hpc-agent 0.7.1

HPC orchestrator for Claude Code and external agent harnesses

hpc-agent

HPC orchestrator for array-batch experiments on SGE/SLURM clusters. Two surfaces over one core:

• Slash commands for humans in Claude Code (/submit-hpc, /monitor-hpc, /aggregate-hpc, /campaign-hpc) — interactive markdown templates in slash_commands/commands/*.md that walk you through choosing a cluster and authoring .hpc/tasks.py. The four workflow triggers cover every end-user moment; entry-point onboarding, axis classification, and axes-init are folded into /submit-hpc's escalation playbook (the worker escalates when it can't proceed; the playbook walks the user through the dialog and the agent invokes the relevant skill with a resolved spec). Environment preflight (SSH agent, cluster reachability) is a one-time-per-machine CLI step: hpc-agent setup --cluster <name> probes the cluster and writes a 24h cache marker /submit-hpc's Step 6b gate reads — runtime workflows assume setup succeeded.
• CLI for agents and automation (hpc-agent <subcommand>) — JSON-in, JSON-out, exit codes. Designed to be invoked via a Bash-style tool by external orchestrators. This is a POSIX-native agent surface: any tool that can shell out and parse JSON can drive a cluster — see docs/reference/agent-surface.md. For integrators: docs/integrations/CONTRACT.md.

Both surfaces invoke hpc-agent <subcommand>. The slash commands are pure markdown that orchestrate the binary; the binary's atomic-ops layer (the per-subject runners under hpc_agent/ops/) ensures cross-surface state — in-flight runs, journal records under ~/.claude/hpc/<repo_hash>/ — is shared automatically.

Quick Start

For humans (Claude Code)

pip install hpc-agent                              # or `pip install -e .` from a checkout
hpc-agent setup                                    # copy commands + skills
hpc-agent setup --cluster hoffman2                 # probe cluster + populate 24h preflight cache (run once per cluster)

hpc-agent setup (no flags) copies the bundled slash commands into ~/.claude/commands/ and the skills into ~/.claude/skills/ — idempotent. Re-run with --cluster <name> once per machine + cluster to probe SSH agent reachability, ssh/transport on PATH, clusters.yaml parseability, and TCP :22; on a green probe it writes a 24h cache marker that /submit-hpc's Step 6b gate consults, so the first submit doesn't re-run the check. Pass --dry-run to preview. Each preflight check's detail field carries actionable remediation prose, so a red probe tells you exactly what to fix. Every command (/submit-hpc, /monitor-hpc, /aggregate-hpc, /campaign-hpc) and skill ships inside the package.

Once installed:

• hpc-agent setup --cluster <name> (once per machine + cluster) — install assets and probe each cluster you'll submit to. Runtime workflows assume setup succeeded; re-run if SSH credentials or a cluster's reachability change.
• /submit-hpc — answer prompts about cluster, executor, grid params. The worker escalates with structured intent prompts (entry-point onboarding, axis classification) when it can't proceed; the in-chat agent walks the user through the escalation playbook and invokes the relevant skill with the resolved spec.
• /monitor-hpc to monitor, /aggregate-hpc to collect results.

For agents and automation

pip install hpc-agent
hpc-agent setup --cluster hoffman2                        # one-time: install assets + probe cluster + write 24h cache marker
hpc-agent interview --spec intent.json --campaign-dir <d> # persist campaign intent next to tasks.py
hpc-agent recall --root ~/experiments --task-kind <kind>  # query past interviews for next-interview grounding
hpc-agent submit --spec spec.json                          # JSON envelope on stdout
hpc-agent status --run-id <id>                             # one-shot snapshot; poll as needed
hpc-agent aggregate --run-id <id> --wave 1                 # combiner + result pull

Stdout is a single-line JSON envelope: {"ok": true, "idempotent": ..., "data": {...}} or {"ok": false, "error_code": ..., "retry_safe": ..., "remediation": ...}. Exit codes: 0 ok, 1 user error, 2 cluster/network, 3 internal. Full schema in docs/reference/cli-spec.md; JSON Schema files for runtime validation under hpc_agent/schemas/.

