all1

.gitignore

+minio_data
+model
+runs
+phobert-base
+newfile.log
+postgres_data
+*__pycache__*
\ No newline at end of file

README.md

@@ -187,18 +187,18 @@ Status 400 Bad Request
 ## Các thành phần trong hệ thống
 ### MinIO
 Quản lý model, dữ liệu huấn luyện.
-### PostgreSQL
-Lưu trữ dữ liệu đã gắn nhãn từ người kiểm duyệt nội dung trên nền tảng MXH, được bổ sung liên tục để tiến hóa mô hình.
+### Postgres DB 
+Lưu trữ dữ liệu đã gán nhãn từ người kiểm duyệt nội dung trên nền tảng MXH, được bổ sung liên tục để cải tiến mô hình.
 ### Adminer
-Công cụ quản trị dữ liệu trên PostgreSQL DB.
-### DataManager
-Server tự động khởi tạo bucket minio, các bảng trong cơ sở dữ liệu và cung cấp api bổ sung dữ liệu.
-### NlpCore
-Server cung cấp api phân loại văn bản.
+Công cụ quản trị dữ liệu trên Postgres DB.
+### NLP Data
+Tự động kiểm tra và khởi tạo các bucket minio, các bảng trong cơ sở dữ liệu khi khởi động hệ thống. Cung cấp api bổ sung dữ liệu đã gắn nhãn vào cơ sở dữ liệu phục vụ cho cải tiến mô hình.
+### NLP Infer
+NLP Infer là thành phần chịu trách nhiệm xử lý và phân loại nội dung văn bản. Cung cấp điểm cuối (endpoint) để các hệ thống bên ngoài và các ứng dụng khác có thể sử dụng tính năng phân loại văn bản.
 ### Nginx
-Thực hiện phân tải: phân phối yêu cầu của người dùng đến các NlpCore.
-### NlpTraining
-Server cung cấp api huấn luyện model.
+Thực hiện phân tải: phân phối yêu cầu của người dùng đến các NLP Core.
+### NLP Training
+NLP Training là thành phần thực hiện huấn luyện mô hình AI giúp cải tiến độ chính xác. Cung cấp điểm cuối (endpoint) để điều khiển (bắt đầu hoặc kết thúc) quá trình huấn luyện.
 ### Tensorboard
 Giao diện theo dõi các chỉ số trong quá trình huấn luyện model.
 

config.yaml

@@ -28,3 +28,5 @@ training:
   load_data_worker: 2
   k_fold: 5
   test_ratio: 0.1
+
+log_file: /src/server.log

docker-compose.yml

@@ -49,7 +49,7 @@ services:
     ports:
       - 8080:8080
 
-  datamanager:
+  nlpdata:
     image: vn-text-moderation-data
     restart: always 
     env_file: 

nginx/log/access.log

@@ -35,3 +35,5 @@
 10.3.2.100 - - [10/Jul/2024:10:23:35 +0000] "POST /text-classify HTTP/1.1" 200 2318 "-" "PostmanRuntime/7.37.3" "-"
 10.3.2.100 - - [11/Jul/2024:06:33:03 +0000] "POST /text-classify HTTP/1.1" 200 2318 "-" "PostmanRuntime/7.37.3" "-"
 10.3.2.100 - - [11/Jul/2024:06:41:13 +0000] "POST /text-classify HTTP/1.1" 200 2318 "-" "PostmanRuntime/7.37.3" "-"
+10.3.2.100 - - [15/Jul/2024:02:13:07 +0000] "POST /text-classify HTTP/1.1" 200 2318 "-" "PostmanRuntime/7.37.3" "-"
+10.3.2.100 - - [15/Jul/2024:02:13:22 +0000] "POST /text-classify HTTP/1.1" 200 2318 "-" "PostmanRuntime/7.37.3" "-"

