get-available-resources

Get Available Resources

Build a conservative picture of resources available to the current process. Keep host inventory, process affinity, cgroup/container limits, scheduler allocation, and accelerator runtime usability separate.

Safety contract

Follow these rules:

• Run detection when the user requests it or a specific workload needs resource planning. Do not persist a fingerprint for every scientific task.
• Use stdout by default. Persist only when the user chooses an explicit generic local filename.
• Do not run stress tests, benchmarks, large allocations, write probes, device resets, driver installation, or clock/power changes.
• Do not dump the environment. Read only the named Slurm and accelerator variables implemented by the detector.
• Do not report hostnames, absolute paths, cgroup paths, job IDs, device UUIDs, PCI addresses, or raw visibility-variable values.
• Treat a missing observation as unknown. Never convert unknown to unlimited.
• Never infer that a visible host CPU, memory pool, or GPU is usable inside a scheduler allocation or container.

The bundled detector uses only fixed executable/argument tuples, no shell, short timeouts, bounded stdout/stderr, and partial-failure warnings.

Quick start

Run from this skill directory.

Ephemeral stdout snapshot

python scripts/detect_resources.py

The command emits only JSON to stdout. Redirect it only when ordinary shell permissions are acceptable.

Explicit private file

python scripts/detect_resources.py --output resource-snapshot.json

Explicit output is restricted to one .json filename in the current directory, uses private permissions, rejects symlinks and path traversal, and refuses overwrite unless --force is supplied.

Optional psutil enhancement

The standard-library detector works without installation. For broader cross-platform physical-core, affinity, available-memory, swap, and disk coverage:

uv pip install "psutil==7.2.2"

The import is lazy. Failure to import psutil becomes a warning, not a fatal error.

Skip management-tool probes

python scripts/detect_resources.py --skip-accelerators

Use this when accelerator discovery latency is undesirable. The detector still summarizes the presence and state of allowlisted visibility variables without returning their values.

Required interpretation

CPU

Read these as different facts:

• cpu.host.logical: system-visible scheduling units.
• cpu.host.physical: physical topology, or null; never inferred from logical count.
• cpu.process.affinity_logical: current affinity-set size when supported.
• cpu.cgroup_v2.cpuset_logical: effective cgroup cpuset size.
• cpu.cgroup_v2.quota_cores: finite cpu.max capacity, possibly fractional.
• scheduler.allocation.cpu_per_process: bounded Slurm per-task interpretation when scope is clear.
• cpu.effective.capacity_cores: minimum positive observed constraint.
• cpu.effective.worker_ceiling: conservative floor for CPU process workers.

A quota of 1.5 is CPU-time capacity, not 1.5 physical cores. Affinity and cpusets constrain placement; quota constrains bandwidth.

Memory

Keep these separate:

• host total/available memory;
• current cgroup usage, hard memory.max, and remaining hierarchical capacity;
• memory.high, which is a pressure/throttle boundary rather than a hard cap;
• scheduler memory allocation and its scope; and
• conservative effective hard limit and available estimate.

On Apple silicon, memory.model is unified_cpu_gpu. Do not add integrated GPU memory to RAM or describe it as separate VRAM.

Accelerators

Each device is a backend candidate:

• NVIDIA GPU → CUDA candidate;
• AMD GPU → ROCm candidate;
• Apple integrated GPU → Metal candidate.

Management-query visibility does not establish:

1. scheduler/container permission;
2. device-node access;
3. driver/runtime compatibility;
4. framework package compatibility; or
5. operator/data-type support.

Therefore runtime_usable_devices remains null and each device says runtime_compatibility: not_tested. Visibility/allocation counts are upper bounds, not guarantees.

Disk

capacity_bytes, filesystem free_bytes, user-available blocks, and a non-writing permission check are distinct. Filesystem or project quotas can still be stricter. The absolute working path is always redacted.

Scheduler and container

Slurm variables describe allocation scope, but enforcement depends on site configuration such as task affinity or cgroups. Prefer affinity and cgroup observations as enforcement evidence.

Container markers identify context; cgroup controls identify limits. A container with no finite cgroup value can still see host inventory, and a non-root cgroup is not automatically labeled a container.

See references/resource_semantics.md for the detailed platform rules.

