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feat: convert ai-optimizer from cron job to manual skill

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- Update README.md for manual execution workflow
- Change model queue to deepseek-coder-v2:16b (better coding model)
- Remove automated scheduling references
- Add skill usage instructions for post-PR#1 merge
This commit is contained in:
Hermes Agent
2026-04-30 16:07:05 +00:00
parent 7ce0e46670
commit 0ec198dec2
2 changed files with 68 additions and 73 deletions
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135
assets/ai-optimizer/README.md
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@@ -1,13 +1,13 @@
# AI Model Optimization Cron Job
# AI Model Optimizer - Manual Skill
**Purpose:** Automatically find optimal ollama/llama.cpp configurations for maximum context size and hardware utilization.
**Purpose:** Find optimal ollama configurations for maximum context size and GPU utilization on AMD MI50 GPUs.
**Schedule:** Every hour
**Usage:** Run manually via Hermes skill when needed (not automated).
**Hardware:**
- 2× AMD MI50 GPUs (32GB VRAM each, 64GB total)
- 128GB system RAM
- ROCm: HSA_OVERRIDE_GFX_VERSION=9.0.6, HIP_VISIBLE_DEVICES=0,1
- ROCm: `HSA_OVERRIDE_GFX_VERSION=9.0.6`, `HIP_VISIBLE_DEVICES=0,1`
---
@@ -21,21 +21,33 @@ REPO: /opt/data/infra (persistent - do not reclone)
---
## Quick Start
```bash
# From Hermes container or any machine with ollama access
ollama-test-model --model devstral-small-2:24b --ctx 65536
```
Or use the full workflow skill for systematic testing.
---
## Model Queues
### GPU Track (Coding - prioritize speed + context on GPU)
1. `devstral-small-2:24b`
2. `qwen2.5-coder:32b`
3. `codellama:34b-instruct`
1. `deepseek-coder-v2:16b` - Best coding model, fits on GPU
2. `qwen2.5-coder:32b` - Alternative coding model
3. `codellama:34b-instruct` - Legacy option
### RAM Track (Knowledge - prioritize max context)
1. `qwen2.5:72b`
2. `nemotron-3-nano:30b`
3. `mixtral:8x7b-instruct`
1. `qwen2.5:72b` - Large knowledge model
2. `nemotron-3-nano:30b` - Efficient large model
3. `mixtral:8x7b-instruct` - MoE architecture
---
## Context Steps (in order)
```
[32768, 65536, 98304, 131072, 163840, 200704, 262144, 327680]
```
@@ -45,45 +57,49 @@ REPO: /opt/data/infra (persistent - do not reclone)
## Optimization Strategy
### GPU Track (Coding)
- Start: num_ctx=32768, num_gpu=99, flash_attn=true
- Start: `num_ctx=32768`, `num_gpu=99`, `flash_attn=true`
- Increase context until OOM or tokens/sec < 5
- Record best config before hitting wall
- Target: >10 tokens/sec with max context
### RAM Track (Knowledge)
- Start: num_ctx=65536, num_gpu=50, flash_attn=true
- Start: `num_ctx=65536`, `num_gpu=50`, `flash_attn=true`
- Allow heavy RAM offload (up to 100GB system RAM)
- Increase context until OOM
- Speed secondary to context size
---
## Each Run - Step by Step
## Manual Testing Workflow
### 1. Quick Model Test
```bash
# Test a model at specific context size
docker exec ollama ollama run <model>:<tag> "Your prompt here"
```
### 2. Check Current State
### 1. Read State
```bash
cd /opt/data/infra
cat assets/ai-optimizer/state.json
```
### 2. Determine Next Test
- Read `track` (gpu or ram)
- Get `current_model` from queue at `model_index`
- Get `current_config` for parameters to test
- Select next context step from `context_steps`
### 3. Pull Model (if needed)
```bash
docker exec ollama ollama list | grep -q "<model>" || docker exec ollama ollama pull <model>
docker exec ollama ollama pull <model>:<tag>
```
### 4. Create Test Modelfile
```bash
docker exec ollama bash -c "cat <<EOF > /root/.ollama/test_${model}.modelfile
FROM ${model}
PARAMETER num_ctx ${current_config.num_ctx}
PARAMETER num_gpu ${current_config.num_gpu}
PARAMETER flash_attn ${current_config.flash_attn}
PARAMETER num_ctx ${num_ctx}
PARAMETER num_gpu ${num_gpu}
PARAMETER flash_attn true
PARAMETER num_predict 4096
PARAMETER num_keep 1024
PARAMETER repeat_penalty 1.1
@@ -93,6 +109,7 @@ docker exec ollama ollama create test-model -f /root/.ollama/test_${model}.model
```
### 5. Run Benchmark
```bash
# Warm up
docker exec ollama ollama run test-model "Hello" > /dev/null
@@ -104,7 +121,8 @@ docker exec ollama ollama run test-model "Write a Python async context manager t
docker exec ollama ollama run test-model "Explain the complete memory hierarchy in modern GPUs, from registers through L1/L2 caches to VRAM, and how data moves between them during matrix multiplication."
```
### 6. Measure VRAM (if possible)
### 6. Measure VRAM
```bash
# Try host first
rocm-smi --showmeminfo vram 2>/dev/null || \
@@ -114,24 +132,14 @@ echo "VRAM unavailable"
```
### 7. Record Results
- Parse tokens/sec from ollama output
- Record VRAM/RAM usage
- Update `best_configs` if improved
### 8. Update State
```python
if test_successful:
if context_step < max_reached:
current_config.num_ctx = next_context_step
else:
model_index += 1
current_config.num_ctx = context_steps[0]
else:
best_configs[track][current_model] = last_good_config
model_index += 1
```
Update `state.json` and append to `results.csv`:
- tokens/sec from ollama output
- VRAM/RAM usage
- Whether this config is the new best
### 8. Commit Changes
### 9. Commit to Repo
```bash
cd /opt/data/infra
git add assets/ai-optimizer/
@@ -139,15 +147,6 @@ git commit -m "ai-optimizer: tested ${model} at ${num_ctx} ctx - ${status}"
git push
```
### 10. Matrix Notification (if available)
```python
import os
if os.getenv("MATRIX_HOME_SERVER") and os.getenv("MATRIX_ACCESS_TOKEN"):
# Send notification
pass
# Else: silent
```
---
## State File Structure
@@ -155,7 +154,7 @@ if os.getenv("MATRIX_HOME_SERVER") and os.getenv("MATRIX_ACCESS_TOKEN"):
```json
{
"track": "gpu",
"current_model": "devstral-small-2:24b",
"current_model": "deepseek-coder-v2:16b",
"model_index": 0,
"phase": "context_scaling",
"backend": "ollama",
@@ -169,10 +168,10 @@ if os.getenv("MATRIX_HOME_SERVER") and os.getenv("MATRIX_ACCESS_TOKEN"):
"ram": {}
},
"completed_models": [],
"gpu_queue": ["devstral-small-2:24b", "qwen2.5-coder:32b", "codellama:34b-instruct"],
"gpu_queue": ["deepseek-coder-v2:16b", "qwen2.5-coder:32b", "codellama:34b-instruct"],
"ram_queue": ["qwen2.5:72b", "nemotron-3-nano:30b", "mixtral:8x7b-instruct"],
"context_steps": [32768, 65536, 98304, 131072, 163840, 200704, 262144, 327680],
"last_updated": "2026-04-28T17:00:00Z"
"last_updated": "2026-04-30T00:00:00Z"
}
```
@@ -182,33 +181,29 @@ if os.getenv("MATRIX_HOME_SERVER") and os.getenv("MATRIX_ACCESS_TOKEN"):
```csv
timestamp,track,model,backend,phase,num_ctx,num_gpu,flash_attn,tokens_per_sec,vram_gb,ram_gb,status,is_best
2026-04-28T17:00:00Z,gpu,devstral-small-2:24b,ollama,context_scaling,65536,99,true,15.2,52.1,18.4,success,false
2026-04-30T00:00:00Z,gpu,deepseek-coder-v2:16b,ollama,context_scaling,65536,99,true,15.2,52.1,18.4,success,false
```
---
## Stop Conditions
## Skill Usage
1. All models in both queues have `best_configs` recorded
2. Manual intervention needed (error in state.json `error` field)
3. No progress for 3 consecutive runs
Once PR #1 (ai-worker-restricted-access) is merged:
---
```bash
# From Hermes container, SSH to host for direct ollama access
ssh -i /path/to/key ai-worker@host docker exec ollama ollama run <model>
## Error Handling
If any step fails:
1. Log error: `"error": {"message": "...", "timestamp": "..."}`
2. Do NOT increment model_index (retry next run)
3. Commit state with error field
4. Exit gracefully
# Or run the skill directly
ollama-benchmark --model deepseek-coder-v2:16b --track gpu
```
---
## Notes
- **No num_parallel**: Removed to avoid limiting other settings
- **Two tracks**: Complete GPU track first, then RAM track
- **Backend**: Start with ollama, llama.cpp optional
- **Manual execution only** - No cron job, run when needed
- **Two tracks**: Complete GPU track first (coding models), then RAM track
- **Backend**: ollama (llama.cpp optional for advanced users)
- **Host access**: Use docker exec or SSH for rocm-smi
- **Ask before deploy**: Show diff before `nh os switch`
- **Commit results**: Push best configs to repo for reference