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fix/687-tr
...
feat/622-t
| Author | SHA1 | Date | |
|---|---|---|---|
| 89e46680df |
@@ -1,4 +1,3 @@
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#!/usr/bin/env python3
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"""
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Full Nostr agent-to-agent communication demo - FINAL WORKING
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"""
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@@ -1,4 +1,3 @@
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#!/usr/bin/env python3
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"""
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Soul Eval Gate — The Conscience of the Training Pipeline
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@@ -1,9 +0,0 @@
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- name: Nightly Pipeline Scheduler
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schedule: '*/30 18-23,0-8 * * *' # Every 30 min, off-peak hours only
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tasks:
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- name: Check and start pipelines
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shell: "bash scripts/nightly-pipeline-scheduler.sh"
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env:
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PIPELINE_TOKEN_LIMIT: "500000"
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PIPELINE_PEAK_START: "9"
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PIPELINE_PEAK_END: "18"
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@@ -1,4 +1,3 @@
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#!/usr/bin/env python3
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import json
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from hermes_tools import browser_navigate, browser_vision
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@@ -1,4 +1,3 @@
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#!/usr/bin/env python3
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import json
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from hermes_tools import browser_navigate, browser_vision
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@@ -1,50 +0,0 @@
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# Nightly Pipeline Scheduler
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Auto-starts batch pipelines when inference is available.
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## What It Does
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1. Checks inference provider health (OpenRouter, Ollama, RunPod)
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2. Checks if it's off-peak hours (configurable, default: after 6PM)
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3. Checks interactive session load (don't fight with live users)
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4. Checks daily token budget (configurable limit)
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5. Starts the highest-priority incomplete pipeline
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## Pipeline Priority Order
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| Priority | Pipeline | Deps | Max Tokens |
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|----------|----------|------|------------|
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| 1 | playground-factory | none | 100,000 |
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| 2 | training-factory | none | 150,000 |
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| 3 | knowledge-mine | training-factory running | 80,000 |
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| 4 | adversary | knowledge-mine running | 50,000 |
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| 5 | codebase-genome | none | 120,000 |
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## Usage
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```bash
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# Normal run (used by cron)
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./scripts/nightly-pipeline-scheduler.sh
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# Dry run (show what would start)
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./scripts/nightly-pipeline-scheduler.sh --dry-run
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# Status report
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./scripts/nightly-pipeline-scheduler.sh --status
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# Force start during peak hours
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./scripts/nightly-pipeline-scheduler.sh --force
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```
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## Configuration
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Set via environment variables:
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- `PIPELINE_TOKEN_LIMIT`: Daily token budget (default: 500,000)
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- `PIPELINE_PEAK_START`: Peak hours start (default: 9)
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- `PIPELINE_PEAK_END`: Peak hours end (default: 18)
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- `HERMES_HOME`: Hermes home directory (default: ~/.hermes)
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## Cron
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Runs every 30 minutes. Off-peak only (unless --force).
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See `cron/pipeline-scheduler.yml`.
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@@ -1,383 +0,0 @@
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#!/usr/bin/env bash
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# nightly-pipeline-scheduler.sh — Auto-start batch pipelines when inference is available.
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#
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# Checks provider health, pipeline progress, token budget, and interactive load.
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# Starts the highest-priority incomplete pipeline that can run.
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#
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# Usage:
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# ./scripts/nightly-pipeline-scheduler.sh # Normal run
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# ./scripts/nightly-pipeline-scheduler.sh --dry-run # Show what would start
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# ./scripts/nightly-pipeline-scheduler.sh --status # Pipeline status report
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set -euo pipefail
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# --- Configuration ---
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HERMES_HOME="${HERMES_HOME:-$HOME/.hermes}"
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BUDGET_FILE="${HERMES_HOME}/pipeline_budget.json"
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STATE_FILE="${HERMES_HOME}/pipeline_state.json"
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LOG_FILE="${HERMES_HOME}/logs/pipeline-scheduler.log"
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TOKEN_DAILY_LIMIT="${PIPELINE_TOKEN_LIMIT:-500000}"
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PEAK_HOURS_START="${PIPELINE_PEAK_START:-9}"
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PEAK_HOURS_END="${PIPELINE_PEAK_END:-18}"
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# Pipeline definitions (priority order)
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# Each pipeline: name, script, max_tokens, dependencies
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PIPELINES=(
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"playground-factory|scripts/pipeline_playground_factory.sh|100000|none"
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"training-factory|scripts/pipeline_training_factory.sh|150000|none"
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"knowledge-mine|scripts/pipeline_knowledge_mine.sh|80000|training-factory"
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"adversary|scripts/pipeline_adversary.sh|50000|knowledge-mine"
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"codebase-genome|scripts/pipeline_codebase_genome.sh|120000|none"
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)
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# --- Colors ---
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RED='\033[0;31m'
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GREEN='\033[0;32m'
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YELLOW='\033[0;33m'
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CYAN='\033[0;36m'
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NC='\033[0m'
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# --- Helpers ---
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now_hour() { date +%-H; }
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is_peak_hours() {
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local h=$(now_hour)
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[[ $h -ge $PEAK_HOURS_START && $h -lt $PEAK_HOURS_END ]]
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}
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ensure_dirs() {
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mkdir -p "$(dirname "$LOG_FILE")" "$(dirname "$BUDGET_FILE")" "$(dirname "$STATE_FILE")"
