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Unified CLI & Telemetry Suite

The ColabBio Unified Management CLI (cb-svc-cli) provides DevOps, MLOps, and bioinformaticians with a sovereign, terminal-native control plane for cluster observation, AI pipelines, model lifecycle, tenant auditing, and conversational clinical assistance.

┌────────────────────────────────────────────────────────────────────────┐
│ COLABBIO UNIFIED MANAGEMENT CLI (cb-svc-cli) │
├────────────────────────────────────────────────────────────────────────┤
│ • Spring Shell 3.2 + Native Wrapper (`cb-devops/scripts/colabbio`) │
│ • Dual Execution Mode: Connected Live Cluster & Standalone Fallback │
│ • Interactive AI Companion with ANSI Rich Widget Rendering │
│ • Real-Time Diagnostics: MicroK8s, MedVFS, Triton, OMERO, OHDSI DQD │
└────────────────────────────────────────────────────────────────────────┘

The CLI is compiled into a self-contained executable Java JAR within cb-svc-parent and accessed via the convenient wrapper script colabbio.

Terminal window
# 1. Compile and package the CLI suite
cd cb-svc-parent
mvn clean package -DskipTests=false
# 2. (Optional) Add colabbio to your PATH
export PATH="$PATH:$(pwd)/../cb-devops/scripts"
# 3. Verify installation
colabbio help

The CLI is organized into focused sub-command suites designed for hospital cluster administrators, MLOps engineers, and bioinformaticians.

2.1. Node & Infrastructure (colabbio node)

Section titled “2.1. Node & Infrastructure (colabbio node)”

Inspect the host machine, GPU accelerators, Tailscale mesh connectivity, and bootstrap initialization logs.

Terminal window
# Check node hardware, available cores, memory, and manifest status
colabbio node status
# Query deep GPU telemetry via nvidia-smi
colabbio node status --gpu
# View bootstrap and initialization logs
colabbio node logs -n 50

Node Manifest Persistence

During cluster initialization via colabbio-node-init.sh, the system persists the node configuration to /etc/colabbio/node-manifest.json and bootstrap logs to /var/log/colabbio/node-init.log.


2.2. Cluster Observability (colabbio cluster)

Section titled “2.2. Cluster Observability (colabbio cluster)”

Audit MicroK8s microservices, Ingress endpoints, and MedVFS storage classes.

Terminal window
# Audit all core pods, services, and Ingress routing in the colabbio namespace
colabbio cluster audit
# Stream logs from a specific core service (omero, triton, fhir, ai)
colabbio cluster logs --service triton --lines 100

2.3. Study & Pipeline Monitoring (colabbio study)

Section titled “2.3. Study & Pipeline Monitoring (colabbio study)”

Track execution progress, step-by-step pipeline stages (SlideLab, PyTorch WSI-MIL training, Triton registration), and generate validation reports.

Terminal window
# Inspect real-time status of a running study pipeline
colabbio study status --run-id run-tcga-brca-001
# Compile and display the Markdown clinical validation report
colabbio study report --run-id run-tcga-brca-001

2.4. Model Registry & Inspection (colabbio model)

Section titled “2.4. Model Registry & Inspection (colabbio model)”

Inspect registered pathology models, ONNX tensor signatures, and MLflow Model Cards.

Terminal window
# List all registered models in the sovereign registry
colabbio model list
# Inspect tensor shapes, normalization methods, and SHA-256 digests
colabbio model inspect --name abmil-brca-v1

2.5. Pathology Visor & Ingestion (colabbio visor)

Section titled “2.5. Pathology Visor & Ingestion (colabbio visor)”

Monitor OMERO.server (Blitz), OMERO.web, and ingest Whole Slide Images (SVS, NDPI, MRXS).

Terminal window
# Check health of OMERO.server (port 4064) and DeepZoom rendering bridge
colabbio visor status
# Batch ingest digital pathology slides into OMERO repository
colabbio visor ingest --path /data/wsi_samples/

2.6. Triton Inference Benchmarking (colabbio inference)

Section titled “2.6. Triton Inference Benchmarking (colabbio inference)”

Benchmark GPU forward-pass latency and attention heatmap computation under load.

Terminal window
# Run latency benchmark with concurrency level 4
colabbio inference benchmark --model abmil-brca-v1 --concurrency 4

2.7. Tenant Governance & OHDSI DQD (colabbio tenant)

Section titled “2.7. Tenant Governance & OHDSI DQD (colabbio tenant)”

Manage hospital tenants and execute Data Quality Dashboard (DQD) checks.

Terminal window
# List registered hospital tenants and active dataset quotas
colabbio tenant list
# Run OHDSI Achilles & DQD conformance and plausibility audit
colabbio tenant audit --tenant hospital-la-fe

3. Conversational Clinical AI Companion (ask & chat)

Section titled “3. Conversational Clinical AI Companion (ask & chat)”

The CLI integrates directly with the Clinical AI Companion backend (cb-svc-ai), enabling engineers and bioinformaticians to query datasets, diagnose failures, and inspect pathology slides in natural language.

Terminal window
# Query active models in Triton
colabbio ask "cuales son los modelos en triton"
# Analyze a biopsy slide and generate hotspots
colabbio ask "analiza la biopsia de la slide 1092"
# Pipe AI markdown reports directly to disk
colabbio ask "Genera un reporte del paciente PAT-77402" > reporte.md

4. A2UI Widget Rendering: Web UI vs CLI Terminal

Section titled “4. A2UI Widget Rendering: Web UI vs CLI Terminal”

The backend emits structured Markdown with A2UI embedded widgets using the fenced code syntax:

```widget:score_bar
{
"label": "Probabilidad de Malignidad",
"score": 0.9412,
"threshold": 0.50,
"status": "HIGH_RISK"
}
```
```widget:omero_viewport_action
{
"imageId": "1092",
"x": 18450,
"y": 9200,
"zoom": 20,
"label": "Centrar Visor en Hotspot 1"
}
```

When rendered in the CLI, the parser transforms JSON blocks into clean, high-contrast ANSI terminal components:

┌─ [SCORE BAR: Probabilidad de Malignidad] ─────────────────────────
│ Valor: 94.12% [HIGH_RISK]
│ Barra: [███████████████████░]
└────────────────────────────────────────────────────────────
[🔬 OMERO Viewport Action] Centrar Visor en Hotspot 1
Coordenadas: X: 18450, Y: 9200, Zoom: 20x (Slide ID: 1092)
Enlace Visor: https://omero.colabbio.local/webclient/?show=image-1092

To ensure operational reliability in disconnected environments (such as isolated edge VMs or air-gapped clinical networks):

  1. Connected Live Mode (🟢 Live Cluster): Connects to cb-svc-ai on http://localhost:8080 (or configured COLABBIO_AI_URL) and queries live Kubernetes API, PostgreSQL, and Triton Inference Server.
  2. Standalone Fallback Mode (🟡 Standalone): If the backend is offline or undergoing maintenance, the CLI operates locally with built-in diagnostic engines and simulated clinical models.