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Deep learning environment configuration jupyter notebook
2022-07-07 00:41:00 【Peng Xiang】
today , Bo mainly systematically studies the relevant contents of in-depth learning , Record your learning process here , Make progress with everyone .
Content introduction
- Fundamentals of deep learning : Linear neural networks . Multilayer perceptron
- Convolutional neural networks :LeNet , AlexNet , VGG , Inception , ResNet
- Cyclic neural network :RNN , GRU , LSTM , seq2seq
- Attention mechanism : Attention , Transformer
- optimization algorithm : SGD , Adam , Momentum
- High performance computing : parallel , many GPU , Distributed
- Computer vision : object detection , Semantic segmentation
- Computer language processing : Word embedding ,BERT
Deep learning is a very “ Fantasy ” The process of , The reason why the model he generates is excellent or how it works is sometimes incomprehensible to us humans , Don't get too tangled here .
The first is the configuration of the environment , Bloggers have already installed PyCharm and Anaconda And configure the relevant environment , Therefore, only a common package is installed here d2l
For related installation tutorials, you can read my previous blog :
Anaconda Installation and PyCharm To configure
This process may take a few minutes , Because there are many packages to install
After installation , Let's start learning
jupyter notebook Use
What we use here is jupyter notebook, After we installed Anaconda Generally, it will be installed for us by default .
Input... At the terminal jupyter notebook --generate-config
enter , The location of the configuration document is shown below .
We can modify its default installation path , It's convenient for us to use
Look in Notepad (Ctrl+F
)NotebookApp.notebook_dir
The position of , Change the folder in the back to your favorite folder location .
Open it in the browser
Run a code and find no corresponding package , It turns out that the default is Anaconda Of base
, In this environment, we have not installed the corresponding environment before , Then we need to switch our environment , Or we want to use it in a different environment jupyter notebook
Single environment creation
The specific way is as follows :
conda create -n my-conda-env # Create a virtual environment
conda activate my-conda-env # Activate our virtual environment
conda install ipykernel # install python kernel
ipython kernel install --user --name=my-conda-env-kernel # python -m ipykernel install --user --name Name of the environment --display-name " stay jupyter The name of the environment shown in "
jupyter notebook # Start operation
conda install ipykernel
ipython kernel install --user --name=my-conda-env-kernel
BUG explain :
ImportError: cannot import name 'generator_to_async_generator'
BUG solve :
pip uninstall -y ipython prompt_toolkit
pip install ipython prompt_toolkit
Re install
Create for all environments
Of course, the above method can only be one conda The environment create , We use conda install nb_conda_kernels
For all conda The environment create jupyter, Thus, there are many choices .
Install code prompt function
At this time, it cannot prompt code after installation , We just need to perform the following steps
1、 Start menu running Anaconda Prompt (Anaconda3)
, Enter commands one by one
pip install jupyter_contrib_nbextensions
jupyter contrib nbextension install
pip install jupyter_nbextensions_configurator
jupyter nbextensions_configurator enable
2、 Open again notebook, The successful implementation will be in http://localhost:8888/tree It appears that Nbextensions
Check Hinterland
Open one again python You can see the code prompt in the file
This completes our environment configuration
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