For integrators

hpc-agent is Bash-invokable from any agent harness with a JSON parser. See docs/integrations/CONTRACT.md for the full contract: the spawn env block, error_code → retry policy table, the find-prior-run → submit → monitor-summary → verify-aggregation-complete workflow, the .hpc/tasks.py boundary, and the executor import allowlist.

The canonical reference for .hpc/tasks.py is shipped inside the package at src/hpc_agent/models/mapreduce/templates/scaffolds/tasks_example.py. It demonstrates three patterns (Cartesian product, chunking by row count, date-window backtests) inline. Integrators locate it at runtime via from hpc_agent import _PACKAGE_ROOT or rglob("tasks_example.py").

The most common first-time failure is the harness's default-empty spawn env dropping SSH_AUTH_SOCK. hpc-agent status/aggregate/reconcile fail fast with error_code: "ssh_unreachable" (exit 2) instead of hanging on auth — run hpc-agent setup --cluster <name> once on each machine to verify the spawn env and populate the 24h cache marker that /submit-hpc's Step 6b gate reads. hpc-agent does not kill cluster jobs by design (settings.json denies scancel/qdel); if the integrator decides a run is bad, stop polling and let it expire.

---

Standalone usage

Organize your experiment repo

Keep standalone executor scripts in a dedicated directory, separate from shared utilities:

my_experiment/
├── executors/           # or src/ — each file is a runnable experiment
│   ├── ml_ridge.py      # python3 executors/ml_ridge.py --help
│   ├── ml_xgboost.py
│   └── dl_patchts.py
├── lib/                 # shared utilities (not executors)
│   ├── loading.py
│   └── transforms.py
└── data/

Each executor accepts experiment-specific arguments (--horizon, --start, --end, --features, etc.). No HPC awareness is needed — all parameters arrive as CLI flags.

Run

hpc-agent setup --cluster <name> → one-time per machine: install assets + probe cluster
/submit-hpc                      → discovers executors, walks you through .hpc/tasks.py, syncs code, submits
/monitor-hpc                     → tracks completion per grid point, diagnoses failures, auto-resubmits
/aggregate-hpc                   → validates completeness, runs aggregation, downloads summaries

Example conversation:

You: /submit run ridge and xgboost with horizon=[1, 5, 25]

Claude: I found these executors in src/:
  ml_ridge.py    — --horizon, --start, --end, --output-file
  ml_xgboost.py  — --horizon, --start, --end, --output-file

Proposed plan:
  Cluster: hoffman2 (SGE)
  Grid: executor=[ml_ridge, ml_xgboost] × horizon=[1, 5, 25] → 6 grid points
  Total: 6 tasks
  Resources: 1 CPU, 16G, 4:00:00
  Confirm?

You: yes

Claude: Submitted job 12345678 (6 tasks). Run /monitor-hpc to track progress.

No config files required. Claude discovers your executors by reading their source and --help, then suggests resources conversationally based on the executor and your input.

How It Works

The framework's contract with your experiment is a @register_run-decorated Python function with typed kwargs — that function can live in a notebook, a .py script, or a package module; discover_runs AST-walks all three indifferently. See docs/internals/experiment-contract.md for the canonical description.

The boundary between hpc-agent and your experiment repo is documented in docs/reference/boundary-contract.md and enforced by tests/test_boundary_contract.py.

1. Claude reads your executor scripts and their --help output.
2. You describe what to run in natural language — Claude walks you through writing .hpc/tasks.py once: a small Python module exposing total() and resolve(task_id) that returns the per-task kwargs. The file is committed to git and reused on every subsequent submit.
3. A per-run sidecar .hpc/runs/<run_id>.json records the executor command, result-dir template, cmd_sha, and wave map for this particular submission.
4. The framework executor _hpc_dispatch.py (zero deps, stdlib-only) is deployed to the cluster's .hpc/ by deploy_runtime.
5. The job template runs the dispatcher, which imports your .hpc/tasks.py, calls resolve(task_id), formats the result_dir, and execs your executor command with kwargs as env vars.
6. Your executor reads kwargs as ordinary env vars (uppercased + HPC_KW_*) — no HPC awareness needed.

