ravnoor

# from https://www.kaggle.com/nothxplz/dogs-vs-cats-redux-kernels-edition/cats-vs-dogs-05-pytorch-example/run/761413
from __future__ import print_function

import argparse
import csv
import os
import os.path
import shutil
import time

ravnoor

# This is an example for the CIFAR-10 dataset.
# There's a function for creating a train and validation iterator.
# There's also a function for creating a test iterator.
# Inspired by https://discuss.pytorch.org/t/feedback-on-pytorch-for-kaggle-competitions/2252/4

from utils import plot_images

def get_train_valid_loader(data_dir,
                           batch_size,
                           augment,

ravnoor

# SGE 8.1.9

qhold 888333 -t 7-12
qalter -h U 888333 -t 7-12

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ls -1 | awk -F'_' '{print $1 $2}' | sort -u

# FLP_001_t2.nii
# FLP_001_t1_nii
# FLP_004_t2.nii
# FLP_004_t1.nii
# ||
# ||
# FLP_001
# FLP_004

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# https://github.com/Rachnog/Deep-Trading/blob/master/volatility/volatility.py

epsilon = 1.0e-9
def qlike_loss(y_true, y_pred):
    y_pred = K.clip(y_pred, epsilon, 1.0 - epsilon)
    loss = K.log(y_pred) + y_true / y_pred
    return K.mean(loss, axis=-1)

def mse_log(y_true, y_pred):
    y_pred = K.clip(y_pred, epsilon, 1.0 - epsilon)

ravnoor

# https://github.com/andreimouraviev/Mets/blob/a8ce43b335584187f0728a4b3975c679ccf06cbc/UNET_2D_AUG17.py
# coding: utf-8

# In[1]:

import os
os.chdir(r'/home/amourav/Python')
import sklearn
from UTILS import *

ravnoor

conda create -n tf-keras python=2.7
pip install numpy

wget https://github.com/bazelbuild/bazel/releases/download/0.5.2/bazel-0.5.2-installer-linux-x86_64.sh
# 0.5.4 incompatibility issues

chmod +x bazel-0.5.2-installer-linux-x86_64.sh
HOME=/00_toolbox ./bazel-0.5.2-installer-linux-x86_64.sh --prefix=/00_toolbox/bazel-build

git clone https://github.com/tensorflow/tensorflow

ravnoor

The fastest remote directory rsync over ssh archival I can muster (40MB/s over 1gb NICs)

This creates an archive that does the following:

rsync (Everyone seems to like -z, but it is much slower for me)

• a: archive mode - rescursive, preserves owner, preserves permissions, preserves modification times, preserves group, copies symlinks as symlinks, preserves device files.
• H: preserves hard-links
• A: preserves ACLs

ravnoor

find . -maxdepth 1 -name '._*' -delete

ravnoor

# Example code to demonstrate parallel for loop implementation using joblib
from joblib import Parallel, delayed
import multiprocessing

# Vars
my_list = range(10)
squares = []

# Function to parallelize
def find_square(i):