Databricks Runtime 7.4 ML

Databricks divulgou esta imagem em novembro de 2020.

Databricks Runtime 7.4 for Machine Learning fornece um ambiente pronto para aprendizagem automática e ciência de dados com base em Databricks Runtime 7.4. Databricks Runtime ML contém muitas bibliotecas populares de aprendizagem automática, incluindo TensorFlow, PyTorch e XGBoost. Também apoia a prática de aprendizagem profunda distribuída utilizando o Horovod.

Para obter mais informações, incluindo instruções para a criação de um cluster ML de runtime de databricks, consulte databricks Runtime for Machine Learning.

Novas funcionalidades e grandes mudanças

Databricks O tempo de execução 7.4 ML é construído em cima do Databricks Runtime 7.4. Para obter informações sobre as novidades em Databricks Runtime 7.4, incluindo Apache Spark MLlib e SparkR, consulte as notas de lançamento databricks Runtime 7.4.

Grandes alterações no ambiente de Runtime ML Scala de Databricks

XGBoost é atualizado para 1.2.0. Esta versão permite que o XGBoost utilize GPUs em clusters Spark para melhorar a velocidade de treino. Há várias outras alterações, incluindo algumas mudanças de rutura. Consulte as notas de lançamento XGBoost 1.2.0 para obter mais informações.

Especificamente, nos clusters da CPU, xgboost4j_2.12 e xgboost4j-spark_2.12 são atualizados de 1.0.0 para 1.2.0. Nos clusters gpu, estes pacotes são removidos e a versão 1.2.0 xgboost4j-gpu_2.12 e xgboost4j-spark-gpu_2.12 são instaladas em vez disso.

O GraphFrames é atualizado de 0.8.0-db2-spark3.0 para 0.8.1-db1-spark3.0.

Principais alterações no ambiente de Runtime ML Python de Databricks

Consulte databricks Runtime 7.4 para as principais alterações no ambiente Databricks Runtime Python. Para obter uma lista completa dos pacotes Python instalados e suas versões, consulte as bibliotecas Python.

Pacotes python atualizados

  • 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
  • tensorfluxo 2.3.0 -> 2.3.1
  • xgboost 1.1.1 -> 1.2.0

Melhorias

Ambiente do sistema

O ambiente do sistema em Databricks Runtime 7.4 ML difere do Databricks Runtime 7.4 da seguinte forma:

Bibliotecas

As secções seguintes listam as bibliotecas incluídas no Databricks Runtime 7.4 ML que diferem das incluídas no Databricks Runtime 7.4.

Nesta secção:

Bibliotecas de topo

Databricks Runtime 7.4 ML inclui as seguintes bibliotecasde topo:

Bibliotecas do Python

Databricks Runtime 7.4 ML usa Conda para a gestão de pacotes Python e inclui muitos pacotes ML populares.

Para além das embalagens especificadas nos ambientes Conda nas seguintes secções, o Databricks Runtime 7.4 ML também instala as seguintes embalagens:

  • hiperopito 0.2.4.db2
  • sparkdl 2.1.0-db2

Bibliotecas python em aglomerados de CPU

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

Bibliotecas python em aglomerados de GPU

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

Pacotes de faíscas contendo módulos Python

Pacote de faísca Módulo Python Versão
quadros gráficos quadros gráficos 0.8.1-db1-spark3.0

Bibliotecas R

As bibliotecas R são idênticas às Bibliotecas R em Databricks Runtime 7.4.

Bibliotecas java e scala (cluster Scala 2.12)

Além das bibliotecas Java e Scala em Databricks Runtime 7.4, databricks Runtime 7.4 ML contém os seguintes JARs:

Aglomerados de CPU

ID do Grupo ID de artefacto Versão
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-cliente 1.11.0
módulos org.scala-lang scala-java8-compat_2.12 0.8.0
org.tensorflow spark-tensorflow-connector_2.12 1.15.0

Aglomerados de GPU

ID do Grupo ID de artefacto Versão
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-cliente 1.11.0
módulos org.scala-lang scala-java8-compat_2.12 0.8.0
org.tensorflow spark-tensorflow-connector_2.12 1.15.0