# Dysweep
Dysweep is a Python library enhancing the functionalities of the [Weights and Biases sweep library](https://docs.wandb.ai/guides/sweeps). It allows entire experiments to be executed using a configuration dictionary (YAML/JSON).
## Features
- **Checkpointing for the Sweep Server**: Dysweep introduces checkpointing that allows resuming certain runs, useful when only a small fraction of runs fail, eliminating the need to re-run the entire sweep.
- **Running Sweeps Over Hierarchies**: Dysweep supports hierarchically structured parameters, thereby eliminating the need for hard-coding the selection between different classes.
Dysweep is inspired by [DyPy](https://github.com/vahidzee/dypy), offering a versatile configuration set that empowers defining experiments at any layer of abstraction.
## Applications
Dysweep aids in large-scale hyperparameter tuning across various models/methods and running models over different configurations and datasets. It provides a systematic way to define a sweep in WandB, allowing parallel execution of experiments.
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"description": "# Dysweep\n\nDysweep is a Python library enhancing the functionalities of the [Weights and Biases sweep library](https://docs.wandb.ai/guides/sweeps). It allows entire experiments to be executed using a configuration dictionary (YAML/JSON).\n\n## Features\n\n- **Checkpointing for the Sweep Server**: Dysweep introduces checkpointing that allows resuming certain runs, useful when only a small fraction of runs fail, eliminating the need to re-run the entire sweep.\n\n- **Running Sweeps Over Hierarchies**: Dysweep supports hierarchically structured parameters, thereby eliminating the need for hard-coding the selection between different classes.\n\nDysweep is inspired by [DyPy](https://github.com/vahidzee/dypy), offering a versatile configuration set that empowers defining experiments at any layer of abstraction.\n\n## Applications\n\nDysweep aids in large-scale hyperparameter tuning across various models/methods and running models over different configurations and datasets. It provides a systematic way to define a sweep in WandB, allowing parallel execution of experiments.\n\n\n\n",
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