Parallelism Model

The parallelization axis lives entirely in user code (.hpc/tasks.py). The framework is agnostic to whether you're doing a Cartesian grid, chunking by row count, date-window backtests, or something else — it just calls total() and resolve(i). The canonical reference at hpc_agent/models/mapreduce/templates/scaffolds/tasks_example.py shows three patterns inline; the agent helps you keep whichever applies and delete the rest.

Memory across campaigns

Two primitives — interview and recall — close the loop between consecutive campaigns. The interview agent (Claude Code or any external orchestrator) persists structured intent (goal, task_count, budget, abort_if, task_generator, cluster_target, transcript, provenance) into <campaign_dir>/interview.json next to the materialized tasks.py. The next interview calls recall --root <experiments-dir> to query past intents, returning recency-sorted summaries plus a 3-tier rollup (counts/histograms/quantiles, optional walltime aggregation, optional per-generator parameter envelopes). Observed ranges only — reasoning over them stays in the calling agent.

See docs/workflows/memory-across-campaigns.md for the full flow, including the task_generator typed materializer (5 shapes: enumerated, cartesian_product, items_x_seeds, numeric_logspace, numeric_linspace) and the ~/.hpc-agent/config.json:experiment_roots default-root config.

Throughput Optimization

hpc-agent automatically optimizes job submissions for cluster constraints. When constraints are configured (max array size, walltime, concurrent job limits), the optimizer packs tasks into batched waves:

• Tasks are split into arrays of ≤max_array_size
• Arrays are grouped into waves of ≤max_concurrent_jobs
• Waves are staggered via scheduler dependencies (SLURM --dependency, SGE -hold_jid)
• Total wall-clock time is estimated when per-task duration is known

Configure constraints in clusters.yaml (cluster-level); per-experiment overrides resolved at /submit time are persisted to the run sidecar at .hpc/runs/<run_id>.json.

Commands

Command	What it does
/submit-hpc	Discover executors (scaffolds inline if none found), build grid conversationally, write .hpc/tasks.py with FLAGS dict + .hpc/cli.py dispatcher, sync code, submit array jobs. Carries an escalation playbook covering entry-point onboarding, axis classification, and axes-init dialogs.
/monitor-hpc	Poll status, diagnose failures, auto-resubmit, self-schedule next check
/aggregate-hpc	Validate completeness, run aggregation on cluster, download summaries
/campaign-hpc	Closed-loop iteration: tag submits, read prior history, repeat /submit-hpc campaign_id=<slug> until the strategy stops. Carries the validate-campaign findings interpretation guide. See docs/workflows/campaign.md.

Setup is a CLI step, not a slash: run hpc-agent setup --cluster <name> once per machine + cluster (see Quick Start above). Each preflight check's detail field carries actionable remediation prose.

Primitives

The slash commands above compose ~50 primitives exposed as hpc-agent <name>. Full machine-readable catalog at docs/generated/operations.md (auto-regenerated). High-traffic ones for agent orchestration:

Primitive	Replaces
submit-flow / submit-flow-batch	rsync + deploy + qsub + record (single or N-spec batch with shared rsync). Auto-dispatches when the spec is {specs: [...]}.
monitor-flow	Poll-and-combine loop the slash command's tick body wraps.
aggregate-flow	rsync_pull _combiner/ + reduce_partials + optional summary pull + ingest runtime samples.
build-submit-spec	Resolved-interview-values → validated submit_flow.input.json spec.
build-tasks-py	Cartesian-product axes → .hpc/tasks.py from the canonical Pattern 1 template.
discover-executors / discover-reducers	Scan repo for executor scripts / aggregator scripts (find existing reducer instead of writing a fresh one).
decide-monitor-arm	Pick cron/loop/none + cadence + cron schedule for scheduling the next monitor tick.
monitor-summary	Canonical user-facing tick summary (byte-stable framing).
summarize-submit-plan	Canonical pre-submit confirmation summary.
verify-canary	Wait + grep + output-check protocol for 1-task canary submissions.
verify-aggregation-complete	All-waves-combined / all-tasks-present / no-cross-run-contamination invariant report.
suggest-setup-action / find-prior-run	/submit-hpc Setup priority cascade + cmd_sha resume detection.
prune-orphan-sidecars	Clean half-baked sidecars from failed batches.

hpc-agent <name> --help shows the per-primitive args; many take --spec <path> for a JSON input. See docs/primitives/<name>.md for the per-primitive contract (idempotency, side effects, error codes, schemas).

