From 94b25f9752a4c032d5aa637a086a532f486afb98 Mon Sep 17 00:00:00 2001 From: Sergio Jimenez Jimenez Date: Mon, 19 Jan 2026 16:32:45 +0100 Subject: [PATCH] raytune as docker --- .gitea/workflows/ci.yaml | 30 + README.md | 20 +- apply_content.py | 9 +- docs/01_introduccion.md | 12 +- docs/03_objetivos_metodologia.md | 32 +- docs/04_desarrollo_especifico.md | 46 +- docs/05_conclusiones_trabajo_futuro.md | 4 + docs/07_anexo_a.md | 34 +- src/README.md | 227 +- src/docker-compose.tuning.doctr.yml | 50 + src/docker-compose.tuning.easyocr.yml | 51 + src/docker-compose.tuning.paddle.yml | 50 + src/docker-compose.tuning.yml | 82 + src/raytune/Dockerfile | 18 + src/raytune/README.md | 131 + src/raytune/raytune_ocr.py | 371 ++ src/raytune/requirements.txt | 4 + src/raytune/run_tuning.py | 80 + thesis_output/plantilla_individual.htm | Bin 1724448 -> 584742 bytes thesis_output/plantilla_individual.htm.bak | 6075 ++++++++++++++++++++ 20 files changed, 7214 insertions(+), 112 deletions(-) create mode 100644 src/docker-compose.tuning.doctr.yml create mode 100644 src/docker-compose.tuning.easyocr.yml create mode 100644 src/docker-compose.tuning.paddle.yml create mode 100644 src/docker-compose.tuning.yml create mode 100644 src/raytune/Dockerfile create mode 100644 src/raytune/README.md create mode 100644 src/raytune/raytune_ocr.py create mode 100644 src/raytune/requirements.txt create mode 100644 src/raytune/run_tuning.py create mode 100644 thesis_output/plantilla_individual.htm.bak diff --git a/.gitea/workflows/ci.yaml b/.gitea/workflows/ci.yaml index 26510e4..f6b2c8b 100644 --- a/.gitea/workflows/ci.yaml +++ b/.gitea/workflows/ci.yaml @@ -25,6 +25,7 @@ jobs: image_easyocr_gpu: seryus.ddns.net/unir/easyocr-gpu image_doctr: seryus.ddns.net/unir/doctr-cpu image_doctr_gpu: seryus.ddns.net/unir/doctr-gpu + image_raytune: seryus.ddns.net/unir/raytune steps: - name: Output version info run: | @@ -205,3 +206,32 @@ jobs: tags: | ${{ needs.essential.outputs.image_doctr_gpu }}:${{ needs.essential.outputs.Version }} ${{ needs.essential.outputs.image_doctr_gpu }}:latest + + # Ray Tune OCR image (amd64 only) + build_raytune: + runs-on: ubuntu-latest + needs: essential + steps: + - name: Checkout + uses: actions/checkout@v4 + + - name: Set up Docker Buildx + uses: docker/setup-buildx-action@v3 + + - name: Login to Gitea Registry + uses: docker/login-action@v3 + with: + registry: ${{ needs.essential.outputs.repo }} + username: username + password: ${{ secrets.CI_READWRITE }} + + - name: Build and push Ray Tune image + uses: docker/build-push-action@v5 + with: + context: src/raytune + file: src/raytune/Dockerfile + platforms: linux/amd64 + push: true + tags: | + ${{ needs.essential.outputs.image_raytune }}:${{ needs.essential.outputs.Version }} + ${{ needs.essential.outputs.image_raytune }}:latest diff --git a/README.md b/README.md index 62bf3e4..66bc978 100644 --- a/README.md +++ b/README.md @@ -18,11 +18,15 @@ Optimizar el rendimiento de PaddleOCR para documentos académicos en español me ## Resultados Principales +**Tabla.** *Comparación de métricas OCR entre configuración baseline y optimizada.* + | Modelo | CER | Precisión Caracteres | WER | Precisión Palabras | |--------|-----|---------------------|-----|-------------------| | PaddleOCR (Baseline) | 7.78% | 92.22% | 14.94% | 85.06% | | **PaddleOCR-HyperAdjust** | **1.49%** | **98.51%** | **7.62%** | **92.38%** | +*Fuente: Elaboración propia.* + **Mejora obtenida:** Reducción del CER en un **80.9%** ### Configuración Óptima Encontrada @@ -56,6 +60,8 @@ PDF (académico UNIR) ### Experimento de Optimización +**Tabla.** *Parámetros de configuración del experimento Ray Tune.* + | Parámetro | Valor | |-----------|-------| | Número de trials | 64 | @@ -64,6 +70,8 @@ PDF (académico UNIR) | Trials concurrentes | 2 | | Tiempo total | ~6 horas (CPU) | +*Fuente: Elaboración propia.* + --- ## Estructura del Repositorio @@ -143,16 +151,20 @@ Se realizó una validación adicional con aceleración GPU para evaluar la viabi ## Requisitos +**Tabla.** *Dependencias principales del proyecto y versiones utilizadas.* + | Componente | Versión | |------------|---------| -| Python | 3.11.9 | +| Python | 3.12.3 | | PaddlePaddle | 3.2.2 | | PaddleOCR | 3.3.2 | | Ray | 2.52.1 | -| Optuna | 4.6.0 | +| Optuna | 4.7.0 | | jiwer | (para métricas CER/WER) | | PyMuPDF | (para conversión PDF) | +*Fuente: Elaboración propia.* + --- ## Uso @@ -262,11 +274,15 @@ python3 apply_content.py ### Archivos de Entrada y Salida +**Tabla.** *Relación de scripts de generación con sus archivos de entrada y salida.* + | Script | Entrada | Salida | |--------|---------|--------| | `generate_mermaid_figures.py` | `docs/*.md` (bloques ```mermaid```) | `thesis_output/figures/figura_*.png`, `figures_manifest.json` | | `apply_content.py` | `instructions/plantilla_individual.htm`, `docs/*.md`, `thesis_output/figures/*.png` | `thesis_output/plantilla_individual.htm` | +*Fuente: Elaboración propia.* + ### Contenido Generado Automáticamente - **30 tablas** con formato APA (Tabla X. *Título* + Fuente: ...) diff --git a/apply_content.py b/apply_content.py index ca2139c..98ebc4e 100644 --- a/apply_content.py +++ b/apply_content.py @@ -6,7 +6,8 @@ import os from bs4 import BeautifulSoup, NavigableString BASE_DIR = os.path.dirname(os.path.abspath(__file__)) -TEMPLATE = os.path.join(BASE_DIR, 'thesis_output/plantilla_individual.htm') +TEMPLATE_INPUT = os.path.join(BASE_DIR, 'instructions/plantilla_individual.htm') +TEMPLATE_OUTPUT = os.path.join(BASE_DIR, 'thesis_output/plantilla_individual.htm') DOCS_DIR = os.path.join(BASE_DIR, 'docs') # Global counters for tables and figures @@ -365,7 +366,7 @@ def main(): global table_counter, figure_counter print("Reading template...") - html_content = read_file(TEMPLATE) + html_content = read_file(TEMPLATE_INPUT) soup = BeautifulSoup(html_content, 'html.parser') print("Reading docs content...") @@ -595,9 +596,9 @@ def main(): print("Saving modified template...") output_html = str(soup) - write_file(TEMPLATE, output_html) + write_file(TEMPLATE_OUTPUT, output_html) - print(f"✓ Done! Modified: {TEMPLATE}") + print(f"✓ Done! Modified: {TEMPLATE_OUTPUT}") print("\nTo convert to DOCX:") print("1. Open the .htm file in Microsoft Word") print("2. Replace [Insertar diagrama Mermaid aquí] placeholders with actual diagrams") diff --git a/docs/01_introduccion.md b/docs/01_introduccion.md index 2608a43..8db0086 100644 --- a/docs/01_introduccion.md +++ b/docs/01_introduccion.md @@ -18,6 +18,8 @@ El procesamiento de documentos en español presenta particularidades que complic La Tabla 1 resume los principales desafíos lingüísticos del OCR en español: +**Tabla 1.** *Desafíos lingüísticos específicos del OCR en español.* + | Desafío | Descripción | Impacto en OCR | |---------|-------------|----------------| | Caracteres especiales | ñ, á, é, í, ó, ú, ü, ¿, ¡ | Confusión con caracteres similares (n/ñ, a/á) | @@ -25,7 +27,7 @@ La Tabla 1 resume los principales desafíos lingüísticos del OCR en español: | Abreviaturas | Dr., Sra., Ud., etc. | Puntos internos confunden segmentación | | Nombres propios | Tildes en apellidos (García, Martínez) | Bases de datos sin soporte Unicode | -*Tabla 1. Desafíos lingüísticos específicos del OCR en español. Fuente: Elaboración propia.* +*Fuente: Elaboración propia.* Además de los aspectos lingüísticos, los documentos académicos y administrativos en español presentan características tipográficas que complican el reconocimiento: variaciones en fuentes entre encabezados, cuerpo y notas al pie; presencia de tablas con bordes y celdas; logotipos institucionales; marcas de agua; y elementos gráficos como firmas o sellos. Estos elementos generan ruido que puede propagarse en aplicaciones downstream como la extracción de entidades nombradas o el análisis semántico. @@ -37,6 +39,8 @@ La adaptación de modelos preentrenados a dominios específicos típicamente req La Tabla 2 ilustra los requisitos típicos para diferentes estrategias de mejora de OCR: +**Tabla 2.** *Comparación de estrategias de mejora de modelos OCR.* + | Estrategia | Datos requeridos | Hardware | Tiempo | Expertise | |------------|------------------|----------|--------|-----------| | Fine-tuning completo | >10,000 imágenes etiquetadas | GPU (≥16GB VRAM) | Días-Semanas | Alto | @@ -44,7 +48,7 @@ La Tabla 2 ilustra los requisitos típicos para diferentes estrategias de mejora | Transfer learning | >500 imágenes etiquetadas | GPU (≥8GB VRAM) | Horas | Medio | | **Optimización de hiperparámetros** | **<100 imágenes de validación** | **CPU suficiente** | **Horas** | **Bajo-Medio** | -*Tabla 2. Comparación de estrategias de mejora de modelos OCR. Fuente: Elaboración propia.* +*Fuente: Elaboración propia.* ### La oportunidad: optimización sin fine-tuning @@ -88,6 +92,8 @@ Una solución técnicamente superior pero impracticable tiene valor limitado. Es Este trabajo se centra específicamente en: +**Tabla 3.** *Delimitación del alcance del trabajo.