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vidai
Commit 5b39ec90 authored by Stefy Lanza (nextime / spora )'s avatar Stefy Lanza (nextime / spora )
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Make all debug prints conditional on get_debug() - suppress debug output when --debug not specified

parent e6634929
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Showing with 68 additions and 36 deletions
vidai/worker_analysis.py
View file @ 5b39ec90
......@@ -183,7 +183,8 @@ def check_job_cancelled(job_id):
def analyze_media(media_path, prompt, model_path, interval=10, job_id_int=None, comm=None):
"""Analyze media using dynamic model loading."""
print(f"DEBUG: Starting analyze_media for job {job_id_int}, media_path={media_path}")
if get_debug():
print(f"DEBUG: Starting analyze_media for job {job_id_int}, media_path={media_path}")
# Send initial progress update
if comm:
......@@ -201,9 +202,11 @@ def analyze_media(media_path, prompt, model_path, interval=10, job_id_int=None,
total_tokens = 0
# Get model with reference counting
print(f"DEBUG: Loading model {model_path} for job {job_id_int}")
if get_debug():
print(f"DEBUG: Loading model {model_path} for job {job_id_int}")
model = get_or_load_model(model_path)
print(f"DEBUG: Model loaded for job {job_id_int}")
if get_debug():
print(f"DEBUG: Model loaded for job {job_id_int}")
# Send progress update after model loading
if comm:
......@@ -217,17 +220,20 @@ def analyze_media(media_path, prompt, model_path, interval=10, job_id_int=None,
print(f"PROGRESS: Job {job_id_int} - 8% - Model loaded successfully")
# Get system prompt
print(f"DEBUG: Retrieving system prompt for job {job_id_int}")
if get_debug():
print(f"DEBUG: Retrieving system prompt for job {job_id_int}")
try:
from .config import get_system_prompt_content
system_prompt = get_system_prompt_content()
full_prompt = system_prompt + " " + prompt if system_prompt else prompt
except:
full_prompt = prompt
print(f"DEBUG: Full prompt set for job {job_id_int}")
if get_debug():
print(f"DEBUG: Full prompt set for job {job_id_int}")
if is_video(media_path):
print(f"DEBUG: Detected video, extracting frames for job {job_id_int}")
if get_debug():
print(f"DEBUG: Detected video, extracting frames for job {job_id_int}")
frames, output_dir = extract_frames(media_path, interval, optimize=True)
total_frames = len(frames)
......@@ -245,7 +251,8 @@ def analyze_media(media_path, prompt, model_path, interval=10, job_id_int=None,
descriptions = []
for i, (frame_path, ts) in enumerate(frames):
print(f"DEBUG: Processing frame {i+1}/{total_frames} at {ts:.2f}s for job {job_id_int}")
if get_debug():
print(f"DEBUG: Processing frame {i+1}/{total_frames} at {ts:.2f}s for job {job_id_int}")
# Send progress update before processing
if comm:
......@@ -261,7 +268,8 @@ def analyze_media(media_path, prompt, model_path, interval=10, job_id_int=None,
# Check for cancellation
if job_id_int and check_job_cancelled(job_id_int):
print(f"DEBUG: Job {job_id_int} cancelled during frame processing")
if get_debug():
print(f"DEBUG: Job {job_id_int} cancelled during frame processing")
# Clean up and return cancelled message
for fp, _ in frames[i:]:
try:
......@@ -278,7 +286,8 @@ def analyze_media(media_path, prompt, model_path, interval=10, job_id_int=None,
desc, tokens = analyze_single_image(frame_path, full_prompt, model)
total_tokens += tokens
print(f"DEBUG: Frame {i+1} analyzed for job {job_id_int}")
if get_debug():
print(f"DEBUG: Frame {i+1} analyzed for job {job_id_int}")
descriptions.append(f"At {ts:.2f}s: {desc}")
os.unlink(frame_path)
......@@ -308,7 +317,8 @@ def analyze_media(media_path, prompt, model_path, interval=10, job_id_int=None,
import shutil
shutil.rmtree(output_dir)
print(f"DEBUG: All frames processed, generating summary for job {job_id_int}")
if get_debug():
print(f"DEBUG: All frames processed, generating summary for job {job_id_int}")
