Parallel and Large Scale Machine Learning with scikit-learn

Parallel and Large Scale Learning with scikit-learn Data Science London
Meetup - Mar. 2013 jeudi 7 mars 13

About me • Regular contributor to scikit-learn • Interested in
NLP, Computer Vision, Predictive Modeling & ML in general • Interested in Cloud Tech and Scaling Stuff • Starting my own ML consulting business: http://ogrisel.com jeudi 7 mars 13

Outline • The Problem and the Ecosystem • Scaling Text
Classiﬁcation • Scaling Forest Models • Introduction to IPython.parallel & StarCluster • Scaling Model Selection & Evaluation jeudi 7 mars 13

Parts of the Ecosystem ——— Multiple Machines with Multiple Cores
——— Single Machine with Multiple Cores multiprocessing jeudi 7 mars 13

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 jeudi 7 mars 13

Cross Validation Labels to Predict Input Data jeudi 7 mars
13

Cross Validation A B C A B C jeudi 7
mars 13

Cross Validation A B C A B C Subset of
the data used to train the model Held-out test set for evaluation jeudi 7 mars 13

Cross Validation A B C A B C A C
B A C B B C A B C A jeudi 7 mars 13

Model Selection the Hyperparameters hell param_1 in [1, 10, 100]
param_2 in [1e3, 1e4, 1e5] Find the best combination of parameters that maximizes the Cross Validated Score jeudi 7 mars 13

Grid Search (1, 1e3) (10, 1e3) (100, 1e3) (1, 1e4)
(10, 1e4) (100, 1e4) (1, 1e5) (10, 1e5) (100, 1e5) param_2 param_1 jeudi 7 mars 13

(1, 1e3) (10, 1e3) (100, 1e3) (1, 1e4) (10, 1e4)
(100, 1e4) (1, 1e5) (10, 1e5) (100, 1e5) jeudi 7 mars 13

Grid Search: Qualitative Results jeudi 7 mars 13

Grid Search: Cross Validated Scores jeudi 7 mars 13

Parallel ML Use Cases • Stateless Feature Extraction • Model
Assessment with Cross Validation • Model Selection with Grid Search • Bagging Models: Random Forests • In-Loop Averaged Models jeudi 7 mars 13

Embarrassingly Parallel ML Use Cases • Stateless Feature Extraction •
Model Assessment with Cross Validation • Model Selection with Grid Search • Bagging Models: Random Forests • In-Loop Averaged Models jeudi 7 mars 13

Inter-Process Comm. Use Cases • Stateless Feature Extraction • Model
Assessment with Cross Validation • Model Selection with Grid Search • Bagging Models: Random Forests • In-Loop Averaged Models jeudi 7 mars 13

Scaling Text Feature Extraction The Hashing Trick jeudi 7 mars
13

(Count|TfIdf)Vectorizer Scalability Issues • Builds an In-Memory Vocabulary from text
tokens to integer feature indices • A Big Python dict: slow to (un)pickle • Large Corpus: ~10^6 tokens • Vocabulary == Statefulness == Sync barrier • No easy way to run in parallel jeudi 7 mars 13

>>> from sklearn.feature_extraction.text ... import TfidfVectorizer >>> vec = TfidfVectorizer()
>>> vec.fit(["The cat sat on the mat."]) >>> vec.vocabulary_ {u'cat': 0, u'mat': 1, u'on': 2, u'sat': 3, u'the': 4} jeudi 7 mars 13

The Hashing Trick • Replace the Python dict by a
hash function: • Does not need any memory storage • Hashing is stateless: can run in parallel! >>> from sklearn.utils.murmurhash import * >>> murmurhash3_bytes_u32('cat', 0) % 10 9L >>> murmurhash3_bytes_u32('sat', 0) % 10 0L jeudi 7 mars 13

>>> from sklearn.feature_extraction.text ... import HashingVectorizer >>> vec = HashingVectorizer()
>>> out = vec.transform([ ... "The cat sat on the mat."]) >>> out.shape (1, 1048576) >>> out.nnz # number of non-zero elements 5 jeudi 7 mars 13

Some Numbers jeudi 7 mars 13

Loading 20 newsgroups dataset for all categories 11314 documents -
22.055MB (training set) 7532 documents - 13.801MB (testing set) Extracting features from the training dataset using a sparse vectorizer done in 12.881007s at 1.712MB/s n_samples: 11314, n_features: 129792 Extracting features from the test dataset using the same vectorizer done in 4.043470s at 3.413MB/s n_samples: 7532, n_features: 129792 TﬁdfVectorizer jeudi 7 mars 13

