Outline of the session:
When doing model evaluations and parameters tuning, many models must be trained independently on the same data. This is an embarrassingly parallel problem but having a copy of the dataset in memory for each process is waste of RAM:
When doing 3 folds cross validation on a 9 parameters grid, a naive implementation could read the data from the disk and load it in memory 27 times. If this happens concurrently (e.g. on a compute node with 32 cores) the RAM might blow up hence breaking the potential linear speed up.
This section gives a primer on some tools best utilizing computational resources when doing predictive modeling in the Python / NumPy ecosystem namely:
optimal usage of available CPUs and cluster nodes with IPython.parallel
optimal memory re-use using shared memory between Python processes using numpy.memmap and joblib
IPython.parallel:%px and %%px magics#priceless)Let start an IPython cluster using the ipcluster common (usually run from your operating system console). To make sure that we are not running several clusters on the same host, let's try to shut down any running IPython cluster first:
In [ ]:
!ipcluster stop
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!ipcluster start -n=2 --daemon
Go to the "Cluster" tab of the notebook and start a local cluster with 2 engines. Then come back here. We should now be able to use our cluster from our notebook session (or any other Python process running on localhost):
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from IPython.parallel import Client
client = Client()
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len(client)
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%%px
import os
import socket
print("This is running in process with pid {0} on host '{1}'.".format(
os.getpid(), socket.gethostname()))
The content of the __main__ namespace can also be read and written via the %px magic:
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%px a = 1
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%px print(a)
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%%px
a *= 2
print(a)
It is possible to restrict the %px and %%px magic instructions to specific engines:
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%%px --targets=-1
a *= 2
print(a)
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%px print(a)
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all_engines = client[:]
all_engines
The namespace of the __main__ module of each running python engine can be accessed in read and write mode as a python dictionary:
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all_engines['a'] = 1
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all_engines['a']
Direct views can also execute the same code in parallel on each engine of the view:
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def my_sum(a, b):
return a + b
my_sum_apply_results = all_engines.apply(my_sum, 11, 31)
my_sum_apply_results
The ouput of the apply method is an asynchronous handle returned immediately without waiting for the end of the computation. To block until the results are ready use:
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my_sum_apply_results.get()
Here is a more useful example to fetch the network hostname of each engine in the cluster. Let's study it in more details:
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def hostname():
"""Return the name of the host where the function is being called"""
import socket
return socket.gethostname()
hostname_apply_result = all_engines.apply(hostname)
When doing the above, the hostname function is first defined locally (the client python process). The DirectView.apply method introspects it, serializes its name and bytecode and ships it to each engine of the cluster where it is reconstructed as local function on each engine. This function is then called on each engine of the view with the optionally provided arguments.
In return, the client gets a python object that serves as an handle to asynchronously fetch the list of the results of the calls:
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hostname_apply_result
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hostname_apply_result.get()
It is also possible to key the results explicitly with the engine ids with the AsyncResult.get_dict method. This is a very simple idiom to fetch metadata on the runtime environment of each engine of the direct view:
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hostnames = hostname_apply_result.get_dict()
hostnames
It can be handy to invert this mapping to find one engine id per host in the cluster so as to execute host specific operation:
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one_engine_by_host = dict((hostname, engine_id) for engine_id, hostname
in hostnames.items())
one_engine_by_host
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one_engine_per_host_view = client[one_engine_by_host.values()]
one_engine_per_host_view
Trick: you can even use those engines ids to execute shell commands in parallel on each host of the cluster:
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one_engine_by_host.values()
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%%px --targets=[1]
!pip install flask
In the previous example we put the import socket statement inside the body of the hostname function to make sure to make sure that is is available when the rest of the function is executed in the python processes of the remote engines.
Alternatively it is possible to import the required modules ahead of time on all the engines of a directview using a context manager / with syntax:
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with all_engines.sync_imports():
import numpy
However this method does not support alternative import syntaxes:
>>> import numpy as np
>>> from numpy import linalgHence the method of importing in the body of the "applied" functions is more flexible. Additionally, this does not pollute the __main__ namespace of the engines as it only impact the local namespace of the function itself.
Exercise:
Hints:
Use the psutil module to collect the runtime info on a specific process or host. For instance to fetch the memory usage of the currently running process in MB:
>>> import os
>>> import psutil
>>> psutil.Process(os.getpid()).get_memory_info().rss / 1e6
To allocate a numpy array with 1000 zeros stored as 64bit floats you can use:
>>> import numpy as np
>>> z = np.zeros(1000, dtype=np.float64)
The size in bytes of such a numpy array can then be fetched with z.nbytes:
>>> z.nbytes / 1e6
0.008
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LoadBalancedView is an alternative to the DirectView to run one function call at a time on a free engine.
