Thomas thomasmooon

## live_loss_plot_keras.ipynb

      
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                thomasmooon
                / live_loss_plot_keras.ipynb
            
            
              Created
              May 25, 2018 10:12
                — forked from stared/live_loss_plot_keras.ipynb
            
              
                Live loss plot for training models in Keras (see: https://github.com/stared/livelossplot/ for a library)
              
          
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## preallocate_memory.py
###################################
## extra imports to set GPU options
###################################
import tensorflow as tf
from keras import backend as k

config = tf.ConfigProto() # TensorFlow wizardry
config.gpu_options.allow_growth = True # Don't pre-allocate memory; allocate as-needed
config.gpu_options.per_process_gpu_memory_fraction = 0.95 # Only allow a total fraction the GPU memory to be allocated
k.tensorflow_backend.set_session(tf.Session(config=config)) # Create a session with the above options specified.

## rsf_introduction.Rmd
---
title: "Random Survival Forest example"
author: "Thomas Gerlach"
date: "2018 MAY 22"
output: html_notebook
---

# Content

This script briefly introduces training and prediction of a random survival forest

## example_hdf5matrix.py
from keras.models import Sequential
from keras.layers import Dense
from keras.utils.io_utils import HDF5Matrix
import numpy as np

def create_dataset():
    import h5py
    X = np.random.randn(200,10).astype('float32')
    y = np.random.randint(0, 2, size=(200,1))
    f = h5py.File('test.h5', 'w')

## stopwatch.py
import datetime

class Timer(object):
    """A simple timer class"""

    def __init__(self):
        pass

    def start(self):
        """Starts the timer"""

## ec2-cuda-setup.bash
#!/bin/bash

NVIDIA_DRIVER_VERSION=352.63
NVIDIA_CUDA_VERSION=7.5
NVIDIA_CUDA_FULL_VERSION=7.5.18

sudo yum update -y
sudo yum groupinstall -y "Development tools"
sudo yum install kernel-devel-`uname -r`

## tensorflow_self_check.py
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,

## example_trials_visualization.ipynb

      
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                thomasmooon
                / example_trials_visualization.ipynb
            
            
              Last active
              November 9, 2018 14:10
            
              
                hyperopt save and reload trials object
              
          
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## memlog.sh
#!/bin/bash -e

echo "      date     time $(free -m | grep total | sed -E 's/^    (.*)/\1/g')"
while true; do
    echo "$(date '+%Y-%m-%d %H:%M:%S') $(free -m | grep Mem: | sed 's/Mem://g')"
    sleep 1
done

## tf_keras_multiGPU.py
def gpu_assignment(gpus, allow_growth = True, per_process_gpu_memory_fraction = 0.95):
    # GPU assignment

    ######################################################
    # !! run this BEFORE importing TF or keras !!
    # run code only on a single, particular GPU
    # from http://kawahara.ca/select-single-gpu-keras/
    ######################################################
    import os
    os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
	###################################
	## extra imports to set GPU options
	###################################
	import tensorflow as tf
	from keras import backend as k

	config = tf.ConfigProto() # TensorFlow wizardry
	config.gpu_options.allow_growth = True # Don't pre-allocate memory; allocate as-needed
	config.gpu_options.per_process_gpu_memory_fraction = 0.95 # Only allow a total fraction the GPU memory to be allocated
	k.tensorflow_backend.set_session(tf.Session(config=config)) # Create a session with the above options specified.
	---
	title: "Random Survival Forest example"
	author: "Thomas Gerlach"
	date: "2018 MAY 22"
	output: html_notebook
	---

	# Content

	This script briefly introduces training and prediction of a random survival forest
	from keras.models import Sequential
	from keras.layers import Dense
	from keras.utils.io_utils import HDF5Matrix
	import numpy as np

	def create_dataset():
	import h5py
	X = np.random.randn(200,10).astype('float32')
	y = np.random.randint(0, 2, size=(200,1))
	f = h5py.File('test.h5', 'w')
	import datetime

	class Timer(object):
	"""A simple timer class"""

	def __init__(self):
	pass

	def start(self):
	"""Starts the timer"""
	#!/bin/bash

	NVIDIA_DRIVER_VERSION=352.63
	NVIDIA_CUDA_VERSION=7.5
	NVIDIA_CUDA_FULL_VERSION=7.5.18

	sudo yum update -y
	sudo yum groupinstall -y "Development tools"
	sudo yum install kernel-devel-`uname -r`
	# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	#!/bin/bash -e

	echo " date time $(free -m \| grep total \| sed -E 's/^ (.*)/\1/g')"
	while true; do
	echo "$(date '+%Y-%m-%d %H:%M:%S') $(free -m \| grep Mem: \| sed 's/Mem://g')"
	sleep 1
	done
	def gpu_assignment(gpus, allow_growth = True, per_process_gpu_memory_fraction = 0.95):
	# GPU assignment

	######################################################
	# !! run this BEFORE importing TF or keras !!
	# run code only on a single, particular GPU
	# from http://kawahara.ca/select-single-gpu-keras/
	######################################################
	import os
	os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"