Support

 start_training_run()

hp.start_training_run(
    provider: str = 'ray',
    cpu_count: int = 1,
    gpu_count: int = 0,
    memory_size: int = 10,
    worker_count: int = 0,
    training_job_file_path: str,
    title: str,
    description: str,
    file_set_id: str,
    project: str,
    additional_dirs: list = [],
    additional_files: list = [],
    requirements_file_path: str = None,
    image_id: str = None,
    use_conda: bool = False,
    output_file_size: int = 5,
)

TIPS

• Explore the HISE AI/ML dashboard before you start. From the top navigation menu in HISE, choose RESEARCH > AI/ML.

• In your Python script, include a main() function that handles every step in order—loading your data, training the model, and saving results.

• Test your script end to end by running it locally with test data.

Note on Ray Integration

When you use Ray, the HISE SDK automatically transforms your script for distributed execution. This allows your compute_square function (and similar tasks) to run in parallel, enabling efficient scaling and faster results. Here's how your script is adapted for Ray:

Transformed app.py for Ray Execution

import ray
import time
import random

# Decorate function so it can run in parallel as a Ray task
@ray.remote
def compute_square(x):
    time.sleep(random.uniform(0.5, 1.5))
    return x * x

def main():
    ray.init()  # Initialize Ray
    
    inputs = list(range(10))
          # Launch distributed Ray tasks
    futures = [compute_square.remote(x) for x in inputs]
    results = ray.get(futures)
    
    with open("/home/workspace/output/empty.txt", "w") as f:
        pass
    print("Results:", results)

if __name__ == "__main__":
    main()

Using default project: aifiDevTest
Ray-conformant code written to: /home/workspace/.artifacts/app.py

The following methods now have ray decorators: ['compute_square']. Please select the methods from which you want to remove the Ray decorators
 1) compute_square
 2) None

[1 - 2]

The following methods now have ray decorators: ['compute_square']. Please select the methods whose Ray decorator parameters you want to edit
 1) compute_square
 2) None

[1 - 2] 

{
'workflowName': 'hise-ray-workflow',
'executionId': '009bfa68-2748-4a71-9056-9c4969935714',
'status': 'ACTIVE',
'message': 'Execution Created',
'trainingJobId': '00000000-0000-0000-0000-000000000000',
'trainingImageId': '00000000-0000-0000-0000-000000000000',
'providerDashboard': 'https://stage.allenimmunology.org/orchestrate/ray/dashboard/workflow/be7327a2-6644-4bbb-8fc8-d94dba2c63eb',
'executionDetails': 'https://console.cloud.google.com/workflows/workflow/us-west1/hise-ray-workflow/execution/be7327a2-6644-4bbb-8fc8-d94dba2c63eb/summary?inv=1&invt=Abx65A&project=stage-pipeline-internal&supportedpurview=project'
}

 TROUBLESHOOTING

• Do not list hisepy in your requirements.in file. It’s a platform SDK and should not be managed as a dependency.

• Use the correct, full path to your script and requirements files—use /private/your-folder/app.py if running outside that folder, or just app.py if running from within.

• Ensure you enter a valid file_set_id (UUID) in the SDK call. Using a placeholder or short string will cause an “Invalid UUID length” error. For details, see Save Your Input File or File Set to a Study (Tutorial).

• If you see a "No matching distribution found" or "Could not find a version" error, check your requirements.in for typos or packages that aren’t available on PyPI.

• If you get a permissions error, make sure your script and data are located in an allowed, writable directory (such as /private).