74 lines
4.2 KiB
Markdown
74 lines
4.2 KiB
Markdown
# Databricks User Guide
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---
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You can run BigDL program on the [Databricks](https://databricks.com/) cluster as follows.
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### **1. Create a Databricks Cluster**
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- Create either [AWS Databricks](https://docs.databricks.com/getting-started/try-databricks.html) workspace or [Azure Databricks](https://docs.microsoft.com/en-us/azure/azure-databricks/) workspace.
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- Create a Databricks [clusters](https://docs.databricks.com/clusters/create.html) using the UI. Choose Databricks runtime version. This guide is tested on Runtime 7.3 (includes Apache Spark 3.0.1, Scala 2.12).
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### **2. Installing BigDL Python libraries**
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In the left pane, click **Clusters** and select your cluster.
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Install BigDL DLLib python environment using prebuilt release Wheel package. Click **Libraries > Install New > Upload > Python Whl**. Download BigDL DLLib prebuilt Wheel [here](https://sourceforge.net/projects/analytics-zoo/files/dllib-py). Choose a wheel with timestamp for the same Spark version and platform as Databricks runtime. Download and drop it on Databricks.
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Install BigDL Orca python environment using prebuilt release Wheel package. Click **Libraries > Install New > Upload > Python Whl**. Download Bigdl Orca prebuilt Wheel [here](https://sourceforge.net/projects/analytics-zoo/files/dllib-py). Choose a wheel with timestamp for the same Spark version and platform as Databricks runtime. Download and drop it on Databricks.
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If you want to use other BigDL libraries (Friesian, Chronos, Nano, Serving, etc.), download prebuilt release Wheel package from [here](https://sourceforge.net/projects/analytics-zoo/files/) and install to cluster in the similar ways.
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### **3. Installing BigDL Java libraries**
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Install BigDL DLLib prebuilt jar package. Click **Libraries > Install New > Upload > Jar**. Download BigDL DLLib prebuilt package from [Release Page](../release.md). Please note that you should choose the same spark version of package as your Databricks runtime version. Find jar named "bigdl-dllib-spark_*-jar-with-dependencies.jar" in the lib directory. Drop the jar on Databricks.
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Install BigDL Orca prebuilt jar package. Click **Libraries > Install New > Upload > Jar**. Download BigDL Orca prebuilt package from [Release Page](../release.md). Please note that you should choose the same spark version of package as your Databricks runtime version. Find jar named "bigdl-orca-spark_*-jar-with-dependencies.jar" in the lib directory. Drop the jar on Databricks.
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If you want to use other BigDL libraries (Friesian, Chronos, Nano, Serving, etc.), download prebuilt jar package from [Release Page](../release.md) and install to cluster in the similar ways.
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Make sure the jar files and whl files are installed on all clusters. In **Libraries** tab of your cluster, check installed libraries and click “Install automatically on all clusters” option in **Admin Settings**.
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### **4. Setting Spark configuration**
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On the cluster configuration page, click the **Advanced Options** toggle. Click the **Spark** tab. You can provide custom [Spark configuration properties](https://spark.apache.org/docs/latest/configuration.html) in a cluster configuration. Please set it according to your cluster resource and program needs.
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See below for an example of Spark config setting needed by BigDL. Here it sets 2 core per executor. Note that "spark.cores.max" needs to be properly set below.
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```
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spark.executor.cores 2
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spark.cores.max 4
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```
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### **5. Running BigDL on Databricks**
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Open a new notebook, and call `init_orca_context` at the beginning of your code (with `cluster_mode` set to "spark-submit").
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```python
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from bigdl.orca import init_orca_context, stop_orca_context
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init_orca_context(cluster_mode="spark-submit")
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```
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Output on Databricks:
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### **6. Install other third-party libraries on Databricks if necessary**
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If you want to use other third-party libraries, check related Databricks documentation of [libraries for AWS Databricks](https://docs.databricks.com/libraries/index.html) and [libraries for Azure Databricks](https://docs.microsoft.com/en-us/azure/databricks/libraries/).
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