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feat(evaluator): submit a live Gym runner as an agent-evaluation job - #1367
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An evaluation someone got working locally with `AgentEvaluator()` can now be
submitted as a governed job without retyping its configuration:
evaluator.submit(tasks=TasksetRef("default/my-suite"), target=gym_runner)
`runner_to_target` describes a live `GymAgentTaskRunner` as the `GymRunnerTarget`
that reproduces it job-side, and refuses runners carrying state the wire cannot
express rather than silently submitting something other than what was tested.
Only Gym is supported; the other runners each need their own decisions about what
survives translation and are deliberately not guessed at.
`submit()` gains a fourth overload discriminated by `tasks`, and agent jobs get
their own `AgentEvaluatorJobResource` / `AgentEvaluatorJob`. The resource is not
related to `EvaluatorJobResource` by inheritance in either direction: a row
evaluation publishes `aggregate-scores` and `row-scores`, an agent evaluation
publishes `agent-eval-results` and `summary`, so sharing a base would put
readers here whose type says "results" and whose behaviour is a 404.
Isolate the integration fixtures' entity store (`NMP_DATA_DIR`) alongside their
file storage. They already pointed file storage at a per-run temp dir but left
the database on the developer's real platform, which both wrote test entities
into a live local install and broke reruns: metric bundles are content-addressed,
so a second run found the first run's bundle entity, skipped the upload as a
duplicate, then failed to download a blob that went away with the old temp dir.
Signed-off-by: Sandy Chapman <schapman@nvidia.com>
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Included review availability: Your plan provides up to 12 included reviews per hour; 10 remain after this review. 📝 WalkthroughWalkthroughThe SDK now supports submitting stored tasksets with live Gym runners. It serializes runner configuration into agent-evaluation targets, creates synchronous agent job resources, validates submission modes, and adds isolated integration coverage. ChangesAgent evaluation submission
Sequence Diagram(s)sequenceDiagram
participant Caller
participant Evaluator
participant SyncExecutor
participant AgentEvaluationAPI
participant AgentEvaluatorJobResource
Caller->>Evaluator: submit(taskset, GymAgentTaskRunner)
Evaluator->>SyncExecutor: submit_agent_eval(taskset, runner)
SyncExecutor->>AgentEvaluationAPI: POST /agent-evaluate/jobs with GymRunnerTarget
AgentEvaluationAPI-->>SyncExecutor: return job identity
SyncExecutor->>AgentEvaluatorJobResource: create resource
AgentEvaluatorJobResource->>AgentEvaluationAPI: retrieve status or poll
Merge Risk: ⚪ Minimal · up to The PR adds governed submission for live Gym evaluations and includes targeted validation; no actionable merge-blocking risk remains beyond normal checks and review. 🚥 Pre-merge checks | ✅ 4 | ❌ 1❌ Failed checks (1 warning)
✅ Passed checks (4 passed)
✨ Finishing Touches 💡 1📝 Generate docstrings 💡
🧪 Generate unit tests (beta)
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In `@plugins/nemo-evaluator/src/nemo_evaluator/jobs/runner_targets.py`:
- Line 74: Update _gym_target to validate that the Gym configuration is
JSON-serializable before constructing GymRunnerTarget, converting invalid values
such as callables or arbitrary objects into UnsubmittableRunnerError rather than
allowing PydanticSerializationError from model_dump(mode="json") to escape. Add
a regression test covering a non-JSON hydra_params value.
In `@plugins/nemo-evaluator/src/nemo_evaluator/sdk/job_resources.py`:
- Around line 255-259: Update the route construction in the job resource
initializer using job_route_base_url so agent jobs resolve through
/agent-evaluate/jobs rather than the hardcoded /evaluate/jobs base, while
preserving the existing route behavior for other jobs. Add a status-route test
covering get_job_status or wait_until_done for an agent job and asserting the
/agent-evaluate/jobs/{name}/status path.
