GPU cloud server configuration keras environment

The solution to the coexistence of anaconda2 and anaconda3 in Ubuntu16.04

1. Type in the terminal

conda create -n py27 python=2.7 anaconda
activates the py2.7 environment
source activate py27
closes the 2.7 environment
source deactivate.

1.2 jupyter notebook switches the kernel
$python2 -m pip install ipykernel
$python2 -m ipykernel install --user

1.3 installs various packages in sequence
conda install nibabel
conda install nipype
sckit-image == 0.13.0
sckit-learn == 0.18.1


2. Install theano, tensorflow, keras,
https://blog.csdn.net/m984789463/article/details/77074451

error: To use MKL 2018 with Theano you MUST set "MKL_THREADING_LAYER=GNU" in your environement.
Solution
conda install mkl=2017
export MKL_THREADING_LAYER=GNU


3. Install tensorflow-gpu
conda install tensorflow-gpu

3.1 After installation, check the version of tensorflow-gpu

Enter python and enter the python command line
import tensorflow as tf
tf.__version__
print('default gpu device {}'.format(tf.test.gpu_device_name()))

query The tensorflow installation path is:
tf.__path__

Check whether tensorflow uses gpu for calculation
import tensorflow as tf
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))


4. Data
 decompression data from /data to /input
unzip /data /datasets.zip -d /input/datasets

5. Solve MKL2018
conda install mkl=2017

6.keras import error
import tensorflow.python.ops.tensor_array_ops

7.ubuntu recursively decompress the gz file in the folder
 gunzip -r /your/path

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