nginx/log/error.log

@@ -7323,3 +7323,91 @@
 2024/07/11 06:40:56 [notice] 1#1: start worker process 29
 2024/07/11 06:40:56 [notice] 1#1: start worker process 30
 2024/07/11 06:40:56 [notice] 1#1: start worker process 31
+2024/07/11 09:35:05 [notice] 1#1: signal 3 (SIGQUIT) received, shutting down
+2024/07/11 09:35:05 [notice] 20#20: gracefully shutting down
+2024/07/11 09:35:05 [notice] 21#21: gracefully shutting down
+2024/07/11 09:35:05 [notice] 22#22: gracefully shutting down
+2024/07/11 09:35:05 [notice] 23#23: gracefully shutting down
+2024/07/11 09:35:05 [notice] 25#25: gracefully shutting down
+2024/07/11 09:35:05 [notice] 24#24: gracefully shutting down
+2024/07/11 09:35:05 [notice] 20#20: exiting
+2024/07/11 09:35:05 [notice] 21#21: exiting
+2024/07/11 09:35:05 [notice] 22#22: exiting
+2024/07/11 09:35:05 [notice] 23#23: exiting
+2024/07/11 09:35:05 [notice] 25#25: exiting
+2024/07/11 09:35:05 [notice] 24#24: exiting
+2024/07/11 09:35:05 [notice] 20#20: exit
+2024/07/11 09:35:05 [notice] 21#21: exit
+2024/07/11 09:35:05 [notice] 22#22: exit
+2024/07/11 09:35:05 [notice] 23#23: exit
+2024/07/11 09:35:05 [notice] 25#25: exit
+2024/07/11 09:35:05 [notice] 24#24: exit
+2024/07/11 09:35:05 [notice] 28#28: gracefully shutting down
+2024/07/11 09:35:05 [notice] 27#27: gracefully shutting down
+2024/07/11 09:35:05 [notice] 28#28: exiting
+2024/07/11 09:35:05 [notice] 31#31: gracefully shutting down
+2024/07/11 09:35:05 [notice] 29#29: gracefully shutting down
+2024/07/11 09:35:05 [notice] 26#26: gracefully shutting down
+2024/07/11 09:35:05 [notice] 27#27: exiting
+2024/07/11 09:35:05 [notice] 30#30: gracefully shutting down
+2024/07/11 09:35:05 [notice] 31#31: exiting
+2024/07/11 09:35:05 [notice] 28#28: exit
+2024/07/11 09:35:05 [notice] 29#29: exiting
+2024/07/11 09:35:05 [notice] 26#26: exiting
+2024/07/11 09:35:05 [notice] 27#27: exit
+2024/07/11 09:35:05 [notice] 31#31: exit
+2024/07/11 09:35:05 [notice] 26#26: exit
+2024/07/11 09:35:05 [notice] 29#29: exit
+2024/07/11 09:35:05 [notice] 30#30: exiting
+2024/07/11 09:35:05 [notice] 30#30: exit
+2024/07/11 09:35:05 [notice] 1#1: signal 17 (SIGCHLD) received from 25
+2024/07/11 09:35:05 [notice] 1#1: worker process 25 exited with code 0
+2024/07/11 09:35:05 [notice] 1#1: signal 29 (SIGIO) received
+2024/07/11 09:35:05 [notice] 1#1: signal 17 (SIGCHLD) received from 28
+2024/07/11 09:35:05 [notice] 1#1: worker process 28 exited with code 0
+2024/07/11 09:35:05 [notice] 1#1: signal 29 (SIGIO) received
+2024/07/11 09:35:05 [notice] 1#1: signal 17 (SIGCHLD) received from 29
+2024/07/11 09:35:05 [notice] 1#1: worker process 29 exited with code 0
+2024/07/11 09:35:05 [notice] 1#1: worker process 26 exited with code 0
+2024/07/11 09:35:05 [notice] 1#1: signal 29 (SIGIO) received
+2024/07/11 09:35:05 [notice] 1#1: signal 17 (SIGCHLD) received from 26
+2024/07/11 09:35:05 [notice] 1#1: signal 17 (SIGCHLD) received from 20
+2024/07/11 09:35:05 [notice] 1#1: worker process 20 exited with code 0
+2024/07/11 09:35:05 [notice] 1#1: signal 29 (SIGIO) received
+2024/07/11 09:35:05 [notice] 1#1: signal 17 (SIGCHLD) received from 31
+2024/07/11 09:35:05 [notice] 1#1: worker process 31 exited with code 0
+2024/07/11 09:35:05 [notice] 1#1: signal 29 (SIGIO) received
+2024/07/11 09:35:05 [notice] 1#1: signal 17 (SIGCHLD) received from 23
+2024/07/11 09:35:05 [notice] 1#1: worker process 23 exited with code 0
+2024/07/11 09:35:05 [notice] 1#1: signal 29 (SIGIO) received
+2024/07/11 09:35:05 [notice] 1#1: signal 17 (SIGCHLD) received from 27
+2024/07/11 09:35:05 [notice] 1#1: worker process 27 exited with code 0
+2024/07/11 09:35:05 [notice] 1#1: signal 29 (SIGIO) received
+2024/07/11 09:35:05 [notice] 1#1: signal 17 (SIGCHLD) received from 24
+2024/07/11 09:35:05 [notice] 1#1: worker process 21 exited with code 0
+2024/07/11 09:35:05 [notice] 1#1: worker process 24 exited with code 0
+2024/07/11 09:35:05 [notice] 1#1: signal 29 (SIGIO) received
+2024/07/11 09:35:05 [notice] 1#1: signal 17 (SIGCHLD) received from 21
+2024/07/11 09:35:05 [notice] 1#1: signal 17 (SIGCHLD) received from 30
+2024/07/11 09:35:05 [notice] 1#1: worker process 30 exited with code 0
+2024/07/11 09:35:05 [notice] 1#1: signal 17 (SIGCHLD) received from 22
+2024/07/11 09:35:05 [notice] 1#1: worker process 22 exited with code 0
+2024/07/11 09:35:05 [notice] 1#1: exit
+2024/07/11 09:36:00 [notice] 1#1: using the "epoll" event method
+2024/07/11 09:36:00 [notice] 1#1: nginx/1.25.0
+2024/07/11 09:36:00 [notice] 1#1: built by gcc 10.2.1 20210110 (Debian 10.2.1-6) 
+2024/07/11 09:36:00 [notice] 1#1: OS: Linux 6.5.0-17-generic
+2024/07/11 09:36:00 [notice] 1#1: getrlimit(RLIMIT_NOFILE): 1048576:1048576
+2024/07/11 09:36:00 [notice] 1#1: start worker processes
+2024/07/11 09:36:00 [notice] 1#1: start worker process 20
+2024/07/11 09:36:00 [notice] 1#1: start worker process 21
+2024/07/11 09:36:00 [notice] 1#1: start worker process 22
+2024/07/11 09:36:00 [notice] 1#1: start worker process 23
+2024/07/11 09:36:00 [notice] 1#1: start worker process 24
+2024/07/11 09:36:00 [notice] 1#1: start worker process 25
+2024/07/11 09:36:00 [notice] 1#1: start worker process 26
+2024/07/11 09:36:00 [notice] 1#1: start worker process 27
+2024/07/11 09:36:00 [notice] 1#1: start worker process 28
+2024/07/11 09:36:00 [notice] 1#1: start worker process 29
+2024/07/11 09:36:00 [notice] 1#1: start worker process 30
+2024/07/11 09:36:00 [notice] 1#1: start worker process 31

