COMMANDS:

# Install & setup pip install terradev-cli terradev configure --provider runpod

# Price discovery (19 clouds) terradev quote -g H100 terradev quote -g A100 --max-price 2.50

# Provision with auto topology optimization terradev provision -g H100 -n 4 --parallel 6 terradev provision -g A100 --dry-run terradev run --gpu H100 --image pytorch/pytorch:latest

# Instance management terradev status --live terradev manage -i <id> -a stop terradev analytics --days 30 terradev optimize Training Pipeline bash # Pre-flight validation terradev preflight

# Launch training terradev train --script train.py --from-provision latest terradev train-status terradev monitor --job my-job terradev checkpoint list --job my-job Inference Optimization bash # vLLM auto-tuning (6 critical knobs) terradev vllm auto-optimize -s workload.json -m meta-llama/Llama-2-7b-hf -g 4 terradev vllm analyze -e http://localhost:8000 terradev vllm benchmark -e http://localhost:8000 -c 10

# MoE deployment with auto-optimizations terradev provision --task clusters/moe-template/task.yaml \ --set model_id=Qwen/Qwen3.5-397B-A17B

# Disaggregated prefill/decode terradev ml ray --deploy-pd --model zai-org/GLM-5-FP8 \ --prefill-tp 8 --decode-tp 1 --decode-dp 24

# LoRA adapters (hot-load on running endpoint) terradev lora add -e http://endpoint:8000 -n customer-a -p /adapters/a terradev lora list -e http://endpoint:8000 terradev lora remove -e http://endpoint:8000 -n customer-a Kubernetes bash # Topology-optimized clusters terradev k8s create my-cluster --gpu H100 --count 8 --prefer-spot terradev k8s list terradev k8s info my-cluster terradev k8s destroy my-cluster Secondary Features bash # HF Spaces (one-click deployment) terradev hf-space my-llama --model-id meta-llama/Llama-2-7b-hf --template llm

# InferX serverless (<2s cold starts) terradev inferx deploy --endpoint my-api --model-id meta-llama/Llama-2-7b-hf terradev inferx status --endpoint my-api

# Observability & Safety terradev phoenix deploy --project my-inference terradev phoenix spans --project my-inference --limit 100 terradev qdrant create-collection --name docs --vector-size 1536 terradev guardrails generate-config --enable-topical --enable-pii

# GitOps automation terradev gitops init --provider github --repo my-org/infra --tool argocd terradev gitops sync --cluster production

# Integrations (BYOAPI) terradev configure --provider wandb --api-key $WANDB_KEY terradev configure --provider prometheus --api-key $PROMETHEUS_URL Quick Workflows bash # 5-minute GPU setup pip install terradev-cli && terradev setup runpod --quick && \ terradev quote -g H100 && terradev run --gpu H100 --image pytorch/pytorch:latest

# Production RAG pipeline terradev qdrant k8s --namespace rag && \ terradev qdrant create-collection --name kb --vector-size 1536 && \ terradev provision --task clusters/moe-template/task.yaml \ --set model_id=Qwen/Qwen3.5-397B-A17B && \ terradev phoenix deploy --project rag-pipeline

# Multi-cloud cost optimization terradev analytics --days 30 && terradev optimize

Key Features: Auto NUMA/RDMA topology optimization, 19-cloud price comparison, vLLM auto-tuning, disaggregated P/D, LoRA hot-loading, BYOAPI security model.