To make it easier to deploy BigDL, Microsoft and Intel have partnered to create a “Deploy to Azure” button on top of the Linux (Ubuntu) edition of the Data Science Virtual Machine (DSVM).
This is available on Github at https://github.com/Azure/DataScienceVM/tree/master/Extensions/BigDL
It may take as long as 10 minutes to fully provision DSVM—perfect time for a coffee break!
Please note: For ease of use, we suggest selecting the password option rather than the SSH option in the DSVM provisioning prompt.
The Azure Virtual Machines provide a mechanism to automatically run a script during post provisioning when using Azure Resource Manager (ARM) templates.
The DSVM team have documented this on Github* ,
Examples of writing DSVM extensions. https://github.com/Azure/DataScienceVM/tree/master/Extensions
The team have published the Azure Resource Manager (ARM) template and the script to install BigDL on the DSVM for Linux (Ubuntu) when creating the VM on Azure. Clicking the Deploy to Azure button takes the user to the Azure portal wizard, http://portal.azure.com and walks them through the VM creation process, and automatically executes the necessary script to install/configure BigDL so that it is ready for use once the VM is successfully provisioned.
As per documentation above you need to include the link within the Variabl to the file uri and command to execute for the Machine config
As per the example above this is the implementation in the ARM taken from https://github.com/Azure/DataScienceVM/blob/master/Extensions/BigDL/azuredeploy.json
"storageAccountName": "[concat(uniquestring(resourceGroup().id), 'lindsvm')]",
"fileUris": https://raw.githubusercontent.com/Azure/DataScienceVM/master/Extensions/BigDL/InstallBigDL.sh ... ,
"commandToExecute": "bash InstallBigDL.sh"
The user can directly run /opt/BigDL/run_notebooks.sh to start a Jupyter* notebook server to execute the samples.
To create the data science steps in case you already have a DSVM (Ubuntu) instance, or just want to understand the details of what the automated steps does, above.
Before you start, you need to provision the Microsoft Data Science Virtual Machine for Linux (Ubuntu) by visiting the Azure product detail page and following the directions in the VM creation wizard.
When DSVM is configured, make a note of its public IP address or DNS name; you will need it to connect to DSVM via your connect tool of choice. The recommended tool for text interface is SSH or Putty. For the graphical interface, Microsoft* recommends an X Client called X2GO*.
Note: You may need to configure your proxy server correctly if your network administrators require all connections to go through your network proxy. The only session type supported by default on DSVM is Xfce*.
Change to root and clone BigDL from Github; switch to released branch-0.1:
git clone https://github.com/intel-anlaytics/BigDL.git
git checkout branch-0.1
If successful, you should see the following messages:
Switch to Python* 2.7.
Confirm Python* version.
In the directory where you cloned BigDL library (/opt/BigDL), create a script, and run_notebook.sh with the following content:#begin run_notebook.sh
In the same BigDL directory, create start_tensorboard.sh with the following content:#begin start_tensorboard.sh
Please note that ‘/anaconda/lib/python2.7/site-packages/’ is installation-dependent and may change in future releases of DSVM. Thus, if these instructions do not work for you out of the box, you may need to update this path.
Note the URL at the end of the log http://10.0.2.4:6006. Open your DSVM browser with it to see the TensorBoard pane.
Execute run_notebook.sh and start_tensorboard.sh via bash commands from different terminals:
Open two browser tabs, one for text_classification.ipynb and another for TensorBoard .
Navigate to the text_classification example:
http://localhost:YOUR_PORT_NUMBER/notebooks/pyspark/dl/example/tutorial/simple_text_classification/text_classfication.ipynb# —Check location of sample.
Run the notebook. This will take a few minutes. In the end, you will see a loss graph like this one:
Your TensorBoard may look like this for the Text Classification example.
Azure Virtual Machines provide a mechanism to automatically run a script during post provisioning when using Azure Resource Manager (ARM) templates. On Github , we published the ARM template and the script to install BigDL on the DSVM for Linux (Ubuntu) when creating the VM on Azure. On the same Github directory there is also a Deploy to Azure button that takes the user to the Azure portal wizard, leads them through the VM creation, and automatically executes the above script to install/configure BigDL so that it is ready for use once the VM is successfully provisioned. The user can directly run /opt/BigDL/run_notebooks.sh to start a Jupyter notebook server to execute the samples.
BigDL continues to evolve and enjoys solid support from the open-source community as well as from Intel’s dedicated software engineering team.
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