terradev-cli 3.1.9

Cross-cloud GPU provisioning with GitOps automation and HuggingFace Spaces deployment

Terradev CLI v3.1.9

BYOAPI: Cross-cloud GPU provisioning and cost optimization platform with GitOps automation, for stateless workloads.

Why Terradev?

Developers overpay by only accessing single-cloud workflows or using sequential provisioning with inefficient egress + rate-limiting.

Terradev is a cross-cloud compute-provisioning CLI that compresses + stages datasets, provisions optimal instances + nodes, and deploys 3-5x faster than sequential provisioning.

GitOps Automation

Production-ready GitOps workflows based on real-world Kubernetes experience:

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

# Bootstrap GitOps tool on cluster
terradev gitops bootstrap --tool argocd --cluster production

# Sync cluster with Git repository
terradev gitops sync --cluster production --environment prod

# Validate configuration
terradev gitops validate --dry-run --cluster production

GitOps Features

• Multi-Provider Support: GitHub, GitLab, Bitbucket, Azure DevOps
• Tool Integration: ArgoCD and Flux CD support
• Repository Structure: Automated GitOps repository setup
• Policy as Code: Gatekeeper/Kyverno policy templates
• Multi-Environment: Dev, staging, production environments
• Resource Management: Automated quotas and network policies
• Validation: Dry-run and apply validation
• Security: Best practices and compliance policies

GitOps Repository Structure

my-infra/
├── clusters/
│   ├── dev/
│   ├── staging/
│   └── prod/
├── apps/
├── infra/
├── policies/
└── monitoring/

HuggingFace Spaces Integration

Deploy any HuggingFace model to Spaces with one command:

# Install HF Spaces support
pip install terradev-cli[hf]

# Set your HF token
export HF_TOKEN=your_huggingface_token

# Deploy Llama 2 with one click
terradev hf-space my-llama --model-id meta-llama/Llama-2-7b-hf --template llm

# Deploy custom model with GPU
terradev hf-space my-model --model-id microsoft/DialoGPT-medium \
  --hardware a10g-large --sdk gradio

# Result:
# Space URL: https://huggingface.co/spaces/username/my-llama
# 100k+ researchers can now access your model!

HF Spaces Features

• One-Click Deployment: No manual configuration required
• Template-Based: LLM, embedding, and image model templates
• Multi-Hardware: CPU-basic to A100-large GPU tiers
• Auto-Generated Apps: Gradio, Streamlit, and Docker support
• Revenue Streams: Hardware upgrades, private spaces, template licensing

Available Templates

# LLM Template (A10G GPU)
terradev hf-space my-llama --model-id meta-llama/Llama-2-7b-hf --template llm

# Embedding Template (CPU-upgrade)
terradev hf-space my-embeddings --model-id sentence-transformers/all-MiniLM-L6-v2 --template embedding

# Image Model Template (T4 GPU)
terradev hf-space my-image --model-id runwayml/stable-diffusion-v1-5 --template image

MoE Cluster Templates (NEW in v3.1.8)

Production-ready cluster configs optimized for Mixture-of-Experts models — the dominant architecture for every major 2026 release (GLM-5, Qwen 3.5, Mistral Large 3, DeepSeek V4, Llama 5).

# Deploy any MoE model with one command
terradev provision --task clusters/moe-template/task.yaml \
  --set model_id=zai-org/GLM-5-FP8 --set tp_size=8

# Or Qwen 3.5 flagship
terradev provision --task clusters/moe-template/task.yaml \
  --set model_id=Qwen/Qwen3.5-397B-A17B

# Kubernetes
kubectl apply -f clusters/moe-template/k8s/

# Helm
helm upgrade --install moe-inf ./helm/terradev \
  -f clusters/moe-template/helm/values-moe.yaml \
  --set model.id=zai-org/GLM-5-FP8

MoE Template Features

• Any MoE Model: Parameterized for GLM-5, Qwen 3.5, Mistral Large 3, DeepSeek V4, Llama 5
• NVLink Topology: Enforced single-node TP with NUMA alignment
• vLLM + SGLang: Both serving backends supported
• FP8 Quantization: Half the VRAM of BF16 on H100/H200
• GPU-Aware Autoscaling: HPA on DCGM metrics and vLLM queue depth
• Multi-Cloud: RunPod, Vast.ai, Lambda, AWS, CoreWeave

See clusters/moe-template/ for full docs and clusters/glm-5/ for a model-specific example.