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lv = client.load_balanced_view()
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def slow_square(x):
import time
time.sleep(2)
return x ** 2
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result = lv.apply(slow_square, 4)
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result
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result.ready()
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result.get() # blocking call
It is possible to spread some tasks among the engines of the LB view by passing a callable and an iterable of task arguments to the LoadBalancedView.map method:
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results = lv.map(slow_square, [0, 1, 2, 3])
results
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results.ready()
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results.progress
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# results.abort()
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# Iteration on AsyncMapResult is blocking
for r in results:
print(r)
The load balanced view will be used in the following to schedule work on the cluster while being able to monitor progress and occasionally add new computing nodes to the cluster while computing to speed up the processing when using EC2 and StarCluster (see later).
Let's restart the cluster to kill the existing python processes and restart with a new client instances to be able to monitor the memory usage in details:
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!ipcluster stop
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!ipcluster start -n=2 --daemon
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from IPython.parallel import Client
client = Client()
len(client)
The numpy package makes it possible to memory map large contiguous chunks of binary files as shared memory for all the Python processes running on a given host:
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%px import numpy as np
Creating a numpy.memmap instance with the w+ mode creates a file on the filesystem and zeros its content. Let's do it from the first engine process or our current IPython cluster:
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%%px --targets=-1
# Cleanup any existing file from past session (necessary for windows)
import os
if os.path.exists('small.mmap'):
os.unlink('small.mmap')
mm_w = np.memmap('small.mmap', shape=10, dtype=np.float32, mode='w+')
print(mm_w)
Assuming the notebook process was launched with:
cd solutions
ipython notebook
or:
cd notebooks
ipython notebook
and the cluster was launched from the ipython notebook UI, the engines will have a the same current working directory as the notebook process, hence we can find the small.mmap file the current folder:
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!ls -lh small.mmap
This binary file can then be mapped as a new numpy array by all the engines having access to the same filesystem. The mode='r+' opens this shared memory area in read write mode:
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%%px
mm_r = np.memmap('small.mmap', dtype=np.float32, mode='r+')
print(mm_r)
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%%px --targets=-1
mm_w[0] = 42
print(mm_w)
print(mm_r)
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%px print(mm_r)
Memory mapped arrays created with mode='r+' can be modified and the modifications are shared with all the engines:
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%%px --targets=1
mm_r[1] = 43
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%%px
print(mm_r)
Be careful those, there is no builtin read nor write lock available on this such datastructures so it's better to avoid concurrent read & write operations on the same array segments unless there engine operations are made to cooperate with some synchronization or scheduling orchestrator.
Memmap arrays generally behave very much like regular in-memory numpy arrays:
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%%px
print("sum={0:.3}, mean={1:.3}, std={2:.3}".format(
mm_r.sum(), np.mean(mm_r), np.std(mm_r)))
Before allocating more data in memory on the cluster let us define a couple of utility functions from the previous exercise (and more) to monitor what is used by which engine and what is still free on the cluster as a whole:
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def get_engines_memory(client):
"""Gather the memory allocated by each engine in MB"""
def memory_mb():
import os
import psutil
return psutil.Process(os.getpid()).get_memory_info().rss / 1e6
return client[:].apply(memory_mb).get_dict()
def get_host_free_memory(client):
"""Free memory on each host of the cluster in MB."""
all_engines = client[:]
def hostname():
import socket
return socket.gethostname()
hostnames = all_engines.apply(hostname).get_dict()
one_engine_per_host = dict((hostname, engine_id)
for engine_id, hostname
in hostnames.items())
def host_free_memory():
import psutil
return psutil.virtual_memory().free / 1e6
host_mem = client[one_engine_per_host.values()].apply(
host_free_memory).get_dict()
return dict((hostnames[eid], m) for eid, m in host_mem.items())
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get_engines_memory(client)
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get_host_free_memory(client)
Let's allocate a 80MB memmap array in the first engine and load it in readwrite mode in all the engines:
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%%px --targets=-1
# Cleanup any existing file from past session (necessary for windows)
import os
if os.path.exists('big.mmap'):
os.unlink('big.mmap')
np.memmap('big.mmap', shape=10 * int(1e6), dtype=np.float64, mode='w+')
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ls -lh big.mmap
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get_host_free_memory(client)
No significant memory was used in this operation as we just asked the OS to allocate the buffer on the hard drive and just maitain a virtual memory area as a cheap reference to this buffer.