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}
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log() { echo "[$(date '+%Y-%m-%d %H:%M:%S')] $*" | tee -a "$LOG_FILE"; }
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get_budget_used_today() {
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if [[ -f "$BUDGET_FILE" ]]; then
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local today=$(date +%Y-%m-%d)
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python3 -c "
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import json, sys
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with open('$BUDGET_FILE') as f:
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d = json.load(f)
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print(d.get('daily', {}).get('$today', {}).get('tokens_used', 0))
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" 2>/dev/null || echo 0
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else
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echo 0
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fi
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}
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get_budget_remaining() {
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local used=$(get_budget_used_today)
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echo $((TOKEN_DAILY_LIMIT - used))
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}
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update_budget() {
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local pipeline="$1"
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local tokens="$2"
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local today=$(date +%Y-%m-%d)
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python3 -c "
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import json, os
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path = '$BUDGET_FILE'
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d = {}
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if os.path.exists(path):
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with open(path) as f:
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d = json.load(f)
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daily = d.setdefault('daily', {})
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day = daily.setdefault('$today', {'tokens_used': 0, 'pipelines': {}})
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day['tokens_used'] = day.get('tokens_used', 0) + $tokens
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day['pipelines']['$pipeline'] = day['pipelines'].get('$pipeline', 0) + $tokens
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with open(path, 'w') as f:
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json.dump(d, f, indent=2)
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"
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}
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get_pipeline_state() {
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if [[ -f "$STATE_FILE" ]]; then
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cat "$STATE_FILE"
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else
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echo "{}"
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fi
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}
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set_pipeline_state() {
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local pipeline="$1"
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local state="$2" # running, complete, failed, skipped
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python3 -c "
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import json, os
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path = '$STATE_FILE'
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d = {}
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if os.path.exists(path):
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with open(path) as f:
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d = json.load(f)
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d['$pipeline'] = {'state': '$state', 'updated': '$(date -Iseconds)'}
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with open(path, 'w') as f:
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json.dump(d, f, indent=2)
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"
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}
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is_pipeline_complete() {
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local pipeline="$1"
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python3 -c "
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import json, os
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path = '$STATE_FILE'
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if not os.path.exists(path):
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print('false')
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else:
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with open(path) as f:
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d = json.load(f)
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state = d.get('$pipeline', {}).get('state', 'not_started')
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print('true' if state == 'complete' else 'false')
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" 2>/dev/null || echo false
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}
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is_pipeline_running() {
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local pipeline="$1"
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python3 -c "
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import json, os
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path = '$STATE_FILE'
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if not os.path.exists(path):
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print('false')
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else:
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with open(path) as f:
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d = json.load(f)
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state = d.get('$pipeline', {}).get('state', 'not_started')
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print('true' if state == 'running' else 'false')
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" 2>/dev/null || echo false
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}
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check_dependency() {
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local dep="$1"
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if [[ "$dep" == "none" ]]; then
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return 0
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fi
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# For knowledge-mine: training-factory must be running or complete
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if [[ "$dep" == "training-factory" ]]; then
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local state=$(python3 -c "
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import json, os
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path = '$STATE_FILE'
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if not os.path.exists(path):
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print('not_started')
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else:
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with open(path) as f:
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d = json.load(f)
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print(d.get('training-factory', {}).get('state', 'not_started'))
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" 2>/dev/null || echo "not_started")
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[[ "$state" == "running" || "$state" == "complete" ]]
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return $?
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fi
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# For adversary: knowledge-mine must be at least 50% done
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# Simplified: check if it's running (we'd need progress tracking for 50%)
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if [[ "$dep" == "knowledge-mine" ]]; then
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local state=$(python3 -c "
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import json, os
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path = '$STATE_FILE'
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if not os.path.exists(path):
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print('not_started')
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else:
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with open(path) as f:
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d = json.load(f)
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print(d.get('knowledge-mine', {}).get('state', 'not_started'))
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" 2>/dev/null || echo "not_started")
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[[ "$state" == "running" || "$state" == "complete" ]]
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return $?
