⬆️ Upload an object to the Giskard server#

You can easily upload to the Giskard server objects, such as model, dataset, slicing & transformation functions and test suite.

Uploading objects to the Giskard server will help you:

  • Debug your model by inspecting the rows that make your test fail

  • Compare the results of your test suite for different models

  • Help you create more insightful tests based on domain knowledge

1. Run the Giskard server#

To install the Giskard server, please check the run the Giskard server page.

2. Create a Giskard client#

To create a Giskard client, please execute the following Python code:

from giskard import GiskardClient

url = "http://localhost:19000"  # If Giskard is installed locally
# url = "http://app.giskard.ai" # If you want to upload in a Giskard server
token = "API_TOKEN"  # you can generate your API token in the Settings tab of the Giskard application
project_name = "enron"

# Create a giskard client to communicate with Giskard
client = GiskardClient(url, token)

You can find the URL and the tokens in the Settings tab of the Giskard application.

Hint

You may also need an extra token (Giskard Space Token) to upload your test suite to a private Space on Hugging Face Spaces. To create your Giskard client, please use the following code snippet instead:

url = "https://huggingface.co/spaces/<user-id>/<space-id>"
token = "API_TOKEN"                 # You can generate your API token in the Settings tab of your Giskard Hugging Face Space
hf_token = "<Giskard Space Token>"  # Find it in Settings in your Giskard Hugging Face Space
client = GiskardClient(
    url=url,
    token=token,
    hf_token=hf_token,
)

3. Create a new project if needed#

If you want to upload your object in a new project, you will need to create a new project. To do so, execute the following Python code:

# Create a project
client.create_project(project_name, "Project name", "Small description of the project")

4. Upload your object#

Upload your suite to the Giskard server to:

  • Compare models to decide which model to promote

  • Debug your tests to diagnose the issues

  • Create more domain-specific tests that are integrating business feedback

  • Share your results

from giskard import demo, Model, testing, Suite, GiskardClient

demo_data_processing_function, demo_sklearn_model = demo.titanic_pipeline()

# Wrap your model with Giskard.Model. Check the dedicated doc page: https://docs.giskard.ai/en/latest/guides/wrap_model/index.html
# you can use any tabular, text or LLM models (PyTorch, HuggingFace, LangChain, etc.),
# for classification, regression & text generation.
def prediction_function(df):
    # The pre-processor can be a pipeline of one-hot encoding, imputer, scaler, etc.
    preprocessed_df = demo_data_processing_function(df)
    return demo_sklearn_model.predict_proba(preprocessed_df)

giskard_model = Model(
    model=prediction_function,  # A prediction function that encapsulates all the data pre-processing steps and that could be executed with the dataset used by the scan.
    model_type="classification",  # Either regression, classification or text_generation.
    name="Titanic model",  # Optional
    classification_labels=demo_sklearn_model.classes_,  # Their order MUST be identical to the prediction_function's output order
    feature_names=['PassengerId', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked'],  # Default: all columns of your dataset
    # classification_threshold=0.5,  # Default: 0.5
)

url = "http://localhost:19000"  # If Giskard is installed locally
# url = "http://app.giskard.ai" # If you want to upload on a an external Giskard server
token = "API_TOKEN"  # you can generate your API token in the Settings tab of the Giskard application
project_name = "my_project_id"

# Create a giskard client to communicate with Giskard
client = GiskardClient(url, token)

# Create a project
client.create_project(project_name, "Project name", "Small description of the project")

suite = Suite() \
    .add_test(testing.test_f1(model=giskard_model)) \
    .add_test(testing.test_accuracy(model=giskard_model)) \
    .upload(client, project_name)

Then the suite that you uploaded will appear in the test tab of the Giskard server.


Uploading the model to the Giskard server enables you to:

  • Compare your model with others using a test suite.

  • Gather feedback from your colleagues regarding your model.

  • Debug your model effectively in case of test failures.

