Amazon
Web Services
Microsoft Azure
Google Cloud PlatformDatabricks Runtime 9.0 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 August 2021.
Databricks Runtime 9.0 for Machine Learning provides a ready-to-go environment for machine learning and data science based on Databricks Runtime 9.0 (EoS). Databricks Runtime ML contains many popular machine learning libraries, including TensorFlow, PyTorch, and XGBoost. It 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.
Correction
A previous version of these release notes stated that support for monitoring cluster GPU metrics with Ganglia was disabled in Databricks Runtime 9.0 ML GPU. That was true for Databricks Runtime 9.0 ML Beta, but the issue was fixed with Databricks Runtime 9.0 ML GA. The statement has been removed.
New features and improvements
Databricks Runtime 9.0 ML is built on top of Databricks Runtime 9.0. For information on what’s new in Databricks Runtime 9.0, including Apache Spark MLlib and SparkR, see the Databricks Runtime 9.0 (EoS) release notes.
Databricks Autologging (Public Preview)
Databricks Autologging is now available for Databricks Runtime 9.0 for Machine Learning in select 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.
Improvements to Databricks Feature Store
Performance when creating a training set has been improved by minimizing the number of joins across source feature tables.
XGBoost integration with PySpark now supports distributed training and GPU clusters
For details, see Use XGBoost on Databricks.
Major changes to Databricks Runtime ML Python environment
Conda environments, along with the %conda command, are removed. Databricks Runtime 9.0 ML is built with pip and virtualenv.
Custom images using Conda-based environments with Databricks Container Services will still be supported,
but will not have notebook-scoped library capabilities. Databricks recommends using virtualenv-based environments with
Databricks Container Services and %pip for all notebook-scoped libraries.
See Databricks Runtime 9.0 (EoS) for the major changes to the Databricks Runtime Python environment. For a full list of installed Python packages and their versions, see Python libraries.
Deprecations and unsupported features
In Databricks Runtime 9.0 ML, HorovodRunner does not support setting
np=0, wherenpis the number of parallel processes to use for the Horovod job.Databricks Runtime 9.0 ML includes r-base 4.1.0 with R graphics engine version 14. This is not supported by RStudio Server version 1.2.x.
nvprofis removed in Databricks Runtime 9.0 ML GPU.
System environment
The system environment in Databricks Runtime 9.0 ML differs from Databricks Runtime 9.0 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 8.1.0.77
NCCL 2.10.3
TensorRT 7.2.2
Libraries
The following sections list the libraries included in Databricks Runtime 9.0 ML that differ from those included in Databricks Runtime 9.0.
In this section:
Top-tier libraries
Databricks Runtime 9.0 ML includes the following top-tier libraries:
Python libraries
Databricks Runtime 9.0 ML uses Virtualenv for Python package management and includes many popular ML packages.
In addition to the packages specified in the following sections, Databricks Runtime 9.0 ML also includes the following packages:
hyperopt 0.2.5.db2
sparkdl 2.2.0_db1
