What Scheduler Meaning, Applications & Example
Component that adjusts learning rate during training.
What is a Scheduler?
A Scheduler is an algorithm or tool used to manage the timing and execution of tasks in machine learning or computing processes. In machine learning, it typically refers to an algorithm used to adjust learning rates or execute training steps at specific intervals during the training process. Schedulers help optimize the training process by adjusting hyperparameters like the learning rate to ensure better convergence and efficiency.
Types of Schedulers
- Learning Rate Scheduler: Adjusts the learning rate during training based on predefined rules or epochs. Common strategies include reducing the learning rate over time or adjusting it in response to performance metrics.
- Batch Scheduler: Controls the scheduling of batches for training, ensuring that mini-batches are fed to the model at appropriate times during the training loop.
- Job Scheduler: In computing, it manages the execution of tasks or jobs, often used in distributed systems or clusters to allocate resources for tasks.
Applications of a Scheduler
- Model Training : In machine learning, learning rate schedulers improve convergence speed and prevent overfitting by adjusting the learning rate during training.
- Resource Allocation: In cloud computing or distributed systems, schedulers manage the allocation of resources (e.g., CPU, GPU) to tasks, ensuring efficient execution.
- Task Automation: In DevOps, schedulers are used to automate and run jobs at scheduled times, such as backups or data processing tasks.
Example of a Scheduler
An example of a learning rate scheduler in machine learning might be a Step Decay scheduler, which reduces the learning rate by a fixed factor after a set number of epochs to allow the model to fine-tune its parameters and avoid overshooting the optimal solution.