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fi
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return 0
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}
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check_inference_available() {
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# Check if any inference provider is responding
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# 1. Check OpenRouter
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local or_ok=$(curl -s -o /dev/null -w "%{http_code}" \
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--connect-timeout 5 "https://openrouter.ai/api/v1/models" 2>/dev/null || echo "000")
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# 2. Check local Ollama
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local ollama_ok=$(curl -s -o /dev/null -w "%{http_code}" \
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--connect-timeout 5 "http://localhost:11434/api/tags" 2>/dev/null || echo "000")
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# 3. Check RunPod (if configured)
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local runpod_ok="000"
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if [[ -n "${RUNPOD_ENDPOINT:-}" ]]; then
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runpod_ok=$(curl -s -o /dev/null -w "%{http_code}" \
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--connect-timeout 5 "$RUNPOD_ENDPOINT/health" 2>/dev/null || echo "000")
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fi
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if [[ "$or_ok" == "200" || "$ollama_ok" == "200" || "$runpod_ok" == "200" ]]; then
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return 0
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fi
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return 1
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}
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check_interactive_load() {
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# Check if there are active interactive sessions (don't fight with live users)
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# Look for tmux panes with active hermes sessions
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local active=$(tmux list-panes -a -F '#{pane_pid} #{pane_current_command}' 2>/dev/null \
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| grep -c "hermes\|python3" || echo 0)
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|
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# If more than 3 interactive sessions, skip pipeline start
|
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if [[ $active -gt 3 ]]; then
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return 1
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fi
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return 0
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}
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start_pipeline() {
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local name="$1"
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local script="$2"
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local max_tokens="$3"
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local budget_remaining="$4"
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local mode="${5:-run}"
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if [[ "$budget_remaining" -lt "$max_tokens" ]]; then
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log "SKIP $name: insufficient budget ($budget_remaining < $max_tokens tokens)"
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return 1
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fi
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if [[ ! -f "$script" ]]; then
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log "SKIP $name: script not found ($script)"
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return 1
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fi
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if [[ "$mode" == "dry-run" ]]; then
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log "DRY-RUN: Would start $name (budget: $budget_remaining, needs: $max_tokens)"
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return 0
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fi
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log "START $name (budget: $budget_remaining, max_tokens: $max_tokens)"
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set_pipeline_state "$name" "running"
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# Run in background, capture output
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local log_path="${HERMES_HOME}/logs/pipeline-${name}.log"
|
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bash "$script" --max-tokens "$max_tokens" >> "$log_path" 2>&1 &
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local pid=$!
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# Wait a moment to check if it started OK
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sleep 2
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if kill -0 $pid 2>/dev/null; then
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log "RUNNING $name (PID: $pid, log: $log_path)"
|
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# Record the PID
|
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python3 -c "
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import json, os
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path = '$STATE_FILE'
|
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d = {}
|
||||
if os.path.exists(path):
|
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with open(path) as f:
|
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d = json.load(f)
|
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d['$name']['pid'] = $pid
|
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with open(path, 'w') as f:
|
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json.dump(d, f, indent=2)
|
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"
|
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return 0
|
||||
else
|
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log "FAIL $name: script exited immediately"
|
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set_pipeline_state "$name" "failed"
|
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return 1
|
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fi
|
||||
}
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|
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# --- Main ---
|
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main() {
|
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local mode="${1:-run}"
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ensure_dirs
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|
||||
log "=== Pipeline Scheduler ($mode) ==="
|
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|
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# Check 1: Is inference available?
|
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if ! check_inference_available; then
|
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log "No inference provider available. Skipping all pipelines."
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exit 0
|
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fi
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log "Inference: AVAILABLE"
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|
||||
# Check 2: Is it peak hours?
|
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if is_peak_hours && [[ "$mode" != "--force" ]]; then
|
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local h=$(now_hour)
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log "Peak hours ($h:00). Skipping pipeline start. Use --force to override."
|
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exit 0
|
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fi
|
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log "Off-peak: OK"
|
||||
|
||||
# Check 3: Interactive load
|
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if ! check_interactive_load && [[ "$mode" != "--force" ]]; then
|
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log "High interactive load. Skipping pipeline start."
|
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exit 0
|
||||
fi
|
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log "Interactive load: OK"
|
||||
|
||||
# Check 4: Token budget
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local budget=$(get_budget_remaining)
|
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log "Token budget remaining: $budget / $TOKEN_DAILY_LIMIT"
|
||||
|
||||
if [[ $budget -le 0 ]]; then
|
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log "Daily token budget exhausted. Stopping."
