Databricks Runtime 5.2 ML

Databricks released this image in January 2019.

Databricks Runtime 5.2 ML provides a ready-to-go environment for machine learning and data science based on Databricks Runtime 5.2 (unsupported). Databricks Runtime for ML contains many popular machine learning libraries, including TensorFlow, PyTorch, Keras, and XGBoost. It also supports distributed TensorFlow training using Horovod.

For more information, including instructions for creating a Databricks Runtime ML cluster, see AI and Machine Learning on Databricks.

New features

Databricks Runtime 5.2 ML is built on top of Databricks Runtime 5.2. For information on what’s new in Databricks Runtime 5.2, see the Databricks Runtime 5.2 (unsupported) release notes. In addition to library updates, Databricks Runtime 5.2 ML introduces the following new features:

  • GraphFrames now supports the Pregel API (Python) with Databricks’s performance optimizations.

  • HorovodRunner adds:

    • On a GPU cluster, training processes are mapped to GPUs instead of worker nodes to simplify the support of multi-GPU instance types. This built-in support allows you to distribute to all of the GPUs on a multi-GPU machine without custom code.

    • HorovodRunner.run() now returns the return value from the first training process.

Note

Databricks Runtime ML releases pick up all maintenance updates to the base Databricks Runtime release. For a list of all maintenance updates, see Maintenance updates for Databricks Runtime (archived).

System environment

The system environment in Databricks Runtime 5.2 ML differs from Databricks Runtime 5.2 as follows:

  • Python: 2.7.15 for Python 2 clusters and 3.6.5 for Python 3 clusters.

  • DBUtils: Databricks Runtime 5.2 ML does not contain (Legacy) Library utility (dbutils.library).

  • For GPU clusters, the following NVIDIA GPU libraries:

    • Tesla driver 396.44

    • CUDA 9.2

    • CUDNN 7.2.1

Libraries

The following sections list the libraries included in Databricks Runtime 5.2 ML that differ from those included in Databricks Runtime 5.2.

Python libraries

Databricks Runtime 5.2 ML uses Conda for Python package management. As a result, there are major differences in pre-installed Python libraries compared to Databricks Runtime. The following is a full list of provided Python packages and versions installed using Conda package manager.

Library

Version

Library

Version

Library

Version

absl-py

0.6.1

argparse

1.4.0

asn1crypto

0.24.0

astor

0.7.1

backports-abc

0.5

backports.functools-lru-cache

1.5

backports.weakref

1.0.post1

bcrypt

3.1.5

bleach

2.1.3

boto

2.48.0

boto3

1.7.62

botocore

1.10.62

certifi

2018.04.16

cffi

1.11.5

chardet

3.0.4

cloudpickle

0.5.3

colorama

0.3.9

configparser

3.5.0

cryptography

2.2.2

cycler

0.10.0

Cython

0.28.2

decorator

4.3.0

docutils

0.14

entrypoints

0.2.3

enum34

1.1.6

et-xmlfile

1.0.1

funcsigs

1.0.2

functools32

3.2.3-2

fusepy

2.0.4

futures

3.2.0

gast

0.2.0

grpcio

1.12.1

h5py

2.8.0

horovod

0.15.2

html5lib

1.0.1

idna

2.6

ipaddress

1.0.22

ipython

5.7.0

ipython_genutils

0.2.0

jdcal

1.4

Jinja2

2.10

jmespath

0.9.3

jsonschema

2.6.0

jupyter-client

5.2.3

jupyter-core

4.4.0

Keras

2.2.4

Keras-Applications

1.0.6

Keras-Preprocessing

1.0.5

kiwisolver

1.0.1

linecache2

1.0.0

llvmlite

0.23.1

lxml

4.2.1

Markdown

3.0.1

MarkupSafe

1.0

matplotlib

2.2.2

mistune

0.8.3

mleap

0.8.1

mock

2.0.0

msgpack

0.5.6

nbconvert

5.3.1

nbformat

4.4.0

nose

1.3.7

nose-exclude

0.5.0

numba

0.38.0+0.g2a2b772fc.dirty

numpy

1.14.3

olefile

0.45.1

openpyxl

2.5.3

pandas

0.23.0

pandocfilters

1.4.2

paramiko

2.4.1

pathlib2

2.3.2

patsy

0.5.0

pbr

5.1.1

pexpect

4.5.0

pickleshare

0.7.4

Pillow

5.1.0

pip

10.0.1

ply

3.11

prompt-toolkit

1.0.15

protobuf

3.6.1

psycopg2

2.7.5

ptyprocess

0.5.2

pyarrow

0.8.0

pyasn1

0.4.4

pycparser

2.18

Pygments

2.2.0

PyNaCl

1.3.0

pyOpenSSL

18.0.0

pyparsing

2.2.0

PySocks

1.6.8

Python

2.7.15

python-dateutil

2.7.3

pytz

2018.4

PyYAML

3.12

pyzmq

17.0.0

requests

2.18.4

s3transfer

0.1.13

scandir

1.7

scikit-learn

0.19.1

scipy

1.1.0

seaborn

0.8.1

setuptools

39.1.0

simplegeneric

0.8.1

singledispatch

3.4.0.3

six

1.11.0

statsmodels

0.9.0

subprocess32

3.5.3

tensorboard

1.12.2

tensorboardX

1.4

tensorflow

1.12.0

termcolor

1.1.0

testpath

0.3.1

torch

0.4.1

torchvision

0.2.1

tornado

5.0.2

traceback2

1.4.0

traitlets

4.3.2

unittest2

1.1.0

urllib3

1.22

virtualenv

16.0.0

wcwidth

0.1.7

webencodings

0.5.1

Werkzeug

0.14.1

wheel

0.31.1

wrapt

1.10.11

wsgiref

0.1.2

In addition, the following Spark packages include Python modules:

Spark Package

Python Module

Version

graphframes

graphframes

0.7.0-db1-spark2.4

spark-deep-learning

sparkdl

1.5.0-db1-spark2.4

tensorframes

tensorframes

0.6.0-s_2.11

R libraries

The R libraries are identical to the R Libraries in Databricks Runtime 5.2.

Java and Scala libraries (Scala 2.11 cluster)

In addition to Java and Scala libraries in Databricks Runtime 5.2, Databricks Runtime 5.2 ML contains the following JARs:

Group ID

Artifact ID

Version

com.databricks

spark-deep-learning

1.5.0-db1-spark2.4

com.typesafe.akka

akka-actor_2.11

2.3.11

ml.combust.mleap

mleap-databricks-runtime_2.11

0.13.0

ml.dmlc

xgboost4j

0.81

ml.dmlc

xgboost4j-spark

0.81

org.graphframes

graphframes_2.11

0.7.0-db1-spark2.4

org.tensorflow

libtensorflow

1.12.0

org.tensorflow

libtensorflow_jni

1.12.0

org.tensorflow

spark-tensorflow-connector_2.11

1.12.0

org.tensorflow

tensorflow

1.12.0

org.tensorframes

tensorframes

0.6.0-s_2.11