This is just an installation guide how to install Keras on Google Compute Engine Centos 7.
yum install python-devel sqlite-devel -y
See Google Reference https://cloud.google.com/compute/docs/gpus/add-gpus
Run this script once as root
#!/bin/bash
echo "Checking for CUDA and installing."
# Check for CUDA and try to install.
if ! rpm -q cuda; then
curl -O http://developer.download.nvidia.com/compute/cuda/repos/rhel7/x86_64/cuda-repo-rhel7-8.0.61-1.x86_64.rpm
rpm -i --force ./cuda-repo-rhel7-8.0.61-1.x86_64.rpm
yum clean all
yum install epel-release -y
yum update -y
yum install cuda -y
fi
# Verify that CUDA installed; retry if not.
if ! rpm -q cuda; then
yum install cuda -y
fi
You can create a new file /etc/profile.d/cuda.sh to make enviorment variables setting globally or just add them into your home directory ~/.bashrc.
export CUDA_HOME=/usr/local/cuda
export PATH=$PATH:$CUDA_HOME/bin
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CUDA_HOME/lib64
~$ tar xzvf cudnn-8.0-linux-x64-v6.0.tgz
~$ sudo cp cuda/lib64/* /usr/local/cuda/lib64/
~$ sudo cp cuda/include/cudnn.h /usr/local/cuda/include/
~$ sudo ln -s /usr/local/cuda/lib64/libcudnn.so.6 /usr/local/cuda/lib64/libcudnn.so.5
The last trick is because Keras package is using libcudnn.so.5 as default dependency. Our download is version 6. We create a symbolic link to avoid errors and it actually works.
Run:
nvidia-smi
You will see pic like below
Create the requirements.txt file:
scikit-learn
futures
scipy
pandas
pysqlite
tensorflow
tensorflow-gpu
Keras
Run pip install -r requirements.txt to install all python packages.
Anaconda is the most popular and trusted data science ecosystem. The following steps shows how to install.
sudo yum install bzip2 -y
curl -O https://repo.continuum.io/archive/Anaconda3-4.3.1-Linux-x86_64.sh
bash Anaconda3-4.3.1-Linux-x86_64.sh
Follow the instrunction to install and add Anacoda HOME into your path.
$ python -V Python 3.6.0 :: Anaconda 4.3.1 (64-bit)
# conda create --name <env_name> [list of packages] , for example.
conda create --name my_python3
# Activate the environment
source activate my_python3
# Deactive the environment
source deactive my_python3
# List all environments
conda info --envs
# Clone an environemnt
conda create --name my_python3_2 --clone my_python3
# Remove an environemnt
conda remove --name my_python3_2 --all
# Export an environment into a yaml file
source activate my_python3
conda env export > environment.yml
# Create an environment from a yaml file
conda env create -f environment.yml
Create the requirements.anacoda.txt file:
$ cat requirements.anacoda.txt
futures
tensorflow
tensorflow-gpu
Keras
Run:
$ conda create --name datascience
$ source activate datascience
(datascience) $ pip install -r requirements.anacoda.txt
Simple try those imports, should be imported correctly with no errors.
from keras.callbacks import ModelCheckpoint
from keras.layers import Masking, LSTM, Dense
from keras.layers.core import Dropout
from keras.layers.wrappers import TimeDistributed
import keras.backend.tensorflow_backend as K
import pandas as pd
import numpy as np
Use mnist as our starter example [https://github.com/tensorflow/tensorflow/tree/r1.3/tensorflow/examples/tutorials/mnist]
mnist_softmax.py
(datascience) $ python mnist_softmax.py --data_dir data
Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.
Extracting data/train-images-idx3-ubyte.gz
Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.
Extracting data/train-labels-idx1-ubyte.gz
Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.
Extracting data/t10k-images-idx3-ubyte.gz
Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.
