Databricks Runtime 7.4 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 November 2020.

Databricks Runtime 7.4 for Machine Learning provides a ready-to-go environment for machine learning and data science based on Databricks Runtime 7.4 (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.

New features and major changes

Databricks Runtime 7.4 ML is built on top of Databricks Runtime 7.4. For information on what’s new in Databricks Runtime 7.4, including Apache Spark MLlib and SparkR, see the Databricks Runtime 7.4 (EoS) release notes.

Major changes to Databricks Runtime ML Scala environment

XGBoost is upgraded to 1.2.0. This version allows XGBoost to use GPUs on Spark clusters to improve training speed. There are several other changes, including some breaking changes. For more information, review the XGBoost 1.2.0 release notes.

Specifically, on CPU clusters, xgboost4j_2.12 and xgboost4j-spark_2.12 are upgraded from 1.0.0 to 1.2.0. On GPU clusters, these packages are removed, and version 1.2.0 of xgboost4j-gpu_2.12 and xgboost4j-spark-gpu_2.12 are installed instead.

GraphFrames is upgraded from 0.8.0-db2-spark3.0 to 0.8.1-db1-spark3.0.

Major changes to Databricks Runtime ML Python environment

See Databricks Runtime 7.4 (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.

Python packages upgraded

• cloudpickle 1.3.0 -> 1.4.1
• databricks-cli 0.11.0 -> 0.13.0
• horovod 0.19.5 -> 0.20.3
• petastorm 0.9.5 -> 0.9.6
• plotly 4.9.0 -> 4.10.0
• sparkdl 2.1.0-db1 -> 2.1.0-db2
• tensorflow 2.3.0 -> 2.3.1
• xgboost 1.1.1 -> 1.2.0

Improvements

• Some pickling issues that caused PyTorch failures were fixed in Databricks Runtime 7.4. For details, see the Databricks Runtime 7.4 release notes.
• Horovod 0.20.3 supports using the horovod.spark package on Azure Databricks. See horovod.spark: distributed deep learning with Horovod.

System environment

The system environment in Databricks Runtime 7.4 ML differs from Databricks Runtime 7.4 as follows:

• Open MPI is upgraded from 4.0.4 to 4.0.5
• DBUtils: Databricks Runtime ML does not contain Library utility (dbutils.library) (legacy). You can use %pip and %conda commands instead. See Notebook-scoped Python libraries.
• For GPU clusters, Databricks Runtime ML includes the following NVIDIA GPU libraries:
  • CUDA 10.1 Update 2
  • cuDNN 7.6.5
  • NCCL 2.7.3
  • TensorRT 6.0.1

Libraries

The following sections list the libraries included in Databricks Runtime 7.4 ML that differ from those included in Databricks Runtime 7.4.

In this section:

• Top-tier libraries
• Python libraries
• R libraries
• Java and Scala libraries (Scala 2.12 cluster)

Top-tier libraries

Databricks Runtime 7.4 ML includes the following top-tier libraries:

• GraphFrames
• Horovod and HorovodRunner
• MLflow
• PyTorch
• spark-tensorflow-connector
• TensorFlow
• TensorBoard

Python libraries

Databricks Runtime 7.4 ML uses Conda for Python package management and includes many popular ML packages.

In addition to the packages specified in the Conda environments in the following sections, Databricks Runtime 7.4 ML also installs the following packages:

• hyperopt 0.2.4.db2
• sparkdl 2.1.0-db2

Python libraries on CPU clusters

name: databricks-ml
channels:
  - pytorch
  - defaults
dependencies:
  - _libgcc_mutex=0.1=main
  - absl-py=0.9.0=py37_0
  - asn1crypto=1.3.0=py37_1
  - astor=0.8.0=py37_0
  - backcall=0.1.0=py37_0
  - backports=1.0=py_2
  - bcrypt=3.2.0=py37h7b6447c_0
  - blas=1.0=mkl
  - blinker=1.4=py37_0
  - boto3=1.12.0=py_0
  - botocore=1.15.0=py_0
  - c-ares=1.16.1=h7b6447c_0
  - ca-certificates=2020.7.22=0
  - cachetools=4.1.1=py_0
  - certifi=2020.6.20=py37_0
  - cffi=1.14.0=py37h2e261b9_0
  - chardet=3.0.4=py37_1003
  - click=7.0=py37_0
  - cloudpickle=1.4.1=py_0
  - configparser=3.7.4=py37_0
  - cpuonly=1.0=0
  - cryptography=2.8=py37h1ba5d50_0
  - cycler=0.10.0=py37_0
  - cython=0.29.15=py37he6710b0_0
  - decorator=4.4.1=py_0
  - dill=0.3.1.1=py37_1
  - docutils=0.15.2=py37_0
  - entrypoints=0.3=py37_0
  - flask=1.1.1=py_1
  - freetype=2.9.1=h8a8886c_1
  - future=0.18.2=py37_1
  - gast=0.3.3=py_0
  - gitdb=4.0.5=py_0
  - gitpython=3.1.0=py_0
  - google-auth=1.11.2=py_0
  - google-auth-oauthlib=0.4.1=py_2
  - google-pasta=0.2.0=py_0
  - grpcio=1.27.2=py37hf8bcb03_0
  - gunicorn=20.0.4=py37_0
  - h5py=2.10.0=py37h7918eee_0
  - hdf5=1.10.4=hb1b8bf9_0
  - icu=58.2=he6710b0_3
  - idna=2.8=py37_0
  - intel-openmp=2020.0=166
  - ipykernel=5.1.4=py37h39e3cac_0
  - ipython=7.12.0=py37h5ca1d4c_0
  - ipython_genutils=0.2.0=py37_0
  - isodate=0.6.0=py_1
  - itsdangerous=1.1.0=py37_0
  - jedi=0.17.2=py37_0
  - jinja2=2.11.1=py_0
  - jmespath=0.10.0=py_0
  - joblib=0.14.1=py_0
  - jpeg=9b=h024ee3a_2
  - jupyter_client=5.3.4=py37_0
  - jupyter_core=4.6.1=py37_0
  - kiwisolver=1.1.0=py37he6710b0_0
  - krb5=1.16.4=h173b8e3_0
  - ld_impl_linux-64=2.33.1=h53a641e_7
  - libedit=3.1.20181209=hc058e9b_0
  - libffi=3.2.1=hf484d3e_1007
  - libgcc-ng=9.1.0=hdf63c60_0
  - libgfortran-ng=7.3.0=hdf63c60_0
  - libpng=1.6.37=hbc83047_0
  - libpq=11.2=h20c2e04_0
  - libprotobuf=3.11.4=hd408876_0
  - libsodium=1.0.16=h1bed415_0
  - libstdcxx-ng=9.1.0=hdf63c60_0
  - libtiff=4.1.0=h2733197_0
  - lightgbm=2.3.0=py37he6710b0_0
  - lz4-c=1.8.1.2=h14c3975_0
  - mako=1.1.2=py_0
  - markdown=3.1.1=py37_0
  - markupsafe=1.1.1=py37h14c3975_1
  - matplotlib-base=3.1.3=py37hef1b27d_0
  - mkl=2020.0=166
  - mkl-service=2.3.0=py37he904b0f_0
  - mkl_fft=1.0.15=py37ha843d7b_0
  - mkl_random=1.1.0=py37hd6b4f25_0
  - ncurses=6.2=he6710b0_1
  - networkx=2.4=py_1
  - ninja=1.10.1=py37hfd86e86_0
  - nltk=3.4.5=py37_0
  - numpy=1.18.1=py37h4f9e942_0
  - numpy-base=1.18.1=py37hde5b4d6_1
  - oauthlib=3.1.0=py_0
