Databricks Runtime 8,3 para (EoS) ML

nota

O suporte para essa versão do Databricks Runtime foi encerrado. Para saber a data do fim do suporte, consulte Histórico do fim do suporte. Para conhecer todas as versões compatíveis do site Databricks Runtime, consulte Databricks Runtime notas sobre as versões e a compatibilidade.

A Databricks lançou essa versão em junho de 2021.

O Databricks Runtime 8.3 for Machine Learning oferece um ambiente pronto para uso para aprendizado de máquina e ciência de dados com base no Databricks Runtime 8.3 (EoS). Databricks Runtime ML Contém muitas bibliotecas populares de aprendizado de máquina, incluindo TensorFlow, PyTorch, e XGBoost. Ele também oferece suporte ao treinamento de aprendizagem profunda distribuída usando o Horovod.

Para obter mais informações, incluindo instruções para criar um cluster Databricks Runtime ML , consulte AI e aprendizado de máquina em Databricks.

Novos recursos e melhorias

O Databricks Runtime 8.3 ML foi desenvolvido com base no Databricks Runtime 8.3. Para obter informações sobre as novidades do Databricks Runtime 8.3, incluindo Apache Spark MLlib e SparkR, , consulte as notas sobre a versão Databricks Runtime 8.3 (EoS).

Databricks Runtime 8.3 O site ML também inclui o seguinte pacote novo:

• recurso engenharia e servindo
• AutoML

Principais mudanças no ambiente do Databricks Runtime ML Python

Consulte Databricks Runtime 8.3 (EoS) para conhecer as principais alterações no ambiente Python do Databricks Runtime. Para obter uma lista completa do pacote Python instalado e suas versões, consulte Python biblioteca.

Python pacote atualizado

• coalas 1.7.0 - > 1.8.0
• mlflow 1.15.0 - > 1.17.0
• Pandas 1.1.3 - > 1,15
• petastorm 0.9.8 - > 0.10.0
• xgboost 1.3.3 - > 1,4,1

Python pacote adicionado

• feriados: 0.10.5.2

Use o Shiny dentro do R Notebook

Agora, o senhor pode desenvolver, hospedar e compartilhar aplicativos Shiny diretamente de um Databricks R Notebook, de forma semelhante ao RStudio hospedado. Para obter detalhes, consulte Shiny on Databricks.

Depreciações

Os ambientes Conda, juntamente com o comando %conda, estão obsoletos em favor de pip e virtualenv e serão removidos em uma próxima versão principal. Além disso, as imagens personalizadas que usam ambientes baseados em Conda com Databricks Container Services ainda serão suportadas, mas não terão recursos de biblioteca com escopo de Notebook. Databricks recomenda o uso de ambientes baseados em virtualenvcom Databricks Container Services e %pip para todas as bibliotecas com escopo de Notebook.

Ambiente do sistema

O ambiente do sistema no Databricks Runtime 8.3 ML difere do Databricks Runtime 8.3 da seguinte forma:

• DBUtils : Databricks Runtime ML não inclui utilidades de biblioteca (dbutils.biblioteca) (legado). Em vez disso, use %pip e %conda comando. NotebookConsulte -scoped Pythonbiblioteca.
• Para o clustering de GPU, o site Databricks Runtime ML inclui a seguinte biblioteca de GPUs NVIDIA:
  • CUDA 11.0
  • cuDNN 8.0.4.30
  • NCCL 2.7.8
  • TensorRT 7.1.3

biblioteca

As seções a seguir listam as bibliotecas incluídas em Databricks Runtime 8.3 ML que diferem daquelas incluídas em Databricks Runtime 8.3.

Nesta secção:

• Biblioteca de primeira linha
• Bibliotecas Python
• R biblioteca
• Java e Scala biblioteca (Scala 2.12 clustering)

Biblioteca de primeira linha

Databricks Runtime 8.3 O site ML inclui as seguintes bibliotecas de primeira linha:

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

Python biblioteca

Databricks Runtime 8.3 O ML usa o Conda para o gerenciamento do pacote Python e inclui muitos pacotes populares do ML.

