hpc-claw 0.2.0

HPC-specific intent modeling and policy-verifiable resource orchestration built on Agentivium Core.

HPC-Claw

HPC-Claw is a user-space, pre-submission decision-support and verification layer for HPC scheduling. It normalizes structured or natural-language intent, captures time-bounded Slurm and policy snapshots, generates candidate decisions, builds evidence, and asks Agentivium Core's generic pipeline to classify relation and admissibility before approval or execution.

HPC-Claw is the first domain extension of agentivium-core. Agentivium Core defines the contracts: conversation state, context packs, memory, retrieval, clarification, intent IRs, parsers, LLM clients, policy validation results, planners, tool adapters, and execution traces. HPC-Claw implements HPC-specific behavior on top of those contracts.

HPC-Claw is not a scheduler replacement and does not modify slurmctld. Dry-run is the default. Explicit submission is protected by verification freshness, admissibility, script integrity, authorization, approval, an available audit sink, and a final exact-command confirmation.

Installation

pip install -e ".[dev]"

The package targets Python 3.10 or newer and targets the public agentivium-core==0.5.0 contracts distributed through PyPI.

Conversational Shell

Normal use now starts from the application home and current workspace:

hpc-claw
hpc-claw start
hpc-claw submit

The first two commands initialize or migrate ~/.hpc-claw, detect static HPC capabilities, confirm workspace trust, create a persistent thread/run, and open the terminal conversation. hpc-claw submit opens the submit-oriented shell in dry-run mode. The shell exposes /status, /context, /skills, /thread, /new, /trace, and /exit; it never invokes the CLI as a subprocess.

Global application state is owned by ~/.hpc-claw/:

config.toml        version.json       providers/       credentials/
clusters/          threads/           runs/            memory/
skills/            audit/             cache/           logs/
migrations/backups/

The home is created with mode 0700; protected files use 0600. Migrations are versioned, backed up before mutation, and preserve legacy state for review. Provider configuration is separate from credentials, and thread/run persistence redacts secret-like fields and bearer or URL credentials.

Useful read-only diagnostics remain available without using the conversation:

hpc-claw detect --json
hpc-claw doctor --json
hpc-claw inspect ./job.sbatch --json

Project-local Workspace Setup

Initialize a hidden assistant workspace before regular use:

hpc-claw init --role hpc_user \
  --llm-base-url http://localhost:11434/v1 \
  --llm-model qwen2.5:7b

For an administrator profile:

hpc-claw init --role admin \
  --llm-base-url https://api.openai.com/v1 \
  --llm-model gpt-4.1-mini \
  --llm-api-key-env OPENAI_API_KEY

This backward-compatible command creates <workspace>/.hpc-claw/ as optional project-local configuration. It is separate from the global application home and contains:

• config.yaml for the active role, LLM endpoint, default policy, and paths
• roles/ for admin or HPC-user permissions
• policies/ with the provenance-aware Slurm policy copied into the workspace
• prompts/ and templates/ for internal assistant instructions and Slurm templates
• contexts/, evidence/, cases/, approvals/, audit/, memory/, runs/, traces/, artifacts/, and logs/ for runtime state

Use the generated LLM config directly:

hpc-claw parse examples/requests/mpi_test.txt \
  --llm-config .hpc-claw/config.yaml \
  --out intent.json

Verifiable Scheduling Path

Start with canonical YAML or JSON; natural-language parsing remains an optional, untrusted adapter:

hpc-claw intent validate examples/intents/cpu_gpu_fallback.yaml --json
hpc-claw context collect --scheduler slurm --out .hpc-claw/contexts/latest.json
hpc-claw policy snapshot hpc_claw/policy/profiles/verifiable_slurm_v0_1.yaml \
  --out .hpc-claw/policies/site-policy-snapshot.json
hpc-claw recommend examples/intents/cpu_gpu_fallback.yaml \
  --context .hpc-claw/contexts/latest.json \
  --policy-snapshot .hpc-claw/policies/site-policy-snapshot.json \
  --dry-run --json

The dry-run result contains normalized intent, context/policy IDs and timestamps, candidate decisions, observed/predicted/derived evidence, relation, admissibility, blocking checks, deviations, re-verification deadline, and next action. It returns the admissible set; the verifier does not choose a globally "best" decision.

The formal pipeline is:

Structured intent
  -> Core Intent
  -> Slurm ContextSnapshot + PolicySnapshot
  -> rule and bounded-enumeration candidate Decisions
  -> HPC EvidenceBundle
  -> Core VerificationPipeline + HPC providers
  -> relation and admissible decision set
  -> explicit approval when required
  -> dry-run script or guarded sbatch
  -> append-only audit events

Machine-readable commands use --json. Operational exit codes are: 0 accepted, 1 rejected/inadmissible, 2 clarification required, 3 re-verification required, and 4 scheduler infrastructure or partial-observability failure.