Plan a workload

The planner consumes a validated snapshot and performs no work:

python scripts/plan_workload.py resource-snapshot.json \
  --workload cpu \
  --tasks 100 \
  --memory-per-worker-mib 2048

Optional controls:

• --workers N: explicit upper bound.
• --reserve-memory-mib N: memory kept outside the worker budget.
• --workload cpu|mixed|io: selects a bounded worker heuristic.
• --accelerator none|any|cuda|rocm|metal: requests a candidate backend decision without claiming usability.
• --output plan.json: explicit private local output; stdout is default.

For CPU or mixed work, use suggested_workers and threads_per_worker together. Process workers multiplied by BLAS/OpenMP native threads can oversubscribe an allocation.

The I/O plan permits bounded oversubscription (maximum 32) but labels it a heuristic. Benchmark only the real representative workload and stay within scheduler/container limits.

Validate or diff snapshots

Validate:

python scripts/snapshot_tools.py validate resource-snapshot.json

Diff resource state while ignoring observed_at:

python scripts/snapshot_tools.py diff before.json after.json

Use --include-volatile to include the timestamp. Inputs must be regular, non-symlink JSON files no larger than 1 MiB. Diffs are bounded.

The schema and null/zero meanings are documented in references/snapshot_schema.md.

Optional accelerator diagnostic plan

Generate a plan without executing any diagnostic:

python scripts/accelerator_diagnostics.py resource-snapshot.json \
  --backend auto

The result contains fixed, read-only management query argument lists and separate gates for visibility, permission, and runtime compatibility. Run a framework's official availability check only in the exact environment that will execute the workload. Do not install or mutate drivers automatically.

Partial failures and provenance

One failed probe must not erase successful observations. Inspect:

• completeness;
• sorted warnings with stable codes;
• sorted provenance source/status records; and
• null fields.

Subprocess stderr and raw exception text are not copied into the snapshot because they can contain identifiers or paths.

Platform notes

• Linux: reads only bounded /proc and cgroup v2 files. Ancestor CPU and memory limits are considered.
• macOS: uses fixed sysctl keys and a bounded system_profiler SPDisplaysDataType -json query. Apple silicon memory is unified.
• Windows: optional psutil improves physical-core, affinity, available memory, and swap observations. Processor-group scope can make host and process counts differ.
• Slurm: reads an allowlist of allocation variables. It never emits job, node, submit-host, GPU-ID, or path values.
• NVIDIA/AMD: management CLIs are optional. Absence is normal; timeout, truncation, parse failure, and runtime uncertainty remain explicit.

Bundled files

• scripts/detect_resources.py — redacted snapshot collector.
• scripts/plan_workload.py — deterministic worker/memory planner.
• scripts/snapshot_tools.py — schema validator and bounded structural diff.
• scripts/accelerator_diagnostics.py — non-executing read-only diagnostic plan.
• tests/get-available-resources/ in the repository root — network-free Linux, macOS, Windows, cgroup, Slurm, and accelerator cases.
• references/resource_semantics.md — interpretation and platform details.
• references/snapshot_schema.md — schema 1.1 contract.
• references/sources.md — dated official-source ledger.

Official documentation was refreshed on 2026-07-23; consult references/sources.md before changing semantics or dependency pins.

Agent operating procedure

1. Check the environment. Confirm the Python environment and library versions (python -c "import pkg; print(pkg.__version__)") and inspect the data's shape, types and missing values.
2. Pin down the inputs. Confirm formats, identifiers and parameters from the data or the user. Ask rather than guess any value that changes the result.
3. Run a small version first. Run on a sample or a single fold first and check runtime and memory.
4. Execute the full task using the instructions and references above.
5. Validate the result. Use held-out data, fixed random seeds and appropriate metrics; check for leakage; report uncertainty (CIs, std over seeds).
6. Report. State what was run (versions, commands, parameters), what was checked, and what is still uncertain.

If this happens	Do this
Out-of-memory or very slow execution	Subsample, use chunked or lazy computation, or reduce model size, and tell the user what changed.
A function, flag or endpoint in these instructions is missing in the installed version	Check the installed version's own documentation (help(), --help, official docs), adapt, and tell the user. Never invent an API.
A required input, identifier or parameter is ambiguous	Ask the user, or state the assumption explicitly before running.

Integrity rules

• Never fabricate results, parameters, identifiers, citations or statistics. If something cannot be run or verified, say so plainly.
• Never report a metric you did not compute in this session; show the code path that produced every number.
• Treat version-specific details here as possibly outdated: confirm them against the official documentation for the installed version.
• Ask before actions that cost money, consume shared GPUs or cloud quota, touch personal or patient data, or cannot be undone.