Skipton // Databricks

Data Science & Machine Learning: platform overview

The materials in this repository accompany the session delivered on TBC.

Environment set-up

1. Install the Databricks CLI and configure for access to your workspace

2. Clone the repository to your local environment and navigate to that folder on the command line

3. Create the demo cluster using the command databricks clusters create --json-file cluster-spec.json

4. Upload the mmlspark python library to the cluster databricks fs cp -r ./libraries dbfs:/FileStore/jars/datasci-overview

5. Install all of the libraries on your cluster using the cluster UI:

   library name (or maven coords)	version	type	repository / location
   fbprophet	latest	PyPI	default
   joblibspark	latest	PyPI	default
   plotly	latest	PyPI	default
   scikit-learn==0.21.3	0.21.3	PyPI	default
   petastorm==0.7.2	0.7.2	PyPI	default
   com.microsoft.ml.spark:mmlspark_2.11:1.0.0-rc1	1.0.0-rc1	Maven	https://mmlspark.azureedge.net/maven
   mmlspark-0.17	0.17	DBFS, Python Whl	dbfs:/FileStore/jars/datasci-overview/mmlspark-0.17-py2.py3-none-any.whl

6. Upload the repository to your workspace using e.g. databricks workspace import_dir . /Users/[email protected]/datasci-overview where [email protected] is the address you use to log into your workspace

7. Attach a notebook to your newly created cluster and try running it.

Notebook descriptions

01_Replicate-traditional-workflow

First example of accessing data, training and evaluating a simple model in a Databricks notebook.

02_Introduction-to-tracking

Building on the first notebook, showing how to log parameters, metrics, models etc. to the MLflow tracking server.

03_Deployment-management

Demonstrating different ways of using MLflow to deploy the models trained in the previous notebook.

00_Reset

Resets some of the MLflow components (for demo purposes)

Fine Grained Demand Forecasting

Example of using pandas_udf to create forecasts for subgroups within a large dataset.

Hyperopt + Spark demo

Simple example of hyperparameter tuning on a single-node or cluster environment using the Hyperopt package.

LightGBM - Quantile Regression for Drug Discovery

Example of data distributed training of a statistical machine learning model using the Microsoft LightGBM implementation.

mnist-tensorflow-keras

Example of data distributed training of a deep neural network by pairing Keras and HorovodRunner.

parallel-model-selection-joblib

Parallel model selection on scikit-learn models using the joblibspark backend.

petastorm

Batch-at-a-time data loading for training of neural networks on datasets whose size exceeds available memory.

Other material

Slides for the session are located TBC and should be accessible immediately after the session.

Questions?

Please contact me at [email protected].