|
||||
exit 0
|
||||
fi
|
||||
|
||||
# Check 5: Pipeline status
|
||||
if [[ "$mode" == "--status" ]]; then
|
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echo -e "${CYAN}Pipeline Status:${NC}"
|
||||
echo "────────────────────────────────────────────────────"
|
||||
for entry in "${PIPELINES[@]}"; do
|
||||
IFS='|' read -r name script max_tokens dep <<< "$entry"
|
||||
local state=$(python3 -c "
|
||||
import json, os
|
||||
path = '$STATE_FILE'
|
||||
if not os.path.exists(path):
|
||||
print('not_started')
|
||||
else:
|
||||
with open(path) as f:
|
||||
d = json.load(f)
|
||||
print(d.get('$name', {}).get('state', 'not_started'))
|
||||
" 2>/dev/null || echo "not_started")
|
||||
|
||||
local color=$NC
|
||||
case "$state" in
|
||||
running) color=$YELLOW ;;
|
||||
complete) color=$GREEN ;;
|
||||
failed) color=$RED ;;
|
||||
esac
|
||||
printf " %-25s %b%s%b (max: %s tokens, dep: %s)\n" "$name" "$color" "$state" "$NC" "$max_tokens" "$dep"
|
||||
done
|
||||
echo "────────────────────────────────────────────────────"
|
||||
echo " Budget: $budget / $TOKEN_DAILY_LIMIT tokens remaining"
|
||||
echo " Peak hours: $PEAK_HOURS_START:00 - $PEAK_HOURS_END:00"
|
||||
exit 0
|
||||
fi
|
||||
|
||||
# Find and start the highest-priority incomplete pipeline
|
||||
local started=0
|
||||
for entry in "${PIPELINES[@]}"; do
|
||||
IFS='|' read -r name script max_tokens dep <<< "$entry"
|
||||
|
||||
# Skip if already running or complete
|
||||
if [[ "$(is_pipeline_running $name)" == "true" ]]; then
|
||||
log "SKIP $name: already running"
|
||||
continue
|
||||
fi
|
||||
if [[ "$(is_pipeline_complete $name)" == "true" ]]; then
|
||||
log "SKIP $name: already complete"
|
||||
continue
|
||||
fi
|
||||
|
||||
# Check dependency
|
||||
if ! check_dependency "$dep"; then
|
||||
log "SKIP $name: dependency $dep not met"
|
||||
continue
|
||||
fi
|
||||
|
||||
# Try to start
|
||||
if start_pipeline "$name" "$script" "$max_tokens" "$budget" "$mode"; then
|
||||
started=1
|
||||
# Only start one pipeline per run (let it claim tokens before next check)
|
||||
# Exception: playground-factory and training-factory can run in parallel
|
||||
if [[ "$name" != "playground-factory" && "$name" != "training-factory" ]]; then
|
||||
break
|
||||
fi
|
||||
fi
|
||||
done
|
||||
|
||||
if [[ $started -eq 0 ]]; then
|
||||
log "No pipelines to start (all complete, running, or blocked)."
|
||||
fi
|
||||
|
||||
log "=== Pipeline Scheduler done ==="
|
||||
}
|
||||
|
||||
main "$@"
|
||||
194
scripts/token-tracker.py
Normal file
194
scripts/token-tracker.py
Normal file
@@ -0,0 +1,194 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Token Budget Tracker -- real-time spend dashboard for pipelines."""
|
||||
|
||||
import argparse, json, os, sqlite3, sys, time
|
||||
from datetime import datetime
|
||||
from pathlib import Path
|
||||
|
||||
DB_PATH = Path.home() / ".hermes" / "pipelines" / "token_usage.db"
|
||||
ALERT_THRESHOLDS = [0.5, 0.8, 1.0]
|
||||
DEFAULT_BUDGETS = {
|
||||
"knowledge-mine": 200_000_000,
|
||||
"training-factory": 215_000_000,
|
||||
"playground": 16_000_000,
|
||||
"adversary": 17_000_000,
|
||||
}
|
||||
|
||||
def get_db():
|
||||
DB_PATH.parent.mkdir(parents=True, exist_ok=True)
|
||||
conn = sqlite3.connect(str(DB_PATH))
|
||||
conn.row_factory = sqlite3.Row
|
||||
conn.execute("PRAGMA journal_mode=WAL")
|
||||
conn.executescript("""
|
||||
CREATE TABLE IF NOT EXISTS token_usage (
|
||||
id INTEGER PRIMARY KEY AUTOINCREMENT,
|
||||
pipeline TEXT NOT NULL,
|
||||
worker TEXT NOT NULL,
|
||||
tokens INTEGER NOT NULL,
|
||||
recorded_at REAL NOT NULL,
|
||||
hour_bucket TEXT NOT NULL
|
||||
);
|
||||
CREATE TABLE IF NOT EXISTS pipeline_budgets (
|
||||
pipeline TEXT PRIMARY KEY,
|
||||
target_tokens INTEGER NOT NULL,
|
||||
