Jetson compute utilities

The AI Accelerator compute box is an embedded NVIDIA Jetson Orin AGX 64GB (Tegra). Use the commands below on the compute module itself — keyboard and display connected, or SSH as user lab (password easybot). Default IP is 192.168.0.100 — see SDK.

For SDK work you usually run perception inside Docker, but CPU load, temperature, and power mode are easiest to read on the host before or while containers are running.

Quick reference

What you want

Command

Live CPU / GPU / RAM / temperature / power

tegrastats

Interactive dashboard (if installed)

jtop

Power mode (MAXN vs low power)

nvpmodel -q / sudo nvpmodel -m 0

Software / BSP version (2.x)

curl -s http://localhost/universal-robots/platform/system/v1/versions | python3 -m json.tool

Tegra L4T release line

head -1 /etc/nv_tegra_release

Disk space

df -h ~

Memory summary

free -h

Running containers

docker ps

Container CPU / RAM

docker stats --no-stream

tegrastats — CPU, GPU, memory, temperature, power

NVIDIA’s built-in monitor. Updates once per second until you press Ctrl+C.

tegrastats

Sample line (values vary with load):

RAM 8234/64341MB (lfb 1234x4MB) SWAP 0/32170MB CPU [45%@2201,12%@729,...] GR3D_FREQ 99% cpu@48.2C gpu@46.3C soc0@46.8C tj@48.2C VDD_IN 45234mW

Field

Meaning

RAM used/total

System memory in use

SWAP

Swap usage (should stay low during normal SDK use)

CPU [N%@freq,...]

Per-core utilization and clock (MHz)

GR3D_FREQ

GPU utilization (high during FoundationPose / model compile)

cpu@, gpu@, soc@, tj@

Temperature sensors in °C (tj = junction — treat as the hot-spot reference)

VDD_IN

Approximate input power in milliwatts

Log a short sample to a file (useful when reporting performance issues):

timeout 60 tegrastats --interval 1000 | tee ~/tegrastats_$(date +%F_%H%M).log

Inside the SDK container, tegrastats is also available (the host binary is bind-mounted by run_sdk_setup.sh):

docker exec -it isaac_pkgs_app tegrastats

jtop — interactive dashboard (optional)

jtop (jetson-stats) shows CPU, GPU, memory, power mode, fan speed, and temperatures in a single terminal UI.

If jtop is not installed:

sudo apt update
sudo apt install -y python3-pip
sudo pip3 install -U jetson-stats
sudo reboot

Then run:

jtop

The SDK container can read jtop’s socket when the jtop group exists on the host (run_sdk_setup.sh mounts /run/jtop.sock).

Power mode — nvpmodel

Jetson power modes cap CPU/GPU clocks and TDP. The SDK expects MAXN (maximum performance), especially when compiling SAM / FoundationPose engines.

nvpmodel -q          # show current mode
sudo nvpmodel -m 0   # set MAXN

Typical modes on the AI Accelerator Jetson Orin AGX 64GB:

ID

Name

Use

0

MAXN

Normal SDK use, perception, model compile

1

MODE_15W

Low power — avoid for AI workloads

2

MODE_30W

Low power

3

MODE_50W

Low power

If model restore fails after a compile, confirm MAXN — see Model restore failed.

Optional: lock clocks at maximum for a benchmark (reverts after reboot):

sudo jetson_clocks

Version and platform info

AI Accelerator software version (2.x) — preferred check from SDK:

curl -s http://localhost/universal-robots/platform/system/v1/versions | python3 -m json.tool

Underlying Tegra / L4T release (useful for NVIDIA support tickets):

head -1 /etc/nv_tegra_release

On 1.x boxes only, the REVISION field in that line helps pick an upgrade file — see Upgrade from 1.x.

General host utilities

These work like on any Linux system and are handy during setup and debugging.

CPU and processes

htop          # if installed; otherwise: top
uptime        # load average

Disk

df -h ~                              # free space on home / data partition
du -sh ~/aia_sdk/ros/data/*          # size of datasets, models, logs

Large recordings and checkpoints live under ~/aia_sdk/ros/data/ — see Clean up data files before deleting anything.

Docker

docker ps                            # running containers (isaac_pkgs_app, aia-core, camera, …)
docker stats --no-stream             # one-shot CPU/RAM per container
docker logs --tail 50 isaac_pkgs_app # recent SDK container output

Network

ip addr show                         # compute box IP (default 192.168.0.100 on 2.x)
ssh lab@192.168.0.100                # from another PC; password easybot
ping -c 3 192.168.0.10               # robot reachability (adjust IP)

When temperatures look high

During FoundationPose, SAM, or first-time TensorRT compile, GPU utilization and tj temperature will rise — that is expected. If the box throttles or shuts down:

  1. Confirm MAXN: nvpmodel -q

  2. Check airflow around the compute box (fan exhaust not blocked).

  3. Capture a short log: timeout 120 tegrastats --interval 1000 | tee ~/thermal.log

  4. Compare load with containers stopped: docker stop isaac_pkgs_app then run tegrastats again.