Loading 20 newsgroups dataset for all categories 11314 documents -
22.055MB (training set) 7532 documents - 13.801MB (testing set) Extracting features from the training dataset using a sparse vectorizer done in 5.281561s at 4.176MB/s n_samples: 11314, n_features: 65536 Extracting features from the test dataset using the same vectorizer done in 3.413027s at 4.044MB/s n_samples: 7532, n_features: 65536 HashingVectorizer jeudi 7 mars 13

HashingVectorizer on Amazon Reviews • Music reviews: 216MB XML ﬁle
140MB raw text / 174,180 reviews: 53s • Books reviews: 1.3GB XML ﬁle 900MB raw text / 975,194 reviews: ~6min • https://gist.github.com/ogrisel/4313514 jeudi 7 mars 13

Parallel Text Classiﬁcation jeudi 7 mars 13

HowTo: Parallel Text Classiﬁcation All Labels to Predict All Text
Data jeudi 7 mars 13

Partition the Text Data Labels 1 Text Data 1 Labels
2 Text Data 2 Labels 3 Text Data 3 jeudi 7 mars 13

Vectorizer in Parallel Labels 1 Text Data 1 Labels 2
Text Data 2 Labels 3 Text Data 3 vec vec vec Labels 1 Vec Data 1 Labels 2 Vec Data 2 Labels 3 Text Data 3 jeudi 7 mars 13

Train Linear Models in Parallel Labels 1 Text Data 1
Labels 2 Text Data 2 Labels 3 Text Data 3 vec vec vec Labels 1 Vec Data 1 Labels 2 Vec Data 2 Labels 3 Text Data 3 clf_1 clf_2 clf_2 clf_3 jeudi 7 mars 13

Collect Models and Average clf = ( clf_1 + clf_2
+ clf_3 ) / 3 jeudi 7 mars 13

>>> clf = clone(clf_1) >>> clf.coef_ += clf_2.coef_ >>> clf.coef_
+= clf_3.coef_ >>> clf.intercept_ += clf_2.intercept_ >>> clf.intercept_ += clf_3.intercept_ >>> clf.coef_ /= 3; clf.intercept_ /= 3 Averaging Linear Models jeudi 7 mars 13

Training Forest Models in Parallel jeudi 7 mars 13

Tricks • Try: ExtraTreesClassiﬁer instead of: RandomForestClassiﬁer • Faster to
train • Sometimes better generalization too • Both kind of Forest Models are naturally embarrassingly parallel models. jeudi 7 mars 13

HowTo: Parallel Forests All Labels to Predict All Data jeudi
7 mars 13

Partition Replicate the Dataset All Labels All Data All Labels
All Data All Labels All Data jeudi 7 mars 13

Train Forest Models in Parallel clf_1 clf_2 clf_2 clf_3 All
Labels All Data All Labels All Data All Labels All Data Seed each model with a different random_state integer! jeudi 7 mars 13

Collect Models and Combine clf = ( clf_1 + clf_2
+ clf_3 ) Forest Models naturally do the averaging at prediction time. >>> clf = clone(clf_1) >>> clf.estimators_ += clf_2.estimators_ >>> clf.estimators_ += clf_3.estimators_ jeudi 7 mars 13

What if my data does not ﬁt in memory? jeudi
7 mars 13

HowTo: Parallel Forests (for large datasets) All Labels to Predict
All Data jeudi 7 mars 13

Partition Replicate Partition the Dataset Labels 1 Data 1 Labels
2 Data 2 Labels 3 Data 3 jeudi 7 mars 13

Train Forest Models in Parallel clf_1 clf_2 clf_2 clf_3 Labels
1 Data 1 Labels 2 Data 2 Labels 3 Data 3 jeudi 7 mars 13

Collect Models and Sum clf = ( clf_1 + clf_2
+ clf_3 ) >>> clf = clone(clf_1) >>> clf.estimators_ += clf_2.estimators_ >>> clf.estimators_ += clf_3.estimators_ jeudi 7 mars 13

Warning • Models trained on the partitioned dataset are not
exactly equivalent of models trained on the unpartitioned dataset • If very much data: does not matter much in practice: Gilles Louppe & Pierre Geurts http://www.cs.bris.ac.uk/~ﬂach/ jeudi 7 mars 13

Implementing Parallelization with Python jeudi 7 mars 13

Single Machine with Multiple Cores — — — — jeudi
7 mars 13

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] jeudi 7 mars 13

multiprocessing • Part of the standard lib • Simple API
• Cross-Platform support (even Windows!) • Some support for shared memory • Support for synchronization (Lock) jeudi 7 mars 13

multiprocessing: limitations • No docstrings in the source code! •
Very tricky to use the shared memory values with NumPy • Bad support for KeyboardInterrupt • fork without exec on POSIX jeudi 7 mars 13