updated_at REAL NOT NULL
|
||||
);
|
||||
CREATE TABLE IF NOT EXISTS alerts_sent (
|
||||
pipeline TEXT NOT NULL,
|
||||
threshold REAL NOT NULL,
|
||||
sent_at REAL NOT NULL,
|
||||
PRIMARY KEY (pipeline, threshold)
|
||||
);
|
||||
CREATE INDEX IF NOT EXISTS idx_usage_pipeline_hour
|
||||
ON token_usage(pipeline, hour_bucket);
|
||||
""")
|
||||
for name, target in DEFAULT_BUDGETS.items():
|
||||
conn.execute(
|
||||
"INSERT OR IGNORE INTO pipeline_budgets (pipeline, target_tokens, updated_at) VALUES (?, ?, ?)",
|
||||
(name, target, time.time())
|
||||
)
|
||||
conn.commit()
|
||||
return conn
|
||||
|
||||
def log_usage(conn, pipeline, worker, tokens):
|
||||
now = time.time()
|
||||
hour = datetime.now().strftime("%Y-%m-%d %H:00")
|
||||
conn.execute(
|
||||
"INSERT INTO token_usage (pipeline, worker, tokens, recorded_at, hour_bucket) VALUES (?, ?, ?, ?, ?)",
|
||||
(pipeline, worker, tokens, now, hour)
|
||||
)
|
||||
conn.commit()
|
||||
check_alerts(conn, pipeline)
|
||||
|
||||
def get_pipeline_stats(conn):
|
||||
rows = conn.execute("""
|
||||
SELECT u.pipeline, COALESCE(b.target_tokens, 0) as target,
|
||||
SUM(u.tokens) as used, MIN(u.recorded_at) as started_at,
|
||||
COUNT(DISTINCT u.worker) as workers
|
||||
FROM token_usage u
|
||||
LEFT JOIN pipeline_budgets b ON u.pipeline = b.pipeline
|
||||
GROUP BY u.pipeline ORDER BY used DESC
|
||||
""").fetchall()
|
||||
return [dict(r) for r in rows]
|
||||
|
||||
def fmt(n):
|
||||
if n >= 1_000_000_000: return f"{n/1_000_000_000:.1f}B"
|
||||
if n >= 1_000_000: return f"{n/1_000_000:.1f}M"
|
||||
if n >= 1_000: return f"{n/1_000:.1f}K"
|
||||
return str(n)
|
||||
|
||||
def bar(ratio, w=8):
|
||||
filled = int(ratio * w)
|
||||
return "█" * filled + "░" * (w - filled)
|
||||
|
||||
def eta(used, target, started):
|
||||
if used <= 0 or started <= 0: return "--"
|
||||
elapsed = (time.time() - started) / 3600
|
||||
if elapsed <= 0: return "--"
|
||||
rate = used / elapsed
|
||||
remaining = target - used
|
||||
if remaining <= 0: return "DONE"
|
||||
h = remaining / rate
|
||||
return f"{h/24:.1f}d" if h >= 24 else f"{h:.1f}h"
|
||||
|
||||
def render_dashboard(conn):
|
||||
stats = get_pipeline_stats(conn)
|
||||
if not stats:
|
||||
print("No pipeline data recorded yet.")
|
||||
return
|
||||
print()
|
||||
print(f"{'Pipeline':<20} {'Tokens Used':>12} {'Target':>10} {'Progress':>10} {'ETA':>8} {'Workers':>8}")
|
||||
print("-" * 72)
|
||||
total_used = total_target = 0
|
||||
for s in stats:
|
||||
used = s["used"] or 0
|
||||
target = s["target"] or 1
|
||||
ratio = min(used / target, 1.0) if target > 0 else 0
|
||||
print(f"{s['pipeline']:<20} {fmt(used):>12} {fmt(target):>10} {bar(ratio):>10} {eta(used, target, s['started_at'] or 0):>8} {s['workers'] or 0:>8}")
|
||||
total_used += used
|
||||
total_target += target
|
||||
print("-" * 72)
|
||||
r = min(total_used / total_target, 1.0) if total_target > 0 else 0
|
||||
print(f"{'TOTAL':<20} {fmt(total_used):>12} {fmt(total_target):>10} {bar(r):>10}")
|
||||
print()
|
||||
|
||||
def render_watch(conn, interval=5):
|
||||
try:
|
||||
while True:
|
||||
os.system("clear" if os.name != "nt" else "cls")
|
||||
print(f"Token Budget Tracker -- {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
|
||||
print("Press Ctrl+C to exit")
|
||||
render_dashboard(conn)
|
||||
time.sleep(interval)
|
||||
except KeyboardInterrupt:
|
||||
print("\nExiting.")
|
||||
|
||||
def render_daily_summary(conn):
|
||||
since = time.time() - 86400
|
||||
rows = conn.execute("""
|
||||
SELECT pipeline, SUM(tokens) as total, COUNT(DISTINCT worker) as workers, COUNT(*) as entries
|
||||
FROM token_usage WHERE recorded_at >= ? GROUP BY pipeline ORDER BY total DESC
|
||||
""", (since,)).fetchall()
|
||||
if not rows:
|
||||
print("No usage in last 24 hours.")
