CredVault Intelligence Engine (CIE) CLI

Command-line interface for the CredVault Intelligence Engine - AI/ML platform for researchers, developers, and enterprises.

Installation

npm install -g credvault-cie

Quick Start

# Login to your account
cie login

# Upload a dataset
cie data upload customers.csv

# Train a model
cie train ds_abc123

# Make predictions
cie predict md_xyz789 --data '{"age": 25, "income": 50000}'

Commands

Authentication

# Login with email/password
cie login

# Login to specific tenant
cie login --tenant tenant_abc123

# Check current session
cie whoami

# Configure defaults
cie configure

# Logout
cie logout

Data Management

# Upload dataset
cie data upload dataset.csv

# List all datasets
cie data list

# List datasets (JSON output)
cie data list --output json

# Delete dataset
cie data delete ds_abc123

# Delete without confirmation
cie data delete ds_abc123 --force

Model Training

# Train model on dataset
cie train ds_abc123

# Train with custom name
cie train ds_abc123 --name "Customer Prediction Model"

# Train specific model type
cie train ds_abc123 --type classification

# List all models
cie models list

# Get model details
cie models info md_xyz789

# Export trained model
cie models export md_xyz789
cie models export md_xyz789 --output ./my-model.zip

Predictions

# Single prediction
cie predict md_xyz789 --data '{"age": 25, "income": 50000}'

# Batch prediction from file
cie predict md_xyz789 --file input.json

# Stream live predictions
cie stream md_xyz789

Deployment

# Deploy model to production
cie deploy md_xyz789 --env production

# Deploy to staging
cie deploy md_xyz789 --env staging

# List all jobs
cie jobs list

# Check job status
cie job status job_abc123

IoT/Robotics Integration

# Connect robot/device
cie robot connect robot_001

# Monitor robot data
cie robot monitor robot_001

# Sync workspace
cie sync

Utilities

# View logs
cie logs

# View last 100 logs
cie logs --lines 100

# Test API connectivity
cie test

# Check CLI version
cie version

# Update CLI
cie update

Global Options

# Verbose output (detailed logging)
cie data list --verbose

# Quiet mode (minimal output, useful for scripts)
cie data upload file.csv --quiet

# JSON output (machine-readable)
cie models list --output json

Configuration

Configuration is stored in ~/.cie/config.json

{
  "apiUrl": "https://api.credvault.com",
  "defaultRegion": "us-east-1",
  "defaultEnvironment": "production",
  "defaultCluster": "cluster_abc123"
}

Authentication

Credentials are securely stored in ~/.cie/credentials

Session tokens expire after 7 days. Run cie login to refresh.

Exit Codes

• 0 - Success
• 1 - General error
• 2 - Authentication error
• 3 - Not found error
• 4 - Validation error

Examples

Complete Workflow

# 1. Login
cie login
# Email: [email protected]
# Password: ********
# Login successful

# 2. Upload training data
cie data upload research-data.csv
# Dataset uploaded: ds_abc123
# Records: 50000

# 3. Start training
cie train ds_abc123 --name "Research Model"
# Training started: job_xyz789
# Model ID: md_123456

# 4. Monitor progress
cie job status job_xyz789
# Status: running
# Progress: 67%

# 5. Make predictions
cie predict md_123456 --data '{"feature1": 10, "feature2": 20}'
# Prediction: {"result": "positive", "confidence": 0.94}

# 6. Deploy to production
cie deploy md_123456 --env production
# Model deployed
# API Endpoint: https://api.credvault.com/v1/models/md_123456/predict

Automation Scripts

#!/bin/bash
# Automated training pipeline

# Upload new data
DATASET_ID=$(cie data upload new-data.csv --quiet)

# Train model
JOB_ID=$(cie train $DATASET_ID --quiet | grep -o 'job_[a-z0-9]*')

# Wait for completion
while [ "$(cie job status $JOB_ID --output json | jq -r '.status')" != "completed" ]; do
  sleep 30
done

# Deploy
cie deploy $(cie job status $JOB_ID --output json | jq -r '.modelId') --env production