The path of data in a machine learning system is complex, with its performance demands changing dramatically at each stage of the lifecycle. A storage solution optimized for cost-effective, long-term archival of raw data is often ill-suited for the high-throughput, parallel reads required by a distributed training job. Selecting the appropriate storage technology for each task is not a matter of optimization, it is a fundamental architectural requirement for building a performant and cost-efficient AI platform.
Matching Storage Characteristics to ML Workloads
Different phases of the machine learning lifecycle impose distinct I/O (Input/Output) patterns on the underlying storage system. A successful storage architecture accommodates these varying needs by employing a tiered approach rather than a one-size-fits-all solution. The primary access patterns to consider are:
- High-Throughput Sequential Reads: This is the classic pattern for model training. The system reads large, contiguous chunks of data, often streaming the entire dataset once per epoch. The primary performance metric here is bandwidth (GB/s), not operations per second.
- Low-Latency Random Access: Required during online inference, where a model needs to fetch a specific feature vector for a single prediction request with minimal delay. It's also relevant for certain data exploration and preprocessing tasks. Here, latency (milliseconds) and IOPS (I/O Operations Per Second) are the most important metrics.
- Mixed Read/Write Parallel I/O: Common during complex data preprocessing, feature engineering, and checkpointing. Multiple processes may be reading raw data, transforming it, and writing new features or model weights simultaneously. This pattern demands a balance of throughput, IOPS, and concurrency control.
Let's examine the three primary storage paradigms and their fit for these ML workloads.
Object Storage
Object storage systems, such as Amazon S3, Google Cloud Storage (GCS), and Azure Blob Storage, manage data as objects within a flat address space. Each object consists of the data itself, a variable amount of metadata, and a globally unique identifier (the key). Access is handled via an HTTP-based API.
Strengths:
- Massive Scalability: Object stores are designed to hold exabytes of data, scaling capacity and throughput horizontally without administrative overhead. This makes them the default choice for building data lakes.
- Durability and Availability: They typically replicate data across multiple physical locations, offering extremely high durability guarantees.
- Cost-Effectiveness: The cost per gigabyte is significantly lower than other storage types, especially with tiered storage classes (e.g., S3 Glacier) for archival.
Weaknesses:
- High Latency: Every request is an HTTP call, which introduces network overhead. This makes object storage slow for workloads that require reading many small files, where per-file latency dominates total read time.
- API-based Access: Object storage is not POSIX-compliant. You cannot mount it like a traditional file system and use standard file I/O operations. Applications must be written to use the S3 (or equivalent) API. While tools like
s3fs exist, they often suffer from performance and consistency issues.
- "Eventually Consistent" Models: Some object stores have consistency models that can introduce delays before a newly written or overwritten object is visible to all subsequent read requests, which can complicate certain data processing pipelines.
Use Cases in ML:
- Primary Data Lake: Storing raw, unstructured data (images, text, logs).
- Model and Artifact Registry: A durable, versioned repository for trained model weights, data splits, and experiment results.
- Checkpoint Storage: A cost-effective location for saving long-term training checkpoints.
Block Storage
Block storage, like Amazon Elastic Block Store (EBS) or Google Persistent Disk, provides raw volumes (blocks) of storage to a compute instance. The operating system formats this volume with a traditional file system (e.g., ext4, XFS) and manages files and directories.
Strengths:
- Low Latency and High IOPS: Since the storage is directly attached (logically) to the instance, it delivers excellent performance for random read/write operations, making it ideal for databases or transactional workloads.
- POSIX-Compliant: It behaves as a standard local disk, allowing any application to use it without modification.
Weaknesses:
- Single-Host Access: A standard block volume can only be mounted by a single compute instance at a time, making it unsuitable for sharing data across a multi-node training cluster.
- Limited Scalability: A single volume has a maximum size (e.g., 64 TiB for some EBS types), and its performance is tied to the attached instance.
- Higher Cost: The cost per gigabyte is substantially higher than object storage.
Use Cases in ML:
- Instance Boot Volumes: The operating system for a training or inference server runs on block storage.
- Scratch Space: Temporary storage for a single-node job that requires high-performance disk I/O.
- Feature Store Databases: Hosting the online component of a feature store that requires low-latency lookups (e.g., a Redis or Cassandra instance).
Parallel File Systems
Parallel file systems, such as Lustre, BeeGFS, and their managed cloud equivalents (e.g., Amazon FSx for Lustre), are designed to provide concurrent, high-performance access to a shared dataset from thousands of clients. They achieve this by striping data across multiple storage servers and separating metadata operations onto dedicated servers.
Diagram of a parallel file system architecture. Client nodes first contact the Metadata Server (MDS) to locate a file's data, then read the data blocks in parallel directly from the multiple Object Storage Servers (OSS).
Strengths:
- Extreme Throughput: By aggregating the bandwidth of many storage servers, these systems can deliver hundreds of GB/s or even TB/s of throughput, scaling with the number of storage servers.
- Shared POSIX Access: All client nodes in a cluster see the same, consistent file system. This drastically simplifies the data loading code for distributed training, as each node can access the data using standard file paths.
- Low-Latency at Scale: Designed for high-performance computing (HPC) environments, they maintain low latency even under heavy, concurrent load.
Weaknesses:
- Cost and Complexity: Parallel file systems are the most expensive option and can be complex to deploy and manage, although managed services abstract away much of this difficulty.
- Suboptimal for Small Files: While better than object storage, performance can still degrade when dealing with many millions of tiny files due to metadata overhead.
Use Cases in ML:
- Hot Training Data: The primary storage for datasets being actively used by large, multi-node distributed training jobs.
- Shared Home Directories: Providing a common, high-performance file space for teams of data scientists.
- High-Performance Scratch: A shared scratch space for multi-node data processing jobs that produce intermediate files.
A Tiered Storage Architecture for AI
No single system provides the optimal balance of performance, cost, and scalability for the entire ML workflow. The industry best practice is a tiered architecture that aligns the storage solution with the data access pattern.
Relative comparison of storage solutions across main metrics, scaled from 1 (Low) to 10 (High). Note that exact performance and cost depend heavily on the specific cloud provider and service configuration.
A common and effective pattern is to use object storage as the permanent, durable data lake and a parallel file system as a high-performance cache for active training jobs.
- Data Ingestion: Raw data lands in the object storage data lake.
- Preprocessing: A Spark or Ray job reads data from the object store, processes it, and writes the prepared training dataset back to the object store in an optimized format (e.g., TFRecord, Petastorm).
- Training Preparation: Before a distributed training job begins, the prepared dataset is copied from the object store to the parallel file system. This is a one-time cost per training run.
- Model Training: The multi-node GPU cluster reads data at maximum speed from the parallel file system, which is designed to handle the concurrent requests from all nodes without creating an I/O bottleneck.
- Archival: Once training is complete, the high-performance copy on the parallel file system can be deleted to save costs, with the master copy remaining safely in the object store.
This hybrid approach combines the low cost and scale of object storage with the extreme performance of parallel file systems, ensuring that the expensive, high-performance tier is only used when absolutely necessary. This prevents storage from becoming the limiting factor in your model training velocity and allows you to scale your compute resources effectively.
