kempnerpulse 0.2.1

Real-time GPU monitoring dashboard for DCGM Prometheus metrics

KempnerPulse

nvidia-smi says 100% GPU utilization - but are your tensor cores even active? KempnerPulse shows what's actually happening.

Real-time GPU monitoring dashboard for DCGM Prometheus metrics. A single-file Rich-based TUI that streams dcgm-exporter /metrics and renders four interactive views in the terminal.

Features

• Fleet View : All GPUs at a glance: utilization, memory, power, temperature, PCIe/NVLink bandwidth, sparkline bars.
• Focus View : Deep dive into one GPU with per-metric sparkline history.
• Plot View : Stacked line charts across all GPUs.
• Job View : Running GPU compute processes with per-GPU metrics.
• Real Utilization : Weighted composite metric from SM active, tensor pipe, DRAM active, and GR engine counters (customizable weights with presets for AI/ML, HPC, and memory-bound workflows).
• Workload Classification : 12-category status based on NVIDIA DCGM profiling metric guidance (idle, tensor-heavy compute, memory-bound, I/O, etc.).
• Health Monitoring : Temperature, PCIe replay errors, and ECC errors with color-coded alerts.
• SLURM/CUDA Aware : Automatically detects CUDA_VISIBLE_DEVICES, SLURM_JOB_GPUS, etc. to show only your allocated GPUs.
• Zero Dependencies beyond Python 3.9+ and rich.

Screenshots

Fleet View

All GPUs at a glance with utilization bars, memory, power, temperature, and bandwidth.

Focus View

Deep dive into a single GPU with per-metric sparkline history.

Plot View

Stacked line charts across all GPUs.

Job View

Running GPU compute processes with per-GPU metrics.

Requirements

• Linux with NVIDIA GPUs
• dcgm-exporter running and exposing /metrics (default: http://localhost:9400/metrics)
• Python >= 3.9
• nvidia-smi on the PATH (for hardware queries and process listing)

Note: KempnerPulse currently supports NVIDIA datacenter GPUs (V100, A100, H100, H200, B200, B300). Grace-Hopper (GH200), Grace-Blackwell (GB200) and RTX support is planned but not yet tested. AMD GPUs are not supported.

Installation

pip install kempnerpulse

Or install from source:

pip install .

Quick Start

# Default: connect to localhost:9400/metrics, show SLURM/CUDA-visible GPUs
kempnerpulse

# Explicit source and GPU selection
kempnerpulse --source http://gpu-node:9400/metrics --gpus 0,1,2,3

# Show all GPUs on the node
kempnerpulse --show-all

# Start in focus view for GPU 0
kempnerpulse --focus-gpu 0

# Use HPC weight preset
kempnerpulse --hpc-weights

# Custom weights (SM, Tensor, DRAM, GR; normalized automatically)
kempnerpulse --weights 0.40,0.30,0.20,0.10

# Export CSV (default columns) — only GPUs with your processes
kempnerpulse --export > metrics.csv

# Export all columns
kempnerpulse --export all > metrics.csv

# Export custom columns
kempnerpulse --export gpu_id,real_util_pct,power_w,tensor_active_pct > metrics.csv

# Single snapshot export
kempnerpulse --export --once

Interactive Commands

Command	Action
:focus <id>	Enter focused view for a specific GPU
:plot	Enter plot view (line charts)
:job	Enter job view (running GPU processes)
:q	Return to fleet view (or exit if in fleet)
:exit	Exit the dashboard
Ctrl+C	Exit the dashboard
Esc	Cancel an unfinished : command

CLI Reference

Flag	Type	Default	Description
--version			Show version and exit.
--source URL	string	http://localhost:9400/metrics	dcgm-exporter /metrics endpoint or a local text file.
--poll SECS	float	1.0	Dashboard redraw interval in seconds (does not change DCGM sampling rate).
--history N	int	120	Number of samples kept for sparkline history.
--focus-gpu ID	string		Start in Focus View for the given GPU id (e.g. 0).
--once	flag		Render a single snapshot and exit instead of running live.
--gpus IDS	string		Explicit GPU ids or ranges (0,1 or 0-3). Overrides SLURM/CUDA env vars.
--show-all	flag		Ignore SLURM/CUDA visibility env vars; show every GPU in the source.
--weights W	4 floats	0.35,0.35,0.20,0.10	Comma-separated Real Util weights: SM,TENSOR,DRAM,GR. Auto-normalized.
--ai-weights	preset		AI/LLM training preset (0.35, 0.35, 0.20, 0.10). This is the default.
--hpc-weights	preset		HPC / mixed CUDA preset (0.45, 0.15, 0.25, 0.15).
--mem-weights	preset		Memory-bound / bandwidth-heavy preset (0.35, 0.10, 0.40, 0.15).
--export	string	(off)	Output CSV to stdout. --export for default columns, --export all for every column, or --export col1,col2,... for a custom set. Only GPUs with processes owned by the current user are included.