* + | Aspecto | Dentro del alcance | Fuera del alcance | |---------|-------------------|-------------------| | **Tipo de documento** | Documentos académicos digitales (PDF) | Documentos escaneados, manuscritos | @@ -96,7 +102,7 @@ Este trabajo se centra específicamente en: | **Método de mejora** | Optimización de hiperparámetros | Fine-tuning, aumento de datos | | **Hardware** | Ejecución en CPU | Aceleración GPU | -*Tabla 3. Delimitación del alcance del trabajo. Fuente: Elaboración propia.* +*Fuente: Elaboración propia.* ### Relevancia y beneficiarios diff --git a/docs/03_objetivos_metodologia.md b/docs/03_objetivos_metodologia.md index 4624210..cdff727 100644 --- a/docs/03_objetivos_metodologia.md +++ b/docs/03_objetivos_metodologia.md @@ -8,6 +8,8 @@ Este capítulo establece los objetivos del trabajo siguiendo la metodología SMA ### Justificación SMART del Objetivo General +**Tabla 4.** *Justificación SMART del objetivo general.* + | Criterio | Cumplimiento | |----------|--------------| | **Específico (S)** | Se define claramente qué se quiere lograr: optimizar PaddleOCR mediante ajuste de hiperparámetros para documentos en español | @@ -16,6 +18,8 @@ Este capítulo establece los objetivos del trabajo siguiendo la metodología SMA | **Relevante (R)** | El impacto es demostrable: mejora la extracción de texto en documentos académicos sin costes adicionales de infraestructura | | **Temporal (T)** | El plazo es un cuatrimestre, correspondiente al TFM | +*Fuente: Elaboración propia.* + ## Objetivos específicos ### OE1: Comparar soluciones OCR de código abierto @@ -115,12 +119,16 @@ class ImageTextDataset: #### Modelos Evaluados +**Tabla 5.** *Modelos OCR evaluados en el benchmark inicial.* + | Modelo | Versión | Configuración | |--------|---------|---------------| | EasyOCR | - | Idiomas: ['es', 'en'] | | PaddleOCR | PP-OCRv5 | Modelos server_det + server_rec | | DocTR | - | db_resnet50 + sar_resnet31 | +*Fuente: Elaboración propia.* + #### Métricas de Evaluación Se utilizó la biblioteca `jiwer` para calcular: @@ -139,6 +147,8 @@ def evaluate_text(reference, prediction): #### Hiperparámetros Seleccionados +**Tabla 6.** *Hiperparámetros seleccionados para optimización.* + | Parámetro | Tipo | Rango/Valores | Descripción | |-----------|------|---------------|-------------| | `use_doc_orientation_classify` | Booleano | [True, False] | Clasificación de orientación del documento | @@ -149,6 +159,8 @@ def evaluate_text(reference, prediction): | `text_det_unclip_ratio` | Fijo | 0.0 | Coeficiente de expansión (fijado) | | `text_rec_score_thresh` | Continuo | [0.0, 0.7] | Umbral de confianza de reconocimiento | +*Fuente: Elaboración propia.* + #### Configuración de Ray Tune ```python @@ -235,23 +247,31 @@ Y retorna métricas en formato JSON: #### Hardware +**Tabla 7.** *Especificaciones de hardware del entorno de desarrollo.* + | Componente | Especificación | |------------|----------------| -| CPU | Intel Core (especificar modelo) | -| RAM | 16 GB | -| GPU | No disponible (ejecución en CPU) | +| CPU | AMD Ryzen 7 5800H | +| RAM | 16 GB DDR4 | +| GPU | NVIDIA RTX 3060 Laptop (5.66 GB VRAM) | | Almacenamiento | SSD | +*Fuente: Elaboración propia.* + #### Software +**Tabla 8.** *Versiones de software utilizadas.* + | Componente | Versión | |------------|---------| -| Sistema Operativo | Windows 10/11 | -| Python | 3.11.9 | +| Sistema Operativo | Ubuntu 24.04.3 LTS | +| Python | 3.12.3 | | PaddleOCR | 3.3.2 | | PaddlePaddle | 3.2.2 | | Ray | 2.52.1 | -| Optuna | 4.6.0 | +| Optuna | 4.7.0 | + +*Fuente: Elaboración propia.* ### Limitaciones Metodológicas diff --git a/docs/04_desarrollo_especifico.md b/docs/04_desarrollo_especifico.md index 4df005c..0241b3b 100644 --- a/docs/04_desarrollo_especifico.md +++ b/docs/04_desarrollo_especifico.md @@ -34,6 +34,11 @@ Se seleccionaron tres soluciones OCR de código abierto representativas del esta *Fuente: Elaboración propia.* +**Imágenes Docker disponibles en el registro del proyecto:** +- PaddleOCR: `seryus.ddns.net/unir/paddle-ocr-gpu`, `seryus.ddns.net/unir/paddle-ocr-cpu` +- EasyOCR: `seryus.ddns.net/unir/easyocr-gpu` +- DocTR: `seryus.ddns.net/unir/doctr-gpu` + ### Criterios de Éxito Los criterios establecidos para evaluar las soluciones fueron: @@ -322,7 +327,7 @@ Esta sección ha presentado: ### Introducción -Esta sección describe el proceso de optimización de hiperparámetros de PaddleOCR utilizando Ray Tune con el algoritmo de búsqueda Optuna. Los experimentos fueron implementados en el notebook `src/paddle_ocr_fine_tune_unir_raytune.ipynb` y los resultados se almacenaron en `src/raytune_paddle_subproc_results_20251207_192320.csv`. +Esta sección describe el proceso de optimización de hiperparámetros de PaddleOCR utilizando Ray Tune con el algoritmo de búsqueda Optuna. Los experimentos fueron implementados en [`src/run_tuning.py`](https://github.com/seryus/MastersThesis/blob/main/src/run_tuning.py) con la librería de utilidades [`src/raytune_ocr.py`](https://github.com/seryus/MastersThesis/blob/main/src/raytune_ocr.py), y los resultados se almacenaron en [`src/results/`](https://github.com/seryus/MastersThesis/tree/main/src/results). La optimización de hiperparámetros representa una alternativa al fine-tuning tradicional que no requiere: - Acceso a GPU dedicada @@ -339,17 +344,17 @@ El experimento se ejecutó en el siguiente entorno: | Componente | Versión/Especificación | |------------|------------------------| -| Sistema operativo | Windows 10/11 | -| Python | 3.11.9 | +| Sistema operativo | Ubuntu 24.04.3 LTS | +| Python | 3.12.3 | | PaddlePaddle | 3.2.2 | | PaddleOCR | 3.3.2 | | Ray | 2.52.1 | -| Optuna | 4.6.0 | -| CPU | Intel Core (multinúcleo) | -| RAM | 16 GB | -| GPU | No disponible (ejecución CPU) | +| Optuna | 4.7.0 | +| CPU | AMD Ryzen 7 5800H | +| RAM | 16 GB DDR4 | +| GPU | NVIDIA RTX 3060 Laptop (5.66 GB VRAM) | -*Fuente: Outputs del notebook `src/paddle_ocr_fine_tune_unir_raytune.ipynb`.* +*Fuente: Configuración del entorno de ejecución. Resultados en `src/results/` generados por `src/run_tuning.py`.* #### Arquitectura de Ejecución @@ -613,7 +618,7 @@ Configuración óptima: | text_det_unclip_ratio | 0.0 | 1.5 | -1.5 (fijado) | | text_rec_score_thresh | **0.6350** | 0.5 | +0.135 | -*Fuente: Análisis del notebook.* +*Fuente: Análisis de [`src/results/`](https://github.com/seryus/MastersThesis/tree/main/src/results) generados por [`src/run_tuning.py`](https://github.com/seryus/MastersThesis/blob/main/src/run_tuning.py).* #### Análisis de Correlación @@ -628,7 +633,7 @@ Se calculó la correlación de Pearson entre los parámetros continuos y las mé | `text_rec_score_thresh` | -0.161 | Correlación débil negativa | | `text_det_unclip_ratio` | NaN | Varianza cero (valor fijo) | -*Fuente: Análisis del notebook.* +*Fuente: Análisis de [`src/results/`](https://github.com/seryus/MastersThesis/tree/main/src/results) generados por [`src/run_tuning.py`](https://github.com/seryus/MastersThesis/blob/main/src/run_tuning.py).* **Tabla 24.** *Correlación de parámetros con WER.* @@ -638,7 +643,7 @@ Se calculó la correlación de Pearson entre los parámetros continuos y las mé | `text_det_box_thresh` | +0.227 | Correlación débil positiva | | `text_rec_score_thresh` | -0.173 | Correlación débil negativa | -*Fuente: Análisis del notebook.* +*Fuente: Análisis de [`src/results/`](https://github.com/seryus/MastersThesis/tree/main/src/results) generados por [`src/run_tuning.py`](https://github.com/seryus/MastersThesis/blob/main/src/run_tuning.py).* **Hallazgo clave**: El parámetro `text_det_thresh` muestra la correlación más fuerte (-0.52 con ambas métricas), indicando que valores más altos de este umbral tienden a reducir el error. Este umbral controla qué píxeles se consideran "texto" en el mapa de probabilidad del detector. @@ -653,7 +658,7 @@ El parámetro booleano `textline_orientation` demostró tener el mayor impacto e | True | 3.76% | 7.12% | 12.73% | 32 | | False | 12.40% | 14.93% | 21.71% | 32 | -*Fuente: Análisis del notebook.* +*Fuente: Análisis de [`src/results/`](https://github.com/seryus/MastersThesis/tree/main/src/results) generados por [`src/run_tuning.py`](https://github.com/seryus/MastersThesis/blob/main/src/run_tuning.py).* **Interpretación:** @@ -741,7 +746,7 @@ optimized_config = { | PaddleOCR (Baseline) | 7.78% | 92.22% | 14.94% | 85.06% | | PaddleOCR-HyperAdjust | **1.49%** | **98.51%** | **7.62%** | **92.38%** | -*Fuente: Ejecución final en notebook `src/paddle_ocr_fine_tune_unir_raytune.ipynb`.* +*Fuente: Validación final. Código en [`src/run_tuning.py`](https://github.com/seryus/MastersThesis/blob/main/src/run_tuning.py), resultados en [`src/results/`](https://github.com/seryus/MastersThesis/tree/main/src/results).