Fount References

Prerequisites

• Python installed
• pip install fount-core
• Valid API key from Fount Developer Portal

1. Authentication

   Set your API key as an environment variable
   Python

   export FOUNT_API_KEY="your-api-key-here"
   

2. Initialise Client
   Python

   from fount import Fount
   client = Fount()

3. Upload Dataset
   Python

   # From DataFrame
   dataset = client.upload_dataframe(df, name="my_dataset")
   
   # From CSV
   dataset = client.upload_csv("data.csv", name="csv_dataset")
   
   # From Excel
   dataset = client.upload_excel("data.xlsx", sheet_name="Sheet1", name="excel_data")

4. Train a Model
   Python

   job = client.train(
       dataset=dataset,
     	model_name="my_model",
   		series_id_cols=["category"]
       categorical_cols=["category","Year", "Month"],
       date_column="date",
       target_columns=["sales"],
       validation_data_required=True,
       validation_split=0.2,
       time_granularity="daily"
   )

5. Execute & Monitor a job
   Python

   # Synchronous
   job.run(wait=True, poll_interval=30)
   
   # Asynchronous
   job.run(wait=False)
   
   # Check status
   status = job.status()
   print(f"Status: {status['status']}")

6. Run inference
   Python

   inference_job = client.inference(
       dataset=dataset,
       model_name="my_model",
       batch_size=1000
   )
   
   # Synchronous
   inference_job.run(wait=True, poll_interval=30)
   
   # Asynchronous
   inference_job.run(wait=False)
   
   # Check status
   status = inference_job.status()
   print(f"Status: {status['status']}")

7. Tune a model
   Python

   tuning_job = client.tune(
       dataset=dataset,
     	model_name="my_tuned_model",
       series_id_cols=["category"]
       categorical_cols=["category","Year", "Month"],
       date_column="date",
       target_columns=["sales"],
       validation_data_required=True,
       validation_split=0.2,
       time_granularity="daily"
   )
   
   # Synchronous
   tuning_job.run(wait=True, poll_interval=30)
   
   # Asynchronous
   tuning_job.run(wait=False)
   
   # Check status
   status = tuning_job.status()
   print(f"Status: {status['status']}")

8. Run Inference on tuned model
   Python

   inference_job_on_tuned_model = client.inference(
       dataset=dataset,
       model_name="my_tuned_model",
       batch_size=1000
   )
   
   # Synchronous
   inference_job_on_tuned_model.run(wait=True, poll_interval=30)
   
   # Asynchronous
   inference_job_on_tuned_model.run(wait=False)
   
   # Check status
   status = inference_job_on_tuned_model.status()
   print(f"Status: {status['status']}")

9. Retrieve results
   Python

   # Get metrics
   metrics = job.metrics()
   
   # Get predictions
   predictions = job.predictions()
   
   # Stop job if needed
   job.stop()

10. Manage jobs and datasets
    Python

    # List all datasets
    datasets = client.get_all_datasets()
    
    # List all models
    models = client.get_all_models()
    
    # List all training jobs
    training_jobs = client.get_all_training_jobs()
    
    # List all tuning jobs
    tuning_jobs = client.get_all_tuning_jobs()