Databricks Runtime 10.2 for Machine Learning (Unsupported)

Databricks released this image in December 2021.

Databricks Runtime 10.2 for Machine Learning provides a ready-to-go environment for machine learning and data science based on Databricks Runtime 10.2 (Unsupported). Databricks Runtime ML contains many popular machine learning libraries, including TensorFlow, PyTorch, and XGBoost. Databricks Runtime ML includes AutoML, a tool to automatically train machine learning pipelines. Databricks Runtime ML also supports distributed deep learning training using Horovod.

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

New features and improvements

Databricks Runtime 10.2 ML is built on top of Databricks Runtime 10.2. For information on what’s new in Databricks Runtime 10.2, including Apache Spark MLlib and SparkR, see the Databricks Runtime 10.2 (Unsupported) release notes.

Databricks Autologging (Public Preview)

Databricks Autologging is now in Public Preview in all regions. Databricks Autologging is a no-code solution that provides automatic experiment tracking for machine learning training sessions on Azure Databricks. With Databricks Autologging, model parameters, metrics, files, and lineage information are automatically captured when you train models from a variety of popular machine learning libraries. Training sessions are recorded as MLflow Tracking Runs. Model files are also tracked so you can easily log them to the MLflow Model Registry and deploy them for real-time scoring with MLflow Model Serving.

For more information about Databricks Autologging, see Databricks Autologging.

Enhancements to Databricks AutoML

The following enhancements have been made to Databricks AutoML.

  • AutoML ignores columns that have only a single value.
  • For classification and regression problems, the time column used to split the dataset into training, validation, and test sets chronologically can now be string type. Previously only timestamp and integer were supported. See Control the train/validation/test split for details.

Enhancements to Databricks Feature Store

The following enhancements have been made to Databricks Feature Store.

Simplified FeatureStoreClient interface

The FeatureStoreClient interface has been simplified.

  • FeatureStoreClient.create_feature_table() has been deprecated. Instead, use FeatureStoreClient.create_table().
  • FeatureStoreClient.get_feature_table() has been deprecated. Instead, use FeatureStoreClient.get_table().
  • All arguments to FeatureStoreClient.publish_table() other than name and online_store must be passed as keyword arguments.

For more information, see Work with feature tables and Databricks Feature Store Python API.

Publish only selected columns to online stores

Databricks Feature Store now supports publishing only selected columns to an online store. For more information, see Publish selected features to an online store.

Major changes to Databricks Runtime ML Python environment

The Automated MLflow Tracking integration for Apache Spark MLlib, which was deprecated in Databricks Runtime 10.1 ML, is now disabled by default in Databricks Runtime 10.2 ML. It has been replaced by MLflow’s PySpark ML Autologging integration, which is enabled by default with Databricks Autologging. Autologging records additional information beyond what Automated MLflow tracking for MLlib captured, including the parameters, metrics, and artifacts associated with the best model.

Python packages upgraded

  • databricks-cli 0.14.3 => 0.16.2
  • keras 2.6.0 => 2.7.0
  • lightgbm 3.3.0 => 3.3.1
  • mlflow 1.21.0 => 1.22.0
  • plotly 5.3.0 => 5.3.1
  • shap 0.39.0 => 0.40.0
  • spacy 3.1.3 => 3.2.0
  • tensorboard 2.6.0 => 2.7.0
  • tensorflow 2.6.0 => 2.7.0
  • torch 1.9.1 => 1.10.0
  • torchvision 0.10.1 => 0.11.1
  • transformers 4.11.3 => 4.12.3
  • xgboost 1.4.2 => 1.5.0

System environment

The system environment in Databricks Runtime 10.2 ML differs from Databricks Runtime 10.2 as follows:

Libraries

The following sections list the libraries included in Databricks Runtime 10.2 ML that differ from those included in Databricks Runtime 10.2.

