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Databricks Runtime 7.6 untuk Pembelajaran Mesin (EoS)

Catatan

Dukungan untuk versi Databricks Runtime ini telah berakhir. Untuk tanggal akhir dukungan, lihat Riwayat akhir dukungan. Untuk semua versi Runtime Databricks yang didukung, lihat Versi dan kompatibilitas catatan rilis Databricks Runtime.

Databricks merilis versi ini pada Februari 2021.

Databricks Runtime 7.6 untuk Pembelajaran Mesin menyediakan lingkungan siap pakai untuk pembelajaran mesin dan ilmu data berdasarkan Databricks Runtime 7.6 (EoS). Runtime Databricks ML berisi banyak pustaka populer untuk pembelajaran mesin, termasuk TensorFlow, PyTorch, dan XGBoost. Ini juga mendukung pelatihan pembelajaran mendalam terdistribusi menggunakan Horovod.

Untuk informasi selengkapnya, termasuk instruksi untuk membuat kluster ML Runtime Databricks, lihat AI dan pembelajaran mesin di Databricks.

Untuk bantuan tentang migrasi dari Databricks Runtime 6.x, lihat Panduan migrasi Databricks Runtime 7.x (EoS).

Fitur baru dan perubahan besar

Databricks Runtime 7.6 ML dibangun di atas basis Databricks Runtime 7.6. Untuk informasi tentang apa yang baru di Databricks Runtime 7.6, termasuk Apache Spark MLlib dan SparkR, lihat catatan rilis Databricks Runtime 7.6 (EoS).

Pemendaman

  • Tensorflow 1.x tidak akan didukung dalam rilis utama Databricks Runtime yang akan datang.
  • Paket CUDA berikut tidak digunakan lagi dan akan dihapus dalam rilis utama Databricks Runtime yang akan datang:
    • Alat Baris Perintah CUDA
    • pengkompilasi CUDA
    • cuda-cudart-dev
    • cuda-manfft
    • cuda-cufft-dev
    • cuda-cuobjdump
    • cuda-cupti
    • cuda-curand
    • cuda-curand-dev
    • cuda-cusolver
    • cuda-cusolver-dev
    • cuda-cusparse
    • cuda-cusparse-dev
    • dokumentasi cuda
    • cuda-driver-dev
    • cuda-gdb
    • penasihat-perpustakaan-cuda-gpu
    • cuda-libraries-dev
    • lisensi CUDA
    • cuda-memcheck
    • cuda-minimal-build
    • cuda-misc-headers
    • cuda-npp
    • cuda-npp-dev
    • cuda-nsight
    • cuda-nvcc
    • cuda-nvdisasm
    • cuda-nvgraph
    • cuda-nvgraph-dev
    • cuda-nvjpeg
    • cuda-nvjpeg-dev
    • cuda-nvml-dev
    • cuda-nvprune
    • cuda-nvrtc-dev
    • cuda-nvvp
    • cuda-samples
    • cuda-sanitizer-api
    • CUDA Toolkit
    • Alat CUDA
    • Alat Visual CUDA
    • freeglut3
    • libcublas-dev
    • libcudnn7-dev
    • libdrm-dev
    • libegl1
    • libegm-mesa0
    • libgbl1-mesa-dev
    • libgbm1
    • libgles1
    • libgles2
    • libglu1-mesa
    • libglu1-mesa-dev
    • libnccl-dev
    • libnvinfer-dev
    • libnvinfer-plugin-dev
    • libopengl0
    • libwayland-server0
    • libx11-xcb-dev
    • libxcb-dri2-0-dev
    • libxcb-dri3-dev
    • libxcb-glx0-dev
    • libxcb-present-dev
    • libxcb-randr0
    • libxcb-randr0-dev
    • libxcb-render0-dev
    • libxcb-shape0-dev
    • libxcb-sync-dev
    • libxcb-xfixes0
    • libxcb-xfixes0-dev
    • libxdamage-dev
    • libxext-dev
    • libxfixes-dev
    • libxi-dev
    • libxmu-dev
    • libxmu-headers
    • libxshmfence-dev
    • libxxf86vm-dev
    • mesa-common-dev
    • nsight-compute
    • nsight-systems
    • x11proto-damage-dev
    • x11proto-fixes-dev
    • x11proto-input-dev
    • x11proto-xext-dev
    • x11proto-xf86vidmode-dev

Perubahan besar pada lingkungan Python ML di Databricks Runtime

Lihat Databricks Runtime 7.6 (EoS) untuk perubahan besar pada lingkungan Databricks Runtime Python. Untuk daftar lengkap paket Python yang diinstal dan versinya, lihat Pustaka Python.