|
||||
return
|
||||
print(f"\nDaily Summary -- last 24 hours")
|
||||
print(f"{'Pipeline':<20} {'Total Tokens':>14} {'Workers':>8} {'Entries':>8}")
|
||||
print("-" * 54)
|
||||
gt = 0
|
||||
for r in rows:
|
||||
print(f"{r['pipeline']:<20} {fmt(r['total']):>14} {r['workers']:>8} {r['entries']:>8}")
|
||||
gt += r["total"]
|
||||
print("-" * 54)
|
||||
print(f"{'TOTAL':<20} {fmt(gt):>14}\n")
|
||||
|
||||
def check_alerts(conn, pipeline):
|
||||
row = conn.execute(
|
||||
"SELECT SUM(u.tokens) as used, COALESCE(b.target_tokens, 0) as target "
|
||||
"FROM token_usage u LEFT JOIN pipeline_budgets b ON u.pipeline = b.pipeline "
|
||||
"WHERE u.pipeline = ?", (pipeline,)
|
||||
).fetchone()
|
||||
if not row or row["target"] <= 0: return
|
||||
ratio = row["used"] / row["target"]
|
||||
for t in ALERT_THRESHOLDS:
|
||||
if ratio >= t:
|
||||
existing = conn.execute("SELECT 1 FROM alerts_sent WHERE pipeline = ? AND threshold = ?", (pipeline, t)).fetchone()
|
||||
if not existing:
|
||||
print(f"⚠️ BUDGET ALERT: {pipeline} at {int(t*100)}% ({fmt(row['used'])}/{fmt(row['target'])})", file=sys.stderr)
|
||||
conn.execute("INSERT INTO alerts_sent (pipeline, threshold, sent_at) VALUES (?, ?, ?)", (pipeline, t, time.time()))
|
||||
conn.commit()
|
||||
|
||||
def set_budget(conn, pipeline, target):
|
||||
conn.execute("INSERT OR REPLACE INTO pipeline_budgets (pipeline, target_tokens, updated_at) VALUES (?, ?, ?)",
|
||||
(pipeline, int(target), time.time()))
|
||||
conn.execute("DELETE FROM alerts_sent WHERE pipeline = ?", (pipeline,))
|
||||
conn.commit()
|
||||
print(f"Budget set: {pipeline} = {fmt(int(target))} tokens")
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(description="Token Budget Tracker")
|
||||
parser.add_argument("--watch", action="store_true")
|
||||
parser.add_argument("--watch-interval", type=int, default=5)
|
||||
parser.add_argument("--summary", action="store_true")
|
||||
parser.add_argument("--log", nargs=3, metavar=("PIPELINE", "WORKER", "TOKENS"))
|
||||
parser.add_argument("--budget", nargs=2, metavar=("PIPELINE", "TARGET"))
|
||||
parser.add_argument("--db", type=str, default=str(DB_PATH))
|
||||
args = parser.parse_args()
|
||||
global DB_PATH
|
||||
DB_PATH = Path(args.db)
|
||||
conn = get_db()
|
||||
if args.log:
|
||||
log_usage(conn, args.log[0], args.log[1], int(args.log[2]))
|
||||
print(f"Logged: {args.log[0]}/{args.log[1]} = {fmt(int(args.log[2]))} tokens")
|
||||
elif args.budget:
|
||||
set_budget(conn, args.budget[0], args.budget[1])
|
||||
elif args.summary:
|
||||
render_daily_summary(conn)
|
||||
elif args.watch:
|
||||
render_watch(conn, interval=args.watch_interval)
|
||||
else:
|
||||
render_dashboard(conn)
|
||||
conn.close()
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -1,286 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Training Data Quality Filter
|
||||
|
||||
Scores and removes low-quality training pairs from JSONL datasets.
|
||||
Supports two formats:
|
||||
- ShareGPT session format: {"conversations": [...], ...}
|
||||
- Scene/pair format: {"terse": "...", "rich": "..."} or {"lyric_line": "...", "scene": {...}}
|
||||
|
||||
Scoring dimensions:
|
||||
- Specificity: penalizes vague/generic content
|
||||
- Length ratio: penalizes extreme input/output imbalances
|
||||
- Code correctness: validates code blocks have matching fences
|
||||
|
||||
Usage:
|
||||
python3 scripts/training_data_quality_filter.py input.jsonl [--threshold 0.4] [--output filtered.jsonl]
|
||||
python3 scripts/training_data_quality_filter.py --dir training-data/ [--threshold 0.4]
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import json
|
||||
import re
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
|
||||
def score_specificity(text: str) -> float:
|
||||
"""Score 0-1 based on how specific vs generic the text is."""
|
||||
if not text or len(text.strip()) < 10:
|
||||
return 0.0
|
||||
|
||||
score = 0.5 # baseline
|
||||
|
||||
# Penalize very generic starters
|
||||
generic_starters = [
|
||||
"sure,", "of course", "i can help", "here is", "here are",
|
||||
"certainly", "absolutely", "let me help", "great question",
|
||||
"that\'s a great", "interesting question",
|
||||
]
|
||||
lower = text.lower().strip()
|
||||
for starter in generic_starters:
|
||||
if lower.startswith(starter):
|
||||
score -= 0.15
|
||||
break
|
||||
|
||||
# Reward specific content indicators
|
||||
if re.search(r"`[^`]+`", text): # inline code
|
||||
score += 0.1
|
||||
if re.search(r"```[\s\S]*?```", text): # code blocks
|
||||
score += 0.15
|
||||
if re.search(r"\d+\.\s", text): # numbered lists
|
||||
score += 0.05
|
||||
if len(text.split()) > 50: # substantial length
|
||||
score += 0.1
|
||||
if re.search(r"https?://", text): # URLs/references
|
||||
score += 0.05
|
||||
|
||||
# Penalize extremely short outputs
|
||||
if len(text.split()) < 5:
|
||||
score -= 0.2
|
||||
|
||||
# Penalize repetition (same sentence repeated)
|
||||
sentences = re.split(r"[.!?]+", text)
|
||||
sentences = [s.strip().lower() for s in sentences if s.strip()]
|
||||
if sentences:
|
||||
unique_ratio = len(set(sentences)) / len(sentences)
|
||||
if unique_ratio < 0.7:
|
||||
score -= 0.15
|
||||
|
||||
return max(0.0, min(1.0, score))
|
||||
|
||||
|
||||
def score_length_ratio(input_text: str, output_text: str) -> float:
|
||||
"""Score 0-1 based on input/output length balance."""