In `@plugins/nemo-evaluator/src/nemo_evaluator/sdk/resources.py`:
- Around line 175-187: Update the tasks branch in submit so it rejects config,
field_mapping, prompt_template, and metric_bundle_packager when tasks is
provided, alongside metric and dataset, before calling
_executor.submit_agent_eval. Add coverage verifying each option raises the
validation error and is not silently discarded.
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JashG
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Aug 19, 2026
Two review findings on #1367, both in error handling rather than the happy path. `submit(tasks=...)` accepted `config`, `field_mapping`, `prompt_template` and `metric_bundle_packager` and then dropped them: only `metric` and `dataset` were rejected. A caller supplying any of the four got a job that silently ignored it. All four are now refused, naming whichever were passed and pointing at `target` as what configures a taskset run. `_gym_target` promised in its docstring to refuse state with no wire form, but `hydra_params` and `env_vars` are typed loosely enough to hold a callable, which survived construction and failed later inside `model_dump(mode="json")` -- a `PydanticSerializationError` raised from the transport, naming neither the runner nor the field. It now checks serializability and raises `UnsubmittableRunnerError` instead, so the code matches what the module documents. Also assert `AgentEvaluatorJobResource.get_job_status()` against a live agent job. A review round asked whether it 404s, since the status route is built from `/evaluate/jobs` while agent jobs live under `/agent-evaluate/jobs`. It does not -- the status lookup ignores the collection prefix -- but nothing covered it, because the execution path polls through `nmp.testing` rather than this resource. Signed-off-by: Sandy Chapman <schapman@nvidia.com>
…et overload The taskset overload declared `metric: None = None` and `dataset: None = None` to express that neither belongs in a taskset submission. They had the opposite effect: `submit(tasks=..., metric=..., dataset=..., target=runner)` matched the overload and type-checked clean, leaving the runtime guard as the only defence. Removing them means no overload matches that call, so `ty` rejects it statically as well. Signed-off-by: Sandy Chapman <schapman@nvidia.com>
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The skill drifted from the code because the NVSkills CI gate blocked any PR touching top-level `skills/`, so several evaluator changes landed with their docs updated and the skill left behind. That gate is gone as of #1302, so this catches the skill up. Stored tasks (#1071, #566). `TaskInput` now carries a runner-discriminated `spec`, and `EvaluatorTaskDefinition` has the grader-only `reference` field. The skill still showed the flat pre-#1071 shape and told readers that held-out ground truth required an inline `AgentEvalTaskInput` -- which would cost them tasksets and revision pinning for a limitation that no longer exists. Three places said it; all three are corrected. Local execution (#1262). The skill lumped `client.evaluator.run()` together with the `nemo evaluator ... run` CLI verb as "being retired", but only the CLI verb still exists -- the method was removed a week ago. SKILL.md now warns about the CLI verb alone: naming a method that cannot be called, four lines from the seven live `.run()` calls the skill teaches (`AgentEvaluator().run`, `Evaluator().run_sync`), invited the wrong generalization. The removal is recorded in `troubleshooting.md` instead, which is symptom-indexed and so only reached by someone who already called it from memory. `GymRunnerTarget` was also missing from SKILL.md's platform-target list, alongside the same omission in the agent-evaluation reference. Taskset submission (#1367). `submit` grew a second shape -- `tasks` + `target` against a live runner -- which was previously CLI-only and went out with no skill or docs coverage. Added to the interface table and the agent-evaluation reference, along with `GymRunnerTarget` in the target table, the four row-only options the taskset path refuses, and the Gym-only translation limit. The returned `AgentEvaluatorJobResource` deliberately has no `get_result()` or `download_artifacts()`, while every other job example in the skill ends in `get_result()`. That trap gets its own troubleshooting row. `evals.json` graded the agent on producing `nemo evaluator evaluate run --spec`, the very path SKILL.md says not to build on. Both verbs take identical spec flags, so the eval was rewarding the discouraged one for no benefit. Deliberately NOT included: the skill updates written for #1173. That PR closed unmerged, so `Evaluator.run_dataset_sync` and `client.evaluator.evaluate_dataset` do not exist. `evaluate_dataset` on main is the *backend* contract method, which makes the rename look landed when it is not. The public surface is still `run_sync` and `submit(metric=..., config=...)