Open-source MLRun can be used for efficient resource management in a number of ways. A few examples include:
- Auto-scaling - Automated resource allocation based on workload needs.
- Experiment tracking to compare models and choose the best-performing one without re-running the entire training pipeline.
- Serverless deployments with auto-scaling.
- Support for model quantization and pruning.
- Monitoring and logging for resource usage.
- Parallel pipeline execution and distributed compute capabilities.
- Micro-batching - Processing multiple requests simultaneously, improving GPU utilization and lowering per-request costs.
Read more about auto-scaling GPUs, experiment tracking, and how to use open-source Nuclio for serverless deployment.