feature_store 0.3.3
automl 1.1.1
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 |
bleach |
3.3.0 |
boto3 |
1.16.7 |
botocore |
1.19.7 |
Bottleneck |
1.3.2 |
cachetools |
4.2.2 |
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 |
Cython |
0.29.23 |
databricks-cli |
0.14.3 |
dbus-python |
1.2.16 |
decorator |
5.0.6 |
defusedxml |
0.7.1 |
dill |
0.3.2 |
diskcache |
5.2.1 |
distlib |
0.3.2 |
distro-info |
0.23ubuntu1 |
entrypoints |
0.3 |
ephem |
4.0.0.2 |
facets-overview |
1.0.0 |
filelock |
3.0.12 |
Flask |
1.1.2 |
flatbuffers |
1.12 |
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 |
1.34.1 |
gunicorn |
20.0.4 |
h5py |
3.1.0 |
hijri-converter |
2.1.3 |
holidays |
0.10.5.2 |
horovod |
0.22.1 |
htmlmin |
0.1.12 |
idna |
2.10 |
ImageHash |
4.2.1 |
ipykernel |
5.3.4 |
ipython |
7.22.0 |
ipython-genutils |
0.2.0 |
ipywidgets |
7.6.4 |
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 |
1.0.1 |
keras-nightly |
2.5.0.dev2021032900 |
Keras-Preprocessing |
1.1.2 |
kiwisolver |
1.3.1 |
koalas |
1.8.1 |
korean-lunar-calendar |
0.2.1 |
lightgbm |
3.1.1 |
llvmlite |
0.36.0 |
LunarCalendar |
0.0.9 |
Mako |
1.1.3 |
Markdown |
3.3.3 |
MarkupSafe |
1.1.1 |
matplotlib |
3.4.2 |
missingno |
0.5.0 |
mistune |
0.8.4 |
mleap |
0.17.0 |
mlflow-skinny |
1.19.0 |
multimethod |
1.4 |
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 |
0.53.1 |
numpy |
1.19.2 |
oauthlib |
3.1.0 |
opt-einsum |
3.3.0 |
packaging |
20.9 |
pandas |
1.2.4 |
pandas-profiling |
3.0.0 |
pandocfilters |
1.4.3 |
paramiko |
2.7.2 |
parso |
0.7.0 |
patsy |
0.5.1 |
petastorm |
0.11.1 |
pexpect |
4.8.0 |
phik |
0.12.0 |
pickleshare |
0.7.5 |
Pillow |
8.2.0 |
pip |
21.0.1 |
plotly |
4.14.3 |
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 |
pycparser |
2.20 |
pydantic |
1.8.2 |
Pygments |
2.8.1 |
PyGObject |
3.36.0 |
PyMeeus |
0.5.11 |
PyNaCl |
1.3.0 |
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 |
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 |
retrying |
1.3.3 |
rsa |
4.7.2 |
s3transfer |
0.3.7 |
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 |
0.39.0 |
simplejson |
3.17.2 |
six |
1.15.0 |
slicer |
0.0.7 |
smmap |
3.0.5 |
spark-tensorflow-distributor |
0.1.0 |
sqlparse |
0.4.1 |
ssh-import-id |
5.10 |
statsmodels |
0.12.2 |
tabulate |
0.8.7 |
tangled-up-in-unicode |
0.1.0 |
tensorboard |
2.5.0 |
tensorboard-data-server |
0.6.1 |
tensorboard-plugin-wit |
1.8.0 |
tensorflow-cpu |
2.5.0 |
tensorflow-estimator |
2.5.0 |
termcolor |
1.1.0 |
terminado |
0.9.4 |
testpath |
0.4.4 |
threadpoolctl |
2.1.0 |
torch |
1.9.0+cpu |
torchvision |
0.10.0+cpu |
tornado |
6.1 |
tqdm |
4.59.0 |
traitlets |
5.0.5 |
typing-extensions |
3.7.4.3 |
ujson |
4.0.2 |
unattended-upgrades |
0.1 |
urllib3 |
1.25.11 |
virtualenv |
20.4.1 |
visions |
0.7.1 |
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 |
1.4.2 |
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 |
bleach |
3.3.0 |
boto3 |
1.16.7 |
botocore |
1.19.7 |
Bottleneck |
1.3.2 |
cachetools |
4.2.2 |
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 |
Cython |
0.29.23 |
databricks-cli |
0.14.3 |
dbus-python |
1.2.16 |
decorator |
5.0.6 |
defusedxml |
0.7.1 |
dill |
0.3.2 |
diskcache |
5.2.1 |
distlib |
0.3.2 |
distro-info |
0.23ubuntu1 |
entrypoints |
0.3 |
ephem |
4.0.0.2 |
facets-overview |
