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iPython & Jupyter: 4 fun & pro t
Лев Тонких
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No content
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Наши проекты
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Лучшее 2015
«Афиша-Рестораны» и «Рамблер.Почта» вошли в список
25 лучших приложений 2015 года для iOS по версии
редакции AppStore, официального онлайн-магазина Apple.
У «Афиши-Рестораны» — 15-е место в рейтинге,
«Рамблер.Почта» — на 24-м.
http://lenta.ru/news/2015/12/09/rambler/
(http://lenta.ru/news/2015/12/09/rambler/)
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Контакты
В группе компаний Rambler&Co всегда есть
открытые вакансии для тех, кто хочет
профессионально расти и развиваться, занимаясь
тем, что по-настоящему нравится
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В чем разница между IPython & Jupyter?
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IPython
2001
Все в консоли, отображение графиков в новом окне и т.д.
IPython Notebook pre 1.0
18 декабря 2011
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8 августа 2013 - IPython 1.0
1 апреля 2014 - IPython 2.0
Появились виджеты
27 февраля 2015 - IPython 3.0
- 54
IPython kernels for other languages (https://github.com/ipython/ipython/wiki/IPython-
kernels-for-other-languages)
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Проблема
В названии IPython
Это уже не только Python
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Jupyter Notebook
И еще одна проблема...
Один большой монолитный репозиторий
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Куда класть исходники - Григорий Петров
Куда класть исходники-Григорий Петров
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Jupyter 4.0 "BigSplit"
ipython
ipykernel
ipywidget
notebook
jupyter_core
etc.
8 января 2016 - Jupyter 4.1
Первый релиз после BigSplit
Будут частыми smaller releases
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Multi-cells
Выделение Shift-Up / Shift-Down
Merge Shift-M
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Command palette
Cmd-Shift-P / Ctrl-Shift-P
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Restart kernel and run-all
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Find & Replace
Hotkey - F
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I
n [ ]
: ! j
u
p
y
t
e
r n
o
t
e
b
o
o
k
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Немного магии
I
n [
1
4
]
: %
l
s
m
a
g
i
c
O
u
t
[
1
4
]
: A
v
a
i
l
a
b
l
e l
i
n
e m
a
g
i
c
s
:
%
a
l
i
a
s %
a
l
i
a
s
_
m
a
g
i
c %
a
u
t
o
c
a
l
l %
a
u
t
o
m
a
g
i
c %
a
u
t
o
s
a
v
e %
b
o
o
k
m
a
r
k %
c
a
t %
c
d %
c
l
e
a
r %
c
o
A
v
a
i
l
a
b
l
e c
e
l
l m
a
g
i
c
s
:
%
%
! %
%
H
T
M
L %
%
S
V
G %
%
b
a
s
h %
%
c
a
p
t
u
r
e %
%
d
e
b
u
g %
%
f
i
l
e %
%
h
t
m
l %
%
j
a
v
a
s
c
r
i
p
t %
%
l
a
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e
x %
%
A
u
t
o
m
a
g
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c i
s O
N
, % p
r
e
f
i
x I
S N
O
T n
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e
d
e
d f
o
r l
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n
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a
g
i
c
s
.
I
n [
1
5
]
: %
q
u
i
c
k
r
e
f
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pwd
Current working directory path
I
n [
1
6
]
: %
p
w
d
O
u
t
[
1
6
]
: '
/
U
s
e
r
s
/
l
.
t
o
n
k
i
k
h
/
i
p
y
t
h
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n
'
What in inside?
I
n [
1
7
]
: i
m
p
o
r
t o
s
o
s
.
g
e
t
c
w
d
(
)
, o
s
.
p
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.
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e
a
l
p
a
t
h
(
'
.
'
)
# o
r s
m
t
h e
l
s
e
?
