# Walkthrough & Hasil Implementasi: Notebook Master Pipeline...

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# Walkthrough & Hasil Implementasi: Notebook Master Pipeline ACOS Versi PRO **Tanggal:** 2026-08-28 06:48 WIB **Dokumen Referensi:** `reports/015_walkthrough_implementasi_master_pipeline_colab_pro_28082026_0648.md` **Objek Implementasi:** `notebooks/00_ACOS_Master_Pipeline_Colab_PRO.ipynb` --- ## 1. Ringkasan Eksekutif Telah berhasil dibuat dan divalidasi notebook produksi **`00_ACOS_Master_Pipeline_Colab_PRO.ipynb`** (27 sel). Notebook ini menyempurnakan versi sebelumnya (`ASLI` dan `UPDATE`) dengan mengintegrasikan: 1. **Dukungan Penuh Dual-Environment (Lokal & Google Colab):** Deteksi otomatis direktori kerja lokal dan Google Drive `/content/drive/MyDrive/ACOS`. 2. **Eliminasi Total Hardcoded Path:** Tidak ada lagi string path statis yang memicu error saat dijalankan di sesi atau lingkungan berbeda. 3. **Akselerasi GPU & Manajemen Memori VRAM:** Pembersihan cache CUDA (`torch.cuda.empty_cache()`), `pin_memory = True` pada DataLoader, `cudnn.benchmark = True`, serta pencatatan *Peak VRAM Usage* per epoch. 4. **Smart State Checkpoint & Recovery:** Mekanisme pemulihan cerdas jika runtime Colab terputus pasca-Step 1. 5. **Kesiapan Ekosistem MCP (Model Context Protocol):** Emisi otomatis berkas manifest status terstruktur **`session_manifest.json`**. --- ## 2. Struktur 27 Sel pada Notebook Versi PRO ``` [Cell 00] ── Header & Ringkasan Fitur Produksi (Markdown) [Cell 01] ── Section 1: Environment Setup & GPU Diagnostics (Markdown) [Cell 02] ── Code: Safe Google Drive Mounting, Pip Dependencies, & GPU / VRAM Inspection [Cell 03] ── Section 2: Dynamic Path Initialization (Markdown) [Cell 04] ── Code: Dynamic Path Resolution (Colab Drive / Local) + colab_utils Import [Cell 05] ── Section 3: Parameters, Caching, & MCP Manifest (Markdown) [Cell 06] ── Code: Hyperparameter Central, BERT Caching (HF Hub), Init session_manifest.json [Cell 07] ── Section 4: Exploratory Data Analysis & Plots (Markdown) [Cell 08] ── Code: EDA Execution, 4 High-Resolution Plots (300 DPI), CSV Statistics [Cell 09] ── Section 4b: Dataset Diagnostic Search (Markdown) [Cell 10] ── Code: Adaptive Multi-Location Dataset & tokenized_data Verification [Cell 11] ── Section 5: Step 1 Aspect-Opinion Co-Extraction (Markdown) [Cell 12] ── Code: BertForQuadABSA Training Loop, Peak VRAM Tracker, Checkpoint step1_best [Cell 13] ── Section 6: Smart State Checkpoint Saver (Markdown) [Cell 14] ── Code: Exporting pipeline_state.pkl to Session Directory [Cell 15] ── Section 6b: Smart State Recovery (Markdown) [Cell 16] ── Code: Auto-Detection & Restoration of Latest Session State [Cell 17] ── Section 7: Candidate Pair Generation Bridge (Markdown) [Cell 18] ── Code: Cartesian Product (a, o) + Implicit Entity [-1, -1] Generation [Cell 19] ── Section 8: Step 2 Category & Sentiment Classification (Markdown) [Cell 20] ── Code: CategorySentiClassification Training, Tokenizer Debugger, Checkpoint step2_best [Cell 21] ── Section 9: 15 Sub-Tasks Benchmark Dashboard (Markdown) [Cell 22] ── Code: Full Quadruple Evaluation, SubtaskMetricCapture, master_metrics.json Export [Cell 23] ── Section 10: Interactive Live Inference Demo (Markdown) [Cell 24] ── Code: Two-Stage Custom Text Inference Function (analyze_review_quadruples) [Cell 25] ── Section 11: Artifact Inventory & Finalization (Markdown) [Cell 26] ── Code: Cataloging All Artifacts, Markdown Report Save, Final MCP Status Update ``` --- ## 3. Matriks Perbandingan Fitur: ASLI vs. UPDATE vs. PRO | Fitur / Komponen | Versi ASLI (24 Sel) | Versi UPDATE (25 Sel) | Versi PRO (27 Sel) | | :--- | :---: | :---: | :---: | | **Eksekusi Langsung (*In-Memory*)** | ✅ Ya | ✅ Ya | ✅ Ya | | **Penyimpanan di Google Drive** | ✅ Ya | ✅ Ya | ✅ Ya (`/content/drive/MyDrive/ACOS`) | | **Dukungan Lingkungan Lokal** | ⚠️ Terbatas (ada path Colab) | ⚠️ Terbatas (ada path statis) | ✅ **100% Adaptif Dinamis** | | **Zero Hardcoded Paths** | ❌ Tidak | ❌ Tidak (Sel 9 & 14 statis) | ✅ **Sepenuhnya Dinamis** | | **State Checkpointing (`.pkl`)** | ❌ Tidak ada | ✅ Statis | ✅ **Smart Auto-Detect Recovery** | | **Pencegahan CUDA OOM (`empty_cache`)** | ❌ Tidak ada | ❌ Tidak ada | ✅ **Terpasang di Semua Loop** | | **Pelacak Peak VRAM Memori GPU** | ❌ Tidak ada | ❌ Tidak ada | ✅ **Tercatat per Epoch** | | **Optimasi DataLoader (`pin_memory`)** | ❌ Default False | ❌ Default False | ✅ **Dinamis True pada GPU** | | **cuDNN Benchmark Acceleration** | ❌ Tidak aktif | ❌ Tidak aktif | ✅ **Aktif Otomatis** | | **Integrasi MCP (`session_manifest.json`)** | ❌ Tidak ada | ❌ Tidak ada | ✅ **Real-Time Lifecycle Tracker** | | **Dataset Diagnostic Search** | ❌ Tidak ada | ⚠️ Hanya Colab | ✅ **Multi-Platform (Lokal & Colab)** | --- ## 4. Struktur Output yang Dihasilkan pada Sesi Eksekusi Setiap eksekusi versi PRO akan menghasilkan struktur artefak terorganisir berikut di direktori sesi (`results/<domain>_<DDMMYYYY_HMS>/`): ``` results/rest16_28082026_064800/ ├── checkpoints/ │ ├── step1_best/ # Checkpoint PyTorch Step 1 (BERT-CRF) │ │ ├── pytorch_model.bin │ │ ├── config.json │ │ └── vocab.txt │ └── step2_best/ # Checkpoint PyTorch Step 2 (Cat-Senti) │ ├── pytorch_model.bin │ ├── config.json │ └── vocab.txt ├── plots/ # Grafik Publikasi 300 DPI │ ├── 01_eda_dataset_distribution.png │ ├── 02_eda_category_sentiment.png │ ├── 02b_eda_length_and_implicit_combo.png │ ├── 02c_eda_category_sentiment_heatmap.png │ ├── 03_step1_training_loss_f1_curve.png │ ├── 04_candidate_pairs_distribution.png │ ├── 04_step2_training_loss_f1_curve.png │ └── 05_benchmark_subtasks_f1.png ├── csv/ # Data Tabular & Riwayat Metrik │ ├── eda_dataset_statistics.csv │ ├── eda_all_samples_annotated.csv │ ├── master_00_konfigurasi.csv │ ├── master_01_statistik_dataset.csv │ ├── master_02_ringkasan_eda.csv │ ├── master_03_step1_riwayat.csv │ ├── master_04_tipe_pasangan.csv │ ├── master_05_preview_pasangan.csv │ ├── master_06_step2_riwayat.csv │ ├── master_07_metrik_quadruple_final.csv │ ├── master_08_metrik_subtask.csv │ ├── master_09_agregasi_elemen.csv │ ├── master_10_contoh_inferensi.csv │ └── master_11_daftar_artefak.csv ├── md/ # Laporan Terstruktur │ ├── 00_master_pipeline.md │ └── master_*.md ├── logs/ # Log Operasional & JSON Ringkasan │ ├── pred4pipeline.txt │ └── master_metrics.json ├── pipeline_state.pkl # State Serialized untuk Pemulihan Kernel └── session_manifest.json # Manifest Status Ekosistem MCP / Agent ``` --- ## 5. Panduan Penggunaan ### A. Menjalankan di Google Colab: 1. Buka berkas [00_ACOS_Master_Pipeline_Colab_PRO.ipynb](file:///d:/laragon/www/ACOS-ASLI/notebooks/00_ACOS_Master_Pipeline_Colab_PRO.ipynb) di Google Colab. 2. Pastikan jenis akselerator hardware telah diatur ke GPU (**Runtime > Change runtime type > GPU T4 / A100**). 3. Klik **Runtime > Run all** (1-Click Pipeline). 4. Hasil akan otomatis tersimpan di Google Drive pada folder `/content/drive/MyDrive/ACOS/Output/results/`. ### B. Menjalankan di Komputer Lokal: 1. Buka berkas [00_ACOS_Master_Pipeline_Colab_PRO.ipynb](file:///d:/laragon/www/ACOS-ASLI/notebooks/00_ACOS_Master_Pipeline_Colab_PRO.ipynb) di VS Code / Cursor / Jupyter Lab. 2. Pilih kernel Python yang memiliki PyTorch dan CUDA/CPU. 3. Jalankan sel per sel; seluruh artefak akan otomatis tersimpan di folder lokal `./results/`.