Upload your ML model & data

How to upload your data and Machine Learning model to Giskard using Python

Prerequisites
To upload the model you want to inspect, you need:
A model. For example, a scikit-learn, Tensorflow, HuggingFace, catboost, PyTorch, ... Python functions
A pandas dataframe composed of the examples you want to inspect. For example, it could be your test dataset or a dataset composed of some wrong predictions of your model
The Giskard's platform. To install it, check Installation & upgrade​
Steps to upload your data & model
1. Install the Giskard library
In order to upload models and datasets to Giskard, you'll need to install the library giskard:
pip install giskard
In case of installation errors related to giskard library, it's sometimes a good idea to remove it with:
pip uninstall giskard
and re-installing again
2. Start ML Worker
​ML worker is the component in Giskard that connect your Python environment to the Giskard server that you just installed. For more technical information, have a look at this page. To start MLworker, execute the following command line in the terminal of the machine where your model was created:
giskard worker start -h [GISKARD IP ADDRESS]
If ML Worker manages to connect to the Giskard instance, you should see the following message in the worker logs: "Connected to Giskard server." 
If you work from your notebook, you will need to start Giskard as a daemon with:
giskard worker start -d -h [GISKARD IP ADDRESS]
If Giskard is installed locally, please only do: giskard worker start. That will establish a connection to the Giskard instance installed on localhost:40051.  
If Giskard is not installed locally, please specify the IP address (and a port in case a custom port is used). For example, giskard worker start -h 192.158.1.38
For more information, see this page.
3. Create a new Giskard project or load an existing project
To create a new project or load an existing one, run the code below in your Python environment:
from giskard import GiskardClient
​
url = "http://localhost:19000" #if Giskard is installed locally (for installation, see: https://docs.giskard.ai/start/guides/installation) 
token = "YOUR GENERATED TOKEN" #you can generate your API token in the Admin tab of the Giskard application (for installation, see: https://docs.giskard.ai/start/guides/installation) 
client = GiskardClient(url, token)
​
project = client.create_project("project_key", "PROJECT_NAME", "DESCRIPTION") #Choose the arguments you want. But "project_key" should be unique and in lower case
#If your project is already created use project = client.get_project("existing_project_key")
If you want to use an existing project, use project=client.get_project("EXISTING_PROJECT_KEY")to load the existing project, then use:
upload_model to upload a new version of the model you want to inspect/test
upload_dataset to upload a new dataset that you want to apply to your existing model
For more details about the arguments of these functions, see our Github repo.
4. Upload a model and a dataset
Apply the upload_model_and_df to the project using the following arguments:
Argument
Description
Type
prediction_function
The model you want to predict. It could be any Python function that takes a Pandas dataframe as input and returns:
the  probabilities for all the classification labels if model_type=classification
the prediction if model_type=regression
If you have preprocessing steps, wrap the whole prediction pipeline: all the preprocessing steps (categorical encoding, scaling, etc.) + ML predict_proba function. Click here for more information.
Callable[
[pd.DataFrame], Iterable[Union[str, float, int]]
model_type
classification for classification model
regression for regression model
str
df
A Pandas dataframe that contains some data examples that might interest you to inspect (test set, train set, production data). Some important remarks:
df can contain more columns than the features of the model such as the actual ground truth variable, sample_id, metadata, etc. 
df should be raw data that comes before all the preprocessing steps
Pandas dataframe
column_types
A dictionary of column names and their types (numeric, category or text) for all columns of df. 
Dict[str, str]
target
The column name in df corresponding to the actual target variable (ground truth).
Optional[str]
feature_names
An optional list of the feature names of prediction_function. By default, feature_namesare all the keys from column_types except from target. Some important remarks:
Make sure that prediction_function(df[feature_names]) does not return an error message
Make sure these features have the same order as in your train set.
Optional[[List[str]]
classification_labels
The classification labels of your prediction when prediction_task="classification". Some important remarks:
If classification_labels is a list of n elements, make sure prediction_function is also returning n probabilities
Make sure the labels have the same order as the output of prediction_function
Optional[List[str]] = None
classification_threshold
The probability threshold in the case of a binary classification model. By default, it's equal to 0.5
Optional[float] = 0.5
model_name
The name of the model you uploaded
Optional[str]
dataset_name
The name of the dataset you uploaded
Optional[str]
It's better to upload prediction_function as a function that wraps the whole prediction pipeline: all the preprocessing steps (categorical encoding, etc.) + ML prediction. This is key for a robust and interpretable inspection stage! Click here for examples with and without pipelines.
Make sure that prediction_function(df[feature_names]) gets executed without an error. This is the only requirement to upload a model on Giskard!
Examples
!pip install giskard
!giskard worker start
​
from giskard import GiskardClient
​
client = GiskardClient(url, token)
​
#If you're creating your project for the first time
credit_scoring = client.create_project("credit_scoring", "Credit scoring project", "Predict the default probabilities of a credit demand")
​
#If your project is already created use 
#project = client.get_project("credit_scoring")
​
credit_scoring.upload_model_and_df(
    prediction_function=clf.predict_proba,
    model_type='classification',
    df=test_data,
    column_types={
        'credit_id':'category',
        'credit_amount':'numeric',
        'credit_category':'category',
        'credit_application':'text',
        'Is_default': 'category'
        },
    target = 'Is_default',
    feature_names=['credit_amount','credit_category','credit_application'],
    classification_labels=['Not default','Default']
    )
Example notebooks:
You can download an example notebook here to execute it in your working environment
To get started with Giskard as fast as possible we've included a demo python notebook in the platform with all the requirements on http://localhost:19000/jupyter (accessible after the Installation & upgrade). Feel free to modify it to adapt it to your case!  
Now you uploaded your model, let's Collect feedback of your ML model​
Troubleshooting​​
If you encounter any issues, join our Discord on our #support channel. Our community will help! 

