Update README.md

README.md

@@ -1,75 +1,103 @@
----
-license: apache-2.0
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-# HLO Feature Dataset for Deep Learning Workloads
+# HLO Feature Dataset for Deep Learning Resource Estimation
 
-[![Dataset](https://img.shields.io/badge/HuggingFace-Dataset-yellow)](https://huggingface.co/datasets/your-username/hlo-feature-dataset)
+[![Dataset](https://img.shields.io/badge/HuggingFace-Dataset-blue)](https://huggingface.co/datasets/your-username/hlo-feature-dataset)
 
 ## Dataset Summary
-The **HLO Feature Dataset** provides High-Level Optimizer (HLO) graph features extracted from various deep learning model training runs. Each sample represents a unique configuration of neural network training, paired with graph-based features suitable for machine learning tasks such as:
+The **HLO Feature Dataset** is a collection of compiler-level graph features (HLO graphs) extracted from deep learning training workloads. Alongside detailed metadata (model configs, GPU stats), this dataset enables machine learning approaches for:
 
-- Runtime and resource prediction
-- AI workload optimization in HPC environments
-- Graph neural network (GNN) research on compiler-level representations
-- Scheduling and efficiency analysis for GPU-based training
+- ⏱️ **Training Time Prediction**
+- 📉 **Resource Consumption Estimation**
+- ⚡ **HPC and GPU Scheduling Optimization**
+- 🧩 **Graph-based Neural Architecture Analysis**
 
-The dataset includes metadata for each training run and corresponding `.npz` files containing serialized HLO graph features.
+This dataset is ideal for experimenting with regression models (e.g., XGBoost) and Graph Neural Networks (GNNs) using compiler features.
 
 ---
 
-## Supported Tasks and Benchmarks
-- 🕒 **Runtime Prediction**
-- 📊 **Resource Utilization Estimation**
-- ⚙️ **Graph-Based Neural Architecture Analysis**
-- 🚀 **HPC & GPU Scheduling Optimization**
-
-This dataset can be used to train models that predict execution time, memory usage, or optimize scheduling strategies based on compiler graph features.
+## Supported Tasks
+- **⚙️ Runtime & Resource Prediction**: Predict training time (`fit_time`) based on HLO features.
+- **📊 ML for Systems Optimization**: Use tabular + graph data for AI workload management.
+- **🔗 Graph Representation Learning**: Apply GNNs on HLO graphs (`node_feat`, `edge_index`).
 
 ---
 
 ## Dataset Structure
 
-Each sample consists of:
-- **Metadata**: Model configuration, hardware specs, and performance metrics (from `dataset-new.csv`).
-- **HLO Graph Features**: Stored in `.npz` files containing:
-  - `node_opcode`: Operation codes
-  - `node_feat`: Node feature matrix
-  - `edge_index`: Graph topology
-  - `node_config_ids`: Config identifiers
-  - `node_splits`: Graph partition info
+Each entry includes:
+- **Metadata**: From `dataset-new.csv` (model, optimizer, GPU specs, timing metrics, etc.)
+- **HLO Graph Features**: `.npz` files containing:
+  - `node_opcode`, `node_feat`, `edge_index`, `node_config_ids`, `node_splits`
 
 ---
 
-## Features
-| Feature            | Type     | Description                          |
-|--------------------|----------|--------------------------------------|
-| name               | string   | Model name                           |
-| optimizer          | string   | Optimizer used                       |
-| batch, epochs      | int      | Training parameters                  |
-| learn_rate         | float    | Learning rate                        |
-| gpu_name           | string   | GPU model                            |
-| fit_time           | float    | Total training time                  |
-| npz_path           | string   | Path to HLO feature `.npz` file      |
-| ...                | ...      | Additional GPU & utilization metrics |
+## Usage Example
+
+This example demonstrates how to load metadata, preprocess features, and train an XGBoost model to predict training time (`fit_time`), as shown in the Colab notebook.
+
+```python
+import pandas as pd
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import mean_squared_error
+from xgboost import XGBRegressor
+
+# Load metadata CSV
+df = pd.read_csv('dataset-new.csv')
+
+# Example feature selection (drop non-numeric/categorical handling needed)
+X = df[['batch', 'epochs', 'learn_rate', 'gpu_core_count', 'gpu_memory_size']]
+y = df['fit_time']
+
+# Train-test split
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Initialize XGBoost Regressor
+xgb_model = XGBRegressor(n_estimators=100, learning_rate=0.1, max_depth=6, random_state=42)
+xgb_model.fit(X_train, y_train)
 
-The `.npz` file contains graph data relevant for GNNs or ML models.
+# Evaluate
+preds = xgb_model.predict(X_test)
+rmse = mean_squared_error(y_test, preds, squared=False)
+print(f"RMSE: {rmse}")
+```
 
 ---
 
-## Usage Example
+### Loading HLO Graph Features
+For graph-based ML tasks, load the `.npz` files:
 
 ```python
-from datasets import load_dataset
-import numpy as np
+npz_file = df.iloc[0]['npz_path']
+graph_data = np.load(npz_file)
+
+node_features = graph_data['node_feat']
+edges = graph_data['edge_index']
+
+print("Node Feature Shape:", node_features.shape)
+print("Edge Index Shape:", edges.shape)
+```
+
+---
 
-# Load metadata
-dataset = load_dataset("your-username/hlo-feature-dataset")
-sample = dataset['train'][0]
+## Citation
+If you use this dataset, please cite:
 
-# Load HLO features
-npz_file = sample['npz_path']
-data = np.load(npz_file)
+```
+@misc{hlofeatures2025,
+  title={HLO Feature Dataset for AI Resource Estimation},
+  author={Your Name},
+  year={2025},
+  url={https://huggingface.co/datasets/your-username/hlo-feature-dataset}
+}
+```
+
+---
+
+## License
+Specify your license here (e.g., MIT, Apache-2.0).
+
+---
 
-node_features = data['node_feat']
-edges = data['edge_index']
+## Contributions
+Open to contributions! Feel free to suggest improvements or share your models trained on this dataset.