AWS#

1. Initialize EC2 instance#

  • In the AWS console, go to the service EC2 and select one of the following zones: N. Virginia (us-east-1), Paris (eu-west-3), or Singapore (ap-southeast-1)

  • Launch an EC2 instance

2. Configure your EC2 instance#

  • Application and OS image: Select the default Ubuntu server 22.04 LTS 64-bit (x86)

  • Instance type: We recommend you to choose at least a t2.large instance type (2vCPU, 8GB memory)

  • Key pair: Choose your usual key pair. If you don’t have one, go to the Amazon document to create the right one

  • Network settings: You need to **open the port 19000 to access the Giskard frontend and upload your model. To do so, click on Edit and add the following security groups:

  • Storage: Choose a minimum of 30 Gigs of SSD (this will mainly depend on the size of your datasets)

3. Launch the instance and install Giskard#

  • Click on Launch instance to create the instance

  • Connect in SSH to your instance. You can for example use the EC2 Instance connect to open a terminal directly in your AWS platform

  • Installation of the Giskard requirements (docker)

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

  • Installation of Giskard

giskard hub start

4. Connect to your instance#

  • Get your IP address: Click on the ID of the instance you just created and copy its Public IPv4 address (or Public IPv4 DNS)

  • Go to http://<your IP address>:19000 in your web browser. For instance, http://ec2-13-50-XXXX.compute.amazonaws.com:19000

  • The user id is admin and the password is admin

Hint

You can stop the instance and restart it when you need to save AWS 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

5. 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.