Enter the official website
Download the M1 version
The official website download is too slow, go to Tsinghua mirror to download https://mirrors.tuna.tsinghua.edu.cn/anaconda/miniconda/
Note: enter the terminal and enter uname -a to know what processor you are. M1/M2 chips are all arm processors, so download the MacOSX-arm64 version
After the download is complete, find the .sh file in the ~/Downloads/ directory, and enter it in the terminal
A software agreement clause will pop up for you to read. At this time, you can directly press Ctrl+C to skip the reading process. Follow the prompts, enter yes, and press Enter to agree to the software agreement terms.
My installation path is /Users/qianying/miniconda3
Next, continue to press Enter to install miniconda on the computer. After installation, the prompt is as follows:
Configure environment variables
At this time, the conda command cannot be called. For example, when you enter conda --version, it prompts that it is not found.
Type in terminal
Add this sentence in it, that is, add the path where conda is located to the environment variable
After saving, exit and enter in the terminal:
The word (base) appears in the terminal, because during installation, the base environment is automatically activated every time the terminal is opened. Enter in the terminal to cancel the automatic activation of the base environment.
conda config --set auto_activate_base false
After restarting the terminal, there will be no (base) words. Check the version number of conda, indicating that miniconda3 is installed successfully.
Configure domestic sources
Enter vim .condarc in the terminal, copy the following content into the .condarc file, save and exit.
ssl_verify: true show_channel_urls: true channels: - https://mirrors.ustc.edu.cn/anaconda/cloud/menpo/ - https://mirrors.ustc.edu.cn/anaconda/cloud/bioconda/ - https://mirrors.ustc.edu.cn/anaconda/cloud/msys2/ - https://mirrors.ustc.edu.cn/anaconda/cloud/conda-forge/ - https://mirrors.ustc.edu.cn/anaconda/pkgs/free/ - https://mirrors.ustc.edu.cn/anaconda/pkgs/main/ - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/ - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/ - defaults
Create a tensorflow environment
Create an environment named tf2, python version 3.8
conda create -n tf2 python=3.8
Note: The sources of University of Science and Technology of China and Tsinghua University added by conda do not support python3.7 and below versions, so a python3.8 environment is created here.
Enter the virtual environment
conda activate tf2
Install the dependency tensorflow-deps of tensorflow on mac
conda install -c apple tensorflow-deps
Install the mac version of tensorflow
python -m pip install tensorflow-macos -i c
Note: This is not installed with conda here, because there is no such installation package in the conda source, so use pip to install the mac version of tensorflow instead.
Note: -i https://pypi.douban.com/simple Indicates the pip domestic source of the specified watercress
Install related plug-ins to accelerate training and rely on tensorflow-metal (enabling mac to support gpu training)
python -m pip install tensorflow-metal -i https://pypi.douban.com/simple
View some information about conda
View conda environment
conda env listconda info --env
Create environment (example is to create an environment called py36, python version 3.6)
conda create -n py36 python=3.6
Remove the environment named py36
conda remove -n py36 --all
Activate the environment
conda activate py36
Downloading from the Tsinghua mirror source (for example, downloading numpy) will significantly increase the download speed
pip install numpy -i https://pypi.tuna.tsinghua.edu.cn/simple
Add a single image
conda config --add channels https://mirrors.ustc.edu.cn/anaconda/pkgs/main/conda config --set show_channel_urls yes
Delete all conda configurations
conda config --remove-key channels
Enter the python environment
Exit the python environment, all three commands are available
exit() quit() ctrl + z
Enter the python environment to check the version number of tf, and check the physical equipment supported by mac for training
python import tensorflow as tf tf.__version__ tf.config.list_physical_devices()
At this point, the environment is fully configured.