通过

用于 ML 的 Databricks Runtime 6.5 (EoS)

  • 项目

注意

对此 Databricks Runtime 版本的支持已结束。 有关终止支持日期，请参阅终止支持历史记录。 有关所有受支持的 Databricks Runtime 版本，请参阅 Databricks Runtime 发行说明版本和兼容性。

Databricks 于 2020 年 4 月发布了此映像。

用于机器学习的 Databricks Runtime 6.5 基于 Databricks Runtime 6.5 (EoS)，为机器学习和数据科学提供了随时可用的环境。 Databricks Runtime ML 包含许多常用的机器学习库，包括 TensorFlow、PyTorch、Keras 和 XGBoost。 它还支持使用 Horovod 进行分布式深度学习训练。

有关详细信息，包括有关如何创建 Databricks Runtime ML 群集的说明，请参阅 Databricks 上的 AI 和机器学习。

新增功能

Databricks Runtime 6.5 ML 基于 Databricks Runtime 6.5 构建。 若要了解 Databricks Runtime 6.5 中的新增功能，请参阅 Databricks Runtime 6.5 (EoS) 发行说明。

改进

升级了机器学习库

弃用项

  • 在即将发布的 Databricks Runtime ML 的主版本中，以下 Python 包会被删除：
    • argparse
    • colorama
    • 已弃用
    • et_xmlfile
    • fusepy
    • html5lib
    • jdcal
    • keras
    • llvmlite
    • lxml
    • nose-exclude
    • nose
    • numba
    • openpyxl
    • pathlib2
    • ply
    • pymongo
    • singledispatch
    • tensorboardx
    • virtualenv
    • webencodings

系统环境

Databricks Runtime 6.5 ML 中的系统环境在以下方面不同于 Databricks Runtime 6.5：

库

以下部分列出了 Databricks Runtime 6.5 ML 中包含的库，这些库不同于 Databricks Runtime 6.5 中包含的库。

本节内容：

顶层库

Databricks Runtime 6.5 ML 包含以下顶层库：

Python 库

Databricks Runtime 6.5 ML 使用 Conda 进行 Python 包管理，并且包含许多常用的 ML 包。 下一部分介绍用于 Databricks Runtime 6.5 ML 的 Conda 环境。

