Databricks Runtime 7.1 MLDatabricks Runtime 7.1 ML

Databricks 于 2020 年 7 月发布了此映像。Databricks released this image in July 2020.

Databricks Runtime 7.1 ML 基于 Databricks Runtime 7.1 为机器学习和数据科学提供了随时可用的环境。Databricks Runtime 7.1 ML provides a ready-to-go environment for machine learning and data science based on Databricks Runtime 7.1. Databricks Runtime ML 包含许多常用的机器学习库,包括 TensorFlow、PyTorch 和 XGBoost。Databricks Runtime ML contains many popular machine learning libraries, including TensorFlow, PyTorch, and XGBoost. 它还支持使用 Horovod 进行分布式深度学习训练。It also supports distributed deep learning training using Horovod.

有关详细信息,包括有关创建 Databricks Runtime ML 群集的说明,请参阅用于机器学习的 Databricks RuntimeFor more information, including instructions for creating a Databricks Runtime ML cluster, see Databricks Runtime for Machine Learning.

新功能和主要更改New features and major changes

Databricks Runtime 7.1 ML 是基于 Databricks Runtime 7.1 构建的。Databricks Runtime 7.1 ML is built on top of Databricks Runtime 7.1. 若要了解 Databricks Runtime 7.1 中的新增功能,包括 Apache Spark MLlib 和 SparkR,请参阅 Databricks Runtime 7.1 发行说明。For information on what’s new in Databricks Runtime 7.1, including Apache Spark MLlib and SparkR, see the Databricks Runtime 7.1 release notes.

Databricks Runtime ML Python 环境的主要更改Major changes to Databricks Runtime ML Python environment

本部分介绍已安装的 Databricks Runtime ML Python 环境与 Databricks Runtime 7.0 ML 相比有哪些主要更改。This section describes the major changes to the installed Databricks Runtime ML Python environment compared to Databricks Runtime 7.0 ML. 你还应该查看 Databricks Runtime 7.1 中 Databricks Runtime Python 环境的主要更改。You should also review the major changes to the Databricks Runtime Python environment in Databricks Runtime 7.1. 有关已安装的 Python 包及其版本的完整列表,请参阅 Python 库For a full list of installed Python packages and their versions, see Python libraries.

pip 和 conda magic 命令现已默认启用(公共预览版)pip and conda magic commands now enabled by default (Public Preview)

在 Databricks Runtime ML 6.4 及更高版本中,通过群集配置设置提供了 %pip%conda magic 命令。In Databricks Runtime ML 6.4 and above, %pip and %conda magic commands were available via a cluster configuation setting. 现在这些命令已默认启用。These commands are now enabled by default. 有关详细信息,请参阅作用域为笔记本的 Python 库For more information, see Notebook-scoped Python libraries.

升级的 Python 包Python packages upgraded

  • pillow 7.0.0 -> 7.1.0pillow 7.0.0 -> 7.1.0
  • pytorch 1.5.0 -> 1.5.1pytorch 1.5.0 -> 1.5.1
  • torchvision 0.6.0 -> 0.6.1torchvision 0.6.0 -> 0.6.1
  • horovod 0.19.1 -> 0.19.5horovod 0.19.1 -> 0.19.5
  • mlflow 1.8.0 -> 1.9.1mlflow 1.8.0 -> 1.9.1

添加的 Python 包Python packages added

  • spark-tensorflow-distributor:0.1.0spark-tensorflow-distributor: 0.1.0

对 ML Spark 包、Java 和 Scala 库的更改Changes to ML Spark packages, Java and Scala libraries

以下包已升级:The following packages are upgraded:

  • mlflow-client:1.9.1mlflow-client: 1.9.1

系统环境System environment

Databricks Runtime 7.1 ML 中的系统环境在以下方面不同于 Databricks Runtime 7.1:The system environment in Databricks Runtime 7.1 ML differs from Databricks Runtime 7.1 as follows:

  • DBUtils:Databricks Runtime ML 未包含库实用工具DBUtils: Databricks Runtime ML does not contain Library utilities. 可改用 %pip%conda 命令。You can use %pip and %conda commands instead. 请参阅作用域为笔记本的 Python 库See Notebook-scoped Python libraries.
  • 对于 GPU 群集,以下 NVIDIA GPU 库不同:For GPU clusters, the following NVIDIA GPU libraries:
    • CUDA 10.1 Update 2CUDA 10.1 Update 2
    • cuDNN 7.6.5cuDNN 7.6.5
    • NCCL 2.7.3NCCL 2.7.3
    • TensorRT 6.0.1TensorRT 6.0.1

Libraries

以下部分列出了 Databricks Runtime 7.1 ML 中包含的库,这些库不同于 Databricks Runtime 7.1 中包含的库。The following sections list the libraries included in Databricks Runtime 7.1 ML that differ from those included in Databricks Runtime 7.1.