* #### Métricas de Mejora @@ -823,9 +828,9 @@ Esta sección ha presentado: 4. **Mejora final**: CER reducido de 7.78% a 1.49% (reducción del 80.9%) **Fuentes de datos:** -- `src/paddle_ocr_fine_tune_unir_raytune.ipynb`: Código del experimento -- `src/raytune_paddle_subproc_results_20251207_192320.csv`: Resultados de 64 trials -- `src/paddle_ocr_tuning.py`: Script de evaluación +- [`src/run_tuning.py`](https://github.com/seryus/MastersThesis/blob/main/src/run_tuning.py): Script principal de optimización +- [`src/raytune_ocr.py`](https://github.com/seryus/MastersThesis/blob/main/src/raytune_ocr.py): Librería de utilidades Ray Tune +- [`src/results/`](https://github.com/seryus/MastersThesis/tree/main/src/results): Resultados CSV de los trials ## Discusión y análisis de resultados @@ -1066,8 +1071,13 @@ Este capítulo ha presentado el desarrollo completo de la contribución: **Resultado principal**: Se logró alcanzar el objetivo de CER < 2% mediante optimización de hiperparámetros, sin requerir fine-tuning ni recursos GPU. **Fuentes de datos:** -- `src/raytune_paddle_subproc_results_20251207_192320.csv`: Resultados de 64 trials -- `src/paddle_ocr_fine_tune_unir_raytune.ipynb`: Notebook principal del experimento +- [`src/run_tuning.py`](https://github.com/seryus/MastersThesis/blob/main/src/run_tuning.py): Script principal de optimización +- [`src/results/`](https://github.com/seryus/MastersThesis/tree/main/src/results): Resultados CSV de los trials + +**Imágenes Docker:** +- `seryus.ddns.net/unir/paddle-ocr-gpu`: PaddleOCR con soporte GPU +- `seryus.ddns.net/unir/easyocr-gpu`: EasyOCR con soporte GPU +- `seryus.ddns.net/unir/doctr-gpu`: DocTR con soporte GPU ### Validación con Aceleración GPU diff --git a/docs/05_conclusiones_trabajo_futuro.md b/docs/05_conclusiones_trabajo_futuro.md index 9db8d7d..5d953e4 100644 --- a/docs/05_conclusiones_trabajo_futuro.md +++ b/docs/05_conclusiones_trabajo_futuro.md @@ -10,10 +10,14 @@ Este Trabajo Fin de Máster ha demostrado que es posible mejorar significativame El objetivo principal del trabajo era alcanzar un CER inferior al 2% en documentos académicos en español. Los resultados obtenidos confirman el cumplimiento de este objetivo: +**Tabla 39.** *Cumplimiento del objetivo de CER.* + | Métrica | Objetivo | Resultado | |---------|----------|-----------| | CER | < 2% | **1.49%** | +*Fuente: Elaboración propia.* + ### Conclusiones Específicas **Respecto a OE1 (Comparativa de soluciones OCR)**: diff --git a/docs/07_anexo_a.md b/docs/07_anexo_a.md index 47b21e4..34d1620 100644 --- a/docs/07_anexo_a.md +++ b/docs/07_anexo_a.md @@ -48,6 +48,8 @@ MastersThesis/ ### Sistema de Desarrollo +**Tabla A1.** *Especificaciones del sistema de desarrollo.* + | Componente | Especificación | |------------|----------------| | Sistema Operativo | Ubuntu 24.04.3 LTS | @@ -56,20 +58,30 @@ MastersThesis/ | GPU | NVIDIA RTX 3060 Laptop (5.66 GB VRAM) | | CUDA | 12.4 | +*Fuente: Elaboración propia.* + ### Dependencias +**Tabla A2.** *Dependencias del proyecto.* + | Componente | Versión | |------------|---------| -| Python | 3.11 | -| Docker | 24+ | +| Python | 3.12.3 | +| Docker | 29.1.5 | | NVIDIA Container Toolkit | Requerido para GPU | -| Ray | 2.52+ | -| Optuna | 4.6+ | +| Ray | 2.52.1 | +| Optuna | 4.7.0 | + +*Fuente: Elaboración propia.* ## A.4 Instrucciones de Ejecución de Servicios OCR ### PaddleOCR (Puerto 8002) +**Imágenes Docker:** +- GPU: `seryus.ddns.net/unir/paddle-ocr-gpu` +- CPU: `seryus.ddns.net/unir/paddle-ocr-cpu` + ```bash cd src/paddle_ocr @@ -82,6 +94,8 @@ docker compose -f docker-compose.cpu-registry.yml up -d ### DocTR (Puerto 8003) +**Imagen Docker:** `seryus.ddns.net/unir/doctr-gpu` + ```bash cd src/doctr_service @@ -91,6 +105,8 @@ docker compose up -d ### EasyOCR (Puerto 8002) +**Imagen Docker:** `seryus.ddns.net/unir/easyocr-gpu` + ```bash cd src/easyocr_service @@ -165,29 +181,37 @@ analyze_results(results, prefix='raytune_paddle', config_keys=PADDLE_OCR_CONFIG_ ### Servicios y Puertos +**Tabla A3.** *Servicios Docker y puertos.* + | Servicio | Puerto | Script de Ajuste | |----------|--------|------------------| | PaddleOCR | 8002 | `paddle_ocr_payload` | | DocTR | 8003 | `doctr_payload` | | EasyOCR | 8002 | `easyocr_payload` | +*Fuente: Elaboración propia.* + ## A.7 Métricas de Rendimiento Los resultados detallados de las evaluaciones y ajustes de hiperparámetros se encuentran en: - [Métricas Generales](metrics/metrics.md) - Comparativa de los tres servicios -- [PaddleOCR](metrics/metrics_paddle.md) - Mejor precisión (7.72% CER) +- [PaddleOCR](metrics/metrics_paddle.md) - Mejor precisión (7.76% CER baseline, **1.49% optimizado**) - [DocTR](metrics/metrics_doctr.md) - Más rápido (0.50s/página) - [EasyOCR](metrics/metrics_easyocr.md) - Balance intermedio ### Resumen de Resultados +**Tabla A4.** *Resumen de resultados del benchmark por servicio.* + | Servicio | CER Base | CER Ajustado | Mejora | |----------|----------|--------------|--------| | **PaddleOCR** | 8.85% | **7.72%** | 12.8% | | DocTR | 12.06% | 12.07% | 0% | | EasyOCR | 11.23% | 11.14% | 0.8% | +*Fuente: Elaboración propia.* + ## A.8 Licencia El código se distribuye bajo licencia MIT. diff --git a/src/README.md b/src/README.md index 7f1834b..678a54c 100644 --- a/src/README.md +++ b/src/README.md @@ -1,74 +1,153 @@ -# Running Notebooks in Background - -## Quick: Check Ray Tune Progress - -```bash -# Is papermill still running? -ps aux | grep papermill | grep -v grep - -# View live log -tail -f papermill.log - -# Find latest Ray Tune run and count completed trials -LATEST=$(ls -td ~/ray_results/trainable_* 2>/dev/null | head -1) -echo "Run: $LATEST" -COMPLETED=$(find "$LATEST" -name "result.json" -size +0 2>/dev/null | wc -l) -TOTAL=$(ls -d "$LATEST"/trainable_*/ 2>/dev/null | wc -l) -echo "Completed: $COMPLETED / $TOTAL" - -# Check workers are healthy -for port in 8001 8002 8003; do - status=$(curl -s "localhost:$port/health" 2>/dev/null | python3 -c "import sys,json; print(json.load(sys.stdin).get('status','down'))" 2>/dev/null || echo "down") - echo "Worker $port: $status" -done - -# Show best result so far -if [ "$COMPLETED" -gt 0 ]; then - find "$LATEST" -name "result.json" -size +0 -exec cat {} \; 2>/dev/null | \ - python3 -c "import sys,json; results=[json.loads(l) for l in sys.stdin if l.strip()]; best=min(results,key=lambda x:x.get('CER',999)); print(f'Best CER: {best[\"CER\"]:.4f}, WER: {best[\"WER\"]:.4f}')" 2>/dev/null -fi -``` - ---- - -## Option 1: Papermill (Recommended) - -Runs notebooks directly without conversion. - -```bash -pip install papermill -nohup papermill .ipynb output.ipynb > papermill.log 2>&1 & -``` - -Monitor: -```bash -tail -f papermill.log -``` - -## Option 2: Convert to Python Script - -```bash -jupyter nbconvert --to script .ipynb -nohup python .py > output.log 2>&1 & -``` - -**Note:** `%pip install` magic commands need manual removal before running as `.py` - -## Important Notes - -- Ray Tune notebooks require the OCR service running first (Docker) -- For Ray workers, imports must be inside trainable functions - -## Example: Ray Tune PaddleOCR - -```bash -# 1. Start OCR service -cd src/paddle_ocr && docker compose up -d ocr-cpu - -# 2. Run notebook with papermill -cd src -nohup papermill paddle_ocr_raytune_rest.ipynb output_raytune.ipynb > papermill.log 2>&1 & - -# 3. Monitor -tail -f papermill.log -``` +# OCR Hyperparameter Tuning with Ray Tune + +This directory contains the Docker setup for running automated hyperparameter optimization on OCR services using Ray Tune with Optuna. + +## Prerequisites + +- Docker with NVIDIA GPU support (`nvidia-container-toolkit`) +- NVIDIA GPU with CUDA support + +## Quick Start + +```bash +cd src + +# Start PaddleOCR service and run tuning (images pulled from registry) +docker compose -f docker-compose.tuning.paddle.yml up -d paddle-ocr-gpu +docker compose -f docker-compose.tuning.paddle.yml run raytune --service paddle --samples 64 +``` + +## Available Services + +| Service | Port | Compose File | +|---------|------|--------------| +| PaddleOCR | 8002 | `docker-compose.tuning.paddle.yml` | +| DocTR | 8003 | `docker-compose.tuning.doctr.yml` | +| EasyOCR | 8002 | `docker-compose.tuning.easyocr.yml` | + +**Note:** PaddleOCR and EasyOCR both use port 8002. Run them separately. + +## Usage Examples + +### PaddleOCR Tuning + +```bash +# Start service +docker compose -f docker-compose.tuning.paddle.yml up -d paddle-ocr-gpu + +# Wait for health check (check with) +curl http://localhost:8002/health + +# Run tuning (64 samples) +docker compose -f docker-compose.tuning.paddle.yml run raytune --service paddle --samples 64 + +# Stop service +docker compose -f docker-compose.tuning.paddle.yml down +``` + +### DocTR Tuning + +```bash +docker compose -f docker-compose.tuning.doctr.yml up -d doctr-gpu +curl http://localhost:8003/health +docker compose -f docker-compose.tuning.doctr.yml run raytune --service doctr --samples 64 +docker compose -f docker-compose.tuning.doctr.yml down +``` + +### EasyOCR Tuning + +```bash +docker compose -f docker-compose.tuning.easyocr.yml up -d easyocr-gpu +curl http://localhost:8002/health +docker compose -f docker-compose.tuning.easyocr.yml run raytune --service easyocr --samples 64 +docker compose -f docker-compose.tuning.easyocr.yml down +``` + +### Run Multiple Services (PaddleOCR + DocTR) + +```bash +# Start both services +docker compose -f docker-compose.tuning.yml up -d paddle-ocr-gpu doctr-gpu + +# Run tuning for each +docker compose -f docker-compose.tuning.yml run raytune --service paddle --samples 64 +docker compose -f docker-compose.tuning.yml run raytune --service doctr --samples 64 + +# Stop all +docker compose -f docker-compose.tuning.yml down +``` + +## Command Line Options + +```bash +docker compose -f run raytune --service --samples +``` + +| Option | Description | Default | +|--------|-------------|---------| +| `--service` | OCR service: `paddle`, `doctr`, `easyocr` | Required | +| `--samples` | Number of hyperparameter trials | 64 | + +## Output + +Results are saved to `src/results/` as CSV files: +- `raytune_paddle_results_.csv` +- `raytune_doctr_results_.csv` +- `raytune_easyocr_results_.csv` + +## Directory Structure + +``` +src/ +├── docker-compose.tuning.yml # All services (PaddleOCR + DocTR) +├── docker-compose.tuning.paddle.yml # PaddleOCR only +├── docker-compose.tuning.doctr.yml # DocTR only +├── docker-compose.tuning.easyocr.yml # EasyOCR only +├── raytune/ +│ ├── Dockerfile +│ ├── requirements.txt +│ ├── raytune_ocr.py +│ └── run_tuning.py +├── dataset/ # Input images and ground truth +├── results/ # Output CSV files +└── debugset/ # Debug output +``` + +## Docker Images + +All images are pre-built and pulled from registry: +- `seryus.ddns.net/unir/raytune:latest` - Ray Tune tuning service +- `seryus.ddns.net/unir/paddle-ocr-gpu:latest` - PaddleOCR GPU +- `seryus.ddns.net/unir/doctr-gpu:latest` - DocTR GPU +- `seryus.ddns.net/unir/easyocr-gpu:latest` - EasyOCR GPU + +### Build locally (development) + +```bash +# Build raytune image locally +docker build -t seryus.ddns.net/unir/raytune:latest ./raytune +``` + +## Troubleshooting + +### Service not ready +Wait for the health check to pass before running tuning: +```bash +# Check service health +curl http://localhost:8002/health +# Expected: {"status": "ok", "model_loaded": true, ...} +``` + +### GPU not detected +Ensure `nvidia-container-toolkit` is installed: +```bash +nvidia-smi # Should show your GPU +docker run --rm --gpus all nvidia/cuda:12.4.1-base nvidia-smi +``` + +### Port already in use +Stop any running OCR services: +```bash +docker compose -f docker-compose.tuning.paddle.yml down +docker compose -f docker-compose.tuning.easyocr.yml down +``` diff --git a/src/docker-compose.tuning.doctr.yml b/src/docker-compose.tuning.doctr.yml new file mode 100644 index 0000000..31233a8 --- /dev/null +++ b/src/docker-compose.tuning.doctr.yml @@ -0,0 +1,50 @@ +# docker-compose.tuning.doctr.yml - Ray Tune with DocTR GPU +# Usage: +# docker compose -f docker-compose.tuning.doctr.yml up -d doctr-gpu +# docker compose -f docker-compose.tuning.doctr.yml run raytune --service doctr --samples 64 +# docker compose -f docker-compose.tuning.doctr.yml down + +services: + raytune: + image: seryus.ddns.net/unir/raytune:latest + command: ["--service", "doctr", "--host", "doctr-gpu", "--port", "8000", "--samples", "64"] + volumes: + - ./results:/app/results:rw + environment: + - PYTHONUNBUFFERED=1 + depends_on: + doctr-gpu: + condition: service_healthy + + doctr-gpu: + image: seryus.ddns.net/unir/doctr-gpu:latest + container_name: doctr-gpu-tuning + ports: + - "8003:8000" + volumes: + - ./dataset:/app/dataset:ro + - ./debugset:/app/debugset:rw + - doctr-cache:/root/.cache/doctr + environment: + - PYTHONUNBUFFERED=1 + - CUDA_VISIBLE_DEVICES=0 + - DOCTR_DET_ARCH=db_resnet50 + - DOCTR_RECO_ARCH=crnn_vgg16_bn + deploy: + resources: + reservations: + devices: + - driver: nvidia + count: 1 + capabilities: [gpu] + restart: unless-stopped + healthcheck: + test: ["CMD", "python", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:8000/health')"] + interval: 30s + timeout: 10s + retries: 3 + start_period: 180s + +volumes: + doctr-cache: + name: doctr-model-cache diff --git a/src/docker-compose.tuning.easyocr.yml b/src/docker-compose.tuning.easyocr.yml new file mode 100644 index 0000000..082e0cf --- /dev/null +++ b/src/docker-compose.tuning.easyocr.yml @@ -0,0 +1,51 @@ +# docker-compose.tuning.easyocr.yml - Ray Tune with EasyOCR GPU +# Usage: +# docker compose -f docker-compose.tuning.easyocr.yml up -d easyocr-gpu +# docker compose -f docker-compose.tuning.easyocr.yml run raytune --service easyocr --samples 64 +# docker compose -f docker-compose.tuning.easyocr.yml down +# +# Note: EasyOCR uses port 8002 (same as PaddleOCR). Cannot run simultaneously. + +services: + raytune: + image: seryus.ddns.net/unir/raytune:latest + command: ["--service", "easyocr", "--host", "easyocr-gpu", "--port", "8000", "--samples", "64"] + volumes: + - ./results:/app/results:rw + environment: + - PYTHONUNBUFFERED=1 + depends_on: + easyocr-gpu: + condition: service_healthy + + easyocr-gpu: + image: seryus.ddns.net/unir/easyocr-gpu:latest + container_name: easyocr-gpu-tuning + ports: + - "8002:8000" + volumes: + - ./dataset:/app/dataset:ro + - ./debugset:/app/debugset:rw + - easyocr-cache:/root/.EasyOCR + environment: + - PYTHONUNBUFFERED=1 + - CUDA_VISIBLE_DEVICES=0 + - EASYOCR_LANGUAGES=es,en + deploy: + resources: + reservations: + devices: + - driver: nvidia + count: 1 + capabilities: [gpu] + restart: unless-stopped + healthcheck: + test: ["CMD", "python", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:8000/health')"] + interval: 30s + timeout: 10s + retries: 3 + start_period: 120s + +volumes: + easyocr-cache: + name: easyocr-model-cache diff --git a/src/docker-compose.tuning.paddle.yml b/src/docker-compose.tuning.paddle.yml new file mode 100644 index 0000000..bddf2c5 --- /dev/null +++ b/src/docker-compose.tuning.paddle.yml @@ -0,0 +1,50 @@ +# docker-compose.tuning.paddle.yml - Ray Tune with PaddleOCR GPU +# Usage: +# docker compose -f docker-compose.tuning.paddle.yml up -d paddle-ocr-gpu +# docker compose -f docker-compose.tuning.paddle.yml run raytune --service paddle --samples 64 +# docker compose -f docker-compose.tuning.paddle.yml down + +services: + raytune: + image: seryus.ddns.net/unir/raytune:latest + command: ["--service", "paddle", "--host", "paddle-ocr-gpu", "--port", "8000", "--samples", "64"] + volumes: + - ./results:/app/results:rw + environment: + - PYTHONUNBUFFERED=1 + depends_on: + paddle-ocr-gpu: + condition: service_healthy + + paddle-ocr-gpu: + image: seryus.ddns.net/unir/paddle-ocr-gpu:latest + container_name: paddle-ocr-gpu-tuning + ports: + - "8002:8000" + volumes: + - ./dataset:/app/dataset:ro + - ./debugset:/app/debugset:rw + - paddlex-cache:/root/.paddlex + environment: + - PYTHONUNBUFFERED=1 + - CUDA_VISIBLE_DEVICES=0 + - PADDLE_DET_MODEL=PP-OCRv5_mobile_det + - PADDLE_REC_MODEL=PP-OCRv5_mobile_rec + deploy: + resources: + reservations: + devices: + - driver: nvidia + count: 1 + capabilities: [gpu] + restart: unless-stopped + healthcheck: + test: ["CMD", "python", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:8000/health')"] + interval: 30s + timeout: 10s + retries: 3 + start_period: 60s + +volumes: + paddlex-cache: + name: paddlex-model-cache diff --git a/src/docker-compose.tuning.yml b/src/docker-compose.tuning.yml new file mode 100644 index 0000000..796d4d0 --- /dev/null +++ b/src/docker-compose.tuning.yml @@ -0,0 +1,82 @@ +# docker-compose.tuning.yml - Ray Tune with all OCR services (PaddleOCR + DocTR) +# Usage: +# docker compose -f docker-compose.tuning.yml up -d paddle-ocr-gpu doctr-gpu +# docker compose -f docker-compose.tuning.yml run raytune --service paddle --samples 64 +# docker compose -f docker-compose.tuning.yml run raytune --service doctr --samples 64 +# docker compose -f docker-compose.tuning.yml down +# +# Note: EasyOCR uses port 8002 (same as PaddleOCR). Use docker-compose.tuning.easyocr.yml separately. + +services: + raytune: + image: seryus.ddns.net/unir/raytune:latest + network_mode: host + shm_size: '5gb' + volumes: + - ./results:/app/results:rw + environment: + - PYTHONUNBUFFERED=1 + + paddle-ocr-gpu: + image: seryus.ddns.net/unir/paddle-ocr-gpu:latest + container_name: paddle-ocr-gpu-tuning + ports: + - "8002:8000" + volumes: + - ./dataset:/app/dataset:ro + - ./debugset:/app/debugset:rw + - paddlex-cache:/root/.paddlex + environment: + - PYTHONUNBUFFERED=1 + - CUDA_VISIBLE_DEVICES=0 + - PADDLE_DET_MODEL=PP-OCRv5_mobile_det + - PADDLE_REC_MODEL=PP-OCRv5_mobile_rec + deploy: + resources: + reservations: + devices: + - driver: nvidia + count: 1 + capabilities: [gpu] + restart: unless-stopped + healthcheck: + test: ["CMD", "python", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:8000/health')"] + interval: 30s + timeout: 10s + retries: 3 + start_period: 60s + + doctr-gpu: + image: seryus.ddns.net/unir/doctr-gpu:latest + container_name: doctr-gpu-tuning + ports: + - "8003:8000" + volumes: + - ./dataset:/app/dataset:ro + - ./debugset:/app/debugset:rw + - doctr-cache:/root/.cache/doctr + environment: + - PYTHONUNBUFFERED=1 + - CUDA_VISIBLE_DEVICES=0 + - DOCTR_DET_ARCH=db_resnet50 + - DOCTR_RECO_ARCH=crnn_vgg16_bn + deploy: + resources: + reservations: + devices: + - driver: nvidia + count: 1 + capabilities: [gpu] + restart: unless-stopped + healthcheck: + test: ["CMD", "python", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:8000/health')"] + interval: 30s + timeout: 10s + retries: 3 + start_period: 180s + +volumes: + paddlex-cache: + name: paddlex-model-cache + doctr-cache: + name: doctr-model-cache diff --git a/src/raytune/Dockerfile b/src/raytune/Dockerfile new file mode 100644 index 0000000..a276510 --- /dev/null +++ b/src/raytune/Dockerfile @@ -0,0 +1,18 @@ +FROM python:3.12-slim + +WORKDIR /app + +# Install dependencies +COPY requirements.txt . +RUN pip install --no-cache-dir -r requirements.txt + +# Copy application files +COPY raytune_ocr.py . +COPY run_tuning.py . + +# Create results directory +RUN mkdir -p /app/results + +ENV PYTHONUNBUFFERED=1 + +ENTRYPOINT ["python", "run_tuning.py"] diff --git a/src/raytune/README.md b/src/raytune/README.md new file mode 100644 index 0000000..a94dd44 --- /dev/null +++ b/src/raytune/README.md @@ -0,0 +1,131 @@ +# Ray Tune OCR Hyperparameter Optimization + +Docker-based hyperparameter tuning for OCR services using Ray Tune with Optuna search. + +## Structure + +``` +raytune/ +├── Dockerfile # Python 3.12-slim with Ray Tune + Optuna +├── requirements.txt # Dependencies +├── raytune_ocr.py # Shared utilities and search spaces +├── run_tuning.py # CLI entry point +└── README.md +``` + +## Quick Start + +```bash +cd src + +# Build the raytune image +docker compose -f docker-compose.tuning.paddle.yml build raytune + +# Or pull from registry +docker pull seryus.ddns.net/unir/raytune:latest +``` + +## Usage + +### PaddleOCR Tuning + +```bash +# Start PaddleOCR service +docker compose -f docker-compose.tuning.paddle.yml up -d paddle-ocr-gpu + +# Wait for health check, then run tuning +docker compose -f docker-compose.tuning.paddle.yml run raytune --service paddle --samples 64 + +# Stop when done +docker compose -f docker-compose.tuning.paddle.yml down +``` + +### DocTR Tuning + +```bash +docker compose -f docker-compose.tuning.doctr.yml up -d doctr-gpu +docker compose -f docker-compose.tuning.doctr.yml run raytune --service doctr --samples 64 +docker compose -f docker-compose.tuning.doctr.yml down +``` + +### EasyOCR Tuning + +```bash +# Note: EasyOCR uses port 8002 (same as PaddleOCR). Cannot run simultaneously. +docker compose -f docker-compose.tuning.easyocr.yml up -d easyocr-gpu +docker compose -f docker-compose.tuning.easyocr.yml run raytune --service easyocr --samples 64 +docker compose -f docker-compose.tuning.easyocr.yml down +``` + +## CLI Options + +``` +python run_tuning.py --service {paddle,doctr,easyocr} --samples N +``` + +| Option | Description | Default | +|------------|--------------------------------------|---------| +| --service | OCR service to tune (required) | - | +| --samples | Number of hyperparameter trials | 64 | + +## Search Spaces + +### PaddleOCR +- `use_doc_orientation_classify`: [True, False] +- `use_doc_unwarping`: [True, False] +- `textline_orientation`: [True, False] +- `text_det_thresh`: uniform(0.0, 0.7) +- `text_det_box_thresh`: uniform(0.0, 0.7) +- `text_rec_score_thresh`: uniform(0.0, 0.7) + +### DocTR +- `assume_straight_pages`: [True, False] +- `straighten_pages`: [True, False] +- `preserve_aspect_ratio`: [True, False] +- `symmetric_pad`: [True, False] +- `disable_page_orientation`: [True, False] +- `disable_crop_orientation`: [True, False] +- `resolve_lines`: [True, False] +- `resolve_blocks`: [True, False] +- `paragraph_break`: uniform(0.01, 0.1) + +### EasyOCR +- `text_threshold`: uniform(0.3, 0.9) +- `low_text`: uniform(0.2, 0.6) +- `link_threshold`: uniform(0.2, 0.6) +- `slope_ths`: uniform(0.0, 0.3) +- `ycenter_ths`: uniform(0.3, 1.0) +- `height_ths`: uniform(0.3, 1.0) +- `width_ths`: uniform(0.3, 1.0) +- `add_margin`: uniform(0.0, 0.3) +- `contrast_ths`: uniform(0.05, 0.3) +- `adjust_contrast`: uniform(0.3, 0.8) +- `decoder`: ["greedy", "beamsearch"] +- `beamWidth`: [3, 5, 7, 10] +- `min_size`: [5, 10, 15, 20] + +## Output + +Results are saved to `src/results/` as CSV files: +- `raytune_paddle_results_YYYYMMDD_HHMMSS.csv` +- `raytune_doctr_results_YYYYMMDD_HHMMSS.csv` +- `raytune_easyocr_results_YYYYMMDD_HHMMSS.csv` + +Each row contains: +- Configuration parameters (prefixed with `config/`) +- Metrics: CER, WER, TIME, PAGES, TIME_PER_PAGE +- Worker URL used for the trial + +## Network Mode + +The raytune container uses `network_mode: host` to access OCR services on localhost ports: +- PaddleOCR: port 8002 +- DocTR: port 8003 +- EasyOCR: port 8002 (conflicts with PaddleOCR) + +## Dependencies + +- ray[tune]==2.52.1 +- optuna==4.7.0 +- requests>=2.28.0 +- pandas>=2.0.0 diff --git a/src/raytune/raytune_ocr.py b/src/raytune/raytune_ocr.py new file mode 100644 index 0000000..40d3969 --- /dev/null +++ b/src/raytune/raytune_ocr.py @@ -0,0 +1,371 @@ +# raytune_ocr.py +# Shared Ray Tune utilities for OCR hyperparameter optimization +# +# Usage: +# from raytune_ocr import check_workers, create_trainable, run_tuner, analyze_results +# +# Environment variables: +# OCR_HOST: Host for OCR services (default: localhost) + +import os +from datetime import datetime +from typing import List, Dict, Any, Callable + +import requests +import pandas as pd + +import ray +from ray import tune +from ray.tune.search.optuna import OptunaSearch + + +def check_workers( + ports: List[int], + service_name: str = "OCR", + timeout: int = 180, + interval: int = 5, +) -> List[str]: + """ + Wait for workers to be fully ready (model + dataset loaded) and return healthy URLs. + + Args: + ports: List of port numbers to check + service_name: Name for error messages + timeout: Max seconds to wait for each worker + interval: Seconds between retries + + Returns: + List of healthy worker URLs + + Raises: + RuntimeError if no healthy workers found after timeout + """ + import time + + host = os.environ.get("OCR_HOST", "localhost") + worker_urls = [f"http://{host}:{port}" for port in ports] + healthy_workers = [] + + for url in worker_urls: + print(f"Waiting for {url}...") + start = time.time() + + while time.time() - start < timeout: + try: + health = requests.get(f"{url}/health", timeout=10).json() + model_ok = health.get('model_loaded', False) + dataset_ok = health.get('dataset_loaded', False) + + if health.get('status') == 'ok' and model_ok: + gpu = health.get('gpu_name', 'CPU') + print(f"✓ {url}: ready ({gpu})") + healthy_workers.append(url) + break + + elapsed = int(time.time() - start) + print(f" [{elapsed}s] model={model_ok}") + except requests.exceptions.RequestException: + elapsed = int(time.time() - start) + print(f" [{elapsed}s] not reachable") + + time.sleep(interval) + else: + print(f"✗ {url}: timeout after {timeout}s") + + if not healthy_workers: + raise RuntimeError( + f"No healthy {service_name} workers found.\n" + f"Checked ports: {ports}" + ) + + print(f"\n{len(healthy_workers)}/{len(worker_urls)} workers ready\n") + return healthy_workers + + +def create_trainable(ports: List[int], payload_fn: Callable[[Dict], Dict]) -> Callable: + """ + Factory to create a trainable function for Ray Tune. + + Args: + ports: List of worker ports for load balancing + payload_fn: Function that takes config dict and returns API payload dict + + Returns: + Trainable function for Ray Tune + + Note: + Ray Tune 2.x API: tune.report(metrics_dict) - pass dict directly, NOT kwargs. + See: https://docs.ray.io/en/latest/tune/api/doc/ray.tune.report.html + """ + def trainable(config): + import os + import random + import requests + from ray.tune import report # Ray 2.x: report(dict), not report(**kwargs) + + host = os.environ.get("OCR_HOST", "localhost") + api_url = f"http://{host}:{random.choice(ports)}" + payload = payload_fn(config) + + try: + response = requests.post(f"{api_url}/evaluate", json=payload, timeout=None) + response.raise_for_status() + metrics = response.json() + metrics["worker"] = api_url + report(metrics) # Ray 2.x API: pass dict directly + except Exception as e: + report({ # Ray 2.x API: pass dict directly + "CER": 1.0, + "WER": 1.0, + "TIME": 0.0, + "PAGES": 0, + "TIME_PER_PAGE": 0, + "worker": api_url, + "ERROR": str(e)[:500] + }) + + return trainable + + +def run_tuner( + trainable: Callable, + search_space: Dict[str, Any], + num_samples: int = 64, + num_workers: int = 1, + metric: str = "CER", + mode: str = "min", +) -> tune.ResultGrid: + """ + Initialize Ray and run hyperparameter tuning. + + Args: + trainable: Trainable function from create_trainable() + search_space: Dict of parameter names to tune.* search spaces + num_samples: Number of trials to run + num_workers: Max concurrent trials + metric: Metric to optimize + mode: "min" or "max" + + Returns: + Ray Tune ResultGrid + """ + ray.init( + ignore_reinit_error=True, + include_dashboard=False, + configure_logging=False, + _metrics_export_port=0, # Disable metrics export to avoid connection warnings + ) + print(f"Ray Tune ready (version: {ray.