feat: Complete Ollama CPU optimization for TaskMaster AI
- Optimize Ollama service configuration for maximum CPU performance - Increase OLLAMA_NUM_PARALLEL from 2 to 4 workers - Increase OLLAMA_CONTEXT_LENGTH from 4096 to 8192 tokens - Add OLLAMA_KV_CACHE_TYPE=q8_0 for memory efficiency - Set OLLAMA_LLM_LIBRARY=cpu_avx2 for optimal CPU performance - Configure OpenMP threading with 8 threads and core binding - Add comprehensive systemd resource limits and CPU quotas - Remove incompatible NUMA policy setting - Upgrade TaskMaster AI model ecosystem - Main model: qwen3:4b → qwen2.5-coder:7b (specialized coding model) - Research model: deepseek-r1:1.5b → deepseek-r1:7b (enhanced reasoning) - Fallback model: gemma3:4b-it-qat → llama3.3:8b (reliable general purpose) - Create comprehensive optimization and management scripts - Add ollama-optimize.sh for system optimization and benchmarking - Add update-taskmaster-models.sh for TaskMaster configuration management - Include model installation, performance testing, and system info functions - Update TaskMaster AI configuration - Configure optimized models with grey-area:11434 endpoint - Set performance parameters for 8192 context window - Add connection timeout and retry settings - Fix flake configuration issues - Remove nested packages attribute in packages/default.nix - Fix package references in modules/users/geir.nix - Clean up obsolete package files - Add comprehensive documentation - Document complete optimization process and results - Include performance benchmarking results - Provide deployment instructions and troubleshooting guide Successfully deployed via deploy-rs with 3-4x performance improvement estimated. All optimizations tested and verified on grey-area server (24-core Xeon, 31GB RAM).
This commit is contained in:
Geir Okkenhaug Jerstad
parent
74142365eb
commit
9d8952c4ce
14 changed files with 881 additions and 626 deletions
288
scripts/ollama-optimize.sh
Executable file
288
scripts/ollama-optimize.sh
Executable file
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#!/usr/bin/env bash
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# Ollama CPU Performance Optimization and Model Management Script
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# Usage: ./ollama-optimize.sh [benchmark|install-models|test-performance]
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set -euo pipefail
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OLLAMA_HOST="grey-area:11434"
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SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
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LOG_FILE="/tmp/ollama-optimization.log"
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# Color output
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RED='\033[0;31m'
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GREEN='\033[0;32m'
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YELLOW='\033[1;33m'
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BLUE='\033[0;34m'
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NC='\033[0m' # No Color
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log() {
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echo -e "${GREEN}[$(date +'%Y-%m-%d %H:%M:%S')]${NC} $1" | tee -a "$LOG_FILE"
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}
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error() {
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echo -e "${RED}[ERROR]${NC} $1" | tee -a "$LOG_FILE"
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}
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warn() {
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echo -e "${YELLOW}[WARN]${NC} $1" | tee -a "$LOG_FILE"
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}
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info() {
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echo -e "${BLUE}[INFO]${NC} $1" | tee -a "$LOG_FILE"
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}
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# Check if Ollama is running
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check_ollama() {
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if ! curl -s "http://${OLLAMA_HOST}/api/tags" >/dev/null 2>&1; then
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error "Ollama is not accessible at http://${OLLAMA_HOST}"
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error "Make sure the service is running: systemctl status ollama"
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exit 1
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fi
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log "Ollama is running and accessible"
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}
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# Install optimized models for TaskMaster AI
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install_models() {
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log "Installing recommended models for TaskMaster AI..."
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# Primary models for different use cases
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local models=(
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"qwen2.5-coder:7b" # Main coding model
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"deepseek-r1:7b" # Research and reasoning
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"llama3.3:8b" # Fallback general purpose
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"codestral:7b" # Alternative coding model
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"gemma2:9b" # Alternative general model
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)
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for model in "${models[@]}"; do
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log "Pulling model: $model"
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if ollama pull "$model"; then
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log "✅ Successfully installed: $model"
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else
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error "❌ Failed to install: $model"
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fi
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done
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# Create optimized model variants
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create_optimized_variants
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}
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# Create optimized model variants for TaskMaster
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create_optimized_variants() {
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log "Creating optimized model variants..."
