Pycharm uses CONDA to call the remote server

PyCharm How to delete items completely

https://www.pycharm.com.cn/t/62

pycharm

home—>dll—>annacoda3—>bin—>python3.9

window Of powershell install conda Link remote , And create the environment

# Log on to remote 
ssh  user name @ website  -p  port 
conda info -e
# Creating a virtual environment 
conda create -n name_pycharm python=3.9# establish 
conda activate name_pycharm# Activate 
conda install jupyter# Just 、 Download Software in virtual environment 
conda list# see 
jupyter notebook# start-up 

1、 ImportError: cannot import name ‘BatchNormalization‘ from ‘keras.layers.normalization‘

Version of the problem

Solution ： take from keras.layers.normalization import BatchNormalization
Change to from keras.layers.normalization.batch_normalization_v1 import BatchNormalization

2、FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead. from pandas import MultiIndex, Int64Index

pip install --upgrade pandas

3、Error! C:\Users\18437\jupyterProjectsTotalNPL\ Fudan University Chinese text classification corpus .xlsx is not UTF-8 encodedSaving disabled.

resolvent ：

Whether it's txt The file or csv The file has such a problem , The solution is the same ：
** Right click on the file - Open it in Notepad - Save as - Notice the bottom of the window – choice UTF-8 Coding format - preservation .
**.
https://www.php.cn/topic/excel/455341.html#:~:text=excel%E8%AE%BE%E7%BD%AE%E6%88%90,%E5%9B%9E%E4%BF%9D%E5%AD%98%E6%96%87%E4%BB%B6%E5%8D%B3%E5%8F%AF%E3%80%82

4、SyntaxError: (unicode error) ‘unicodeescape’ codec can’t decode bytes in position 2-3: truncated \UXXXXXXXX escape

https://www.cnblogs.com/rychh/p/9743864.html

 There are three ways to summarize ：

 One : Replace with absolute path 
func1("C:\\Users\\renyc")

 Two : Explicitly declare that the string does not need to be escaped （ Add r）
func1(r"C:\Users\renyc")

 3、 ... and : Use Linux The path of /
func1("C:/Users/renyc")

5、ImportError: Missing optional dependency ‘openpyxl’. Use pip or conda to install openpyxl.

pip install openpyxl

6、tensorflow call GPU

Environmental installation

 First of all, I'll turn it on anaconda Inside Anaconda powershell prompt
# Create a virtual environment inside tensorflowMy
conda create -n tensorflowMy python=3.9

# Activate the virtual environment 
conda activate tensorflowMy

# install cudatoolkit
conda install cudatoolkit -c conda-forge

# install cudnn
conda install cudnn -c conda-forge

# Download in the virtual environment tensorflow package 
pip install tensorflow

# Download in the virtual environment keras package 
pip install keras

Code calls

import os
import tensorflow as tf
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
gpus = tf.config.list_physical_devices("GPU")

# View using GPU still CPU：
from tensorflow.python.client import device_lib
print(device_lib.list_local_devices())

# see GPU Usage rate ： Execute at the terminal nvidia-smi

7、Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.

Through online search , This is generally due to GPU The cause of insufficient video memory , My solution is to reduce it directly batch_size Size , take batch_size The value of the original 16 Revised to 8, It can run normally

8、 Douban mirror image Fast installation

pip install -i https://pypi.douban.com/simple < Packages that need to be installed >
such as ：
pip install -i https://pypi.douban.com/simple requests

tensorflow 2.5.0 
pip install -i https://pypi.douban.com/simple grpcio==1.34.0
pip install -i https://pypi.douban.com/simple  h5py==3.1.0
pip install -i https://pypi.douban.com/simple numpy==1.19.2
pip install -i https://pypi.douban.com/simple six==1.15.0
pip install -i https://pypi.douban.com/simple tensorflow-estimator==2.5.0
pip install -i https://pypi.douban.com/simple typing-extensions==3.7.4
pip install -i https://pypi.douban.com/simple wrapt==1.12.1

9、pytorch install GPU

python：3.8.5
conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch

(pytorch_envs) C:\Users\Fishc>python
Python 3.8.12 (default, Oct 12 2021, 03:01:40) [MSC v.1916 64 bit (AMD64)] :: Anaconda, Inc. on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import torch
>>> torch.__version__
'1.10.2'
>>>torch.cuda.is_available()

10、TensorFlow call GPU

 Install in the virtual environment tensorflow2.9.0+keras2.9.0+cudnn8.2.1.32+cudatoolkit11.4.2+python3.9
 First of all, I'll turn it on anaconda Inside Anaconda powershell prompt

 Create a virtual environment inside tensorflowMy

conda create -n tensorflowMy python=3.6

 Activate the virtual environment 

conda activate tensorflowMy
pip install tensorflow-gpu==1.12.0 -i https://pypi.tuna.tsinghua.edu.cn/simple
 install cudatoolkit

conda install cudatoolkit -c conda-forge

 install cudnn

conda install cudnn -c conda-forge

 Download in the virtual environment tensorflow package 

pip install tensorflow

 Download in the virtual environment keras package 

pip install keras

import os
os.environ['CUDA_VISIBLE_DEVICES'] = '/gpu:0' 
  # The key to this sentence 
import time

# View using GPU still CPU：
from tensorflow.python.client import device_lib
print(device_lib.list_local_devices())

11、 Configuration environment

# Create an environment 
conda create -n rebot python=3.6
conda activate rebot
# Installation package 
# Upgrade instruction 
python -m pip install --upgrade pip
conda install jupyter 
pip install  tornado==4.4.3 -i https://pypi.tuna.tsinghua.edu.cn/simple
pip install tensorflow==1.2 -i https://pypi.tuna.tsinghua.edu.cn/simple
#（tensorflow 2.0 There will be no  tensorflow.contrib）
pip list

12、 There is not enough video memory

import tensorflow as tf
import os
os.environ["CUDA_VISIBLE_DEVICES"] = '0'   # Specify the first block GPU You can use 
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.5  #  The program can only occupy the specified space at most gpu50% Explicit memory of 
config.gpu_options.allow_growth = True      # The program requests memory on demand 
sess = tf.Session(config = config)

13、tensorflow1 edition GPU

conda create -n robot python=3.6
conda activate robot


conda install jupyter 
python -m pip install --upgrade pip
pip install  tornado==4.4.3 -i https://pypi.tuna.tsinghua.edu.cn/simple

pip install tensorflow-gpu==1.12  -i https://pypi.tuna.tsinghua.edu.cn/simple
conda install cudatoolkit=9.0 
conda install cudnn=7.3

pip install numpy==1.16.0

import tensorflow as tf
tf.test.is_gpu_available()

14、gitgit

 Start using our and interface gitgit-lfs

 If you need to create a model repository from the command line （ If you create a repository from a website , Then skip ）

pip install huggingface_hub
Or use transformers-cli if you have transformers

huggingface-cli login
Log in using the same credentials as huggingface.co/join
Create a model repo from the CLI if needed
huggingface-cli repo create model_name
 Clone the model locally 

Make sure you have git-lfs installed
(https://git-lfs.github.com)
git lfs install
git clone https://huggingface.co/username/model_name
 Then add , Submit and push weights , Word splitter and configuration 

save files via `.save_pretrained()` or move them here
git add .
git commit -m "commit from $USER"
git push
 then , Your model will be accessed through its identifier ： user name /model_name

 Anyone can load it from the code ：

tokenizer = AutoTokenizer.from_pretrained("username/model_name")
model = AutoModel.from_pretrained("username/model_name")