jupyter学习手记

红太狼 2023-05-29 12:00 16阅读 0赞

1.  怎样在jupyter中显示图片

from matplotlib.pyplot import imshow
    import numpy as np
    from PIL import Image
    
    %matplotlib inline
    pil_im = Image.open('data/empire.jpg', 'r').convert('RGB')
    imshow(np.asarray(pil_im))

1.  怎样在jupyter中使用多个conda环境

conda create -n keras2_py3 python=3.6
    
    pip install ipykernel
    
    python -m ipykernel install --user --name keras2_py3 --display-name 'keras2_py3'
    
    # remove
    conda remove -n 'name' --all

1.  Error : Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.

import tensorflow as tf
    config = tf.ConfigProto()
    config.gpu_options.allow_growth = True
    tf.keras.backend.set_session(tf.Session(config=config))

1.  theano配置

in case you passed the cudnn test [https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#verify], yet still facing the same problem for Theano:
    
    in ~/.theanorc, ensure you have:
    [cuda]
    root=/usr/local/cuda/bin
    [dnn]
    include_path=/usr/local/cuda/include
    library_path=/usr/local/cuda/lib64
    cd /usr/local/cuda/include
    sudo cp /usr/include/cudnn.h .
    cd /usr/local/cuda/lib64
    sudo cp /usr/lib/x86_64-linux-gnu/libcudnn* .
    
    python -c 'import theano'
    Using cuDNN version 7103 on context None
    Mapped name None to device cuda: GeForce GTX 1080 Ti (0000:02:00.0)
    passed.

Error (theano.gpuarray): could not initialize pygpu, support disabled
    
    
    
    1) Clean-up:
    
    theano-cache purge
    
    
    
    2) Install:
    
    #conda install numpy scipy mkl-service libpython m2w64-toolchain nose
    
    #pip install --upgrade --no-deps git+git://github.com/Theano/Theano.git
    
    #conda install pygpu
    
    #REM Create a new conda env named "python2" with python 2.7
    conda create -n python2 python=2
    
    #REM activate python2
    activate python2
    
    #REM Then install basic required packages for Theano
    conda install numpy scipy mkl-service libpython m2w64-toolchain
    
    #REM Now install theano and pygpu
    conda install theano pygpu
    
    #conda install theano=0.9.0
    
    #REM And check if all is OK.
    import theano, pygpu, numpy
    
    print(theano.__version__, pygpu.__version__, numpy.__version__)
    
    #REM If all is OK, consider using this conda env ("python2") for your works.
    #REM You can deactivate it at anytime:
    deactivate
    
    #REM If you want to delete the env, be sure it is deactivated and then run:
    conda remove --all -n python2
    
    
    
    3) Modify backend:
    
    "image_data_format": "channels_first"
    
    "backend": "theano"
    
    #https://github.com/keras-team/keras/blob/master/keras/backend/common.py#L111
    
    
    
    4) Configure to use GPU:
    
    [global]
    device=cuda
    floatX = float32
    optimizer_including=cudnn
    openmp=False
    allow_input_downcast=True
    cxx=C:Anaconda2Librarymingw-w64bing++.exe
    
    [dnn]
    enabled=True
    include_path=C:Program FilesNVIDIA GPU Computing ToolkitCUDAv8.0include
    library_path=C:Program FilesNVIDIA GPU Computing ToolkitCUDAv8.0libx64
    
    [cuda]
    root = C:Program FilesNVIDIA GPU Computing ToolkitCUDAv8.0
    
    
    
    5) Test http://deeplearning.net/software/theano/tutorial/using_gpu.html
    
    from theano import function, config, shared, sandboximporttheano.tensorasfrom theano import function, config, shared, sandbox
    import theano.tensor as T
    import numpy
    import time
    vlen = 10 * 30 * 768  # 10 x #cores x # threads per core
    iters = 1000
    rng = numpy.random.RandomState(22)
    x = shared(numpy.asarray(rng.rand(vlen), config.floatX))
    f = function([], T.exp(x))
    print(f.maker.fgraph.toposort())
    t0 = time.time()
    for i in range(iters):
       r = f()
    t1 = time.time()
    print("Looping %d times took %f seconds" % (iters, t1 - t0))
    print("Result is %s" % (r,))
    if numpy.any([isinstance(x.op, T.Elemwise) for x in f.maker.fgraph.toposort()]):
       print('Used the cpu')
    else:
       print('Used the gpu')
    
    
    
    
    
    Sources:
    
    https://github.com/Theano/libgpuarray/issues/264
    
    https://github.com/Theano/Theano/issues/5831
    
    https://github.com/Theano/Theano/issues/5838
    
    https://zhuanlan.zhihu.com/p/26473699

1.  周期性输出GPU资源利用率

watch -n 10 nvidia-smi

1.  [在Vscode中Jupyter Notebook的一些技巧（快捷键）][Vscode_Jupyter Notebook]
2.  升级jupyter后提示 `500: Internal Server Error`，查看log发现`ImportError: cannot import name configparser`，解决办法：

pip install configparser==3.5.0b2

[Vscode_Jupyter Notebook]: https://zhuanlan.zhihu.com/p/101646637