NVIDIA cuOpt Python API Docs | dltHub

The cuOpt Routing Python API Reference provides a REST API for setting custom costs between waypoints and calculating total travel costs. The API focuses on routing optimization, allowing users to map problems to data concepts like environments and fleets. The cuOpt Open-API Reference offers a Swagger interface for easy access and documentation. The REST API base URL is http://localhost:5000 and Requests require client_id and client_secret credentials which are exchanged for a bearer token..

dlt is an open-source Python library that handles authentication, pagination, and schema evolution automatically. dlthub provides AI context files that enable code assistants to generate production-ready pipelines. Install with uv pip install "dlt[workspace]" and start loading NVIDIA cuOpt data in under 10 minutes.

---

What data can I load from NVIDIA cuOpt?

Here are some of the endpoints you can load from NVIDIA cuOpt:

---

How do I authenticate with the NVIDIA cuOpt API?

Authentication uses client credentials (CUOPT_CLIENT_ID and CUOPT_CLIENT_SECRET) provided via environment variables; the client exchanges them for a bearer token that is sent in the Authorization header.

1. Get your credentials

1. Log in to the NVIDIA Developer portal.
2. Navigate to the cuOpt service section.
3. Create a new API client or select an existing one.
4. Copy the generated Client ID and Client Secret.
5. Store them in the environment variables CUOPT_CLIENT_ID and CUOPT_CLIENT_SECRET or add them to the secrets.toml file.

2. Add them to .dlt/secrets.toml

Copy
[sources.nvidia_cuopt_source]
client_id = "your_client_id_here"
client_secret = "your_client_secret_here"

dlt reads this automatically at runtime — never hardcode tokens in your pipeline script. For production environments, see setting up credentials with dlt for environment variable and vault-based options.

---

How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

Copy
uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

Copy
dlt ai init --agent <your-agent> # <agent>: claude | cursor | codex

This installs project rules, a secrets management skill, appropriate ignore files, and configures the dlt MCP server for your agent. Learn more →

2. Install the rest-api-pipeline toolkit:

Copy
dlt ai toolkit rest-api-pipeline install

This loads the skills and context about dlt the agent uses to build the pipeline iteratively, efficiently, and safely. The agent uses MCP tools to inspect credentials — it never needs to read your secrets.toml directly. Learn more →

3. Start LLM-assisted coding:

Use /find-source to load data from the NVIDIA cuOpt API into DuckDB.

The rest-api-pipeline toolkit takes over from here — it reads relevant API documentation, presents you with options for which endpoints to load, and follows a structured workflow to scaffold, debug, and validate the pipeline step by step.

4. Run the pipeline:

Copy
python nvidia_cuopt_pipeline.py

If everything is configured correctly, you'll see output like this:

Copy
Pipeline nvidia_cuopt_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset nvidia_cuopt_data
The duckdb destination used duckdb:/nvidia_cuopt.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

Copy
dlt pipeline nvidia_cuopt_pipeline show

This opens the Pipeline Dashboard where you can verify pipeline state, load metrics, schema (tables, columns, types), and query the loaded data directly.

---

Python pipeline example

This example loads cuopt_health and cuopt_request from the NVIDIA cuOpt API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

Copy
import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def nvidia_cuopt_source(client_id=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "http://localhost:5000",
            "auth": {
                "type": "bearer",
                "token": client_id,
            },
        },
        "resources": [
            {"name": "cuopt_health", "endpoint": {"path": "cuopt/health"}},
            {"name": "cuopt_request", "endpoint": {"path": "cuopt/request/{id}"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="nvidia_cuopt_pipeline",
        destination="duckdb",
        dataset_name="nvidia_cuopt_data",
    )
    load_info = pipeline.run(nvidia_cuopt_source())
    print(load_info)

To add more endpoints, append entries from the resource table to the "resources" list using the same name, path, and data_selector pattern.

---

How do I query the loaded data?

Once the pipeline runs, dlt creates one table per resource. You can query with Python or SQL.

Python (pandas DataFrame):

Copy
import dlt

data = dlt.pipeline("nvidia_cuopt_pipeline").dataset()
sessions_df = data.cuopt_health.df()
print(sessions_df.head())

SQL (DuckDB example):

Copy
SELECT * FROM nvidia_cuopt_data.cuopt_health LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

data = dlt.pipeline("nvidia_cuopt_pipeline").dataset()
data.cuopt_health.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

---

What destinations can I load NVIDIA cuOpt data to?

dlt supports loading into any of these destinations — only the destination parameter changes:

Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.

---

Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI Workbench:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-runtime — Deploy, schedule, and monitor your pipeline in production.

Copy
dlt ai toolkit data-exploration install
dlt ai toolkit dlthub-runtime install

• How to deploy a pipeline
• How to explore your data in marimo Notebooks
• How to query your data in Python with dataset
• Join the dlt community on Slack