`. Every claim was verified by executing it against main rather than reading the source, which caught two errors in my own first draft: an example missing the required `resources_server`, and a claim that `env_vars` can hold a callable. It cannot -- it is `dict[str, str]`, so pydantic refuses one at construction and it never reaches the serializability guard. Only `hydra_params` is `dict[str, Any]`. (The `_gym_target` docstring names both and is likewise overstated, but that is merged code and out of scope here.) Four tests added, each mutation-verified. The largest gap they close is that `store_resources` -- the skill's canonical stored-task example -- was only ever asserted as text, so no schema change to `TaskInput` could fail it. It now runs against the real resource signatures and re-validates through the wire form `create` actually posts. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Signed-off-by: Sandy Chapman <schapman@nvidia.com>
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The skill drifted from the code because the NVSkills CI gate blocked any PR touching top-level `skills/`, so several evaluator changes landed with their docs updated and the skill left behind. That gate is gone as of #1302, so this catches the skill up. Stored tasks (#1071, #566). `TaskInput` now carries a runner-discriminated `spec`, and `EvaluatorTaskDefinition` has the grader-only `reference` field. The skill still showed the flat pre-#1071 shape and told readers that held-out ground truth required an inline `AgentEvalTaskInput` -- which would cost them tasksets and revision pinning for a limitation that no longer exists. Three places said it; all three are corrected. Local execution (#1262). The skill lumped `client.evaluator.run()` together with the `nemo evaluator ... run` CLI verb as "being retired", but only the CLI verb still exists -- the method was removed a week ago. SKILL.md now warns about the CLI verb alone: naming a method that cannot be called, four lines from the seven live `.run()` calls the skill teaches (`AgentEvaluator().run`, `Evaluator().run_sync`), invited the wrong generalization. The removal is recorded in `troubleshooting.md` instead, which is symptom-indexed and so only reached by someone who already called it from memory. `GymRunnerTarget` was also missing from SKILL.md's platform-target list, alongside the same omission in the agent-evaluation reference. Taskset submission (#1367). `submit` grew a second shape -- `tasks` + `target` against a live runner -- which was previously CLI-only and went out with no skill or docs coverage. Added to the interface table and the agent-evaluation reference, along with `GymRunnerTarget` in the target table, the four row-only options the taskset path refuses, and the Gym-only translation limit. The returned `AgentEvaluatorJobResource` deliberately has no `get_result()` or `download_artifacts()`, while every other job example in the skill ends in `get_result()`. That trap gets its own troubleshooting row. `evals.json` graded the agent on producing `nemo evaluator evaluate run --spec`, the very path SKILL.md says not to build on. Both verbs take identical spec flags, so the eval was rewarding the discouraged one for no benefit. Deliberately NOT included: the skill updates written for #1173. That PR closed unmerged, so `Evaluator.run_dataset_sync` and `client.evaluator.evaluate_dataset` do not exist. `evaluate_dataset` on main is the *backend* contract method, which makes the rename look landed when it is not. The public surface is still `run_sync` and `submit(metric=..., config=...)`. Every claim was verified by executing it against main rather than reading the source, which caught two errors in my own first draft: an example missing the required `resources_server`, and a claim that `env_vars` can hold a callable. It cannot -- it is `dict[str, str]`, so pydantic refuses one at construction and it never reaches the serializability guard. Only `hydra_params` is `dict[str, Any]`. (The `_gym_target` docstring names both and is likewise overstated, but that is merged code and out of scope here.) Four tests added, each mutation-verified. The largest gap they close is that `store_resources` -- the skill's canonical stored-task example -- was only ever asserted as text, so no schema change to `TaskInput` could fail it. It now runs against the real resource signatures and re-validates through the wire form `create` actually posts. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Signed-off-by: Sandy Chapman <schapman@nvidia.com>
