dgx-spark-playbooks/nvidia/station-healthcare-agent/assets/SETUP-GUIDE.md

Local Healthcare Agent — Setup Guide

From-scratch setup for a DGX Station. Produces a working multi-agent clinical analysis system with OpenFold3 protein structure prediction.

Hardware: DGX Station (284 GB VRAM, aarch64)

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Prerequisites

• Docker + NVIDIA Container Toolkit (docker info --format '{{.ServerVersion}}' ≥ 23.0.1)
• Node.js v22+ (the DGX Station ships with v18 — upgrade with curl -fsSL https://deb.nodesource.com/setup_22.x | sudo -E bash - && sudo apt-get install -y nodejs)
• OpenShell CLI ≥ 0.0.33
• At least 200 GB free on / (86 GB Ollama model + Docker images + working space; verify with df -h /)
• A single GPU with ≥150 GB free VRAM (target the GB300, not the RTX PRO 6000, on dual-GPU stations)
• Network access to r4.smarthealthit.org (FHIR test server) and nvcr.io (NGC registry)
• NVIDIA NGC API key (get one here) and docker login nvcr.io (run make ngc-login after Step 1)

Tip

make prereq checks all of the above (Docker, Node.js v22, OpenShell, disk, GPU, port 11434, NGC docker login) in one shot.

1. Clone the repo

git clone https://github.com/jaival-nvidia/local-healthcare-agent.git
cd local-healthcare-agent
cp .env.example .env
# Edit .env: set NGC_API_KEY=nvapi-...
make ngc-login   # docker login nvcr.io with NGC_API_KEY (required to pull OpenFold3)

2. Install OpenShell

pip install openshell --upgrade --pre \
  --index-url https://urm.nvidia.com/artifactory/api/pypi/nv-shared-pypi/simple
openshell --version   # >= 0.0.33

Note

Ollama runs as a Docker container in this playbook (Step 3) — you do not need a host Ollama install. If host Ollama is already running on port 11434 (e.g., from the NemoClaw playbook), stop it first (sudo systemctl stop ollama) or override OLLAMA_PORT in .env. Both docker-compose.yml and setup_sandbox.sh honor the override.

3. Start infrastructure

make up
# Starts Ollama and OpenFold3 via Docker Compose.
# Auto-pulls nemotron-3-super:120b-a12b (~86 GB) if not cached.
# Wait for both services to report healthy:
make status

This starts:

• Ollama (port ${OLLAMA_PORT:-11434}) — LLM inference with nemotron-3-super:120b-a12b
• OpenFold3 NIM (port ${OPENFOLD_PORT:-8000}) — protein structure prediction (~3 min startup)

Tip

On dual-GPU stations, set LLM_GPU and OPENFOLD_GPU in .env to the GB300 index (find with nvidia-smi --query-gpu=index,name --format=csv,noheader). Nemotron 3 Super (~94 GB resident) does not fit safely on the RTX PRO 6000 (98 GB), and OpenFold3 crashes on multi-GPU containers.

4. Start OpenShell gateway

# Ubuntu 24.04 needs the cgroup fix:
OPENSHELL_K3S_ARGS='--kubelet-arg=cgroup-driver=systemd' openshell gateway start

# k3s takes 10-15s to accept connections; poll until ready:
for i in $(seq 1 30); do
  openshell status 2>/dev/null | grep -q "Connected" && break
  sleep 2
done
openshell status   # Should show: Status: Connected

5. (Optional) Configure inference provider manually

make setup (Step 6) creates the inference provider for you. To do it by hand:

BRIDGE_IP=$(ip -4 addr show docker0 | grep -oP 'inet \K[\d.]+')

openshell provider create \
  --name ollama-local \
  --type openai \
  --credential OPENAI_API_KEY=ollama \
  --config OPENAI_BASE_URL=http://${BRIDGE_IP}:${OLLAMA_PORT:-11434}/v1

openshell inference set \
  --provider ollama-local \
  --model nemotron-3-super:120b-a12b

Note

Current OpenShell releases do not accept the --base-url shorthand for provider create — use --config OPENAI_BASE_URL=... as shown above.

6. Create sandbox and deploy everything

make setup
# Creates sandbox, installs Python packages, deploys skills/agents/config,
# starts OpenClaw gateway, runs smoke test.
# Takes ~5-15 min (PyPI downloads are slow through the sandbox proxy).

Or for local access without SSH tunnel:

make setup-local

7. Verify

make test           # L1-3: infrastructure + OpenShell + config (~1 min)
make test-full      # L1-5: includes agent functional + E2E (~20 min)

8. Access the demo

Remote (SSH tunnel):

ssh -f -N -L 18789:localhost:18789 <user>@<dgx-ip>
open http://localhost:18789/

Local:

open http://localhost:18789/

Canvas (charts + molecular viewers): http://localhost:18789/__openclaw__/canvas/

---

Day-to-day commands

Command	What
make up	Start Ollama + OpenFold3
make down	Stop all Docker services
make status	Health dashboard
make restart	Restart OpenClaw gateway in sandbox
make test	Quick validation (L1-3, ~1 min)
make test-full	Full validation (L1-5, ~20 min)
make logs	Tail all service logs
make clean	Remove test results + volumes

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Demo Prompts

Clinical cohort analysis:

Find all diabetic patients, get their latest A1c and medications. Identify gap patients with A1c above 9% not on insulin or GLP-1. Show the A1c distribution as a histogram.

Patient case summary:

Look up the first patient. Compile a case summary: demographics, conditions, recent labs (flag abnormal), and medications.

Molecular visualization:

Show me the 3D protein structure of atorvastatin bound to its target

End-to-end investigation:

Find all diabetic patients with poorly controlled A1c, identify what medications they are on, show me the distribution, and visualize the molecular target of the most common therapy.

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Known Issues

PyPI slow through sandbox proxy: Package installs take 10-15 minutes due to the privacy router throttling. The setup script retries automatically.

Canvas proxy strips <script src=...>: The build_viewer.py script inlines JS libraries to work around this.

Model misspellings: nemotron-3-super:120b-a12b (120B total, 12B active MoE) occasionally misspells drug names. The actual code execution is correct.

Ollama model unloading: The model unloads after the keep_alive timeout. First request after unloading takes ~30s to reload. For demos, send a warmup message first.

OpenFold3 startup: Takes ~3 minutes to load the model. The healthcheck waits for it automatically.