by Facingsouth | 2026-03-06 01:30:55+00:00
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.