Let's open new references to the same buffer from all the engines at once:
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%px %time big_mmap = np.memmap('big.mmap', dtype=np.float64, mode='r+')
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%px big_mmap
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get_host_free_memory(client)
No physical memory was allocated in the operation as it just took a couple of ms to do so. This is also confirmed by the engines process stats:
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get_engines_memory(client)
Let's trigger an actual load of the data from the drive into the in-memory disk cache of the OS, this can take some time depending on the speed of the hard drive (on the order of 100MB/s to 300MB/s hence 3s to 8s for this dataset):
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%%px --targets=-1
%time np.sum(big_mmap)
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get_engines_memory(client)
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get_host_free_memory(client)
We can see that the first engine has now access to the data in memory and the free memory on the host has decreased by the same amount.
We can now access this data from all the engines at once much faster as the disk will no longer be used: the shared memory buffer will instead accessed directly by all the engines:
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%px %time np.sum(big_mmap)
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get_engines_memory(client)
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get_host_free_memory(client)
So it seems that the engines have loaded a whole copy of the data but this actually not the case as the total amount of free memory was not impacted by the parallel access to the shared buffer. Furthermore, once the data has been preloaded from the hard drive using one process, all the of the other processes on the same host can access it almost instantly saving a lot of IO wait.
This strategy makes it very interesting to load the readonly datasets of machine learning problems, especially when the same data is reused over and over by concurrent processes as can be the case when doing learning curves analysis or grid search.
joblib is a utility library included in the sklearn package. Among other things it provides tools to serialize objects that comprise large numpy arrays and reload them as memmap backed datastructures.
To demonstrate it, let's create an arbitrary python datastructure involving numpy arrays:
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import numpy as np
class MyDataStructure(object):
def __init__(self, shape):
self.float_zeros = np.zeros(shape, dtype=np.float32)
self.integer_ones = np.ones(shape, dtype=np.int64)
data_structure = MyDataStructure((3, 4))
data_structure.float_zeros, data_structure.integer_ones
We can now persist this datastructure to disk:
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from sklearn.externals import joblib
joblib.dump(data_structure, 'data_structure.pkl')
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!ls -l data_structure*
A memmapped copy of this datastructure can then be loaded:
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memmaped_data_structure = joblib.load('data_structure.pkl', mmap_mode='r+')
memmaped_data_structure.float_zeros, memmaped_data_structure.integer_ones
We can leverage the previous tools to build a utility function that extracts Cross Validation splits ahead of time to persist them on the hard drive in a format suitable for memmaping by IPython engine processes.
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from sklearn.externals import joblib
from sklearn.cross_validation import ShuffleSplit
import os
def persist_cv_splits(X, y, n_cv_iter=5, name='data',
suffix="_cv_%03d.pkl", test_size=0.25, random_state=None):
"""Materialize randomized train test splits of a dataset."""
cv = ShuffleSplit(X.shape[0], n_iter=n_cv_iter,
test_size=test_size, random_state=random_state)
cv_split_filenames = []
for i, (train, test) in enumerate(cv):
cv_fold = (X[train], y[train], X[test], y[test])
cv_split_filename = name + suffix % i
cv_split_filename = os.path.abspath(cv_split_filename)
joblib.dump(cv_fold, cv_split_filename)
cv_split_filenames.append(cv_split_filename)
return cv_split_filenames
Let's try it on the digits dataset, we can run this from the :
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from sklearn.datasets import load_digits
digits = load_digits()
digits_split_filenames = persist_cv_splits(digits.data, digits.target,
name='digits', random_state=42)
digits_split_filenames
In [ ]:
ls -lh digits*
Each of the persisted CV splits can then be loaded back again using memmaping:
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X_train, y_train, X_test, y_test = joblib.load(
'digits_cv_002.pkl', mmap_mode='r+')
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X_train
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y_train
Let's leverage IPython.parallel and the Memory Mapping features of joblib to write a custom grid search utility that runs on cluster in a memory efficient manner.
