kny5/infotec_hcia_ai

Step-by-step instructions to install Conda, Jupyter, and MindSpore for the repository https://github.com/kny5/infotec_hcia_ai:

1. Install Conda:

For Linux/macOS:

# Download the Miniconda installer
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh

# Run the installer
bash miniconda.sh -b -p $HOME/miniconda

# Add conda to your PATH
echo 'export PATH="$HOME/miniconda/bin:$PATH"' >> ~/.bashrc
source ~/.bashrc

For Windows:

Download the Miniconda installer from https://repo.anaconda.com/miniconda/Miniconda3-latest-Windows-x86_64.exe and run the installer.

2. Create a Conda environment:

# Navigate to the repository directory
cd infotec_hcia_ai

# Create a Conda environment
conda create --name infotec_hcia_ai python=3.7

# Activate the environment
conda activate infotec_hcia_ai

3. Install Jupyter:

# Install Jupyter
conda install jupyter

4. Install MindSpore:

# Install MindSpore (CPU version)
conda install -c mindspore mindspore

# OR Install MindSpore (GPU version, if you have a compatible GPU)
conda install -c mindspore mindspore-gpu

5. Start Jupyter Notebook:

# Launch Jupyter Notebook
jupyter notebook

This will open Jupyter in your default web browser. You can then navigate to the repository directory in the Jupyter interface and start working with the provided notebooks.

Note: Make sure to adapt these instructions if you are using a specific operating system or have specific requirements. Additionally, verify the MindSpore version compatibility with your system and adjust the installation command accordingly.

6. Install mindspore-gpu for Windows Linux Subsystem (WSL2)

To install MindSpore with GPU support on WSL 2 running Ubuntu 20.04 with CUDA 11.1, you can follow these steps:

1. Install CUDA Toolkit 11.1 on WSL 2:

# Update package information
sudo apt update

# Install basic dependencies
sudo apt install -y build-essential

# Download CUDA Toolkit 11.1 deb (network) installer
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/cuda-ubuntu2004.pin
sudo mv cuda-ubuntu2004.pin /etc/apt/preferences.d/cuda-repository-pin-600
wget https://developer.download.nvidia.com/compute/cuda/11.1.1/local_installers/cuda-repo-ubuntu2004-11-1-local_11.1.1-455.32.00-1_amd64.deb
sudo dpkg -i cuda-repo-ubuntu2004-11-1-local_11.1.1-455.32.00-1_amd64.deb
sudo apt-key add /var/cuda-repo-ubuntu2004-11-1-local/7fa2af80.pub
sudo apt-get update
sudo apt-get -y install cuda

2. Set up Environment Variables:

Add the following lines to your ~/.bashrc or ~/.zshrc file:

export PATH=/usr/local/cuda-11.1/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda-11.1/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}

Then, run:

source ~/.bashrc   # or source ~/.zshrc

3. Install cuDNN:

Download cuDNN from the NVIDIA website (requires registration): https://developer.nvidia.com/rdp/cudnn-download

Follow the installation instructions provided on the cuDNN download page.

4. Install MindSpore with GPU support:

# Install additional dependencies
sudo apt install -y python3-pip libgl1-mesa-glx

# Install MindSpore GPU version
pip3 install https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.7.0/MindSpore/gpu/ubuntu_x86/cuda-11.1/mindspore_gpu-1.7.0-cp38-cp38-linux_x86_64.whl

5. Verify Installation:

# Verify MindSpore installation
python3 -c "import mindspore; print(mindspore.__version__)"

This should output the installed MindSpore version.

Now, you have successfully installed MindSpore with GPU support on WSL 2 with Ubuntu 20.04 and CUDA 11.1. Make sure to check the official MindSpore documentation for any updates or changes to the installation process.