O
u
t
[
1
7
]
: (
'
/
U
s
e
r
s
/
l
.
t
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n
k
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k
h
/
i
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, '
/
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s
/
l
.
t
o
n
k
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k
h
/
i
p
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t
h
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n
'
)
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Docstrings & examples
I
n [
1
8
]
: %
p
i
n
f
o %
p
w
d
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I
n [
1
9
]
: f
r
o
m I
P
y
t
h
o
n
.
c
o
r
e
.
m
a
g
i
c
s
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o
s
m i
m
p
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t O
S
M
a
g
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c
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m
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c = O
S
M
a
g
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c
s
(
)
m
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g
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c
.
p
w
d
(
)
O
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t
[
1
9
]
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/
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r
s
/
l
.
t
o
n
k
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k
h
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p
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n
'
I
n [
2
0
]
: %
p
s
o
u
r
c
e m
a
g
i
c
.
p
w
d
I
n [
2
1
]
: %
p
s
o
u
r
c
e %
p
s
o
u
r
c
e
I
n [
2
2
]
: %
p
s
o
u
r
c
e %
p
w
d
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env
Virtual Environment
I
n [ ]
: %
e
n
v
I
n [
2
4
]
: e
n
v = %
e
n
v
%
p
a
g
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n
v
True or False?
I
n [
1
]
: e
n
v = %
e
n
v
%
s
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t
_
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v l
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(
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: l
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=
l
o
l
I
n [
2
]
: q
O
u
t
[
2
]
: F
a
l
s
e
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Slide 23 text
I
n [
2
7
]
: # %
m
k
d
i
r t
e
s
t
# %
c
d t
e
s
t
I
n [ ]
: # %
l
o
a
d t
e
s
t
.
p
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s
t
.
p
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p
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t
(
'
H
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l
l
o w
o
r
l
d
'
)
I
n [
3
1
]
: %
p
y
c
a
t t
e
s
t
.
p
y
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Shell capture
I
n [
3
2
]
: %
s
c f
i
l
e
s = l
s
I
n [
3
3
]
: f
i
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O
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t
[
3
3
]
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n
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g
.
p
y
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Slide 25 text
I
n [
3
4
]
: ! l
s
U
n
t
i
t
l
e
d
.
i
p
y
n
b e
n
v i
m
g i
p
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h
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p
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v
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w
r
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t
I
n [
3
5
]
: f
i
l
e
s = ! l
s
f
i
l
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s
, f
i
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s
.
n
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s
.
s
O
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t
[
3
5
]
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[
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Slide 26
Slide 26 text
Shell execute
Slide 27
Slide 27 text
I
n [
3
6
]
: f
i
l
e
s = %
s
x l
s # =
= %
s
c -
l f
i
l
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s = l
f
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# f
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n
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I
n [
3
7
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! l
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[
3
7
]
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Slide 28
Slide 28 text
One more example
I
n [
3
8
]
: ! p
i
p f
r
e
e
z
e | g
r
e
p i
p
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t
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0
I
n [
3
9
]
: !
! p
i
p f
r
e
e
z
e | g
r
e
p i
p
y
t
h
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O
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t
[
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]
Slide 29
Slide 29 text
Bash
I
n [
4
0
]
: %
%
b
a
s
h
f
o
r i i
n {
1
.
.
3
}
;
d
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c
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$
i
"
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1
2
3
Slide 30
Slide 30 text
Who
Slide 31
Slide 31 text
I
n [
4
1
]
: %
w
h
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S
M
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Slide 32
Slide 32 text
I
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4
3
]
: %
w
h
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_
l
s d
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O
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[
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V
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Slide 33
Slide 33 text
psearch
Поиск по имени переменной
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c
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Slide 34
Slide 34 text
Logging
I
n [
6
9
]
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l
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Slide 35
Slide 35 text
I
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1
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Slide 36
Slide 36 text
Python2 & Python3
I
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9
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Interest over time. Web Search. Worldwide, 2004 - present.
View full report in Google Trends
python 2 python 3
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Slide 37
Slide 37 text
I
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%
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Slide 38
Slide 38 text
No content
Slide 39
Slide 39 text
I
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7
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Slide 40
Slide 40 text
Write & Sharing
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5
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Slide 41
Slide 41 text
Time
Время выполнения
I
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7
9
]
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I
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Slide 42
Slide 42 text
LaTeX
I
n [
3
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: %
%
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x
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exp
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I
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∇ ×
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Slide 43
Slide 43 text
Demos
Slide 44
Slide 44 text
Куда спрятаться от ядерного взрыва?