In this section:

Top-tier libraries

Databricks Runtime 10.2 ML includes the following top-tier libraries:

Python libraries

Databricks Runtime 10.2 ML uses Virtualenv for Python package management and includes many popular ML packages.

In addition to the packages specified in the in the following sections, Databricks Runtime 10.2 ML also includes the following packages:

  • hyperopt 0.2.7.db1
  • sparkdl 2.2.0-db5
  • feature_store 0.3.6
  • automl 1.5.0

Python libraries on CPU clusters

Library Version Library Version Library Version
absl-py 0.11.0 Antergos Linux 2015.10 (ISO-Rolling) appdirs 1.4.4
argon2-cffi 20.1.0 astor 0.8.1 astunparse 1.6.3
async-generator 1.10 attrs 20.3.0 backcall 0.2.0
bcrypt 3.2.0 bidict 0.21.4 bleach 3.3.0
blis 0.7.4 boto3 1.16.7 botocore 1.19.7
cachetools 4.2.4 catalogue 2.0.6 certifi 2020.12.5
cffi 1.14.5 chardet 4.0.0 click 7.1.2
cloudpickle 1.6.0 cmdstanpy 0.9.68 configparser 5.0.1
convertdate 2.3.2 cryptography 3.4.7 cycler 0.10.0
cymem 2.0.5 Cython 0.29.23 databricks-automl-runtime 0.2.4
databricks-cli 0.16.2 dbus-python 1.2.16 decorator 5.0.6
defusedxml 0.7.1 dill 0.3.2 diskcache 5.2.1
distlib 0.3.3 distro-info 0.23ubuntu1 entrypoints 0.3
ephem 4.1.1 facets-overview 1.0.0 fasttext 0.9.2
filelock 3.0.12 Flask 1.1.2 flatbuffers 2.0
fsspec 0.9.0 future 0.18.2 gast 0.4.0
gitdb 4.0.7 GitPython 3.1.12 google-auth 1.22.1
google-auth-oauthlib 0.4.2 google-pasta 0.2.0 grpcio 1.39.0
gunicorn 20.0.4 gviz-api 1.10.0 h5py 3.1.0
hijri-converter 2.2.2 holidays 0.11.3.1 horovod 0.23.0
htmlmin 0.1.12 huggingface-hub 0.1.2 idna 2.10
ImageHash 4.2.1 imbalanced-learn 0.8.1 importlib-metadata 3.10.0
ipykernel 5.3.4 ipython 7.22.0 ipython-genutils 0.2.0
ipywidgets 7.6.3 isodate 0.6.0 itsdangerous 1.1.0
jedi 0.17.2 Jinja2 2.11.3 jmespath 0.10.0
joblib 1.0.1 joblibspark 0.3.0 jsonschema 3.2.0
jupyter-client 6.1.12 jupyter-core 4.7.1 jupyterlab-pygments 0.1.2
jupyterlab-widgets 1.0.0 keras 2.7.0 Keras-Preprocessing 1.1.2
kiwisolver 1.3.1 koalas 1.8.2 korean-lunar-calendar 0.2.1
langcodes 3.3.0 libclang 12.0.0 lightgbm 3.3.1
llvmlite 0.37.0 LunarCalendar 0.0.9 Mako 1.1.3
Markdown 3.3.3 MarkupSafe 2.0.1 matplotlib 3.4.2