__version__})") + + tuner = tune.Tuner( + trainable, + tune_config=tune.TuneConfig( + metric=metric, + mode=mode, + search_alg=OptunaSearch(), + num_samples=num_samples, + max_concurrent_trials=num_workers, + ), + param_space=search_space, + ) + + return tuner.fit() + + +def analyze_results( + results: tune.ResultGrid, + output_folder: str = "results", + prefix: str = "raytune", + config_keys: List[str] = None, +) -> pd.DataFrame: + """ + Analyze and save tuning results. + + Args: + results: Ray Tune ResultGrid + output_folder: Directory to save CSV + prefix: Filename prefix + config_keys: List of config keys to show in best result (without 'config/' prefix) + + Returns: + Results DataFrame + """ + os.makedirs(output_folder, exist_ok=True) + df = results.get_dataframe() + + # Save to CSV + timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") + filename = f"{prefix}_results_{timestamp}.csv" + filepath = os.path.join(output_folder, filename) + df.to_csv(filepath, index=False) + print(f"Results saved: {filepath}") + + # Best configuration + best = df.loc[df["CER"].idxmin()] + print(f"\nBest CER: {best['CER']:.6f}") + print(f"Best WER: {best['WER']:.6f}") + + if config_keys: + print(f"\nOptimal Configuration:") + for key in config_keys: + col = f"config/{key}" + if col in best: + val = best[col] + if isinstance(val, float): + print(f" {key}: {val:.4f}") + else: + print(f" {key}: {val}") + + return df + + +def correlation_analysis(df: pd.DataFrame, param_keys: List[str]) -> None: + """ + Print correlation of numeric parameters with CER/WER. + + Args: + df: Results DataFrame + param_keys: List of config keys (without 'config/' prefix) + """ + param_cols = [f"config/{k}" for k in param_keys if f"config/{k}" in df.columns] + numeric_cols = [c for c in param_cols if df[c].dtype in ['float64', 'int64']] + + if not numeric_cols: + print("No numeric parameters for correlation analysis") + return + + corr_cer = df[numeric_cols + ["CER"]].corr()["CER"].sort_values(ascending=False) + corr_wer = df[numeric_cols + ["WER"]].corr()["WER"].sort_values(ascending=False) + + print("Correlation with CER:") + print(corr_cer) + print("\nCorrelation with WER:") + print(corr_wer) + + +# ============================================================================= +# OCR-specific payload functions +# ============================================================================= + +def paddle_ocr_payload(config: Dict) -> Dict: + """Create payload for PaddleOCR API. Uses pages 5-10 (first doc) for tuning.""" + return { + "pdf_folder": "/app/dataset", + "use_doc_orientation_classify": config.get("use_doc_orientation_classify", False), + "use_doc_unwarping": config.get("use_doc_unwarping", False), + "textline_orientation": config.get("textline_orientation", True), + "text_det_thresh": config.get("text_det_thresh", 0.0), + "text_det_box_thresh": config.get("text_det_box_thresh", 0.0), + "text_det_unclip_ratio": config.get("text_det_unclip_ratio", 1.5), + "text_rec_score_thresh": config.get("text_rec_score_thresh", 0.0), + "start_page": 5, + "end_page": 10, + "save_output": False, + } + + +def doctr_payload(config: Dict) -> Dict: + """Create payload for DocTR API. Uses pages 5-10 (first doc) for tuning.""" + return { + "pdf_folder": "/app/dataset", + "assume_straight_pages": config.get("assume_straight_pages", True), + "straighten_pages": config.get("straighten_pages", False), + "preserve_aspect_ratio": config.get("preserve_aspect_ratio", True), + "symmetric_pad": config.get("symmetric_pad", True), + "disable_page_orientation": config.get("disable_page_orientation", False), + "disable_crop_orientation": config.get("disable_crop_orientation", False), + "resolve_lines": config.get("resolve_lines", True), + "resolve_blocks": config.get("resolve_blocks", False), + "paragraph_break": config.get("paragraph_break", 0.035), + "start_page": 5, + "end_page": 10, + "save_output": False, + } + + +def easyocr_payload(config: Dict) -> Dict: + """Create payload for EasyOCR API. Uses pages 5-10 (first doc) for tuning.""" + return { + "pdf_folder": "/app/dataset", + "text_threshold": config.get("text_threshold", 0.7), + "low_text": config.get("low_text", 0.4), + "link_threshold": config.get("link_threshold", 0.4), + "slope_ths": config.get("slope_ths", 0.1), + "ycenter_ths": config.get("ycenter_ths", 0.5), + "height_ths": config.get("height_ths", 0.5), + "width_ths": config.get("width_ths", 0.5), + "add_margin": config.get("add_margin", 0.1), + "contrast_ths": config.get("contrast_ths", 0.1), + "adjust_contrast": config.get("adjust_contrast", 0.5), + "decoder": config.get("decoder", "greedy"), + "beamWidth": config.get("beamWidth", 5), + "min_size": config.get("min_size", 10), + "start_page": 5, + "end_page": 10, + "save_output": False, + } + + +# ============================================================================= +# Search spaces +# ============================================================================= + +PADDLE_OCR_SEARCH_SPACE = { + "use_doc_orientation_classify": tune.choice([True, False]), + "use_doc_unwarping": tune.choice([True, False]), + "textline_orientation": tune.choice([True, False]), + "text_det_thresh": tune.uniform(0.0, 0.7), + "text_det_box_thresh": tune.uniform(0.0, 0.7), + "text_det_unclip_ratio": tune.choice([0.0]), + "text_rec_score_thresh": tune.uniform(0.0, 0.7), +} + +DOCTR_SEARCH_SPACE = { + "assume_straight_pages": tune.choice([True, False]), + "straighten_pages": tune.choice([True, False]), + "preserve_aspect_ratio": tune.choice([True, False]), + "symmetric_pad": tune.choice([True, False]), + "disable_page_orientation": tune.choice([True, False]), + "disable_crop_orientation": tune.choice([True, False]), + "resolve_lines": tune.choice([True, False]), + "resolve_blocks": tune.choice([True, False]), + "paragraph_break": tune.uniform(0.01, 0.1), +} + +EASYOCR_SEARCH_SPACE = { + "text_threshold": tune.uniform(0.3, 0.9), + "low_text": tune.uniform(0.2, 0.6), + "link_threshold": tune.uniform(0.2, 0.6), + "slope_ths": tune.uniform(0.0, 0.3), + "ycenter_ths": tune.uniform(0.3, 1.0), + "height_ths": tune.uniform(0.3, 1.0), + "width_ths": tune.uniform(0.3, 1.0), + "add_margin": tune.uniform(0.0, 0.3), + "contrast_ths": tune.uniform(0.05, 0.3), + "adjust_contrast": tune.uniform(0.3, 0.8), + "decoder": tune.choice(["greedy", "beamsearch"]), + "beamWidth": tune.choice([3, 5, 7, 10]), + "min_size": tune.choice([5, 10, 15, 20]), +} + + +# ============================================================================= +# Config keys for results display +# ============================================================================= + +PADDLE_OCR_CONFIG_KEYS = [ + "use_doc_orientation_classify", "use_doc_unwarping", "textline_orientation", + "text_det_thresh", "text_det_box_thresh", "text_det_unclip_ratio", "text_rec_score_thresh", +] + +DOCTR_CONFIG_KEYS = [ + "assume_straight_pages", "straighten_pages", "preserve_aspect_ratio", "symmetric_pad", + "disable_page_orientation", "disable_crop_orientation", "resolve_lines", "resolve_blocks", + "paragraph_break", +] + +EASYOCR_CONFIG_KEYS = [ + "text_threshold", "low_text", "link_threshold", "slope_ths", "ycenter_ths", + "height_ths", "width_ths", "add_margin", "contrast_ths", "adjust_contrast", + "decoder", "beamWidth", "min_size", +] diff --git a/src/raytune/requirements.txt b/src/raytune/requirements.txt new file mode 100644 index 0000000..0e76c4e --- /dev/null +++ b/src/raytune/requirements.txt @@ -0,0 +1,4 @@ +ray[tune]==2.52.1 +optuna==4.7.0 +requests>=2.28.0 +pandas>=2.0.0 diff --git a/src/raytune/run_tuning.py b/src/raytune/run_tuning.py new file mode 100644 index 0000000..a3396ad --- /dev/null +++ b/src/raytune/run_tuning.py @@ -0,0 +1,80 @@ +#!/usr/bin/env python3 +"""Run hyperparameter tuning for OCR services.""" + +import os +import sys +import argparse +from raytune_ocr import ( + check_workers, create_trainable, run_tuner, analyze_results, + paddle_ocr_payload, doctr_payload, easyocr_payload, + PADDLE_OCR_SEARCH_SPACE, DOCTR_SEARCH_SPACE, EASYOCR_SEARCH_SPACE, + PADDLE_OCR_CONFIG_KEYS, DOCTR_CONFIG_KEYS, EASYOCR_CONFIG_KEYS, +) + +SERVICES = { + "paddle": { + "payload_fn": paddle_ocr_payload, + "search_space": PADDLE_OCR_SEARCH_SPACE, + "config_keys": PADDLE_OCR_CONFIG_KEYS, + "name": "PaddleOCR", + }, + "doctr": { + "payload_fn": doctr_payload, + "search_space": DOCTR_SEARCH_SPACE, + "config_keys": DOCTR_CONFIG_KEYS, + "name": "DocTR", + }, + "easyocr": { + "payload_fn": easyocr_payload, + "search_space": EASYOCR_SEARCH_SPACE, + "config_keys": EASYOCR_CONFIG_KEYS, + "name": "EasyOCR", + }, +} + +def main(): + parser = argparse.ArgumentParser(description="Run OCR hyperparameter tuning") + parser.add_argument("--service", choices=["paddle", "doctr", "easyocr"], required=True) + parser.add_argument("--host", type=str, default="localhost", help="OCR service host") + parser.add_argument("--port", type=int, default=8000, help="OCR service port") + parser.add_argument("--samples", type=int, default=64, help="Number of samples") + args = parser.parse_args() + + # Set environment variable for raytune_ocr module + os.environ["OCR_HOST"] = args.host + + cfg = SERVICES[args.service] + ports = [args.port] + + print(f"\n{'='*50}") + print(f"Hyperparameter Tuning: {cfg['name']}") + print(f"Host: {args.host}:{args.port}") + print(f"Samples: {args.samples}") + print(f"{'='*50}\n") + + # Check workers + healthy = check_workers(ports, cfg["name"]) + + # Create trainable and run tuning + trainable = create_trainable(ports, cfg["payload_fn"]) + results = run_tuner( + trainable=trainable, + search_space=cfg["search_space"], + num_samples=args.samples, + num_workers=len(healthy), + ) + + # Analyze results + df = analyze_results( + results, + output_folder="results", + prefix=f"raytune_{args.service}", + config_keys=cfg["config_keys"], + ) + + print(f"\n{'='*50}") + print("Tuning complete!") + print(f"{'='*50}") + +if __name__ == "__main__": + main() diff --git 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+ +

