| /* | |
| Implementation of circular buffer counter for counting events per time. | |
| It is thead safe and has high increment/add performance and good enough avg/count performance. | |
| Usage is as simple as: | |
| var counter = new CircularBufferCounter(TimeSpan.FromSeconds(1), TimeSpan.FromHours(2)); | |
| counter.Increment(); | |
| var avgPerMinuteLastHour = counter.Avg(TimeSpan.FromMinutes(1), TimeSpan.FromHours(1)); | |
| */ |
| """ | |
| Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy) | |
| BSD License | |
| """ | |
| import numpy as np | |
| # data I/O | |
| data = open('input.txt', 'r').read() # should be simple plain text file | |
| chars = list(set(data)) | |
| data_size, vocab_size = len(data), len(chars) |
Picking the right architecture = Picking the right battles + Managing trade-offs
| # -*- coding: utf-8 -*- | |
| # pylint: disable=line-too-long, unused-argument, invalid-name, too-many-arguments, too-many-locals | |
| """ | |
| Utilities to support integration of Vowpal Wabbit and scikit-learn | |
| """ | |
| import numpy as np | |
| import sklearn | |
| from pyvw import vw | |
| import re |
| # Nathan Lehrer | |
| def get_best_path(grid): | |
| # Finds the best path through an M x N grid of point values, and that path's score | |
| # Input: grid = grid of point values = M x N list of lists | |
| # Returns: best_score = best possible score = int, path = best possible path = string | |
| M,N = len(grid),len(grid[0]) | |
| scores = {(0,0):grid[0][0]} # best score for a path to each cell; score of (0,0) is grid value | |
| trace = {} # whether we optimally come from up ('U') or left ('L') into each cell | |
| """Run Length Encoding utilities for NumPy arrays. | |
| Authors | |
| ------- | |
| - Nezar Abdennur | |
| - Anton Goloborodko | |
| """ | |
| from __future__ import division, print_function | |
| import numpy as np |
| import pandas as pd | |
| import itertools | |
| import time | |
| import multiprocessing | |
| from typing import Callable, Tuple, Union | |
| def groupby_parallel( | |
| groupby_df: pd.core.groupby.DataFrameGroupBy, | |
| func: Callable[[Tuple[str, pd.DataFrame]], Union[pd.DataFrame, pd.Series]], | |
| num_cpus: int = multiprocessing.cpu_count() - 1, |
Nothing gives you more detail about spark internals than actually reading it source code. In addition, you get to learn many design techniques and improve your scala coding skills. These are the random notes I make while reading the spark code. The best way to comprehend the notes is to load spark code into an IDE, e.g. IntelliJ, and navigate the code on the side.
The scripts for creating a spark cluster are: start-master.sh and start-slave.sh. Read them carefully, and you can see that both scripts are very similar except the values for $CLASS variable. For start-master.sh, the value is CLASS="org.apache.spark.deploy.master.Master", while the value for start-slave.sh is shown below with more context.
# NOTE: This exact class name is matched downstream by SparkSubmit.