server_infer/constants.py

+from utils import get_data_from_yaml
+
+config = get_data_from_yaml("/src/config.yaml")
+DEVICE = config.get("device")
+CLASSES = config.get("classes")
+MINIO_SERVER = config.get("minio")["server"]
+MINIO_DATA_LABELED = config.get("minio")["data_labeled"]
+MINIO_MODEL_TRAINED = config.get("minio")["model_trained"]
+VNCORENLP_DIR = config.get("vncorenlp")["save_dir"]
+PHOBERTBASE_DIR = config.get("phobert_base")["save_dir"]
+MAX_TOKEN_LENGTH = config.get("phobert_base")["max_token_length"]
+MODEL_CHECKPOINT = config.get("model_checkpoint")
+CHUNK_SIZE = config.get("chunk_size")
+INFER_LENGTH = config.get("limit_infer_length")
+LOG_FILE = config.get("log_file")
\ No newline at end of file

server_infer/logger.py

+import logging
+import logging.handlers
+import sys
+
+from constants import LOG_FILE
+
+# Định nghĩa hàm để cấu hình logger
+def configure_logger(log_file=LOG_FILE):
+    # Tạo logger
+    logger = logging.getLogger('NLP_Infer')
+    logger.setLevel(logging.DEBUG)
+
+    # Định nghĩa handler để ghi log vào file
+    file_handler = logging.FileHandler(log_file)
+    file_handler.setLevel(logging.DEBUG)
+
+    # Định nghĩa handler để in log ra stderr
+    stream_handler = logging.StreamHandler(sys.stderr)
+    stream_handler.setLevel(logging.DEBUG)
+
+    # Định nghĩa format cho log
+    formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+    file_handler.setFormatter(formatter)
+    stream_handler.setFormatter(formatter)
+
+    # Thêm handler vào logger
+    logger.addHandler(file_handler)
+    logger.addHandler(stream_handler)
+
+    return logger
+
+# Sử dụng hàm để cấu hình logger
+logger = configure_logger()

server_infer/minio_client.py

+import os
+from minio import Minio
+from minio.error import S3Error
+
+from constants import MINIO_SERVER, MINIO_MODEL_TRAINED, MODEL_CHECKPOINT
+from logger import logger
+
+minio_client = Minio(
+    endpoint=MINIO_SERVER,
+    access_key=os.getenv("MINIO_ROOT_USER"),
+    secret_key=os.getenv("MINIO_ROOT_PASSWORD"),
+    secure=False
+)
+
+def download_latest_model():
+    # List objects in the bucket
+    objects = minio_client.list_objects(MINIO_MODEL_TRAINED)
+    latest_obj = None
+    latest_time = None
+    
+    for obj in objects:
+        if "best" in obj.object_name:
+            if latest_time is None or obj.last_modified > latest_time:
+                latest_time = obj.last_modified
+                latest_obj = obj
+
+    if latest_obj is not None:
+        try:
+            minio_client.fget_object(MINIO_MODEL_TRAINED, latest_obj.object_name, MODEL_CHECKPOINT)
+        except S3Error as exc:
+            logger.error(f"Error occurred: {exc}")
+        return latest_obj.object_name
+    else:
+        raise Exception("No *best* models found in the bucket")
\ No newline at end of file

server_infer/server.py

@@ -3,57 +3,14 @@ from pydantic import BaseModel
 import torch
 from transformers import AutoTokenizer
 from contextlib import asynccontextmanager
-from minio import Minio
-from minio.error import S3Error
-import os
 from typing import List
 
 from bert_model import BERTClassifier
 from utils import get_data_from_yaml, split_chunk
 from preprocess import preprocess_text
-
-config = get_data_from_yaml("/src/config.yaml")
-DEVICE = config.get("device")
-CLASSES = config.get("classes")
-MINIO_SERVER = config.get("minio")["server"]
-MINIO_DATA_LABELED = config.get("minio")["data_labeled"]
-MINIO_MODEL_TRAINED = config.get("minio")["model_trained"]
-VNCORENLP_DIR = config.get("vncorenlp")["save_dir"]
-PHOBERTBASE_DIR = config.get("phobert_base")["save_dir"]
-MAX_TOKEN_LENGTH = config.get("phobert_base")["max_token_length"]
-MODEL_CHECKPOINT = config.get("model_checkpoint")
-CHUNK_SIZE = config.get("chunk_size")
-INFER_LENGTH = config.get("limit_infer_length")
-
-minio_client = Minio(
-    endpoint=MINIO_SERVER,
-    access_key=os.getenv("MINIO_ROOT_USER"),
-    secret_key=os.getenv("MINIO_ROOT_PASSWORD"),
-    secure=False
-)
-
-def download_latest_model():
-    # List objects in the bucket
-    objects = minio_client.list_objects(MINIO_MODEL_TRAINED)
-    latest_obj = None
-    latest_time = None
-    
-    for obj in objects:
-        if "best" in obj.object_name:
-            if latest_time is None or obj.last_modified > latest_time:
-                latest_time = obj.last_modified
-                latest_obj = obj
-
-    if latest_obj is not None:
-        try:
-            minio_client.fget_object(MINIO_MODEL_TRAINED, latest_obj.object_name, MODEL_CHECKPOINT)
-        except S3Error as exc:
-            print(f"Error occurred: {exc}")
-        return latest_obj.object_name
-    else:
-        raise Exception("No *best* models found in the bucket")
-
-
+from minio_client import download_latest_model
+from constants import PHOBERTBASE_DIR, MAX_TOKEN_LENGTH, DEVICE, CLASSES, MODEL_CHECKPOINT, INFER_LENGTH, CHUNK_SIZE
+from logger import logger
 
 tokenizer = AutoTokenizer.from_pretrained(PHOBERTBASE_DIR, local_files_only=True, use_fast=False)
 
@@ -88,9 +45,9 @@ async def lifespan(app: FastAPI):
         update_model = download_latest_model()
         model.load_state_dict(torch.load(MODEL_CHECKPOINT))
         model.eval()
-        print(f"Model updated: {update_model}")
+        logger.info(f"Model updated: {update_model}")
     except Exception as e:
-        print(f"An error occurred: {e}")
+        logger.error(f"An error occurred: {e}")
     yield
     torch.cuda.empty_cache()
     torch.cuda.ipc_collect()

server_manage_data/config.py → server_manage_data/constants.py

@@ -5,4 +5,5 @@ DB_SERVER = config.get("sqldb")["server"]
 DB_TABLENAME = config.get("sqldb")["table"]
 MINIO_SERVER = config.get("minio")["server"]
 MINIO_DATA_LABELED = config.get("minio")["data_labeled"]
-MINIO_MODEL_TRAINED = config.get("minio")["model_trained"]
+MINIO_MODEL_TRAINED = config.get("minio")["model_trained"]
\ No newline at end of file
+LOG_FILE = config.get("log_file")
\ No newline at end of file

server_manage_data/database.py

 from sqlalchemy.ext.asyncio import AsyncSession, create_async_engine
 from sqlalchemy.orm import sessionmaker
 import os
-from config import DB_SERVER
+from constants import DB_SERVER
 
 DATABASE_URL = f'postgresql+asyncpg://{os.getenv("POSTGRES_USER")}:{os.getenv("POSTGRES_PASSWORD")}@{DB_SERVER}/{os.getenv("POSTGRES_DB")}'
 

server_manage_data/logger.py

+import logging
+import logging.handlers
+import sys
+
+from constants import LOG_FILE
+
+# Định nghĩa hàm để cấu hình logger
+def configure_logger(log_file=LOG_FILE):
+    # Tạo logger
+    logger = logging.getLogger('NLP_Data')
+    logger.setLevel(logging.DEBUG)
+
+    # Định nghĩa handler để ghi log vào file
+    file_handler = logging.FileHandler(log_file)
+    file_handler.setLevel(logging.DEBUG)
+
+    # Định nghĩa handler để in log ra stderr
+    stream_handler = logging.StreamHandler(sys.stderr)
+    stream_handler.setLevel(logging.DEBUG)
+
+    # Định nghĩa format cho log
+    formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+    file_handler.setFormatter(formatter)
+    stream_handler.setFormatter(formatter)
+
+    # Thêm handler vào logger
+    logger.addHandler(file_handler)
+    logger.addHandler(stream_handler)
+
+    return logger
+
+# Sử dụng hàm để cấu hình logger
+logger = configure_logger()

server_manage_data/minio_client.py

+from minio import Minio
+import os
+
+from constants import MINIO_SERVER, MINIO_DATA_LABELED, MINIO_MODEL_TRAINED
+from logger import logger
+
+minio_client = Minio(
+    endpoint=MINIO_SERVER,
+    access_key=os.getenv("MINIO_ROOT_USER"),
+    secret_key=os.getenv("MINIO_ROOT_PASSWORD"),
+    secure=False
+)
+bucket_names = [MINIO_DATA_LABELED, MINIO_MODEL_TRAINED]
+
+def check_bucket(minio_client, bucket_names):
+    # Check if bucket exists
+    for bucket_name in bucket_names:
+        found = minio_client.bucket_exists(bucket_name)
+        if not found:
+            # Create bucket
+            minio_client.make_bucket(bucket_name)
+            logger.info(f"Bucket '{bucket_name}' created successfully.")
+        else:
+            logger.info(f"Bucket '{bucket_name}' already exists.")
\ No newline at end of file

server_manage_data/models.py

 from sqlalchemy import Column, Integer, String
 from sqlalchemy.ext.declarative import declarative_base
 
-from config import DB_TABLENAME
+from constants import DB_TABLENAME
 
 Base = declarative_base()
 

server_manage_data/server.py

 from fastapi import FastAPI, Depends, HTTPException
 from contextlib import asynccontextmanager
 from sqlalchemy.ext.asyncio import AsyncSession
-from sqlalchemy.future import select
-from sqlalchemy import MetaData, Table, inspect
-from minio import Minio
-from minio.error import S3Error
-import os
 
 from models import Base, LabeledData
 from schemas import LabeledDataCreate
-from sqlalchemy import create_engine, MetaData, Table
-from sqlalchemy.orm import sessionmaker
-import pandas as pd
-from io import StringIO, BytesIO
 from database import engine, get_db, SessionLocal
-from config import MINIO_SERVER, MINIO_MODEL_TRAINED, MINIO_DATA_LABELED
-from utils import check_bucket
-
-minio_client = Minio(
-    endpoint=MINIO_SERVER,
-    access_key=os.getenv("MINIO_ROOT_USER"),
-    secret_key=os.getenv("MINIO_ROOT_PASSWORD"),
-    secure=False
-)
-bucket_names = [MINIO_DATA_LABELED, MINIO_MODEL_TRAINED]
-
+from constants import MINIO_SERVER, MINIO_MODEL_TRAINED, MINIO_DATA_LABELED
+from minio_client import check_bucket
 
 @asynccontextmanager
 async def lifespan(app: FastAPI):
-    check_bucket(minio_client=minio_client, bucket_names=bucket_names)
+    check_bucket()
     async with engine.begin() as conn:
         await conn.run_sync(Base.metadata.create_all)
     yield

server_manage_data/utils.py

@@ -8,14 +8,3 @@ def get_data_from_yaml(filename):
         raise IOError(f"Error opening file: {filename}")
 
     return data
-
-def check_bucket(minio_client, bucket_names):
-    # Check if bucket exists
-    for bucket_name in bucket_names:
-        found = minio_client.bucket_exists(bucket_name)
-        if not found:
-            # Create bucket
-            minio_client.make_bucket(bucket_name)
-            print(f"Bucket '{bucket_name}' created successfully.")
-        else:
-            print(f"Bucket '{bucket_name}' already exists.")
\ No newline at end of file

server_train/constants.py

+from utils import get_data_from_yaml
+
+config = get_data_from_yaml("/src/config.yaml")
+DEVICE = config.get("device")
+CLASSES = config.get("classes")
+MINIO_SERVER = config.get("minio")["server"]
+MINIO_DATA_LABELED = config.get("minio")["data_labeled"]
+MINIO_MODEL_TRAINED = config.get("minio")["model_trained"]
+VNCORENLP_DIR = config.get("vncorenlp")["save_dir"]
+PHOBERTBASE_DIR = config.get("phobert_base")["save_dir"]
+MAX_TOKEN_LENGTH = config.get("phobert_base")["max_token_length"]
+MODEL_CHECKPOINT = config.get("model_checkpoint")
+CHUNK_SIZE = config.get("chunk_size")
+EPOCH = config.get("training")["epoch"]
+K_FOLD = config.get("training")["k_fold"]
+TEST_RATIO = config.get("training")["test_ratio"]
+BATCH_SIZE = config.get("training")["batch_size"]
+LOAD_DATA_WORKER = config.get("training")["load_data_worker"]
+LOG_FILE = config.get("log_file")
\ No newline at end of file

server_train/logger.py

+import logging
+import logging.handlers
+import sys
+
+from constants import LOG_FILE
+
+# Định nghĩa hàm để cấu hình logger
+def configure_logger(log_file=LOG_FILE):
+    # Tạo logger
+    logger = logging.getLogger('NLP_Training')
+    logger.setLevel(logging.DEBUG)
+
+    # Định nghĩa handler để ghi log vào file
+    file_handler = logging.FileHandler(log_file)
+    file_handler.setLevel(logging.DEBUG)
+
+    # Định nghĩa handler để in log ra stderr
+    stream_handler = logging.StreamHandler(sys.stderr)
+    stream_handler.setLevel(logging.DEBUG)
+
+    # Định nghĩa format cho log
+    formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+    file_handler.setFormatter(formatter)
+    stream_handler.setFormatter(formatter)
+
+    # Thêm handler vào logger
+    logger.addHandler(file_handler)
+    logger.addHandler(stream_handler)
+
+    return logger
+
+# Sử dụng hàm để cấu hình logger
+logger = configure_logger()

server_train/minio_client.py

+import os
+from minio import Minio
+from minio.error import S3Error
+
+from constants import MINIO_SERVER, MINIO_DATA_LABELED, MINIO_MODEL_TRAINED, MODEL_CHECKPOINT
+from logger import logger
+
+minio_client = Minio(
+    endpoint=MINIO_SERVER,
+    access_key=os.getenv("MINIO_ROOT_USER"),
+    secret_key=os.getenv("MINIO_ROOT_PASSWORD"),
+    secure=False
+)
+bucket_names = [MINIO_DATA_LABELED, MINIO_MODEL_TRAINED]
+
+def download_latest_model():
+    # List objects in the bucket
+    objects = minio_client.list_objects(MINIO_MODEL_TRAINED)
+    latest_obj = None
+    latest_time = None
+    
+    for obj in objects:
+        if "last" in obj.object_name:
+            if latest_time is None or obj.last_modified > latest_time:
+                latest_time = obj.last_modified
+                latest_obj = obj
+
+    if latest_obj is not None:
+        try:
+            minio_client.fget_object(MINIO_MODEL_TRAINED, latest_obj.object_name, MODEL_CHECKPOINT)
+        except S3Error as exc:
+            logger.error(f"Error occurred: {exc}")
+        return latest_obj.object_name
+    else:
+        raise Exception("No *last* models found in the bucket")
\ No newline at end of file

server_train/server.py

-from fastapi import FastAPI, HTTPException
-from pydantic import BaseModel
+import datetime
 import threading
-import torch
 import numpy as np
+import pandas as pd
+from io import BytesIO
+from fastapi import FastAPI, HTTPException
+from contextlib import asynccontextmanager
+from pydantic import BaseModel
 from transformers import AutoTokenizer
+from transformers import get_linear_schedule_with_warmup, AutoTokenizer
 from sklearn.model_selection import train_test_split
-import pandas as pd
-import os
-
 from gensim.utils import simple_preprocess
 from sklearn.model_selection import StratifiedKFold
-from contextlib import asynccontextmanager
-
+import torch
 import torch.nn as nn
 from torch.optim import AdamW
 from torch.utils.data import Dataset, DataLoader
 from torch.utils.tensorboard import SummaryWriter
-from transformers import get_linear_schedule_with_warmup, AutoTokenizer, AutoModel
-from io import BytesIO
-import pandas as pd
-import sys
-import logging
-import datetime
-from typing import List
-from minio import Minio
+from typing import List 
 from minio.error import S3Error
 
 from bert_model import BERTClassifier
-from utils import get_data_from_yaml, seed_everything
+from utils import seed_everything
 from preprocess import preprocess_row
+from minio_client import minio_client, download_latest_model
+from constants import PHOBERTBASE_DIR, CLASSES, DEVICE, MODEL_CHECKPOINT, EPOCH, \
+                        MAX_TOKEN_LENGTH, BATCH_SIZE, LOAD_DATA_WORKER, MINIO_MODEL_TRAINED, \
+                        MINIO_DATA_LABELED, TEST_RATIO, K_FOLD
+from logger import logger
 
 # Global variables to manage the training thread and the stop flag
 train_thread = None
 is_training = False
 stop_training_flag = threading.Event()
 
-logger = logging.getLogger()
-logger.setLevel(logging.INFO)
-
-handler = logging.StreamHandler(sys.stderr)
-formatter = logging.Formatter('%(asctime)s %(levelname)s: %(message)s')
-handler.setFormatter(formatter)
-
-logger.addHandler(handler)
-
-
-config = get_data_from_yaml("/src/config.yaml")
-DEVICE = config.get("device")
-CLASSES = config.get("classes")
-MINIO_SERVER = config.get("minio")["server"]
-MINIO_DATA_LABELED = config.get("minio")["data_labeled"]
-MINIO_MODEL_TRAINED = config.get("minio")["model_trained"]
-VNCORENLP_DIR = config.get("vncorenlp")["save_dir"]
-PHOBERTBASE_DIR = config.get("phobert_base")["save_dir"]
-MAX_TOKEN_LENGTH = config.get("phobert_base")["max_token_length"]
-MODEL_CHECKPOINT = config.get("model_checkpoint")
-CHUNK_SIZE = config.get("chunk_size")
-EPOCH = config.get("training")["epoch"]
-K_FOLD = config.get("training")["k_fold"]
-TEST_RATIO = config.get("training")["test_ratio"]
-BATCH_SIZE = config.get("training")["batch_size"]
-LOAD_DATA_WORKER = config.get("training")["load_data_worker"]
-
-minio_client = Minio(
-    endpoint=MINIO_SERVER,
-    access_key=os.getenv("MINIO_ROOT_USER"),
-    secret_key=os.getenv("MINIO_ROOT_PASSWORD"),
-    secure=False
-)
-bucket_names = [MINIO_DATA_LABELED, MINIO_MODEL_TRAINED]
-
-def download_latest_model():
-    # List objects in the bucket
-    objects = minio_client.list_objects(MINIO_MODEL_TRAINED)
-    latest_obj = None
-    latest_time = None
-    
-    for obj in objects:
-        if "last" in obj.object_name:
-            if latest_time is None or obj.last_modified > latest_time:
-                latest_time = obj.last_modified
-                latest_obj = obj
-
-    if latest_obj is not None:
-        try:
-            minio_client.fget_object(MINIO_MODEL_TRAINED, latest_obj.object_name, MODEL_CHECKPOINT)
-        except S3Error as exc:
-            print(f"Error occurred: {exc}")
-        return latest_obj.object_name
-    else:
-        raise Exception("No *last* models found in the bucket")
-    
-
 def release_training_vram():
     # Giải phóng bộ nhớ GPU của quá trình training
     torch.cuda.empty_cache()
@@ -214,7 +155,7 @@ def train_model(skf, train_df, tokenizer, use_pretrain=False):
 
     for fold in range(skf.n_splits):
         if stop_training_flag.is_set():
-            print("Training stopped.")
+            logger.info("Training stopped.")
             writer.close()
             return "Training stopped"
         logger.info(f'-----------Fold: {fold+1} ------------------')
@@ -233,7 +174,7 @@ def train_model(skf, train_df, tokenizer, use_pretrain=False):
         best_acc = 0
         for e in range(epoch_each_fold):
             if stop_training_flag.is_set():
-                print("Training stopped.")
+                logger.info("Training stopped.")
                 writer.close()
                 return "Training stopped"
             logger.info(f'Fold {fold+1} Epoch {cur_epoch+1}/{EPOCH}')
@@ -245,7 +186,7 @@ def train_model(skf, train_df, tokenizer, use_pretrain=False):
 
             for data in train_loader:
                 if stop_training_flag.is_set():
-                    print("Training stopped.")
+                    logger.info("Training stopped.")
                     writer.close()
                     return "Training stopped"
                 if data is not None:         
@@ -278,7 +219,7 @@ def train_model(skf, train_df, tokenizer, use_pretrain=False):
             writer.add_scalar("Loss/train", train_loss, cur_epoch)
             # End train ----------------------------------------------------------------------------------
             if stop_training_flag.is_set():
-                print("Training stopped.")
+                logger.info("Training stopped.")
                 writer.close()
                 return "Training stopped"
             # Valid --------------------------------------------------------------------------------------
@@ -289,7 +230,7 @@ def train_model(skf, train_df, tokenizer, use_pretrain=False):
             with torch.no_grad():
                 for data in valid_loader:
                     if stop_training_flag.is_set():
-                        print("Training stopped.")
+                        logger.info("Training stopped.")
                         writer.close()
                         return "Training stopped"
                     input_ids = data['input_ids'].to(DEVICE)
@@ -315,7 +256,7 @@ def train_model(skf, train_df, tokenizer, use_pretrain=False):
             # End valid ----------------------------------------------------------------------------------
             
             if stop_training_flag.is_set():
-                print("Training stopped.")
+                logger.info("Training stopped.")
                 writer.close()
                 return "Training stopped"
             
@@ -333,11 +274,11 @@ def train_model(skf, train_df, tokenizer, use_pretrain=False):
                         '/src/phobert_best.pth'      # Path to the file you want to upload
                     )
                 except S3Error as exc:
-                    print(f"Error occurred: {exc}")
-                print(f"File uploaded successfully. {checkpoint_best}")
+                    logger.error(f"Error occurred: {exc}")
+                logger.info(f"File uploaded successfully. {checkpoint_best}")
 
             if stop_training_flag.is_set():
-                print("Training stopped.")
+                logger.info("Training stopped.")
                 writer.close()
                 return "Training stopped"
             
@@ -350,12 +291,12 @@ def train_model(skf, train_df, tokenizer, use_pretrain=False):
                     '/src/phobert_last.pth'      # Path to the file you want to upload
                 )
             except S3Error as exc:
-                print(f"Error occurred: {exc}")
-            print(f"File uploaded successfully. {checkpoint_last}")
+                logger.error(f"Error occurred: {exc}")
+            logger.info(f"File uploaded successfully. {checkpoint_last}")
 
             cur_epoch = cur_epoch + 1
 
-    print("Training completed.")
+    logger.info("Training completed.")
     writer.close()
     release_training_vram()
     is_training = False
@@ -372,7 +313,7 @@ class TrainingResponse(BaseModel):
 
 @asynccontextmanager
 async def lifespan(app: FastAPI):
-    print(f'CUDA available: {torch.cuda.is_available()}')
+    logger.info(f'CUDA available: {torch.cuda.is_available()}')
     yield
     torch.cuda.empty_cache()
     torch.cuda.ipc_collect()
@@ -389,7 +330,7 @@ async def start_training(request: TrainingRequest):
     use_pretrain = False
 
     if request.pretrain == "":
-        print("Training from zero")
+        logger.info("Training from zero")
     elif request.pretrain == "latest":
         try:
             latest_model = download_latest_model()
@@ -397,7 +338,7 @@ async def start_training(request: TrainingRequest):
             is_training = False
             raise HTTPException(status_code=500, detail=str(exc))
         use_pretrain = True
-        print(f"Training from latest: {latest_model}")
+        logger.info(f"Training from latest: {latest_model}")
     else:
         try:
             minio_client.fget_object(MINIO_MODEL_TRAINED, request.pretrain, MODEL_CHECKPOINT)
@@ -405,7 +346,7 @@ async def start_training(request: TrainingRequest):
             is_training = False
             raise HTTPException(status_code=500, detail=str(exc))
         use_pretrain = True
-        print(f"Training from : {request.pretrain}")
+        logger.info(f"Training from : {request.pretrain}")
 
     objects = minio_client.list_objects(MINIO_DATA_LABELED, recursive=True)
 

server_train/utils.py

@@ -2,6 +2,8 @@ import numpy as np
 import torch
 import yaml 
 
+from logger import logger
+
 def get_data_from_yaml(filename):
     try:
         with open(filename, 'r') as f:
@@ -16,8 +18,8 @@ def seed_everything(seed_value):
     torch.manual_seed(seed_value)
     
     if torch.cuda.is_available(): 
-        print("Torch available")
+        logger.info("Torch available. Start seed_everything.")
         torch.cuda.manual_seed(seed_value)
         torch.cuda.manual_seed_all(seed_value)
         torch.backends.cudnn.deterministic = True
-        torch.backends.cudnn.benchmark = True
+        torch.backends.cudnn.benchmark = True