Azure#

Installing Giskard in Azure enables you to inspect & test models that you created in the Microsoft Azure environment (ex: Azure Machine Learning, Synapse Analytics, etc.). Here are the 3 steps to install Giskard in a new VM instance in Azure:

1. Create a Giskard VM Instance in Azure#

  1. Select “Create a resource” and choose Virtual Machine

  2. In the configuration of your VM, select the default configuration:

    1. Choose a Linux machine. For instance, it can be the default Ubuntu server 20.04 LTS

    2. We recommend you choose at least the Standard_D2s machine (2vCPU, 8GB memory)

    3. Enable the default SSH connection by selecting Inbound ports: SSH (22)

  3. Create your VM instance. Make sure you downloaded the certificate file containing the private key you will need to SSH

  4. On the home page, select the VM you just created by selecting Go to Resource

  5. Go to Settings, Networking, and click on Add inbound port rule with the following properties:

  6. Connect to your VM in SSH by using the path of the private key file you downloaded. To do so, go to the tab Overview, select Connect and SSH then follow the different steps to get the right command to execute in your terminal.

Note

For example, the terminal command line to SSH connect to your install from your computer can be:

sudo ssh -i /Users/bob/Downloads/Giskard2_key.cer azureuser@52.142.236.215

2. Install Giskard in the VM#

  • Installation of the Giskard requirements (git and docker)

 curl -fsSL https://get.docker.com -o get-docker.sh
 sudo sh get-docker.sh

  • Installation of Giskard

giskard hub start

3. Connect to your instance#

  • Get the Public IP address of your Giskard VM by clicking on the Overview tab

  • Go to http://<your IP address>:19000 in your web browser

Note

You can stop the instance and restart it when you need to save your Azure compute costs. However, note that

  • the IP address will not necessarily be the same. So make sure you copy it again when it’s launched.

  • you will need to re-start the Giskard hub, by executing in the Giskard folder:

giskard hub start

  • The user id is admin and the password is admin

4. Start the ML worker#

Giskard executes your model using a worker that runs the model directly in your Python environment, with all the dependencies required by your model. You can either execute the ML worker:

  • From your local notebook within the kernel that contains all the dependencies of your model

  • From Google Colab within the kernel that contains all the dependencies of your model

  • Or from your terminal within the Python environment that contains all the dependencies of your model

Note

If you plan to use LLM-assisted tests or transformations, don’t forget to set the OPENAI_API_KEY environment variable before starting the Giskard worker.

To start the ML worker from your notebook, run the following code in your notebook:

!giskard worker start -d -k YOUR_KEY -u http://<your IP address>:19000/

The API Access Key (YOUR_KEY) can be found in the Settings tab of the Giskard Hub.

⚠️ Warning

To see the available commands of the worker, you can execute:

!giskard worker --help

You’re all set to try Giskard in action. Upload your first model, dataset or test suite by following the upload an object page.

To start the ML worker from your Colab notebook, run in your Colab cell:

!giskard worker start -d -k YOUR_KEY -u http://<your IP address>:19000/

The API Access Key (YOUR_KEY) can be found in the Settings tab of the Giskard Hub.

⚠️ Warning

To see the available commands of the worker, you can execute:

!giskard worker --help

You’re all set to try Giskard in action. Upload your first model, dataset or test suite by following the upload an object page.

  • Run the following command within the Python environment that contains all the dependencies of your model:

giskard worker start -k YOUR_KEY -u http://<your IP address>:19000/

The API Access Key (YOUR_KEY) can be found in the Settings tab of the Giskard Hub.

⚠️ Warning

To see the available commands of the worker, you can execute:

!giskard worker --help

You’re all set to try Giskard in action. Upload your first model, dataset or test suite by following the upload an object page.