Configuration

clusters.yaml (required)

Cluster infrastructure definitions. Ships inside the package at hpc_agent/config/clusters.yaml. Override the active path with HPC_CLUSTERS_CONFIG=/your/clusters.yaml (useful for integrators who want to keep their cluster definitions outside the package):

hoffman2:
  host: hoffman2.idre.ucla.edu
  user: <your_user>
  scheduler: sge
  scratch: <your_scratch>
  modules: [python/3.11.9]
  conda_source: /u/local/apps/anaconda3/2024.06/etc/profile.d/conda.sh
  conda_envs: [<your_env>]          # optional — Claude presents these as options
  gpu_types: [a100, h200, a6000]

~/.hpc-agent/config.json (optional)

Per-user config for the recall primitive's default --root. List one or more directories under experiment_roots and recall walks them all when --root is omitted:

{
  "experiment_roots": [
    "/home/user/experiments",
    "/scratch/user/campaigns"
  ]
}

The --root CLI flag still wins when set. If neither flag nor config is present, recall errors with spec_invalid rather than silently falling back to cwd.

Caching

Claude remembers your preferences (cluster, executor directory, environment, resources) across conversations via Claude Code memory. The .hpc/runs/<run_id>.json sidecars (paired with .hpc/tasks.py) serve as the submission record for monitoring and resubmission.

Job Templates

Template	SGE	SLURM
CPU array	hpc_agent/models/mapreduce/templates/runtime/sge/cpu_array.sh	hpc_agent/models/mapreduce/templates/runtime/slurm/cpu_array.slurm
GPU array	hpc_agent/models/mapreduce/templates/runtime/sge/gpu_array.sh	hpc_agent/models/mapreduce/templates/runtime/slurm/gpu_array.slurm

Templates are parameterized via environment variables injected at submission time. Resolve paths via hpc_agent.get_template_path(scheduler, template). The GPU template is used when the configured resources include gpus; otherwise the CPU template is used.

Supported Clusters

Cluster	Institution	Scheduler
Hoffman2	UCLA IDRE	SGE
Discovery	USC CARC	SLURM

Cluster connection details are in hpc_agent/config/clusters.yaml (or whatever HPC_CLUSTERS_CONFIG points at).

Python API

# Framework subdirectory layout
from hpc_agent import RUNS_SUBDIR, RepoLayout, TASKS_FILENAME, load_tasks_module

# Per-run sidecars (canonical home)
from hpc_agent.state.runs import (
    compute_cmd_sha,
    find_existing_runs,
    find_run_by_cmd_sha,
    read_run_sidecar,
    write_run_sidecar,
)

# Cluster config + templates
from hpc_agent import _PACKAGE_ROOT, get_template_path, load_clusters_config

# Submission planning
from hpc_agent.infra.constraints import ClusterConstraints, parse_constraints
from hpc_agent.infra.throughput import (
    WorkloadSpec,
    build_wave_map,
    compute_submission_plan,
)

# Remote execution + backends
from hpc_agent.infra.backends import get_backend
from hpc_agent.infra.remote import deploy_runtime

Development

pip install -e '.[dev]'
pre-commit install        # auto-runs ruff, frontmatter regen, index regen
pytest -q                 # 1400+ tests

The pre-commit hook regenerates docs/primitives/*.md frontmatter, docs/primitives/README.md catalog, and docs/generated/operations.md from the @primitive registry, then auto-stages the result. Without it you'll see CI fail on the corresponding --check gates and have to push a follow-up chore: regenerate ... commit.