CPU 群集上的 Python

name: databricks-ml
channels:
  - Databricks
  - pytorch
  - defaults
dependencies:
  - _libgcc_mutex=0.1=main
  - _py-xgboost-mutex=2.0=cpu_0
  - _tflow_select=2.3.0=mkl
  - absl-py=0.9.0=py37_0
  - asn1crypto=0.24.0=py37_0
  - astor=0.8.0=py37_0
  - backcall=0.1.0=py37_0
  - backports=1.0=py_2
  - bcrypt=3.1.7=py37h7b6447c_0
  - blas=1.0=mkl
  - boto=2.49.0=py37_0
  - boto3=1.9.162=py_0
  - botocore=1.12.163=py_0
  - c-ares=1.15.0=h7b6447c_1001
  - ca-certificates=2019.1.23=0
  - certifi=2019.3.9=py37_0
  - cffi=1.12.2=py37h2e261b9_1
  - chardet=3.0.4=py37_1003
  - click=7.0=py_0
  - cloudpickle=0.8.0=py37_0
  - colorama=0.4.1=py_0
  - configparser=3.7.4=py37_0
  - cpuonly=1.0=0
  - cryptography=2.6.1=py37h1ba5d50_0
  - cycler=0.10.0=py37_0
  - cython=0.29.6=py37he6710b0_0
  - decorator=4.4.0=py37_1
  - docutils=0.14=py37_0
  - entrypoints=0.3=py37_0
  - et_xmlfile=1.0.1=py37_0
  - flask=1.0.2=py37_1
  - freetype=2.9.1=h8a8886c_1
  - future=0.17.1=py37_0
  - gast=0.2.2=py37_0
  - gitdb2=2.0.6=py_0
  - gitpython=2.1.11=py37_0
  - google-pasta=0.1.8=py_0
  - grpcio=1.16.1=py37hf8bcb03_1
  - gunicorn=19.9.0=py37_0
  - h5py=2.9.0=py37h7918eee_0
  - hdf5=1.10.4=hb1b8bf9_0
  - html5lib=1.0.1=py_0
  - icu=58.2=h9c2bf20_1
  - idna=2.8=py37_0
  - intel-openmp=2019.3=199
  - ipykernel=5.1.0=py37h39e3cac_0
  - ipython=7.4.0=py37h39e3cac_0
  - ipython_genutils=0.2.0=py37_0
  - itsdangerous=1.1.0=py_0
  - jdcal=1.4=py37_0
  - jedi=0.13.3=py37_0
  - jinja2=2.10=py37_0
  - jmespath=0.9.4=py_0
  - jpeg=9b=h024ee3a_2
  - jupyter_client=5.2.4=py37_0
  - jupyter_core=4.4.0=py37_0
  - keras-applications=1.0.8=py_0
  - keras-preprocessing=1.1.0=py_1
  - kiwisolver=1.0.1=py37hf484d3e_0
  - krb5=1.16.1=h173b8e3_7
  - libedit=3.1.20181209=hc058e9b_0
  - libffi=3.2.1=hd88cf55_4
  - libgcc-ng=8.2.0=hdf63c60_1
  - libgfortran-ng=7.3.0=hdf63c60_0
  - libpng=1.6.36=hbc83047_0
  - libpq=11.2=h20c2e04_0
  - libprotobuf=3.11.4=hd408876_0
  - libsodium=1.0.16=h1bed415_0
  - libstdcxx-ng=8.2.0=hdf63c60_1
  - libtiff=4.0.10=h2733197_2
  - libxgboost=0.90=he6710b0_1
  - libxml2=2.9.9=hea5a465_1
  - libxslt=1.1.33=h7d1a2b0_0
  - llvmlite=0.28.0=py37hd408876_0
  - lxml=4.3.2=py37hefd8a0e_0
  - mako=1.0.10=py_0
  - markdown=3.1.1=py37_0
  - markupsafe=1.1.1=py37h7b6447c_0
  - mkl=2019.3=199
  - mkl_fft=1.0.10=py37ha843d7b_0
  - mkl_random=1.0.2=py37hd81dba3_0
  - ncurses=6.1=he6710b0_1
  - networkx=2.2=py37_1
  - ninja=1.9.0=py37hfd86e86_0
  - nose=1.3.7=py37_2
  - numba=0.43.1=py37h962f231_0
  - numpy=1.16.2=py37h7e9f1db_0
  - numpy-base=1.16.2=py37hde5b4d6_0
  - olefile=0.46=py_0
