Databricks Runtime 5.1 ML (EoS)
Note
Support for this Databricks Runtime version has ended. For the end-of-support date, see End-of-support history. For all supported Databricks Runtime versions, see Databricks Runtime release notes versions and compatibility.
Databricks released this version in December 2018.
Databricks Runtime 5.1 ML provides a ready-to-go environment for machine learning and data science based on Databricks Runtime 5.1 (EoS). Databricks Runtimes for ML contain many popular machine learning libraries, including TensorFlow, PyTorch, Keras, and XGBoost. It also supports distributed TensorFlow training using Horovod.
For more information, including instructions for creating a Databricks Runtime ML cluster, see AI and machine learning on Databricks.
New features
Databricks Runtime 5.1 ML is built on top of Databricks Runtime 5.1. For information on what’s new in Databricks Runtime 5.1, see the Databricks Runtime 5.1 (EoS) release notes. In addition to the updates to existing libraries in Libraries, Databricks Runtime 5.1 ML includes the following new features:
- PyTorch for building deep learning networks.
Note
Databricks Runtime ML releases pick up all maintenance updates to the base Databricks Runtime release. For a list of all maintenance updates, see Maintenance updates for Databricks Runtime (archived).
System environment
The difference in system environment in Databricks Runtime 5.1 and that in Databricks Runtime 5.1 ML is:
- Python: 2.7.15 for Python 2 clusters and 3.6.5 for Python 3 clusters.
- DBUtils: Databricks Runtime 5.1 ML does not contain Library utility (dbutils.library) (legacy).
- For GPU clusters, the following NVIDIA GPU libraries:
- Tesla driver 396.44
- CUDA 9.2
- CUDNN 7.2.1
Libraries
The differences in the libraries included in Databricks Runtime 5.1 and those included in Databricks Runtime 5.1 ML are listed in this section.
Python libraries
Databricks Runtime 5.1 ML uses Conda for Python package management. As a result, there are major changes in pre-installed Python libraries compared to Databricks Runtime. Following is the full list of provided Python packages and versions installed using Conda package manager.
| Library | Version | Library | Version | Library | Version |
|---|---|---|---|---|---|
| absl-py | 0.6.1 | argparse | 1.4.0 | asn1crypto | 0.24.0 |
| astor | 0.7.1 | backports-abc | 0.5 | backports.functools-lru-cache | 1.5 |
| backports.weakref | 1.0.post1 | bcrypt | 3.1.4 | bleach | 2.1.3 |
| boto | 2.48.0 | boto3 | 1.7.62 | botocore | 1.10.62 |
| certifi | 2018.04.16 | cffi | 1.11.5 | chardet | 3.0.4 |
| cloudpickle | 0.5.3 | colorama | 0.3.9 | configparser | 3.5.0 |
| cryptography | 2.2.2 | cycler | 0.10.0 | Cython | 0.28.2 |
| decorator | 4.3.0 | docutils | 0.14 | entrypoints | 0.2.3 |
| enum34 | 1.1.6 | et-xmlfile | 1.0.1 | funcsigs | 1.0.2 |
| functools32 | 3.2.3-2 | fusepy | 2.0.4 | futures | 3.2.0 |
| gast | 0.2.0 | grpcio | 1.12.1 | h5py | 2.8.0 |
