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jeremylongshore

vastai-migration-deep-dive

by jeremylongshore

Hundreds of Claude Code plugins with embedded AI skills. Learn via interactive Jupyter tutorials.

1,042🍴 135📅 Jan 23, 2026

SKILL.md


name: vastai-migration-deep-dive description: | Execute Vast.ai major re-architecture and migration strategies with strangler fig pattern. Use when migrating to or from Vast.ai, performing major version upgrades, or re-platforming existing integrations to Vast.ai. Trigger with phrases like "migrate vastai", "vastai migration", "switch to vastai", "vastai replatform", "vastai upgrade major". allowed-tools: Read, Write, Edit, Bash(npm:), Bash(node:), Bash(kubectl:*) version: 1.0.0 license: MIT author: Jeremy Longshore jeremy@intentsolutions.io

Vast.ai Migration Deep Dive

Overview

Comprehensive guide for migrating to or from Vast.ai, or major version upgrades.

Prerequisites

  • Current system documentation
  • Vast.ai SDK installed
  • Feature flag infrastructure
  • Rollback strategy tested

Migration Types

TypeComplexityDurationRisk
Fresh installLowDaysLow
From competitorMediumWeeksMedium
Major versionMediumWeeksMedium
Full replatformHighMonthsHigh

Pre-Migration Assessment

Step 1: Current State Analysis

# Document current implementation
find . -name "*.ts" -o -name "*.py" | xargs grep -l "vastai" > vastai-files.txt

# Count integration points
wc -l vastai-files.txt

# Identify dependencies
npm list | grep vastai
pip freeze | grep vastai

Step 2: Data Inventory

interface MigrationInventory {
  dataTypes: string[];
  recordCounts: Record<string, number>;
  dependencies: string[];
  integrationPoints: string[];
  customizations: string[];
}

async function assessVast.aiMigration(): Promise<MigrationInventory> {
  return {
    dataTypes: await getDataTypes(),
    recordCounts: await getRecordCounts(),
    dependencies: await analyzeDependencies(),
    integrationPoints: await findIntegrationPoints(),
    customizations: await documentCustomizations(),
  };
}

Migration Strategy: Strangler Fig Pattern

Phase 1: Parallel Run
┌─────────────┐     ┌─────────────┐
│   Old       │     │   New       │
│   System    │ ──▶ │  Vast.ai   │
│   (100%)    │     │   (0%)      │
└─────────────┘     └─────────────┘

Phase 2: Gradual Shift
┌─────────────┐     ┌─────────────┐
│   Old       │     │   New       │
│   (50%)     │ ──▶ │   (50%)     │
└─────────────┘     └─────────────┘

Phase 3: Complete
┌─────────────┐     ┌─────────────┐
│   Old       │     │   New       │
│   (0%)      │ ──▶ │   (100%)    │
└─────────────┘     └─────────────┘

Implementation Plan

Phase 1: Setup (Week 1-2)

# Install Vast.ai SDK
npm install @vastai/sdk

# Configure credentials
cp .env.example .env.vastai
# Edit with new credentials

# Verify connectivity
node -e "require('@vastai/sdk').ping()"

Phase 2: Adapter Layer (Week 3-4)

// src/adapters/vastai.ts
interface ServiceAdapter {
  create(data: CreateInput): Promise<Resource>;
  read(id: string): Promise<Resource>;
  update(id: string, data: UpdateInput): Promise<Resource>;
  delete(id: string): Promise<void>;
}

class Vast.aiAdapter implements ServiceAdapter {
  async create(data: CreateInput): Promise<Resource> {
    const vastaiData = this.transform(data);
    return vastaiClient.create(vastaiData);
  }

  private transform(data: CreateInput): Vast.aiInput {
    // Map from old format to Vast.ai format
  }
}

Phase 3: Data Migration (Week 5-6)

async function migrateVast.aiData(): Promise<MigrationResult> {
  const batchSize = 100;
  let processed = 0;
  let errors: MigrationError[] = [];

  for await (const batch of oldSystem.iterateBatches(batchSize)) {
    try {
      const transformed = batch.map(transform);
      await vastaiClient.batchCreate(transformed);
      processed += batch.length;
    } catch (error) {
      errors.push({ batch, error });
    }

    // Progress update
    console.log(`Migrated ${processed} records`);
  }

  return { processed, errors };
}

Phase 4: Traffic Shift (Week 7-8)

// Feature flag controlled traffic split
function getServiceAdapter(): ServiceAdapter {
  const vastaiPercentage = getFeatureFlag('vastai_migration_percentage');

  if (Math.random() * 100 < vastaiPercentage) {
    return new Vast.aiAdapter();
  }

  return new LegacyAdapter();
}

Rollback Plan

# Immediate rollback
kubectl set env deployment/app VASTAI_ENABLED=false
kubectl rollout restart deployment/app

# Data rollback (if needed)
./scripts/restore-from-backup.sh --date YYYY-MM-DD

# Verify rollback
curl https://app.yourcompany.com/health | jq '.services.vastai'

Post-Migration Validation

async function validateVast.aiMigration(): Promise<ValidationReport> {
  const checks = [
    { name: 'Data count match', fn: checkDataCounts },
    { name: 'API functionality', fn: checkApiFunctionality },
    { name: 'Performance baseline', fn: checkPerformance },
    { name: 'Error rates', fn: checkErrorRates },
  ];

  const results = await Promise.all(
    checks.map(async c => ({ name: c.name, result: await c.fn() }))
  );

  return { checks: results, passed: results.every(r => r.result.success) };
}

Instructions

Step 1: Assess Current State

Document existing implementation and data inventory.

Step 2: Build Adapter Layer

Create abstraction layer for gradual migration.

Step 3: Migrate Data

Run batch data migration with error handling.

Step 4: Shift Traffic

Gradually route traffic to new Vast.ai integration.

Output

  • Migration assessment complete
  • Adapter layer implemented
  • Data migrated successfully
  • Traffic fully shifted to Vast.ai

Error Handling

IssueCauseSolution
Data mismatchTransform errorsValidate transform logic
Performance dropNo cachingAdd caching layer
Rollback triggeredErrors spikedReduce traffic percentage
Validation failedMissing dataCheck batch processing

Examples

Quick Migration Status

const status = await validateVast.aiMigration();
console.log(`Migration ${status.passed ? 'PASSED' : 'FAILED'}`);
status.checks.forEach(c => console.log(`  ${c.name}: ${c.result.success}`));

Resources

Flagship+ Skills

For advanced troubleshooting, see vastai-advanced-troubleshooting.

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Total Score

85/100

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