  - openpyxl=2.6.1=py37_1
  - openssl=1.1.1b=h7b6447c_1
  - opt_einsum=3.1.0=py_0
  - pandas=0.24.2=py37he6710b0_0
  - paramiko=2.4.2=py37_0
  - parso=0.3.4=py37_0
  - pathlib2=2.3.3=py37_0
  - patsy=0.5.1=py37_0
  - pexpect=4.6.0=py37_0
  - pickleshare=0.7.5=py37_0
  - pillow=5.4.1=py37h34e0f95_0
  - pip=19.0.3=py37_0
  - ply=3.11=py37_0
  - prompt_toolkit=2.0.9=py37_0
  - protobuf=3.11.4=py37he6710b0_0
  - psutil=5.6.1=py37h7b6447c_0
  - psycopg2=2.7.6.1=py37h1ba5d50_0
  - ptyprocess=0.6.0=py37_0
  - py-xgboost=0.90=py37he6710b0_1
  - py-xgboost-cpu=0.90=py37_1
  - pyasn1=0.4.8=py_0
  - pycparser=2.19=py_0
  - pygments=2.3.1=py37_0
  - pymongo=3.8.0=py37he6710b0_1
  - pynacl=1.3.0=py37h7b6447c_0
  - pyopenssl=19.0.0=py37_0
  - pyparsing=2.3.1=py37_0
  - pysocks=1.6.8=py37_0
  - python=3.7.3=h0371630_0
  - python-dateutil=2.8.0=py37_0
  - python-editor=1.0.4=py_0
  - pytorch=1.4.0=py3.7_cpu_0
  - pytz=2018.9=py37_0
  - pyyaml=5.1=py37h7b6447c_0
  - pyzmq=18.0.0=py37he6710b0_0
  - readline=7.0=h7b6447c_5
  - requests=2.21.0=py37_0
  - s3transfer=0.2.1=py37_0
  - scikit-learn=0.20.3=py37hd81dba3_0
  - scipy=1.2.1=py37h7c811a0_0
  - setuptools=40.8.0=py37_0
  - simplejson=3.16.0=py37h14c3975_0
  - singledispatch=3.4.0.3=py37_0
  - six=1.12.0=py37_0
  - smmap2=2.0.5=py_0
  - sqlite=3.27.2=h7b6447c_0
  - sqlparse=0.3.0=py_0
  - statsmodels=0.9.0=py37h035aef0_0
  - tabulate=0.8.3=py37_0
  - tensorboard=1.15.0+db2=pyhb230dea_0
  - tensorflow=1.15.0+db2=mkl_py37hc5fbf04_0
  - tensorflow-base=1.15.0+db2=mkl_py37h2ae1e84_0
  - tensorflow-estimator=1.15.1+db2=pyh2649769_0
  - tensorflow-mkl=1.15.0+db2=h4fcabd2_0
  - termcolor=1.1.0=py37_1
  - tk=8.6.8=hbc83047_0
  - torchvision=0.5.0=py37_cpu
  - tornado=6.0.2=py37h7b6447c_0
  - tqdm=4.31.1=py37_1
  - traitlets=4.3.2=py37_0
  - urllib3=1.24.1=py37_0
  - virtualenv=16.0.0=py37_0
  - wcwidth=0.1.7=py37_0
  - webencodings=0.5.1=py37_1
  - websocket-client=0.56.0=py37_0
  - werkzeug=0.14.1=py37_0
  - wheel=0.33.1=py37_0
  - wrapt=1.11.1=py37h7b6447c_0
  - xz=5.2.4=h14c3975_4
  - yaml=0.1.7=had09818_2
  - zeromq=4.3.1=he6710b0_3
  - zlib=1.2.11=h7b6447c_3
  - zstd=1.3.7=h0b5b093_0
  - pip:
    - argparse==1.4.0
    - databricks-cli==0.9.1
    - deprecated==1.2.7
    - docker==4.2.0
    - fusepy==2.0.4
    - gorilla==0.3.0
    - horovod==0.19.0
    - hyperopt==0.2.2.db1
    - keras==2.2.5
    - matplotlib==3.0.3
    - mleap==0.8.1
    - mlflow==1.7.0
    - nose-exclude==0.5.0
    - pyarrow==0.13.0
    - querystring-parser==1.2.4
    - seaborn==0.9.0
    - tensorboardx==1.9
prefix: /databricks/conda/envs/databricks-ml

