Install on Azure¶
Driverless AI can be run on Azure with fixed pricing (BYOL) or with a pay-per-hour option. The installation steps are similar for both.
Watch the installation video here. Note that some of the images in this video may change between releases, but the installation steps remain the same.
|Provider||Instance Type||Num GPUs||Suitable for|
Installing the Azure Instance¶
- Log in to your Azure portal at https://portal.azure.com, and click the Create a Resource button.
- Search for H2O DriverlessAI in the Marketplace. Select one of the following options:
- H2O Driverless AI: This provides you with a “pay-per-hour” method for running Driverless AI on Azure without a Driverless AI license. The price will include running both the Azure compute instance and Driverless AI.
- H2O Driverless AI (BYOL). If you already have a Driverless AI license, then select the BYOL (Bring-Your-Own-License) option. You will only be charged for the Azure instance. You will also be prompted to paste your license into the UI the first time that you log in.
- Click Create. This launches the H2O DriverlessAI Virtual Machine creation process.
- On the Basics tab:
- Enter a name for the VM.
- Select the Disk Type for the VM. Use HDD for GPU instances.
- Enter the name that you will use when connecting to the machine through SSH.
- Enter and confirm a password that will be used when connecting to the machine through SSH.
- Specify the Subscription option. (This should be Pay-As-You-Go.)
- Enter a name unique name for the resource group.
- Specify the VM region.
Click OK when you are done.
- On the Size tab, select your virtual machine size. Specify the HDD disk type and select a configuration. We recommend using an N-Series type, which comes with a GPU. Also note that Driverless AI requires 10 GB of free space in order to run and will stop working of less than 10 GB is available. We recommend a minimum of 30 GB of disk space. Click OK when you are done.
- On the Settings tab, select or create the Virtual Network and Subnet where the VM is going to be located and then click OK.
- The Summary tab performs a validation on the specified settings and will report back any errors. When the validation passes successfully, click Create to create the VM.
- After the VM is created, it will be available under the list of Virtual Machines. Select this Driverless AI VM to view the IP address of your newly created machine. Then open a terminal window and ssh into the machine running the VM. Optionally run
pwdto retrieve your current location in the VM, and optionally run
nvidia-smito verify that the NVIDIA driver is running.
- If you selected a GPU machine, then enable persistence of the GPU. Note that this only needs to be run once. Refer to the following for more information: http://docs.nvidia.com/deploy/driver-persistence/index.html.
sudo nvidia-persistenced --user <USER> sudo nvidia-smi -pm 1
- Use the following command to retrieve the latest Driverless AI version.
sudo h2oai update
- At this point, you can copy data into the data directory on the host machine using
scp. For example:
scp <data_file>.csv <username>@<vm_address>:/etc/h2oai/data
The data will be visible inside the Docker container.
- Start the Driverless AI Docker image
sudo h2oai start
Driverless AI will begin running:-------------------------------- Welcome to H2O.ai's Driverless AI --------------------------------- version: 1.3.1 - Put data in the volume mounted at /data - Logs are written to the volume mounted at /log/20180606-044258 - Connect to Driverless AI on port 12345 inside the container - Connect to Jupyter notebook on port 8888 inside the container
- Connect to Driverless AI with your browser:
Stopping the Azure Instance¶
The Azure instance will continue to run even when you close the Azure portal. To stop the instance:
- Click the Virtual Machines left menu item.
- Select the checkbox beside your DriverlessAI virtual machine.
- On the right side of the row, click the … button, then select Stop. (Note that you can then restart this by selecting Start.)
Upgrading the Driverless AI Image¶
If you have a valid license and are running a Driverless AI image from a cloud offering, there are bash commands to make the upgrade process easy. This example shows how to upgrade Driverless AI from version 1.0.18 to the latest version.
WARNING: Experiments, MLIs, and MOJOs are not automatically upgraded when Driverless AI is upgraded.
- Build MLI models before upgrading.
- Build MOJO pipelines before upgrading.
If you did not build MLI on a model before upgrading Driverless AI, then you will not be able to view MLI on that model after upgrading. Before upgrading, be sure to run MLI jobs on models that you want to continue to interpret in future releases. If that MLI job appears in the list of Interpreted Models in your current version, then it will be retained after upgrading.
If you did not build a MOJO pipeline on a model before upgrading Driverless AI, then you will not be able to build a MOJO pipeline on that model after upgrading. Before upgrading, be sure to build MOJO pipelines on all desired models.
- SSH into the IP address of the image instance and copy the existing experiments to a backup location:
# Set up a directory of the previous version name mkdir dai_rel_1.0.18 # Copy the data, log, license, and tmp directories as backup cp -r ./data dai_rel_1.0.18/data cp -r ./log dai_rel_1.0.18/log cp -r ./license dai_rel_1.0.18/license cp -r ./tmp dai_rel_1.0.18/tmp
- Use the following commands to upgrade the Driverless AI version:
# Stop Driverless AI h2oai stop # Run the following command h2oai update # Start Driverless AI h2oai start
And to see all available Driverless AI commands, type
- Connect to Driverless AI with your browser at http://Your-Driverless-AI-Host-Machine:12345.