Terradev CLI v5.5.6

An imperative command-line-interface for AI workload orchestration.

License: Apache 2.0 - Free and open source for commercial and personal use.

pypi.org/project/terradev-cli/

Terradev is a cross-cloud compute control plane for AI workloads, not just a provisioning wrapper.

Combines quoting, provisioning, topology optimization, training orchestration, inference tuning, and cost analytics in one CLI, with a Rust-accelerated idempotent runtime underneath.

Continued focus on lower cost, faster provisioning, and topology-aware execution with local credential storage.

Model agnostic. Dataset agnostic. GPU agnostic. Provider agnostic. The only thing Terradev is not agnostic about is correctness: it enforces topology, idempotency, and sequencing.

NOTES ON 5.3.9

Added LoRAX (LoRA eXchange) integration and HuggingFace PEFT import for production-grade multi-LoRA inference serving:

• LoRAX Service (ml_services/lorax_service.py): Async HTTP client for Predibase LoRAX multi-LoRA inference server that serves thousands of fine-tuned models on a single GPU with dynamic adapter loading, heterogeneous continuous batching, and adapter exchange scheduling.

• LoRAX CLI commands (terradev lora lorax):

  terradev lora lorax deploy -m mistralai/Mistral-7B-Instruct-v0.1 --docker
  terradev lora lorax test --host localhost --port 8080
  terradev lora lorax list-adapters
  terradev lora lorax load-adapter -a vineetsharma/qlora-adapter-Mistral-7B-Instruct-v0.1-gsm8k
  terradev lora lorax unload-adapter -a my-adapter
  terradev lora lorax generate -p "What is 2+2?" -a my-adapter
  terradev lora lorax sync-registry

• PEFT Import Service (ml_services/peft_import_service.py): Download, validate, and prepare LoRA adapters from HuggingFace using the PEFT library with auto-detection of rank, alpha, and target modules.

• PEFT CLI commands (terradev lora peft):

  terradev lora peft import -a vineetsharma/qlora-adapter-Mistral-7B-Instruct-v0.1-gsm8k
  terradev lora peft import -a username/adapter --local-name my-adapter --register --base-model mistralai/Mistral-7B-Instruct-v0.1
  terradev lora peft list
  terradev lora peft validate -p ~/.terradev/peft_adapters/username--adapter
  terradev lora peft delete -a username/adapter

• LoRAX Helm Template (clusters/lorax-template/helm/): Production-ready Kubernetes manifests with GPU resource limits, storage configuration, and Prometheus metrics support.

• Registry Integration: One-step import from HuggingFace and automatic registration in Terradev LoRA registry with version tracking, cross-replica sync, and cost attribution.

NOTES ON 5.3.3

Added provider registration and profiling system for intelligent quirk-aware routing across 23 cloud providers, and registration for custom providers from .yaml import:

• ProviderProfile schema (providers/types.py): Encodes provider-specific behaviors including API style (REST/GraphQL/JSON:API), authentication type (Bearer/Basic/HMAC/X-Api-Key), rate limits, spot instance support, egress costs, fallback routing, capacity checks, container image pinning, and spot interruption handling.

• Built-in profiles (providers/provider_profiles.py): Pre-configured profiles for all 23 providers (RunPod, Vast.ai, Lambda Labs, AWS, GCP, Azure, Oracle, Crusoe, CoreWeave, DigitalOcean, Yotta Labs, E2E Networks, FluidStack, Alibaba, OVHcloud, Hetzner, SiliconFlow, TensorDock, Baseten, HuggingFace, Hyperstack, InferX, Latitude).

• Dynamic registration: Users can register custom provider profiles programmatically or load from YAML/JSON files for internal clusters or proprietary cloud providers.

• Profile-aware routing (providers/registry.py): ProviderRegistry.ranked_providers() now incorporates provider profiles into scoring, using egress costs, fallback routing preferences, and spot preemption rates for intelligent provider selection.

