Strategies and Tools for Parallel Machine Learning in Python

Strategies & Tools for Parallel Machine Learning

in PythonPyConFR 2012 - Paris

Sunday, September 16, 2012

The Story

scikit-learn

The Cloud

stackoverflow

& kaggle


...


Parts of the Ecosystem

Multiple Machines with Multiple Cores ♥♥♥

Single Machine with Multiple Cores ♥♥♥

multiprocessing


The Problem

Big CPU (Supercomputers - MPI)

Simulating stuff from models

Big Data (Google scale - MapReduce)

Counting stuff in logs / Indexing the Web

Machine Learning?

often somewhere in the middle


Parallel ML Use Cases

• Model Evaluation with Cross Validation

• Model Selection with Grid Search

• Bagging Models: Random Forests

• Averaged Models


Embarrassingly Parallel ML Use Cases




• Averaged Models


Inter-Process Comm. Use Cases




• Averaged Models


Cross Validation

Labels to Predict

Input Data


Cross Validation

A B C

A B C


Cross Validation

A B C

A B C

Subset of the data usedto train the model

Held-outtest set

for evaluation


Cross Validation

A B C

A B C

A C B

A C B

B C A

B C A


Model Selection

the Hyperparameters hell

p1 in [1, 10, 100]

p2 in [1e3, 1e4, 1e5]

Find the best combination of parameters that maximizes the Cross Validated Score


Grid Search

(1, 1e3) (10, 1e3) (100, 1e3)

(1, 1e4) (10, 1e4) (100, 1e4)

(1, 1e5) (10, 1e5) (100, 1e5)

p1

p2


(1, 1e3) (10, 1e3) (100, 1e3)

(1, 1e4) (10, 1e4) (100, 1e4)

(1, 1e5) (10, 1e5) (100, 1e5)


Grid Search:Qualitative Results


Grid Search:Cross Validated Scores


Enough ML theory!

Lets go shopping^Wparallel computing!


Single Machinewith

Multiple Cores

♥ ♥

♥ ♥


multiprocessing>>> from multiprocessing import Pool>>> p = Pool(4)

>>> p.map(type, [1, 2., '3'])[int, float, str]

>>> r = p.map_async(type, [1, 2., '3'])>>> r.get()[int, float, str]


multiprocessing

• Part of the standard lib

• Nice API

• Cross-Platform support (even Windows!)

• Some support for shared memory

• Support for synchronization (Lock)


multiprocessing: limitations

• No docstrings in a stdlib module? WTF?

• Tricky / impossible to use the shared memory values with NumPy

• Bad support for KeyboardInterrupt



• transparent disk-caching of the output values and lazy re-evaluation (memoize pattern)

• easy simple parallel computing

• logging and tracing of the execution


>>> from os.path.join>>> from joblib import Parallel, delayed

>>> Parallel(2)(... delayed(join)('/ect', s)... for s in 'abc')['/ect/a', '/ect/b', '/ect/c']

joblib.Parallel


Usage in scikit-learn

• Cross Validationcross_val(model, X, y, n_jobs=4, cv=3)

• Grid SearchGridSearchCV(model, n_jobs=4, cv=3).fit(X, y)

• Random ForestsRandomForestClassifier(n_jobs=4).fit(X, y)


>>> from joblib import Parallel, delayed>>> import numpy as np

>>> Parallel(2, max_nbytes=1e6)(... delayed(type)(np.zeros(int(i)))... for i in [1e4, 1e6])[<type 'numpy.ndarray'>, <class 'numpy.core.memmap.memmap'>]

joblib.Parallel:shared memory


(1, 1e3) (10, 1e3) (100, 1e3)

(1, 1e4) (10, 1e4) (100, 1e4)

(1, 1e5) (10, 1e5) (100, 1e5)

Only 3 allocated datasets shared by all the concurrent workers performing

the grid search.


Multiple Machineswith

Multiple Cores

♥ ♥

♥ ♥♥ ♥

♥ ♥♥ ♥

♥ ♥♥ ♥

♥ ♥


MapReduce?

[ (k1, v1), (k2, v2), ...]

mapper

mapper

mapper

[ (k3, v3), (k4, v4), ...]

reducer

reducer

[ (k5, v6), (k6, v6), ...]


Why MapReduce does not always work

Write a lot of stuff to disk for failover

Inefficient for small to medium problems

[(k, v)] mapper [(k, v)] reducer [(k, v)]

Data and model params as (k, v) pairs?

Complex to leverage for Iterative Algorithms


• Parallel Processing Library

• Interactive Exploratory Shell

Multi Core & Distributed

IPython.parallel


Demo!


The AllReduce Pattern

• Compute an aggregate (average) of active node data

• Do not clog a single node with incoming data transfer

• Traditionally implemented in MPI systems


AllReduce 0/3Initial State

Value: 2.0 Value: 0.5

Value: 1.1 Value: 3.2 Value: 0.9

Value: 1.0


AllReduce 1/3Spanning Tree



Value: 1.0


AllReduce 2/3Upward Averages


Value: 1.1(1.1, 1)

Value: 3.2(3.1, 1)

Value: 0.9(0.9, 1)

Value: 1.0



Value: 2.0(2.1, 3)

Value: 0.5(0.7, 2)

Value: 1.1(1.1, 1)

Value: 3.2(3.1, 1)

Value: 0.9(0.9, 1)

Value: 1.0



Value: 2.0(2.1, 3)

Value: 0.5(0.7, 2)

Value: 1.1(1.1, 1)

Value: 3.2(3.1, 1)

Value: 0.9(0.9, 1)

Value: 1.0(1.38, 6)


AllReduce 3/3Downward Updates

Value: 2.0(2.1, 3)

Value: 0.5(0.7, 2)

Value: 1.1(1.1, 1)

Value: 3.2(3.1, 1)

Value: 0.9(0.9, 1)

Value: 1.38




Value: 1.1(1.1, 1)

Value: 3.2(3.1, 1)

Value: 0.9(0.9, 1)

Value: 1.38





Value: 1.38


AllReduce Final State



Value: 1.38


AllReduce Implementations

http://mpi4py.scipy.org

IPC directly w/ IPython.parallel

https://github.com/ipython/ipython/tree/master/docs/examples/parallel/interengine








Working in the Cloud

• Launch a cluster of machines in one cmd: starcluster start mycluster -b 0.07

starcluster sshmaster mycluster

• Supports spotinstances!

• Ships blas, atlas, numpy, scipy!

• IPython plugin!


Perspectives


2012 results by Stanford / Google


The YouTube Neuron


Thanks

• http://j.mp/ogrisel-pyconfr-2012

• http://scikit-learn.org

• http://packages.python.org/joblib

• http://ipython.org

• http://star.mit.edu/cluster/

@ogrisel


https://speakerdeck.com/u/ogrisel/p/strategies-for-parallel-machine-learning-in-python

https://speakerdeck.com/u/ogrisel/p/strategies-for-parallel-machine-learning-in-python

http://scikit-learn.org

http://scikit-learn.org

http://joblib.org

http://joblib.org

http://ipython.org

http://ipython.org

http://star.mit.edu/cluster/

http://star.mit.edu/cluster/

Goodies

Super Ctrl-C to killall nosetests


psutil nosetests killer script in Automator


Bind killnose to Shift-Ctrl-C


ps aux | grep IPython.parallel \ | grep -v grep \ | cut -d ' ' -f 2 | xargs kill

Or simpler:


Technology

Strategies and Tools for Parallel Machine Learning in Python