fix: replace production print() calls with logger in rl_training_tool (salvage #1981) (#2462)

* fix: respect DashScope v1 runtime mode for alibaba

Remove the hardcoded Alibaba branch from resolve_runtime_provider()
that forced api_mode='anthropic_messages' regardless of the base URL.

Alibaba now goes through the generic API-key provider path, which
auto-detects the protocol from the URL:
- /apps/anthropic → anthropic_messages (via endswith check)
- /v1 → chat_completions (default)

This fixes Alibaba setup with OpenAI-compatible DashScope endpoints
(e.g. coding-intl.dashscope.aliyuncs.com/v1) that were broken because
runtime always forced Anthropic mode even when setup saved a /v1 URL.

Based on PR #2024 by @kshitijk4poor.

* docs(skill): add split, merge, search examples to ocr-and-documents skill

Adds pymupdf examples for PDF splitting, merging, and text search
to the existing ocr-and-documents skill. No new dependencies — pymupdf
already covers all three operations natively.

* fix: replace all production print() calls with logger in rl_training_tool

Replace all bare print() calls in production code paths with proper logger calls.

- Add `import logging` and module-level `logger = logging.getLogger(__name__)`
- Replace print() in _start_training_run() with logger.info()
- Replace print() in _stop_training_run() with logger.info()
- Replace print(Warning/Note) calls with logger.warning() and logger.info()

Using the logging framework allows log level filtering, proper formatting,
and log routing instead of always printing to stdout.

---------

Co-authored-by: kshitijk4poor <kshitijk4poor@users.noreply.github.com>
Co-authored-by: memosr.eth <96793918+memosr@users.noreply.github.com>

tools/rl_training_tool.py

@@ -37,12 +37,15 @@ import subprocess
 import sys
 import time
 import uuid
+import logging
 from datetime import datetime
 import yaml
 from dataclasses import dataclass, field
 from pathlib import Path
 from typing import Any, Dict, List, Optional
 
+logger = logging.getLogger(__name__)
+
 # ============================================================================
 # Path Configuration
 # ============================================================================
@@ -206,7 +209,7 @@ def _scan_environments() -> List[EnvironmentInfo]:
                             ))
                             break
         except Exception as e:
-            print(f"Warning: Could not parse {py_file}: {e}")
+            logger.warning("Could not parse %s: %s", py_file, e)
     
     return environments
 
@@ -243,7 +246,7 @@ def _get_env_config_fields(env_file_path: str) -> Dict[str, Dict[str, Any]]:
             config_class = type(env_config)
         except Exception as config_error:
             # Fallback: try to import BaseEnvConfig directly from atroposlib
-            print(f"Note: config_init failed ({config_error}), using BaseEnvConfig defaults")
+            logger.info("config_init failed (%s), using BaseEnvConfig defaults", config_error)
             try:
                 from atroposlib.envs.base import BaseEnvConfig
                 config_class = BaseEnvConfig
@@ -291,7 +294,7 @@ def _get_env_config_fields(env_file_path: str) -> Dict[str, Dict[str, Any]]:
         return fields
         
     except Exception as e:
-        print(f"Warning: Could not introspect environment config: {e}")
+        logger.warning("Could not introspect environment config: %s", e)
         return {}
 
 
@@ -324,7 +327,7 @@ async def _spawn_training_run(run_state: RunState, config_path: Path):
     
     try:
         # Step 1: Start the Atropos API server (run-api)
-        print(f"[{run_id}] Starting Atropos API server (run-api)...")
+        logger.info("[%s] Starting Atropos API server (run-api)...", run_id)
         
         # File must stay open while the subprocess runs; we store the handle
         # on run_state so _stop_training_run() can close it when done.
@@ -346,10 +349,10 @@ async def _spawn_training_run(run_state: RunState, config_path: Path):
             _stop_training_run(run_state)
             return
         
-        print(f"[{run_id}] Atropos API server started")
+        logger.info("[%s] Atropos API server started", run_id)
         
         # Step 2: Start the Tinker trainer
-        print(f"[{run_id}] Starting Tinker trainer: launch_training.py --config {config_path}")
+        logger.info("[%s] Starting Tinker trainer: launch_training.py --config %s", run_id, config_path)
         
         trainer_log_file = open(trainer_log, "w")  # closed by _stop_training_run
         run_state.trainer_log_file = trainer_log_file
@@ -362,7 +365,7 @@ async def _spawn_training_run(run_state: RunState, config_path: Path):
         )
         
         # Wait for trainer to initialize (it starts FastAPI inference server on 8001)
-        print(f"[{run_id}] Waiting 30 seconds for trainer to initialize...")
+        logger.info("[%s] Waiting 30 seconds for trainer to initialize...", run_id)
         await asyncio.sleep(30)
         
         if run_state.trainer_process.poll() is not None:
@@ -371,10 +374,10 @@ async def _spawn_training_run(run_state: RunState, config_path: Path):
             _stop_training_run(run_state)
             return
         
-        print(f"[{run_id}] Trainer started, inference server on port 8001")
+        logger.info("[%s] Trainer started, inference server on port 8001", run_id)
         
         # Step 3: Start the environment
-        print(f"[{run_id}] Waiting 90 more seconds before starting environment...")
+        logger.info("[%s] Waiting 90 more seconds before starting environment...", run_id)
         await asyncio.sleep(90)
         
         # Find the environment file
@@ -390,7 +393,7 @@ async def _spawn_training_run(run_state: RunState, config_path: Path):
             _stop_training_run(run_state)
             return
         
-        print(f"[{run_id}] Starting environment: {env_info.file_path} serve")
+        logger.info("[%s] Starting environment: %s serve", run_id, env_info.file_path)
         
         env_log_file = open(env_log, "w")  # closed by _stop_training_run
         run_state.env_log_file = env_log_file
@@ -412,7 +415,7 @@ async def _spawn_training_run(run_state: RunState, config_path: Path):
         
         run_state.status = "running"
         run_state.start_time = time.time()
-        print(f"[{run_id}] Training run started successfully!")
+        logger.info("[%s] Training run started successfully!", run_id)
         
         # Start background monitoring
         asyncio.create_task(_monitor_training_run(run_state))
@@ -460,7 +463,7 @@ def _stop_training_run(run_state: RunState):
     """Stop all processes for a training run."""
     # Stop in reverse order: env -> trainer -> api
     if run_state.env_process and run_state.env_process.poll() is None:
-        print(f"[{run_state.run_id}] Stopping environment process...")
+        logger.info("[%s] Stopping environment process...", run_state.run_id)
         run_state.env_process.terminate()
         try:
             run_state.env_process.wait(timeout=10)
@@ -468,7 +471,7 @@ def _stop_training_run(run_state: RunState):
             run_state.env_process.kill()
     
     if run_state.trainer_process and run_state.trainer_process.poll() is None:
-        print(f"[{run_state.run_id}] Stopping trainer process...")
+        logger.info("[%s] Stopping trainer process...", run_state.run_id)
         run_state.trainer_process.terminate()
         try:
             run_state.trainer_process.wait(timeout=10)
@@ -476,7 +479,7 @@ def _stop_training_run(run_state: RunState):
             run_state.trainer_process.kill()
     
     if run_state.api_process and run_state.api_process.poll() is None:
-        print(f"[{run_state.run_id}] Stopping API server...")
+        logger.info("[%s] Stopping API server...", run_state.run_id)
         run_state.api_process.terminate()
         try:
             run_state.api_process.wait(timeout=10)