Cluster tooling: GPU heartbeat + in-container rebuild helper by eugenevinitsky · Pull Request #397 · Emerge-Lab/PufferDrive

eugenevinitsky · 2026-04-11T17:45:54Z

Summary

Three scripts-only changes that make cluster workflows easier:

scripts/gpu_heartbeat.py — standalone watchdog that runs dummy matmuls when GPU utilization drops below 65%. Prevents jobs from being reclaimed on shared clusters during periods of low GPU usage (policy eval, checkpointing, map loading, etc.).
--heartbeat flag in scripts/submit_cluster.py — wires the heartbeat script into the singularity command. Uses a bash brace group { ... ; } so the backgrounded python process is properly scoped within the container; training runs in the foreground, exits cleanly, and the heartbeat is killed on exit with TRAIN_EXIT propagated. Opt-in only — existing jobs are unaffected.
scripts/rebuild_on_cluster.py — submits a SLURM job that rebuilds the drive C extension inside the container overlay. Writes a bash script to scratch and sbatches it, avoiding the nested-quoting hell of sbatch --wrap. Supports --wait (poll until finish, print log), --dry (inspect before submitting), and configurable --account / --overlay / --user. Also fixes the stale default overlay path in submit_cluster.py — the real overlay lives at /scratch/<user>/images/PufferDrive/overlay-15GB-500K.ext3, not the old containers/pufferdrive/overlay.ext3 path.

Motivation

On the NYU cluster:

Jobs get reclaimed when GPU util dips below a threshold for too long. During map loading, policy eval, and checkpointing, training utilization can hit 0% for tens of seconds. The heartbeat keeps the GPU visibly busy during those gaps.
The in-container rebuild was previously an ad-hoc sbatch --wrap 'singularity exec ...' one-liner with triple-nested quoting that was easy to get wrong. rebuild_on_cluster.py templatizes it and makes it scriptable.
The stale overlay default caused silent "torch not found" failures because the old overlay path doesn't have torch installed. Fixed.

Bash heartbeat wiring (technical note)

Naive attempt (broken):

cd $PROJECT && python scripts/gpu_heartbeat.py & cmd...

The & has lower precedence than &&, so this parses as (cd $PROJECT && python heartbeat.py) & — backgrounding the cd. Training runs from the wrong dir.

Working version:

{ python scripts/gpu_heartbeat.py > /tmp/gpu_heartbeat.log 2>&1 & HEARTBEAT_PID=$!; \\
  training_cmd; TRAIN_EXIT=$?; \\
  kill $HEARTBEAT_PID 2>/dev/null; exit $TRAIN_EXIT; }

The brace group { ... ; } runs in the current shell (unlike ( ... )), so the preceding cd and env exports still apply. The & backgrounds only the python call. Training's exit code is captured and propagated.

Usage

# Rebuild C extension in container (with --wait to see logs live)
python scripts/rebuild_on_cluster.py --user \$USER --wait

# Submit training with heartbeat enabled
python scripts/submit_cluster.py \\
    --save_dir /scratch/\$USER/experiments \\
    --compute_config scripts/cluster_configs/nyu_greene.yaml \\
    --program_config scripts/cluster_configs/train_base.yaml \\
    --container --heartbeat

Test plan

rebuild_on_cluster.py --dry --user <me> prints the script without submitting
rebuild_on_cluster.py --wait rebuilds successfully and exits 0 on a working branch
submit_cluster.py --container --heartbeat ... launches a training job that runs normally + has a heartbeat child process (verified via /tmp/gpu_heartbeat.log on the compute node)
Training exit code propagates through the brace group (tested locally with false in place of the training command)

🤖 Generated with Claude Code

…tion GPU heartbeat script runs dummy matmuls to keep utilization above 65% when training isn't fully saturating the GPU. Opt-in via --heartbeat. Uses bash brace group `{ ... ; }` to run in current shell, so the cd and env exports still apply to training. The `&` only backgrounds the python heartbeat, not the whole compound statement (fixing the precedence bug from the previous attempt). Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

The old default (/scratch/<user>/containers/pufferdrive/overlay.ext3) was stale; actual working overlay lives at /scratch/<user>/images/PufferDrive/ overlay-15GB-500K.ext3 and uses /ext3/miniforge3 via source /ext3/env.sh. rebuild_on_cluster.py writes a bash script to scratch (avoiding nested quoting hell in sbatch --wrap) and submits it. Supports --wait to poll for completion and tail the log, --dry for inspection. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

Copilot

Pull request overview

This PR adds optional cluster helpers to keep GPU jobs from being reclaimed during low-utilization phases and to simplify rebuilding the in-container C extension on SLURM.

Changes:

Add scripts/gpu_heartbeat.py to generate GPU load when utilization drops below a threshold.
Add --heartbeat option to scripts/submit_cluster.py and wire it into container and non-container launch paths.
Add scripts/rebuild_on_cluster.py to submit and optionally wait on a remote SLURM rebuild job, and update the default overlay path.