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The skill drifted from the code because the NVSkills CI gate blocked any PR touching top-level `skills/`, so several evaluator changes landed with their docs updated and the skill left behind. That gate is gone as of #1302, so this catches the skill up. Stored tasks (#1071, #566). `TaskInput` now carries a runner-discriminated `spec`, and `EvaluatorTaskDefinition` has the grader-only `reference` field. The skill still showed the flat pre-#1071 shape and told readers that held-out ground truth required an inline `AgentEvalTaskInput` -- which would cost them tasksets and revision pinning for a limitation that no longer exists. Three places said it; all three are corrected. Local execution (#1262). The skill lumped `client.evaluator.run()` together with the `nemo evaluator ... run` CLI verb as "being retired", but only the CLI verb still exists -- the method was removed a week ago. SKILL.md now warns about the CLI verb alone: naming a method that cannot be called, four lines from the seven live `.run()` calls the skill teaches (`AgentEvaluator().run`, `Evaluator().run_sync`), invited the wrong generalization. The removal is recorded in `troubleshooting.md` instead, which is symptom-indexed and so only reached by someone who already called it from memory. `GymRunnerTarget` was also missing from SKILL.md's platform-target list, alongside the same omission in the agent-evaluation reference. Taskset submission (#1367). `submit` grew a second shape -- `tasks` + `target` against a live runner -- which was previously CLI-only and went out with no skill or docs coverage. Added to the interface table and the agent-evaluation reference, along with `GymRunnerTarget` in the target table, the four row-only options the taskset path refuses, and the Gym-only translation limit. The returned `AgentEvaluatorJobResource` deliberately has no `get_result()` or `download_artifacts()`, while every other job example in the skill ends in `get_result()`. That trap gets its own troubleshooting row. `evals.json` graded the agent on producing `nemo evaluator evaluate run --spec`, the very path SKILL.md says not to build on. Both verbs take identical spec flags, so the eval was rewarding the discouraged one for no benefit. Deliberately NOT included: the skill updates written for #1173. That PR closed unmerged, so `Evaluator.run_dataset_sync` and `client.evaluator.evaluate_dataset` do not exist. `evaluate_dataset` on main is the *backend* contract method, which makes the rename look landed when it is not. The public surface is still `run_sync` and `submit(metric=..., config=...)`. Every claim was verified by executing it against main rather than reading the source, which caught two errors in my own first draft: an example missing the required `resources_server`, and a claim that `env_vars` can hold a callable. It cannot -- it is `dict[str, str]`, so pydantic refuses one at construction and it never reaches the serializability guard. Only `hydra_params` is `dict[str, Any]`. (The `_gym_target` docstring names both and is likewise overstated, but that is merged code and out of scope here.) Four tests added, each mutation-verified. The largest gap they close is that `store_resources` -- the skill's canonical stored-task example -- was only ever asserted as text, so no schema change to `TaskInput` could fail it. It now runs against the real resource signatures and re-validates through the wire form `create` actually posts. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Signed-off-by: Sandy Chapman <schapman@nvidia.com>
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The skill drifted from the code because the NVSkills CI gate blocked any PR touching top-level `skills/`, so several evaluator changes landed with their docs updated and the skill left behind. That gate is gone as of #1302, so this catches the skill up. Stored tasks (#1071, #566). `TaskInput` now carries a runner-discriminated `spec`, and `EvaluatorTaskDefinition` has the grader-only `reference` field. The skill still showed the flat pre-#1071 shape and told readers that held-out ground truth required an inline `AgentEvalTaskInput` -- which would cost them tasksets and revision pinning for a limitation that no longer exists. Three places said it; all three are corrected. Local execution (#1262). The skill lumped `client.evaluator.run()` together with the `nemo evaluator ... run` CLI verb as "being retired", but only the CLI verb still exists -- the method was removed a week ago. SKILL.md now warns