# Send progress update for summary generation
if comm:
......@@ -323,7 +333,8 @@ def analyze_media(media_path, prompt, model_path, interval=10, job_id_int=None,
# Check for cancellation before summary
if job_id_int and check_job_cancelled(job_id_int):
print(f"DEBUG: Job {job_id_int} cancelled before summary")
if get_debug():
print(f"DEBUG: Job {job_id_int} cancelled before summary")
return "Job cancelled by user", total_tokens
# Generate summary
......@@ -361,7 +372,8 @@ def analyze_media(media_path, prompt, model_path, interval=10, job_id_int=None,
summary_tokens = 0
total_tokens += summary_tokens
print(f"DEBUG: Summary generated for job {job_id_int}")
if get_debug():
print(f"DEBUG: Summary generated for job {job_id_int}")
# Send final progress update
if comm:
......@@ -377,7 +389,8 @@ def analyze_media(media_path, prompt, model_path, interval=10, job_id_int=None,
result = f"Frame Descriptions:\n" + "\n".join(descriptions) + f"\n\nSummary:\n{summary}"
return result, total_tokens
else:
print(f"DEBUG: Detected image, analyzing for job {job_id_int}")
if get_debug():
print(f"DEBUG: Detected image, analyzing for job {job_id_int}")
# Send progress update for image analysis start
if comm:
......@@ -392,7 +405,8 @@ def analyze_media(media_path, prompt, model_path, interval=10, job_id_int=None,
# Check for cancellation before processing image
if job_id_int and check_job_cancelled(job_id_int):
print(f"DEBUG: Job {job_id_int} cancelled before image analysis")
if get_debug():
print(f"DEBUG: Job {job_id_int} cancelled before image analysis")
return "Job cancelled by user", total_tokens
# Send progress update before model inference
......@@ -408,7 +422,8 @@ def analyze_media(media_path, prompt, model_path, interval=10, job_id_int=None,
result, tokens = analyze_single_image(media_path, full_prompt, model)
total_tokens += tokens
print(f"DEBUG: Image analysis completed for job {job_id_int}")
if get_debug():
print(f"DEBUG: Image analysis completed for job {job_id_int}")
# Send progress update for completion
if comm:
......@@ -436,32 +451,38 @@ def analyze_media(media_path, prompt, model_path, interval=10, job_id_int=None,
return result, total_tokens
def worker_process(backend_type: str):
"""Main worker process."""
print(f"DEBUG: Starting Analysis Worker for {backend_type}...")
print(f"DEBUG: Worker PID: {os.getpid()}")
if get_debug():
print(f"DEBUG: Starting Analysis Worker for {backend_type}...")
print(f"DEBUG: Worker PID: {os.getpid()}")
# Workers use TCP for interprocess communication
comm = SocketCommunicator(host='127.0.0.1', port=get_backend_worker_port(), comm_type='tcp')
print(f"DEBUG: Worker connecting to {comm.host}:{comm.port}")
if get_debug():
print(f"DEBUG: Worker connecting to {comm.host}:{comm.port}")
comm.connect()
print(f"Analysis Worker connected to backend")
if get_debug():
print(f"Analysis Worker connected to backend")
# Register with backend
register_msg = Message('register', 'register', {'type': f'analysis_{backend_type}'})
comm.send_message(register_msg)
print(f"Analysis Worker registered as analysis_{backend_type}")
if get_debug():
print(f"Analysis Worker registered as analysis_{backend_type}")
while True:
try:
message = comm.receive_message()
if message:
if message and get_debug():
print(f"DEBUG: Worker {os.getpid()} received message: {message}")
if message and message.msg_type == 'analyze_request':
print(f"DEBUG: Worker received analyze_request: {message.msg_id}")
if get_debug():
print(f"DEBUG: Worker received analyze_request: {message.msg_id}")
data = message.data
media_path = data.get('local_path', data.get('file_name', ''))
if not media_path:
result = 'No media path provided'
print(f"DEBUG: No media path provided for job {message.msg_id}")
if get_debug():
print(f"DEBUG: No media path provided for job {message.msg_id}")
else:
prompt = data.get('prompt', 'Describe this image.')