Assume that we want to reproduce the grid search from the previous session:
In [ ]:
import numpy as np
from pprint import pprint
svc_params = {
'C': np.logspace(-1, 2, 4),
'gamma': np.logspace(-4, 0, 5),
}
pprint(svc_params)
GridSearchCV internally uses the following ParameterGrid utility iterator class to build the possible combinations of parameters:
In [ ]:
from sklearn.grid_search import ParameterGrid
list(ParameterGrid(svc_params))
Let's write a function to load the data from a CV split file and compute the validation score for a given parameter set and model:
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def compute_evaluation(cv_split_filename, model, params):
"""Function executed by a worker to evaluate a model on a CV split"""
# All module imports should be executed in the worker namespace
from sklearn.externals import joblib
X_train, y_train, X_validation, y_validation = joblib.load(
cv_split_filename, mmap_mode='c')
model.set_params(**params)
model.fit(X_train, y_train)
validation_score = model.score(X_validation, y_validation)
return validation_score
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def grid_search(lb_view, model, cv_split_filenames, param_grid):
"""Launch all grid search evaluation tasks."""
all_tasks = []
all_parameters = list(ParameterGrid(param_grid))
for i, params in enumerate(all_parameters):
task_for_params = []
for j, cv_split_filename in enumerate(cv_split_filenames):
t = lb_view.apply(
compute_evaluation, cv_split_filename, model, params)
task_for_params.append(t)
all_tasks.append(task_for_params)
return all_parameters, all_tasks
Let's try on the digits dataset that we splitted previously as memmapable files:
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from sklearn.svm import SVC
from IPython.parallel import Client
client = Client()
lb_view = client.load_balanced_view()
model = SVC()
svc_params = {
'C': np.logspace(-1, 2, 4),
'gamma': np.logspace(-4, 0, 5),
}
all_parameters, all_tasks = grid_search(
lb_view, model, digits_split_filenames, svc_params)
The grid_search function is using the asynchronous API of the LoadBalancedView, we can hence monitor the progress:
In [ ]:
def progress(tasks):
return np.mean([task.ready() for task_group in tasks
for task in task_group])
In [ ]:
print("Tasks completed: {0}%".format(100 * progress(all_tasks)))
Even better, we can introspect the completed task to find the best parameters set so far:
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def find_bests(all_parameters, all_tasks, n_top=5):
"""Compute the mean score of the completed tasks"""
mean_scores = []
for param, task_group in zip(all_parameters, all_tasks):
scores = [t.get() for t in task_group if t.ready()]
if len(scores) == 0:
continue
mean_scores.append((np.mean(scores), param))
return sorted(mean_scores, reverse=True)[:n_top]
In [ ]:
from pprint import pprint
print("Tasks completed: {0}%".format(100 * progress(all_tasks)))
pprint(find_bests(all_parameters, all_tasks))
It is often wasteful to search all the possible combinations of parameters as done previously, especially if the number of parameters is large (e.g. more than 3).
To speed up the discovery of good parameters combinations, it is often faster to randomized the search order and allocate a budget of evaluations, e.g. 10 or 100 combinations.
See this JMLR paper by James Bergstra for an empirical analysis of the problem. The interested reader should also have a look at hyperopt that further refines this parameter search method using meta-optimizers.
Randomized Parameter Search has just been implemented in the master branch of scikit-learn be part of the 0.14 release.
In [ ]:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
# Some nice default configuration for plots
plt.rcParams['figure.figsize'] = 10, 7.5
plt.rcParams['axes.grid'] = True
plt.gray()
In [ ]:
lb_view = client.load_balanced_view()
model = SVC()
In [ ]:
import sys
from collections import OrderedDict
sys.path.append('..')
import model_selection, mmap_utils
reload(model_selection), reload(mmap_utils)
lb_view.abort()
svc_params = OrderedDict([
('gamma', np.logspace(-4, 0, 5)),
('C', np.logspace(-1, 2, 4)),
])
search = model_selection.RandomizedGridSeach(lb_view)
search.launch_for_splits(model, svc_params, digits_split_filenames)
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print(search.report())
In [ ]:
print(search.report())
search.boxplot_parameters(display_train=False)
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#search.abort()
To provision a cheap transient compute cluster on Amazon EC2, the first step is to register on EC2 with a credit card and put your EC2 credentials as environment variables. For instance under Linux / OSX:
[laptop]% export AWS_ACCESS_KEY_ID=XXXXXXXXXXXXXXXXXXXXX
[laptop]% export AWS_SECRET_ACCESS_KEY=XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
You can put those exports in your ~/.bashrc to automatically get those credentials loaded in new shell sessions.