about the CLI verb alone: naming a method that cannot be called, four lines from the seven live `.run()` calls the skill teaches (`AgentEvaluator().run`, `Evaluator().run_sync`), invited the wrong generalization. The removal is recorded in `troubleshooting.md` instead, which is symptom-indexed and so only reached by someone who already called it from memory. `GymRunnerTarget` was also missing from SKILL.md's platform-target list, alongside the same omission in the agent-evaluation reference. Taskset submission (#1367). `submit` grew a second shape -- `tasks` + `target` against a live runner -- which was previously CLI-only and went out with no skill or docs coverage. Added to the interface table and the agent-evaluation reference, along with `GymRunnerTarget` in the target table, the four row-only options the taskset path refuses, and the Gym-only translation limit. The returned `AgentEvaluatorJobResource` deliberately has no `get_result()` or `download_artifacts()`, while every other job example in the skill ends in `get_result()`. That trap gets its own troubleshooting row. `evals.json` graded the agent on producing `nemo evaluator evaluate run --spec`, the very path SKILL.md says not to build on. Both verbs take identical spec flags, so the eval was rewarding the discouraged one for no benefit. Deliberately NOT included: the skill updates written for #1173. That PR closed unmerged, so `Evaluator.run_dataset_sync` and `client.evaluator.evaluate_dataset` do not exist. `evaluate_dataset` on main is the *backend* contract method, which makes the rename look landed when it is not. The public surface is still `run_sync` and `submit(metric=..., config=...)`. Every claim was verified by executing it against main rather than reading the source, which caught two errors in my own first draft: an example missing the required `resources_server`, and a claim that `env_vars` can hold a callable. It cannot -- it is `dict[str, str]`, so pydantic refuses one at construction and it never reaches the serializability guard. Only `hydra_params` is `dict[str, Any]`. (The `_gym_target` docstring names both and is likewise overstated, but that is merged code and out of scope here.) Four tests added, each mutation-verified. The largest gap they close is that `store_resources` -- the skill's canonical stored-task example -- was only ever asserted as text, so no schema change to `TaskInput` could fail it. It now runs against the real resource signatures and re-validates through the wire form `create` actually posts. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Signed-off-by: Sandy Chapman <schapman@nvidia.com>
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The skill drifted from the code because the NVSkills CI gate blocked any PR touching top-level `skills/`, so several evaluator changes landed with their docs updated and the skill left behind. That gate is gone as of #1302, so this catches the skill up. Stored tasks (#1071, #566). `TaskInput` now carries a runner-discriminated `spec`, and `EvaluatorTaskDefinition` has the grader-only `reference` field. The skill still showed the flat pre-#1071 shape and told readers that held-out ground truth required an inline `AgentEvalTaskInput` -- which would cost them tasksets and revision pinning for a limitation that no longer exists. Three places said it; all three are corrected. Local execution (#1262). The skill lumped `client.evaluator.run()` together with the `nemo evaluator ... run` CLI verb as "being retired", but only the CLI verb still exists -- the method was removed a week ago. SKILL.md now warns about the CLI verb alone: naming a method that cannot be called, four lines from the seven live `.run()` calls the skill teaches (`AgentEvaluator().run`, `Evaluator().run_sync`), invited the wrong generalization. The removal is recorded in `troubleshooting.md` instead, which is symptom-indexed and so only reached by someone who already called it from memory. `GymRunnerTarget` was also missing from SKILL.md's platform-target list, alongside the same omission in the agent-evaluation reference. Taskset submission (#1367). `submit` grew a second shape -- `tasks` + `target` against a live runner -- which was previously CLI-only and went out with no skill or docs coverage. Added to the interface table and the agent-evaluation reference, along with `GymRunnerTarget` in the target table, the four row-only options the taskset path refuses, and the Gym-only translation limit. The returned `AgentEvaluatorJobResource` deliberately has no `get_result()` or `download_artifacts()`, while every other job example in the skill ends in `get_result()`. That trap gets its own troubleshooting row. `evals.json` graded the agent on producing `nemo evaluator evaluate run --spec`, the very path SKILL.md says not to build on. Both verbs take identical spec flags, so the eval was rewarding the discouraged one for no benefit. Deliberately NOT included: the skill updates written for #1173. That PR closed unmerged, so `Evaluator.run_dataset_sync` and `client.evaluator.evaluate_dataset` do not exist. `evaluate_dataset` on main is the *backend* contract method, which makes the rename look landed when it is not. The public surface is still `run_sync` and `submit(metric=..., config=...)