🤗 HuggingFace Spaces#

For an easy use of the Giskard Hub, you can directly access the Hub online through Hugging Face with:

  • The public HF Space: That’s perfect if you don’t want to upload your own model and just want to check some Giskard demo projects.

  • The private HF Space: That’s perfect if you want to use Giskard with your own model without installing the Giskard Hub. You need to have an Hugging Face account.

For other ways to install the Giskard Hub, the On-premise or Private Cloud installation.

The Giskard Hub is the app adapted for an enterprise use of Giskard. Extending the features of the open-source library, it enables you to:

  • Debug tests to diagnose your issues

  • Create domain-specific tests thanks to automatic model insights

  • Compare models to decide which model to promote

  • Collect business feedback of your model results

  • Share your results with your colleagues for alignment

  • Store all your QA objects (tests, data slices, evaluation criteria, etc.) in one place to work more efficiently

Public HF Space: Try the Hub on demo models in 1 click#

If you want to try the Giskard Hub on some demo ML projects (not on your own ML models), navigate to our public demo Space:

Hint

The demo Giskard Space is read-only. To upload your own models, datasets and projects in the Giskard Space, we recommend that you duplicate the Space. More on this in the following sections.

Private HF Space: Test & debug your own ML model in the Hub#

Leverage the Hugging Face (HF) Space to easily test & debug your own ML models. This implies that you deploy a private HF space containing the Giskard Hub and upload your Python objects (such as ML models, test suites, datasets, slicing functions, or transformation functions) to your HF Space. To do so, follow these steps:

1. Duplicate the demo Space from Giskard#

Begin by visiting the Giskard HF space and duplicate the space (as depicted below).

Duplication image

During duplication, you’re presented with options to modify the owner, the visibility and the hardware:

Space Duplication popup

Hint

Owner and visibility: If you don’t want to publicly share your model, set your Space to private and assign the owner as your organization Hardware: We recommend to use paid hardware to get the best out of Giskard’s HF Space. You can also incorporate persistent storage to retain your data even after the Space reboots. With free hardware that lacks persistent storage, any inactivity beyond 48 hours will result in the space being shut down. This will lead to a loss of all data within your Giskard Space.

Once you’re ready, click on Duplicate Space. The building process will take several minutes.

2. Create a new Giskard project#

Create a new Giskard project

3. Enter your HF Access token#

On your first access on a private HF Space, Giskard needs a HF access token to generate the Giskard Space Token. To do so, follow the instructions in the pop-up that you encounter when creating your first project.

Input Hugging Face access token

Alternatively, provide your HF access token through the Giskard Settings.

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 https://XXX.hf.space -t HF-TOKEN

To find the exact command with the right API Access Key (YOUR_KEY) and HuggingFace token (HF-TOKEN), go to the “Ml Worker” section in the Settings tab in the Giskard Hub that you install in HF Space.

⚠️ Warning

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

!giskard worker --help

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

!giskard worker start -d -k YOUR_KEY -u https://XXX.hf.space -t HF-TOKEN

To find the exact command with the right API Access Key (YOUR_KEY) and HuggingFace token (HF-TOKEN), go to the “Ml Worker” section in the Settings tab in the Giskard Hub that you install in HF Space.

⚠️ Warning

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

!giskard worker --help

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

giskard worker start -d -k YOUR_KEY -u https://XXX.hf.space -t HF-TOKEN

To find the exact command with the right API Access Key (YOUR_KEY) and HuggingFace token (HF-TOKEN), go to the “Ml Worker” section in the Settings tab in the Giskard Hub that you install in HF Space.

⚠️ Warning

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

!giskard worker --help

5. Upload your test suite by creating a Giskard Client for your HF Space#

You can then upload the test suite generated by the Giskard scan from your Python notebook to your HF Space. Achieve this by initializing a Giskard Client: simply copy the “Create a Giskard Client” snippet from the Giskard Hub settings and run it within your Python notebook. For more details, have a look at our upload object documentation page

You are now ready to debug the tests which you’ve just uploaded in the test tab of the Giskard Hub.

Feedback and support#

If you have suggestions or need specialized support, please join us on the Giskard Discord community or reach out on Giskard’s GitHub repository.