Creating a SimAI Project and Uploading Training Data

This example demonstrates how to connect to SimAI, create a new project, and upload training data folders.

Before you begin

Make sure you have:

• Valid SimAI credentials and organization access.

• A dataset folder containing subdirectories with your training data.

• The ansys-simai-core library installed.

Import necessary libraries

import os

import ansys.simai.core as asc
from ansys.simai.core.errors import NotFoundError

Configure your settings

Update these variables with your specific settings:

ORGANIZATION_NAME = "your_organization"  # Replace with your organization name
PROJECT_NAME = "your_project_name"  # Your project name
DATASET_PATH = "path/to/your/data/folder"  # Directory containing subdirectories with training data

Initialize the SimAI client

Create a client to connect to the SimAI platform:

simai_client = asc.SimAIClient(organization=ORGANIZATION_NAME)

Set up the project

Try to get an existing project by name, or create it if it does not exist:

try:
    project = simai_client.projects.get(name=PROJECT_NAME)
    print(f"Using existing project: {PROJECT_NAME}")
except NotFoundError:
    project = simai_client.projects.create(PROJECT_NAME)
    print(f"Created new project: {PROJECT_NAME}")

Upload training data

Upload all directories from the dataset path as training data. Each subdirectory should contain the files for one training data sample.

Note

If training data with the same name already exists, the API will raise an error. You can use simai_client.training_data.get(name=dir_name) to check beforehand.

print("\nUploading training data files:")

for dir_name in os.listdir(DATASET_PATH):
    complete_path = os.path.join(DATASET_PATH, dir_name)
    if not os.path.isdir(complete_path):
        continue

    # Skip if already uploaded
    try:
        existing_td = simai_client.training_data.get(name=dir_name)
        print(f"  '{dir_name}' already exists, adding to project.")
        existing_td.add_to_project(project)
        continue
    except NotFoundError:
        pass

    print(f"  Uploading '{dir_name}'...")
    td = simai_client.training_data.create(dir_name, project=project)
    td.upload_folder(complete_path)
    print(f"  '{dir_name}' uploaded successfully.")

print("\nAll training data uploaded.")

Wait for data processing

After uploading, SimAI processes the training data. Wait for all data in the project to be ready.

project_data = project.list_training_data()

print("\nWaiting for data processing to complete...")
for data in project_data:
    print(f"  Processing '{data.name}'...")
    data.wait()

    if data.is_ready:
        print(f"  '{data.name}' is ready.")
    else:
        print(f"  '{data.name}' failed: {data.failure_reason}")

Display project summary

ready_count = sum(1 for d in project_data if d.is_ready)
print(f"\nProject '{PROJECT_NAME}': {ready_count}/{len(project_data)} training data ready.")

Next steps

Once all data are ready, you can proceed to configure and build a model. See the next tutorial: Building a SimAI Model