Reviewed changes

Copilot reviewed 3 out of 3 changed files in this pull request and generated 11 comments.

File	Description
scripts/submit_cluster.py	Adds `--heartbeat` flag, wraps training command with a bash brace group, updates default overlay path.
scripts/rebuild_on_cluster.py	New SSH+SLURM helper that writes a rebuild script to scratch, submits it via `sbatch`, and optionally polls/logs.
scripts/gpu_heartbeat.py	New watchdog that polls `nvidia-smi` and runs dummy matmuls when utilization is low.

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Copilot · 2026-04-11T17:49:34Z

    parser.add_argument(
        "--container_overlay",
        type=str,
-        default="/scratch/ev2237/containers/pufferdrive/overlay.ext3",
+        default=f"/scratch/{os.environ.get('USER', '')}/images/PufferDrive/overlay-15GB-500K.ext3",
        help="Singularity overlay path",
    )


--container_overlay default is built from os.environ.get('USER', ''); if USER is unset this becomes /scratch//images/... and silently points to a non-existent overlay. Consider falling back to a more reliable username lookup (e.g., getpass.getuser()), or erroring when USER is empty so the user must pass --container_overlay explicitly.

Copilot · 2026-04-11T17:49:34Z

+            train_str = " ".join(full_cmd)
+            if args.heartbeat:
+                train_str = wrap_with_heartbeat(train_str)
+            inner_cmd = f"{env_setup} && {cache_exports} && cd {project_root} && {train_str}"


The training command is converted to a shell string via ' '.join(full_cmd) and then embedded into bash -c (and potentially into a brace group). This will break if any argument contains spaces or shell-special characters, and it also makes quoting/escaping fragile. Prefer building the shell string with shlex.join(full_cmd) (Python 3.8+) and applying shlex.quote to interpolated paths like project_root/save_dir.

Copilot · 2026-04-11T17:49:34Z

+        # preceding `cd` and env exports still apply to the training command. The `&`
+        # backgrounds only the python call, not the whole compound statement.
+        def wrap_with_heartbeat(train_cmd_str):
+            hb = "python scripts/gpu_heartbeat.py > /tmp/gpu_heartbeat.log 2>&1 & HEARTBEAT_PID=$!"


Heartbeat uses a fixed log file /tmp/gpu_heartbeat.log. On nodes that run multiple jobs for the same user (or requeues), this can be overwritten/contended. Consider including $SLURM_JOB_ID (or $HEARTBEAT_PID) in the log filename to make it per-job/per-process.

Suggested change

hb = "python scripts/gpu_heartbeat.py > /tmp/gpu_heartbeat.log 2>&1 & HEARTBEAT_PID=$!"

hb = (

'HEARTBEAT_LOG="/tmp/gpu_heartbeat.${SLURM_JOB_ID:-$$}.log"; '

'python scripts/gpu_heartbeat.py > "$HEARTBEAT_LOG" 2>&1 & HEARTBEAT_PID=$!'

)

Copilot · 2026-04-11T17:49:34Z

+        def wrap_with_heartbeat(train_cmd_str):
+            hb = "python scripts/gpu_heartbeat.py > /tmp/gpu_heartbeat.log 2>&1 & HEARTBEAT_PID=$!"
+            return (
+                f"{{ {hb}; {train_cmd_str}; TRAIN_EXIT=$?; "
+                f"kill $HEARTBEAT_PID 2>/dev/null; exit $TRAIN_EXIT; }}"
+            )


The heartbeat command uses python scripts/gpu_heartbeat.py .... If the environment only provides python3 (common on some systems), this will fail. Consider using python3 for consistency with other scripts, or invoking the same interpreter as training via an explicit path (e.g., from the activated env) to avoid ambiguity.

Copilot · 2026-04-11T17:49:35Z

+    parser.add_argument("--mem", default="16gb", help="SLURM memory")
+    parser.add_argument("--cpus", default="8", help="SLURM cpus-per-task")
+    parser.add_argument("--wait", action="store_true", help="Poll until the job finishes and print its log")
+    parser.add_argument("--dry", action="store_true", help="Print the script and sbatch command without submitting")


--dry help text says it prints "the script and sbatch command without submitting", but the dry-run branch currently only prints the script plus destination/log paths and never prints the actual sbatch ... command that would be executed. Either print sbatch_cmd in the dry-run output or adjust the flag help text.