| horovod | 0.15.0 | html5lib | 1.0.1 | idna | 2.6 |
| ipaddress | 1.0.22 | ipython | 5.7.0 | ipython_genutils | 0.2.0 |
| jdcal | 1.4 | Jinja2 | 2.10 | jmespath | 0.9.3 |
| jsonschema | 2.6.0 | jupyter-client | 5.2.3 | jupyter-core | 4.4.0 |
| Keras | 2.2.4 | Keras-Applications | 1.0.6 | Keras-Preprocessing | 1.0.5 |
| kiwisolver | 1.0.1 | linecache2 | 1.0.0 | llvmlite | 0.23.1 |
| lxml | 4.2.1 | Markdown | 3.0.1 | MarkupSafe | 1.0 |
| matplotlib | 2.2.2 | mistune | 0.8.3 | mleap | 0.8.1 |
| mock | 2.0.0 | msgpack | 0.5.6 | nbconvert | 5.3.1 |
| nbformat | 4.4.0 | nose | 1.3.7 | nose-exclude | 0.5.0 |
| numba | 0.38.0+0.g2a2b772fc.dirty | numpy | 1.14.3 | olefile | 0.45.1 |
| openpyxl | 2.5.3 | pandas | 0.23.0 | pandocfilters | 1.4.2 |
| paramiko | 2.4.1 | pathlib2 | 2.3.2 | patsy | 0.5.0 |
| pbr | 5.1.1 | pexpect | 4.5.0 | pickleshare | 0.7.4 |
| Pillow | 5.1.0 | pip | 10.0.1 | ply | 3.11 |
| prompt-toolkit | 1.0.15 | protobuf | 3.6.1 | psycopg2 | 2.7.5 |
| ptyprocess | 0.5.2 | pyarrow | 0.8.0 | pyasn1 | 0.4.4 |
| pycparser | 2.18 | Pygments | 2.2.0 | PyNaCl | 1.3.0 |
| pyOpenSSL | 18.0.0 | pyparsing | 2.2.0 | PySocks | 1.6.8 |
| Python | 2.7.15 | python-dateutil | 2.7.3 | pytz | 2018.4 |
| PyYAML | 3.12 | pyzmq | 17.0.0 | requests | 2.18.4 |
| s3transfer | 0.1.13 | scandir | 1.7 | scikit-learn | 0.19.1 |
| scipy | 1.1.0 | seaborn | 0.8.1 | setuptools | 39.1.0 |
| simplegeneric | 0.8.1 | singledispatch | 3.4.0.3 | six | 1.11.0 |
| statsmodels | 0.9.0 | subprocess32 | 3.5.3 | tensorboard | 1.12.0 |
| tensorboardX | 1.4 | tensorflow | 1.12.0 | termcolor | 1.1.0 |
| testpath | 0.3.1 | torch | 0.4.1 | torchvision | 0.2.1 |
| tornado | 5.0.2 | traceback2 | 1.4.0 | traitlets | 4.3.2 |
| unittest2 | 1.1.0 | urllib3 | 1.22 | virtualenv | 16.0.0 |
| wcwidth | 0.1.7 | webencodings | 0.5.1 | Werkzeug | 0.14.1 |
| wheel | 0.31.1 | wrapt | 1.10.11 | wsgiref | 0.1.2 |
In addition, the following Spark packages include Python modules:
| Spark Package | Python Module | Version |
|---|---|---|
| tensorframes | tensorframes | 0.6.0-s_2.11 |
| graphframes | graphframes | 0.6.0-db3-spark2.4 |
| spark-deep-learning | sparkdl | 1.4.0-db2-spark2.4 |
R libraries
The R libraries are identical to R Libraries on Databricks Runtime 5.1.
Java and Scala libraries (Scala 2.11 cluster)
In addition to Java and Scala libraries in Databricks Runtime 5.1, Databricks Runtime 5.1 ML contains the following JARs:
| Group ID | Artifact ID | Version |
|---|---|---|
| com.databricks | spark-deep-learning | 1.4.0-db2-spark2.4 |
| org.tensorframes | tensorframes | 0.6.0-s_2.11 |
| org.graphframes | graphframes_2.11 | 0.6.0-db3-spark2.4 |
| org.tensorflow | libtensorflow | 1.12.0 |
| org.tensorflow | libtensorflow_jni | 1.12.0 |
| org.tensorflow | spark-tensorflow-connector_2.11 | 1.12.0 |
| org.tensorflow | tensorflow | 1.12.0 |
| ml.dmlc | xgboost4j | 0.81 |
| ml.dmlc | xgboost4j-spark | 0.81 |
| ml.combust.mleap | mleap-databricks-runtime_2.11 | 0.13.0 |