Storage throughput is frequently the limiting factor in AI training pipelines, not compute. This webinar shows how to use AWS's S3 connector for PyTorch with MinIO AIStor as the data layer, covering why it outperforms direct SDK access for training workloads and how to configure it for AIStor with a single flag change. The session also covers model state management using AIStor object versioning: checkpointing every training epoch, tagging known-good versions, promoting older checkpoints using MCCP, and bulk-removing poor epochs with MC undo. A live CIFAR-10 demo runs training across 20 epochs, illustrating how accuracy patterns across checkpoints make rollback a routine operational tool rather than a recovery measure. Lifecycle management for checkpoint cost control is covered in the Q&A.
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The AWS S3 connector for PyTorch connects to AIStor with a single configuration change (force_path_style=true) and delivers higher throughput than direct boto3 or MinIO SDK access because it is purpose-built for distributed PyTorch training and checkpointing.
AIStor object tagging lets teams mark known-good model checkpoints by epoch number; tags persist through version promotions, enabling lifecycle rules that protect tagged versions while automatically retiring or tiering untagged ones to cold storage.
In the live CIFAR-10 demo, accuracy plateaued between epochs 15 and 20 (60% vs 59%), demonstrating the diminishing returns pattern that makes version rollback and selective epoch deletion a practical model management workflow, not just a recovery tool.
ML engineers and data platform teams setting up AIStor as the data layer for PyTorch training workloads, and anyone managing model checkpoint versioning, rollback, and storage cost control at scale.
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