Then proceed to the installation of StarCluster it-self:
[laptop]% pip install StarClusterLet's run the help command a first time and create a template configuration file:
[laptop]% starcluster help
StarCluster - (http://star.mit.edu/cluster)
Software Tools for Academics and Researchers (STAR)
Please submit bug reports to starcluster@mit.edu
cli.py:87 - ERROR - config file /home/user/.starcluster/config does not exist
Options:
--------
[1] Show the StarCluster config template
[2] Write config template to /home/user/.starcluster/config
[q] Quit
Please enter your selection:
2
and create a password-less ssh key that will be dedicated to this transient cluster:
[laptop]% starcluster createkey mykey -o ~/.ssh/mykey.rsa
You can now edit the file /home/user/.starcluster/config and remplace its content with the following sample configuration:
[global]
DEFAULT_TEMPLATE=iptemplate
REFRESH_INTERVAL=5
[key mykey]
KEY_LOCATION=~/.ssh/mykey.rsa
[plugin ipcluster]
SETUP_CLASS = starcluster.plugins.ipcluster.IPCluster
ENABLE_NOTEBOOK = True
NOTEBOOK_PASSWD = aaaa
[plugin ipclusterstop]
SETUP_CLASS = starcluster.plugins.ipcluster.IPClusterStop
[plugin ipclusterrestart]
SETUP_CLASS = starcluster.plugins.ipcluster.IPClusterRestartEngines
[plugin pypackages]
setup_class = starcluster.plugins.pypkginstaller.PyPkgInstaller
packages = scikit-learn, psutil
# Base configuration for IPython.parallel cluster
[cluster iptemplate]
KEYNAME = mykey
CLUSTER_SIZE = 1
CLUSTER_USER = ipuser
CLUSTER_SHELL = bash
REGION = us-east-1
NODE_IMAGE_ID = ami-5b3fb632 # REGION and NODE_IMAGE_ID go in pair
NODE_INSTANCE_TYPE = c1.xlarge # 8 CPUs
DISABLE_QUEUE = True # We don't need SGE, faster cluster startup
PLUGINS = pypackages, ipclusterStart a new cluster using the myclustertemplate section of the ~/.startcluster/config file:
[laptop]% starcluster start -c iptemplate -s 3 -b 0.5 mycluster
The -s option makes it possible to select the number of EC2 instance to start.
The -b option makes it possible to provision non-master instances on the Spot Instance market
To also provision the master node on the Spot Instance market you can further add the --force-spot-master flag to the previous commandline.
Provisioning Spot Instances is typically up to 5x cheaper than regular instances for largish instance types such as c1.xlarge but you run the risk of having your instances shut down if the price goes up. Also provisioning new instances on the Spot market can be slower: often a couple of minutes instead of 30s for On Demand instances.
You can access the price history of spot instances of a specific region with:
[laptop]% starcluster -r us-west-1 spothistory c1.xlarge
StarCluster - (http://star.mit.edu/cluster) (v. 0.9999)
Software Tools for Academics and Researchers (STAR)
Please submit bug reports to starcluster@mit.edu
>>> Current price: $0.11
>>> Max price: $0.75
>>> Average price: $0.13Connect to the master node via ssh:
[laptop]% starcluster sshmaster -A -u ipuser
The -A flag makes it possible to use your local ssh agent to manage your keys: makes it possible to git clone / git push github repositories from the master node as you would from your local folder.
The StarCluster AMI comes with tmux installed by default.
It is possible to ssh into other cluster nodes from the master using local DNS aliases such as:
[myuser@master]% ssh node001When using the LoadBalancedView API of IPython.parallel.Client is it possible to dynamically grow the cluster to shorten the duration of the processing of a queue of task without having to restart from scratch.
This can be achieved using the addnode command, for instance to add 3 more nodes using $0.50 bid price on the Spot Instance market:
[laptop]% starcluster addnode -s 3 -b 0.5 mycluster
Each node will automatically run the IPCluster plugin and register new IPEngine processes to the existing IPController process running on master.
It is also possible to terminate individual running nodes of the cluster with removenode command but this will kill any task running on that node and IPython.parallel will not restart the failed task automatically.
Once your are done with your computation, don't forget to shutdown the whole cluster and EBS volume so as to only pay for the resources you used.
Before doing so, don't forget to backup any result file you would like to keep, by either pushing them to the S3 storage service (recommended for large files that you would want to reuse on EC2 later) or fetching them locally using the starcluster get command.
The cluster shutdown itself can be achieved with a single command:
[laptop]% starcluster terminate mycluster
Alternatively to can also keep your data by preserving the EBS volume attached to the master node by remplacing the terminate command with the stop command:
[laptop]% starcluster stop mycluster
You can then later restart the same cluster again with the start command to automatically remount the EBS volume.