GPU 群集上的 Python

name: databricks-ml-gpu
channels:
  - Databricks
  - pytorch
  - defaults
dependencies:
  - _libgcc_mutex=0.1=main
  - _py-xgboost-mutex=1.0=gpu_0
  - _tflow_select=2.1.0=gpu
  - absl-py=0.9.0=py37_0
  - asn1crypto=0.24.0=py37_0
  - astor=0.8.0=py37_0
  - backcall=0.1.0=py37_0
  - backports=1.0=py_2
  - bcrypt=3.1.7=py37h7b6447c_0
  - blas=1.0=mkl
  - boto=2.49.0=py37_0
  - boto3=1.9.162=py_0
  - botocore=1.12.163=py_0
  - c-ares=1.15.0=h7b6447c_1001
  - ca-certificates=2019.1.23=0
  - certifi=2019.3.9=py37_0
  - cffi=1.12.2=py37h2e261b9_1
  - chardet=3.0.4=py37_1003
  - click=7.0=py_0
  - cloudpickle=0.8.0=py37_0
  - colorama=0.4.1=py_0
  - configparser=3.7.4=py37_0
  - cryptography=2.6.1=py37h1ba5d50_0
  - cudatoolkit=10.0.130=0
  - cudnn=7.6.4=cuda10.0_0
  - cupti=10.0.130=0
  - cycler=0.10.0=py37_0
  - cython=0.29.6=py37he6710b0_0
  - decorator=4.4.0=py37_1
  - docutils=0.14=py37_0
  - entrypoints=0.3=py37_0
  - et_xmlfile=1.0.1=py37_0
  - flask=1.0.2=py37_1
  - freetype=2.9.1=h8a8886c_1
  - future=0.17.1=py37_0
  - gast=0.2.2=py37_0
  - gitdb2=2.0.6=py_0
  - gitpython=2.1.11=py37_0
  - google-pasta=0.1.8=py_0
  - grpcio=1.16.1=py37hf8bcb03_1
  - gunicorn=19.9.0=py37_0
  - h5py=2.9.0=py37h7918eee_0
  - hdf5=1.10.4=hb1b8bf9_0
  - html5lib=1.0.1=py_0
  - icu=58.2=h9c2bf20_1
  - idna=2.8=py37_0
  - intel-openmp=2019.3=199
  - ipykernel=5.1.0=py37h39e3cac_0
  - ipython=7.4.0=py37h39e3cac_0
  - ipython_genutils=0.2.0=py37_0
  - itsdangerous=1.1.0=py_0
  - jdcal=1.4=py37_0
  - jedi=0.13.3=py37_0
  - jinja2=2.10=py37_0
  - jmespath=0.9.4=py_0
  - jpeg=9b=h024ee3a_2
  - jupyter_client=5.2.4=py37_0
  - jupyter_core=4.4.0=py37_0
  - keras-applications=1.0.8=py_0
  - keras-preprocessing=1.1.0=py_1
  - kiwisolver=1.0.1=py37hf484d3e_0
  - krb5=1.16.1=h173b8e3_7
  - libedit=3.1.20181209=hc058e9b_0
  - libffi=3.2.1=hd88cf55_4
  - libgcc-ng=8.2.0=hdf63c60_1
  - libgfortran-ng=7.3.0=hdf63c60_0
  - libpng=1.6.36=hbc83047_0
  - libpq=11.2=h20c2e04_0
  - libprotobuf=3.11.4=hd408876_0
  - libsodium=1.0.16=h1bed415_0
  - libstdcxx-ng=8.2.0=hdf63c60_1
  - libtiff=4.0.10=h2733197_2
  - libxgboost=0.90=h688424c_0
  - libxml2=2.9.9=hea5a465_1
  - libxslt=1.1.33=h7d1a2b0_0
  - llvmlite=0.28.0=py37hd408876_0
  - lxml=4.3.2=py37hefd8a0e_0
  - mako=1.0.10=py_0
  - markdown=3.1.1=py37_0
  - markupsafe=1.1.1=py37h7b6447c_0
  - mkl=2019.3=199
  - mkl_fft=1.0.10=py37ha843d7b_0
  - mkl_random=1.0.2=py37hd81dba3_0
  - ncurses=6.1=he6710b0_1
  - networkx=2.2=py37_1
  - ninja=1.9.0=py37hfd86e86_0
  - nose=1.3.7=py37_2
  - numba=0.43.1=py37h962f231_0
  - numpy=1.16.2=py37h7e9f1db_0
  - numpy-base=1.16.2=py37hde5b4d6_0
  - olefile=0.46=py_0
  - openpyxl=2.6.1=py37_1
  - openssl=1.1.1b=h7b6447c_1
  - opt_einsum=3.1.0=py_0
  - pandas=0.24.2=py37he6710b0_0
  - paramiko=2.4.2=py37_0
  - parso=0.3.4=py37_0
  - pathlib2=2.3.3=py37_0
  - patsy=0.5.1=py37_0
  - pexpect=4.6.0=py37_0
  - pickleshare=0.7.5=py37_0
  - pillow=5.4.1=py37h34e0f95_0
  - pip=19.0.3=py37_0
