Run container-backed AutoML / hyperparameter optimization (HPO) for NVIDIA TAO networks using AutoMLRunner. Handles algorithm selection (bayesian, hyperband, asha, bohb, llm, hybrid, autoresearch), WandB experiment tracking, job execution on any TAO SDK platform, result interpretation, and per-rec custom evaluation hooks. Use when the user mentions TAO AutoML, hyperparameter optimization, HPO, automl, automl_settings, AutoMLRunner, tao_automl, bayesian search, hyperband, ASHA, LLM-guided search, autoresearch, or wants to tune train/evaluate/inference/distill/prune/quantize for a TAO network. Model actions use the resolved image; venv training requires an explicit request. Platform-agnostic — runs on any SDK (Brev, SLURM, Kubernetes, Docker).
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name
tao-run-automl
description
Run container-backed AutoML / hyperparameter optimization (HPO) for NVIDIA TAO networks using AutoMLRunner. Handles algorithm selection (bayesian, hyperband, asha, bohb, llm, hybrid, autoresearch), WandB experiment tracking, job execution on any TAO SDK platform, result interpretation, and per-rec custom evaluation hooks. Use when the user mentions TAO AutoML, hyperparameter optimization, HPO, automl, automl_settings, AutoMLRunner, tao_automl, bayesian search, hyperband, ASHA, LLM-guided search, autoresearch, or wants to tune train/evaluate/inference/distill/prune/quantize for a TAO network. Model actions use the resolved image; venv training requires an explicit request. Platform-agnostic — runs on any SDK (Brev, SLURM, Kubernetes, Docker).
Standalone install? If this session was not initialized by the TAO skill bank plugin, run the tao-setup skill first (host preflight, credentials, cross-skill discovery).
Run automated hyperparameter optimization for a TAO model by combining:
The selected model skill under skills/models/<model_skill>/.
The selected platform skill under skills/platform/<platform>/.
AutoMLRunner, which generates recommendations, launches selected action jobs,
extracts metrics, and feeds results back to the optimizer.
Do not launch until model metadata, platform preflight, data visibility,
credentials, image choice, and compute shape are all proven.
Execution Runtime — Hard Gate
Every recommendation, baseline evaluation, per-recommendation evaluation, and
final evaluation runs in the selected model action's resolved
container_image by default. Resolve it from the model skill before any
training-environment setup. A local checkpoint or Hugging Face model ID does not
change this rule.
Use venv-based model execution only when explicitly requested. Never infer
venv mode from local-docker, local GPUs, Python, or pyproject.toml. If
absent, execution is container-backed. A host/controller venv for tao_automl,
TAO SDK, or a platform adapter is
control-plane-only; keep child model actions in the resolved container image.
Reference Map
references/skill_info.yaml: this workflow's structured metadata.
Split detailed references: automl-preflight-concepts.md for prerequisites
and support checks; automl-intent-algorithms.md for search policy;
automl-compression-literature.md for distill/prune/quantize algorithm
sufficiency and future compression-search roadmap;
automl-runner-configuration.md for runner/API/WandB details;
automl-advanced-monitoring.md for hooks, resume, and pitfalls; and
automl-examples.md for conversation examples; and
automl-common-pitfalls.md for recurring safety checks. detailed-guide.md
is only the map.
skills/models/<network>/SKILL.md: model-specific dataset requirements, metrics,
HPO notes, checkpoint handoff, and known failures.
skills/platform/<platform>/SKILL.md: selected platform preflight, credentials,
resource shape, monitoring, and cancellation.
skills/core/tao-launch-workflow/SKILL.md: shared intake pattern for platform,
credentials, dataset visibility, image confirmation, and user confirmation.
Preflight
Run the shared launch intake. If the user has not chosen a platform, ask;
Brev, SLURM, Kubernetes, and Docker are equal peers.
Run the selected platform skill's preflight before generating runner files.