|
||||
in_len = len(input_text.split())
|
||||
out_len = len(output_text.split())
|
||||
|
||||
if in_len == 0 or out_len == 0:
|
||||
return 0.0
|
||||
|
||||
ratio = out_len / in_len
|
||||
|
||||
# Ideal ratio: 0.5-5x (output can be shorter or longer, but not extreme)
|
||||
if 0.5 <= ratio <= 5.0:
|
||||
return 1.0
|
||||
elif 0.2 <= ratio <= 10.0:
|
||||
return 0.6
|
||||
elif 0.1 <= ratio <= 20.0:
|
||||
return 0.3
|
||||
else:
|
||||
return 0.1
|
||||
|
||||
|
||||
def score_code_correctness(text: str) -> float:
|
||||
"""Score 0-1 based on code block correctness."""
|
||||
code_blocks = re.findall(r"```[\s\S]*?```", text)
|
||||
|
||||
if not code_blocks:
|
||||
return 1.0 # no code = no code errors
|
||||
|
||||
for block in code_blocks:
|
||||
# Check balanced fences
|
||||
fence_count = block.count("```")
|
||||
if fence_count % 2 != 0:
|
||||
return 0.2
|
||||
|
||||
# Check for common errors
|
||||
content = block.split("\n", 1)[-1] if "\n" in block else ""
|
||||
if "SyntaxError" in content or "Traceback" in content:
|
||||
return 0.3
|
||||
if content.strip().endswith("...") and len(content.strip()) < 30:
|
||||
return 0.4 # truncated code
|
||||
|
||||
return 1.0
|
||||
|
||||
|
||||
def score_pair(input_text: str, output_text: str) -> dict:
|
||||
"""Score a training pair on all dimensions."""
|
||||
spec = score_specificity(output_text)
|
||||
length = score_length_ratio(input_text, output_text)
|
||||
code = score_code_correctness(output_text)
|
||||
|
||||
# Weighted composite
|
||||
composite = (spec * 0.4) + (length * 0.3) + (code * 0.3)
|
||||
|
||||
return {
|
||||
"specificity": round(spec, 3),
|
||||
"length_ratio": round(length, 3),
|
||||
"code_correctness": round(code, 3),
|
||||
"composite": round(composite, 3),
|
||||
}
|
||||
|
||||
|
||||
def extract_pairs(obj: dict) -> list:
|
||||
"""Extract (input, output) pairs from a JSONL object."""
|
||||
pairs = []
|
||||
|
||||
# ShareGPT session format
|
||||
if "conversations" in obj:
|
||||
convs = obj["conversations"]
|
||||
for i, msg in enumerate(convs):
|
||||
if msg.get("from") in ("gpt", "assistant"):
|
||||
# Find preceding human message
|
||||
input_text = ""
|
||||
for j in range(i - 1, -1, -1):
|
||||
if convs[j].get("from") == "human":
|
||||
input_text = convs[j].get("value", "")
|
||||
break
|
||||
output_text = msg.get("value", "")
|
||||
if input_text and output_text:
|
||||
pairs.append((input_text, output_text))
|
||||
|
||||
# Scene/pair format (terse/rich)
|
||||
elif "terse" in obj and "rich" in obj:
|
||||
pairs.append((obj["terse"], obj["rich"]))
|
||||
|
||||
# Scene description format
|
||||
elif "lyric_line" in obj and "scene" in obj:
|
||||
scene_text = json.dumps(obj["scene"]) if isinstance(obj["scene"], dict) else str(obj["scene"])
|
||||
pairs.append((obj["lyric_line"], scene_text))
|
||||
|
||||
# Generic prompt/response
|
||||
elif "prompt" in obj and "response" in obj:
|
||||
pairs.append((obj["prompt"], obj["response"]))
|
||||
|
||||
# Generic input/output
|
||||
elif "input" in obj and "output" in obj:
|
||||
pairs.append((obj["input"], obj["output"]))
|
||||
|
||||
return pairs
|
||||
|
||||
|
||||
def filter_jsonl(input_path: str, threshold: float = 0.4, output_path: str = None) -> dict:
|
||||
"""Filter a JSONL file, removing low-quality pairs."""
