Configure Vast.ai local development with hot reload and testing.
Use when setting up a development environment, configuring test workflows,
or establishing a fast iteration cycle with Vast.ai.
Trigger with phrases like "vastai dev setup", "vastai local development",
"vastai dev environment", "develop with vastai".
Set up a fast, reproducible local development workflow for Vast.ai GPU workloads. Test Docker images locally, mock API responses for CI, and minimize cloud GPU costs during development.
Prerequisites
Completed vastai-install-auth setup
Docker installed locally
Python 3.8+ with pytest
Instructions
Step 1: Project Structure
vastai-project/
src/
vastai_client.py # API client wrapper
job_runner.py # Job orchestration logic
instance_manager.py # Instance lifecycle management
docker/
Dockerfile # GPU workload image
requirements.txt # Python dependencies for GPU job
tests/
test_client.py # Unit tests with mocked API
test_job_runner.py # Integration tests
conftest.py # Shared fixtures and mocks
scripts/
test-connection.sh # Quick API verification
benchmark-gpu.py # GPU benchmark script
.env.development # Dev API key (low spending limit)
.env.production # Prod API key (gitignored)
# Build and test your GPU image locally (CPU mode)
docker build -t my-training:dev -f docker/Dockerfile .
docker run --rm my-training:dev python -c "import torch; print('OK')"
# Test training script in CPU mode
docker run --rm -v $(pwd)/data:/workspace/data my-training:dev \
python train.py --epochs 1 --batch-size 4 --device cpu --dry-run
Step 4: Quick Connection Test Script
#!/bin/bash
set -euo pipefail
echo "Testing Vast.ai connection..."
vastai show user 2>/dev/null && echo " CLI auth: OK" || echo " CLI auth: FAIL"
BALANCE=$(vastai show user --raw 2>/dev/null | python3 -c "import sys,json; print(json.load(sys.stdin).get('balance',0))")
echo " Balance: \$$BALANCE"
echo "Connection verified."
Step 5: Development Workflow
# 1. Edit Docker image and training code locally
# 2. Test locally with CPU mode
docker build -t my-training:dev . && docker run --rm my-training:dev python train.py --dry-run
# 3. Push image to registry
docker tag my-training:dev ghcr.io/yourorg/training:dev && docker push ghcr.io/yourorg/training:dev
# 4. Rent cheapest GPU for real test
vastai create instance OFFER_ID --image ghcr.io/yourorg/training:dev --disk 20
# 5. Monitor, verify, destroy
vastai show instances && vastai destroy instance INSTANCE_ID
Output
Project structure with client, tests, and Docker setup
Mocked Vast.ai client for unit tests (no API calls)
Proceed to vastai-sdk-patterns for production-ready API patterns.
Examples
TDD workflow: Write tests that mock search_offers and create_instance, implement the job runner to pass tests, then run one real integration test against the API.
Cost-controlled dev: Set dph_total<=0.10 in search queries and auto-destroy after 30 minutes to keep testing costs under $0.05.