Installation

pip install terradev-cli

With HF Spaces support:

pip install terradev-cli[hf]        # HuggingFace Spaces deployment
pip install terradev-cli[all]        # All cloud providers + ML services + HF Spaces

Quick Start

# 1. Get setup instructions for any provider
terradev setup runpod --quick
terradev setup aws --quick

# 2. Configure your cloud credentials (BYOAPI — you own your keys)
terradev configure --provider runpod
terradev configure --provider aws
terradev configure --provider vastai

# 3. Deploy to HuggingFace Spaces (NEW!)
terradev hf-space my-llama --model-id meta-llama/Llama-2-7b-hf --template llm
terradev hf-space my-embeddings --model-id sentence-transformers/all-MiniLM-L6-v2 --template embedding
terradev hf-space my-image --model-id runwayml/stable-diffusion-v1-5 --template image

# 4. Get enhanced quotes with conversion prompts
terradev quote -g A100
terradev quote -g A100 --quick  # Quick provision best quote

# 5. Provision the cheapest instance (real API call)
terradev provision -g A100

# 6. Configure ML services
terradev configure --provider wandb --dashboard-enabled true
terradev configure --provider langchain --tracing-enabled true

# 7. Use ML services
terradev ml wandb --test
terradev ml langchain --create-workflow my-workflow

# 8. View analytics
python user_analytics.py

# 9. Provision 4x H100s in parallel across multiple clouds
terradev provision -g H100 -n 4 --parallel 6

# 10. Dry-run to see the allocation plan without launching
terradev provision -g A100 -n 2 --dry-run

# 11. Manage running instances
terradev status --live
terradev manage -i <instance-id> -a stop
terradev manage -i <instance-id> -a start
terradev manage -i <instance-id> -a terminate

# 12. Execute commands on provisioned instances
terradev execute -i <instance-id> -c "python train.py"

# 13. Stage datasets near compute (compress + chunk + upload)
terradev stage -d ./my-dataset --target-regions us-east-1,eu-west-1

# 14. View cost analytics from the tracking database
terradev analytics --days 30

# 15. Find cheaper alternatives for running instances
terradev optimize

# 16. One-command Docker workload (provision + deploy + run)
terradev run --gpu A100 --image pytorch/pytorch:latest -c "python train.py"

# 17. Keep an inference server alive
terradev run --gpu H100 --image vllm/vllm-openai:latest --keep-alive --port 8000

BYOAuth — Bring Your Own Authentication

Terradev never touches, stores, or proxies your cloud credentials through a third party. Your API keys stay on your machine in ~/.terradev/credentials.json — encrypted at rest, never transmitted.

How it works:

1. You run terradev configure --provider <name> and enter your API key
2. Credentials are stored locally in your home directory — never sent to Terradev servers
3. Every API call goes directly from your machine to the cloud provider
4. No middleman account, no shared credentials, no markup on provider pricing

Why this matters:

• Zero trust exposure — No third party holds your AWS/GCP/Azure keys
• No vendor lock-in — If you stop using Terradev, your cloud accounts are untouched
• Enterprise-ready — Compliant with SOC2, HIPAA, and internal security policies that prohibit sharing credentials with SaaS vendors
• Full audit trail — Every provision is logged locally with provider, cost, and timestamp

