Databricks Runtime 5.5 with Conda (unsupported)

Note

This release is no longer available. If you want to use Conda to manage Python libraries and environments, use a supported version of Databricks Runtime for Machine Learning.

We’re excited to introduce Databricks Runtime 5.5 with Conda (Beta), which lets you take advantage of Conda to manage Python libraries and environments. This runtime offers two root Conda environment options at cluster creation:

  • Databricks Standard environment includes updated versions of many popular Python packages. This environment is intended as a drop-in replacement for existing notebooks that run on Databricks Runtime. This is the default Databricks Conda-based runtime environment.

  • Databricks Minimal environment contains a minimum number of packages that are required for PySpark and Databricks Python notebook functionality. This environment is ideal if you want to customize the runtime with various Python packages.

Both include support for Databricks Library utility (dbutils.library) (legacy).

Note

The Scala, Java, and R libraries in Databricks Runtime 5.5 with Conda are identical to those in Databricks Runtime 5.5. For details, see the Databricks Runtime 5.5 LTS (unsupported) release notes. For information about how to use Databricks Runtime with Conda, see Conda.

New features

A new notebook-scoped library API is provided to support updating the notebook’s Conda environment with a YAML specification (see Conda documentation).

dbutils.library.updateCondaEnv('''envYmlContent''')

For example, to update the numpy library to 1.16.4, call the following:

dbutils.library.updateCondaEnv(
"""channels:
  - default
dependencies:
  - numpy=1.16.4""")

Note

You can get detailed information about updateCondaEnv using dbutils.library.help("updateCondaEnv").

Improvements

  • Packages in the standard and minimal environments are updated to newer versions. See Libraries for the full list of package versions. Here are some key package updates:

    • Python updated to 3.7.3, from 3.7.0

    • IPython updated to 7.4.0, from 6.5.0

    • pip updated to 19.0.3, from 10.0.1

  • To improve environment isolation between notebooks, process isolation and ADLS passthrough is enabled.

  • To allow you to install packages using conda install without needing to pass the easily-forgotten -y flag, the always_yes Conda configuration option is now set to True in the .condarc.

System environment

The system environment in Databricks Runtime 5.5 with Conda differs from Databricks Runtime 5.5 as follows:

  • Python: 3.7.x. Only Python 3 is supported.

Libraries

The following is the exported environment.yml file for default root environments on Databricks Runtime 5.5 with Conda.

Databricks Standard 

name: databricks-standard
channels:
  - defaults
dependencies:
  - asn1crypto=0.24.0=py37_0
  - backcall=0.1.0=py37_0
  - blas=1.0=openblas
  - boto=2.49.0=py37_0
  - boto3=1.9.162=py_0
  - botocore=1.12.163=py_0
  - ca-certificates=2019.1.23=0
  - certifi=2019.3.9=py37_0
  - cffi=1.12.2=py37h2e261b9_1
  - chardet=3.0.4=py37_1
  - cryptography=2.6.1=py37h1ba5d50_0
  - cython=0.29.6=py37he6710b0_0
  - decorator=4.4.0=py37_1
  - docutils=0.14=py37_0
  - idna=2.8=py37_0
  - ipython=7.4.0=py37h39e3cac_0
  - ipython_genutils=0.2.0=py37_0
  - jedi=0.13.3=py37_0
  - jmespath=0.9.4=py_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
  - libopenblas=0.3.6=h5a2b251_0
  - libpq=11.2=h20c2e04_0
  - libstdcxx-ng=8.2.0=hdf63c60_1
  - ncurses=6.1=he6710b0_1
  - nomkl=3.0=0
  - numpy=1.16.2=py37h99e49ec_0
  - numpy-base=1.16.2=py37h2f8d375_0
  - openssl=1.1.1b=h7b6447c_1
  - pandas=0.24.2=py37he6710b0_0
  - parso=0.3.4=py37_0
  - patsy=0.5.1=py37_0
  - pexpect=4.6.0=py37_0
  - pickleshare=0.7.5=py37_0
  - pip=19.0.3=py37_0
  - prompt_toolkit=2.0.9=py37_0
  - psycopg2=2.7.6.1=py37h1ba5d50_0
  - ptyprocess=0.6.0=py37_0
  - pycparser=2.19=py37_0
  - pygments=2.3.1=py37_0
  - pyopenssl=19.0.0=py37_0
  - pysocks=1.6.8=py37_0
  - python=3.7.3=h0371630_0
  - python-dateutil=2.8.0=py37_0
  - pytz=2018.9=py37_0
  - readline=7.0=h7b6447c_5
  - requests=2.21.0=py37_0
  - s3transfer=0.2.0=py37_0
  - scikit-learn=0.20.3=py37h22eb022_0
  - scipy=1.2.1=py37he2b7bc3_0
  - setuptools=40.8.0=py37_0
  - six=1.12.0=py37_0
  - sqlite=3.27.2=h7b6447c_0
  - statsmodels=0.9.0=py37h035aef0_0
  - tk=8.6.8=hbc83047_0
  - traitlets=4.3.2=py37_0
  - urllib3=1.24.1=py37_0
  - wcwidth=0.1.7=py37_0
  - wheel=0.33.1=py37_0
  - xz=5.2.4=h14c3975_4
  - zlib=1.2.11=h7b6447c_3
  - pip:
    - cycler==0.10.0
    - kiwisolver==1.1.0
    - matplotlib==3.0.3
    - pyarrow==0.12.0
    - pyparsing==2.4.0
    - seaborn==0.9.0
prefix: /databricks/conda/envs/databricks-standard

Databricks Minimal 

name: databricks-minimal
channels:
  - defaults
dependencies:
  - backcall=0.1.0=py37_0
  - blas=1.0=openblas
  - ca-certificates=2019.1.23=0
  - certifi=2019.3.9=py37_0
  - decorator=4.4.0=py37_1
  - ipython=7.4.0=py37h39e3cac_0
  - ipython_genutils=0.2.0=py37_0
  - jedi=0.13.3=py37_0
  - 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
  - libopenblas=0.3.6=h5a2b251_0
  - libstdcxx-ng=8.2.0=hdf63c60_1
  - ncurses=6.1=he6710b0_1
  - nomkl=3.0=0
  - numpy=1.16.2=py37h99e49ec_0
  - numpy-base=1.16.2=py37h2f8d375_0
  - openssl=1.1.1b=h7b6447c_1
  - pandas=0.24.2=py37he6710b0_0
  - parso=0.3.4=py37_0
  - pexpect=4.6.0=py37_0
  - pickleshare=0.7.5=py37_0
  - pip=19.0.3=py37_0
  - prompt_toolkit=2.0.9=py37_0
  - ptyprocess=0.6.0=py37_0
  - pygments=2.3.1=py37_0
  - python=3.7.3=h0371630_0
  - python-dateutil=2.8.0=py37_0
  - pytz=2018.9=py37_0
  - readline=7.0=h7b6447c_5
  - setuptools=40.8.0=py37_0
  - six=1.12.0=py37_0
  - sqlite=3.27.2=h7b6447c_0
  - tk=8.6.8=hbc83047_0
  - traitlets=4.3.2=py37_0
  - wcwidth=0.1.7=py37_0
  - wheel=0.33.1=py37_0
  - xz=5.2.4=h14c3975_4
  - zlib=1.2.11=h7b6447c_3
  - pip:
    - pyarrow==0.12.0
prefix: /databricks/conda/envs/databricks-minimal