DASK and Apache Spark

For a Python driven Data Science team, DASK presents a very obvious logical next step for distributed analysis. However, today the de-facto standard choice for exact same purpose is Apache Spark. DASK is a pure Python framework, which does more of same i.e. it allows one to run the same Pandas or NumPy code either locally or on a cluster. Whereas, Apache Spark brings about a learning curve involving a new API and execution model although with a Python wrapper. Given the above statement, do we even need to compare and contrast to make a choice? Shouldn’t DASK be the default choice? Well, that’s what this session is about. It goes in detail explaining the various viewpoints and dimensions that need to be considered to pick one over other.
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1.WIFI SSID:SparkAISummit | Password: UnifiedAnalytics

2.DASK and Apache Spark Gurpreet Singh, Microsoft #UnifiedAnalytics #SparkAISummit

3.…this talk is also about Scaling Python for Data Analysis & Machine Learning! We’ll start with briefly reviewing the Scalability Challenge in the PyData stack… …before Comparing and Contrasting … #UnifiedAnalytics #SparkAISummit 3

4.Pandas & Scikit-learn Options to Scale… GIL Challenge Multiprocessing Concurrent Futures Hashing Trick JobLib Partial_Fit() #UnifiedAnalytics #SparkAISummit 4

5.Design DASK Arrays [Parallel NumPy] GraphX d support oesn’t Python Approach DASK Bag [Parallel Lists] DASK-ML [Parallel Scikit-learn] Spark SQL / GraphFrames Spark Streaming DASK DataFrames Custom Custom DataFrames [Parallel Pandas] Algorithms Graphs Spark MLlib High Level APIs DASK Delayed DASK Futures RDD Low Level APIs it Subm s a graph n Pytho y nar Dictio ct obje Lazy n tio Execu Directed Acyclic Graph (DAG) Pluggable Task Scheduling System Local Scheduler Local Spark Standalone Synchronous Multiprocessing Mesos YARN Threaded Distributed Task Scheduler #UnifiedAnalytics #SparkAISummit 5

6.DASK DataFrame & PySpark DASK DataFrames [Parallel Pandas] Challenges Challenges § DASK DataFrames API is not identical with Pandas API § Performance Concerns due to the PySpark Design § Performance Concerns with Operations involving Shuffling § Inefficiencies of Pandas are carried over Recommendations Recommendations § Follow the Pandas Performance tips § Use DataFrames API § Avoid Shuffle, Use pre-sorting, Persist the Results § Use Vectorized/Pandas UDF (Spark v2.3 onwards) #UnifiedAnalytics #SparkAISummit 6

7.Code Review Wildcard Wildcard Additional Details #UnifiedAnalytics #SparkAISummit 7

8.Code Review While Pandas display a sample of the data, DASK and PySpark show metadata of the DataFrame. The npartitions value shows how many partitions the DataFrame is split into. DASK created a DAG with 99 nodes to process the data. #UnifiedAnalytics #SparkAISummit 8

9.Code Review .compute() or .head() method tells Dask to go ahead and run the computation and display the results. .show() displays the DataFrame in a tabular form. #UnifiedAnalytics #SparkAISummit 9

10.Scalable Machine Learning Spark MLlib - As of Spark 2.0, the primary Machine Learning API for Spark is now the DataFrame-based API in the spark.ml package. It OCT ‘17 - DASK-ML provides: How does DASK-ML Algorithms Common algorithms e.g. classification, regression, & clustering work? Featurization Feature extraction, Transformation, Dimensionality Reduction Re-implement Partner with existing Constructing, evaluating and tuning ML pipelines Parallelize Scikit-Learn Algorithms Libraries Pipelines Persistence Save/Load Algorithms, Models, and Pipelines Utilities Linear Algebra, Statistics, and Data Handling Distributed Pipelines Chains multiple Transformers and Estimators as per the ML Flow JobLib Transformers Algorithm that transfers one DataFrame into another from sklearn.externals.joblib import _dask, parallel_backend Estimators Algorithm that trains and produces a model from sklearn.utils import register_parallel_backend register_parallel_backend('distributed', _dask.DaskDistributedBackend) Scalable ML Approaches from dask_ml.xgboost import XGBRegressor § Spark for Feature Engineering + Scikit-learn etc. for Learning est = XGBRegressor(...) est.fit(train, train_labels) § Distributed ML Algorithms from Spark MLlib prediction = est.predict(test) § Train/evaluate Scikit-learn models in parallel (spark-sklearn) #UnifiedAnalytics #SparkAISummit 10

11.Distributed Deep Learning Deep § APIs for scalable deep learning in Python from Databricks Learning § Provides a suite of tools covering loading, Training, Pipelines Tuning and Deploying Peer a DASK Distributed cluster with with TensorFlow running in distributed mode. § Simplifies Distributed Deep Learning Training § Supports TensorFlow, Keras and PyTorch § Integration with PySpark Project New scheduling option called Gang Scheduler Hydrogen #UnifiedAnalytics #SparkAISummit 11

12.Other Dev Considerations… § Workloads/APIs § Custom Algorithms (only in DASK) § SQL, Graph (only in Spark) § Debugging Challenges § DASK Distributed may not align with normal Python Debugging Tools/Practices § PySpark errors may have a mix of JVM and Python Stack Trace § Visualization Options § Down-sample and use Pandas DataFrames § Use open source Libraries e.g. D3, Seaborn, Datashader (only for DASK) etc. § Use Databricks Visualization Feature #UnifiedAnalytics #SparkAISummit 12

13.Which one to Use? There Are No Solutions, There Are Only Tradeoffs! – Thomas Sowell #UnifiedAnalytics #SparkAISummit 13

14.Questions? #UnifiedAnalytics #SparkAISummit 14

15.DON’T FORGET TO RATE AND REVIEW THE SESSIONS SEARCH SPARK + AI SUMMIT