Extracting data/t10k-labels-idx1-ubyte.gz
2017-08-23 20:40:27.074516: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
2017-08-23 20:40:27.074577: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
2017-08-23 20:40:27.074587: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
2017-08-23 20:40:27.074594: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
2017-08-23 20:40:27.074601: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
2017-08-23 20:40:29.468172: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:893] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2017-08-23 20:40:29.469181: I tensorflow/core/common_runtime/gpu/gpu_device.cc:955] Found device 0 with properties:
name: Tesla K80
major: 3 minor: 7 memoryClockRate (GHz) 0.8235
pciBusID 0000:00:04.0
Total memory: 11.17GiB
Free memory: 11.11GiB
2017-08-23 20:40:29.469238: I tensorflow/core/common_runtime/gpu/gpu_device.cc:976] DMA: 0
2017-08-23 20:40:29.469246: I tensorflow/core/common_runtime/gpu/gpu_device.cc:986] 0: Y
2017-08-23 20:40:29.469258: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1045] Creating TensorFlow device (/gpu:0) -> (device: 0, name: Tesla K80, pci bus id: 0000:00:04.0)
0.9182
mnist_deep.py
(datascience) $ python mnist_deep.py --data_dir data
Extracting data/train-images-idx3-ubyte.gz
Extracting data/train-labels-idx1-ubyte.gz
Extracting data/t10k-images-idx3-ubyte.gz
Extracting data/t10k-labels-idx1-ubyte.gz
Saving graph to: /tmp/tmp3x1g8twx
2017-08-23 21:09:14.432169: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
2017-08-23 21:09:14.432228: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
2017-08-23 21:09:14.432236: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
2017-08-23 21:09:14.432242: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
2017-08-23 21:09:14.432247: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
2017-08-23 21:09:16.876049: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:893] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2017-08-23 21:09:16.876992: I tensorflow/core/common_runtime/gpu/gpu_device.cc:955] Found device 0 with properties:
name: Tesla K80
major: 3 minor: 7 memoryClockRate (GHz) 0.8235
pciBusID 0000:00:04.0
Total memory: 11.17GiB
Free memory: 11.11GiB
2017-08-23 21:09:16.877020: I tensorflow/core/common_runtime/gpu/gpu_device.cc:976] DMA: 0
2017-08-23 21:09:16.877037: I tensorflow/core/common_runtime/gpu/gpu_device.cc:986] 0: Y
2017-08-23 21:09:16.877049: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1045] Creating TensorFlow device (/gpu:0) -> (device: 0, name: Tesla K80, pci bus id: 0000:00:04.0)
step 0, training accuracy 0.06
step 100, training accuracy 0.88
step 200, training accuracy 0.8
step 300, training accuracy 0.98
step 400, training accuracy 0.96
step 500, training accuracy 0.9
step 600, training accuracy 0.96
step 700, training accuracy 0.96
step 800, training accuracy 0.94
step 900, training accuracy 1
step 1000, training accuracy 0.92
step 1100, training accuracy 0.98
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step 1400, training accuracy 0.96
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step 2000, training accuracy 0.96
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step 2200, training accuracy 0.98
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step 2700, training accuracy 0.96
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step 3600, training accuracy 0.98
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step 5700, training accuracy 0.96
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step 6000, training accuracy 0.98
step 6100, training accuracy 1
step 6200, training accuracy 0.96
step 6300, training accuracy 1
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step 6800, training accuracy 0.98
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step 7000, training accuracy 1
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step 7200, training accuracy 0.98
step 7300, training accuracy 0.96
step 7400, training accuracy 1
step 7500, training accuracy 0.98
step 7600, training accuracy 1
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step 8000, training accuracy 1
step 8100, training accuracy 1
step 8200, training accuracy 1
step 8300, training accuracy 0.96
step 8400, training accuracy 1
step 8500, training accuracy 1
step 8600, training accuracy 0.98
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step 8800, training accuracy 0.98
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step 9100, training accuracy 1
step 9200, training accuracy 0.98
step 9300, training accuracy 1
step 9400, training accuracy 1
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step 9900, training accuracy 0.98
step 10000, training accuracy 1
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step 10200, training accuracy 1
step 10300, training accuracy 1
step 10400, training accuracy 1
step 10500, training accuracy 1
step 10600, training accuracy 0.96
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step 10900, training accuracy 0.98
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step 13700, training accuracy 0.98
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step 14300, training accuracy 1
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step 15000, training accuracy 1
step 15100, training accuracy 1
step 15200, training accuracy 0.98
step 15300, training accuracy 1
step 15400, training accuracy 0.98
step 15500, training accuracy 1
step 15600, training accuracy 1
step 15700, training accuracy 0.98
step 15800, training accuracy 1
step 15900, training accuracy 1
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step 16100, training accuracy 0.98
step 16200, training accuracy 1
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step 19200, training accuracy 1
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step 19400, training accuracy 1
step 19500, training accuracy 1
step 19600, training accuracy 1
step 19700, training accuracy 0.98
step 19800, training accuracy 1
step 19900, training accuracy 1
test accuracy 0.9918