  - ply=3.11=py37_0
  - prompt_toolkit=2.0.9=py37_0
  - protobuf=3.11.4=py37he6710b0_0
  - psutil=5.6.1=py37h7b6447c_0
  - psycopg2=2.7.6.1=py37h1ba5d50_0
  - ptyprocess=0.6.0=py37_0
  - py-xgboost=0.90=py37h688424c_0
  - py-xgboost-gpu=0.90=py37h28bbb66_0
  - pyasn1=0.4.8=py_0
  - pycparser=2.19=py_0
  - pygments=2.3.1=py37_0
  - pymongo=3.8.0=py37he6710b0_1
  - pynacl=1.3.0=py37h7b6447c_0
  - pyopenssl=19.0.0=py37_0
  - pyparsing=2.3.1=py37_0
  - pysocks=1.6.8=py37_0
  - python=3.7.3=h0371630_0
  - python-dateutil=2.8.0=py37_0
  - python-editor=1.0.4=py_0
  - pytorch=1.4.0=py3.7_cuda10.0.130_cudnn7.6.3_0
  - pytz=2018.9=py37_0
  - pyyaml=5.1=py37h7b6447c_0
  - pyzmq=18.0.0=py37he6710b0_0
  - readline=7.0=h7b6447c_5
  - requests=2.21.0=py37_0
  - s3transfer=0.2.1=py37_0
  - scikit-learn=0.20.3=py37hd81dba3_0
  - scipy=1.2.1=py37h7c811a0_0
  - setuptools=40.8.0=py37_0
  - simplejson=3.16.0=py37h14c3975_0
  - singledispatch=3.4.0.3=py37_0
  - six=1.12.0=py37_0
  - smmap2=2.0.5=py_0
  - sqlite=3.27.2=h7b6447c_0
  - sqlparse=0.3.0=py_0
  - statsmodels=0.9.0=py37h035aef0_0
  - tabulate=0.8.3=py37_0
  - tensorboard=1.15.0+db2=pyhb230dea_0
  - tensorflow=1.15.0+db2=gpu_py37h9fd0ff8_0
  - tensorflow-base=1.15.0+db2=gpu_py37hd56f5dd_0
  - tensorflow-estimator=1.15.1+db2=pyh2649769_0
  - tensorflow-gpu=1.15.0+db2=h0d30ee6_0
  - termcolor=1.1.0=py37_1
  - tk=8.6.8=hbc83047_0
  - torchvision=0.5.0=py37_cu100
  - tornado=6.0.2=py37h7b6447c_0
  - tqdm=4.31.1=py37_1
  - traitlets=4.3.2=py37_0
  - urllib3=1.24.1=py37_0
  - virtualenv=16.0.0=py37_0
  - wcwidth=0.1.7=py37_0
  - webencodings=0.5.1=py37_1
  - websocket-client=0.56.0=py37_0
  - werkzeug=0.14.1=py37_0
  - wheel=0.33.1=py37_0
  - wrapt=1.11.1=py37h7b6447c_0
  - xz=5.2.4=h14c3975_4
  - yaml=0.1.7=had09818_2
  - zeromq=4.3.1=he6710b0_3
  - zlib=1.2.11=h7b6447c_3
  - zstd=1.3.7=h0b5b093_0
  - pip:
    - argparse==1.4.0
    - databricks-cli==0.9.1
    - deprecated==1.2.7
    - docker==4.2.0
    - fusepy==2.0.4
    - gorilla==0.3.0
    - horovod==0.19.0
    - hyperopt==0.2.2.db1
    - keras==2.2.5
    - matplotlib==3.0.3
    - mleap==0.8.1
    - mlflow==1.7.0
    - nose-exclude==0.5.0
    - pyarrow==0.13.0
    - querystring-parser==1.2.4
    - seaborn==0.9.0
    - tensorboardx==1.9
prefix: /databricks/conda/envs/databricks-ml-gpu

包含 Python 模块的 Spark 包

Spark 包 Python 模块 版本
graphframes graphframes 0.7.0-db1-spark2.4
spark-deep-learning sparkdl 1.6.0-db1-spark2.4
tensorframes tensorframes 0.8.2-s_2.11

R 库

R 库与 Databricks Runtime 6.5 中的 R 库完全相同。

Java 和 Scala 库（Scala 2.11 群集）

除了 Databricks Runtime 6.5 中的 Java 库和 Scala 库之外，Databricks Runtime 6.5 ML 还包含以下 JAR：

组 ID 项目 ID 版本
com.typesafe.akka akka-actor_2.11 2.3.11
ml.combust.mleap mleap-databricks-runtime_2.11 0.15.0
ml.dmlc xgboost4j 0.90
ml.dmlc xgboost4j-spark 0.90
org.mlflow mlflow-client 1.6.0
org.tensorflow libtensorflow 1.15.0
org.tensorflow libtensorflow_jni 1.15.0
org.tensorflow spark-tensorflow-connector_2.11 1.15.0
org.tensorflow tensorflow 1.15.0