GPU Visibility Selection

The dashboard picks the first available source in this order:

1. --gpus flag
2. CUDA_VISIBLE_DEVICES env var
3. NVIDIA_VISIBLE_DEVICES env var
4. SLURM_STEP_GPUS env var
5. SLURM_JOB_GPUS env var

If none are set, all GPUs on the node are shown. Use --show-all to explicitly override all env vars. All GPU selections are filtered against GPUs accessible to the current process (as reported by nvidia-smi), which respects cgroup and container restrictions.

Weight Presets

Preset	Flag	SM	Tensor	DRAM	GR	Best For
AI/ML (default)	--ai-weights	0.35	0.35	0.20	0.10	DL training, LLM inference, transformers
HPC	--hpc-weights	0.45	0.15	0.25	0.15	Scientific computing, mixed CUDA
Memory-bound	--mem-weights	0.35	0.10	0.40	0.15	Bandwidth-heavy workloads, stencil codes

Custom: --weights 0.40,0.30,0.20,0.10 (values are normalized automatically).

How It Works

KempnerPulse reads Prometheus text-format metrics from dcgm-exporter via HTTP (or a local file). It computes a Real Utilization score as a weighted combination of four DCGM profiling counters:

Real Util = clamp(0, 100,
              W_sm    × SM_ACTIVE
            + W_tensor × TENSOR_ACTIVE
            + W_dram   × DRAM_ACTIVE
            + W_gr     × GR_ENGINE_ACTIVE)

This gives a more accurate picture of GPU utilization than nvidia-smi's GPU-Util alone, which only reports kernel-launch duty cycle.

Workload Classification

Each GPU is classified into one of 12 categories every refresh cycle, based on thresholds from NVIDIA's DCGM profiling metric guidance. Categories are evaluated in order and the first matching rule wins.

Status	Thresholds	Rationale
idle	Real Util < 5 %, GR < 5 %, DRAM < 5 %, no I/O	Nothing running.
tensor-heavy compute	Tensor ≥ 50 % and SM ≥ 60 %	DL training / large-scale inference.
tensor compute	Tensor ≥ 15 % and SM ≥ 40 %	Mixed-precision, moderate tensor use.
FP64 / HPC compute	FP64 ≥ 20 % and SM ≥ 50 %	Scientific double-precision workload.
I/O or data-loading	Memcpy ≥ 40 % or PCIe ≥ 1 GB/s, SM < 30 %	Heavy transfer; SMs idle.
memory-bound	DRAM ≥ 50 % and SM < 50 %	Bandwidth limited.
compute-heavy	SM ≥ 80 %	Effective SM use (NVIDIA: ≥ 80 % needed).
compute-active	SM ≥ 50 %	Moderate compute, no tensor dominance.
memory-active	DRAM ≥ 40 %	Significant DRAM traffic.
busy, low SM use	GR ≥ 40 % and SM < 25 %	Overhead / sync / small kernels.
low utilization	GR < 15 %, SM < 15 %, DRAM < 15 %	Barely active.
mixed / moderate	(fallthrough)	No single dominant pattern.

Full details, bottleneck color key, and NVIDIA reference points: docs/classification.md

Health Monitoring

Status	Condition	Meaning
OK	(none of the below)	Normal operation.
WARN	PCIe replay rate > 0/s	PCIe link retransmissions occurring.
HOT	GPU or memory temp ≥ warning threshold	Approaching thermal throttling.
CRIT	Row-remap failure > 0 or uncorrectable remapped rows > 0	Hardware memory errors. Remove from production.

Temperature warning thresholds are per-model (A100: 93 °C, H100/H200: 95 °C, RTX 6000: 92 °C, default: 93 °C). Full threshold table: docs/classification.md

CSV Export

Export GPU metrics as CSV for offline analysis or terminal monitoring. Only GPUs where the current user has running compute processes are included.

kempnerpulse --export > metrics.csv            # default columns
kempnerpulse --export all > metrics.csv        # all 34 columns
kempnerpulse --export gpu_id,real_util_pct,power_w > metrics.csv  # custom
kempnerpulse --export --once                   # single snapshot

Default columns: timestamp, gpu_id, model, gpu_util_pct, mem_used_mib, real_util_pct, sm_active_pct, tensor_active_pct, dram_active_pct

Full column reference and usage details: docs/export.md

DCGM Metrics

KempnerPulse consumes ~30 DCGM fields covering profiling counters, memory, temperature, power, clocks, PCIe, NVLink, and error counters. The complete list with descriptions and NVIDIA doc links: docs/metrics.md

Performance Overhead

KempnerPulse introduces minimal runtime overhead, using approximately 8.2% of a single CPU core on an AMD EPYC 9374F processor, with negligible memory usage (below the reporting resolution of top).

License

MIT. See LICENSE for details.