RDkit透過conda安裝

執行下面其中一項指令

$ conda install -c rdkit rdkit
$ conda install -c rdkit/label/nightly rdkit
$ conda install -c rdkit/label/attic rdkit
$ conda install -c rdkit/label/beta rdkit



參考
https://anaconda.org/rdkit/rdkit

Open Babel

https://open-babel.readthedocs.io/en/latest/Installation/install.html#install-binaries

最簡單的python Tensorflow-gpu安裝方法

https://youyouyou.pixnet.net/blog/post/120576553?fbclid=IwAR3ZCMSIKkZpyWCeqyUKweumQY3RtpRr-aqo3i8byca0xnJj0Lp0M20bejk

Windows jupyter notebook 發生 InternalError: Blas GEMM launch failed

原因:GPU記憶體不足

解決方式:
from sklearn.preprocessing import RobustScaler
from keras.models import Model, load_model, Sequential
from keras.layers import Input
from keras.layers import LSTM, GRU, Bidirectional, BatchNormalization
from keras.layers import Dense, Dropout
from keras.layers import Concatenate
from keras import regularizers

在import Keras module下方再增加一段程式碼

import tensorflow as tf
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = '0' # Set to -1 if CPU should be used CPU = -1 , GPU = 0

gpus = tf.config.experimental.list_physical_devices('GPU')
cpus = tf.config.experimental.list_physical_devices('CPU')

if gpus:
    try:
        # Currently, memory growth needs to be the same across GPUs
        for gpu in gpus:
            tf.config.experimental.set_memory_growth(gpu, True)
        logical_gpus = tf.config.experimental.list_logical_devices('GPU')
        print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
    except RuntimeError as e:
        # Memory growth must be set before GPUs have been initialized
        print(e)
elif cpus:
    try:
        # Currently, memory growth needs to be the same across GPUs
        logical_cpus= tf.config.experimental.list_logical_devices('CPU')
        print(len(cpus), "Physical CPU,", len(logical_cpus), "Logical CPU")
    except RuntimeError as e:
        # Memory growth must be set before GPUs have been initialized
        print(e)

https://github.com/tensorflow/tensorflow/issues/11812

如何在 Windows 10 安裝 Anaconda 3.7 和 TensorFlow-GPU 2.0

https://machinelearningtw.blogspot.com/2019/11/windows-10-anaconda-37-tensorflow-gpu-20.html?fbclid=IwAR0Vax41lFd7LJUOO76y-wnBxJDdzWCvXZ0r4csqOsISsRiLBbeGvXXggWY

Windows install RDkit Tensorflow Keras

1.安裝RDkit

打開Anaconda3 Prompt (Anaconda3)終端機
$ conda create -c rdkit -n my-rdkit-env rdkit python=3.7
$ conda activate my-rdkit-env

create -c rdkit -n my-rdkit-env意思是在Windows Anaconda3環境裡產生一個名叫my-rdkit-env的環境，因為tensorflow只支援Python3.7，所以在最後還要輸入python=3.7代表在my-rdkit-env環境下是使用python=3.7，接著就可以依序安裝tensorflow跟Keras了!

2.在my-rdkit-env環境下安裝tensorflow2.0
$ conda install tensorflow-gpu=2.0 python=3.7

3.在my-rdkit-env環境下安裝Keras
$ conda install Keras

4.在my-rdkit-env環境下安裝jupyter notebook
$ conda install jupyter

5.安裝CUDA
因為RDkit只支援CUDA 10.0，所以到下面連結下載CUDA 10.0
https://developer.nvidia.com/cuda-toolkit-archiv