`. Every claim was verified by executing it against main rather than reading the source, which caught two errors in my own first draft: an example missing the required `resources_server`, and a claim that `env_vars` can hold a callable. It cannot -- it is `dict[str, str]`, so pydantic refuses one at construction and it never reaches the serializability guard. Only `hydra_params` is `dict[str, Any]`. (The `_gym_target` docstring names both and is likewise overstated, but that is merged code and out of scope here.) Four tests added, each mutation-verified. The largest gap they close is that `store_resources` -- the skill's canonical stored-task example -- was only ever asserted as text, so no schema change to `TaskInput` could fail it. It now runs against the real resource signatures and re-validates through the wire form `create` actually posts. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Signed-off-by: Sandy Chapman <schapman@nvidia.com>
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The skill drifted from the code because the NVSkills CI gate blocked any PR touching top-level `skills/`, so several evaluator changes landed with their docs updated and the skill left behind. That gate is gone as of #1302, so this catches the skill up. Stored tasks (#1071, #566). `TaskInput` now carries a runner-discriminated `spec`, and `EvaluatorTaskDefinition` has the grader-only `reference` field. The skill still showed the flat pre-#1071 shape and told readers that held-out ground truth required an inline `AgentEvalTaskInput` -- which would cost them tasksets and revision pinning for a limitation that no longer exists. Three places said it; all three are corrected. Local execution (#1262). The skill lumped `client.evaluator.run()` together with the `nemo evaluator ... run` CLI verb as "being retired", but only the CLI verb still exists -- the method was removed a week ago. SKILL.md now warns about the CLI verb alone: naming a method that cannot be called, four lines from the seven live `.run()` calls the skill teaches (`AgentEvaluator().run`, `Evaluator().run_sync`), invited the wrong generalization. The removal is recorded in `troubleshooting.md` instead, which is symptom-indexed and so only reached by someone who already called it from memory. `GymRunnerTarget` was also missing from SKILL.md's platform-target list, alongside the same omission in the agent-evaluation reference. Taskset submission (#1367). `submit` grew a second shape -- `tasks` + `target` against a live runner -- which was previously CLI-only and went out with no skill or docs coverage. Added to the interface table and the agent-evaluation reference, along with `GymRunnerTarget` in the target table, the four row-only options the taskset path refuses, and the Gym-only translation limit. The returned `AgentEvaluatorJobResource` deliberately has no `get_result()` or `download_artifacts()`, while every other job example in the skill ends in `get_result()`. That trap gets its own troubleshooting row. `evals.json` graded the agent on producing `nemo evaluator evaluate run --spec`, the very path SKILL.md says not to build on. Both verbs take identical spec flags, so the eval was rewarding the discouraged one for no benefit. Deliberately NOT included: the skill updates written for #1173. That PR closed unmerged, so `Evaluator.run_dataset_sync` and `client.evaluator.evaluate_dataset` do not exist. `evaluate_dataset` on main is the *backend* contract method, which makes the rename look landed when it is not. The public surface is still `run_sync` and `submit(metric=..., config=...)`. Every claim was verified by executing it against main rather than reading the source, which caught two errors in my own first draft: an example missing the required `resources_server`, and a claim that `env_vars` can hold a callable. It cannot -- it is `dict[str, str]`, so pydantic refuses one at construction and it never reaches the serializability guard. Only `hydra_params` is `dict[str, Any]`. (The `_gym_target` docstring names both and is likewise overstated, but that is merged code and out of scope here.) Four tests added, each mutation-verified. The largest gap they close is that `store_resources` -- the skill's canonical stored-task example -- was only ever asserted as text, so no schema change to `TaskInput` could fail it. It now runs against the real resource signatures and re-validates through the wire form `create` actually posts. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Signed-off-by: Sandy Chapman <schapman@nvidia.com>
