python-functions

Write solve(values: list[int]) -> int from a one-line specification; private cases decide the verdict.

Field	Value
Reference	nodus/python-functions@1.0.0
Image	nodus/env-python-functions:1.0.0
Publisher	Nodus
Category	Code
Readiness	Stable
Modes	Train, Evaluate
Reward	Binary
Held-out measures	TrainedTask
Splits	20 train, 16 test (disjoint by canonical identity)
Licenses	code Apache-2.0, data Apache-2.0
Source	https://github.com/nodus-compute/nodus-platform/tree/main/images/environments

Grading

Completions are graded by the platform, never by the trainer: the trainer submits {taskId, completion} batches and the verdicts come back as task events.

Grader	Kind	What it checks
private-cases	Program	Runs the candidate as an unprivileged uid on public inputs; every private expected output must match

Grading runs in a sandbox (nodus/env-python-functions:1.0.0, 1 CPU, 1Gi memory, 30s timeout, no network); candidate code runs as an unprivileged user that cannot read the expected answers.

Sample task

One line of nodus-env tasks --split test --seed 0; tasks never carry answers.

{
  "metadata": {
    "function": "count-divisible-by-last"
  },
  "prompt": "Write Python source defining solve(values: list[int]) -\u003e int. Return how many values are divisible by the last value, or 0 when it is zero. Return 0 for an empty list. Output only Python source, without Markdown fences.",
  "taskId": "python-functions:test:0:0"
}

Examples

Each example is a TrainingJob template. A baseline is shown only where the example was measured by running it.

Example	Mode	Runtime	Model	Tasks	Baseline	Trained	Measured on
python-functions-grpo	Train	nodus/grpo-lora	Qwen/Qwen3-0.6B @ c1899de	16	not measured	not measured	not measured

Run one with a server dry-run first:

$ nodus create trainingjob my-run --from-example nodus/python-functions:python-functions-grpo --dry-run=server -o estimate

import nodus

job = nodus.recipes.TrainingJob.from_example("nodus/python-functions:python-functions-grpo")
plan = job.preview()
run = plan.run(max_cost=5)
print(run.wait().summary)