  - olefile=0.46=py37_0
  - openssl=1.1.1h=h7b6447c_0
  - packaging=20.1=py_0
  - pandas=1.0.1=py37h0573a6f_0
  - paramiko=2.7.1=py_0
  - parso=0.7.0=py_0
  - patsy=0.5.1=py37_0
  - pexpect=4.8.0=py37_1
  - pickleshare=0.7.5=py37_1001
  - pillow=7.0.0=py37hb39fc2d_0
  - pip=20.0.2=py37_3
  - plotly=4.10.0=py_0
  - prompt_toolkit=3.0.3=py_0
  - protobuf=3.11.4=py37he6710b0_0
  - psutil=5.6.7=py37h7b6447c_0
  - psycopg2=2.8.4=py37h1ba5d50_0
  - ptyprocess=0.6.0=py37_0
  - pyasn1=0.4.8=py_0
  - pyasn1-modules=0.2.8=py_0
  - pycparser=2.19=py37_0
  - pygments=2.5.2=py_0
  - pyjwt=1.7.1=py37_0
  - pynacl=1.3.0=py37h7b6447c_0
  - pyodbc=4.0.30=py37he6710b0_0
  - pyopenssl=19.1.0=py_1
  - pyparsing=2.4.6=py_0
  - pysocks=1.7.1=py37_1
  - python=3.7.6=h0371630_2
  - python-dateutil=2.8.1=py_0
  - python-editor=1.0.4=py_0
  - pytorch=1.6.0=py3.7_cpu_0
  - pytz=2019.3=py_0
  - pyzmq=18.1.1=py37he6710b0_0
  - readline=7.0=h7b6447c_5
  - requests=2.22.0=py37_1
  - requests-oauthlib=1.3.0=py_0
  - retrying=1.3.3=py37_2
  - rsa=4.0=py_0
  - s3transfer=0.3.3=py37_1
  - scikit-learn=0.22.1=py37hd81dba3_0
  - scipy=1.4.1=py37h0b6359f_0
  - setuptools=45.2.0=py37_0
  - simplejson=3.17.0=py37h7b6447c_0
  - six=1.14.0=py37_0
  - smmap=3.0.4=py_0
  - sqlite=3.31.1=h62c20be_1
  - sqlparse=0.3.0=py_0
  - statsmodels=0.11.0=py37h7b6447c_0
  - tabulate=0.8.3=py37_0
  - tenacity=6.2.0=py37_0
  - tk=8.6.8=hbc83047_0
  - torchvision=0.7.0=py37_cpu
  - tornado=6.0.3=py37h7b6447c_3
  - tqdm=4.42.1=py_0
  - traitlets=4.3.3=py37_0
  - unixodbc=2.3.7=h14c3975_0
  - urllib3=1.25.8=py37_0
  - wcwidth=0.1.8=py_0
  - websocket-client=0.56.0=py37_0
  - werkzeug=1.0.0=py_0
  - wheel=0.34.2=py37_0
  - wrapt=1.11.2=py37h7b6447c_0
  - xz=5.2.4=h14c3975_4
  - zeromq=4.3.1=he6710b0_3
  - zlib=1.2.11=h7b6447c_3
  - zstd=1.3.7=h0b5b093_0
  - pip:
    - astunparse==1.6.3
    - azure-core==1.8.2
    - azure-storage-blob==12.5.0
    - databricks-cli==0.13.0
    - diskcache==5.0.3
    - docker==4.3.1
    - gorilla==0.3.0
    - horovod==0.20.3
    - joblibspark==0.2.0
    - keras-preprocessing==1.1.2
    - koalas==1.3.0
    - mleap==0.16.1
    - mlflow==1.11.0
    - msrest==0.6.19
    - opt-einsum==3.3.0
    - petastorm==0.9.6
    - pyarrow==1.0.1
    - pyyaml==5.3.1
    - querystring-parser==1.2.4
    - seaborn==0.10.0
    - spark-tensorflow-distributor==0.1.0
    - tensorboard==2.3.0
    - tensorboard-plugin-wit==1.7.0
    - tensorflow-cpu==2.3.1
    - tensorflow-estimator==2.3.0
    - termcolor==1.1.0
    - xgboost==1.2.0
prefix: /databricks/conda/envs/databricks-ml