1.0.0 |
filelock |
3.0.12 |
Flask |
1.1.2 |
flatbuffers |
1.12 |
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 |
1.34.1 |
gunicorn |
20.0.4 |
h5py |
3.1.0 |
hijri-converter |
2.1.3 |
holidays |
0.10.5.2 |
horovod |
0.22.1 |
htmlmin |
0.1.12 |
idna |
2.10 |
ImageHash |
4.2.1 |
ipykernel |
5.3.4 |
ipython |
7.22.0 |
ipython-genutils |
0.2.0 |
ipywidgets |
7.6.4 |
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 |
1.0.1 |
keras-nightly |
2.5.0.dev2021032900 |
Keras-Preprocessing |
1.1.2 |
kiwisolver |
1.3.1 |
koalas |
1.8.1 |
korean-lunar-calendar |
0.2.1 |
lightgbm |
3.1.1 |
llvmlite |
0.36.0 |
LunarCalendar |
0.0.9 |
Mako |
1.1.3 |
Markdown |
3.3.3 |
MarkupSafe |
1.1.1 |
matplotlib |
3.4.2 |
missingno |
0.5.0 |
mistune |
0.8.4 |
mleap |
0.17.0 |
mlflow-skinny |
1.19.0 |
multimethod |
1.4 |
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 |
0.53.1 |
numpy |
1.19.2 |
oauthlib |
3.1.0 |
opt-einsum |
3.3.0 |
packaging |
20.9 |
pandas |
1.2.4 |
pandas-profiling |
3.0.0 |
pandocfilters |
1.4.3 |
paramiko |
2.7.2 |
parso |
0.7.0 |
patsy |
0.5.1 |
petastorm |
0.11.1 |
pexpect |
4.8.0 |
phik |
0.12.0 |
pickleshare |
0.7.5 |
Pillow |
8.2.0 |
pip |
21.0.1 |
plotly |
4.14.3 |
prometheus-client |
0.11.0 |
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 |
pycparser |
2.20 |
pydantic |
1.8.2 |
Pygments |
2.8.1 |
PyGObject |
3.36.0 |
PyMeeus |
0.5.11 |
PyNaCl |
1.3.0 |
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 |
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 |
retrying |
1.3.3 |
rsa |
4.7.2 |
s3transfer |
0.3.7 |
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 |
0.39.0 |
simplejson |
3.17.2 |
six |
1.15.0 |
slicer |
0.0.7 |
smmap |
3.0.5 |
spark-tensorflow-distributor |
0.1.0 |
sqlparse |
0.4.1 |
ssh-import-id |
5.10 |
statsmodels |
0.12.2 |
tabulate |
0.8.7 |
tangled-up-in-unicode |
0.1.0 |
tensorboard |
2.5.0 |
tensorboard-data-server |
0.6.1 |
tensorboard-plugin-wit |
1.8.0 |
tensorflow |
2.5.0 |
tensorflow-estimator |
2.5.0 |
termcolor |
1.1.0 |
terminado |
0.9.4 |
testpath |
0.4.4 |
threadpoolctl |
2.1.0 |
torch |
1.9.0+cu111 |
torchvision |
0.10.0+cu111 |
tornado |
6.1 |
tqdm |
4.59.0 |
traitlets |
5.0.5 |
typing-extensions |
3.7.4.3 |
ujson |
4.0.2 |
unattended-upgrades |
0.1 |
urllib3 |
1.25.11 |
virtualenv |
20.4.1 |
visions |
0.7.1 |
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 |
1.4.2 |
R libraries
The R libraries are identical to the R Libraries in Databricks Runtime 9.0.
Java and Scala libraries (Scala 2.12 cluster)
In addition to Java and Scala libraries in Databricks Runtime 9.0, Databricks Runtime 9.0 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 |
0.17.0-4882dc3 |
ml.dmlc |
xgboost4j-spark_2.12 |
1.4.1 |
ml.dmlc |
xgboost4j_2.12 |
1.4.1 |
org.graphframes |
graphframes_2.12 |
0.8.1-db2-spark3.1 |
org.mlflow |
mlflow-client |
1.19.0 |
org.mlflow |
mlflow-spark |
1.19.0 |
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 |
0.17.0-4882dc3 |
ml.dmlc |
xgboost4j-gpu_2.12 |
1.4.1 |
ml.dmlc |
xgboost4j-spark-gpu_2.12 |
1.4.1 |
org.graphframes |
graphframes_2.12 |
0.8.1-db2-spark3.1 |
org.mlflow |
mlflow-client |
1.19.0 |
org.mlflow |
mlflow-spark |
1.19.0 |
org.scala-lang.modules |
scala-java8-compat_2.12 |
0.8.0 |
org.tensorflow |
spark-tensorflow-connector_2.12 |
1.15.0 |