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Aug 21, 2026
The skill drifted from the code because the NVSkills CI gate blocked any PR touching top-level `skills/`, so several evaluator changes landed with their docs updated and the skill left behind. That gate is gone as of NVIDIA-NeMo#1302, so this catches the skill up. Stored tasks (NVIDIA-NeMo#1071, NVIDIA-NeMo#566). `TaskInput` now carries a runner-discriminated `spec`, and `EvaluatorTaskDefinition` has the grader-only `reference` field. The skill still showed the flat pre-NVIDIA-NeMo#1071 shape and told readers that held-out ground truth required an inline `AgentEvalTaskInput` -- which would cost them tasksets and revision pinning for a limitation that no longer exists. Three places said it; all three are corrected. Local execution (NVIDIA-NeMo#1262). The skill lumped `client.evaluator.run()` together with the `nemo evaluator ... run` CLI verb as "being retired", but only the CLI verb still exists -- the method was removed a week ago. SKILL.md now warns about the CLI verb alone: naming a method that cannot be called, four lines from the seven live `.run()` calls the skill teaches (`AgentEvaluator().run`, `Evaluator().run_sync`), invited the wrong generalization. The removal is recorded in `troubleshooting.md` instead, which is symptom-indexed and so only reached by someone who already called it from memory. `GymRunnerTarget` was also missing from SKILL.md's platform-target list, alongside the same omission in the agent-evaluation reference. Taskset submission (NVIDIA-NeMo#1367). `submit` grew a second shape -- `tasks` + `target` against a live runner -- which was previously CLI-only and went out with no skill or docs coverage. Added to the interface table and the agent-evaluation reference, along with `GymRunnerTarget` in the target table, the four row-only options the taskset path refuses, and the Gym-only translation limit. The returned `AgentEvaluatorJobResource` deliberately has no `get_result()` or `download_artifacts()`, while every other job example in the skill ends in `get_result()`. That trap gets its own troubleshooting row. `evals.json` graded the agent on producing `nemo evaluator evaluate run --spec`, the very path SKILL.md says not to build on. Both verbs take identical spec flags, so the eval was rewarding the discouraged one for no benefit. Deliberately NOT included: the skill updates written for NVIDIA-NeMo#1173. That PR closed unmerged, so `Evaluator.run_dataset_sync` and `client.evaluator.evaluate_dataset` do not exist. `evaluate_dataset` on main is the *backend* contract method, which makes the rename look landed when it is not. The public surface is still `run_sync` and `submit(metric=..., config=...)`. Every claim was verified by executing it against main rather than reading the source, which caught two errors in my own first draft: an example missing the required `resources_server`, and a claim that `env_vars` can hold a callable. It cannot -- it is `dict[str, str]`, so pydantic refuses one at construction and it never reaches the serializability guard. Only `hydra_params` is `dict[str, Any]`. (The `_gym_target` docstring names both and is likewise overstated, but that is merged code and out of scope here.) Four tests added, each mutation-verified. The largest gap they close is that `store_resources` -- the skill's canonical stored-task example -- was only ever asserted as text, so no schema change to `TaskInput` could fail it. It now runs against the real resource signatures and re-validates through the wire form `create` actually posts. Signed-off-by: Sandy Chapman <schapman@nvidia.com> Co-authored-by: Claude Opus 5 <noreply@anthropic.com>
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Summary
An evaluation someone got working locally with
AgentEvaluator()can now be submitted as a governed job, without retyping its configuration into a target spec and getting it subtly wrong. Before, a liveGymAgentTaskRunnercould only run in-process; there was no supported way to hand one to the platform. After,evaluator.submit(tasks=TasksetRef("default/my-suite"), target=runner)derives the target spec from the runner and the job runs the same evaluation.Related Issue
Tracked by AALGO-485 (no GitHub issue).