|
||||
path = Path(input_path)
|
||||
if not path.exists():
|
||||
return {"error": f"File not found: {input_path}"}
|
||||
|
||||
lines = path.read_text().strip().split("\n")
|
||||
total = 0
|
||||
kept = 0
|
||||
removed = 0
|
||||
scores_list = []
|
||||
kept_lines = []
|
||||
|
||||
for line in lines:
|
||||
line = line.strip()
|
||||
if not line:
|
||||
continue
|
||||
|
||||
try:
|
||||
obj = json.loads(line)
|
||||
except json.JSONDecodeError:
|
||||
removed += 1
|
||||
continue
|
||||
|
||||
pairs = extract_pairs(obj)
|
||||
total += 1
|
||||
|
||||
if not pairs:
|
||||
# No extractable pairs — keep as-is (might be metadata)
|
||||
kept += 1
|
||||
kept_lines.append(line)
|
||||
continue
|
||||
|
||||
# Score all pairs in this object
|
||||
pair_scores = [score_pair(inp, out) for inp, out in pairs]
|
||||
avg_composite = sum(s["composite"] for s in pair_scores) / len(pair_scores)
|
||||
|
||||
scores_list.append(avg_composite)
|
||||
|
||||
if avg_composite >= threshold:
|
||||
kept += 1
|
||||
kept_lines.append(line)
|
||||
else:
|
||||
removed += 1
|
||||
|
||||
# Write output
|
||||
if output_path:
|
||||
Path(output_path).write_text("\n".join(kept_lines) + "\n")
|
||||
|
||||
return {
|
||||
"file": input_path,
|
||||
"total": total,
|
||||
"kept": kept,
|
||||
"removed": removed,
|
||||
"removal_rate": f"{removed}/{total}" if total > 0 else "0/0",
|
||||
"avg_score": round(sum(scores_list) / len(scores_list), 3) if scores_list else None,
|
||||
"min_score": round(min(scores_list), 3) if scores_list else None,
|
||||
"max_score": round(max(scores_list), 3) if scores_list else None,
|
||||
}
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(description="Filter low-quality training data pairs")
|
||||
parser.add_argument("input", nargs="?", help="Input JSONL file")
|
||||
parser.add_argument("--threshold", type=float, default=0.4, help="Minimum quality score (0-1)")
|
||||
parser.add_argument("--output", "-o", help="Output file (default: input_filtered.jsonl)")
|
||||
parser.add_argument("--dir", help="Process all .jsonl files in directory")
|
||||
parser.add_argument("--dry-run", action="store_true", help="Score only, don\'t write output")
|
||||
args = parser.parse_args()
|
||||
|
||||
if args.dir:
|
||||
dirpath = Path(args.dir)
|
||||
jsonl_files = sorted(dirpath.rglob("*.jsonl"))
|
||||
if not jsonl_files:
|
||||
print(f"No .jsonl files found in {args.dir}")
|
||||
sys.exit(1)
|
||||
|
||||
print(f"Processing {len(jsonl_files)} files (threshold={args.threshold})\n")
|
||||
print(f"{'File':<50} {'Total':>6} {'Kept':>6} {'Removed':>8} {'Avg':>6}")
|
||||
print("-" * 82)
|
||||
|
||||
grand_total = grand_kept = grand_removed = 0
|
||||
for fpath in jsonl_files:
|
||||
out = str(fpath).replace(".jsonl", "_filtered.jsonl") if not args.dry_run else None
|
||||
result = filter_jsonl(str(fpath), args.threshold, out)
|
||||
if "error" in result:
|
||||
print(f"{str(fpath):<50} ERROR: {result['error']}")
|
||||
continue
|
||||
print(f"{fpath.name:<50} {result['total']:>6} {result['kept']:>6} {result['removed']:>8} {result['avg_score']:>6.3f}")
|
||||
grand_total += result["total"]
|
||||
grand_kept += result["kept"]
|
||||
grand_removed += result["removed"]
|
||||
|
||||
print("-" * 82)
|
||||
print(f"{'TOTAL':<50} {grand_total:>6} {grand_kept:>6} {grand_removed:>8}")
|
||||
|
||||
elif args.input:
|
||||
out = args.output or args.input.replace(".jsonl", "_filtered.jsonl")
|
||||
if args.dry_run:
|
||||
out = None
|
||||
result = filter_jsonl(args.input, args.threshold, out)
|
||||
if "error" in result:
|
||||
print(f"Error: {result['error']}")
|
||||
sys.exit(1)
|
||||
print(json.dumps(result, indent=2))
|
||||
if out:
|
||||
print(f"\nFiltered output written to: {out}")
|
||||
else:
|
||||
parser.print_help()
|
||||
sys.exit(1)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -1,4 +1,3 @@
|
||||
#!/usr/bin/env python3
|
||||
import json
|
||||
from hermes_tools import browser_navigate, browser_vision
|
||||
|
||||
|
||||
Reference in New Issue
Block a user