• CLI commands (terradev providers): New command group for managing custom provider profiles:

  terradev providers load-profiles ~/.terradev/custom_providers.yaml
  terradev providers list-profiles
  terradev providers show-profile runpod
  terradev providers remove-profile my_custom_provider
  terradev providers export-example -o ~/.terradev/custom_providers.yaml

NOTES ON 5.2.1

Added two new BYOAPI providers: Yotta Labs (Shakti Cloud) and E2E Networks — India's leading GPU clouds. Yotta Labs uses a pod-based compute model (similar to RunPod), and E2E Networks is a traditional VM-style hyperscaler that is NSE-listed and MeitY empanelled. Both are BYOAPI: your key, stored locally, never touches a Terradev server.

terradev configure --provider yottalabs
terradev configure --provider e2enetworks

NOTES ON 5.0.0

We removed the paywall, open-sourced Terradev, and added Rust accelerators for safe and snappy delivery...

With the Rust DAG orchestrator, the execution graph enforces correct sequencing and idempotency at the runtime level. You or the agent can issue commands freely... the orchestrator ensures they're safe to execute.

218 tools not including subcommand/flags require heavy context. The Rust MCP orchestrator processes tool calls with minimal overhead: deserializing, routing, executing, and responding faster than pure-Python-based MCP servers by an order of magnitude. For an agent running a complex provisioning workflow across 23 cloud providers, that compounds across every tool call in the chain.

BYOAPI Configuration

Your API keys are stored locally at ~/.terradev/credentials.json and never sent to Terradev servers.

# Configure multiple providers
terradev configure --provider runpod
terradev configure --provider vastai
terradev configure --provider aws
terradev configure --provider gcp

Performance

• 2-8x throughput improvements with vLLM optimization
• 30-50% bandwidth penalty eliminated with NUMA topology
• 2-5x CUDA Graph speedup with optimal topology
• Up to 90% cost savings with automatic provider switching
• <2 minute spot recovery with KV cache checkpointing
• up to 3.6x faster cold starts with weight streaming
• Up to 50% cost savings with MLA-aware VRAM estimation

Complete Tutorial

Step 1: Install Terradev

pip install terradev-cli

For all cloud provider SDKs and ML integrations:

pip install terradev-cli[all]

Verify and list commands:

terradev --help

Step 2: Configure Your First Cloud Provider

Terradev supports 23 GPU cloud providers. Start with one, RunPod is the fastest to set up:

terradev setup runpod --quick

This shows you where to get your API key. Then configure it:

terradev configure --provider runpod

Paste your API key when prompted. It's stored locally at ~/.terradev/credentials.json, never sent to a Terradev server. Add more providers later:

terradev configure --provider vastai
terradev configure --provider lambda_labs
terradev configure --provider aws

The more providers you configure, the better your price coverage.

Step 3: Get Real-Time GPU Prices

Check pricing across every provider you've configured:

terradev quote -g A100

Output is a table sorted cheapest-first: price/hour, provider, region, spot vs. on-demand. Try different GPUs:

terradev quote -g H100
terradev quote -g L40S
terradev quote -g RTX4090

Step 4: Provision

Most clouds hand you GPUs with suboptimal topology by default. Your GPU and NIC end up on different NUMA nodes, RDMA is disabled, and the kubelet Topology Manager is set to none. That's a 30-50% bandwidth penalty on every distributed operation and you'll never see it in nvidia-smi.

When you provision through Terradev, topology optimization is automatic:

terradev provision -g H100 -n 4 --parallel 6

What happens behind the scenes:

• NUMA alignment — GPU and NIC forced to the same NUMA node
• GPUDirect RDMA — nvidia_peermem loaded, zero-copy GPU-to-GPU transfers
• CPU pinning — static CPU manager policy, no core migration
• SR-IOV — virtual functions created per GPU for isolated RDMA paths
• NCCL tuning — InfiniBand enabled, GDR_LEVEL=PIX, GDR_READ=1

You don't configure any of this. It's applied automatically.