Verify nvidia-tao-automl imports:
python -c "import tao_automl; from tao_automl.runner import AutoMLRunner; print('OK')"
Then verify the selected platform's SDK constructs — importing tao_automl does
not prove the platform backend is installed (e.g. DockerSDK() raises
CredentialError without the docker package). See
automl-preflight-concepts.md.
If missing, show the exact install command from versions.yaml and ask before
installing:
Valid platform wheel keys are tao_automl_brev, tao_automl_slurm,
tao_automl_kubernetes, tao_automl_docker, and tao_automl_all. Use
all only for development machines that need every backend. Add ,llm only
when the user requests LLM-guided algorithms.
Model Support Gate
Before every run:
Read the model SKILL.md and references/skill_info.yaml.
Confirm automl_enabled: true for the model or that the model skill
explicitly routes the selected action to AutoML.
Confirm <skill_dir>/schemas/<action>.schema.json exists and parses. This
is the AutoML search-space gate.
For non-TAO-Core models such as Cosmos-RL and CLIP, also require
references/spec_template_<action>.yaml; otherwise the runner has no
complete action defaults.
If any gate fails, do not improvise a search space. Report the missing
package artifact.
Inputs
Collect these before runner construction:
Input
Requirement
model_skill
Resolved model skill directory under skills/models/. Accept user aliases such as network_arch only after resolving them to the packaged skill directory.
network_arch
Read from the resolved model skill metadata.
action
Action to optimize, usually train, evaluate, inference, distill, prune, or quantize when that action has a packaged schema/template.
platform
One of the supported TAO platform skills.
train_dataset / eval_dataset / action inputs
Use model-specific spec keys and dataset layout. Non-train actions often also require parent checkpoints, teacher checkpoints, calibration data, or pruned artifacts.
results_root
Local, Lustre, or S3 path appropriate for the platform.
gpu_count, num_nodes
Respect model and platform limits.
container_image
Resolve through model metadata and versions.yaml; show it to the user.
automl_algorithm
Default bayesian unless user asks for another algorithm or the model skill recommends one.
metric, direction
Prefer the model skill's validation/task metric.
automl_budget
Recommendation count, max epochs/rungs, concurrency, or population size as required by the algorithm.
Never ask for secret values. Verify required env vars with
[ -n "$VAR_NAME" ] && echo SET || echo UNSET.
Pre-Launch Review Gate
Before launching any recommendation jobs, show a concrete launch review and get
user confirmation. This gate applies to every AutoML run for every
AutoML-supported model/network; it is not Cosmos-specific and must not be
scoped to a single model skill. This applies even when platform and image
preflight already passed. The review must include:
model/network, platform, image, GPU/node shape, and result/workspace root
dataset mode and concrete spec keys, including train/eval sample counts when
they can be read cheaply
algorithm, budget, max concurrent jobs, metric, and direction
searchable parameters and ranges, including default values when the user did
not provide an explicit search space
exact generated recommendation configs for the initial launch batch, produced
in a review-only step before any recommendation job is submitted
estimated runtime per recommendation and total expected wall time, with the
assumptions used
the automatic baseline eval job id, metric value, and result path from the
post-preflight eval job, or an explicit blocker if the model has no runnable
evaluate action or validation data
the post-AutoML final evaluation plan for the selected best checkpoint/model,
including metric, dataset, and record path
If the estimate is longer than the user's stated limit or materially longer
than a normal interactive run, ask whether to reduce recommendations, epochs,
dataset size, validation frequency, or search space before launch. Do not hide
multi-day estimates in logs.
Automatic Baseline Eval Job
After platform, image, credential, data, and model preflight pass, run the
model's evaluate action once on the selected validation/eval data before
submitting any AutoML recommendation jobs. This is required AutoML setup, not an
optional "pretrained eval" question for the user. Use the same base model or
checkpoint that the AutoML training run starts from, the model skill's evaluate
spec/template, and the selected platform's normal job submission path. If the
model skill recommends a smaller shape for evaluation than training, use that
shape and call it out in the launch review.
Share the eval metric number in the launch review before asking for confirmation. If a
starting checkpoint exists but the baseline cannot be
produced — no packaged evaluate action, missing eval dataset, failed eval job —
stop and report the blocker instead of silently falling back to a
training-loss-only run.