Além do pacote especificado nos ambientes Conda nas seções a seguir, Databricks Runtime 8.3 ML também inclui o seguinte pacote:

• Hyperopt 0.2.5.db1
• sparkdl 2.1.0.db4
• recurso 0.3.1
• automl 1.0.0

Python biblioteca sobre clustering de CPU

YAML

name: databricks-ml
channels:
  - pytorch
  - defaults
dependencies:
  - _libgcc_mutex=0.1=main
  - absl-py=0.11.0=pyhd3eb1b0_1
  - aiohttp=3.7.4=py38h27cfd23_1
  - asn1crypto=1.4.0=py_0
  - astor=0.8.1=py38h06a4308_0
  - async-timeout=3.0.1=py38h06a4308_0
  - attrs=20.3.0=pyhd3eb1b0_0
  - backcall=0.2.0=pyhd3eb1b0_0
  - bcrypt=3.2.0=py38h7b6447c_0
  - blas=1.0=mkl
  - blinker=1.4=py38h06a4308_0
  - boto3=1.16.7=pyhd3eb1b0_0
  - botocore=1.19.7=pyhd3eb1b0_0
  - brotlipy=0.7.0=py38h27cfd23_1003
  - bzip2=1.0.8=h7b6447c_0
  - c-ares=1.17.1=h27cfd23_0
  - ca-certificates=2021.4.13=h06a4308_1
  - cachetools=4.2.2=pyhd3eb1b0_0
  - certifi=2020.12.5=py38h06a4308_0
  - cffi=1.14.3=py38h261ae71_2
  - chardet=3.0.4=py38h06a4308_1003
  - click=7.1.2=pyhd3eb1b0_0
  - cloudpickle=1.6.0=py_0
  - configparser=5.0.1=py_0
  - cpuonly=1.0=0
  - cryptography=3.1.1=py38h1ba5d50_0
  - cycler=0.10.0=py38_0
  - cython=0.29.21=py38h2531618_0
  - decorator=4.4.2=pyhd3eb1b0_0
  - dill=0.3.2=py_0
  - docutils=0.15.2=py38h06a4308_1
  - entrypoints=0.3=py38_0
  - ffmpeg=4.2.2=h20bf706_0
  - flask=1.1.2=pyhd3eb1b0_0
  - freetype=2.10.4=h5ab3b9f_0
  - fsspec=0.8.3=py_0
  - future=0.18.2=py38_1
  - gitdb=4.0.7=pyhd3eb1b0_0
  - gitpython=3.1.12=pyhd3eb1b0_1
  - gmp=6.1.2=h6c8ec71_1
  - gnutls=3.6.15=he1e5248_0
  - google-auth=1.22.1=py_0
  - google-auth-oauthlib=0.4.2=pyhd3eb1b0_2
  - google-pasta=0.2.0=py_0
  - gunicorn=20.0.4=py38h06a4308_0
  - h5py=2.10.0=py38h7918eee_0
  - hdf5=1.10.4=hb1b8bf9_0
  - icu=58.2=he6710b0_3
  - idna=2.10=pyhd3eb1b0_0
  - importlib-metadata=2.0.0=py_1
  - intel-openmp=2019.4=243
  - ipykernel=5.3.4=py38h5ca1d4c_0
  - ipython=7.19.0=py38hb070fc8_1
  - ipython_genutils=0.2.0=pyhd3eb1b0_1
  - isodate=0.6.0=py_1
  - itsdangerous=1.1.0=pyhd3eb1b0_0
  - jedi=0.17.2=py38h06a4308_1
  - jinja2=2.11.2=pyhd3eb1b0_0
  - jmespath=0.10.0=py_0
  - joblib=0.17.0=py_0
  - jpeg=9b=h024ee3a_2
  - jupyter_client=6.1.7=py_0
  - jupyter_core=4.6.3=py38_0
  - kiwisolver=1.3.0=py38h2531618_0
  - krb5=1.17.1=h173b8e3_0
  - lame=3.100=h7b6447c_0
  - lcms2=2.11=h396b838_0
  - ld_impl_linux-64=2.33.1=h53a641e_7
  - libedit=3.1.20191231=h14c3975_1
  - libffi=3.3=he6710b0_2
  - libgcc-ng=9.1.0=hdf63c60_0
  - libgfortran-ng=7.3.0=hdf63c60_0
  - libidn2=2.3.0=h27cfd23_0
  - libopus=1.3.1=h7b6447c_0
