全局安装一次jupyter notebook，服务于所有虚拟环境

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You're absolutely right! Installing Jupyter separately for every virtual environment can be inefficient. Fortunately, there's a way to avoid duplicating Jupyter installations across environments by leveraging a global Jupyter installation. Here’s how you can do it efficiently:

Optimized Approach: Use One Global Jupyter with Multiple Virtual Environments

The idea is to:

1. Install Jupyter globally (or in a base environment).
2. Register your virtual environments as kernels with the global Jupyter installation.

Steps to Use Global Jupyter with Virtual Environments

1. Install Jupyter Globally or in Base Environment

In your base Python environment (or Conda environment):

pip install jupyter

2. Create a Virtual Environment

Create a virtual environment for your specific project:

python -m venv myenv

3. Activate the Virtual Environment

Activate it:

• Windows:

  myenv\Scripts\activate
  

• Linux/macOS:

  source myenv/bin/activate
  

4. Install ipykernel in the Virtual Environment

Install ipykernel (but not Jupyter) inside the virtual environment:

pip install ipykernel

5. Register the Virtual Environment as a Kernel

Run this command inside the virtual environment to make it available in the global Jupyter installation:

python -m ipykernel install --user --name myenv --display-name "Python (myenv)"

6. Select the Virtual Environment in Jupyter

• Launch Jupyter:

  jupyter notebook
  

• In your notebook, go to Kernel → Change Kernel and select "Python (myenv)".

Explanation

• By using one global Jupyter installation, you avoid installing it multiple times.
• Only ipykernel is installed in each virtual environment, which is lightweight and allows Jupyter to use that environment as a kernel.

Alternative with Conda (if applicable)

If you use Conda instead, you can install Jupyter in the base environment, and each environment only needs ipykernel. The process is similar.

Summary

With this optimized approach, you:

1. Use one global Jupyter installation.
2. Register each virtual environment as a Jupyter kernel.
3. Keep disk usage minimal while still being able to switch environments easily in Jupyter.

This method saves both disk space and setup time while maintaining the flexibility to use isolated environments in your notebooks.