Suggested change

parser.add_argument("--dry", action="store_true", help="Print the script and sbatch command without submitting")

parser.add_argument("--dry", action="store_true", help="Print the script, destination, and log paths without submitting")

Copilot · 2026-04-11T17:49:35Z

+def run_ssh(cmd: str, check: bool = True) -> str:
+    """Run a command on the cluster via ssh and return stdout."""
+    result = subprocess.run(["ssh", "torch", cmd], capture_output=True, text=True)
+    if check and result.returncode != 0:
+        print(result.stdout)
+        print(result.stderr, file=sys.stderr)
+        raise SystemExit(f"ssh command failed: {cmd}")
+    return result.stdout


The SSH destination host is hardcoded as torch in both run_ssh() and the script upload call. This makes the helper unusable in environments where that host alias doesn’t exist. Consider adding a --host (or --ssh-target) argument with default torch, and using it consistently.

Copilot · 2026-04-11T17:49:35Z

+def get_gpu_utilization():
+    try:
+        result = subprocess.run(
+            ["nvidia-smi", "--query-gpu=utilization.gpu", "--format=csv,noheader,nounits"],
+            capture_output=True,
+            text=True,
+            timeout=5,
+        )
+        return int(result.stdout.strip().split("\n")[0])
+    except Exception:
+        return 100  # assume busy if query fails
+
+
+def main():
+    device = torch.device("cuda")
+    x = torch.randn(N, N, device=device)
+    y = torch.randn(N, N, device=device)


get_gpu_utilization() only reads the first line of nvidia-smi output, and main() always uses torch.device('cuda') (i.e., the first visible GPU). For multi-GPU jobs, other GPUs can still sit at 0% utilization and may still trigger reclamation depending on policy. Consider querying/monitoring all visible GPUs and either keeping each above the threshold or using the minimum utilization across them to decide when to generate load.

Copilot · 2026-04-11T17:49:36Z

+        return 100  # assume busy if query fails
+
+
+def main():


main() assumes CUDA is available and will crash with a RuntimeError on CPU-only nodes or if the job was submitted without GPUs. If --heartbeat can be enabled independently of GPU allocation, it would be safer for the heartbeat script to check torch.cuda.is_available() (and/or CUDA_VISIBLE_DEVICES) and exit with a clear message or no-op when no GPU is present.

Suggested change

def main():

def main():

if not torch.cuda.is_available():

print("GPU heartbeat skipped: CUDA is not available.")

return

Copilot · 2026-04-11T17:49:36Z

+THRESHOLD = 65  # percent GPU utilization to maintain
+CHECK_INTERVAL = 0.05  # seconds between checks
+N = 6144  # matrix size for dummy work
+BURST_ITERATIONS = 60  # number of matmuls per burst
+


The default heartbeat workload allocates two 6144x6144 FP32 matrices (plus outputs), which can consume ~450MB+ of GPU memory and may be too large for smaller GPUs or memory-constrained jobs. Consider making N, THRESHOLD, and BURST_ITERATIONS configurable via CLI flags/env vars (with a safer default), so users can tune the heartbeat to their hardware and policies.

Copilot · 2026-04-11T17:49:36Z

+        ).strip()
+        if not state:
+            print("  (job not yet registered in sacct)")
+            continue


The sacct call uses -P, which outputs pipe-delimited fields and typically includes a trailing | (e.g., COMPLETED|). Since state is compared against strings like "COMPLETED", the loop may never break and --wait can hang indefinitely. Consider dropping -P or normalizing with state = state.split('|', 1)[0] before comparing.

Suggested change

continue

continue

state = state.split("|", 1)[0]

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

eugenevinitsky · 2026-04-11T18:01:59Z

I'm the only one who uses this so I'm going to force merge it

eugenevinitsky and others added 2 commits April 11, 2026 13:45

Copilot AI review requested due to automatic review settings April 11, 2026 17:45

Copilot started reviewing on behalf of eugenevinitsky April 11, 2026 17:46 View session

Copilot AI reviewed Apr 11, 2026

View reviewed changes

Fix ruff format: collapse wrap_with_heartbeat f-string

da9a05a

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

eugenevinitsky merged commit 1daf985 into 3.0 Apr 11, 2026
10 checks passed

eugenevinitsky deleted the ev/cluster-tooling branch April 11, 2026 18:05

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Cluster tooling: GPU heartbeat + in-container rebuild helper#397

Cluster tooling: GPU heartbeat + in-container rebuild helper#397
eugenevinitsky merged 3 commits into
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ev/cluster-tooling

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-            hb = "python scripts/gpu_heartbeat.py > /tmp/gpu_heartbeat.log 2>&1 & HEARTBEAT_PID=$!"
+            hb = (
+                'HEARTBEAT_LOG="/tmp/gpu_heartbeat.${SLURM_JOB_ID:-$$}.log"; '
+                'python scripts/gpu_heartbeat.py > "$HEARTBEAT_LOG" 2>&1 & HEARTBEAT_PID=$!'
+            )

	parser.add_argument("--dry", action="store_true", help="Print the script and sbatch command without submitting")
	parser.add_argument("--dry", action="store_true", help="Print the script, destination, and log paths without submitting")

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eugenevinitsky commented Apr 11, 2026

Summary

Motivation

Bash heartbeat wiring (technical note)

Usage

Test plan

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