  • Develop new tests that incorporate additional domain knowledge.

from giskard import demo, Model, GiskardClient

demo_data_processing_function, demo_sklearn_model = demo.titanic_pipeline()

# Wrap your model with Giskard.Model. Check the dedicated doc page: https://docs.giskard.ai/en/latest/guides/wrap_model/index.html
# you can use any tabular, text or LLM models (PyTorch, HuggingFace, LangChain, etc.),
# for classification, regression & text generation.
def prediction_function(df):
    # The pre-processor can be a pipeline of one-hot encoding, imputer, scaler, etc.
    preprocessed_df = demo_data_processing_function(df)
    return demo_sklearn_model.predict_proba(preprocessed_df)

giskard_model = Model(
    model=prediction_function,  # A prediction function that encapsulates all the data pre-processing steps and that could be executed with the dataset used by the scan.
    model_type="classification",  # Either regression, classification or text_generation.
    name="Titanic model",  # Optional
    classification_labels=demo_sklearn_model.classes_,  # Their order MUST be identical to the prediction_function's output order
    feature_names=['PassengerId', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked'],  # Default: all columns of your dataset
    # classification_threshold=0.5,  # Default: 0.5
)

url = "http://localhost:19000"  # If Giskard is installed locally
# url = "http://app.giskard.ai" # If you want to upload on a an external Giskard server
token = "API_TOKEN"  # you can generate your API token in the Settings tab of the Giskard application
project_name = "my_project_id"

# Create a giskard client to communicate with Giskard
client = GiskardClient(url, token)

# Create a project
client.create_project(project_name, "Project name", "Small description of the project")

giskard_model.upload(client, project_name)

Uploading your dataset to the Giskard server enables you to:

  • Inspect and debug your dataset

  • Use your dataset as the input for your tests (unit datasets)

from giskard import demo, Dataset, GiskardClient

df = demo.titanic_df()

# Wrap your Pandas DataFrame with Giskard.Dataset (test set, a golden dataset, etc.). Check the dedicated doc page: https://docs.giskard.ai/en/latest/guides/wrap_dataset/index.html
giskard_dataset = Dataset(
    df=df,  # A pandas.DataFrame that contains the raw data (before all the pre-processing steps) and the actual ground truth variable (target).
    target="Survived",  # Ground truth variable
    name="Titanic dataset", # Optional
    cat_columns=['Pclass', 'Sex', "SibSp", "Parch", "Embarked"]  # List of categorical columns. Optional, but is a MUST if available. Inferred automatically if not.
)

url = "http://localhost:19000"  # If Giskard is installed locally
# url = "http://app.giskard.ai" # If you want to upload on a an external Giskard server
token = "API_TOKEN"  # you can generate your API token in the Settings tab of the Giskard application
project_name = "my_project_id"

# Create a giskard client to communicate with Giskard
client = GiskardClient(url, token)

# Create a project
client.create_project(project_name, "Project name", "Small description of the project")

giskard_dataset.upload(client, project_name)

Saving your slicing function to the Giskard server will enable you to:

  • Use your slicing functions for testing purposes: your slices can be used as fixtures of your test suite

  • Further debug the examples inside your data slice using explanation

  • Apply the saved slicing functions to other datasets (new production data, etc.)

from giskard import slicing_function, GiskardClient
import pandas as pd

@slicing_function(name="females")
def slice_sex(row: pd.Series):
    return row["Sex"] == "female"

url = "http://localhost:19000"  # If Giskard is installed locally
# url = "http://app.giskard.ai" # If you want to upload on a an external Giskard server
token = "API_TOKEN"  # you can generate your API token in the Settings tab of the Giskard application
project_name = "my_project_id"

# Create a giskard client to communicate with Giskard
client = GiskardClient(url, token)

# Create a project
client.create_project(project_name, "Project name", "Small description of the project")

slice_sex.upload(client, project_name)

Saving your transformation function in the Giskard server will enable you to:

  • Use your transformations for testing purposes: your transformations can be used as fixtures of your test suite

  • Use the saved transformations to debug your dataset

  • Use the saved transformations to augment your dataset

from giskard import transformation_function, GiskardClient

@transformation_function(name="increase age")
def increase_age(row):
    row["Age"] = row["Age"] * 0.1
    return row

url = "http://localhost:19000"  # If Giskard is installed locally
# url = "http://app.giskard.ai" # If you want to upload on a an external Giskard server
token = "API_TOKEN"  # you can generate your API token in the Settings tab of the Giskard application
project_name = "my_project_id"

# Create a giskard client to communicate with Giskard
client = GiskardClient(url, token)

# Create a project
client.create_project(project_name, "Project name", "Small description of the project")

increase_age.upload(client, project_name)