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85 changes: 43 additions & 42 deletions README.md
Expand Up @@ -65,25 +65,25 @@ PySpark](http://mmlspark.azureedge.net/docs/pyspark/).

See our [notebooks](notebooks/samples/) for all examples.

[example:101]: notebooks/samples/101%20-%20Adult%20Census%20Income%20Training.ipynb
[example:101]: notebooks/samples/Classification%20-%20Adult%20Census.ipynb
"Adult Census Income Training"
[example:102]: notebooks/samples/102%20-%20Regression%20Example%20with%20Flight%20Delay%20Dataset.ipynb
[example:102]: notebooks/samples/Regression%20-%20Flight%20Delays.ipynb
"Regression Example with Flight Delay Dataset"
[example:106]: notebooks/samples/106%20-%20Quantile%20Regression%20with%20LightGBM.ipynb
[example:106]: notebooks/samples/LightGBM%20-%20Quantile%20Regression%20for%20Drug%20Discovery.ipynb
"Quantile Regression with LightGBM"
[example:201]: notebooks/samples/201%20-%20Amazon%20Book%20Reviews%20-%20TextFeaturizer.ipynb
[example:201]: notebooks/samples/TextAnalytics%20-%20Amazon%20Book%20Reviews.ipynb
"Amazon Book Reviews - TextFeaturizer"
[example:203]: notebooks/samples/203%20-%20Breast%20Cancer%20-%20Tune%20Hyperparameters.ipynb
[example:203]: notebooks/samples/HyperParameterTuning%20-%20Fighting%20Breast%20Cancer.ipynb
"Hyperparameter Tuning with MMLSpark"
[example:301]: notebooks/samples/301%20-%20CIFAR10%20CNTK%20CNN%20Evaluation.ipynb
[example:301]: notebooks/samples/DeepLearning%20-%20CIFAR10%20Convolutional%20Network.ipynb
"CIFAR10 CNTK CNN Evaluation"
[example:302]: notebooks/samples/302%20-%20Pipeline%20Image%20Transformations.ipynb
[example:302]: notebooks/samples/OpenCV%20-%20Pipeline%20Image%20Transformations.ipynb
"Pipeline Image Transformations"
[example:304]: notebooks/samples/304%20-%20Medical%20Entity%20Extraction.ipynb
[example:304]: notebooks/samples/DeepLearning%20-%20BiLSTM%20Medical%20Entity%20Extraction.ipynb
"Medical Entity Extraction"
[example:305]: notebooks/samples/305%20-%20Flowers%20ImageFeaturizer.ipynb
[example:305]: notebooks/samples/DeepLearning%20-%20Flower%20Image%20Classification.ipynb
"Deep Flower Classification"
[example:401]: notebooks/gpu/401%20-%20CNTK%20train%20on%20HDFS.ipynb
[example:401]: notebooks/gpu/DeepLearning%20-%20Distributed%20CNTK%20training.ipynb
"CIFAR10 CNTK CNN Training"

## A short example
Expand All @@ -110,6 +110,39 @@ documentation for [Scala](http://mmlspark.azureedge.net/docs/scala/) and

## Setup and installation

### Spark package

MMLSpark can be conveniently installed on existing Spark clusters via the
`--packages` option, examples:

```bash
spark-shell --packages Azure:mmlspark:0.13
pyspark --packages Azure:mmlspark:0.13
spark-submit --packages Azure:mmlspark:0.13 MyApp.jar
```

This can be used in other Spark contexts too, for example, you can use MMLSpark
in [AZTK](https://github.com/Azure/aztk/) by [adding it to the
`.aztk/spark-defaults.conf`
file](https://github.com/Azure/aztk/wiki/PySpark-on-Azure-with-AZTK#optional-set-up-mmlspark).

### Databricks

To install MMLSpark on the [Databricks
cloud](http://community.cloud.databricks.com), create a new [library from Maven
coordinates](https://docs.databricks.com/user-guide/libraries.html#libraries-from-maven-pypi-or-spark-packages)
in your workspace.

For the coordinates use: `Azure:mmlspark:0.13`. Ensure this library is
attached to all clusters you create.

Finally, ensure that your Spark cluster has at least Spark 2.1 and Scala 2.11.

You can use MMLSpark in both your Scala and PySpark notebooks. To get started with our example notebooks import the following databricks archive:

```https://mmlspark.blob.core.windows.net/dbcs/MMLSpark%20Examples%20v0.13.dbc```


### Docker

The easiest way to evaluate MMLSpark is via our pre-built Docker container. To
Expand All @@ -131,22 +164,6 @@ MMLSpark can be used to train deep learning models on GPU nodes from a Spark
application. See the instructions for [setting up an Azure GPU
VM](docs/gpu-setup.md).

### Spark package

MMLSpark can be conveniently installed on existing Spark clusters via the
`--packages` option, examples:

```bash
spark-shell --packages Azure:mmlspark:0.13
pyspark --packages Azure:mmlspark:0.13
spark-submit --packages Azure:mmlspark:0.13 MyApp.jar
```

This can be used in other Spark contexts too, for example, you can use MMLSpark
in [AZTK](https://github.com/Azure/aztk/) by [adding it to the
`.aztk/spark-defaults.conf`
file](https://github.com/Azure/aztk/wiki/PySpark-on-Azure-with-AZTK#optional-set-up-mmlspark).

### Python

To try out MMLSpark on a Python (or Conda) installation you can get Spark
Expand Down Expand Up @@ -181,22 +198,6 @@ the rest of the options as shown on the screenshot to the right.

Submit, and the cluster should finish configuring within 10 minutes or so.

### Databricks cloud

To install MMLSpark on the [Databricks
cloud](http://community.cloud.databricks.com), create a new [library from Maven
coordinates](https://docs.databricks.com/user-guide/libraries.html#libraries-from-maven-pypi-or-spark-packages)
in your workspace.

For the coordinates use: `Azure:mmlspark:0.13`. Ensure this library is
attached to all clusters you create.

Finally, ensure that your Spark cluster has at least Spark 2.1 and Scala 2.11.

You can use MMLSpark in both your Scala and PySpark notebooks. To get started with our example notebooks import the following databricks archive:

```https://mmlspark.blob.core.windows.net/dbcs/MMLSpark%20Examples%20v0.13.dbc```

### SBT

If you are building a Spark application in Scala, add the following lines to
Expand Down
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