本节内容:In this section:

顶层库Top-tier libraries

Databricks Runtime 7.1 ML 包含以下顶层Databricks Runtime 7.1 ML includes the following top-tier libraries:

Python 库Python libraries

Databricks Runtime 7.1 ML 使用 Conda 进行 Python 包管理,并且包含许多常用的 ML 包。Databricks Runtime 7.1 ML uses Conda for Python package management and includes many popular ML packages. 下一部分介绍用于 Databricks Runtime 7.1 ML 的 Conda 环境。The following section describes the Conda environment for Databricks Runtime 7.1 ML.

CPU 群集上的 PythonPython 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_0
  - astor=0.8.0=py37_0
  - backcall=0.1.0=py37_0
  - backports=1.0=py_2
  - bcrypt=3.1.7=py37h7b6447c_1
  - blas=1.0=mkl
  - blinker=1.4=py37_0
  - boto3=1.12.0=py_0
  - botocore=1.15.0=py_0
  - c-ares=1.15.0=h7b6447c_1001
  - ca-certificates=2020.6.24=0
  - cachetools=4.1.0=py_1
  - certifi=2020.6.20=py37_0
  - cffi=1.14.0=py37h2e261b9_0
  - chardet=3.0.4=py37_1003
  - click=7.0=py37_0
  - cloudpickle=1.3.0=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
  - gitdb2=4.0.2=py_0
  - gitpython=3.0.5=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=0.14.1=py37_0
  - jinja2=2.11.1=py_0
  - jmespath=0.9.4=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=hd88cf55_4
  - 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=py37h7b6447c_0
  - 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_0
  - ninja=1.9.0=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=1.1.1g=h7b6447c_0
  - packaging=20.1=py_0
  - pandas=1.0.1=py37h0573a6f_0
  - paramiko=2.7.1=py_0
  - parso=0.5.2=py_0
  - patsy=0.5.1=py37_0
  - pexpect=4.8.0=py37_0
  - pickleshare=0.7.5=py37_0
  - pillow=7.0.0=py37hb39fc2d_0
  - pip=20.0.2=py37_3
  - plotly=4.8.1=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=0.2.7=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=py37_0
  - pyparsing=2.4.6=py_0
  - pysocks=1.7.1=py37_0
  - python=3.7.6=h0371630_2
  - python-dateutil=2.8.1=py_0
  - python-editor=1.0.4=py_0
  - pytorch=1.5.1=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_0
  - 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=3.0.2=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
  - tk=8.6.8=hbc83047_0
  - torchvision=0.6.1=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==1.6.0
    - azure-storage-blob==12.3.2
    - databricks-cli==0.11.0
    - diskcache==4.1.0
    - docker==4.2.2
    - gorilla==0.3.0
    - horovod==0.19.5
    - hyperopt==0.2.4.db2
    - keras-preprocessing==1.1.2
    - koalas==1.0.1
    - mleap==0.16.0
    - mlflow==1.9.1
    - msrest==0.6.17
    - opt-einsum==3.2.1
    - petastorm==0.9.2
    - pyarrow==0.15.1
    - pyyaml==5.3.1
    - querystring-parser==1.2.4
    - seaborn==0.10.0
    - spark-tensorflow-distributor==0.1.0
    - sparkdl==2.1.0-db1
    - tensorboard==2.2.2
    - tensorboard-plugin-wit==1.7.0
    - tensorflow-cpu==2.2.0
    - tensorflow-estimator==2.2.0
    - termcolor==1.1.0
    - xgboost==1.1.1
prefix: /databricks/conda/envs/databricks-ml