To preview the plan without launching:

terradev provision -g A100 -n 2 --dry-run

To set a price ceiling:

terradev provision -g A100 --max-price 2.50

Step 5: Run a Workload

Option A — Run a command on your provisioned instance:

terradev execute -i <instance-id> -c "nvidia-smi"
terradev execute -i <instance-id> -c "python train.py"

Option B — One command that provisions, deploys a container, and runs:

terradev run --gpu A100 --image pytorch/pytorch:latest -c "python train.py"

Option C — Keep an inference server alive:

terradev run --gpu H100 --image vllm/vllm-openai:latest --keep-alive --port 8000

Step 6: Manage Your Instances

# See all running instances and current cost
terradev status --live

# Stop (keeps allocation)
terradev manage -i <instance-id> -a stop

# Restart
terradev manage -i <instance-id> -a start

# Terminate and release
terradev manage -i <instance-id> -a terminate

Step 7: Track Costs and Find Savings

# View spend over the last 30 days
terradev analytics --days 30

# Find cheaper alternatives for running instances
terradev optimize

Step 8: Distributed Training Pipeline

Now that your nodes have correct topology, distributed training actually runs at full bandwidth:

# Validate GPUs, NCCL, RDMA, and drivers before launching
terradev preflight

# Launch training on the nodes you just provisioned
terradev train --script train.py --from-provision latest

# Watch GPU utilization and cost in real time
terradev monitor --job my-job

# Check status
terradev train-status

# 6. List checkpoints when done
terradev checkpoint list --job my-job

The --from-provision latest flag auto-resolves IPs from your last provision command. Supports torchrun, DeepSpeed, Accelerate, and Megatron.

Step 9: Optimize vLLM Inference (The 6 Knobs)

If you're serving a model with vLLM, there are 6 settings most teams leave at defaults — each one costs throughput:

Knob	Default	Optimized	Impact
max-num-batched-tokens	2048	16384	8x throughput
gpu-memory-utilization	0.90	0.95	5% more VRAM
max-num-seqs	256/1024	512-2048	Prevent queuing
enable-prefix-caching	OFF	ON	Free throughput win
enable-chunked-prefill	OFF	ON	Better prefill
CPU Cores	2 + #GPUs	Optimized	Prevent starvation

Auto-tune all six from your workload profile:

terradev ml vllm auto-optimize -s workload.json -m meta-llama/Llama-2-7b-hf -g 4

Or analyze a running server:

terradev ml vllm analyze -e http://localhost:8000

Benchmark:

terradev ml vllm benchmark -e http://localhost:8000 -c 10

Step 10: Deploy a MoE Model with Auto-Applied Optimizations

For large Mixture-of-Experts models (GLM-5, Qwen 3.5, DeepSeek V4), Terradev's MoE templates include every optimization auto-applied — KV cache offloading, speculative decoding, sleep mode, expert load balancing:

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

Or a smaller model:

terradev provision --task clusters/moe-template/task.yaml \
  --set model_id=Qwen/Qwen3.5-122B-A10B --set tp_size=4 --set gpu_count=4

What's auto-applied (no flags needed):

• KV cache offloading — spills to CPU DRAM, up to 9x throughput
• MTP speculative decoding — up to 2.8x faster generation
• Sleep mode — idle models hibernate to CPU RAM, 18-200x faster than cold restart
• Expert load balancing — rebalances routing at runtime
• LMCache — distributes KV cache across instances via Redis

Step 11: Disaggregated Prefill/Decode (Advanced)

This separates inference into two GPU pools optimized for each phase:

• Prefill (compute-bound) — processes input prompt, wants high FLOPS
• Decode (memory-bound) — generates tokens, wants high HBM bandwidth

The KV cache transfers between them via NIXL — zero-copy GPU-to-GPU over RDMA. This is why getting the NUMA topology right in Step 4 matters: NIXL only runs at full speed when the GPU and NIC share a PCIe switch.

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

Terradev's inference router automatically uses sticky routing. Once a prefill GPU hands off a KV cache to a decode GPU, future requests with the same prefix go to that same decode GPU, avoiding redundant transfers.

Step 12: Create a Kubernetes GPU Cluster

For production, create a topology-optimized K8s cluster:

terradev k8s create my-cluster --gpu H100 --count 8 --prefer-spot

This auto-configures Karpenter NodePools with NUMA-aligned kubelet Topology Manager, GPUDirect RDMA, and PCIe locality enforcement.