missingno 0.5.0 mistune 0.8.4 mleap 0.18.1
mlflow-skinny 1.22.0 multimethod 1.6 murmurhash 1.0.5
nbclient 0.5.3 nbconvert 6.0.7 nbformat 5.1.3
nest-asyncio 1.5.1 networkx 2.5 nltk 3.6.1
notebook 6.3.0 numba 0.54.1 numpy 1.19.2
oauthlib 3.1.0 opt-einsum 3.3.0 packaging 21.3
pandas 1.2.4 pandas-profiling 3.1.0 pandocfilters 1.4.3
paramiko 2.7.2 parso 0.7.0 pathy 0.6.0
patsy 0.5.1 petastorm 0.11.3 pexpect 4.8.0
phik 0.12.0 pickleshare 0.7.5 Pillow 8.2.0
pip 21.0.1 plotly 5.3.1 preshed 3.0.5
prometheus-client 0.10.1 prompt-toolkit 3.0.17 prophet 1.0.1
protobuf 3.17.2 psutil 5.8.0 psycopg2 2.8.5
ptyprocess 0.7.0 pyarrow 4.0.0 pyasn1 0.4.8
pyasn1-modules 0.2.8 pybind11 2.8.1 pycparser 2.20
pydantic 1.8.2 Pygments 2.8.1 PyGObject 3.36.0
PyMeeus 0.5.11 PyNaCl 1.4.0 pyodbc 4.0.30
pyparsing 2.4.7 pyrsistent 0.17.3 pystan 2.19.1.1
python-apt 2.0.0+ubuntu0.20.4.6 python-dateutil 2.8.1 python-editor 1.0.4
python-engineio 4.3.0 python-socketio 5.4.1 pytz 2020.5
PyWavelets 1.1.1 PyYAML 5.4.1 pyzmq 20.0.0
regex 2021.4.4 requests 2.25.1 requests-oauthlib 1.3.0
requests-unixsocket 0.2.0 rsa 4.7.2 s3transfer 0.3.7
sacremoses 0.0.46 scikit-learn 0.24.1 scipy 1.6.2
seaborn 0.11.1 Send2Trash 1.5.0 setuptools 52.0.0
setuptools-git 1.2 shap 0.40.0 simplejson 3.17.2
six 1.15.0 slicer 0.0.7 smart-open 5.2.0
smmap 3.0.5 spacy 3.2.0 spacy-legacy 3.0.8
spacy-loggers 1.0.1 spark-tensorflow-distributor 1.0.0 sqlparse 0.4.1
srsly 2.4.1 ssh-import-id 5.10 statsmodels 0.12.2
tabulate 0.8.7 tangled-up-in-unicode 0.1.0 tenacity 6.2.0
tensorboard 2.7.0 tensorboard-data-server 0.6.1 tensorboard-plugin-profile 2.5.0
tensorboard-plugin-wit 1.8.0 tensorflow-cpu 2.7.0 tensorflow-estimator 2.7.0
tensorflow-io-gcs-filesystem 0.22.0 termcolor 1.1.0 terminado 0.9.4
testpath 0.4.4 thinc 8.0.12 threadpoolctl 2.1.0
tokenizers 0.10.3 torch 1.10.0+cpu torchvision 0.11.1+cpu
tornado 6.1 tqdm 4.59.0 traitlets 5.0.5
transformers 4.12.3 typer 0.3.2 typing-extensions 3.7.4.3
ujson 4.0.2 unattended-upgrades 0.1 urllib3 1.25.11
virtualenv 20.4.1 visions 0.7.4 wasabi 0.8.2
wcwidth 0.2.5 webencodings 0.5.1 websocket-client 0.57.0
Werkzeug 1.0.1 wheel 0.36.2 widgetsnbextension 3.5.1
wrapt 1.12.1 xgboost 1.5.0 zipp 3.4.1

Python libraries on GPU clusters

Library Version Library Version Library Version
absl-py 0.11.0 Antergos Linux 2015.10 (ISO-Rolling) appdirs 1.4.4
argon2-cffi 20.1.0 astor 0.8.1 astunparse 1.6.3
async-generator 1.10 attrs 20.3.0 backcall 0.2.0
bcrypt 3.2.0 bidict 0.21.4 bleach 3.3.0
blis 0.7.4 boto3 1.16.7 botocore 1.19.7
cachetools 4.2.4 catalogue 2.0.6 certifi 2020.12.5
cffi 1.14.5 chardet 4.0.0 click 7.1.2
cloudpickle 1.6.0 cmdstanpy 0.9.68 configparser 5.0.1
convertdate 2.3.2 cryptography 3.4.7 cycler 0.10.0
cymem 2.0.5 Cython 0.29.23 databricks-automl-runtime 0.2.4
databricks-cli 0.16.2 dbus-python 1.2.16 decorator 5.0.6
defusedxml 0.7.1 dill 0.3.2 diskcache 5.2.1
distlib 0.3.3 distro-info 0.23ubuntu1 entrypoints 0.3
ephem 4.1.1 facets-overview 1.0.0 fasttext 0.9.2
filelock 3.0.12 Flask 1.1.2 flatbuffers 2.0
fsspec 0.9.0 future 0.18.2 gast 0.4.0
gitdb 4.0.7 GitPython 3.1.12 google-auth 1.22.1
google-auth-oauthlib 0.4.2 google-pasta 0.2.0 grpcio 1.39.0
gunicorn 20.0.4 gviz-api 1.10.0 h5py 3.1.0
hijri-converter 2.2.2 holidays 0.11.3.1 horovod 0.23.0
htmlmin 0.1.12 huggingface-hub 0.1.2 idna 2.10
ImageHash 4.2.1 imbalanced-learn 0.8.1 importlib-metadata 3.10.0
ipykernel 5.3.4 ipython 7.22.0 ipython-genutils 0.2.0
ipywidgets 7.6.3 isodate 0.6.0 itsdangerous 1.1.0
jedi 0.17.2 Jinja2 2.11.3 jmespath 0.10.0
joblib 1.0.1 joblibspark 0.3.0 jsonschema 3.2.0
jupyter-client 6.1.12 jupyter-core 4.7.1 jupyterlab-pygments 0.1.2
jupyterlab-widgets 1.0.0 keras 2.7.0 Keras-Preprocessing 1.1.2
kiwisolver 1.3.1 koalas 1.8.2 korean-lunar-calendar 0.2.1
langcodes 3.3.0 libclang 12.0.0 lightgbm 3.3.1
llvmlite 0.37.0 LunarCalendar 0.0.9 Mako 1.1.3
Markdown 3.3.3 MarkupSafe 2.0.1 matplotlib 3.4.2
missingno 0.5.0 mistune 0.8.4 mleap 0.18.1
mlflow-skinny 1.22.0 multimethod 1.6 murmurhash 1.0.5
nbclient 0.5.3 nbconvert 6.0.7 nbformat 5.1.3
nest-asyncio 1.5.1 networkx 2.5 nltk 3.6.1
notebook 6.3.0 numba 0.54.1 numpy 1.19.2
oauthlib 3.1.0 opt-einsum 3.3.0 packaging 21.3
pandas 1.2.4 pandas-profiling 3.1.0 pandocfilters 1.4.3
paramiko 2.7.2 parso 0.7.0 pathy 0.6.0
patsy 0.5.1 petastorm 0.11.3 pexpect 4.8.0
phik 0.12.0 pickleshare 0.7.5 Pillow 8.2.0
pip 21.0.1 plotly 5.3.1 preshed 3.0.5
prompt-toolkit 3.0.17 prophet 1.0.1 protobuf 3.17.2
psutil 5.8.0 psycopg2 2.8.5 ptyprocess 0.7.0
pyarrow 4.0.0 pyasn1 0.4.8 pyasn1-modules 0.2.8
pybind11 2.8.1 pycparser 2.20 pydantic 1.8.2
Pygments 2.8.1 PyGObject 3.36.0 PyMeeus 0.5.11
PyNaCl 1.4.0 pyodbc 4.0.30 pyparsing 2.4.7
pyrsistent 0.17.3 pystan 2.19.1.1 python-apt 2.0.0+ubuntu0.20.4.6
python-dateutil 2.8.1 python-editor 1.0.4 python-engineio 4.3.0
python-socketio 5.4.1 pytz 2020.5 PyWavelets 1.1.1
PyYAML 5.4.1 pyzmq 20.0.0 regex 2021.4.4
requests 2.25.1 requests-oauthlib 1.3.0 requests-unixsocket 0.2.0
rsa 4.7.2 s3transfer 0.3.7 sacremoses 0.0.46
scikit-learn 0.24.1 scipy 1.6.2 seaborn 0.11.1
Send2Trash 1.5.0 setuptools 52.0.0 setuptools-git 1.2
shap 0.40.0 simplejson 3.17.2 six 1.15.0
slicer 0.0.7 smart-open 5.2.0 smmap 3.0.5
spacy 3.2.0 spacy-legacy 3.0.8 spacy-loggers 1.0.1
spark-tensorflow-distributor 1.0.0 sqlparse 0.4.1 srsly 2.4.1
ssh-import-id 5.10 statsmodels 0.12.2 tabulate 0.8.7
tangled-up-in-unicode 0.1.0 tenacity 6.2.0 tensorboard 2.7.0
tensorboard-data-server 0.6.1 tensorboard-plugin-profile 2.5.0 tensorboard-plugin-wit 1.8.0
tensorflow 2.7.0 tensorflow-estimator 2.7.0 tensorflow-io-gcs-filesystem 0.22.0
termcolor 1.1.0 terminado 0.9.4 testpath 0.4.4
thinc 8.0.12 threadpoolctl 2.1.0 tokenizers 0.10.3
torch 1.10.0+cu111 torchvision 0.11.1+cu111 tornado 6.1
tqdm 4.59.0 traitlets 5.0.5 transformers 4.12.3
typer 0.3.2 typing-extensions 3.7.4.3 ujson 4.0.2
unattended-upgrades 0.1 urllib3 1.25.11 virtualenv 20.4.1
visions 0.7.4 wasabi 0.8.2 wcwidth 0.2.5
webencodings 0.5.1 websocket-client 0.57.0 Werkzeug 1.0.1
wheel 0.36.2 widgetsnbextension 3.5.1 wrapt 1.12.1
xgboost 1.5.0 zipp 3.4.1

Spark packages containing Python modules

Spark Package Python Module Version
graphframes graphframes 0.8.2-db1-spark3.2

R libraries

The R libraries are identical to the R Libraries in Databricks Runtime 10.2.

Java and Scala libraries (Scala 2.12 cluster)

In addition to Java and Scala libraries in Databricks Runtime 10.2, Databricks Runtime 10.2 ML contains the following JARs:

CPU clusters

Group ID Artifact ID Version
com.typesafe.akka akka-actor_2.12 2.5.23
ml.combust.mleap mleap-databricks-runtime_2.12 0.18.1-23eb1ef
ml.dmlc xgboost4j-spark_2.12 1.5.1
ml.dmlc xgboost4j_2.12 1.5.1
org.graphframes graphframes_2.12 0.8.2-db1-spark3.2
org.mlflow mlflow-client 1.22.0
org.mlflow mlflow-spark 1.22.0
org.scala-lang.modules scala-java8-compat_2.12 0.8.0
org.tensorflow spark-tensorflow-connector_2.12 1.15.0

GPU clusters

Group ID Artifact ID Version
com.typesafe.akka akka-actor_2.12 2.5.23
ml.combust.mleap mleap-databricks-runtime_2.12 0.18.1-23eb1ef
ml.dmlc xgboost4j-spark_2.12 1.5.1
ml.dmlc xgboost4j_2.12 1.5.1
org.graphframes graphframes_2.12 0.8.2-db1-spark3.2
org.mlflow mlflow-client 1.22.0
org.mlflow mlflow-spark 1.22.0
org.scala-lang.modules scala-java8-compat_2.12 0.8.0
org.tensorflow spark-tensorflow-connector_2.12 1.15.0