CLI Commands

Command	Description
terradev configure	Set up API credentials for any provider
terradev quote	Get real-time GPU pricing across all clouds
terradev provision	Provision instances with parallel multi-cloud arbitrage
terradev manage	Stop, start, terminate, or check instance status
terradev status	View all instances and cost summary
terradev execute	Run commands on provisioned instances
terradev stage	Compress, chunk, and stage datasets near compute
terradev analytics	Cost analytics with daily spend trends
terradev optimize	Find cheaper alternatives for running instances
terradev run	Provision + deploy Docker container + execute in one command
terradev hf-space	NEW: One-click HuggingFace Spaces deployment
terradev inferx	NEW: InferX serverless inference platform - <2s cold starts
terradev up	NEW: Manifest cache + drift detection
terradev rollback	NEW: Versioned rollback to any deployment
terradev manifests	NEW: List cached deployment manifests
terradev integrations	Show status of W&B, Prometheus, and infra hooks

HF Spaces Commands (NEW!)

# Deploy Llama 2 to HF Spaces
terradev hf-space my-llama --model-id meta-llama/Llama-2-7b-hf --template llm

# Deploy with custom hardware
terradev hf-space my-model --model-id microsoft/DialoGPT-medium \
  --hardware a10g-large --sdk gradio --private

# Deploy embedding model
terradev hf-space my-embeddings --model-id sentence-transformers/all-MiniLM-L6-v2 \
  --template embedding --env BATCH_SIZE=64

Manifest Cache Commands (NEW!)

# Provision with manifest cache
terradev up --job my-training --gpu-type A100 --gpu-count 4

# Fix drift automatically
terradev up --job my-training --fix-drift

# Rollback to previous version
terradev rollback my-training@v2

# List all cached manifests
terradev manifests --job my-training

InferX Commands (NEW!)

# Start InferX serverless inference platform
terradev inferx start --model-id meta-llama/Llama-2-7b-hf --hardware a10g

# Deploy inference endpoint with auto-scaling
terradev inferx deploy --endpoint my-llama-api --model-id microsoft/DialoGPT-medium \
  --hardware t4 --max-concurrency 100

# Get inference endpoint status and health
terradev inferx status --endpoint my-llama-api

# Route inference requests to optimal endpoint
terradev inferx route --query "What is machine learning?" --model-type llm

# Run failover tests for high availability
terradev inferx failover --endpoint my-llama-api --test-load 1000

# Get cost analysis for inference workloads
terradev inferx cost-analysis --days 30 --endpoint my-llama-api

Observability & ML Integrations

Terradev facilitates connections to your existing tools via BYOAPI — your keys stay local, all data flows directly from your instances to your services.

Integration	What Terradev Does	Setup
Weights & Biases	Auto-injects WANDB_* env vars into provisioned containers	terradev configure --provider wandb --api-key YOUR_KEY
Prometheus	Pushes provision/terminate metrics to your Pushgateway	terradev configure --provider prometheus --api-key PUSHGATEWAY_URL
Grafana	Exports a ready-to-import dashboard JSON	terradev integrations --export-grafana

Prices queried in real-time from all 10+ providers. Actual savings vary by availability.

Pricing Tiers

Feature	Research (Free)	Research+ ($49.99/mo)	Enterprise ($299.99/mo)	Enterprise+ ($0.09/GPU-hr)
Max concurrent instances	1	8	32	Unlimited
Provisions/month	10	100	Unlimited	Unlimited
User seats	1	1	5	Unlimited
Providers	All 11	All 11	All 11 + priority	All 11+ + dedicated support
Cost tracking	Yes	Yes	Yes	Yes + fleet dashboard
Dataset staging	Yes	Yes	Yes	Yes
Egress optimization	Basic	Full	Full + custom routes	Full + custom routes
GPU-hour metering	-	-	-	$0.09/GPU-hr (32 GPU min)
Fleet management	-	-	-	Yes
SLA guarantee	-	-	Yes	Yes

Enterprise+: Metered billing at $0.09 per GPU-hour with a minimum commitment of 32 GPUs. You always pay for at least 32 GPU-hours per hour ($2.88/hr floor) whether you use them or not — same model as AWS Reserved Instances. Billed monthly to your card via Stripe. Run terradev upgrade -t enterprise_plus to get started.

