174 lines
No EOL
7.5 KiB
Markdown
174 lines
No EOL
7.5 KiB
Markdown
# Databricks User Guide
|
||
|
||
---
|
||
|
||
You can run BigDL program on the [Databricks](https://databricks.com/) cluster as follows.
|
||
### 1. Create a Databricks Cluster
|
||
|
||
- 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 [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. Generate initialization script
|
||
|
||
[Init script](https://learn.microsoft.com/en-us/azure/databricks/clusters/init-scripts) is used to Install BigDL or other libraries. First, you need to put the **init script** into [DBFS](https://docs.databricks.com/dbfs/index.html), you can use one of the following ways.
|
||
|
||
__Note:__
|
||
* To use the Python 3.8 environment (recommended version by BigDL), please set the Databricks runtime version to 9.1 LTS or 10.4 LTS.
|
||
|
||
**a. Generate init script in Databricks notebook**
|
||
|
||
Create a Databricks notebook and execute
|
||
|
||
```python
|
||
init_script = """
|
||
#!/bin/bash
|
||
|
||
# install bigdl-orca, add other bigdl modules if you need
|
||
/databricks/python/bin/pip install pip install --pre --upgrade bigdl-orca-spark3[ray]
|
||
|
||
# install other necessary libraries, here we install libraries needed in this tutorial
|
||
/databricks/python/bin/pip install tensorflow==2.9.1
|
||
/databricks/python/bin/pip install tqdm
|
||
/databricks/python/bin/pip install torch==1.11.0+cpu torchvision==0.12.0+cpu tensorboard -f https://download.pytorch.org/whl/torch_stable.html
|
||
|
||
# copy bigdl jars to databricks
|
||
cp /databricks/python/lib/python3.8/site-packages/bigdl/share/*/lib/*.jar /databricks/jars
|
||
"""
|
||
|
||
# Change the first parameter to your DBFS path
|
||
dbutils.fs.put("dbfs:/FileStore/scripts/init.sh", init_script, True)
|
||
```
|
||
|
||
To make sure the init script is in DBFS, in the left panel, click **Data > DBFS > check your script save path**.
|
||
|
||
> if you do not see DBFS in your panel, see [Appendix A](#appendix-a).
|
||
|
||
**b. Create init script in local and upload to DBFS**
|
||
|
||
Create a file **init.sh**(or any other filename) in your computer, the file content is
|
||
|
||
```bash
|
||
#!/bin/bash
|
||
|
||
# install bigdl-orca, add other bigdl modules if you need
|
||
/databricks/python/bin/pip install pip install --pre --upgrade bigdl-orca-spark3[ray]
|
||
|
||
# install other necessary libraries, here we install libraries needed in this tutorial
|
||
/databricks/python/bin/pip install tensorflow==2.9.1
|
||
/databricks/python/bin/pip install tqdm
|
||
/databricks/python/bin/pip install torch==1.11.0+cpu torchvision==0.12.0+cpu tensorboard -f https://download.pytorch.org/whl/torch_stable.html
|
||
|
||
# copy bigdl jars to databricks
|
||
cp /databricks/python/lib/python3.8/site-packages/bigdl/share/*/lib/*.jar /databricks/jars
|
||
```
|
||
|
||
Then upload **init.sh** to DBFS. In Databricks left panel, click **Data > DBFS > Choose or create upload directory > Right click > Upload here**.
|
||
|
||
|
||
|
||
Now the init script is in DBFS, right click the init.sh and choose **Copy path**, copy the **Spark API Format** path.
|
||
|
||
|
||
|
||
__Notes:__
|
||
* If Databricks returns an init script failure, please check your Databricks runtime and Python version.
|
||
* If your Databricks runtime version is 11.2 or later, click the `Edit` button and change the version to 9.1 LTS or 10.4 LTS.
|
||
|
||
### 3. Set Spark configuration
|
||
|
||
In the left panel, click **Compute > Choose your cluster > edit > Advanced options > Spark > Confirm**. 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.
|
||
|
||
|
||
|
||
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.
|
||
|
||
```
|
||
spark.executor.cores 2
|
||
spark.cores.max 4
|
||
```
|
||
|
||
### 4. Install BigDL Libraries
|
||
|
||
Use the init script from [step 2](#2-generate-initialization-script) to install BigDL libraries. In the left panel, click **Compute > Choose your cluster > edit > Advanced options > Init Scripts > Paste init script path > Add > Confirm**.
|
||
|
||
|
||
|
||
Then start or restart the cluster. After starting/restarting the cluster, the libraries specified in the init script are all installed.
|
||
|
||
### 5. 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").
|
||
|
||
```python
|
||
from bigdl.orca import init_orca_context, stop_orca_context
|
||
init_orca_context(cluster_mode="spark-submit")
|
||
```
|
||
|
||
Output on Databricks:
|
||
|
||
|
||
|
||
**Run Examples**
|
||
|
||
- [Keras example on Databricks](https://github.com/intel-analytics/BigDL/blob/main/python/orca/tutorial/databricks/tf_keras_ncf.ipynb)
|
||
- [Pytorch example on Databricks](https://github.com/intel-analytics/BigDL/blob/main/python/orca/tutorial/databricks/pytorch_fashion_mnist.ipynb)
|
||
|
||
> Note that if you want to save model to DBFS, or load model from DBFS, the save/load path should be the **File API Format** on Databricks, which means your save/load path should start with `/dbfs`.
|
||
|
||
### 6. Other ways to install third-party libraries on Databricks if necessary
|
||
|
||
If you want to use other ways to install 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**
|
||
|
||
|
||
|
||
### Appendix B
|
||
|
||
Use **Databricks CLI** to upload file to DBFS. When you upload a large file to DBFS, using Databricks CLI could be faster than using the Databricks web UI.
|
||
|
||
**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**.
|
||
|
||
|
||
|
||
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.
|
||
|
||
|
||
|
||
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".
|
||
|
||
|
||
|
||
**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)**.
|
||
|
||
 |