managing-lambda-labs

name	managing-lambda-labs
description	Use when working with Lambda Labs — lambda Labs GPU cloud management covering instance inventory, GPU type availability, SSH key management, filesystem status, instance pricing, and capacity analysis. Use for comprehensive Lambda Labs workspace assessment and GPU instance optimization.
connection_type	lambda-labs
preload	false

Lambda Labs Management

Analyze Lambda Labs GPU instances, availability, filesystems, and pricing.

Phase 1: Discovery

#!/bin/bash
TOKEN="${LAMBDA_API_KEY}"
BASE="https://cloud.lambdalabs.com/api/v1"
AUTH=(-H "Authorization: Bearer ${TOKEN}" -H "Content-Type: application/json")

echo "=== Running Instances ==="
curl -s "${BASE}/instances" "${AUTH[@]}" \
  | jq -r '.data[] | "\(.name // .id[0:12])\t\(.id[0:12])\t\(.status)\t\(.instance_type.name)\t\(.region.name)\t\(.ip)"' \
  | column -t | head -20

echo ""
echo "=== Instance Types & Pricing ==="
curl -s "${BASE}/instance-types" "${AUTH[@]}" \
  | jq -r '.data | to_entries[] | "\(.key)\t\(.value.instance_type.description)\tGPUs:\(.value.instance_type.specs.gpus)\tRAM:\(.value.instance_type.specs.ram)GB\t$\(.value.instance_type.price_cents_per_hour / 100)/hr"' \
  | column -t | head -15

echo ""
echo "=== GPU Availability by Region ==="
curl -s "${BASE}/instance-types" "${AUTH[@]}" \
  | jq -r '.data | to_entries[] | .key as $type | .value.regions_with_capacity_available[]? | "\($type)\t\(.name)\t\(.description)"' \
  | column -t | head -20

echo ""
echo "=== SSH Keys ==="
curl -s "${BASE}/ssh-keys" "${AUTH[@]}" \
  | jq -r '.data[] | "\(.name)\t\(.id[0:12])\t\(.public_key[0:40])..."' \
  | column -t | head -10

Phase 2: Analysis

#!/bin/bash
TOKEN="${LAMBDA_API_KEY}"
BASE="https://cloud.lambdalabs.com/api/v1"
AUTH=(-H "Authorization: Bearer ${TOKEN}" -H "Content-Type: application/json")

echo "=== Instance Details ==="
for INST in $(curl -s "${BASE}/instances" "${AUTH[@]}" | jq -r '.data[].id'); do
  curl -s "${BASE}/instances/${INST}" "${AUTH[@]}" \
    | jq -r '.data | "\(.name // .id[0:12])\t\(.instance_type.name)\t\(.status)\tGPUs:\(.instance_type.specs.gpus)\tVCPUs:\(.instance_type.specs.vcpus)\tRAM:\(.instance_type.specs.ram)GB\tStorage:\(.instance_type.specs.storage_in_gb)GB"' 2>/dev/null
done | column -t | head -15

echo ""
echo "=== Filesystem Attachments ==="
for INST in $(curl -s "${BASE}/instances" "${AUTH[@]}" | jq -r '.data[].id'); do
  NAME=$(curl -s "${BASE}/instances/${INST}" "${AUTH[@]}" | jq -r '.data.name // .data.id[0:12]')
  curl -s "${BASE}/instances/${INST}" "${AUTH[@]}" \
    | jq -r  2>/dev/null
 | column -t |  -10

 
 
curl -s   \
  | jq -r  \
  | column -t |  -10

 
 
INSTANCES=$(curl -s  )
  | jq 

 
 
curl -s   \
  | jq 

Output Format

LAMBDA LABS ANALYSIS
======================
Instance         Type           GPUs   Region     IP              Status
──────────────────────────────────────────────────────────────────────────
ml-train-1       gpu_8x_a100    8xA100 us-tx-3    192.168.1.10    active
dev-box          gpu_1x_a10     1xA10  us-az-1    192.168.1.11    active

Running: 2 instances | GPUs: A100(8) A10(1)
Hourly Cost: $12.49 | Daily Est: $299.76
SSH Keys: 3 configured | Filesystems: 1 attached
Available Types: gpu_1x_a10, gpu_8x_a100 | Sold Out: gpu_8x_h100

Safety Rules

• Read-only: Only use GET endpoints against the Lambda Labs API
• Never launch or terminate instances without confirmation
• API keys: Never output API key values
• Cost awareness: Always highlight running cost estimates for active instances

Anti-Hallucination Rules

1. NEVER assume resource names — always discover via CLI/API in Phase 1 before referencing in Phase 2.
2. NEVER fabricate metric names or dimensions — verify against the service documentation or --help output.
3. NEVER mix CLI commands between service versions — confirm which version/API you are targeting.
4. ALWAYS use the discovery → verify → analyze chain — every resource referenced must have been discovered first.
5. ALWAYS handle empty results gracefully — an empty response is valid data, not an error to retry.

Counter-Rationalizations

Shortcut	Counter	Why
"I'll skip discovery and check known resources"	Always run Phase 1 discovery first	Resource names change, new resources appear — assumed names cause errors
"The user only asked for a quick check"	Follow the full discovery → analysis flow	Quick checks miss critical issues; structured analysis catches silent failures
"Default configuration is probably fine"	Audit configuration explicitly	Defaults often leave logging, security, and optimization features disabled
"Metrics aren't needed for this"	Always check relevant metrics when available	API/CLI responses show current state; metrics reveal trends and intermittent issues
"I don't have access to that"	Try the command and report the actual error	Assumed permission failures prevent useful investigation; actual errors are informative