We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Lu Qiu - Maximize GPU Utilization for Model Training | PyData Global 2023
Maximize GPU utilization for model training with Alassio's caching layer, which improves performance by caching data and reducing remote data fetching, and accelerates model deployment time up to 2x.
- To maximize GPU utilization for model training, caching is crucial to reduce repeated data fetching from remote storage.
- Alassio’s caching layer can improve GPU utilization rate by up to 4 times.
- The current system has a performance bottleneck at the master node, which may fail to serve requests.
- To solve this issue, Alassio provides affinity caching, which caches data on the same node to reduce RPC calls.
- Elastio Data Platform is designed to provide a high-performance layer for cache data and fetch data from local path, reducing the need for remote data fetching.
- The current system has a poor data loader rate, which may not be able to serve concurrent requests.
- Alassio’s benchmark shows that its caching layer can improve the data loader rate from 82% to 1%.
- The current system may have issues with data locality, especially when dealing with concurrent requests.
- Alassio’s distributed caching approaches can help mitigate this issue.
- The current system may have limited caching capabilities, which may not be able to cache all data.
- Alassio provides a caching system that can cache both data and metadata on workers.
- The current system may have issues with node imbalance, which may lead to node failure.
- Alassio provides a solution to prevent node imbalance, ensuring that all nodes are utilized evenly.
- Alassio’s caching layer can also improve model deployment time by up to 2 times.
- Elastio Data Platform provides a unified interface for accessing different storage systems, making it easier to integrate with different frameworks.