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0900a2bab0aa3f38d02afaae93f53797ade542e1
timmy-home/big_brain_deploy.py

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8.5 KiB
Python
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#!/usr/bin/env python3
"""Deploy GPU instance on RunPod for Big Brain Gemma 4."""
import subprocess, json, os, time, requests
# Read RunPod API key
RUNPOD_API_KEY = open(os.path.expanduser('~/.config/runpod/access_key')).read().strip()
GITEA_TOKEN = open(os.path.expanduser('~/.hermes/gitea_token_vps')).read().strip()
GITEA_FORGE = 'https://forge.alexanderwhitestone.com/api/v1/repos/Timmy_Foundation/timmy-home'
def log(msg):
print(f"[{time.strftime('%H:%M:%S')}] {msg}")
def comment_issue(issue_num, body):
"""Add comment to Gitea issue."""
subprocess.run(
['curl', '-s', '-X', 'POST', f'{GITEA_FORGE}/issues/{issue_num}/comments',
'-H', f'Authorization: token {GITEA_TOKEN}',
'-H', 'Content-Type: application/json',
'-d', json.dumps({"body": body})],
capture_output=True, timeout=10
)
def graphql_query(query, variables=None):
"""Run GraphQL query against RunPod API."""
payload = {"query": query}
if variables:
payload["variables"] = variables
r = requests.post(
'https://api.runpod.io/graphql',
headers={
'Authorization': f'Bearer {RUNPOD_API_KEY}',
'Content-Type': 'application/json',
},
json=payload,
timeout=30
)
return r.json()
def deploy_pod(gpu_type, name, cloud_type="COMMUNITY"):
"""Deploy a RunPod pod with Ollama."""
query = """
mutation($input: PodFindAndDeployOnDemandInput!) {
podFindAndDeployOnDemand(input: $input) {
id
desiredStatus
machineId
warning
}
}
"""
variables = {
"input": {
"cloudType": cloud_type,
"gpuCount": 1,
"gpuTypeId": gpu_type,
"name": name,
"containerDiskInGb": 100,
"imageName": "runpod/ollama:latest",
"ports": "11434/http",
"volumeInGb": 50,
"volumeMountPath": "/workspace",
}
}
try:
result = graphql_query(query, variables)
return result
except Exception as e:
return {"error": str(e)}
def check_if_endpoint_exists(name):
"""Check if endpoint already exists."""
query = "{ endpoints { id name } }"
result = graphql_query(query)
endpoints = result.get('data', {}).get('endpoints', [])
matching = [e for e in endpoints if name.lower() in e.get('name', '').lower()]
return matching
# Main deployment logic
log("Starting Big Brain GPU deployment")
log(f"RunPod API key: {RUNPOD_API_KEY[:20]}...{RUNPOD_API_KEY[-10:]}")
# Step 1: Get available GPU types
log("\n=== Step 1: Getting GPU types ===")
gpu_query = "{ gpuTypes { id displayName memoryInGb secureCloud communityCloud } }"
result = graphql_query(gpu_query)
gpus = result.get('data', {}).get('gpuTypes', [])
log(f"Total GPU types: {len(gpus)}")
# Filter GPUs with 24GB+ VRAM for Gemma 3 27B
suitable_gpus = []
for gpu in gpus:
mem = gpu.get('memoryInGb', 0)
if mem >= 24:
suitable_gpus.append(gpu)
log(f"\nGPUs with 24GB+ VRAM:")
for gpu in suitable_gpus[:15]:
log(f" {gpu.get('id')}: {gpu.get('displayName')} - {gpu.get('memoryInGb')}GB, Secure: {gpu.get('secureCloud')}, Community: {gpu.get('communityCloud')}")
# Step 2: Try to find GPU availability
# The error was "no instances available" - we need to find available ones
# The GPU ID format matters - try the ones from the list
pod_name = "big-brain-timmy"
# Try different GPUs in order of preference (cheapest first with enough memory)
gpu_attempts = [
("NVIDIA RTX 4090", "COMMUNITY"), # 24GB, ~$0.44/hr
("NVIDIA A40", "COMMUNITY"), # 48GB
("NVIDIA RTX 3090", "COMMUNITY"), # 24GB
("NVIDIA RTX 3090 Ti", "COMMUNITY"), # 24GB
("NVIDIA L40S", "COMMUNITY"), # 48GB
("NVIDIA A6000", "COMMUNITY"), # 48GB
# Try secure cloud
("NVIDIA RTX 4090", "SECURE"),
("NVIDIA A40", "SECURE"),
("NVIDIA L40S", "SECURE"),
]
log("\n=== Step 2: Attempting deployment ===")
deployed = False
for gpu_type, cloud_type in gpu_attempts:
log(f"Trying {gpu_type} ({cloud_type})...")