• transparent disk-caching of the output values and lazy re-evaluation
(memoization) • easy simple parallel computing • logging and tracing of the execution jeudi 7 mars 13

>>> 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 jeudi 7 mars 13

Usage in scikit-learn • Cross Validation cross_val(model, X, y, n_jobs=4,
cv=3) • Grid Search GridSearchCV(model, n_jobs=4, cv=3).fit(X, y) • Random Forests RandomForestClassifier(n_jobs=4).fit(X, y) jeudi 7 mars 13

>>> 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 (dev) jeudi 7 mars 13

(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. jeudi 7 mars 13

Problems with multiprocessing & joblib • Current Implementation uses fork
without exec under Unix • Break some optimized runtimes: • OpenBlas • Grand Central Dispatch under OSX • Will be ﬁxed in Python 3 at some point... jeudi 7 mars 13

Multiple Machines with Multiple Cores — — — — —
— — — — — — — — — — — jeudi 7 mars 13

• Parallel Processing Library • Interactive Exploratory Shell Multi Core
& Distributed IPython.parallel jeudi 7 mars 13

Working in the Cloud • Launch a cluster of machines
in one cmd: $ starcluster start mycluster -s 3 \ -b 0.07 --force-spot-master $ starcluster sshmaster mycluster • Supports Spot Instances provisioning • Ships blas, atlas, numpy, scipy • IPython plugin, Hadoop plugin and more jeudi 7 mars 13

[global] DEFAULT_TEMPLATE=ip [key mykey] KEY_LOCATION=~/.ssh/mykey.rsa [plugin ipcluster] SETUP_CLASS = starcluster.plugins.ipcluster.IPCluster
ENABLE_NOTEBOOK = True [plugin packages] setup_class = pypackage.PyPackageSetup packages = msgpack-python, scikit-learn [cluster ip] KEYNAME = mykey CLUSTER_USER = ipuser NODE_IMAGE_ID = ami-999d49f0 NODE_INSTANCE_TYPE = c1.xlarge DISABLE_QUEUE = True SPOT_BID = 0.10 PLUGINS = packages, ipcluster jeudi 7 mars 13

$ starcluster start -s 3 --force-spot-master demo_cluster StarCluster - (http://star.mit.edu/cluster)
(v. 0.9999) Software Tools for Academics and Researchers (STAR) Please submit bug reports to [email protected] >>> Using default cluster template: ip >>> Validating cluster template settings... >>> Cluster template settings are valid >>> Starting cluster... >>> Launching a 3-node cluster... >>> Launching master node (ami: ami-999d49f0, type: c1.xlarge)... >>> Creating security group @sc-demo_cluster... SpotInstanceRequest:sir-d10e3412 >>> Launching node001 (ami: ami-999d49f0, type: c1.xlarge) SpotInstanceRequest:sir-3cad4812 >>> Launching node002 (ami: ami-999d49f0, type: c1.xlarge) SpotInstanceRequest:sir-1a918014 >>> Waiting for cluster to come up... (updating every 5s) >>> Waiting for open spot requests to become active... 3/3 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| 100% >>> Waiting for all nodes to be in a 'running' state... 3/3 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| 100% >>> Waiting for SSH to come up on all nodes... 3/3 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| 100% >>> Waiting for cluster to come up took 5.087 mins >>> The master node is ec2-54-243-24-93.compute-1.amazonaws.com jeudi 7 mars 13

>>> Configuring cluster... >>> Running plugin starcluster.clustersetup.DefaultClusterSetup >>> Configuring hostnames...
3/3 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| 100% >>> Creating cluster user: ipuser (uid: 1001, gid: 1001) 3/3 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| 100% >>> Configuring scratch space for user(s): ipuser 3/3 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| 100% >>> Configuring /etc/hosts on each node 3/3 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| 100% >>> Starting NFS server on master >>> Configuring NFS exports path(s): /home >>> Mounting all NFS export path(s) on 2 worker node(s) 2/2 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| 100% >>> Setting up NFS took 0.151 mins >>> Configuring passwordless ssh for root >>> Configuring passwordless ssh for ipuser >>> Running plugin ippackages >>> Installing Python packages on all nodes: >>> $ pip install -U msgpack-python >>> $ pip install -U scikit-learn >>> Installing 2 python packages took 1.12 mins jeudi 7 mars 13

