Dask-ML Python API Docs | dltHub

Dask-ML is a scalable machine learning library that leverages Dask for distributed workloads across clusters, exposing various endpoints via the Dask scheduler and worker HTTP services. The REST API base URL is https://<scheduler-host>:8787 and authentication is deployment-dependent as core Dask components do not include built-in HTTP auth.

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 add "dlt[hub]" and start loading Dask-ML data in under 10 minutes.

---

What data can I load from Dask-ML?

Here are some of the endpoints you can load from Dask-ML:

---

How do I authenticate with the Dask-ML API?

Core Dask components have no built-in authentication for their HTTP APIs, though specific deployments (e.g., Dask Gateway or managed clusters) may implement session-based, token-based, or Basic Auth.

1. Get your credentials

The core Dask scheduler HTTP API (used by Dask-ML environments) does not provide a built-in credential issuance dashboard. Authentication is typically handled at the deployment layer (e.g., Dask Gateway, reverse proxy) or via a shared API key. 1. If using a managed service or Dask Gateway, log in to the provider's specific dashboard to retrieve your API token or credentials. 2. For custom or self-hosted deployments, an API key is typically generated by the infrastructure administrator and must be set in the configuration file under distributed.scheduler.http.api-key. Retrieve this key from your cluster's configuration or deployment settings.

2. Add them to .dlt/secrets.toml

Copy
[sources.dask_ml_source]
api_key = "your_generated_api_key_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 init
uv add "dlt[hub]"

1. Install the dlt AI harness:

Copy
uv run dlthub 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
uv run dlthub ai toolkit install rest-api-pipeline

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 Dask-ML 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
uv run python dask_ml_pipeline.py

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

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

Inspect your pipeline and data:

Copy
uv run dlthub 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 /api/v1/get_workers and /sitemap.json from the Dask-ML 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 dask_ml_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://<scheduler-host>:8787",
            "auth": {"type": "api_key", "api_key": api_key, "name": "token", "location": "header"},
        },
        "resources": [
            {"name": "workers", "endpoint": {"path": "api/v1/get_workers"}},
            {"name": "sitemap", "endpoint": {"path": "sitemap.json"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="dask_ml_pipeline",
        destination="duckdb",
        dataset_name="dask_ml_data",
    )
    load_info = pipeline.run(dask_ml_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("dask_ml_pipeline").dataset()
sessions_df = data.workers.df()
print(sessions_df.head())

SQL (DuckDB example):

Copy
SELECT * FROM dask_ml_data.workers LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

data = dlt.pipeline("dask_ml_pipeline").dataset()
data.workers.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 Dask-ML 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 harness:

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

Copy
uv run dlthub ai toolkit install data-exploration
uv run dlthub ai toolkit install dlthub-platform

• 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