AI workload storage options

Key Concepts

• AI/ML Workloads: Computationally intensive tasks involving artificial intelligence and machine learning, requiring significant data and processing power.
• Storage Performance: The speed at which data can be read from or written to a storage system, crucial for keeping compute resources utilized.
• Throughput: The rate at which data can be transferred, measured in units like terabytes per second (TB/s).
• Latency: The time delay between a request for data and the delivery of that data, measured in milliseconds (ms).
• Parallel File System: A file system designed to distribute data and operations across multiple storage devices and servers to enhance performance.
• Object Store: A data storage architecture that manages data as objects, typically used for unstructured data and offering scalability.
• Managed Lustre: A specific implementation of a parallel file system, optimized for high-performance computing and AI workloads.
• Google Cloud Storage (GCS): Google Cloud's scalable object storage service.
• GCS Fuse: A tool that allows GCS buckets to be mounted as a file system on compute instances.
• GCS Anywhere Cache: A feature of GCS that creates a local, high-performance cache on SSDs near compute instances.
• Training: The phase in AI/ML development where models learn from large datasets.
• Inference: The phase where a trained AI model is used to make predictions or decisions on new data.
• KV Cache (Key-Value Cache): A cache used in some AI models to store frequently accessed key-value pairs, crucial for low-latency operations.

Storage Recommendations for AI Workloads

This video discusses the critical role of storage performance in AI and machine learning workloads, emphasizing that even powerful accelerators can be bottlenecked by slow storage. It outlines the top storage choices for two common AI phases: training and inference, considering performance, cost, and ease of use.

1. AI Training Workloads

Main Topic: Identifying the optimal storage solution for the data-intensive and compute-heavy phase of AI model training.

Key Points & Details:

• Challenge: Training requires massive datasets and high compute, but storage speed can limit accelerator utilization.
• Top Choice: Managed Lustre
  • Description: A parallel file system designed for high throughput.
  • Performance: Stripes data across multiple disks, delivering up to 1 TB/s of throughput for both reads and writes.
  • Latency: Offers sub-millisecond latency.
  • Suitability: Ideal for workloads that frequently write large checkpoints or access millions of small files, ensuring accelerators remain saturated.
  • Ease of Use: Generally provides high performance out-of-the-box with minimal manual tuning for most training jobs.
• Alternative: Google Cloud Storage (GCS) with Anywhere Cache
  • Description: GCS is an object store, fundamentally different from a traditional file system.
  • Integration: GCS Fuse allows compute instances to mount GCS buckets as local file systems.
  • Performance Enhancement: GCS Anywhere Cache creates a zonal cache on SSDs closer to compute instances.
  • Performance Figures: Delivers file throughput up to 2.5 TB/s and offers 70% lower latency compared to direct GCS bucket access.
  • Trade-offs: Requires potential job adjustments to accommodate the object storage model and manual tuning of the cache for optimal performance. This contrasts with Managed Lustre's out-of-the-box performance.

2. AI Inference Workloads

Main Topic: Recommending storage solutions for the phase where trained AI models are deployed to make predictions.

Key Points & Details:

• Shift in Priorities: For inference, cost-effectiveness and flexibility become more important than raw throughput for training.
• Primary Recommendation: GCS with Anywhere Cache
  • Rationale: Offers a balance of cost and flexibility.
  • Deployment: Models can be stored in a multi-region GCS bucket.
  • Caching: Anywhere Cache creates high-performance read caches in any zone with inference servers, bringing models closer to users.
• Alternative: Managed Lustre
  • Scenarios for Use:
    • Existing Infrastructure: If Managed Lustre is already in use for training within a single zone, it's efficient to use it for serving in the same zone.
    • Strict Latency Requirements: It is the top performer for workloads with the most demanding latency needs, such as those relying on a KV cache.

Summary of Recommendations

• For AI Training:
  • Choose Managed Lustre: When the highest performance is needed with the least manual tuning.
  • Consider GCS with Anywhere Cache: As an alternative, especially if willing to adapt jobs for object storage.
• For AI Inference:
  • Primary Choice: GCS with Anywhere Cache: For its cost-effectiveness and flexibility.
  • Secondary Choice: Managed Lustre: When absolute lowest latency is critical or if already using it for training.

Conclusion

The video concludes by emphasizing that selecting storage based on specific AI workload requirements is crucial for building a robust foundation on Google Cloud. By understanding the distinct needs of training and inference, users can make informed decisions to optimize performance, manage costs, and ensure ease of use.

Technical Terms Explained

• Parallel File System: A file system that distributes data and operations across multiple storage nodes and disks, enabling higher performance and scalability than traditional single-node file systems.
• Object Store: A data storage architecture that manages data as discrete units called objects, each with metadata and a unique identifier. It's highly scalable and suitable for unstructured data.
• GCS Fuse: A user-space file system driver that allows users to mount a Google Cloud Storage bucket as a local file system on a Linux machine.
• GCS Anywhere Cache: A feature that deploys a local cache of GCS objects on SSDs within a specific zone, reducing latency and improving read performance for frequently accessed data.
• KV Cache (Key-Value Cache): A cache that stores frequently accessed pairs of keys and their corresponding values. In AI, it's often used to speed up lookups in large models, particularly for tasks like text generation.