>>> Running plugin ipcluster >>> Writing IPython cluster config files
>>> Starting the IPython controller and 7 engines on master >>> Waiting for JSON connector file... /Users/ogrisel/.starcluster/ipcluster/SecurityGroup:@sc-demo_cluster-us-east-1.json 100% || Time: 00:00:00 0.00 B/s >>> Authorizing tcp ports [1000-65535] on 0.0.0.0/0 for: IPython controller >>> Adding 16 engines on 2 nodes 2/2 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| 100% >>> Setting up IPython web notebook for user: ipuser >>> Creating SSL certificate for user ipuser >>> Authorizing tcp ports [8888-8888] on 0.0.0.0/0 for: notebook >>> IPython notebook URL: https://ec2-54-243-24-93.compute-1.amazonaws.com:8888 >>> The notebook password is: zYHoMhEA8rTJSCXj *** WARNING - Please check your local firewall settings if you're having *** WARNING - issues connecting to the IPython notebook >>> IPCluster has been started on SecurityGroup:@sc-demo_cluster for user 'ipuser' with 23 engines on 3 nodes. To connect to cluster from your local machine use: from IPython.parallel import Client client = Client('/Users/ogrisel/.starcluster/ipcluster/SecurityGroup:@sc-demo_cluster-us- east-1.json', sshkey='/Users/ogrisel/.ssh/mykey.rsa') See the IPCluster plugin doc for usage details: http://star.mit.edu/cluster/docs/latest/plugins/ipython.html >>> IPCluster took 0.679 mins >>> Configuring cluster took 3.454 mins >>> Starting cluster took 8.596 mins jeudi 7 mars 13

Demo! jeudi 7 mars 13

Perspectives jeudi 7 mars 13

2012 results by Stanford / Google jeudi 7 mars 13

The YouTube Neuron jeudi 7 mars 13

Thanks • http://scikit-learn.org • http://packages.python.org/joblib • http://ipython.org • http://star.mit.edu/cluster/ •
http://speakerdeck.com/ogrisel @ogrisel jeudi 7 mars 13

If we had more time... jeudi 7 mars 13

MapReduce? [ (k1, v1), (k2, v2), ... ] mapper mapper
mapper [ (k3, v3), (k4, v4), ... ] reducer reducer [ (k5, v6), (k6, v6), ... ] jeudi 7 mars 13

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 jeudi 7 mars 13

When MapReduce is useful for ML • Data Preprocessing &
Feature Extraction • Parsing, Filtering, Cleaning • Computing big JOINs & Aggregates • Random Sampling • Computing ensembles on partitions jeudi 7 mars 13

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 jeudi 7 mars 13

AllReduce 0/3 Initial State Value: 2.0 Value: 0.5 Value: 1.1
Value: 3.2 Value: 0.9 Value: 1.0 jeudi 7 mars 13

AllReduce 1/3 Spanning Tree Value: 2.0 Value: 0.5 Value: 1.1

AllReduce 2/3 Upward Averages Value: 2.0 Value: 0.5 Value: 1.1
(1.1, 1) Value: 3.2 (3.1, 1) Value: 0.9 (0.9, 1) Value: 1.0 jeudi 7 mars 13

AllReduce 2/3 Upward Averages 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 jeudi 7 mars 13

AllReduce 2/3 Upward Averages 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) jeudi 7 mars 13

AllReduce 3/3 Downward 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 jeudi 7 mars 13

AllReduce 3/3 Downward Updates Value: 1.38 Value: 1.38 Value: 1.1
(1.1, 1) Value: 3.2 (3.1, 1) Value: 0.9 (0.9, 1) Value: 1.38 jeudi 7 mars 13

AllReduce 3/3 Downward Updates Value: 1.38 Value: 1.38 Value: 1.38

AllReduce Final State Value: 1.38 Value: 1.38 Value: 1.38 Value:
1.38 Value: 1.38 Value: 1.38 jeudi 7 mars 13

AllReduce Implementations http://mpi4py.scipy.org IPC directly w/ IPython.parallel https://github.com/ipython/ipython/tree/ master/docs/examples/parallel/interengine jeudi
7 mars 13

Killall IPython engines on StarCluster [plugin ipcluster] SETUP_CLASS = starcluster.plugins.ipcluster.IPCluster
ENABLE_NOTEBOOK = True NOTEBOOK_DIRECTORY = notebooks [plugin ipclusterrestart] SETUP_CLASS = starcluster.plugins.ipcluster.IPClusterRestartEngines jeudi 7 mars 13

$ starcluster runplugin ipclusterrestart demo_cluster StarCluster - (http://star.mit.edu/cluster) (v. 0.9999)
Software Tools for Academics and Researchers (STAR) Please submit bug reports to [email protected] >>> Running plugin ipclusterrestart >>> Restarting 23 engines on 3 nodes 3/3 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| 100% jeudi 7 mars 13

Parallel and Large Scale Machine Learning with ...

Parallel and Large Scale Machine Learning with scikit-learn

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