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Databricks Runtime 9.1 LTS for Machine Learning

  • Artikkeli

Databricks released this image and declared it Long Term Support (LTS) in September 2021.

Databricks Runtime 9.1 LTS for Machine Learning provides a ready-to-go environment for machine learning and data science based on Databricks Runtime 9.1 LTS. 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.

Note

LTS means this version is under long-term support. See Databricks Runtime LTS version lifecycle.

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

Tip

To see release notes for Databricks Runtime versions that have reached end-of-support (EoS), see End-of-support Databricks Runtime release notes. The EoS Databricks Runtime versions have been retired and might not be updated.

New features and improvements

AutoML

The following improvements are available in Databricks Runtime 9.1 LTS ML and above.

AutoML supports larger datasets by sampling

AutoML now samples datasets that might exceed memory constraints, allowing it to run on larger datasets with less risk of out-of-memory errors. For details, see Sampling large datasets.

AutoML preprocesses columns based on semantic type

AutoML detects certain columns that have a semantic type that differs from their Spark or pandas data type. AutoML then converts and applies data preprocessing steps based on the detected semantic type. Specifically, AutoML performs the following conversions:

  • String and integer columns that represent date or timestamp data are converted to a timestamp type.
  • String columns that represent numeric data are converted to a numeric type.

Improvements to AutoML generated notebooks

Preprocessing steps for date and timestamp columns are now incorporated in the databricks-automl-runtime package, simplifying the notebooks generated by AutoML training. databricks-automl-runtime is included in Databricks Runtime 9.1 LTS ML and above, and is also available on PyPI.

Feature store

The following improvements are available in Databricks Runtime 9.1 LTS ML and above.

  • When you create a TrainingSet, you can now set label=None to support unsupervised learning applications.
  • You can now specify more than one feature in a single FeatureLookup.
  • You can now specify a custom path for feature tables. Use the path parameter in create_feature_table(). The default is the database location.
  • New supported PySpark data types: ArrayType and ShortType.

Mlflow

The following improvements are available starting in Mlflow version 1.20.2, which is included in Databricks Runtime 9.1 LTS ML.

  • Autologging for scikit-learn now records post-training metrics whenever a scikit-learn evaluation API, such as sklearn.metrics.mean_squared_error, is called.
  • Autologging for PySpark ML now records post-training metrics whenever a model evaluation API, such as Evaluator.evaluate(), is called.
  • mlflow.*.log_model and mlflow.*.save_model now have pip_requirements and extra_pip_requirements arguments so that you can directly specify the pip requirements of the model to log or save.
  • mlflow.*.log_model and mlflow.*.save_model now automatically infer the pip requirements of the model to log or save based on the current software environment.
  • stdMetrics entries are now recorded as training metrics during PySpark CrossValidator autologging.
  • PyTorch Lightning autologging now supports distributed execution.

Databricks Autologging (Public Preview)

The Databricks Autologging Public Preview has been expanded to new 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.

Major changes to Databricks Runtime ML Python environment

Python packages upgraded

  • automl 1.1.1 => 1.2.1
  • feature_store 0.3.3 => 0.3.4.1
  • holidays 0.10.5.2 => 0.11.2
  • keras 2.5.0 => 2.6.0
  • mlflow 1.19.0 => 1.20.2
  • petastorm 0.11.1 => 0.11.2
  • plotly 4.14.3 => 5.1.0
  • spark-tensorflow-distributor 0.1.0 => 1.0.0
  • sparkdl 2.2.0_db1 => 2.2.0_db3
  • tensorboard 2.5.0 => 2.6.0
  • tensorflow 2.5.0 => 2.6.0

Python packages added

  • databricks-automl-runtime 0.1.0

System environment

The system environment in Databricks Runtime 9.1 LTS ML differs from Databricks Runtime 9.1 LTS as follows:

Libraries

The following sections list the libraries included in Databricks Runtime 9.1 LTS ML that differ from those included in Databricks Runtime 9.1 LTS.

In this section:

Top-tier libraries

Databricks Runtime 9.1 LTS ML includes the following top-tier libraries:

Python libraries

Databricks Runtime 9.1 LTS 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 9.1 LTS ML also includes the following packages:

  • hyperopt 0.2.5.db2
  • sparkdl 2.2.0_db3
  • feature_store 0.3.4.1
  • automl 1.2.1

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
clang 5.0 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-automl-runtime 0.1.0 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 1.39.0
gunicorn 20.0.4 h5py 3.1.0 hijri-converter 2.2.1
holidays 0.11.2 horovod 0.22.1 htmlmin 0.1.12
idna 2.10 ImageHash 4.2.1 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 2.6.0 Keras-Preprocessing 1.1.2
kiwisolver 1.3.1 koalas 1.8.1 korean-lunar-calendar 0.2.1
lightgbm 3.1.1 llvmlite 0.37.0 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 1.20.2 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 0.54.0 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 0.11.2 pexpect 4.8.0 phik 0.12.0
pickleshare 0.7.5 Pillow 8.2.0 pip 21.0.1
plotly 5.1.0 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 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 1.0.0 sqlparse 0.4.1 ssh-import-id 5.10
statsmodels 0.12.2 tabulate 0.8.7 tangled-up-in-unicode 0.1.0
tenacity 6.2.0 tensorboard 2.6.0 tensorboard-data-server 0.6.1
tensorboard-plugin-wit 1.8.0 tensorflow-cpu 2.6.0 tensorflow-estimator 2.6.0
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 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 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
clang 5.0 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-automl-runtime 0.1.0 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 1.39.0
gunicorn 20.0.4 h5py 3.1.0 hijri-converter 2.2.1
holidays 0.11.2 horovod 0.22.1 htmlmin 0.1.12
idna 2.10 ImageHash 4.2.1 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 2.6.0 Keras-Preprocessing 1.1.2
kiwisolver 1.3.1 koalas 1.8.1 korean-lunar-calendar 0.2.1
lightgbm 3.1.1 llvmlite 0.37.0 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 1.20.2 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 0.54.0 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 0.11.2 pexpect 4.8.0 phik 0.12.0
pickleshare 0.7.5 Pillow 8.2.0 pip 21.0.1
plotly 5.1.0 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 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 1.0.0
sqlparse 0.4.1 ssh-import-id 5.10 statsmodels 0.12.2
tabulate 0.8.7 tangled-up-in-unicode 0.1.0 tenacity 6.2.0
tensorboard 2.6.0 tensorboard-data-server 0.6.1 tensorboard-plugin-wit 1.8.0
tensorflow 2.6.0 tensorflow-estimator 2.6.0 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 zipp 3.4.1

Spark packages containing Python modules

Spark Package Python Module Version
graphframes graphframes 0.8.1-db3-spark3.1

R libraries

The R libraries are identical to the R Libraries in Databricks Runtime 9.1 LTS.

Java and Scala libraries (Scala 2.12 cluster)

In addition to Java and Scala libraries in Databricks Runtime 9.1 LTS, Databricks Runtime 9.1 LTS 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 1.20.2
org.mlflow mlflow-spark 1.20.2
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 1.20.2
org.mlflow mlflow-spark 1.20.2
org.scala-lang.modules scala-java8-compat_2.12 0.8.0
org.tensorflow spark-tensorflow-connector_2.12 1.15.0