Custom Python Environments with Anaconda & Jupyter Notebooks

Learn how to create custom Python environments to run multiple versions of TensorFlow (and other Python packages) at the same time in different Jupyter notebooks.
★ Cheatsheet below ↓

```
# Create a new conda environment named `tf221`
# -y is for `yes`, to avoid conda from asking whether to proceed
# -n is for name
conda create -n tf221 ipykernel -y

# Activate our freshly created environment
conda activate tf221

# (Not required) Verify that we're using the right version of Python
which python3 # returns /usr/local/anaconda3/envs/tf221/bin/python3

# Install our environment as a Python kernel to use it in Jupyter
python -m ipykernel install --user --name=tf221 --display-name="TensorFlow 2.2.1"

# Start Jupyter Lab
jupyter lab
```

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