Slide 45
Slide 45 text
I
n [
3
2
]
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I
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Slide 46
Slide 46 text
I
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6
]
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m
p
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r
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i
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# L
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;
I
n [
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8
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F
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+
-
Leaflet (http://leafletjs.com) | Map data (c) OpenStreetMap (http://openstreetmap.org) contributors
Slide 47
Slide 47 text
No content
Slide 48
Slide 48 text
Github Commits
Introducing the New GitHub Graphs - April 25, 2012 link (https://github.com/blog/1093-
introducing-the-new-github-graphs)
Slide 49
Slide 49 text
I
n [
4
]
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r
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m g
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a
n
d
a
s a
s p
d
%
m
a
t
p
l
o
t
l
i
b i
n
l
i
n
e
I
n [
3
]
: G
I
T
H
U
B
_
U
S
E
R = o
s
.
g
e
t
e
n
v
(
'
G
I
T
H
U
B
_
U
S
E
R
'
)
G
I
T
H
U
B
_
T
O
K
E
N = o
s
.
g
e
t
e
n
v
(
'
G
I
T
H
U
B
_
T
O
K
E
N
'
)
R
E
P
O
_
P
A
T
H = '
p
y
t
h
o
n
/
c
p
y
t
h
o
n
'
I
n [
8
]
: g = G
i
t
h
u
b
(
G
I
T
H
U
B
_
U
S
E
R
, G
I
T
H
U
B
_
T
O
K
E
N
)
r
e
p
o = g
.
g
e
t
_
r
e
p
o
(
R
E
P
O
_
P
A
T
H
)
d
f = p
d
.
D
a
t
a
F
r
a
m
e
(
{
'
d
a
t
e
'
: p
d
.
S
e
r
i
e
s
(
(
i
.
c
o
m
m
i
t
.
c
o
m
m
i
t
t
e
r
.
d
a
t
e f
o
r i i
n r
e
p
o
.
g
e
t
_
c
o
m
m
i
t
s
(
)
s
t
a
t
s = d
f
.
g
r
o
u
p
b
y
(
p
d
.
G
r
o
u
p
e
r
(
k
e
y
=
'
d
a
t
e
'
, f
r
e
q
=
'
M
'
)
)
.
s
i
z
e
(
)
Slide 50
Slide 50 text
I
n [
3
5
]
: p
l
t
.
f
i
g
u
r
e
(
f
i
g
s
i
z
e
=
(
1
6
,
4
)
)
s
t
a
t
s
.
p
l
o
t
(
t
i
t
l
e
=
'
S
t
a
t
s o
f c
o
m
m
i
t
s
'
, l
a
b
e
l
=
'
C
o
m
m
i
t
s
'
, l
e
g
e
n
d
=
T
r
u
e
)
p
l
t
.
x
l
a
b
e
l
(
'
D
a
t
e
'
)
; p
l
t
.
y
l
a
b
e
l
(
'
C
o
m
m
i
t
s
'
)
p
l
t
.
s
u
p
t
i
t
l
e
(
R
E
P
O
_
P
A
T
H
, y
=
1
.
0
5
, f
o
n
t
s
i
z
e
=
1
4
, f
o
n
t
w
e
i
g
h
t
=
'
b
o
l
d
'
)
O
u
t
[
3
5
]
: <
m
a
t
p
l
o
t
l
i
b
.
t
e
x
t
.
T
e
x
t a
t 0
x
1
7
8
9
e
3
2
b
0
>
Slide 51
Slide 51 text
ipyparallel
Powerful architecture for parallel and distributed computing
Single program, multiple data (SPMD) parallelism.
Multiple program, multiple data (MPMD) parallelism.
Message passing using MPI.
Task farming.
Data parallel.
Combinations of these approaches.
