Databricks Runtime 5.0 ML (EoS)

Note

Support for this Databricks Runtime version has ended. For the end-of-support date, see End-of-support history. For all supported Databricks Runtime versions, see Databricks Runtime release notes versions and compatibility.

Databricks released this version in November 2018.

Databricks Runtime 5.0 ML provides a ready-to-go environment for machine learning and data science. It contains many popular libraries, including TensorFlow, 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 5.0 ML is built on top of Databricks Runtime 5.0. For information on what’s new in Databricks Runtime 5.0, see the Databricks Runtime 5.0 (EoS) release notes. In addition to the new features in Databricks Runtime 5.0, Databricks Runtime 5.0 ML includes the following new features:

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 difference in system environment in Databricks Runtime 5.0 and that in Databricks Runtime 5.0 ML is:

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

  • For GPU clusters, the following NVIDIA GPU libraries:

    • Tesla driver 396.44

    • CUDA 9.2

    • CUDNN 7.2.1

Libraries

The differences in the libraries included in Databricks Runtime 5.0 and those included in Databricks Runtime 5.0 ML are listed in this section.

Python libraries

Databricks Runtime 5.0 ML uses Conda for Python package management. Following is the 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

3.1.4

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

0.15.0

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

5.1.0

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

1.10.0

tensorflow

1.10.0

termcolor

1.1.0

testpath

0.3.1

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

tensorframes

tensorframes

0.5.0-s_2.11

graphframes

graphframes

0.6.0-db3-spark2.4

spark-deep-learning

sparkdl

1.3.0-db2-spark2.4

R libraries

The R libraries are identical to R Libraries on Databricks Runtime 5.0.

Java and Scala libraries (Scala 2.11 cluster)

In addition to Java and Scala libraries in Databricks Runtime 5.0, Databricks Runtime 5.0 ML contains the following JARs:

Group ID

Artifact ID

Version

com.databricks

spark-deep-learning

1.3.0-db2-spark2.4

org.tensorframes

tensorframes

0.5.0-s_2.11

org.graphframes

graphframes_2.11

0.6.0-db3-spark2.4

org.tensorflow

libtensorflow

1.10.0

org.tensorflow

libtensorflow_jni

1.10.0

org.tensorflow

spark-tensorflow-connector_2.11

1.10.0-spark2.4-001

org.tensorflow

tensorflow

1.10.0

ml.dmlc

xgboost4j

0.80

ml.dmlc

xgboost4j-spark

0.80

ml.combust.mleap

mleap-databricks-runtime_2.11

0.13.0-SNAPSHOT