com

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7 of the Best Alternatives to Google Colab For 2024 (With Free Compute!)
https://saturncloud.io/blog/7-of-the-best-alternatives-to-google-colab-for-2023-with-free-compute/