Custom user de ned approaches
Slide 52
Slide 52 text
No content
Slide 53
Slide 53 text
No content
Slide 54
Slide 54 text
No content
Slide 55
Slide 55 text
Engine
IPython instance, that takes commands
handle incoming and outgoing Python objects
Slide 56
Slide 56 text
Controller
provide an interface for working with a set of engines (Direct or LoadBalanced)
collection of processes to which IPython engines and clients can connect
Controller = Hub + Schedulers
Slide 57
Slide 57 text
Hub
Center of a Cluster
Process that keeps track of engine connections, schedulers, clients
Slide 58
Slide 58 text
Schedulers
All actions that can be performed on the engine go through a Scheduler
provide a fully asynchronous interface to a set of engines
Slide 59
Slide 59 text
I
n [ ]
: ! i
p
c
l
u
s
t
e
r s
t
a
r
t -
n 4
I
n [
3
]
: %
%
b
a
s
h
p
y
t
h
o
n n
o
t
_
t
r
a
c
k
/
h
a
b
r
a
p
a
r
s
e
.
p
y s
a
v
e
_
f
a
v
s
_
l
i
s
t s
t
l
e
o
n n
o
t
_
t
r
a
c
k
/
t
e
s
t
.
t
x
t
p
y
t
h
o
n n
o
t
_
t
r
a
c
k
/
h
a
b
r
a
p
a
r
s
e
.
p
y s
a
v
e
_
f
a
v
s
_
l
i
s
t -
-
g
t s
t
l
e
o
n n
o
t
_
t
r
a
c
k
/
t
e
s
t
1
.
t
x
t
p
y
t
h
o
n n
o
t
_
t
r
a
c
k
/
h
a
b
r
a
p
a
r
s
e
.
p
y s
a
v
e
_
f
a
v
s
_
l
i
s
t -
-
m
m s
t
l
e
o
n n
o
t
_
t
r
a
c
k
/
t
e
s
t
2
.
t
x
t
c
a
t n
o
t
_
t
r
a
c
k
/
t
e
s
t
1
.
t
x
t n
o
t
_
t
r
a
c
k
/
t
e
s
t
2
.
t
x
t >
> n
o
t
_
t
r
a
c
k
/
t
e
s
t
.
t
x
t
I
n [
4
7
]
: f
r
o
m i
p
y
p
a
r
a
l
l
e
l i
m
p
o
r
t C
l
i
e
n
t
, r
e
q
u
i
r
e
c
l
i
e
n
t = C
l
i
e
n
t
(
)
d
v
i
e
w = c
l
i
e
n
t
[
:
]
I
n [
4
8
]
: d
v
i
e
w
O
u
t
[
4
8
]
: <
D
i
r
e
c
t
V
i
e
w [
0
, 1
, 2
, 3
]
>
I
n [
4
9
]
: l
i
n
k
s = (
l
i
n
k
.
s
t
r
i
p
(
) f
o
r l
i
n
k i
n o
p
e
n
(
'
n
o
t
_
t
r
a
c
k
/
t
e
s
t
.
t
x
t
'
)
)
I
n [
5
0
]
: d
v
i
e
w
.
s
c
a
t
t
e
r
(
'
l
i
n
k
s
'
, l
i
s
t
(
l
i
n
k
s
)
)
O
u
t
[
5
0
]
: <
A
s
y
n
c
R
e
s
u
l
t
: f
i
n
i
s
h
e
d
>
Slide 60
Slide 60 text
I
n [
5
1
]
: l
e
n
(
d
v
i
e
w
[
'
l
i
n
k
s
'
]
)
O
u
t
[
5
1
]
: 4
I
n [
5
2
]
: @
d
v
i
e
w
.
r
e
m
o
t
e
(
b
l
o
c
k
=
F
a
l
s
e
)
@
r
e
q
u
i
r
e
(
'
r
e
q
u
e
s
t
s
'
, '
b
s
4
'
)
d
e
f t
a
g
_
m
a
k
e
r
(
)
:
t
a
g
s = {
}
f
o
r l
i
n
k i
n l
i
n
k
s
:
s
o
u
p = b
s
4
.
B
e
a
u
t
i
f
u
l
S
o
u
p
(
r
e
q
u
e
s
t
s
.
g
e
t
(
l
i
n
k
)
.
t
e
x
t
, '
h
t
m
l
.
p
a
r
s
e
r
'
)
f
o
r i i
n s
o
u
p
.
f
i
n
d
A
l
l
(
"
a
"
, r
e
l
=
"
t
a
g
"
)
:
t
a
g = i
.
s
t
r
i
n
g
.
l
o
w
e
r
(
)
t
a
g
s
[
t
a
g
] = 1 + t
a
g
s
.
g
e
t
(
t
a
g
, 0
)
r
e
t
u
r
n t
a
g
s
I
n [
5
3
]
: t
a
g
s = t
a
g
_
m
a
k
e
r
(
)
.