Python libraries on GPU clusters

name: databricks-ml-gpu
channels:
  - pytorch
  - defaults
dependencies:
  - _libgcc_mutex=0.1=main
  - absl-py=0.9.0=py37_0
  - asn1crypto=1.3.0=py37_1
  - astor=0.8.0=py37_0
  - backcall=0.1.0=py37_0
  - backports=1.0=py_2
  - bcrypt=3.2.0=py37h7b6447c_0
  - blas=1.0=mkl
  - blinker=1.4=py37_0
  - boto3=1.12.0=py_0
  - botocore=1.15.0=py_0
  - c-ares=1.16.1=h7b6447c_0
  - ca-certificates=2020.7.22=0
  - cachetools=4.1.1=py_0
  - certifi=2020.6.20=py37_0
  - cffi=1.14.0=py37h2e261b9_0
  - chardet=3.0.4=py37_1003
  - click=7.0=py37_0
  - cloudpickle=1.4.1=py_0
  - configparser=3.7.4=py37_0
  - cryptography=2.8=py37h1ba5d50_0
  - cudatoolkit=10.1.243=h6bb024c_0
  - cycler=0.10.0=py37_0
  - cython=0.29.15=py37he6710b0_0
  - decorator=4.4.1=py_0
  - dill=0.3.1.1=py37_1
  - docutils=0.15.2=py37_0
  - entrypoints=0.3=py37_0
  - flask=1.1.1=py_1
  - freetype=2.9.1=h8a8886c_1
  - future=0.18.2=py37_1
  - gast=0.3.3=py_0
  - gitdb=4.0.5=py_0
  - gitpython=3.1.0=py_0
  - google-auth=1.11.2=py_0
  - google-auth-oauthlib=0.4.1=py_2
  - google-pasta=0.2.0=py_0
  - grpcio=1.27.2=py37hf8bcb03_0
  - gunicorn=20.0.4=py37_0
  - h5py=2.10.0=py37h7918eee_0
  - hdf5=1.10.4=hb1b8bf9_0
  - icu=58.2=he6710b0_3
  - idna=2.8=py37_0
  - intel-openmp=2020.0=166
  - ipykernel=5.1.4=py37h39e3cac_0
  - ipython=7.12.0=py37h5ca1d4c_0
  - ipython_genutils=0.2.0=py37_0
  - isodate=0.6.0=py_1
  - itsdangerous=1.1.0=py37_0
  - jedi=0.17.2=py37_0
  - jinja2=2.11.1=py_0
  - jmespath=0.10.0=py_0
  - joblib=0.14.1=py_0
  - jpeg=9b=h024ee3a_2
  - jupyter_client=5.3.4=py37_0
  - jupyter_core=4.6.1=py37_0
  - kiwisolver=1.1.0=py37he6710b0_0
  - krb5=1.16.4=h173b8e3_0
  - ld_impl_linux-64=2.33.1=h53a641e_7
  - libedit=3.1.20181209=hc058e9b_0
  - libffi=3.2.1=hf484d3e_1007
  - libgcc-ng=9.1.0=hdf63c60_0
  - libgfortran-ng=7.3.0=hdf63c60_0
  - libpng=1.6.37=hbc83047_0
  - libpq=11.2=h20c2e04_0
  - libprotobuf=3.11.4=hd408876_0
  - libsodium=1.0.16=h1bed415_0
  - libstdcxx-ng=9.1.0=hdf63c60_0
  - libtiff=4.1.0=h2733197_0
  - lightgbm=2.3.0=py37he6710b0_0
  - lz4-c=1.8.1.2=h14c3975_0
  - mako=1.1.2=py_0
  - markdown=3.1.1=py37_0
  - markupsafe=1.1.1=py37h14c3975_1
  - matplotlib-base=3.1.3=py37hef1b27d_0
  - mkl=2020.0=166
  - mkl-service=2.3.0=py37he904b0f_0
  - mkl_fft=1.0.15=py37ha843d7b_0
  - mkl_random=1.1.0=py37hd6b4f25_0
  - ncurses=6.2=he6710b0_1
  - networkx=2.4=py_1
  - ninja=1.10.1=py37hfd86e86_0
  - nltk=3.4.5=py37_0
  - numpy=1.18.1=py37h4f9e942_0
  - numpy-base=1.18.1=py37hde5b4d6_1
  - oauthlib=3.1.0=py_0
  - olefile=0.46=py37_0
  - openssl=1.1.1h=h7b6447c_0
  - packaging=20.1=py_0
  - pandas=1.0.1=py37h0573a6f_0
  - paramiko=2.7.1=py_0
  - parso=0.7.0=py_0
  - patsy=0.5.1=py37_0
  - pexpect=4.8.0=py37_1
  - pickleshare=0.7.5=py37_1001
  - pillow=7.0.0=py37hb39fc2d_0
  - pip=20.0.2=py37_3
  - plotly=4.10.0=py_0
  - prompt_toolkit=3.0.3=py_0
  - protobuf=3.11.4=py37he6710b0_0
  - psutil=5.6.7=py37h7b6447c_0
