Machine Learning — Anaconda Enterprise 4.4 documentation

Anaconda Enterprise: Documentation

# Machine Learning

Dask facilitates machine learning, statistics, and optimization workloads in a variety of ways. Generally Dask tries to support other high-quality solutions within the PyData ecosystem rather than reinvent new systems. Dask makes it easier to scale single-machine libraries like Scikit-Learn where possible and makes using distributed libraries like XGBoost or Tensorflow more comfortable for average users.

In all cases you will have to install extra dependencies like scikit-learn to gain full advantage. These dependencies are listed at the beginning of each section and are both conda installable from conda-forge or pip installable.

Dask can be helpful in the following ways:

## SKLearn: Model selection

Extra Dependencies: This depends on scikit-learn and dask-searchcv.

Dask can help to accelerate model selection and hyper parameter searches (GridSearchCV, RandomSearchCV) with dask-searchcv.

    from sklearn.feature_extraction.text import CountVectorizer
    from sklearn.feature_extraction.text import TfidfTransformer
    from sklearn.linear_model import SGDClassifier
    from sklearn.pipeline import Pipeline

    pipeline = Pipeline([('vect', CountVectorizer()),
                         ('tfidf', TfidfTransformer()),
                         ('clf', SGDClassifier())])

    # from sklearn.model_selection import GridSearchCV  # use SKLearn
    from dask_searchcv import GridSearchCV              # or use Dask

    grid_search = GridSearchCV(pipeline, parameters)
    grid_search.fit(data.data, data.target)

## SKLearn: Accelerated Joblib

Extra Dependencies: This depends on scikit-learn and distributed.

Scikit-Learn parallelizes its algorithms internally with Joblib a simple parallel computing API used throughout sklearn’s codebase. While Joblib normally runs from a local thread or processing pool Dask can also step in and provide distributed computing support. See http://distributed.readthedocs.io/en/latest/joblib.html for more information.

    import distributed.joblib
    import joblib

    with joblib.parallel_backend('dask.distributed', scheduler_host='localhost:8786'):
        # normal sklearn code

## Convex Optimization Algorithms with Dask.Array

Extra Dependencies: This depends on dask-glm.

Just as Scikit-Learn implements machine learning algorithms like gradient descent with the NumPy API we can implement many of those same algorithms with the Dask.array API (which is a drop-in replacement). Many algorithms for convex optimization are implemented in dask-glm

    from dask_glm.algorithms import newton, bfgs, proximal_gradient, admm
    from dask_glm.families import Logistic, Normal

    import dask.array as da
    X = da....
    y = da....

    beta = admm(X, y, family=Logistic)

## Other Distributed Systems

Extra Dependencies: This depends on xgboost and dask-xgboost or tensorflow and dask-tensorflow.

Other high-quality parallel and distributed systems for machine learning already exist. For example XGBoost supports gradient boosted trees and Tensorflow supports deep learning. Both of these libraries already have robust support for distributed computing. In these cases Dask does not compete with these libraries, but instead facilitates setting them up on a pre-existing Dask cluster and then handles the smooth transfer of data from Dask workers to XGBoost or Tensorflow workers running in the same Python processes.

For more information, see the following subprojects:

- dask-xgboost
- dask-tensorflow