r
e
s
u
l
t
I
n [
1
]
: # t
a
g
s
Slide 61
Slide 61 text
I
n [
5
5
]
: f
r
o
m c
o
l
l
e
c
t
i
o
n
s i
m
p
o
r
t C
o
u
n
t
e
r
f
r
o
m f
u
n
c
t
o
o
l
s i
m
p
o
r
t r
e
d
u
c
e
f
r
o
m o
p
e
r
a
t
o
r i
m
p
o
r
t a
d
d
n
e
w
_
t
a
g
s = d
i
c
t
(
r
e
d
u
c
e
(
a
d
d
, (
C
o
u
n
t
e
r
(
t
a
g
) f
o
r t
a
g i
n t
a
g
s
)
)
)
I
n [
5
6
]
: l
e
n
(
n
e
w
_
t
a
g
s
.
k
e
y
s
(
)
)
O
u
t
[
5
6
]
: 8
2
7
I
n [ ]
: f
r
o
m w
o
r
d
c
l
o
u
d i
m
p
o
r
t W
o
r
d
C
l
o
u
d
w
o
r
d
c
l
o
u
d = W
o
r
d
C
l
o
u
d
(
w
i
d
t
h
=
1
9
2
0
, h
e
i
g
h
t
=
1
0
8
0
, s
c
a
l
e
=
1
,
f
o
n
t
_
p
a
t
h
=
'
/
L
i
b
r
a
r
y
/
F
o
n
t
s
/
V
e
r
d
a
n
a
.
t
t
f
'
,
m
a
x
_
w
o
r
d
s
=
l
e
n
(
n
e
w
_
t
a
g
s
.
k
e
y
s
(
)
)
)
w
o
r
d
c
l
o
u
d
.
g
e
n
e
r
a
t
e
_
f
r
o
m
_
f
r
e
q
u
e
n
c
i
e
s
(
n
e
w
_
t
a
g
s
.
i
t
e
m
s
(
)
)
Slide 62
Slide 62 text
I
n [ ]
: p
l
t
.
i
m
s
h
o
w
(
w
o
r
d
c
l
o
u
d
)
p
l
t
.
a
x
i
s
(
"
o
f
f
"
)
p
l
t
.
s
h
o
w
(
)
w
o
r
d
c
l
o
u
d
.
t
o
_
f
i
l
e
(
'
t
e
s
t
.
p
n
g
'
)
I
n [ ]
: ! i
p
c
l
u
s
t
e
r s
t
o
p
Slide 63
Slide 63 text
nbconvert
https://github.com/jupyter/nbconvert (https://github.com/jupyter/nbconvert)
http://nbconvert.readthedocs.org/en/latest/usage.html
(http://nbconvert.readthedocs.org/en/latest/usage.html)
I
n [ ]
: ! j
u
p
y
t
e
r n
b
c
o
n
v
e
r
t -
-
t
o s
l
i
d
e
s i
p
y
t
h
o
n
.
i
p
y
n
b -
-
p
o
s
t s
e
r
v
e
Slide 64
Slide 64 text
Links
https://github.com/stleon/ipython-slides (https://github.com/stleon/ipython-
slides)
https://talkpython.fm/episodes/show/44/project-jupyter-and-ipython
(https://talkpython.fm/episodes/show/44/project-jupyter-and-ipython)
http://blog.jupyter.org/2016/01/08/notebook-4-1-release/
(http://blog.jupyter.org/2016/01/08/notebook-4-1-release/)
https://jupyter.readthedocs.org/en/latest/
(https://jupyter.readthedocs.org/en/latest/)
https://folium.readthedocs.org/en/latest/
(https://folium.readthedocs.org/en/latest/)
http://matplotlib.org (http://matplotlib.org)
http://stanford.edu/~mwaskom/software/seaborn/
(http://stanford.edu/~mwaskom/software/seaborn/)
https://github.com/PyGithub/PyGithub (https://github.com/PyGithub/PyGithub)
http://ipyparallel.readthedocs.org/en/latest/index.html
(http://ipyparallel.readthedocs.org/en/latest/index.html)
Slide 65
Slide 65 text
Спасибо!