  - libpng=1.6.37=hbc83047_0
  - libpq=12.2=h20c2e04_0
  - libprotobuf=3.13.0.1=hd408876_0
  - libsodium=1.0.18=h7b6447c_0
  - libstdcxx-ng=9.1.0=hdf63c60_0
  - libtasn1=4.16.0=h27cfd23_0
  - libtiff=4.1.0=h2733197_1
  - libunistring=0.9.10=h27cfd23_0
  - libuv=1.40.0=h7b6447c_0
  - libvpx=1.7.0=h439df22_0
  - lightgbm=3.1.1=py38h2531618_0
  - lz4-c=1.9.2=heb0550a_3
  - mako=1.1.3=py_0
  - markdown=3.3.3=py38h06a4308_0
  - markupsafe=1.1.1=py38h7b6447c_0
  - matplotlib-base=3.2.2=py38hef1b27d_0
  - mkl=2019.4=243
  - mkl-service=2.3.0=py38he904b0f_0
  - mkl_fft=1.2.0=py38h23d657b_0
  - mkl_random=1.1.0=py38h962f231_0
  - more-itertools=8.6.0=pyhd3eb1b0_0
  - multidict=5.1.0=py38h27cfd23_2
  - ncurses=6.2=he6710b0_1
  - nettle=3.7.2=hbbd107a_1
  - networkx=2.5.1=pyhd3eb1b0_0
  - ninja=1.10.2=hff7bd54_1
  - nltk=3.5=py_0
  - numpy=1.19.2=py38h54aff64_0
  - numpy-base=1.19.2=py38hfa32c7d_0
  - oauthlib=3.1.0=py_0
  - olefile=0.46=py_0
  - openh264=2.1.0=hd408876_0
  - openssl=1.1.1k=h27cfd23_0
  - packaging=20.4=py_0
  - pandas=1.1.5=py38ha9443f7_0
  - paramiko=2.7.2=py_0
  - parso=0.7.0=py_0
  - patsy=0.5.1=py38_0
  - pexpect=4.8.0=pyhd3eb1b0_3
  - pickleshare=0.7.5=pyhd3eb1b0_1003
  - pillow=8.0.1=py38he98fc37_0
  - pip=20.2.4=py38h06a4308_0
  - plotly=4.14.3=pyhd3eb1b0_0
  - prompt-toolkit=3.0.8=py_0
  - prompt_toolkit=3.0.8=0
  - protobuf=3.13.0.1=py38he6710b0_1
  - psutil=5.7.2=py38h7b6447c_0
  - psycopg2=2.8.5=py38h3c74f83_1
  - ptyprocess=0.6.0=pyhd3eb1b0_2
  - pyasn1=0.4.8=py_0
  - pyasn1-modules=0.2.8=py_0
  - pycparser=2.20=py_2
  - pygments=2.7.2=pyhd3eb1b0_0
  - pyjwt=1.7.1=py38_0
  - pynacl=1.4.0=py38h7b6447c_1
  - pyodbc=4.0.30=py38he6710b0_0
  - pyopenssl=19.1.0=pyhd3eb1b0_1
  - pyparsing=2.4.7=pyhd3eb1b0_0
  - pysocks=1.7.1=py38h06a4308_0
  - python=3.8.8=hdb3f193_4
  - python-dateutil=2.8.1=pyhd3eb1b0_0
  - python-editor=1.0.4=py_0
  - pytorch=1.8.1=py3.8_cpu_0
  - pytz=2020.5=pyhd3eb1b0_0
  - pyzmq=19.0.2=py38he6710b0_1
  - readline=8.0=h7b6447c_0
  - regex=2020.10.15=py38h7b6447c_0
  - requests=2.24.0=py_0
  - requests-oauthlib=1.3.0=py_0
  - retrying=1.3.3=py_2
  - rsa=4.7.2=pyhd3eb1b0_1
  - s3transfer=0.3.6=pyhd3eb1b0_0
  - scikit-learn=0.23.2=py38h0573a6f_0
  - scipy=1.5.2=py38h0b6359f_0
  - setuptools=50.3.1=py38h06a4308_1
  - simplejson=3.17.2=py38h27cfd23_2
  - six=1.15.0=py38h06a4308_0
  - smmap=3.0.5=pyhd3eb1b0_0
  - sqlite=3.33.0=h62c20be_0
  - sqlparse=0.4.1=py_0
  - statsmodels=0.12.0=py38h7b6447c_0
  - tabulate=0.8.7=py38h06a4308_0
  - threadpoolctl=2.1.0=pyh5ca1d4c_0
  - tk=8.6.10=hbc83047_0
