anaconda+pytorch+tensorflow+keras

环境

conda create --name py37 python=3.7
activate py37







新版本

pip install jupyterlab

jupyter lab





经典版本


#pip install ipykernel

pip install jupyter


python -m ipykernel install --user --name py37 --display-name "py37"




外部访问

生成配置文件

jupyter notebook --generate-config

（记录下地址）




生成密码
打开ipython，创建一个密文的密码

In [1]: from notebook.auth import passwd
In [2]: passwd()

Enter password:
Verify password:
Out[2]: 'sha1:22bd77296e00:01bc151a1f7a6de107d31772e9c6c2ccaa773529'





修改默认配置文件
vi ~/.jupyter/jupyter_notebook_config.py




c.NotebookApp.ip='*'
c.NotebookApp.password = u'sha1:22...刚才复制的那个密文'
c.NotebookApp.open_browser = False
c.NotebookApp.port =8888




jupyter notebook







pytorch

conda install pytorch torchvision cudatoolkit=10.1 -c pytorch

更新的版本1.13.1：

pip install torch==1.13.1+cu117 torchvision==0.14.1+cu117 --extra-index-url https://download.pytorch.org/whl/cu117


https://pytorch.org/get-started/locally/

版本2.0：

pip install torch==2.0.0+cu118 torchvision==0.15.1+cu118 -f https://mirror.sjtu.edu.cn/pytorch-wheels/torch_stable.html -i https://mirrors.bfsu.edu.cn/pypi/web/simple -U
pip install -U -I --no-deps xformers==0.0.17rc482 -i https://mirrors.aliyun.com/pypi/simple/

版本2.0.1：

pip install torch==2.0.1+cu118 torchvision==0.15.2+cu118 torchaudio --extra-index-url https://download.pytorch.org/whl/cu118









tensorflow

https://developer.nvidia.com/cuda-downloads
https://developer.nvidia.com/compute/cuda/8.0/Prod2/local_installers/cuda_8.0.61_win10-exe




https://developer.nvidia.com/cudnn
http://developer2.download.nvidia.com/compute/machine-learning/cudnn/secure/v5.1/prod_20161129/8.0/cudnn-8.0-windows10-x64-v5.1.zip


pip install tensorflow-gpu




keras

conda install theano
conda install mingw libpython
pip install tensorflow
pip install keras
conda install h5py










mxnet

By popular demand, DMLC has added MXNet support for Keras. Please follow these steps for having it:



    After having CUDA driver, install MXNet like
    pip install mxnet-cu80

    Install Keras with MXNet support:
    git clone --recursive https://github.com/dmlc/keras
    cd keras
    python setup.py install

    Assign MXNet as Keras backend:
    KERAS_BACKEND=mxnet python -c "from keras import backend"

    “Using MXNet backend.” means Keras+MXNet is successfully installed. Enjoy.

    Q&A:
        I am using Windows, can I have it? Yes, just replace step 1 with pip install mxnet-cu80-win
        I don’t have a GPU, can I have a try? Yes, just replace step 1 with pip install mxnet or pip install mxnet–mkl if you have Intel CPU(s).
        How is compared to TensorFlow backend? em, do you want to benchmark it? Please feel free to submit benchmark results and bugs to github issue.