Argument	Description	Type
`prediction_function`	The model you want to predict. It could be any Python function that takes a Pandas dataframe as input and returns: the probabilities for all the classification labels if `model_type=classification` the prediction if `model_type=regression` If you have preprocessing steps, wrap the whole prediction pipeline: all the preprocessing steps (categorical encoding, scaling, etc.) + ML predict_proba function. Click here for more information.	Callable[ [pd.DataFrame], Iterable[Union[str, float, int]]
`model_type`	`classification` for classification model `regression` for regression model	str
`df`	A Pandas dataframe that contains some data examples that might interest you to inspect (test set, train set, production data). Some important remarks: `df` can contain more columns than the features of the model such as the actual ground truth variable, sample_id, metadata, etc. `df` should be raw data that comes before all the preprocessing steps	Pandas dataframe
`column_types`	A dictionary of column names and their types (`numeric`, `category` or `text`) for all columns of `df`.	Dict[str, str]
`target`	The column name in `df` corresponding to the actual target variable (ground truth).	Optional[str]
`feature_names`	An optional list of the feature names of `prediction_function.` By default, `feature_names`are all the keys from `column_types` except from `target`. Some important remarks: Make sure that `prediction_function`(`df`[`feature_names`]) does not return an error message Make sure these features have the same order as in your train set.	Optional[[List[str]]
`classification_labels`	The classification labels of your prediction when `prediction_task`="classification". Some important remarks: If `classification_labels` is a list of n elements, make sure `prediction_function` is also returning n probabilities Make sure the labels have the same order as the output of `prediction_function`	Optional[List[str]] = None
`classification_threshold`	The probability threshold in the case of a binary classification model. By default, it's equal to 0.5	Optional[float] = 0.5
`model_name`	The name of the model you uploaded	Optional[str]
`dataset_name`	The name of the dataset you uploaded	Optional[str]

Installation in Azure

Wrap your prediction function

Last modified 14d ago

Upload your ML model & data

Prerequisites

Steps to upload your data & model

1. Install the Giskard library

2. Start ML Worker

3. Create a new Giskard project or load an existing project

4. Upload a model and a dataset

Examples

Troubleshooting​​

Troubleshooting