Skip to main content
Using an AI coding assistant? Install the Konduktor Skills plugin (npx skills add Trainy-ai/konduktor-skills) to let your AI generate Python SDK code for you — run /draft-python-task to get started.

​
When to use the Python API

The CLI YAML format remains the primary way to describe jobs, but the same capabilities are exposed through the Python package. Use it when you want to:
  • Generate tasks dynamically inside your own tooling.
  • Share a reusable Python helper that sets up common resources or file mounts.
  • Launch quick experiments without writing YAML files.

​
Available entry points

​
konduktor.Task

konduktor.Task(
    name: str,                                              # required
    run: str | Callable[[int, list[str]], str | None] | None = None,  # required
    envs: dict[str, str] | None = None,                     # optional
    workdir: str | None = None,                             # optional
    num_nodes: int | None = None,                           # optional; defaults to 1
)
Useful setters:
  • task.set_resources(konduktor.Resources(...))
  • task.set_file_mounts({"/remote/path": "./local"})
  • task.set_serving(konduktor.Serving(...)) when serving deployments.

​
konduktor.Resources

konduktor.Resources(
    cpus: int | float | str | None = None,      # required;
    memory: int | float | str | None = None,    # required;
    accelerators: str | None = None,            # optional;
    image_id: str | None = None,                # required;
    labels: dict[str, str] | None = None,       # required: kueue.x-k8s.io/queue-name
    job_config: dict[str, int | str] | None = None, # optional; only max_restarts and completions
)

​
konduktor.Serving

konduktor.Serving(
    min_replicas: int | None = None,    # required
    max_replicas: int | None = None,    # optional; defaults to min_replicas
    ports: int | None = 8000,           # optional; defaults to 8000
    probe: str | None = '/health',      # optional; defaults to probe=None (general) or /health (vLLM)
                                        # EXCLUDE PROBE COMPLETELY w VLLM DEPLOYMENTS
)

​
konduktor.launch

konduktor.launch(
    task: konduktor.Task,
    dryrun: bool = False,
    detach_run: bool = False,
) -> str | None
Submits the task. When dryrun=True, Konduktor prints the rendered spec instead of launching. detach_run=True returns immediately after submission; otherwise logs stream until completion. The return value is the job ID when available.

​
Example

import konduktor

# Describe the work to run on each node.
task = konduktor.Task(
    name="python-api-demo",
    run="python train.py --epochs 10",
    workdir=".",
    envs={"myenv": "foo"},
)

# Specify compute requirements and image
resources = konduktor.Resources(
    cpus=4,
    memory=12,
    accelerators="H100",
    image_id="docker.io/ryanattrainy/pytorch-mnist:cpu",
    labels={"mylabel": "bar"},
    job_config={
        "max_restarts": 3,
        "completions": 2,
    },
)

task.set_resources(resources)

job_id = konduktor.launch(task)
print(f"Submitted job: {job_id}")
Save the file (for example as launch_from_python.py) and run: 
python launch_from_python.py