update Databricks user guide (#5779)

* update databricks doc

* update databricks doc

* update databricks doc

* update databricks doc

* update databricks doc

* update databricks doc

Co-authored-by: Zhou <jian.zhou@intel.com>

docs/readthedocs/source/doc/UserGuide/databricks.md

@@ -5,44 +5,63 @@
 You can run BigDL program on the [Databricks](https://databricks.com/) cluster as follows.
 ### **1. Create a Databricks Cluster**
 
-- 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. 
+- Create either an [AWS Databricks](https://docs.databricks.com/getting-started/try-databricks.html) workspace or an [Azure Databricks](https://docs.microsoft.com/en-us/azure/azure-databricks/) workspace. 
-- 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).
+- Create a Databricks [cluster](https://docs.databricks.com/clusters/create.html) using the UI. Choose Databricks runtime version. This guide is tested on Runtime 9.1 LTS (includes Apache Spark 3.1.2, Scala 2.12).
 
-### **2. Installing BigDL Python libraries**
+### 2. Download BigDL Libraries
 
-In the left pane, click **Clusters** and select your cluster.
+Download the BigDL package from [here](https://oss.sonatype.org/content/repositories/snapshots/com/intel/analytics/bigdl/bigdl-assembly-spark_3.1.2/2.1.0-SNAPSHOT/), scroll down to the bottom, choose the **latest** release **bigdl-assembly-spark_3.1.2-2.1.0-*-fat-jars.zip**.
 
-![](images/cluster.png)
+![](images/fat-jars.png)
 
-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.
+Unzip the zip file, we only need two files:
 
-![](images/dllib-whl.png)
+- jars/**bigdl-assembly-spark_3.1.2-2.1.0-SNAPSHOT-jar-with-dependencies.jar**
+- python/**bigdl-spark_3.1.2-2.1.0-SNAPSHOT-python-api.zip**
 
-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.
+### 3. Install BigDL Java dependencies
 
-![](images/orca-whl.png)
+In the Databricks left panel, click **Compute** and select your cluster.
 
-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.
+![](images/compute.png)
 
+Install BigDL java packages using **bigdl-assembly-spark_3.1.2-2.1.0-SNAPSHOT-jar-with-dependencies.jar** from [step 2](#2-download-bigdl-libraries). Click **Libraries > Install New > Library Source(Upload) > Library Type (Jar)**. Drop the jar on Databricks.
 
-### **3. Installing BigDL Java libraries**
+![](images/assembly-jar.png)
 
-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.
+After upload finishes, click **Install**.
 
-![](images/dllib-jar.png)
+> Tips: if you find your upload process is really slow, try to use **Databricks CLI** to upload, see [Appendix B](#appendix-b) for details.
 
-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.
+### 4. Install BigDL Python libraries
 
-![](images/orca-jar.png)
+Install BigDL python environment using **bigdl-spark_3.1.2-2.1.0-SNAPSHOT-python-api.zip** from [step 2](#2-download-bigdl-libraries). However, Databricks can only upload **Jar**, **Python Egg** and **Python Whl**, but doesn't support **Zip**, so we can not simply upload the python api zip and install it like what we do in [step 3](#3-install-bigdl-java-dependencies). You can upload and install the zip package in one of the following ways.
 
-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.
+#### 4.1 Upload and Install through DBFS
 
+**First, upload the zip package to [DBFS](https://docs.databricks.com/dbfs/index.html).** In the left panel, click **Data > DBFS**, if your panel don't have DBFS, see [Appendix A](#appendix-a). then choose or create a folder and right click in the folder, choose **Upload here**.
 
-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**.
+![](images/upload.png)
 
-![](images/apply-all.png)
+Upload your zip package.
 
-### **4. Setting Spark configuration**
+![](images/upload-success.png)
+
+Right click the uploaded zip package and choose **Copy path**, copy the **Spark API Format** path.
+
+![](images/copy-path.png)
+
+**Then install the zip package from DBFS.** In the left panel, click **Compute > choose your cluster > Libraries > Install new > Library Source(DBFS/ADLS) > Library Type(Python Egg) > paste the path > Install**
+
+![](images/install-zip.png)
+
+#### 4.2 Change the File Extension Name
+
+You can simply change the **bigdl-spark_3.1.2-2.1.0-SNAPSHOT-python-api.zip** extension name(**.zip**) to **.egg**, since Egg is essentially a zip format package. Then in the left panel, click **Compute > choose your cluster > Libraries > Install new > Library Source(Upload) > Library Type(Python Egg) > Install**
+
+![](images/egg.png)
+
+### **5. Set Spark configuration**
 
 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.
 
@@ -55,7 +74,7 @@ spark.executor.cores 2
 spark.cores.max 4
 ```
 
-### **5. Running BigDL on Databricks**
+### **6. Run BigDL on Databricks**
 
 Open a new notebook, and call `init_orca_context` at the beginning of your code (with `cluster_mode` set to "spark-submit").
 
@@ -66,9 +85,61 @@ init_orca_context(cluster_mode="spark-submit")
 
 Output on Databricks:
 
-![](images/spark-context.png)
+![](images/init-orca-context.png)
 
 
-### **6. Install other third-party libraries on Databricks if necessary**
+### **7. Install other third-party libraries on Databricks if necessary**
 
 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/).
+
+### Appendix A
+
+If there is no DBFS in your panel,  go to **User profile > Admin Console > Workspace settings > Advanced > Enabled DBFS File Browser**
+
+![](images/dbfs.png)
+
+### Appendix B
+
+Use **Databricks CLI** to upload file to DBFS.
+
+**Install and config Azure Databricks CLI**
+
+1. Install Python, need Python version 2.7.9 and above if you’re using Python 2 or Python 3.6 and above if you’re using Python 3.
+
+2. Run `pip install databricks-cli`
+
+3. Set authentication, Click **user profile icon > User Settings > Access tokens > Generate new token > generate > copy the token**, make sure to **copy** the token and store it in a secure location, **it won't show again**.
+
+   ![](images/token.png)
+
+4. Copy the URL of Databricks host, the format is `https://adb-<workspace-id>.<random-number>.azuredatabricks.net`, you can copy it from your Databricks web page URL.
+
+   ![](images/url.png)
+
+5. In cmd run `dbfs config --token` as shown below:
+
+   ```
+   dbfs configure --token
+   Databricks Host (should begin with https://): https://your.url.from.step.4
+   Token: your-token-from-step-3
+   ```
+
+6. Verify whether you are able to connect to DBFS, run "databricks fs ls".
+
+   ![](images/verify-dbfs.png)
+
+**Upload through Databricks CLI**
+
+Now, we can use Databricks CLI to upload file to DBFS. run command:
+
+```
+dbfs cp /your/local/filepath/bigdl-assembly-spark_3.1.2-2.1.0-SNAPSHOT-jar-with-dependencies.jar dbfs:/FileStore/jars/stable/bigdl-assembly-spark_3.1.2-2.1.0-SNAPSHOT-jar-with-dependencies.jar
+```
+
+After command finished, check DBFS in Databricks, in left panel, click **Data > DBFS > your upload directory**, if you do not see DBFS in your panel, see [Appendix A](#appendix-a).
+
+**Install package from DBFS**
+
+In the left panel, click **Compute > choose your cluster > Libraries > Install new > Library Source(DBFS/ADLS) > Library Type(your package type)**.
+
+![](images/install-zip.png)