model_path = data.get('model_path', 'Qwen/Qwen2.5-VL-7B-Instruct')
......@@ -469,16 +490,19 @@ def worker_process(backend_type: str):
job_id = message.msg_id # Use message ID for job identification
job_id_int = int(message.msg_id.split('_')[1]) # Extract integer job ID
print(f"PROGRESS: Job {job_id_int} accepted - Starting analysis")
print(f"DEBUG: Starting analysis of {media_path} with model {model_path} for job {job_id}")
if get_debug():
print(f"DEBUG: Starting analysis of {media_path} with model {model_path} for job {job_id}")
result, tokens_used = analyze_media(media_path, prompt, model_path, interval, job_id_int, comm)
print(f"DEBUG: Analysis completed for job {message.msg_id}, used {tokens_used} tokens")
if get_debug():
print(f"DEBUG: Analysis completed for job {message.msg_id}, used {tokens_used} tokens")
# Release model reference (don't unload yet, per requirements)
release_model(model_path)
# Send result back
response = Message('analyze_response', message.msg_id, {'result': result, 'tokens_used': tokens_used})
print(f"DEBUG: Sending analyze_response for job {message.msg_id}")
if get_debug():
print(f"DEBUG: Sending analyze_response for job {message.msg_id}")
comm.send_message(response)
# If in cluster mode, also notify cluster master
......
vidai/worker_training.py
View file @ 5b39ec90
......@@ -27,11 +27,12 @@ import shutil
import json
import time
from .comm import SocketCommunicator, Message
from .config import get_comm_type, get_backend_worker_port
from .config import get_comm_type, get_backend_worker_port, get_debug
def train_model(train_path, output_model, description):
"""Perform training."""
print(f"DEBUG: Starting training with videotrain for output_model {output_model}")
if get_debug():
print(f"DEBUG: Starting training with videotrain for output_model {output_model}")
desc_file = os.path.join(train_path, "description.txt")
with open(desc_file, "w") as f:
f.write(description)
......@@ -39,7 +40,8 @@ def train_model(train_path, output_model, description):
# Assume videotrain is available
cmd = ["python", "videotrain", train_path, "--output_dir", output_model]
result = subprocess.run(cmd, capture_output=True, text=True)
print(f"DEBUG: Training subprocess completed with returncode {result.returncode}")
if get_debug():
print(f"DEBUG: Training subprocess completed with returncode {result.returncode}")
if result.returncode == 0:
return "Training completed!"
else:
......@@ -47,7 +49,8 @@ def train_model(train_path, output_model, description):
def worker_process(backend_type: str):
"""Main worker process."""
print(f"Starting Training Worker for {backend_type}...")
if get_debug():
print(f"Starting Training Worker for {backend_type}...")
# Workers use TCP for interprocess communication
comm = SocketCommunicator(host='127.0.0.1', port=get_backend_worker_port(), comm_type='tcp')
......@@ -60,10 +63,11 @@ def worker_process(backend_type: str):
while True:
try:
message = comm.receive_message()
if message:
if message and get_debug():
print(f"DEBUG: Worker {os.getpid()} received message: {message}")
if message and message.msg_type == 'train_request':
print(f"DEBUG: Worker received train_request: {message.msg_id}")
if get_debug():
print(f"DEBUG: Worker received train_request: {message.msg_id}")
data = message.data
output_model = data.get('output_model', './VideoModel')
description = data.get('description', '')
......@@ -71,16 +75,20 @@ def worker_process(backend_type: str):
if train_dir and os.path.isdir(train_dir):
print(f"PROGRESS: Job {message.msg_id} accepted - Starting training")
print(f"DEBUG: Starting training for job {message.msg_id}")
if get_debug():
print(f"DEBUG: Starting training for job {message.msg_id}")
result = train_model(train_dir, output_model, description)
print(f"PROGRESS: Job {message.msg_id} - 100% - Training completed")
print(f"DEBUG: Training completed for job {message.msg_id}")
if get_debug():
print(f"DEBUG: Training completed for job {message.msg_id}")
else:
result = "No valid training directory provided"
print(f"DEBUG: No valid training directory for job {message.msg_id}")
if get_debug():
print(f"DEBUG: No valid training directory for job {message.msg_id}")
response = Message('train_response', message.msg_id, {'message': result})
print(f"DEBUG: Sending train_response for job {message.msg_id}")
if get_debug():
print(f"DEBUG: Sending train_response for job {message.msg_id}")
comm.send_message(response)
time.sleep(0.1)
except Exception as e:
......
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