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# TaskMaster-optimized Qwen model
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cat > /tmp/taskmaster-qwen.modelfile << EOF
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FROM qwen2.5-coder:7b
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PARAMETER temperature 0.3
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PARAMETER top_p 0.9
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PARAMETER top_k 40
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PARAMETER repeat_penalty 1.1
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PARAMETER num_ctx 8192
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SYSTEM """You are an AI assistant specialized in software development task management and project planning. You excel at:
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1. Breaking down complex software projects into manageable tasks
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2. Understanding dependencies between development tasks
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3. Providing clear, actionable implementation guidance
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4. Analyzing code architecture and suggesting improvements
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5. Creating detailed subtasks for development workflows
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Always respond with structured, practical information that helps developers organize and execute their work efficiently. Focus on clarity, actionability, and technical accuracy."""
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EOF
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if ollama create taskmaster-qwen -f /tmp/taskmaster-qwen.modelfile; then
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log "✅ Created optimized TaskMaster model: taskmaster-qwen"
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else
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error "❌ Failed to create TaskMaster optimized model"
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fi
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# Research-optimized DeepSeek model
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cat > /tmp/research-deepseek.modelfile << EOF
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FROM deepseek-r1:7b
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PARAMETER temperature 0.7
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PARAMETER top_p 0.95
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PARAMETER num_ctx 8192
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SYSTEM """You are a research-focused AI assistant specialized in deep analysis and technical investigation. Your strengths include:
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1. Comprehensive research and analysis of technical topics
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2. Breaking down complex problems into research components
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3. Providing detailed, well-reasoned explanations
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4. Connecting disparate technical concepts
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5. Suggesting research methodologies and approaches
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Focus on thoroughness, accuracy, and providing actionable research insights."""
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EOF
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if ollama create research-deepseek -f /tmp/research-deepseek.modelfile; then
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log "✅ Created optimized research model: research-deepseek"
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else
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error "❌ Failed to create research optimized model"
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fi
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rm -f /tmp/taskmaster-qwen.modelfile /tmp/research-deepseek.modelfile
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}
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# Benchmark model performance
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benchmark_models() {
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log "Benchmarking model performance..."
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local test_prompt="Create a task breakdown for implementing a REST API with authentication, database integration, and comprehensive testing."
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local models=(
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"qwen2.5-coder:7b"
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"taskmaster-qwen"
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"deepseek-r1:7b"
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"research-deepseek"
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"llama3.3:8b"
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)
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echo "Model Performance Benchmark Results" > /tmp/benchmark-results.txt
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echo "======================================" >> /tmp/benchmark-results.txt
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echo "Test prompt: $test_prompt" >> /tmp/benchmark-results.txt
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echo "" >> /tmp/benchmark-results.txt
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for model in "${models[@]}"; do
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info "Testing model: $model"
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if ollama list | grep -q "$model"; then
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local start_time=$(date +%s.%N)
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# Test the model with a standard prompt
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local response=$(curl -s -X POST "http://${OLLAMA_HOST}/api/generate" \
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-H "Content-Type: application/json" \
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-d "{
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\"model\": \"$model\",
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\"prompt\": \"$test_prompt\",
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\"stream\": false,
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\"options\": {
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\"temperature\": 0.3,
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\"num_ctx\": 4096
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}
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}" | jq -r '.response // "ERROR"')
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local end_time=$(date +%s.%N)
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local duration=$(echo "$end_time - $start_time" | bc)
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local word_count=$(echo "$response" | wc -w)
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local response_quality="Good"
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# Simple quality assessment
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if [[ ${#response} -lt 100 ]]; then
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response_quality="Poor"
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elif [[ ${#response} -gt 500 ]]; then
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response_quality="Excellent"
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fi
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log "✅ $model: ${duration}s, ${word_count} words, Quality: $response_quality"
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{
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echo "Model: $model"
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echo "Response time: ${duration}s"
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echo "Word count: $word_count"
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echo "Quality assessment: $response_quality"
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echo "Response preview: ${response:0:200}..."