result = deploy_pod(gpu_type, pod_name, cloud_type)
errors = result.get('errors', [])
data = result.get('data', {}).get('podFindAndDeployOnDemand', {})
if errors:
for err in errors:
msg = err.get('message', '')
if 'no longer any instances' in msg or 'no instances' in msg:
log(f" No instances available")
elif 'invalid' in msg.lower() or 'not found' in msg.lower():
log(f" GPU type not found: {msg[:100]}")
else:
log(f" Error: {msg[:100]}")
elif data and data.get('id'):
log(f" ✅ SUCCESS! Pod ID: {data.get('id')}")
log(f" Machine ID: {data.get('machineId')}")
log(f" Status: {data.get('desiredStatus')}")
deployed = True
break
else:
log(f" Response: {json.dumps(result)[:200]}")
if deployed:
pod_id = data.get('id')
# Wait for pod to be running
log(f"\n=== Step 3: Waiting for pod {pod_id} to start ===")
pod_status_query = """
query($podId: String!) {
pod(id: $podId) {
id
desiredStatus
runtimeStatus
machineId
ports
}
}
"""
for attempt in range(30): # Wait up to 15 minutes
time.sleep(30)
result = graphql_query(pod_status_query, {"podId": pod_id})
pod = result.get('data', {}).get('pod', {})
runtime = pod.get('runtimeStatus', 'unknown')
desired = pod.get('desiredStatus', 'unknown')
log(f" Attempt {attempt+1}: desired={desired}, runtime={runtime}")
if runtime == 'RUNNING':
log(f" ✅ Pod is RUNNING!")
# Get the IP/port
ip = f"{pod_id}-11434.proxy.runpod.net"
log(f" Ollama endpoint: http://{ip}:11434")
log(f" Ollama endpoint: http://{pod_id}.proxy.runpod.net:11434")
# Comment on Gitea tickets
comment_text = f"""# ✅ SUCCESS: GPU Instance Deployed
## Pod Details
- **Pod ID:** {pod_id}
- **GPU:** {gpu_type} ({cloud_type} cloud)
- **Status:** RUNNING
- **Endpoint:** http://{pod_id}.proxy.runpod.net:11434
## Next Steps
1. **SSH into pod:**
```bash
ssh root@{pod_id}.proxy.runpod.net
```
2. **Pull Gemma 3 27B:**
```bash
ollama pull gemma3:27b-instruct-q4_K_M
```
3. **Verify Ollama is working:**
```bash
curl http://localhost:11434/api/tags
```
4. **Test inference:**
```bash
curl http://localhost:11434/api/chat \\
-H "Content-Type: application/json" \\
-d '{{"model": "gemma3:27b-instruct-q4_K_M", "messages": [{{"role": "user", "content": "Hello from Timmy"}}]}}'
```
5. **Wire to Mac Hermes:**
Add to `~/.hermes/config.yaml`:
```yaml
providers:
big_brain:
base_url: "http://{pod_id}.proxy.runpod.net:11434/v1"
api_key: ""
model: "gemma3:27b-instruct-q4_K_M"
```
6. **Test Hermes:**
```bash
hermes chat --model gemma3:27b-instruct-q4_K_M --provider big_brain
```"""
comment_issue(543, comment_text)
comment_issue(544, comment_text.replace("Timmy", "Bezalel").replace("Mac Hermes", "Bezalel Hermes"))
log("\n🎉 Big Brain GPU deployed successfully!")
log(f"Pod: {pod_id}")
log(f"Endpoint: http://{pod_id}.proxy.runpod.net:11434")
log(f"Gitea tickets updated with deployment details")
break
elif runtime == 'ERROR' or desired == 'TERMINATED' or desired == 'SUSPENDED':
log(f" ❌ Pod failed: runtime={runtime}, desired={desired}")
break
if runtime != 'RUNNING':
log(f"\n⚠️ Pod is not running after waiting. Check RunPod dashboard.")
else:
log("\n❌ No GPU instances available on RunPod")
log("Try Vertex AI or check back later")
# Comment on tickets
comment_text = """# Deployment Status: RunPod Failed
## Issue
No GPU instances available on RunPod. All GPU types returned "no instances available" error.
## Alternatives
1. **Vertex AI** - Google Cloud's managed Gemma endpoints (see ticket for instructions)
2. **Lambda Labs** - Another GPU cloud provider
3. **Vast.ai** - Community GPU marketplace
4. **Wait for RunPod** - Check back in a few hours"""
comment_issue(543, comment_text)
comment_issue(544, comment_text)
"""
Write the deployment script
write_file('~/.timmy/big_brain_deploy.py', script_content)
# Also run it (with timeout)
print("Running deployment script... (will check Gitea tickets for results in parallel)")
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