Google
Cloud Platform
Amazon Web Services
Microsoft AzureDatabricks Runtime 10.2 for 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 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 (EoS). 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 AI and machine learning on Databricks.
Note
These release notes may include references to features that are not available on Google Cloud as of this release.
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 (EoS) 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 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 Mosaic AutoML
The following enhancements have been made to Mosaic 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 Split data into train, validation, and test sets 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, useFeatureStoreClient.create_table().FeatureStoreClient.get_feature_table()has been deprecated. Instead, useFeatureStoreClient.get_table().All arguments to
FeatureStoreClient.publish_table()other thannameandonline_storemust be passed as keyword arguments.
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:
DBUtils: Databricks Runtime ML does not include Library utility (dbutils.library) (legacy). Use
%pipcommands instead. See Notebook-scoped Python libraries.For GPU clusters, Databricks Runtime ML includes the following NVIDIA GPU libraries:
CUDA 11.0
cuDNN 8.0.5.39
NCCL 2.10.3
TensorRT 7.2.2
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 |
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 |