Add pytorch LLM

README.md

@@ -102,6 +102,8 @@ you can see the sample [fil read in csv](./samples/charts/sbk-file-read.csv) and
 
 ### Setup Instructions
 
+#### Setup with Python virtual environment 
+
 ```
 #create the env
 python3 -m venv venv-sbk-charts
@@ -123,6 +125,31 @@ to deactivate from the venv
 deactivate
 ```
 
+#### Setup with conda
+
+```
+# Create a new conda environment with Python 3.14 or higher
+conda create -n conda-sbk-charts python=3.14 -y
+
+# Activate the environment
+conda activate conda-sbk-charts
+
+# Install pip if not already installed
+conda install pip -y
+
+# Install the project in editable mode using pip
+pip install -e .
+
+# Build the sbk-charts package
+python -m build
+```
+
+To deactivate
+
+```
+conda deactivate
+```
+
 ## Generative AI-Powered Analysis
 
 SBK Charts includes AI-powered analysis descriptions to provide deeper insights into your storage benchmark results.

requirements.txt

@@ -5,8 +5,11 @@ ordered-set~=4.1.0
 jproperties~=2.1.1
 pillow~=12.0.0
 openpyxl-image-loader~=1.0.5
-huggingface_hub~=1.2.3
+huggingface_hub~=0.34.0
 build~=1.3.0
 lmstudio~=1.5.0
 openai~=2.14.0
-requests~=2.32.5
+requests~=2.32.5
+torch~=2.9.1
+transformers~=4.57.3
+accelerate>=0.20.0  # Required for model parallelism and memory optimization

src/ai/sbk_ai.py

@@ -51,6 +51,7 @@
               "The summary generated by generative AI models may not be complete and accurate. "
               "It's recommended to analyze the graphs along with the generated summary.")
 
+DEFAULT_TIMEOUT_SECONDS = 120
 
 def get_t_num_sheet_name(r_num_name):
     """
@@ -134,7 +135,7 @@ def __init__(self):
         self.file =  None
         self.ai_instance = None
         self.web = None
-        self.timeout_seconds = 120
+        self.timeout_seconds = DEFAULT_TIMEOUT_SECONDS
         self.no_threads = False
 
     def add_args(self, parser):
@@ -177,19 +178,27 @@ def parse_args(self, args):
 
         Args:
             args (argparse.Namespace): Parsed command-line arguments
-
+            
         Side Effects:
             - Sets the output file path
-            - Configures timeout and threading settings
+            - Configures timeout (converted to int) and threading settings
             - Activates the selected AI backend instance if specified
         """
+        self.timeout_seconds = int(args.seconds) if hasattr(args, 'seconds') and args.seconds is not None else DEFAULT_TIMEOUT_SECONDS
         self.file = args.ofile
-        self.timeout_seconds = args.seconds
         self.no_threads = args.nothreads
         if args.ai_class:
             self.ai_instance = self.ai_instance_map[args.ai_class.lower()]
             self.ai_instance.parse_args(args)
 
+    def open(self, args):
+        if self.ai_instance:
+            self.ai_instance.open(args)
+
+    def close(self, args):
+        if self.ai_instance:
+            self.ai_instance.close(args)
+
     def load_workbook(self):
         """
         Load the Excel workbook from the specified file path.

src/custom_ai/pytorch_llm/README.md

@@ -0,0 +1,187 @@
+<!--
+Copyright (c) KMG. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+-->
+# PyTorch LLM Implementation for SBK Charts
+
+This document describes the PyTorch LLM implementation for SBK Charts, which enables local AI-powered analysis of storage benchmark results using PyTorch and Hugging Face models.
+
+## Overview
+
+The PyTorch LLM implementation allows SBK Charts to leverage local language models through PyTorch's inference capabilities.
+This implementation is particularly useful for users who want to run AI analysis locally without relying on external APIs.
+This implementation is based on the Hugging Face Transformers library and PyTorch.
+you can train the model by providing '--pt-train' option, but the default model : 'openai/gpt-oss-20b' consumes more than 200GB RAM.
+
+## Implementation Details
+
+The PyTorch LLM implementation is located in the `src/custom_ai/pytorch_llm` directory and extends the base AI interface defined in `src/genai/genai.py`.
+
+### Key Features
+
+1. **Local Model Inference**: Runs entirely on your hardware using PyTorch
+2. **Hugging Face Integration**: Supports any Causal Language Model from the Hugging Face Hub
+3. **Hardware Acceleration**: Automatically utilizes CUDA, MPS, or CPU based on availability
+4. **Memory Efficient**: Uses 16-bit or 32-bit precision based on hardware support
+5. **Configurable Parameters**: Adjust model parameters like temperature and top-p sampling
+
+## Prerequisites
+
+### Python Dependencies
+
+- PyTorch (with CUDA support recommended for GPU acceleration)
+- Transformers library from Hugging Face
+- A compatible pre-trained language model (e.g., from Hugging Face Hub)
+
+### Hardware Requirements
+
+- CPU: Modern x86-64 or ARM processor
+- RAM: At least 32GB (128GB+ recommended for larger models)
+- GPU: NVIDIA GPU with CUDA support recommended for better performance
+- Disk Space: 10GB+ for model storage (varies by model size)
+
+## Installation
+
+1. Install PyTorch (with CUDA if available):
+   ```bash
+   # For CUDA 11.8
+   pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
+
+   # For CPU-only
+   # pip3 install torch torchvision torchaudio
+   ```
+
+2. Install the Transformers library:
+   ```bash
+   pip install transformers
+   ```
+
+3. (Optional) Install additional dependencies for specific models:
+   ```bash
+   pip install accelerate bitsandbytes
+   ```
+
+## Configuration
+
+The implementation supports the following configuration options:
+
+- **Model**: Any Hugging Face model ID or local path (default: `openai/gpt-oss-20b`)
+- **Device**: Automatically detects CUDA/MPS/CPU (can be overridden)
+- **Max Length**: Maximum sequence length for generation (default: 2048)
+- **Temperature**: Controls randomness (default: 0.4)
+- **Top-p**: Nucleus sampling parameter (default: 0.9)
+
+## Usage
+
+### Basic Usage
+
+```bash
+# Process a single CSV file with default settings
+sbk-charts -i input.csv -o output.xlsx pytorch_llm
+
+# Process multiple CSV files
+sbk-charts -i file1.csv,file2.csv -o output.xlsx pytorch_llm
+```
+
+### Advanced Options
+
+```bash
+# Specify a different model
+sbk-charts -i input.csv -o output.xlsx pytorch_llm --pt-model mistralai/Mistral-7B-v0.1
+
+# Adjust generation parameters
+sbk-charts -i input.csv -o output.xlsx pytorch_llm \
+    --pt-temperature 0.7 \
+    --pt-top-p 0.95 \
+    --pt-max-length 1024
+
+# Force CPU usage
+sbk-charts -i input.csv -o output.xlsx pytorch_llm --pt-device cpu
+```
+
+## Example Commands
+
+```bash
+# Process file with default settings
+sbk-charts -i ./samples/charts/sbk-file-read.csv -o ./samples/charts/sbk-file-read-pytorch.xlsx pytorch_llm
+
+# Use a smaller model with custom parameters
+sbk-charts -i ./samples/charts/sbk-file-read.csv -o ./samples/charts/sbk-file-read-mistral.xlsx pytorch_llm \
+    --pt-model mistralai/Mistral-7B-v0.1 \
+    --pt-temperature 0.5 \
+    --pt-max-length 1024
+
+# Process multiple files
+sbk-charts -i ./samples/charts/sbk-file-read.csv,./samples/charts/sbk-rocksdb-read.csv \
+    -o ./samples/charts/sbk-combined-pytorch.xlsx pytorch_llm
+```
+
+## Model Management
+
+### Using Local Models
+
+1. Download a model from Hugging Face Hub:
+   ```bash
+   from transformers import AutoModelForCausalLM, AutoTokenizer
+
+   model_name = "mistralai/Mistral-7B-v0.1"
+   model = AutoModelForCausalLM.from_pretrained(model_name)
+   tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+   # Save locally
+   save_path = "./saved_models/mistral-7b"
+   model.save_pretrained(save_path)
+   tokenizer.save_pretrained(save_path)
+   ```
+
+2. Use the local model:
+   ```bash
+   sbk-charts -i input.csv -o output.xlsx pytorch_llm --pt-model ./saved_models/mistral-7b
+   ```
+
+## Performance Tips
+
+1. **Use GPU Acceleration**: Ensure CUDA is properly installed for best performance
+2. **Quantization**: For large models, consider 4-bit or 8-bit quantization
+3. **Batch Processing**: Process multiple files in a single command when possible
+4. **Model Size**: Choose an appropriately sized model for your hardware
+5. **Execution time**: choose the execution time at least 30 minutes ; you can use the parameter '-secs' parameter
+
+## Troubleshooting
+
+### Common Issues
+
+1. **Out of Memory (OOM) Errors**:
+   - Reduce `--pt-max-length`
+   - Use a smaller model
+   - Enable gradient checkpointing
+   - Use quantization
+
+2. **Model Loading Issues**:
+   - Ensure the model name is correct
+   - Check internet connection if downloading
+   - Verify disk space is available
+
+3. **CUDA Errors**:
+   - Check CUDA installation: `nvidia-smi`
+   - Ensure PyTorch was installed with CUDA support
+   - Try reducing batch size or model size
+
+## Directory Structure
+
+```
+src/
+└── custom_ai/
+    └── pytorch_llm/
+        ├── __init__.py
+        ├── pytorch_llm.py      # Main implementation
+        └── README.md           # This document
+```
+## License
+
+Apache License 2.0