dltop 0.2.1

top/htop-style GPU monitor for CV and AI workloads -- splits CUDA/Tensor/FP* compute from NVENC/NVDEC media engines

dltop

A htop/nvitop-style GPU monitor, tailored for computer-vision and deep-learning workloads.

Where nvitop gives you one uniform compute-utilization bar, dltop splits live GPU utilization into the two things an AI/CV engineer actually cares about on the same GPU:

• Compute utilization — CUDA Streaming Multiprocessor / Tensor / FP32 / FP16 / FP64 activity (via DCGM)
• Media engines — NVENC (encode) and NVDEC (decode) throughput (via NVML)

This helps identify the choke point for different scales of load on a streaming-analytics pipeline. Many metrics share one interleaved chart instead of stacking into tall bars: overlapping series alternate colour cell-by-cell, so no line is ever hidden behind another while each keeps its true value.

When DCGM is unavailable (consumer GeForce cards, or DCGM not installed), dltop falls back to NVML's single lumped SM% (the aggregate compute-utilization proxy shown by nvidia-smi) and prints a footer telling you how to enable the full split on a data-center GPU.

Multi-GPU

Every chart is grouped one domain per GPU — never mixed onto a shared axis. On a box with more than one GPU, dltop adds a host chart plus one chart per GPU per tab (so a 2-GPU box shows 3 stacked charts on the Compute tab: Host, GPU 0, GPU 1), an info card row per GPU, and a toggle bar to show/hide individual GPUs across every chart at once.

Screenshots

Captured in --demo mode (synthetic 2-GPU telemetry — see Demo mode). Five tabs, all on one shared time-series axis.

All

The default overview — every metric on one axis, with CPU, RAM, GPU SM and GPU VRAM on by default. Glance once: is anything busy?

Compute

Host CPU plus the GPU compute engines. On data-center GPUs the DCGM split breaks SM into Tensor, FP32, FP16 and FP64 — see exactly which math your model is doing.

Memory

Host RAM, GPU VRAM and VRAM bandwidth — watch memory fill as a model loads and catch bandwidth saturation.

System

PCIe in/out, GPU power draw, disk and network — the plumbing around the GPU.

Table

Every series — host, per-GPU, and any auto-discovered Prometheus metric — as a row of rolling-window stats (Now, Mean, Median, Stddev) over the last --window seconds. Four buttons copy the current snapshot straight to the clipboard: Markdown, web table (HTML), Excel (TSV), or a capture-metadata block (timestamp, dltop version, hostname, OS, CPU, RAM, GPU(s), driver/CUDA versions, and any Prometheus endpoints) — handy for pasting into a bug report or a training-run log.

The screenshots above are hosted as assets on the latest release rather than committed to the repo, so a clone stays small and the images render identically on GitHub and PyPI. Regenerate them anytime with pip install -e ".[screenshots]" then python scripts/capture_screenshots.py (writes to assets/screenshots/, which is git-ignored), and re-upload with gh release upload <tag> assets/screenshots/*.png.

Installation

From PyPI (recommended)

pipx install dltop

pipx installs dltop into its own isolated environment while still putting the dltop command on your PATH — the right way to install a standalone CLI tool. A plain pip install works too:

pip install dltop

For development

git clone https://github.com/rsnk96/dltop && cd dltop
pip install -e ".[test]"

Python 3.11+ required. The tool is Linux-only (needs NVML + optionally dcgmi).

Usage

Once installed, dltop is callable from any directory — the same way htop is:

dltop                   # 0.5s refresh, DCGM if available
dltop -i 1              # 1s refresh
dltop --no-dcgm         # NVML-only (skip DCGM even if installed)
dltop --window 120      # stats window for the Table tab, seconds (default 60)
dltop --no-discover     # disable Prometheus /metrics auto-discovery
dltop --demo            # synthetic 2-GPU telemetry, no NVIDIA hardware needed
dltop --demo 4          # synthetic N-GPU telemetry
dltop --version         # print the installed version and exit
dltop --help

Key bindings (in the TUI)

Key	Action
q, Q	Quit
space, p	Pause / resume sampling

Switch tabs (All · Compute · Memory · System · Table) by clicking them, or with ← / → when the tab bar is focused. Each chart has per-series checkboxes underneath to toggle individual lines on or off.

Demo mode (no GPU needed)

--demo [N] swaps NVML/DCGM for a synthetic N-GPU source (default 2) so you can try, develop, or screenshot dltop on a laptop with no NVIDIA hardware at all — it's also what scripts/capture_screenshots.py uses to generate the images above.

Prometheus auto-discovery

On startup (unless --no-discover is passed), dltop enumerates locally listening TCP ports (via psutil, falling back to ss -lnt), probes each with a short-timeout GET /metrics, and treats any response that looks like Prometheus text-exposition format as a hit. Up to 30 gauge/counter metrics per discovered endpoint are added as extra, default-off series on the All tab, scraped on a background thread every max(--interval, 1) seconds. Histograms and summaries are skipped (their component series aren't meaningful as single lines). Disable the whole probe with --no-discover if you'd rather not have dltop touch other local sockets.

Related projects

dltop owes a lot to the terminal monitors that came before it:

• htop — the classic interactive process and CPU monitor.
• btop — a polished all-round system monitor (CPU, memory, disk, network).
• nvtop — an htop-style monitor for AMD / NVIDIA / Intel GPUs.
• nvitop — an interactive NVIDIA GPU process viewer.

We use and admire all of these. None of them, individually or together, met the needs we had internally: a single view that splits GPU compute into the lanes a CV/AI engineer reasons about — SM, Tensor, FP16/FP32/FP64 — and the media engines (NVENC/NVDEC), overlaid against host CPU/RAM/IO on one time-series, so you can see where a streaming-analytics pipeline actually bottlenecks. So we built dltop.

Enabling the full compute split (DCGM)

On a data-center GPU (A100, H100, L4, T4, etc.) you can install DCGM to unlock the per-lane breakdown:

# Ubuntu / Debian
sudo apt install datacenter-gpu-manager
sudo systemctl --now enable nvidia-dcgm

Consumer GeForce cards (RTX 4090, 3090, etc.) have these profiling fields gated by NVIDIA at the driver level — DCGM will install but return no profiling data. dltop detects this at startup and falls back silently.

Runtime requirements

• NVIDIA driver + CUDA runtime (any version supported by nvidia-ml-py)
• DCGM service (optional, for the full compute-lane split)
• A terminal (TTY) if you want the keyboard shortcuts — piping to a non-TTY still renders frames but disables keystroke handling

License

MIT