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+# Hyperparameter Tuning with SLURM and Optuna
+
+This package integrates Optuna with SLURM to efficiently run hyperparameter optimization jobs on a cluster. It supports customizable trial parameters and allows you to track the results via a CSV file. The SLURM jobs process the parameter configurations and log their results, which are then read by Optuna to calculate the objective loss.
+## Features
+
+- Submit SLURM jobs with trial parameters generated by Optuna.
+- Customizable loss functions that process results stored in a CSV file.
+- Support for multiple parameter types (integers, floats, and categorical values).
+- Flexible interface for handling different experiment configurations.
+- Logs all SLURM submissions and errors using Python's logging package.
+
+
+## Installation
+
+Install with `poetry` or `pip`.
+
+```bash
+poetry add git+https://github.com/C4theBomb/hyperparameter-tuner.git
+pip install git+https://github.com/C4theBomb/hyperparameter-tuner.git
+```
+
+## Usage
+
+### 1. Define your loss function
+
+```python
+from hyperparameter_tuner import Loss
+
+class CustomLossFunction(Loss):
+  def calculate(self, row: pd.Series) -> float:
+    return row[0]
+```
+
+### 2. Create your objective function
+
+```python
+from hyperparameter_tuner import create_objective
+
+# Define your parameter types { name: (type, args, kwargs) }
+trial_param_types = {
+    'trajectories': ('int', (1, 5000), {}),
+    'learning_rate': ('float', (1e-5, 1e-2), {'log': True}),
+    'optimizer': ('categorical', (['adam', 'sgd'],), {})
+}
+
+loss = MyLoss()
+slurm_script = 'path/to/slurm_script.sh'
+results_path = 'path/to/results.csv'
+
+objective = create_objective(slurm_script, results_path, loss, trial_param_types)
+```
+
+### 3. Write your SLURM script
+
+```sh
+#!/bin/bash
+RESULTS_PATH=$1 # This is always required to be the first parameter
+TRIAL_ID=$2 # This is required to be the second parameter
+
+# Extract your trial parameters
+TRAJECTORIES=$3
+LEARNING_RATE=$4
+OPTIMIZER=$5
+
+REWARDS=$((RANDOM % 100))  # Replace with your experiment logic
+
+# Write results to CSV (first column must be TRIAL_ID so that Optuna can identify the run).
+echo "$TRIAL_ID,$REWARDS,$TRAJECTORIES,$LEARNING_RATE,$OPTIMIZER" >> $RESULTS_PATH
+```
+
+### 4. Run your script
+```
+python main.py
+```
+## License
+
+[AGPL-3.0](https://choosealicense.com/licenses/agpl-3.0/)
+
+
+## Authors
+
+- [Ceferino Patino](https://www.github.com/C4theBomb)
+