Integrations

Jupyter / Colab / VS Code Notebooks

pip install terradev-jupyter
%load_ext terradev_jupyter

%terradev quote -g A100
%terradev provision -g H100 --dry-run
%terradev run --gpu A100 --image pytorch/pytorch:latest --dry-run

GitHub Actions

- uses: theodden/terradev-action@v1
  with:
    gpu-type: A100
    max-price: "1.50"
  env:
    TERRADEV_RUNPOD_KEY: ${{ secrets.RUNPOD_API_KEY }}

Docker (One-Command Workloads)

terradev run --gpu A100 --image pytorch/pytorch:latest -c "python train.py"
terradev run --gpu H100 --image vllm/vllm-openai:latest --keep-alive --port 8000

GPU Topology Optimization (v3.1)

Terradev v3.1 automatically optimizes GPU infrastructure topology — NUMA alignment, PCIe switch pairing, SR-IOV, RDMA, and kubelet Topology Manager configuration. You never configure any of this. It's applied automatically when you create clusters or provision GPU nodes.

What happens behind the scenes

When you run terradev k8s create my-cluster --gpu H100 --count 4:

Layer	What Terradev auto-configures
NUMA Alignment	Kubelet Topology Manager set to restricted with prefer-closest-numa-nodes=true
CPU Pinning	cpuManagerPolicy: static for deterministic core assignment
GPUDirect RDMA	nvidia_peermem kernel module loaded on all GPU nodes
SR-IOV	VF-per-GPU pairing enabled for multi-node clusters
NCCL Tuning	NCCL_NET_GDR_LEVEL=PIX, NCCL_NET_GDR_READ=1, IB enabled
PCIe Locality	GPU-NIC pairs forced to same NUMA node (eliminates cross-socket penalty)
Karpenter	Topology-aware NodePool with correct instance families per GPU type

Why this matters

Without topology optimization, Kubernetes randomly assigns GPUs and NICs across NUMA nodes and PCIe switches. A cross-socket GPU-NIC pairing can cut RDMA bandwidth by 30-50%. Terradev eliminates this class of performance bug entirely.

# All of this is automatic — just provision normally
terradev k8s create training-cluster --gpu H100 --count 8 --prefer-spot

# Output includes topology confirmation:
# 🧬 Topology optimization (auto-applied):
#    Kubelet Topology Manager: restricted (NUMA-aligned)
#    CPU Manager: static (pinned cores)
#    GPUDirect RDMA: enabled (nvidia_peermem)
#    SR-IOV: enabled (8 nodes, VF-per-GPU pairing)
#    NCCL: IB enabled, GDR_LEVEL=PIX, GDR_READ=1
#    PCIe locality: GPU-NIC pairs forced to same NUMA node

DRA / DRANET Ready

Terradev's topology module includes DRA (Dynamic Resource Allocation) and DRANET resource claim generation for K8s 1.31+. When KEP-4381 lands, Terradev will automatically use resource.kubernetes.io/pcieRoot constraints to enforce PCIe-switch-level GPU-NIC pairing — the finest granularity possible.

Claude Code Integration (NEW!)

Access Terradev directly from Claude Code with the MCP server:

# Install the MCP server
npm install -g terradev-mcp

# Add to your Claude Code MCP configuration:
{
  "mcpServers": {
    "terradev": {
      "command": "terradev-mcp"
    }
  }
}

# Check MCP connection
/mcp

# Use Terradev commands naturally in Claude Code:
terradev quote -g H100
terradev provision -g A100 -n 4 --parallel 6
terradev k8s create my-cluster --gpu H100 --count 4 --multi-cloud

Features available through Claude Code:

• GPU price quotes across 11+ providers
• Instance provisioning with cost optimization
• Kubernetes cluster creation and management
• Inference endpoint deployment (InferX)
• HuggingFace Spaces deployment
• Cost analytics and optimization
• Multi-cloud provider management

Security: BYOAPI - All credentials stay on your machine. Terradev never proxies API keys.

Requirements

• Python >= 3.9
• Cloud provider API keys (configured via terradev configure)

License

Business Source License 1.1 (BUSL-1.1) - see LICENSE file for details