 

+ +

+ +

Universidad +Internacional de La Rioja

+ +

Escuela +Superior de Ingeniería y

+ +

Tecnología

+ +

 

+ +

 

+ +

 

+ +

 

+ +

Máster Universitario +en Inteligencia artificial

+ +

Optimización de Hiperparámetros OCR +con Ray Tune para Documentos Académicos en Español

+ + + +

 

+ +

           

+ + + + + + + + + + + + + + + + + + +
+

Trabajo fin de + estudio presentado por:

+ 		+

Sergio Jiménez Jiménez

+
+

Tipo de + trabajo:

+ 		+

Desarrollo + Software

+
+

Director/a:

+ 		+

Javier Rodrigo + Villazón Terrazas

+
+

Fecha:

+ 		+

06.10.2025

+
+ +

 

+ +
+ + +

Resumen

+ +

En este +apartado se introducirá un breve resumen en español del trabajo realizado +(extensión entre 150 y 300 palabras). Este resumen debe incluir el objetivo o +propósito de la investigación, la metodología, los resultados y las +conclusiones.

+ +

El resumen +debe contener lo qué se ha pretendido realizar (objetivo o propósito de la +investigación), cómo se ha realizado (método o proceso desarrollado) y para qué +se ha realizado (resultados y conclusiones).

+ +

 

+ +
+ +

Importante: La extensión mínima en un TFE individual es de 50 páginas, sin contar +portada, resumen, abstract, índices y anexos.

+ +
+ +

 

+ +

Palabras clave: (De 3 a 5 palabras) Descriptores +del trabajo que lo enmarcan en unas temáticas determinadas. Serán los +utilizados para localizar tu trabajo si llega a ser publicado.

+ +

 

+ +

 

+ +
+ + +

 

+ +

Abstract

+ +

En +este apartado se introducirá un breve resumen en inglés del trabajo +realizado (extensión entre 150 y 300 palabras). Este resumen debe incluir el +objetivo o propósito de la investigación, la metodología, los resultados y las +conclusiones.

+ +

 

+ +

Keywords: (De 3 a 5 palabras en inglés)

+ +

 

+ +

 

+ +
+ + +

 

+ + + +

Índice de contenidos

+ +

1.    Introducción. 1

+ +

1.1.      Motivación. 1

+ +

1.2.      Planteamiento +del trabajo. 3

+ +

1.3.      Estructura +del trabajo. 3

+ +

2.    Contexto +y estado del arte. 4

+ +

2.1.      Contexto +del problema. 4

+ +

2.2.      Estado +del arte. 4

+ +

2.3.      Conclusiones. 5

+ +

3.    Objetivos +concretos y metodología de trabajo. 6

+ +

3.1.      Objetivo +general 6

+ +

3.2.      Objetivos +específicos. 7

+ +

3.3.      Metodología +del trabajo. 8

+ +

4.    Desarrollo específico de la contribución. 9

+ +

5.    Conclusiones +y trabajo futuro. 13

+ +

5.1.      Conclusiones. 13

+ +

5.2.      Líneas +de trabajo futuro. 13

+ +

Referencias bibliográficas. 14

+ +

Anexo A.     Código +fuente y datos analizados 15

+ +


+Índice de figuras

+ +

Figura 1. Ejemplo +de figura realizada para nuestro trabajo. 2

+ +


+Índice de tablas

+ +

Tabla 1. Ejemplo +de tabla con sus principales elementos. 2

+ +

 

+ +

 

+ +

 

+ +

 

+ +

 

+ +

 

+ +

 

+ +

 

+ +

 

+ +

 

+ +

 

+ +

 

+ +

 

+ +

 

+ +

 

+ +

 

+ +
+ +
+ + +
+ +

1.   +Introducción

+ +

El primer capítulo es siempre +una introducción. En ella debes resumir de forma esquemática pero +suficientemente clara lo esencial de cada una de las partes del trabajo. La +lectura de este primer capítulo ha de dar una primera idea clara de lo que se +pretendía, las conclusiones a las que se ha llegado y del procedimiento +seguido.

+ +

Como tal, es uno de los +capítulos más importantes de la memoria. Las ideas +principales a transmitir son la identificación del problema a tratar, la +justificación de su importancia, los objetivos generales (a grandes rasgos) y +un adelanto de la contribución que esperas hacer.

+ +

Típicamente una introducción +tiene tres apartados: Motivación, Planteamiento del trabajo, Estructura del +trabajo. (Texto Normal del menú de estilos.)

+ +

Ejemplo de nota al pie[1].

+ +

 

+ +

1.1. Motivación

+ +

En este apartado se deberá +presentar el problema de estudio al que se quiere dar solución y justificar su +importancia para la comunidad educativa y científica.

+ +

La lectura de este apartado +debe dar una idea clara de las razones, motivos e intereses que han llevado a +la elección de este tema. Recuerda que para poder justificar este trabajo debe +haber referencias a la investigación previa sobre el tema objeto de estudio, +independientemente de que luego se profundice en otros apartados.

+ +

Las siguientes preguntas +puedan ayudar a la redacción de este apartado:

+ +

„  ¿Cuál es el problema que quieres tratar?

+ +

„  ¿Cuáles crees que son las causas?

+ +

„  ¿Por qué es relevante el problema?

+ +

 

+ +

A continuación, se indica con +un ejemplo cómo deben introducirse los títulos y las fuentes en Tablas y Figuras.

+ +

 

+ +

Tabla 1. Ejemplo de tabla con sus principales +elementos.

+ +
+ +

+ +
+ +

Fuente: American Psychological Association, +2020a.

+ + + +

 

+ +

Figura 1. Ejemplo de figura realizada para nuestro +trabajo.

+ +

+ +

Fuente: +American Psychological Association, 2020b.

+ +

 

+ +

 

+ +

 

+ +

 

+ +

 

+ +

1.2. Planteamiento del trabajo

+ +

Se debe plantear, de forma +breve, el problema / necesidad detectada de la que se parte para proponer la +propuesta y la finalidad del TFE. Los objetivos se van a plantear +posteriormente, pero en este apartado debe quedar claro qué te planteas con la +intervención.

+ +

Es necesario que los temas +escogidos tengan una vinculación directa con la ingeniería de software, el +desarrollo web y/o la ciberseguridad y, por tanto, el tema trabajado debe estar +en consonancia con la titulación.

+ +

Las siguientes preguntas +puedan ayudar a la redacción de este apartado:

+ +

„  ¿Cómo se podría solucionar el problema?

+ +

„  +¿Qué es lo que se propone? Aquí +describes tus objetivos en términos generales.

+ +

 

+ +

1.3. Estructura del trabajo

+ +

Aquí describes brevemente lo +que vas a contar en cada uno de los capítulos siguientes.

+ +

 

+ +
+ + +

 

+ +

2.   +Contexto +y estado del arte

+ +

Después de la introducción, se suele describir el contexto de +aplicación. Suele ser un capítulo (o dos en ciertos casos) en el que se estudia +a fondo el dominio de aplicación, citando numerosas referencias. Debe aportar +un buen resumen del conocimiento que ya existe en el campo de los problemas +habituales identificados.

+ +

Es conveniente que revises los estudios actuales publicados en la línea +elegida, y deberás consultar diferentes fuentes. No es suficiente con la +consulta on-line, es necesario acudir a la biblioteca y consultar manuales +técnicos.

+ +

Hay que tener presente los autores de referencia en la temática del +trabajo de investigación. Si se ha excluido a alguno de los relevantes hay que +justificar adecuadamente su exclusión. Si por la extensión del trabajo no se +puede señalar a todos los autores, habrá que justificar por qué se han elegido +unos y se ha prescindido de otros.

+ +

La organización específica en +secciones dependerá estrechamente el trabajo concreto que vayas a realizar. En +este punto será fundamental la colaboración con tu DIRECTOR, él podrá +asesorarte y guiarte, aunque siempre debes tener claro que el trabajo fundamental +es tuyo.

+ +

El capítulo debería concluir +con una última sección de resumen de conclusiones, resumiendo las principales +averiguaciones del estudio del dominio y cómo van a afectar al desarrollo +específico del trabajo.

+ +

Recuerda que para citar trabajos de diferentes autores es fundamental e +imprescindible seguir el formato APA, según se describe en +el documento Normativa_APA.pdf disponible en el apartado de Documentación del +Aula de información general del Máster Universitario en Inteligencia Artificial +(MIA). No se debe mencionar, ni utilizar ninguna fuente, sin citarla apropiadamente.

+ +

2.1. Contexto del problema

+ +

2.2. Estado del arte

+ +

Estado del arte (base +teórica): antecedentes, estudios actuales, comparativa de herramientas +existentes, etc.

+ +

2.3. Conclusiones

+ +

Conclusiones (nexo de unión de lo investigado con el trabajo a realizar).

+ +

 

+ +
+ + +

 

+ +

3.   +Objetivos +concretos y metodología de trabajo

+ +

Este tercer capítulo es el +puente entre el estudio del dominio y la contribución a realizar. Según el +trabajo concreto, el bloque se puede organizar de distintas formas, pero hay +tres elementos que deben estar presentes con mayor o menor detalle: (1) objetivo +general, (2) objetivos específicos y (3) metodología de trabajo.

+ +

Es muy importante, por no +decir imprescindible, que los objetivos (general y específicos) sean SMART +(Doran, 1981) según la idea de George T. Doran que utilizó la palabra smart (inteligente en inglés) para definir las +características de un objetivo:

+ +

•          S: Specific / Específico: que +exprese claramente qué es exactamente +lo que se quiere conseguir.

+ +

•          M: Measurable / Medible: que se +puedan establecer medidas que determinen el éxito o fracaso y también el +progreso en la consecución del objetivo.

+ +

•          A: Attainable / Alcanzable: que +sea viable su consecución en base al esfuerzo, tiempo y recursos disponibles +para conseguirlo.

+ +

•          R: Relevant / Relevante: que +tenga un impacto demostrable, es decir que sea útil para un propósito concreto.

+ +

•          T: Time-Related / Con un tiempo +determinado: que se pueda llevar a cabo en una fecha determinada.

+ +

3.1. Objetivo general

+ +

Los trabajos aplicados se +centran en conseguir un impacto concreto, demostrando la efectividad de una +tecnología, proponiendo una nueva metodología o aportando nuevas herramientas +tecnológicas. El objetivo por tanto no debe ser sin más “crear una herramienta” +o “proponer una metodología”, sino que debe centrarse en conseguir un efecto +observable. Además, como se ha dicho antes el objetivo general debe ser SMART

+ +

Ejemplo de objetivo general +SMART: Mejorar el servicio de audio guía de un museo convirtiéndolo en una guía +interactiva controlada por voz y valorada positivamente, un mínimo 4 sobre 5, +por los visitantes del museo.

+ +

Este objetivo descrito +anteriormente podría dar lugar a un trabajo de tipo 2 (desarrollo de software) +que plantease el desarrollo de un bot conversacional +que procesara la señal de voz recogida a través del micrófono y a través de +técnicas de procesamiento del lenguaje natural fuera capaz de mantener una +conversación con el visitante para determinar el contenido en el que está +interesado o resolver las posibles dudas o preguntas que pudiera tener a lo +largo de su visita.

+ +

3.2. Objetivos específicos

+ +

Independientemente del tipo de +trabajo, la hipótesis o el objetivo general típicamente se dividirán en un +conjunto de objetivos más específicos analizables por separado. Estos objetivos +específicos suelen ser explicaciones de los diferentes pasos o tareas a seguir +en la consecución del objetivo general.

+ +

Con los objetivos específicos +has de concretar qué pretendes conseguir. Estos objetivos que deben ser SMART +se formulan con un verbo en infinitivo más el contenido del objeto de estudio. +Se suelen usar viñetas para cada uno de los objetivos. Se pueden utilizar +fórmulas verbales, como las siguientes:

+ +
    +
  • Analizar
    • +
  • Calcular
    • +
  • Clasificar
    • +
  • Comparar
    • +
  • Conocer
    • +
  • Cuantificar
    • +
  • Desarrollar
    • +
  • Describir
    • +
  • Descubrir
    • +
  • Determinar
    • +
  • Establecer
    • +
  • Explorar
    • +
  • Identificar
    • +
  • Indagar
    • +
  • Medir
    • +
  • Sintetizar
    • +
  • Verificar
    • +
+ +

Los objetivos específicos +suelen ser alrededor de 5. Normalmente uno o dos sobre el marco teórico o +estado del arte y dos o tres sobre el desarrollo específico de la contribución.

+ +

En un trabajo como el anterior +se incluirían objetivos específicos tales como:

+ +
    +
  • Identificar + las tecnologías disponibles para crear un chatbot
    • +
+ +
    +
  • Explorar + recursos lingüísticos online que describan el dominio de los muesos en + español
    • +
  • Diseñar + e implementar el módulo de gestión del dialogo
    • +
  • Evaluar + el agente conversacional con 10 visitantes del museo.
    • +
+ +

 

+ +

3.3. Metodología del trabajo

+ +

De cara a alcanzar los objetivos específicos (y con ellos el objetivo +general o la validación/refutación de la hipótesis), será necesario realizar +una serie de pasos. La metodología del trabajo debe describir qué pasos se van +a dar, el porqué de cada paso, qué instrumentos se van a utilizar, cómo se van +a analizar los resultados, etc.

+ +
+ + +

 

+ +

4.   Desarrollo +específico de la contribución

+ +

En este +apartado debes desarrollar la descripción de tu contribución. Es muy +dependiente del tipo de trabajo concreto, y puedes contar con la ayuda de tu +director para estudiar cómo comunicar los detalles de tu contribución. A +continuación, te presentamos la estructura habitual para cada uno de los tipos +de trabajo, aunque suele ser común desarrollar los apartados en función de las +fases o actividades que se hayan establecido en la metodología de trabajo.

+ +

 

+ +

Tipo 1. Piloto experimental

+ +

 

+ +

Este +tipo de trabajos suelen seguir la estructura típica al describir experimentos +científicos, dividida en descripción del experimento, presentación de los +resultados y discusión de los resultados.

+ +

 

+ +

Capítulo 4 - Descripción +detallada del experimento

+ +

En el capítulo de Objetivos y Metodología del Trabajo ya habrás +descrito a grandes rasgos la metodología experimental que vas a seguir. Pero si +tu trabajo se centra en describir un piloto, deberás dedicar un capítulo a +describir con todo detalle las características del piloto. Como mínimo querrás +mencionar:

+ +

„ +Qué tecnologías se utilizaron +(incluyendo justificación de por qué se emplearon y descripciones detalladas de +las mismas).

+ +

„ +Cómo se organizó el piloto

+ +

„ +Qué personas participaron (con +datos demográficos)

+ +

„ +Qué técnicas de evaluación +automática se emplearon.

+ +

„ +Cómo transcurrió el experimento.

+ +

„ +Qué instrumentos de seguimiento y +evaluación se utilizaron.

+ +

„ +Qué tipo de análisis estadísticos +se ha empleado (si procede).

+ +

 

+ +

Capítulo 5 - Descripción de +los resultados

+ +

En el siguiente capítulo deberás detallar los resultados obtenidos, con +tablas de resumen, gráficas de resultados, identificación de datos relevantes, +etc. Es una exposición objetiva, sin valorar los resultados ni justificarlos.

+ +

 

+ +

Capítulo 6 - Discusión

+ +

Tras la presentación objetiva de los resultados, querrás aportar una +discusión de los mismos. En este capítulo puedes +discutir la relevancia de los resultados, presentar posibles explicaciones para +los datos anómalos y resaltar aquellos datos que sean particularmente +relevantes para el análisis del experimento.

+ +

 

+ +

Tipo 2. Desarrollo de +software

+ +

 

+ +

En un trabajo de desarrollo de software es importante justificar los +criterios de diseño seguidos para desarrollar el programa, seguido de la +descripción detallada del producto resultante y finalmente una evaluación de la +calidad y aplicabilidad del producto. Esto suele verse reflejado en la +siguiente estructura de capítulos:

+ +

 

+ +

Capítulo 4 - Identificación +de requisitos

+ +

En este capítulo se debe indicar el trabajo previo realizado para guiar +el desarrollo del software. Esto debería incluir la identificación adecuada del +problema a tratar, así como del contexto habitual de uso o funcionamiento de la +aplicación. Idealmente, la identificación de requisitos se debería hacer +contando con expertos en la materia a tratar.

+ +

 

+ +

Capítulo 5 - Descripción de +la herramienta software desarrollada

+ +

En el caso de desarrollos de +software, deberían aportarse detalles del proceso de desarrollo, incluyendo +las fases e hitos del proceso. También deben presentarse diagramas explicativos +de la arquitectura o funcionamiento, así como capturas de pantalla que permitan +al lector entender el funcionamiento del programa.

+ +

 

+ +

Capítulo 6 - Evaluación

+ +

La evaluación debería cubrir por lo menos una mínima evaluación de la +usabilidad de la herramienta, así como de su aplicabilidad para resolver el +problema propuesto. Estas evaluaciones suelen realizarse con usuarios expertos.

+ +

 

+ +

Tipo 3. Comparativa de +soluciones

+ +

 

+ + + +

Este tipo de trabajos suelen +seguir la estructura típica de un estudio comparativo, parten de plantear la +comparativa a realizar, describen el desarrollo de la misma +y analizan los resultados.

+ +

 

+ +

Capítulo 4 - Planteamiento de la comparativa

+ +

En este capítulo se debe +indicar el trabajo previo realizado para identificar el problema concreto a +tratar, así como las posibles soluciones alternativas que se van a evaluar. +También se deben identificar los criterios de éxito para la comparativa, las medidas +que se van a tomar, etc.

+ +

 

+ +

Capítulo 5 - Desarrollo de la comparativa

+ +

En este capítulo se debería +desarrollar con todo detalle la comparativa realizada, con todos los resultados +y mediciones obtenidos. Puede ser útil acompañar las descripciones con +gráficas, tablas y otros instrumentos para plasmar los datos obtenidos.

+ +

 

+ +

Capítulo 6 - Discusión y análisis de resultados

+ +

Mientras que el capítulo +anterior se centraría en informar de los resultados y comparaciones obtenidos, +en este capítulo se abordará la discusión sobre su posible significado, así +como el análisis de las ventajas y desventajas de las distintas soluciones +evaluadas.

+ +

 

+ +

En +el capítulo de Objetivos y Metodología del Trabajo ya habrás descrito a grandes +rasgos la metodología experimental que vas a seguir. Pero si tu trabajo se +centra en describir un piloto, deberás dedicar un capítulo a describir con todo +detalle las características del piloto. Como mínimo querrás mencionar:

+ +

·       Qué tecnologías se utilizaron +(incluyendo justificación de por qué se emplearon y descripciones +detalladas de las mismas).

+ +

·       Cómo se organizó el piloto

+ +

·       Qué personas participaron (con +datos demográficos)

+ +

·       Qué técnicas de evaluación +automática se emplearon.

+ +

·       Cómo transcurrió́ el experimento.

+ +

·       Qué instrumentos de seguimiento y +evaluación se utilizaron.

+ +

·       +Qué +tipo de análisis estadísticos se ha empleado (si procede).

+ +

 

+ +

.
+

+ + + +

5.   +Conclusiones +y trabajo futuro

+ +

5.1.  Conclusiones

+ +

Este último capítulo (en ocasiones, +dos capítulos complementarios) es habitual en todos los tipos de trabajos y presenta +el resumen final de tu trabajo y debe servir para informar del alcance y +relevancia de tu aportación.

+ +

Suele estructurarse empezando con un +resumen del problema tratado, de cómo se ha abordado y de por qué la solución +sería válida.

+ +

Es +recomendable que incluya también un resumen de las contribuciones del trabajo, +en el que relaciones las contribuciones y los resultados obtenidos con los +objetivos que habías planteado para el trabajo, discutiendo hasta qué punto has +conseguido resolver los objetivos planteados.

+ +

5.2.  Líneas de trabajo futuro

+ +

Finalmente, se suele dedicar una última sección a hablar de líneas de +trabajo futuro que podrían aportar valor añadido al TFE realizado. La sección +debería señalar las perspectivas de futuro que abre el trabajo desarrollado +para el campo de estudio definido. En el fondo, debes justificar de qué modo +puede emplearse la aportación que has desarrollado y en qué campos.

+ +

 

+ +

 

+ +

 

+ +
+ + +

 

+ +

Referencias +bibliográficas

+ +

Según la normativa APA debe ponerse +con sangría francesa y debe estar ordenado por orden alfabético según el +apellido del primer autor.

+ +

Toda la bibliografía que aparezca en +este apartado debe estar citada en el trabajo. La mayor parte de las citas +deben aparecer en el capítulo 2, que es donde se realiza el estudio del estado +del arte. Además, se recomienda evitar citas que hagan referencia a Wikipedia y +que no todas las referencias sean solo enlaces de internet, es decir, que se +vea alguna variabilidad entre libros, congresos, artículos y enlaces puntuales +de internet.

+ +

Se recomienda encarecidamente +utilizar el gestor de bibliografía de Word para gestionar la bibliografía.

+ +

Ejemplo:

+ +

Doran, G. T. +(1981). There's a S.M.A.R.T. way to write management's goals and objectives. Management Review (AMA FORUM), 70, 35-36.

+ +


+ 

+ +

Anexo A.    +Código fuente y datos analizados

+ +

Es recomendable que el estudiante incluya en +su memoria la URL del repositorio donde tiene alojado el código fuente +desarrollado durante el TFE. El estudiante debe ser el único autor del código y +único propietario del repositorio. En el repositorio no debe haber commit de ningún otro usuario del repositorio.

+ +

De igual forma, los datos que hayan utilizado +para el análisis, siempre que así se considere oportuno, también deberían están +alojamos en el mismo repositorio.

+ +

Si el TFE está asociado a una actividad o +proyecto de Empresa, se debe justificar en la memoria que, por temas de +confidencialidad, no se deja disponible ni el código fuente ni los datos +utilizados.

+ +

 

+ +

 

+ +

 

+ +
+ +

+ +
+ + + +
+ +

[1] Ejemplo de nota al pie.

+ +
+ +
+ + + +