Solution: Application of SkyPilot's task queue management system
BACKGROUND: Traditional hyperparametric tuning requires manual management of hundreds of experiments, which is low in resource utilization and prone to errors.
- Implementation steps
- In the YAML configuration use the
${env}Syntax to define variable parameters, for example:run: python train.py --lr ${lr} --batch_size ${bs} - Prepare parameter CSV files or generate parameter combinations via the Python API
- Performs a batch submission:
sky jobs launch -c hp-tuning task.yaml --num-jobs 2000
- In the YAML configuration use the
- management function
- real time monitoring::
sky queue hp-tuningView the status of each task - dynamic adjustment: The runtime can be accessed through the
sky jobs cancel/cancel-allTermination of specific experiments - Collection of results: The logs and output of all tasks are stored uniformly in the
~/sky_jobs/hp-tuning/directory
- real time monitoring::
- Advanced Techniques
- Adaptive parameter sampling in combination with tuning libraries such as Optuna
- set up
resources.use_spot: trueGetting non-critical experiments to use Spot examples - pass (a bill or inspection etc)
sky.job.storage_mountsMounting a shared storage save checkpoint
Effectiveness: In the case of ImageNet tuning, 2000 experiments can be completed in 8 hours, a 4x speedup compared to traditional methods.
This answer comes from the articleSkyPilot: an open-source framework for efficiently running AI and batch tasks in any cloudThe
May not be reproduced without permission:AI productivity tools " How to overcome the complexity of managing large-scale hyperparameter tuning tasks?