Evidence and relation semantics

HPC-Claw keeps scheduler validity, resource feasibility, current availability, policy compliance, deadline feasibility, preference alignment, admissibility, approval, and optimality separate. The Core relation is one of Exact, PermittedRelaxation, JustifiedDeviation, UnjustifiedDeviation, Violation, or operational Undetermined. Explanations never substitute for structured, traceable evidence.

Every observed source records command, timestamp, duration, and status. Missing, permission-denied, timed-out, unparseable, or unsupported sources stay explicit. Queue and policy facts have independent freshness bounds and stale decisions must be rebuilt and reverified before execution.

Research package

research/verifiable_scheduling/ is independent of production verification. It contains a separate exhaustive bounded oracle, 100 provisional gold cases queued for hand review, 1,000 balanced generated cases, 200 challenge cases, 3,600 evidence-fault cases, A0-A5 ablations, trace replay, three deterministic decisors, and frozen metrics. The oracle does not import production predicates. Generate the versioned fixtures via:

python -m research.verifiable_scheduling.scenarios.generate

No claim of global schedule optimality or improved cluster utilization is made.

Basic CLI Usage

Parse a request with a mock LLM response:

hpc-claw parse examples/requests/mpi_test.txt \
  --mock-response examples/outputs/mpi_test_ir.json \
  --out intent.json

Validate the intent:

hpc-claw validate intent.json \
  --policy hpc_claw/policy/profiles/hcmut_slurm_v1.yaml

Generate a Slurm script:

hpc-claw generate slurm intent.json --out job.sbatch

Run the full parse, validate, plan, and generate path:

hpc-claw run examples/requests/mpi_test.txt \
  --policy hpc_claw/policy/profiles/hcmut_slurm_v1.yaml \
  --mock-response examples/outputs/mpi_test_ir.json \
  --out job.sbatch \
  --trace-out trace.json

Expected script:

#!/bin/bash
#SBATCH --job-name=mpi_test
#SBATCH --account=school_g001
#SBATCH --partition=cpu
#SBATCH --nodes=1
#SBATCH --time=00:30:00
#SBATCH --ntasks=8

# TODO: add command

LLM Configuration

HPC-Claw uses the Agentivium Core LLMClient abstraction. The included provider client supports OpenAI-compatible chat completion endpoints.

Local Ollama-style endpoint:

llm:
  provider: openai_compatible
  base_url: http://localhost:11434/v1
  api_key_env: null
  model: qwen2.5:7b
  timeout: 60

Cloud endpoint:

llm:
  provider: openai_compatible
  base_url: https://api.openai.com/v1
  api_key_env: OPENAI_API_KEY
  model: gpt-4.1-mini
  timeout: 60

Use it with:

hpc-claw parse examples/requests/mpi_test.txt \
  --llm-config llm.yaml \
  --out intent.json

Slurm Validation

The initial policy profile is hpc_claw/policy/profiles/hcmut_slurm_v1.yaml. It requires:

• policy.account
• policy.partition
• time.walltime

It also validates allowed accounts, partitions, GPU placement, GPU count, node count, and walltime limits.

Legacy Assistant Runtime

The hpc_claw.assistant package retains the existing conversational 0.1 path for compatibility. LLM output is never verification authority and must enter the same structured validation and formal verification path before execution.

One assistant turn:

User request
  -> ConversationState
  -> HPCContextBuilder
  -> HPCIntentIR
  -> HPCPolicyValidator
  -> HPCPlanner
  -> ClarificationRequest or reviewable Slurm artifact

This path generates reviewable artifacts only.

Current Limitations

Slurm is implemented first. PBS and Flux remain behind future scheduler interfaces. The v0.1 intent profile excludes workflow DAGs, malleable jobs, multi-site federation, and complex co-scheduling. Predictions use trace-derived or deterministic evidence rather than claiming a new prediction model. Human-study extensions remain optional while the formal Core verification path stays stable.

This update delivers the local-shell foundation, global home lifecycle, basic TUI, static detection, trust, persistence, skill discovery, revision proposal support, and stricter execution guard. Full conversational Core event integration, pause/resume verification orchestration, provider health onboarding, and live scheduler execution through the TUI remain follow-up work. Live mutation stays fail-closed; the headless guard additionally requires a verified script hash, authorization confirmation, and durable audit path.

See the work-package map for the module mapping, assumptions, and Core integration details.