For training from scratch, record the baseline as unavailable and proceed; do
not evaluate an empty checkpoint. See automl-preflight-concepts.md for the
checkpoint, baseline, and from-scratch rules.
The runner owns final evaluation. When eval is runnable, pass
final_eval_fn(best_rec, train_job_id) to AutoMLRunner.run; the result then
carries result["final_evaluation"]. See automl-preflight-concepts.md for
the callback and from-scratch rules.
Dependency And Data Preflight
If the selected workflow needs object storage or a platform CLI and the tool is
missing, report the missing dependency and offer the exact install command
before continuing. After user approval, rerun
scripts/check_tao_launch_preflight.py with --install-missing-tools so it
installs the smallest needed package and immediately retries path verification.
For S3 paths, verify both credentials and path readability from the launch
platform before creating runner artifacts. Do not wait for the first training
container to discover a missing AWS CLI, S3 client, or unreadable URI.
For models that read large media archives or directories during every training
trial, stage or extract the dataset once to storage visible from the execution
platform, then point all recommendation specs at that staged path. Record the
source URI, staged path, byte/file-count evidence when available, and timestamp
in <workspace>/evaluations/data_staging.json. If staging is not possible,
include the repeated S3 I/O risk in the pre-launch review and ask before
spending a long AutoML budget on it.
When the model skill defines sample-count-sensitive constraints, enforce them
before launch. Reject or cap every batch-size recommendation that would create
zero training steps for the selected dataset and GPU shard count. Use
scripts/check_tao_launch_preflight.py --effective-batch-limit train_annotation=<batch_size>,<shard_count> for each generated recommendation
before submitting it. If a recommendation later fails because the data is too
small for the effective batch size, classify it as an invalid configuration,
replace or adjust it only when remaining budget exists, and report the
correction in the final summary.
When train sample count is known from an annotation file or cheap manifest read,
pass it as automl_settings["train_sample_count"] to AutoMLRunner.run so the
runner can cap impossible recommendations before submitting a job and record the
adjustment in result["history"][i]["adjustments"].
Algorithm Policy
Algorithm
Good fit
Required knobs
bayesian
Default for small/medium budgets and few parameters.
num_recommendations, metric, direction
hyperband, asha
Many configs with cheap early rungs; ASHA supports parallelism.
Mixed Bayesian/evolutionary search with multi-fidelity budgets.
same rung budget fields as Hyperband
pbt
Long training where schedules should mutate during training.
population and generation budget
llm, hybrid, autoresearch
User explicitly wants LLM-guided search and has an endpoint configured.
LLM endpoint config plus budget
For evaluate or inference, default to Bayesian/BFBO-style search over the
selected action's prompt, decoding, preprocessing, or runtime config knobs.
Use a task metric from the action outputs/logs and set direction explicitly
when the metric name is ambiguous. Do not use training-loss assumptions for
actions that do not update weights.
For distill, use the same train-like policy when the distill action performs
epoch-based optimization and writes checkpoints. For single-shot prune and
quantize, default to bayesian or bfbo unless the action schema/model skill
declares an epoch-like or calibration-budget field that makes
hyperband/asha/bohb/dehb meaningful. Use eval_fn when the selected
metric must be computed by a follow-up evaluate/inference action after the
compression action completes.
Prefer the model skill's recommendation over generic defaults. Avoid ASHA or
Hyperband when the model skill says startup, validation, or checkpoint cost
dominates short trials.
Spec And Search Space
Build specs as nested dictionaries. If a model skill lists paths in dotted
notation for readability, walk the path and assign the nested leaf; do not store
flat dotted strings as spec keys.
Use the packaged selected-action schema for:
automl_default_parameters
automl_disabled_parameters
valid min/max ranges
enums, option weights, conditions, dependencies, and popular parameters
User-provided search spaces must stay inside schema constraints. For integer
knobs with discrete choices, include the schema's required integer option shape
instead of a loose list if the model skill calls that out.