GPU 群集上的 PythonPython 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_0
  - astor=0.8.0=py37_0
  - backcall=0.1.0=py37_0
  - backports=1.0=py_2
  - bcrypt=3.1.7=py37h7b6447c_1
  - blas=1.0=mkl
  - blinker=1.4=py37_0
  - boto3=1.12.0=py_0
  - botocore=1.15.0=py_0
  - c-ares=1.15.0=h7b6447c_1001
  - ca-certificates=2020.6.24=0
  - cachetools=4.1.0=py_1
  - certifi=2020.6.20=py37_0
  - cffi=1.14.0=py37h2e261b9_0
  - chardet=3.0.4=py37_1003
  - click=7.0=py37_0
  - cloudpickle=1.3.0=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
  - gitdb2=4.0.2=py_0
  - gitpython=3.0.5=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=0.14.1=py37_0
  - jinja2=2.11.1=py_0
  - jmespath=0.9.4=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=hd88cf55_4
  - 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=py37h7b6447c_0
  - 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_0
  - ninja=1.9.0=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=1.1.1g=h7b6447c_0
  - packaging=20.1=py_0
  - pandas=1.0.1=py37h0573a6f_0
  - paramiko=2.7.1=py_0
  - parso=0.5.2=py_0
  - patsy=0.5.1=py37_0
  - pexpect=4.8.0=py37_0
  - pickleshare=0.7.5=py37_0
  - pillow=7.0.0=py37hb39fc2d_0
  - pip=20.0.2=py37_3
  - plotly=4.8.1=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=0.2.7=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=py37_0
  - pyparsing=2.4.6=py_0
  - pysocks=1.7.1=py37_0
  - python=3.7.6=h0371630_2
  - python-dateutil=2.8.1=py_0
  - python-editor=1.0.4=py_0
  - pytorch=1.5.1=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_0
  - 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=3.0.2=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
  - tk=8.6.8=hbc83047_0
  - torchvision=0.6.1=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==1.6.0
    - azure-storage-blob==12.3.2
    - databricks-cli==0.11.0
    - diskcache==4.1.0
    - docker==4.2.2
    - gorilla==0.3.0
    - horovod==0.19.5
    - hyperopt==0.2.4.db1
    - keras-preprocessing==1.1.2
    - koalas==1.0.1
    - mleap==0.16.0
    - mlflow==1.9.1
    - msrest==0.6.17
    - opt-einsum==3.2.1
    - petastorm==0.9.2
    - pyarrow==0.15.1
    - pyyaml==5.3.1
    - querystring-parser==1.2.4
    - seaborn==0.10.0
    - spark-tensorflow-distributor==0.1.0
    - sparkdl==2.1.0-db1
    - tensorboard==2.2.2
    - tensorboard-plugin-wit==1.7.0
    - tensorflow==2.2.0
    - tensorflow-estimator==2.2.0
    - termcolor==1.1.0
    - xgboost==1.1.1
prefix: /databricks/conda/envs/databricks-ml-gpu

包含 Python 模块的 Spark 包Spark packages containing Python modules

Spark 包Spark Package Python 模块Python Module 版本Version
graphframesgraphframes graphframesgraphframes 0.8.0-db2-spark3.00.8.0-db2-spark3.0

R 库R libraries

R 库与 Databricks Runtime 7.1 中的 R 库完全相同。The R libraries are identical to the R Libraries in Databricks Runtime 7.1.

Java 库和 Scala 库(Scala 2.12 群集)Java and Scala libraries (Scala 2.12 cluster)

除了 Databricks Runtime 7.1 中的 Java 库和 Scala 库之外,Databricks Runtime 7.1 ML 还包含以下 JAR:In addition to Java and Scala libraries in Databricks Runtime 7.1, Databricks Runtime 7.1 ML contains the following JARs:

组 IDGroup ID 项目 IDArtifact ID 版本Version
com.typesafe.akkacom.typesafe.akka akka-actor_2.12akka-actor_2.12 2.5.232.5.23
ml.combust.mleapml.combust.mleap mleap-databricks-runtime_2.12mleap-databricks-runtime_2.12 0.17.1-4882dc30.17.1-4882dc3
ml.dmlcml.dmlc xgboost4j-spark_2.12xgboost4j-spark_2.12 1.0.01.0.0
ml.dmlcml.dmlc xgboost4j_2.12xgboost4j_2.12 1.0.01.0.0
org.mlfloworg.mlflow mlflow-clientmlflow-client 1.9.11.9.1
org.scala-lang.modulesorg.scala-lang.modules scala-java8-compat_2.12scala-java8-compat_2.12 0.8.00.8.0
org.tensorfloworg.tensorflow spark-tensorflow-connector_2.12spark-tensorflow-connector_2.12 1.15.01.15.0