Changes
runner_to_target(jobs/runner_targets.py): describes a liveGymAgentTaskRunneras theGymRunnerTargetthat reproduces it job-side. RaisesUnsubmittableRunnerErrorfor runners with no wire form rather than silently submitting something other than what was tested. Gym only; the other runners each need their own decisions about what survives translation and are deliberately not guessed at.Evaluator.submit()overload: a fourth shape discriminated bytasks. Supplying both shapes, neither, or a non-runnertargeteach raise a distinctTypeErrorat the boundary. A runner passed to the row path is also refused, rather than travelling on to be described as a model endpoint.AgentEvaluatorJobResource/AgentEvaluatorJob: agent jobs get their own resource and job model. Deliberately unrelated toEvaluatorJobResourceby inheritance in either direction — a row evaluation publishesaggregate-scores/row-scores, an agent evaluation publishesagent-eval-results/summary, so a shared base would put readers here whose type says "results" and whose behaviour is a 404.AgentEvaluatorJobisBaseJob[AgentEvalSpec]; validating an agent job's response asEvaluateSpecfailed on every field of the spec.NMP_DATA_DIR) alongside their file storage. They already pointed file storage at a per-run temp dir but left the database on the developer's real platform (~/.local/share/nemo/nmp-platform.db), which both wrote test entities into a live local install and broke reruns: metric bundles are content-addressed, so a second run found the first run's bundle entity, skipped the upload as a duplicate, then failed to download a blob that went away with the old temp dir.Relationship to #1315 / #1366
Scoped to avoid overlap with @JashG's Gym work:
TaskInputssogym_rowcan move frommetadataintoinputs. An earlier revision of this PR carried its own version of that change; it has been dropped so the widening lands once, in test(evaluator): Add Gym agent evaluation e2e coverage #1315.mcqaevaluation to completion in CI on Kind with a dedicated task image (feat(evaluator): Add dedicated Gym task image for agent evaluation #1366). An earlier revision of this PR carried a local-only execution test that skipped unless thegymCLI was onPATH; test(evaluator): Add Gym agent evaluation e2e coverage #1315's CI job supersedes it, so it was removed.GymRunnerTarget, whereas this PR derives the target from a live runner and resolves a stored taskset reference.Type of Change
Quality Gates
Verification
Signed-off-by:traileruv run pre-commit run -apasses, or any blocked checks are identified belowTargeted validation:
uv run --frozen pytest plugins/nemo-evaluator/tests --ignore=.../integrationuv run --frozen pytest packages/nemo_evaluator_sdk/testsRUN_AGENT_EVAL_INTEGRATION=1 pytest .../integration/{test_evaluate_job,test_metric_filtering,test_docs_manage_tasks_tasksets,test_task_revisions,test_submit_gym_agent_eval}.pyuv run ruff check plugins/nemo-evaluator packages/nemo_evaluator_sdkuv run ruff format --check plugins/nemo-evaluatoruv run --frozen ty check plugins/nemo-evaluatoruv run pre-commit run -ais not fully green, and neither failure is caused by this branch:Run UI lint-stagedfails withmise ERROR No version is set for shim: pnpm— Studio tooling is not bootstrapped in this worktree.skills/**, other plugins'openapi.yaml, bothpnpm-lock.yaml).mainis missing those headers too, so this is pre-existing repo-wide drift that-asurfaces and a normal staged commit does not. Those edits were reverted rather than swept into this PR.No SDK regeneration is needed.
make update-sdkwas run against Stainless and produced no model changes — this PR's surface is not part of the generated SDK. Its only effect was stripping SPDX headers from 33 vendored files (the post-generation license step covers Python files only), so that was reverted. The one vendored change kept is theGymRuntimeConfigaccessor this PR adds.Summary by CodeRabbit
New Features
Bug Fixes
Tests