Data source overrides are mandatory unless the model skill says the launcher can
derive them. Preserve exact user-provided spec keys when the dataset uses direct
annotation/media paths.
Metric Policy
Training loss is cheap but can be misleading. Prefer the model skill's task
metric. Use one of these:
metric_extractor(logs, metric_name): parse the model's logs when the
default resolver is ambiguous.
eval_fn(rec, train_job_id): run the model's evaluate action after each
recommendation when the user wants a downstream task metric.
Do not map kpi to a metric unless the model skill explicitly defines that
mapping.
For every AutoML run with a runnable evaluate action and validation/eval data,
run the automatic baseline eval job after preflight and before recommendations.
The final report must compare that baseline metric, each recommendation's
metric, and the selected best metric so users can see the impact of tuning. For
model skills that require an eval_fn to compute the real task metric, use
that evaluator instead of optimizing a convenient training loss unless the user
explicitly accepts the proxy metric.
Runner Construction
Use the selected platform SDK only after its preflight passes. Construct SDKs
without embedding credentials in code.
from pathlib import Path
from tao_automl.runner import AutoMLRunner
skill_bank = Path("<absolute-tao-skill-bank>")
model_skill = "<resolved-model-skill-directory>"
skill_dir = skill_bank / "skills" / "models" / model_skill
runner = AutoMLRunner(
sdk=sdk,
skill_dir=str(skill_dir),
action=action, # train, distill, prune, quantize, ...
)
result = runner.run(
workspace_path="<automl_workspace>", # timestamp it to avoid collisions
automl_settings=automl_settings,
spec_overrides=spec_overrides,
automl_hyperparameters=automl_hyperparameters,
custom_param_ranges=custom_param_ranges,
metric_extractor=metric_extractor, # optional
eval_fn=eval_fn, # optional
final_eval_fn=final_eval_fn, # optional but required when final eval is runnable
)
Only resume an existing workspace when the user explicitly asks to resume,
continue, recover, or inspect an existing experiment. Treat a plain "run
AutoML" request as a fresh run.
Monitoring
Use runner status output and the platform SDK's get_job_status,
get_job_logs, and get_failure_analysis. For active jobs, report:
recommendation id / trial id
platform job id
status
current metric
best metric so far
selected hyperparameters for the current/best recommendation
elapsed time and updated ETA when enough timing data exists
On failure, classify whether it is infrastructure, data visibility, image,
credential, spec/schema, or model-code failure. Fix only the minimal cause and
do not silently spend additional budget on repeated invalid recommendations.
If a blocker is fixed during run setup, continue from the original task after
showing the updated preflight/launch review instead of leaving the user to
restate the request.
For LLM-based algorithms, inspect the brain logs before calling the run valid.
Verify that LLM calls succeeded, proposals were generated, prior metrics were
used to choose later parameter changes, and logs show keep/discard or
equivalent algorithm decisions. If the brain falls back to random sampling,
classify the LLM workflow as failed or blocked instead of treating it as a
valid LLM-guided run.
Result Handoff
At completion:
Identify the best recommendation by the selected metric and direction.
Return the best child job id and its result path.
Resolve the model checkpoint or action artifact using the model skill's
checkpoint/artifact metadata and SDK helpers; do not guess filenames such as
latest.
Report the exact search space, algorithm, budget, metric, and platform.
Report the automatic baseline eval job id/result path/metric, all
recommendation metrics, final evaluation status/result path/metric, failed
recommendations and root causes, elapsed time, and final runtime notes.
If this feeds a workflow such as AutoML + DEFT, pass the winning spec
overrides and checkpoint through the workflow's declared handoff fields.
With the default retention policy, verify that cleanup-supported, safely
prunable terminal trial artifacts were deleted and that the winning
training artifacts remain. Report protected promotion/resume parents or
conservative Hybrid results explicitly. A remote bind, named volume, or
other output route the SDK cannot reclaim must fail retention preflight
before the first trial rather than be silently retained.
Common Pitfalls
See references/automl-common-pitfalls.md before launching or recovering an
AutoML run.
