Cloud-native model training jobs require fast data access to achieve shorter training cycles. Accessing data can be challenging when your datasets are distributed across different regions and clouds. Additionally, as GPUs remain scarce and expensive resources, it becomes more common to set up remote training clusters from where data resides. This multi-region/cloud scenario introduces the challenges of losing data locality, resulting in operational overhead, latency and expensive cloud costs.
In the third webinar of the multi-cloud webinar series, Chanchan and Shawn dive deep into:
- The data locality challenges in the multi-region/cloud ML pipeline
- Using a cloud-native distributed caching system to overcome these challenges
- The architecture and integration of PyTorch/Ray+Alluxio+S3 using POSIX or RESTful APIs
- Live demo with ResNet and BERT benchmark results showing performance gains and cost savings analysis
Cloud-native model training jobs require fast data access to achieve shorter training cycles. Accessing data can be challenging when your datasets are distributed across different regions and clouds. Additionally, as GPUs remain scarce and expensive resources, it becomes more common to set up remote training clusters from where data resides. This multi-region/cloud scenario introduces the challenges of losing data locality, resulting in operational overhead, latency and expensive cloud costs.
In the third webinar of the multi-cloud webinar series, Chanchan and Shawn dive deep into:
- The data locality challenges in the multi-region/cloud ML pipeline
- Using a cloud-native distributed caching system to overcome these challenges
- The architecture and integration of PyTorch/Ray+Alluxio+S3 using POSIX or RESTful APIs
- Live demo with ResNet and BERT benchmark results showing performance gains and cost savings analysis
Video:
Presentation slides:
Cloud-native model training jobs require fast data access to achieve shorter training cycles. Accessing data can be challenging when your datasets are distributed across different regions and clouds. Additionally, as GPUs remain scarce and expensive resources, it becomes more common to set up remote training clusters from where data resides. This multi-region/cloud scenario introduces the challenges of losing data locality, resulting in operational overhead, latency and expensive cloud costs.
In the third webinar of the multi-cloud webinar series, Chanchan and Shawn dive deep into:
- The data locality challenges in the multi-region/cloud ML pipeline
- Using a cloud-native distributed caching system to overcome these challenges
- The architecture and integration of PyTorch/Ray+Alluxio+S3 using POSIX or RESTful APIs
- Live demo with ResNet and BERT benchmark results showing performance gains and cost savings analysis
Videos:
Presentation Slides:
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