# List clusters
terradev k8s list

# Get cluster info
terradev k8s info my-cluster

# Tear down
terradev k8s destroy my-cluster

Why This Order Matters

Each step builds on the one before it:

• Step 4: NUMA / RDMA / SR-IOV topology ← foundation
• Step 8: Distributed training at full BW ← depends on topology
• Step 9: vLLM knob tuning ← depends on correct memory layout
• Step 10: KV cache offloading + sleep mode ← depends on CPU bus not saturated
• Step 11: Disaggregated P/D ← depends on RDMA for KV transfer

If the provisioning layer is wrong, every optimization above it underperforms. A disaggregated P/D setup with a cross-NUMA KV transfer is slower than a monolithic setup with correct topology.

Terradev handles the foundation automatically so the rest of the stack works the way it's supposed to.

Complete Workflow Examples

Example 1: LLM Inference Service

#!/bin/bash

# Complete LLM deployment workflow

# 1. Find cheapest GPU
terradev quote -g A100 --quick
# 2. Provision with auto-optimization
terradev provision -g A100 -n 2 --parallel 4
# 3. Deploy optimized vLLM
terradev ml vllm --start --instance-ip $(terradev status --json | jq -r '.[0].ip') --model meta-llama/Llama-2-7b-hf --tp-size 2
# 4. Set up monitoring
terradev monitor --endpoint llama-api --live
# 5. Add customer adapter
terradev lora add -e http://$(terradev status --json | jq -r '.[0].ip'):8000 -n customer-a -p ./adapters/customer-a

Example 2: MoE Model Production Deployment

#!/bin/bash

# GLM-5 production deployment

# 1. Deploy MoE cluster
terradev provision --task clusters/moe-template/task.yaml --set model_id=zai-org/GLM-5-FP8 --set tp_size=8
# 2. Deploy monitoring
terradev k8s monitoring-stack --cluster glm-5-cluster
# 3. Set up warm pool for bursty traffic
terradev ml warm-pool --configure --strategy traffic_based --max-warm-models 5 --endpoint glm-5-api
# 4. Test failover
terradev inferx failover --endpoint glm-5-api --test-load 5000

Example 3: InferX + LoRA Hybrid Deployment (Production Multi-Tenant)

#!/bin/bash

# Production deployment with cold start failover and multi-tenant LoRA adapters

echo " Deploying InferX + LoRA Hybrid Inference Service"

# 1. Deploy baseline reserved GPUs for steady traffic
echo " Step 1: Provision reserved baseline capacity"
terradev provision -g H100 -n 2 --parallel 4 \
  --tag baseline-llm \
  --max-price 2.50

BASELINE_IP=$(terradev status --json | jq -r '.[] | select(.tags[] | contains("baseline-llm")) | .ip' | head -1)

# 2. Deploy optimized vLLM with LoRA support on baseline
echo " Step 2: Deploy vLLM with LoRA adapter support"
terradev ml vllm --start \
  --instance-ip $BASELINE_IP \
  --model meta-llama/Llama-2-7b-hf \
  --tp-size 2 \
  --enable-lora \
  --enable-kv-offloading \
  --enable-sleep-mode \
  --port 8000

# 3. Load customer-specific LoRA adapters
echo " Step 3: Load multi-tenant LoRA adapters"
terradev lora add -e http://$BASELINE_IP:8000 \
  -n customer-enterprise-a \
  -p ./adapters/customer-enterprise-a

terradev lora add -e http://$BASELINE_IP:8000 \
  -n customer-startup-b \
  -p ./adapters/customer-startup-b

terradev lora add -e http://$BASELINE_IP:8000 \
  -n customer-internal \
  -p ./adapters/customer-internal

# 4. Configure InferX for cold start and burst handling
echo " Step 4: Configure InferX for serverless burst capacity"
terradev inferx deploy \
  --endpoint burst-llm-api \
  --model-id meta-llama/Llama-2-7b-hf \
  --baseline-endpoint http://$BASELINE_IP:8000 \
  --cold-start-threshold 100 \
  --burst-capacity 10 \
  --failover-strategy active-passive