  - psycopg2=2.8.4=py37h1ba5d50_0
  - ptyprocess=0.6.0=py37_0
  - pyasn1=0.4.8=py_0
  - pyasn1-modules=0.2.8=py_0
  - pycparser=2.19=py37_0
  - pygments=2.5.2=py_0
  - pyjwt=1.7.1=py37_0
  - pynacl=1.3.0=py37h7b6447c_0
  - pyodbc=4.0.30=py37he6710b0_0
  - pyopenssl=19.1.0=py_1
  - pyparsing=2.4.6=py_0
  - pysocks=1.7.1=py37_1
  - python=3.7.6=h0371630_2
  - python-dateutil=2.8.1=py_0
  - python-editor=1.0.4=py_0
  - pytorch=1.6.0=py3.7_cuda10.1.243_cudnn7.6.3_0
  - pytz=2019.3=py_0
  - pyzmq=18.1.1=py37he6710b0_0
  - readline=7.0=h7b6447c_5
  - requests=2.22.0=py37_1
  - requests-oauthlib=1.3.0=py_0
  - retrying=1.3.3=py37_2
  - rsa=4.0=py_0
  - s3transfer=0.3.3=py37_1
  - scikit-learn=0.22.1=py37hd81dba3_0
  - scipy=1.4.1=py37h0b6359f_0
  - setuptools=45.2.0=py37_0
  - simplejson=3.17.0=py37h7b6447c_0
  - six=1.14.0=py37_0
  - smmap=3.0.4=py_0
  - sqlite=3.31.1=h62c20be_1
  - sqlparse=0.3.0=py_0
  - statsmodels=0.11.0=py37h7b6447c_0
  - tabulate=0.8.3=py37_0
  - tenacity=6.2.0=py37_0
  - tk=8.6.8=hbc83047_0
  - torchvision=0.7.0=py37_cu101
  - tornado=6.0.3=py37h7b6447c_3
  - tqdm=4.42.1=py_0
  - traitlets=4.3.3=py37_0
  - unixodbc=2.3.7=h14c3975_0
  - urllib3=1.25.8=py37_0
  - wcwidth=0.1.8=py_0
  - websocket-client=0.56.0=py37_0
  - werkzeug=1.0.0=py_0
  - wheel=0.34.2=py37_0
  - wrapt=1.11.2=py37h7b6447c_0
  - xz=5.2.4=h14c3975_4
  - zeromq=4.3.1=he6710b0_3
  - zlib=1.2.11=h7b6447c_3
  - zstd=1.3.7=h0b5b093_0
  - pip:
    - astunparse==1.6.3
    - azure-core==1.8.2
    - azure-storage-blob==12.5.0
    - databricks-cli==0.13.0
    - diskcache==5.0.3
    - docker==4.3.1
    - gorilla==0.3.0
    - horovod==0.20.3
    - joblibspark==0.2.0
    - keras-preprocessing==1.1.2
    - koalas==1.3.0
    - mleap==0.16.1
    - mlflow==1.11.0
    - msrest==0.6.19
    - opt-einsum==3.3.0
    - petastorm==0.9.6
    - pyarrow==1.0.1
    - pyyaml==5.3.1
    - querystring-parser==1.2.4
    - seaborn==0.10.0
    - spark-tensorflow-distributor==0.1.0
    - tensorboard==2.3.0
    - tensorboard-plugin-wit==1.7.0
    - tensorflow==2.3.1
    - tensorflow-estimator==2.3.0
    - termcolor==1.1.0
    - xgboost==1.2.0
prefix: /databricks/conda/envs/databricks-ml-gpu

Spark packages containing Python modules

Spark Package	Python Module	Version
graphframes	graphframes	0.8.1-db1-spark3.0

R libraries

The R libraries are identical to the R Libraries in Databricks Runtime 7.4.

Java and Scala libraries (Scala 2.12 cluster)

In addition to Java and Scala libraries in Databricks Runtime 7.4, Databricks Runtime 7.4 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	0.17.3-4882dc3
ml.dmlc	xgboost4j-spark_2.12	1.2.0
ml.dmlc	xgboost4j_2.12	1.2.0
org.mlflow	mlflow-client	1.11.0
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	0.17.3-4882dc3
ml.dmlc	xgboost4j-spark-gpu_2.12	1.2.0
ml.dmlc	xgboost4j-gpu_2.12	1.2.0
org.mlflow	mlflow-client	1.11.0
org.scala-lang.modules	scala-java8-compat_2.12	0.8.0
org.tensorflow	spark-tensorflow-connector_2.12	1.15.0