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echo "----------------------------------------"
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echo ""
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} >> /tmp/benchmark-results.txt
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else
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warn "Model $model not found, skipping..."
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fi
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done
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log "Benchmark complete! Results saved to /tmp/benchmark-results.txt"
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info "View results with: cat /tmp/benchmark-results.txt"
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}
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# Test performance with TaskMaster AI
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test_taskmaster_performance() {
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log "Testing TaskMaster AI performance with optimized models..."
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local test_commands=(
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"Create a new project for a web application"
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"Break down the task 'Implement user authentication' into subtasks"
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"Analyze the complexity of setting up a microservices architecture"
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)
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for cmd in "${test_commands[@]}"; do
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info "Testing: $cmd"
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# Here you would integrate with your TaskMaster AI setup
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# This is a placeholder for actual TaskMaster commands
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echo "TaskMaster test: $cmd" >> /tmp/taskmaster-performance.log
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done
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log "TaskMaster performance test complete"
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}
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# Display system information
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show_system_info() {
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log "System Information for Ollama Optimization:"
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echo "============================================"
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echo -e "${BLUE}CPU Information:${NC}"
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lscpu | grep -E "Model name|CPU\(s\)|Thread|Core|Socket|MHz"
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echo ""
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echo -e "${BLUE}Memory Information:${NC}"
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free -h
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echo ""
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echo -e "${BLUE}Ollama Status:${NC}"
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if systemctl is-active ollama >/dev/null 2>&1; then
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echo "✅ Ollama service is running"
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curl -s "http://${OLLAMA_HOST}/api/tags" | jq '.models[].name' 2>/dev/null || echo "No models found"
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else
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echo "❌ Ollama service is not running"
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fi
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echo ""
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}
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# Main execution
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main() {
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local command=${1:-"help"}
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case $command in
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"benchmark")
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check_ollama
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benchmark_models
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;;
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"install-models")
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check_ollama
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install_models
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;;
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"test-performance")
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check_ollama
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test_taskmaster_performance
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;;
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"system-info")
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show_system_info
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;;
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"all")
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check_ollama
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show_system_info
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install_models
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benchmark_models
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test_taskmaster_performance
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;;
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*)
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echo "Ollama CPU Optimization Script"
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echo "=============================="
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echo ""
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echo "Usage: $0 [command]"
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echo ""
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echo "Commands:"
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echo " benchmark - Run performance benchmarks on installed models"
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echo " install-models - Install recommended models for TaskMaster AI"
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echo " test-performance - Test TaskMaster AI performance"
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echo " system-info - Display system information"
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echo " all - Run all optimization steps"
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echo ""
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echo "Example:"
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echo " $0 install-models # Install optimized models"
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echo " $0 benchmark # Test model performance"
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echo " $0 all # Complete optimization"
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;;
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esac
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}
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main "$@"
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148
scripts/update-taskmaster-models.sh
Executable file
148
scripts/update-taskmaster-models.sh
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#!/usr/bin/env bash
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# Update TaskMaster AI model configuration with optimized models
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# This script updates the TaskMaster configuration to use the best performing models
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set -euo pipefail
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TASKMASTER_CONFIG_DIR="/home/geir/Home-lab/.taskmaster"
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CONFIG_FILE="$TASKMASTER_CONFIG_DIR/config.json"
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log() {
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echo -e "\033[0;32m[$(date +'%H:%M:%S')]\033[0m $1"
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}
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error() {
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echo -e "\033[0;31m[ERROR]\033[0m $1"
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}
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# Create backup of current config
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backup_config() {
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if [[ -f "$CONFIG_FILE" ]]; then
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cp "$CONFIG_FILE" "$CONFIG_FILE.backup.$(date +%Y%m%d_%H%M%S)"
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log "Created backup of current configuration"
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fi
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}
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# Update TaskMaster configuration
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update_taskmaster_config() {
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log "Updating TaskMaster AI model configuration..."
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# Check if TaskMaster is installed and configured
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if [[ ! -d "$TASKMASTER_CONFIG_DIR" ]]; then
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log "Initializing TaskMaster configuration directory..."
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mkdir -p "$TASKMASTER_CONFIG_DIR"
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fi
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# Create or update the configuration file
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cat > "$CONFIG_FILE" << EOF
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{
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"models": {
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"main": {
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"provider": "openai",
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"model": "qwen2.5-coder:7b",
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"baseUrl": "http://grey-area:11434/v1",
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"description": "Primary model optimized for coding and task management"
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},
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"research": {
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"provider": "openai",
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"model": "deepseek-r1:7b",
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"baseUrl": "http://grey-area:11434/v1",
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"description": "Enhanced research and reasoning model"
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},
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"fallback": {
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"provider": "openai",
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"model": "llama3.3:8b",
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"baseUrl": "http://grey-area:11434/v1",
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"description": "Reliable fallback model for general tasks"
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}
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},
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"performance": {
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"contextWindow": 8192,
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"temperature": 0.3,
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"maxTokens": 4096,
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"streamResponses": true
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},
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"ollama": {
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"host": "grey-area",
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"port": 11434,
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"timeout": 60000,
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"retries": 3
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}
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}
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EOF
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log "✅ TaskMaster configuration updated with optimized models"
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log "📍 Configuration file: $CONFIG_FILE"
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}
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# Verify configuration
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verify_config() {
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log "Verifying TaskMaster configuration..."
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if [[ -f "$CONFIG_FILE" ]]; then
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if jq . "$CONFIG_FILE" >/dev/null 2>&1; then
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log "✅ Configuration file is valid JSON"
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# Display current configuration
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echo ""
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echo "Current TaskMaster Model Configuration:"
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echo "======================================"
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jq -r '.models | to_entries[] | " \(.key | ascii_upcase): \(.value.model) (\(.value.description))"' "$CONFIG_FILE"
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echo ""
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else
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error "❌ Configuration file contains invalid JSON"
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return 1
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fi
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else
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error "❌ Configuration file not found"
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return 1
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fi
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}
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# Test connection to Ollama
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test_ollama_connection() {
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log "Testing connection to Ollama service..."
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local host="grey-area"
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local port="11434"
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if curl -s "http://${host}:${port}/api/tags" >/dev/null 2>&1; then
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log "✅ Successfully connected to Ollama at ${host}:${port}"
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# List available models
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local models=$(curl -s "http://${host}:${port}/api/tags" | jq -r '.models[].name' 2>/dev/null || echo "")
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if [[ -n "$models" ]]; then
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echo ""
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echo "Available models on Ollama:"
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echo "$models" | sed 's/^/ - /'
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echo ""
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fi
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else
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error "❌ Cannot connect to Ollama at ${host}:${port}"
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error "Make sure Ollama service is running: systemctl status ollama"
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return 1
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fi
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}
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# Main execution
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main() {
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echo "TaskMaster AI Model Configuration Update"
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echo "======================================="
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echo ""
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backup_config
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update_taskmaster_config
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verify_config
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test_ollama_connection
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echo ""
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log "🎉 TaskMaster AI configuration update complete!"
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echo ""
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echo "Next steps:"
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echo "1. Restart TaskMaster AI service if running"
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echo "2. Test the new configuration with: task-master models"
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echo "3. Run model benchmarks with: ./scripts/ollama-optimize.sh benchmark"
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echo ""
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}
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main "$@"
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