# 5. Set up intelligent routing with semantic awareness
echo " Step 5: Configure semantic routing for multi-tenant requests"
cat > routing-config.yaml << EOF
rules:
  - name: "enterprise_customers"
    condition: "header:x-customer-id == 'enterprise-a'"
    route_to: "baseline"
    lora_adapter: "customer-enterprise-a"
    strategy: "latency"

  - name: "startup_customers" 
    condition: "header:x-customer-id == 'startup-b'"
    route_to: "baseline"
    lora_adapter: "customer-startup-b"
    strategy: "cost"

  - name: "internal_workloads"
    condition: "header:x-api-key starts_with 'internal_'"
    route_to: "baseline"
    lora_adapter: "customer-internal"
    strategy: "throughput"

  - name: "burst_traffic"
    condition: "request_rate > 50"
    route_to: "inferx"
    strategy: "auto-scale"

  - name: "fallback"
    condition: "default"
    route_to: "baseline"
    lora_adapter: "customer-internal"
    strategy: "round-robin"
EOF

terradev semantic-router --deploy --config routing-config.yaml

# 6. Configure warm pool for frequently used adapters
echo " Step 6: Configure warm pool for LoRA adapters"
terradev ml warm-pool --configure \
  --strategy adapter_based \
  --max-warm-models 5 \
  --warm-adapters customer-enterprise-a,customer-internal \
  --idle-eviction-minutes 10 \
  --enable-predictive-warming

# 7. Set up comprehensive monitoring and alerting
echo " Step 7: Deploy monitoring stack"
terradev k8s monitoring-stack --cluster production

# Configure W&B for ML observability
terradev ml wandb --setup-alerts \
  --endpoint http://$BASELINE_IP:8000 \
  --metric-thresholds "latency_p95<2000,throughput>100,gpu_utilization>80" \
  --alert-channels slack,email

# Configure InferX-specific monitoring
terradev inferx status --endpoint burst-llm-api --detailed
terradev inferx failover --endpoint burst-llm-api --test-load 1000

# 8. Test the complete setup
echo " Step 8: Testing complete deployment"
echo "Testing baseline endpoint with LoRA..."
curl -X POST http://$BASELINE_IP:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "x-customer-id: enterprise-a" \
  -d '{
    "model": "meta-llama/Llama-2-7b-hf",
    "messages": [{"role": "user", "content": "Hello from enterprise customer!"}],
    "max_tokens": 100
  }'

echo "Testing InferX burst endpoint..."
curl -X POST https://inferx.terradev.cloud/burst-llm-api/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $INFERX_API_KEY" \
  -d '{
    "model": "meta-llama/Llama-2-7b-hf", 
    "messages": [{"role": "user", "content": "Hello from burst traffic!"}],
    "max_tokens": 100
  }'

echo " Step 9: Deployment summary"
echo " Baseline endpoint: http://$BASELINE_IP:8000"
echo " InferX endpoint: https://inferx.terradev.cloud/burst-llm-api"
echo " LoRA adapters loaded: $(terradev lora list -e http://$BASELINE_IP:8000 --count)"
echo " Semantic routing: Active"
echo " Warm pool: Configured for top adapters"
echo " Monitoring: W&B + Prometheus + Grafana"

# 10. Set up automated LoRA updates
echo " Step 10: Configure automated LoRA adapter updates"
cat > lora-update-config.yaml << EOF
adapters:
  - name: "customer-enterprise-a"
    path: "./adapters/customer-enterprise-a"
    update_strategy: "rolling"
    health_check: true
    rollback_on_failure: true
    
  - name: "customer-startup-b"
    path: "./adapters/customer-startup-b" 
    update_strategy: "blue_green"
    health_check: true
    rollback_on_failure: true

monitoring:
  update_frequency: "hourly"
  health_check_timeout: "30s"
  rollback_threshold: "error_rate > 0.05"
EOF

terradev lora auto-update --config lora-update-config.yaml

echo " InferX + LoRA Hybrid Deployment Complete!"
echo ""
echo " Next Steps:"
echo "1. Monitor performance: terradev monitor --endpoint hybrid-llm --live"
echo "2. Check LoRA performance: terradev lora metrics --endpoint http://$BASELINE_IP:8000"
echo "3. Test failover: terradev inferx failover --endpoint burst-llm-api --test-load 5000"
echo "4. Update adapters: terradev lora update -n customer-enterprise-a -p ./new-adapters/"