Paket Phyton ditingkatkan

  • databricks-cli 0.14.0 -> 0.14.1
  • koala 1.4.0 -> 1.5.0
  • lightgbm 2.3.0 -> 3.1.1
  • mlflow 1.12.1 -> 1.13.1
  • plotly 4.12.0 -> 4.14.1
  • pytorch 1.7.0 -> 1.7.1
  • torchvision 0.8.1 -> 0.8.2
  • xgboost 1.2.1 -> 1.3.1

Penyempurnaan

Integrasi XGBoost ke dalam PySpark (Pratinjau Umum)

Integrasi XGBoost dengan PySpark telah ditingkatkan. Paket sparkdl 2.1.0-db5 ini mencakup dua estimator pyspark ML baru, XgboostRegressor dan XgboostClassifier, yang memungkinkan pengguna untuk melatih model XGBoost di Alur PySpark ML.

Sebelum versi ini, XGBoost tidak terintegrasi dengan PySpark. Pengguna harus menggunakan xgboost4j-spark di Scala atau memecahkan Pipeline PySpark ML, mengumpulkan Spark DataFrame pada driver sebagai DataFrame Pandas, dan menggunakan paket Python xgboost. Lihat dokumentasi sparkdl API dan Menggunakan XGBoost di Azure Databricks untuk detail lebih lanjut.

Lingkungan sistem

Lingkungan sistem di Databricks Runtime 7.6 ML berbeda dari Databricks Runtime 7.6 berikut ini:

Pustaka

Bagian berikut mencantumkan pustaka yang disertakan dalam Databricks Runtime 7.6 ML yang berbeda dari yang termasuk dalam Databricks Runtime 7.6.

Di bagian ini:

Pustaka tingkat atas

Databricks Runtime 7.6 ML mencakup pustaka tingkat atas berikut:

Perpustakaan Python

Databricks Runtime 7.6 ML menggunakan Conda untuk manajemen paket Python dan mencakup banyak paket ML populer.

Selain paket yang ditentukan di lingkungan Conda di bagian berikut, Databricks Runtime 7.6 ML juga menginstal paket berikut:

  • hyperopt 0.2.5.db1
  • sparkdl 2.1.0-db5

Pustaka Python di kluster 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=pyhd3eb1b0_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=1.17.1=h27cfd23_0
  - ca-certificates=2021.1.19=h06a4308_1 # (updated from h06a4308_0 in May 26, 2021 maintenance update)
  - cachetools=4.2.0=pyhd3eb1b0_0
  - certifi=2020.12.5=py37h06a4308_0
  - cffi=1.14.0=py37he30daa8_1 # (updated from py37h2e261b9_0 in May 26, 2021 maintenance update)
  - chardet=3.0.4=py37h06a4308_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=pyhd3eb1b0_1
  - isodate=0.6.0=py_1
  - itsdangerous=1.1.0=py37_0
  - jedi=0.17.2=py37h06a4308_1
  - 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=1.17.1=h173b8e3_0 # (updated from 1.16.4 in May 26, 2021 maintenance update)
  - ld_impl_linux-64=2.33.1=h53a641e_7
  - libedit=3.1.20181209=hc058e9b_0
  - libffi=3.3=he6710b0_2 # (updated from 3.2.1 in May 26, 2021 maintenance update)
  - 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 # (updated from 11.2 in May 26, 2021 maintenance update)
  - 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
  - libuv=1.40.0=h7b6447c_0
  - lightgbm=3.1.1=py37h2531618_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=py37he8ac12f_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=1.10.2=py37hff7bd54_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=1.1.1k=h27cfd23_0 # (updated from 1.1.1i in May 26, 2021 maintenance update)
  - 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=pyhd3eb1b0_3
  - pickleshare=0.7.5=pyhd3eb1b0_1003
  - pillow=7.0.0=py37hb39fc2d_0
  - pip=20.0.2=py37_3
  - plotly=4.14.1=pyhd3eb1b0_0
  - prompt_toolkit=3.0.3=py_0
  - protobuf=3.11.4=py37he6710b0_0
  - psutil=5.6.7=py37h7b6447c_0
  - psycopg2=2.8.6=py37h3c74f83_1 # (updated from 2.8.4 in May 26, 2021 maintenance update)
  - ptyprocess=0.6.0=pyhd3eb1b0_2
  - 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=2.0.1=py37h06a4308_0
  - pynacl=1.3.0=py37h7b6447c_0
  - pyodbc=4.0.30=py37he6710b0_0
  - pyopenssl=19.1.0=pyhd3eb1b0_1
  - pyparsing=2.4.6=py_0
  - pysocks=1.7.1=py37_1
  - python=3.7.10=hdb3f193_0 # (updated from 3.7.6 in May 26, 2021 maintenance update)
  - python-dateutil=2.8.1=py_0
  - python-editor=1.0.4=py_0
  - pytorch=1.7.1=py3.7_cpu_0
  - pytz=2019.3=py_0
  - pyzmq=18.1.1=py37he6710b0_0
  - readline=8.1=h27cfd23_0 # (updated from 7.0 in May 26, 2021 maintenance update)
  - 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=0.3.4=pyhd3eb1b0_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=py37h06a4308_0
  - smmap=3.0.4=py_0
  - sqlite=3.35.4=hdfb4753_0 # (updated from 3.31.1 in May 26, 2021 maintenance update)
  - sqlparse=0.4.1=py_0
  - statsmodels=0.11.0=py37h7b6447c_0
  - tabulate=0.8.3=py37_0
  - tk=8.6.10=hbc83047_0 # (updated from 8.6.8 in May 26, 2021 maintenance update)
  - torchvision=0.8.2=py37_cpu
  - tornado=6.0.3=py37h7b6447c_3
  - tqdm=4.42.1=py_0
  - traitlets=4.3.3=py37_0
  - typing_extensions=3.7.4.3=py_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=5.2.5=h7b6447c_0 # (updated from 5.2.4 in May 26, 2021 maintenance update)
  - 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==1.10.0
      - azure-storage-blob==12.7.0
      - databricks-cli==0.14.1
      - diskcache==5.1.0
      - docker==4.4.1
      - gorilla==0.3.0
      - horovod==0.20.3
      - joblibspark==0.3.0
      - keras-preprocessing==1.1.2
      - koalas==1.5.0
      - mleap==0.16.1
      - mlflow==1.13.1
      - msrest==0.6.19
      - opt-einsum==3.3.0
      - petastorm==0.9.7
      - pyarrow==1.0.1
      - pyyaml==5.4
      - querystring-parser==1.2.4
      - seaborn==0.10.0
      - spark-tensorflow-distributor==0.1.0
      - tensorboard==2.3.0
      - tensorboard-plugin-wit==1.8.0
      - tensorflow-cpu==2.3.1
      - tensorflow-estimator==2.3.0
      - termcolor==1.1.0
      - xgboost==1.3.1
prefix: /databricks/conda/envs/databricks-ml

Perpustakaan Python di kluster 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=pyhd3eb1b0_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=1.17.1=h27cfd23_0
  - ca-certificates=2021.1.19=h06a4308_1 # (updated from h06a4308_0 in May 26, 2021 maintenance update)
  - cachetools=4.2.0=pyhd3eb1b0_0
  - certifi=2020.12.5=py37h06a4308_0
  - cffi=1.14.0=py37he30daa8_1 # (updated from py37h2e261b9_0 in May 26, 2021 maintenance update)
  - chardet=3.0.4=py37h06a4308_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=pyhd3eb1b0_1
  - isodate=0.6.0=py_1
  - itsdangerous=1.1.0=py37_0
  - jedi=0.17.2=py37h06a4308_1
  - 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=1.17.1=h173b8e3_0 # (updated from 1.16.4 in May 26, 2021 maintenance update)
  - ld_impl_linux-64=2.33.1=h53a641e_7
  - libedit=3.1.20181209=hc058e9b_0
  - libffi=3.3=he6710b0_2 # (updated from 3.2.1 in May 26, 2021 maintenance update)
  - 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 # (updated from 11.2 in May 26, 2021 maintenance update)
  - 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
  - libuv=1.40.0=h7b6447c_0
  - lightgbm=3.1.1=py37h2531618_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=py37he8ac12f_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=1.10.2=py37hff7bd54_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=1.1.1k=h27cfd23_0 # (updated from 1.1.1i in May 26, 2021 maintenance update)
  - 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=pyhd3eb1b0_3
  - pickleshare=0.7.5=pyhd3eb1b0_1003
  - pillow=7.0.0=py37hb39fc2d_0
  - pip=20.0.2=py37_3
  - plotly=4.14.1=pyhd3eb1b0_0
  - prompt_toolkit=3.0.3=py_0
  - protobuf=3.11.4=py37he6710b0_0
  - psutil=5.6.7=py37h7b6447c_0
  - psycopg2=2.8.6=py37h3c74f83_1 # (updated from 2.8.4 in May 26, 2021 maintenance update)
  - ptyprocess=0.6.0=pyhd3eb1b0_2
  - 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=2.0.1=py37h06a4308_0
  - pynacl=1.3.0=py37h7b6447c_0
  - pyodbc=4.0.30=py37he6710b0_0
  - pyopenssl=19.1.0=pyhd3eb1b0_1
  - pyparsing=2.4.6=py_0
  - pysocks=1.7.1=py37_1
  - python=3.7.10=hdb3f193_0 # (updated from 3.7.6 in May 26, 2021 maintenance update)
  - python-dateutil=2.8.1=py_0
  - python-editor=1.0.4=py_0
  - pytorch=1.7.1=py3.7_cuda10.1.243_cudnn7.6.3_0
  - pytz=2019.3=py_0
  - pyzmq=18.1.1=py37he6710b0_0
  - readline=8.1=h27cfd23_0 # (updated from 7.0 in May 26, 2021 maintenance update)
  - 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=0.3.4=pyhd3eb1b0_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=py37h06a4308_0
  - smmap=3.0.4=py_0
  - sqlite=3.35.4=hdfb4753_0 # (updated from 3.31.1 in May 26, 2021 maintenance update)
  - sqlparse=0.4.1=py_0
  - statsmodels=0.11.0=py37h7b6447c_0
  - tabulate=0.8.3=py37_0
  - tk=8.6.10=hbc83047_0 # (updated from 8.6.8 in May 26, 2021 maintenance update)
  - torchvision=0.8.2=py37_cu101
  - tornado=6.0.3=py37h7b6447c_3
  - tqdm=4.42.1=py_0
  - traitlets=4.3.3=py37_0
  - typing_extensions=3.7.4.3=py_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=5.2.5=h7b6447c_0 # (updated from 5.2.4 in May 26, 2021 maintenance update)
  - 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==1.10.0
      - azure-storage-blob==12.7.0
      - databricks-cli==0.14.1
      - diskcache==5.1.0
      - docker==4.4.1
      - gorilla==0.3.0
      - horovod==0.20.3
      - joblibspark==0.3.0
      - keras-preprocessing==1.1.2
      - koalas==1.5.0
      - mleap==0.16.1
      - mlflow==1.13.1
      - msrest==0.6.19
      - opt-einsum==3.3.0
      - petastorm==0.9.7
      - pyarrow==1.0.1
      - pyyaml==5.4
      - querystring-parser==1.2.4
      - seaborn==0.10.0
      - spark-tensorflow-distributor==0.1.0
      - tensorboard==2.3.0
      - tensorboard-plugin-wit==1.8.0
      - tensorflow==2.3.1
      - tensorflow-estimator==2.3.0
      - termcolor==1.1.0
      - xgboost==1.3.1
prefix: /databricks/conda/envs/databricks-ml-gpu

Paket Spark yang berisi modul Python

Paket Spark Modul Python Versi
graphframe graphframe 0.8.1-db1-spark3.0

Pustaka R

Pustaka R identik dengan Pustaka R di Databricks Runtime 7.6.

Pustaka Java dan Scala (Kluster Scala 2.12)

Selain pustaka Java dan Scala di Databricks Runtime 7.6, Databricks Runtime 7.6 ML berisi JAR berikut ini:

Kluster CPU

ID Grup ID Artefak Versi
com.typeafe.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 1.13.1
org.scala-lang.modules scala-java8-compat_2.12 0.8.0
org.tensorflow spark-tensorflow-connector_2.12 1.15.0

Kluster GPU

ID Grup ID Artefak Versi
com.typeafe.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 1.13.1
org.scala-lang.modules scala-java8-compat_2.12 0.8.0
org.tensorflow spark-tensorflow-connector_2.12 1.15.0