Load NVIDIA cuOpt data to DuckDB

NVIDIA cuOpt is an accelerated optimization service for complex, real-time fleet routing workflows. Everything needed to build a working NVIDIA cuOpt → DuckDB pipeline is on this page: the API's base URL, authentication, endpoints, pagination and incremental field — plus a prompt that hands the whole job to your coding agent.

---

Build your NVIDIA cuOpt to DuckDB pipeline

Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.

PromptCopy prompt
Run uvx dlthub-init@latest to build a pipeline from NVIDIA cuOpt to DuckDB and run it on dltHub

That scaffolds a dltHub workspace and installs the dltHub AI harness — the project rules, the secrets-management skill, and the dlt MCP server your agent needs to work safely. From there it reads the NVIDIA cuOpt API, proposes the endpoints to load, then writes, runs and validates the pipeline while you review rather than type. Credentials are inspected through MCP tools, so your agent never reads secrets.toml itself. How the LLM-native workflow works →

Prefer to write it yourself? Every fact the agent uses is below.

---

NVIDIA cuOpt API at a glance

These values come from the NVIDIA cuOpt API reference — the authoritative source if anything here looks out of date.

---

How do I authenticate with the NVIDIA cuOpt API?

Authentication requires a JWT (Bearer token) passed in the 'Authorization' header. The token is obtained by authenticating with NVIDIA Identity Federation using Client ID and Secret or SAK credentials.

1. Get your credentials

To obtain credentials for the NVIDIA cuOpt managed service: 1. Log in to the NVIDIA NGC portal. 2. Navigate to the NGC Catalog dropdown and select Cloud Functions. 3. Within the interface (specifically under the Cloud Function tab/menu), locate the section for API Keys. 4. Generate a Personal API Key (often referred to as an NVIDIA Identity Federation API Key or SAK). 5. Save the key securely, as it is used to authenticate requests to the service. Note that legacy methods involving Client ID and Client Secret are now deprecated in favor of this API Key (SAK).

2. Add them to .dlt/secrets.toml

Copy
[sources.nvidia_cuopt_source]
CUOPT_CLIENT_SAK = "your_personal_api_key_here"

dlt reads this file automatically at runtime. With the harness, the setup-secrets skill prompts you for the values and never handles the raw credential in chat. For production, see setting up credentials with dlt.

---

What NVIDIA cuOpt data can I load into DuckDB?

These are the NVIDIA cuOpt endpoints dlt can load into DuckDB:

---

How do I load only new NVIDIA cuOpt records?

The NVIDIA cuOpt API reference does not document a timestamp or sequence field for these endpoints, so there is nothing to advertise here as verified. Pick a field from the endpoints table above that increases with every write, then set it as the cursor_path.

Copy
{"name": "cuopt_request", "endpoint": {
    "path": "cuopt/request/{id}",
    # Replace with a field that increases on every write.
    "incremental": {"cursor_path": "REPLACE_ME", "initial_value": "2024-01-01T00:00:00Z"},
}}

On the first run dlt loads everything from initial_value; on every run after that it requests only what changed and appends with write_disposition="merge" if you set a primary key. See incremental loading.

---

What does the generated NVIDIA cuOpt pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /v2/nvcf/functions and /v2/nvcf/assets from the NVIDIA cuOpt API into DuckDB:

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

@dlt.source
def nvidia_cuopt_source(client_secret=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.nvcf.nvidia.com/v2/nvcf",
            "auth": {"type": "bearer", "token": client_secret},
        },
        "resources": [
            {"name": "cuopt_request", "endpoint": {"path": "cuopt/request/{id}"}},
            {"name": "cuopt_solution", "endpoint": {"path": "cuopt/solution/{id}"}}
        ],
    }
    yield from rest_api_resources(config)


def load_nvidia_cuopt_to_duckdb() -> 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)


if __name__ == "__main__":
    load_nvidia_cuopt_to_duckdb()

Run it with python nvidia_cuopt_pipeline.py. The agent iterates on this until it loads cleanly — you review and approve, rather than write it from scratch.

---

How do I query NVIDIA cuOpt data in DuckDB?

dlt creates one table per resource. Query the loaded data with Python or SQL — or ask your agent to, through the MCP server's execute_sql_query tool.

Python (pandas DataFrame):

Copy
import dlt

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

SQL:

Copy
SELECT * FROM nvidia_cuopt_data.cuopt_request LIMIT 10;

See querying your data with dataset and exploring it in marimo notebooks.

---

How do I deploy the NVIDIA cuOpt to DuckDB pipeline in production?

The pipeline runs locally, which is ideal for prototyping and one-off analysis. When you need it on a schedule, monitored on every load, and shared with your team, deploy the same dlt code on the dltHub platform — no infrastructure to maintain. The prompt above already ends with "run it on dltHub", so your agent can take it there directly.

• Deploy & schedule — run the pipeline as a managed job with automatic retries.
• Monitor — observable job queues, alerting, and load metrics for every run.
• Transform — promote raw NVIDIA cuOpt loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

---

What other destinations can I load NVIDIA cuOpt data to?

dlt loads into any of these — only the destination argument changes:

Set dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. On the dltHub platform the same pipeline runs against a managed Iceberg lakehouse. See the full destinations list.

---

Next steps

• Ready for production? Book a demo of the dltHub platform →
• dlt LLM-native workflow docs
• REST API source reference
• Join the dlt community on Slack