---

Quick Reference

# Set up cloud provider credentials
terradev configure

# Real-time GPU pricing across 21+ clouds
terradev quote -g H100 

# Provision with auto topology optimization
terradev provision -g H100 -n 4

# Provision + deploy + run in one command
terradev run --gpu A100 --image ...

# View running instances and costs
terradev status --live

# Launch training on provisioned nodes
terradev train --from-provision latest

# Auto-tune 6 critical vLLM knobs
terradev ml vllm auto-optimize

# Topology-optimized Kubernetes cluster
terradev k8s create

# Cost analytics
terradev analytics --days 30

# Find cheaper alternatives
terradev optimize

Troubleshooting Training Workflows

NCCL Connectivity Problems

# Symptoms: Training hangs, NCCL errors, slow communication

# Diagnosis: Check inter-node connectivity
terradev preflight --detailed
terradev execute -i <node-id> -c "nccl_test -b 8G -e 8G -s 1073741824"

# Fix: Re-provision with proper NUMA alignment
terradev provision -g H100 -n 4 --parallel 6 --ensure-numa-alignment

GPU Memory Issues

# Symptoms: OOM errors, CUDA out of memory

# Diagnosis: Check memory usage across nodes
terradev monitor --job <job-id> --memory-usage
terradev execute -i <node-id> -c "nvidia-smi --query-gpu=memory.used,memory.total --format=csv"

# Fix: Reduce batch size or enable gradient checkpointing
terradev train --script train.py --from-provision latest --script-args "--batch-size 16 --gradient-checkpointing"

Dataset Staging Failures

# Symptoms: Slow data loading, transfer timeouts

# Diagnosis: Check dataset cache status
terradev stage --status --dataset-id <dataset-id>
terradev stage --list-cached --region us-east-1

# Fix: Re-stage with higher parallelism or compression
terradev stage -d ./my-dataset --target-regions us-east-1 --parallel-streams 64 --compression zstd

FlashOptim Compatibility Issues

# Symptoms: FlashOptim fails to apply, training crashes

# Diagnosis: Check FlashOptim compatibility
terradev train-status --job <job-id> | grep flashoptim
terradev preflight --flashoptim-check

# Fix: Disable FlashOptim or adjust configuration
terradev train --script train.py --flashoptim off --from-provision latest
# or with manual configuration
terradev train --script train.py --flashoptim on --flashoptim-optimizer adamw --flashoptim-master-weight-bits 8

Checkpoint Recovery Issues

# Symptoms: Can't resume from checkpoint, corrupted checkpoints

# Diagnosis: Verify checkpoint integrity
terradev checkpoint list --job <job-id> --verify
terradev checkpoint validate --checkpoint <checkpoint-path>

# Fix: Create new checkpoint or repair existing
terradev checkpoint save --job <job-id> --force
terradev checkpoint repair --checkpoint <checkpoint-path>

Performance Optimization

Slow Training Speed

# Diagnose bottlenecks
terradev monitor --job <job-id> --bottleneck-analysis
terradev execute -i <node-id> -c "nvtop --interval 1"

# Common fixes
# 1. Enable mixed precision training
terradev train --script train.py --script-args "--mixed-precision --fp16"

# 2. Optimize data loading
terradev stage --hf-dataset <dataset> --target-regions us-east-1 --preprocess "shuffle,cache"

# 3. Increase parallelism
terradev provision -g H100 -n 8 --parallel 12

Network Bottlenecks

# Check network performance between nodes
terradev preflight --network-test
terradev execute -i <node-id> -c "ibstat -v"

# Fixes for RDMA/InfiniBand issues
terradev provision -g H100 -n 4 --ensure-rdma --enable-gpudirect

Contributing

We welcome contributions! Please see our Contributing Guide for details.

License

Apache 2.0.

Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions