Weights & Biases Audit Trail Logging — ระบบ

Weights & Biases Audit Trail

W&B wandb MLOps Experiment Tracking Hyperparameters Metrics Artifacts Dashboard Model Registry Sweeps Audit Trail Compliance Reproducibility

เนื้อหาเกี่ยวข้อง — Tmgm ดีไหม — คู่มือฉบับสมบูรณ์ 2026

W&B Setup และ Tracking

# === Weights & Biases Setup ===

# pip install wandb
# wandb login

# experiment_tracking.py
# import wandb
# import torch
# import torch.nn as nn
# from datetime import datetime
#
# # Initialize W&B Run
# run = wandb.init(
#     project="image-classifier",
#     name=f"resnet50-{datetime.now():%Y%m%d-%H%M}",
#     config={
#         "model": "ResNet50",
#         "dataset": "CIFAR-10",
#         "epochs": 50,
#         "batch_size": 64,
#         "learning_rate": 0.001,
#         "optimizer": "Adam",
#         "weight_decay": 1e-4,
#         "scheduler": "CosineAnnealing",
#         "augmentation": ["RandomCrop", "HorizontalFlip", "Normalize"],
#     },
#     tags=["production", "v2", "resnet"],
#     notes="ResNet50 with CosineAnnealing scheduler",
# )
#
# # Log Metrics
# for epoch in range(config.epochs):
#     train_loss, train_acc = train_one_epoch(model, train_loader)
#     val_loss, val_acc = validate(model, val_loader)
#
#     wandb.log({
#         "epoch": epoch,
#         "train/loss": train_loss,
#         "train/accuracy": train_acc,
#         "val/loss": val_loss,
#         "val/accuracy": val_acc,
#         "learning_rate": scheduler.get_last_lr()[0],
#     })
#
#     # Log Model Checkpoint
#     if val_acc > best_acc:
#         best_acc = val_acc
#         torch.save(model.state_dict(), "best_model.pth")
#         artifact = wandb.Artifact(
#             name="model-checkpoint",
#             type="model",
#             metadata={"accuracy": val_acc, "epoch": epoch},
#         )
#         artifact.add_file("best_model.pth")
#         run.log_artifact(artifact)
#
# # Log Dataset
# dataset_artifact = wandb.Artifact("cifar10-processed", type="dataset")
# dataset_artifact.add_dir("data/processed/")
# run.log_artifact(dataset_artifact)
#
# wandb.finish()

from dataclasses import dataclass, field
from typing import List, Dict
from datetime import datetime

@dataclass
class ExperimentRun:
    run_id: str
    project: str
    model: str
    config: Dict
    metrics: Dict
    user: str
    timestamp: str
    tags: List[str]
    status: str

runs = [
    ExperimentRun("run-abc123", "image-classifier", "ResNet50",
                  {"lr": 0.001, "epochs": 50, "batch": 64},
                  {"val_acc": 0.942, "val_loss": 0.18},
                  "สมชาย", "2024-01-15 10:30", ["production", "v2"], "completed"),
    ExperimentRun("run-def456", "image-classifier", "EfficientNet-B0",
                  {"lr": 0.0005, "epochs": 100, "batch": 32},
                  {"val_acc": 0.956, "val_loss": 0.14},
                  "สมหญิง", "2024-01-16 14:20", ["experiment"], "completed"),
    ExperimentRun("run-ghi789", "image-classifier", "ViT-B/16",
                  {"lr": 0.0001, "epochs": 30, "batch": 16},
                  {"val_acc": 0.961, "val_loss": 0.12},
                  "สมศักดิ์", "2024-01-17 09:15", ["experiment", "transformer"], "completed"),
]

print("=== W&B Experiment Runs ===")
for r in runs:
    print(f"\n  [{r.run_id}] {r.model}")
    print(f"    User: {r.user} | {r.timestamp}")
    print(f"    Accuracy: {r.metrics['val_acc']:.1%} | Loss: {r.metrics['val_loss']:.3f}")
    print(f"    Tags: {', '.join(r.tags)}")

Model Registry

# model_registry.py — Model Registry & Versioning
from dataclasses import dataclass
from typing import Optional
from enum import Enum

class ModelStage(Enum):
    DEVELOPMENT = "Development"
    STAGING = "Staging"
    PRODUCTION = "Production"
    ARCHIVED = "Archived"

@dataclass
class RegisteredModel:
    name: str
    version: str
    stage: ModelStage
    accuracy: float
    created_by: str
    approved_by: Optional[str]
    artifact_path: str

models = [
    RegisteredModel("image-classifier", "v1.0", ModelStage.ARCHIVED, 0.920, "สมชาย", "ผู้จัดการ", "s3://models/v1.0/"),
    RegisteredModel("image-classifier", "v2.0", ModelStage.ARCHIVED, 0.942, "สมชาย", "ผู้จัดการ", "s3://models/v2.0/"),
    RegisteredModel("image-classifier", "v2.1", ModelStage.PRODUCTION, 0.956, "สมหญิง", "ผู้จัดการ", "s3://models/v2.1/"),
    RegisteredModel("image-classifier", "v3.0", ModelStage.STAGING, 0.961, "สมศักดิ์", None, "s3://models/v3.0/"),
]

print("=== Model Registry ===")
for m in models:
    approved = m.approved_by or "Pending"
    print(f"  {m.name} {m.version} [{m.stage.value}]")
    print(f"    Accuracy: {m.accuracy:.1%} | By: {m.created_by} | Approved: {approved}")

# W&B Model Registry Commands
# import wandb
# run = wandb.init()
#
# # Log Model as Artifact
# artifact = wandb.Artifact("image-classifier", type="model",
#                           metadata={"accuracy": 0.956})
# artifact.add_file("model.pth")
# run.log_artifact(artifact)
#
# # Link to Registry
# run.link_artifact(artifact, "model-registry/image-classifier",
#                   aliases=["latest", "v2.1"])
#
# # Download from Registry
# api = wandb.Api()
# artifact = api.artifact("myproject/model-registry/image-classifier:v2.1")
# artifact.download()

registry_workflow = [
    "1. Train Model — บันทึก Metrics และ Artifacts",
    "2. Evaluate — ทดสอบ Test Set, Bias Check, A/B Test",
    "3. Register — บันทึกใน Model Registry พร้อม Metadata",
    "4. Review — ส่งให้ Reviewer ตรวจสอบ",
    "5. Approve — ผู้มีอำนาจ Approve เพื่อ Deploy",
    "6. Deploy — GitOps Deploy ไป Staging -> Production",
    "7. Monitor — ตรวจ Performance, Data Drift, Model Drift",
]

print(f"\n\nModel Registry Workflow:")
for step in registry_workflow:
    print(f"  {step}")

เคล็ดลับ

• Log Everything: บันทึกทุก Hyperparameter, Metric, Artifact
• Tags: ใช้ Tags จัดกลุ่ม Runs (production, experiment, baseline)
• Artifacts: เก็บ Dataset และ Model เป็น Versioned Artifacts
• Approval: ใส่ Approval Workflow ก่อน Deploy Production
• Lineage: ติดตาม Data -> Model -> Deployment Chain

Weights & Biases คืออะไร

MLOps Experiment Tracking Hyperparameters Metrics Artifacts Dashboard Model Registry Sweeps PyTorch TensorFlow Keras

แนะนำเพิ่มเติม — แหล่งความรู้ Forex iCafeForex

เนื้อหาเกี่ยวข้อง — อ่านต่อ: warzone storing — ข้อมูลครบถ้วน 2026

เนื้อหาเกี่ยวข้อง — ดูเพิ่มเติมเรื่อง Soda Data Quality Community Building