  - torchvision=0.9.1=py38_cpu
  - tornado=6.0.4=py38h7b6447c_1
  - tqdm=4.50.2=py_0
  - traitlets=5.0.5=pyhd3eb1b0_0
  - typing-extensions=3.7.4.3=hd3eb1b0_0
  - typing_extensions=3.7.4.3=pyh06a4308_0
  - unixodbc=2.3.9=h7b6447c_0
  - urllib3=1.25.11=py_0
  - wcwidth=0.2.5=py_0
  - websocket-client=0.57.0=py38_2
  - werkzeug=1.0.1=pyhd3eb1b0_0
  - wheel=0.35.1=pyhd3eb1b0_0
  - wrapt=1.12.1=py38h7b6447c_1
  - x264=1!157.20191217=h7b6447c_0
  - xz=5.2.5=h7b6447c_0
  - yarl=1.6.3=py38h27cfd23_0
  - zeromq=4.3.3=he6710b0_3
  - zipp=3.4.0=pyhd3eb1b0_0
  - zlib=1.2.11=h7b6447c_3
  - zstd=1.4.5=h9ceee32_0
  - pip:
      - argon2-cffi==20.1.0
      - astunparse==1.6.3
      - async-generator==1.10
      - azure-core==1.11.0
      - azure-storage-blob==12.7.1
      - bleach==3.3.0
      - confuse==1.4.0
      - convertdate==2.3.2
      - databricks-cli==0.14.3
      - defusedxml==0.7.1
      - diskcache==5.2.1
      - docker==4.4.4
      - facets-overview==1.0.0
      - flatbuffers==1.12
      - gast==0.3.3
      - grpcio==1.32.0
      - hijri-converter==2.1.1
      - holidays==0.10.5.2
      - horovod==0.21.3
      - htmlmin==0.1.12
      - imagehash==4.2.0
      - ipywidgets==7.6.3
      - joblibspark==0.3.0
      - jsonschema==3.2.0
      - jupyterlab-pygments==0.1.2
      - jupyterlab-widgets==1.0.0
      - keras-preprocessing==1.1.2
      - koalas==1.8.0
      - korean-lunar-calendar==0.2.1
      - llvmlite==0.36.0
      - missingno==0.4.2
      - mistune==0.8.4
      - mleap==0.16.1
      - mlflow-skinny==1.17.0
      - msrest==0.6.21
      - nbclient==0.5.3
      - nbconvert==6.0.7
      - nbformat==5.1.3
      - nest-asyncio==1.5.1
      - notebook==6.4.0
      - numba==0.53.1
      - opt-einsum==3.3.0
      - pandas-profiling==2.11.0
      - pandocfilters==1.4.3
      - petastorm==0.10.0
      - phik==0.11.2
      - prometheus-client==0.10.1
      - pyarrow==1.0.1
      - pymeeus==0.5.11
      - pyrsistent==0.17.3
      - pywavelets==1.1.1
      - pyyaml==5.4.1
      - querystring-parser==1.2.4
      - seaborn==0.10.0
      - send2trash==1.5.0
      - shap==0.39.0
      - slicer==0.0.7
      - spark-tensorflow-distributor==0.1.0
      - tangled-up-in-unicode==0.1.0
      - tensorboard==2.4.1
      - tensorboard-plugin-wit==1.8.0
      - tensorflow-cpu==2.4.1
      - tensorflow-estimator==2.4.0
      - termcolor==1.1.0
      - terminado==0.9.5
      - testpath==0.5.0
      - visions==0.6.0
      - webencodings==0.5.1
      - widgetsnbextension==3.5.1
      - xgboost==1.4.1
prefix: /databricks/conda/envs/databricks-ml

Python biblioteca sobre clustering de GPU

YAML

name: databricks-ml-gpu
channels:
  - defaults
dependencies:
  - _libgcc_mutex=0.1=main
  - absl-py=0.11.0=pyhd3eb1b0_1
  - aiohttp=3.7.4=py38h27cfd23_1
  - asn1crypto=1.4.0=py_0
  - astor=0.8.1=py38h06a4308_0
  - async-timeout=3.0.1=py38h06a4308_0
  - attrs=20.3.0=pyhd3eb1b0_0
  - backcall=0.2.0=pyhd3eb1b0_0
  - bcrypt=3.2.0=py38h7b6447c_0
  - blas=1.0=mkl
  - blinker=1.4=py38h06a4308_0
  - boto3=1.16.7=pyhd3eb1b0_0
  - botocore=1.19.7=pyhd3eb1b0_0
  - brotlipy=0.7.0=py38h27cfd23_1003
  - c-ares=1.17.1=h27cfd23_0
  - ca-certificates=2021.4.13=h06a4308_1
  - cachetools=4.2.2=pyhd3eb1b0_0
  - certifi=2020.12.5=py38h06a4308_0
  - cffi=1.14.3=py38h261ae71_2
  - chardet=3.0.4=py38h06a4308_1003
  - click=7.1.2=pyhd3eb1b0_0
  - cloudpickle=1.6.0=py_0
  - configparser=5.0.1=py_0
  - cryptography=3.1.1=py38h1ba5d50_0
  - cycler=0.10.0=py38_0
  - cython=0.29.21=py38h2531618_0
  - decorator=4.4.2=pyhd3eb1b0_0
  - dill=0.3.2=py_0
  - docutils=0.15.2=py38h06a4308_1
  - entrypoints=0.3=py38_0
  - flask=1.1.2=pyhd3eb1b0_0
  - freetype=2.10.4=h5ab3b9f_0
  - fsspec=0.8.3=py_0
  - future=0.18.2=py38_1
  - gitdb=4.0.7=pyhd3eb1b0_0
  - gitpython=3.1.12=pyhd3eb1b0_1
  - google-auth=1.22.1=py_0
  - google-auth-oauthlib=0.4.2=pyhd3eb1b0_2
  - google-pasta=0.2.0=py_0
  - grpcio=1.31.0=py38hf8bcb03_0
  - gunicorn=20.0.4=py38h06a4308_0
  - h5py=2.10.0=py38h7918eee_0
  - hdf5=1.10.4=hb1b8bf9_0
  - icu=58.2=he6710b0_3
  - idna=2.10=pyhd3eb1b0_0
  - importlib-metadata=2.0.0=py_1
  - intel-openmp=2019.4=243
  - ipykernel=5.3.4=py38h5ca1d4c_0
  - ipython=7.19.0=py38hb070fc8_1
  - ipython_genutils=0.2.0=pyhd3eb1b0_1
  - isodate=0.6.0=py_1
  - itsdangerous=1.1.0=pyhd3eb1b0_0
  - jedi=0.17.2=py38h06a4308_1
  - jinja2=2.11.2=pyhd3eb1b0_0
  - jmespath=0.10.0=py_0
  - joblib=0.17.0=py_0
  - jpeg=9b=h024ee3a_2
  - jupyter_client=6.1.7=py_0
  - jupyter_core=4.6.3=py38_0
  - kiwisolver=1.3.0=py38h2531618_0
  - krb5=1.17.1=h173b8e3_0
  - lcms2=2.11=h396b838_0
  - ld_impl_linux-64=2.33.1=h53a641e_7
  - libedit=3.1.20191231=h14c3975_1
  - libffi=3.3=he6710b0_2
  - libgcc-ng=9.1.0=hdf63c60_0
  - libgfortran-ng=7.3.0=hdf63c60_0
  - libpng=1.6.37=hbc83047_0
  - libpq=12.2=h20c2e04_0
  - libprotobuf=3.13.0.1=hd408876_0
  - libsodium=1.0.18=h7b6447c_0
  - libstdcxx-ng=9.1.0=hdf63c60_0
  - libtiff=4.1.0=h2733197_1
  - lightgbm=3.1.1=py38h2531618_0
  - lz4-c=1.9.2=heb0550a_3
  - mako=1.1.3=py_0
  - markdown=3.3.3=py38h06a4308_0
  - markupsafe=1.1.1=py38h7b6447c_0
  - matplotlib-base=3.2.2=py38hef1b27d_0
  - mkl=2019.4=243
  - mkl-service=2.3.0=py38he904b0f_0
  - mkl_fft=1.2.0=py38h23d657b_0
  - mkl_random=1.1.0=py38h962f231_0
  - more-itertools=8.6.0=pyhd3eb1b0_0
  - multidict=5.1.0=py38h27cfd23_2
  - ncurses=6.2=he6710b0_1
  - networkx=2.5.1=pyhd3eb1b0_0
  - nltk=3.5=py_0
  - numpy=1.19.2=py38h54aff64_0
  - numpy-base=1.19.2=py38hfa32c7d_0
  - oauthlib=3.1.0=py_0
  - olefile=0.46=py_0
  - openssl=1.1.1k=h27cfd23_0
  - packaging=20.4=py_0
  - pandas=1.1.5=py38ha9443f7_0
  - paramiko=2.7.2=py_0
  - parso=0.7.0=py_0
  - patsy=0.5.1=py38_0
  - pexpect=4.8.0=pyhd3eb1b0_3
  - pickleshare=0.7.5=pyhd3eb1b0_1003
  - pillow=8.0.1=py38he98fc37_0
  - pip=20.2.4=py38h06a4308_0
  - plotly=4.14.3=pyhd3eb1b0_0
  - prompt-toolkit=3.0.8=py_0
  - prompt_toolkit=3.0.8=0
  - protobuf=3.13.0.1=py38he6710b0_1
  - psutil=5.7.2=py38h7b6447c_0
  - psycopg2=2.8.5=py38h3c74f83_1
  - ptyprocess=0.6.0=pyhd3eb1b0_2
  - pyasn1=0.4.8=py_0
  - pyasn1-modules=0.2.8=py_0
  - pycparser=2.20=py_2
  - pygments=2.7.2=pyhd3eb1b0_0
  - pyjwt=1.7.1=py38_0
  - pynacl=1.4.0=py38h7b6447c_1
  - pyodbc=4.0.30=py38he6710b0_0
  - pyopenssl=19.1.0=pyhd3eb1b0_1
  - pyparsing=2.4.7=pyhd3eb1b0_0
  - pysocks=1.7.1=py38h06a4308_0
  - python=3.8.8=hdb3f193_4
  - python-dateutil=2.8.1=pyhd3eb1b0_0
  - python-editor=1.0.4=py_0
  - pytz=2020.5=pyhd3eb1b0_0
  - pyzmq=19.0.2=py38he6710b0_1
  - readline=8.0=h7b6447c_0
  - regex=2020.10.15=py38h7b6447c_0
  - requests=2.24.0=py_0
  - requests-oauthlib=1.3.0=py_0
  - retrying=1.3.3=py_2
  - rsa=4.7.2=pyhd3eb1b0_1
  - s3transfer=0.3.6=pyhd3eb1b0_0
  - scikit-learn=0.23.2=py38h0573a6f_0
  - scipy=1.5.2=py38h0b6359f_0
  - setuptools=50.3.1=py38h06a4308_1
  - simplejson=3.17.2=py38h27cfd23_2
  - six=1.15.0=py38h06a4308_0
  - smmap=3.0.5=pyhd3eb1b0_0
  - sqlite=3.33.0=h62c20be_0
  - sqlparse=0.4.1=py_0
  - statsmodels=0.12.0=py38h7b6447c_0
  - tabulate=0.8.7=py38h06a4308_0
  - threadpoolctl=2.1.0=pyh5ca1d4c_0
  - tk=8.6.10=hbc83047_0
  - tornado=6.0.4=py38h7b6447c_1
  - tqdm=4.50.2=py_0
  - traitlets=5.0.5=pyhd3eb1b0_0
  - typing-extensions=3.7.4.3=hd3eb1b0_0
  - typing_extensions=3.7.4.3=pyh06a4308_0
  - unixodbc=2.3.9=h7b6447c_0
  - urllib3=1.25.11=py_0
  - wcwidth=0.2.5=py_0
  - websocket-client=0.57.0=py38_2
  - werkzeug=1.0.1=pyhd3eb1b0_0
  - wheel=0.35.1=pyhd3eb1b0_0
  - wrapt=1.12.1=py38h7b6447c_1
  - xz=5.2.5=h7b6447c_0
  - yarl=1.6.3=py38h27cfd23_0
  - zeromq=4.3.3=he6710b0_3
  - zipp=3.4.0=pyhd3eb1b0_0
  - zlib=1.2.11=h7b6447c_3
  - zstd=1.4.5=h9ceee32_0
  - pip:
      - argon2-cffi==20.1.0
      - astunparse==1.6.3
      - async-generator==1.10
      - azure-core==1.11.0
      - azure-storage-blob==12.7.1
      - bleach==3.3.0
      - confuse==1.4.0
      - convertdate==2.3.2
      - databricks-cli==0.14.3
      - defusedxml==0.7.1
      - diskcache==5.2.1
      - docker==4.4.4
      - facets-overview==1.0.0
      - flatbuffers==1.12
      - gast==0.3.3
      - hijri-converter==2.1.1
      - holidays==0.10.5.2
      - horovod==0.21.3
      - htmlmin==0.1.12
      - imagehash==4.2.0
      - ipywidgets==7.6.3
      - joblibspark==0.3.0
      - jsonschema==3.2.0
      - jupyterlab-pygments==0.1.2
      - jupyterlab-widgets==1.0.0
      - keras-preprocessing==1.1.2
      - koalas==1.8.0
      - korean-lunar-calendar==0.2.1
      - llvmlite==0.36.0
      - missingno==0.4.2
      - mistune==0.8.4
      - mleap==0.16.1
      - mlflow-skinny==1.17.0
      - msrest==0.6.21
      - nbclient==0.5.3
      - nbconvert==6.0.7
      - nbformat==5.1.3
      - nest-asyncio==1.5.1
      - notebook==6.4.0
      - numba==0.53.1
      - opt-einsum==3.3.0
      - pandas-profiling==2.11.0
      - pandocfilters==1.4.3
      - petastorm==0.10.0
      - phik==0.11.2
      - pyarrow==1.0.1
      - pymeeus==0.5.11
      - pyrsistent==0.17.3
      - pywavelets==1.1.1
      - pyyaml==5.4.1
      - querystring-parser==1.2.4
      - seaborn==0.10.0
      - send2trash==1.5.0
      - shap==0.39.0
      - slicer==0.0.7
      - spark-tensorflow-distributor==0.1.0
      - tangled-up-in-unicode==0.1.0
      - tensorboard==2.4.1
      - tensorboard-plugin-wit==1.8.0
      - tensorflow==2.4.1
      - tensorflow-estimator==2.4.0
      - termcolor==1.1.0
      - terminado==0.9.5
      - testpath==0.5.0
      - torch==1.8.1
      - torchvision==0.9.1
      - visions==0.6.0
      - webencodings==0.5.1
      - widgetsnbextension==3.5.1
      - xgboost==1.4.1
prefix: /databricks/conda/envs/databricks-ml-gpu

Spark pacote contendo os módulos Python

Spark pacote	Módulo Python	Versão
graphframes	graphframes	0.8.1-db3-spark3.1

R biblioteca

A biblioteca R é idêntica à biblioteca R em Databricks Runtime 8.3.

Java e biblioteca ( 2.12 clustering) Scala Scala

Além de Java e Scala biblioteca em Databricks Runtime 8.3, Databricks Runtime 8.3 ML contém os seguintes JARs:

Agrupamento de CPU

ID do grupo	ID do artefato	Versão
com.typesafe.akka	também conhecido como actor_2.12	2.5.23
ml.combust.mleap	mleap-databricks-runtime_2.12	0,17.3-4882dc3
ml.dmlc	xgboost4j-spark_2.12	1.4.1
ml.dmlc	xgboost4j_2,12	1.4.1
org.mlflow	cliente mlflow	1,17.0
org.Scala-lang.modules	Scala-java8-compat_2.12	0,8.0
org.tensorflow	spark-tensorflow-connector_2.12	1,15.0

Agrupamento de GPU

ID do grupo	ID do artefato	Versão
com.typesafe.akka	também conhecido como 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	1.4.1
ml.dmlc	xgboost4j-gpu_2,12	1.4.1
org.mlflow	cliente mlflow	1,17.0
org.Scala-lang.modules	Scala-java8-compat_2.12	0,8.0
org.tensorflow	spark-tensorflow-connector_2.12	1,15.0