SAN MATEO, Calif. – Alluxio, the AI and data acceleration platform, today announced the latest enhancements in Alluxio Enterprise AI. Version 3.5 showcases the platform’s capability to accelerate AI model training and streamline operations with features such as a new Cache Only Write Mode, advanced cache management, and enhanced Python SDK integrations. These updates empower organizations to train models faster, handle massive datasets more efficiently, and streamline the complexity of AI infrastructure operations.
AI-driven workloads face significant challenges in managing the sheer volume and complexity of data, which can lead to inefficiencies and increased training times. Ensuring fast, prioritized access to critical data and seamless integration with common AI frameworks is essential for optimizing performance and accelerating model development.
“The latest release of Alluxio Enterprise AI is packed with new capabilities designed to further accelerate AI workload performance,” said Haoyuan (HY) Li, Founder and CEO of Alluxio. “Our customers are training AI models with enormous datasets that often span billions of files. Alluxio Enterprise AI 3.5 was built to ensure workloads perform at peak performance while also simplifying management and operations of AI infrastructure.”
Alluxio Enterprise AI version 3.5 includes the following key features:
- New caching mode accelerates AI checkpoints – Alluxio’s new CACHE_ONLY Write Mode significantly improves the performance of write operations, such as writing checkpoint files during AI model training. When enabled, this mode writes data exclusively to the Alluxio cache instead of the underlying file system (UFS). By bypassing the UFS, write performance is enhanced by eliminating bottlenecks typically associated with underlying storage systems. This feature is experimental.
- Advanced cache eviction policies provide fine-grained control – Alluxio’s TTL Cache Eviction Policies allow administrators to enforce time-to-live (TTL) settings on cached data, ensuring less frequently accessed data is automatically evicted based on defined policies. Alluxio’s priority-based cache eviction policies enable administrators to define caching priorities for specific data that override Alluxio’s default Least Recently Used (LRU) algorithm, ensuring critical data remains in cache even if it would otherwise be evicted. This is ideal for workloads requiring consistent low-latency access to key datasets. Both TTL and Priority-based Cache Eviction Policies are generally available.
- Python SDK integrations enhance AI framework compatibility – Alluxio’s Python SDK now supports leading AI frameworks, including PyTorch, PyArrow, and Ray. These integrations provide a unified Python filesystem interface, enabling applications to interact seamlessly with various storage backends. This simplifies the adoption of Alluxio Enterprise AI for Python applications, particularly those handling data-intensive workloads and AI model training, by facilitating quick and repeated access to both local and remote storage systems. This feature is experimental.
This release also introduces several enhancements to Alluxio’s S3 API, which are immediately available:
- Support for HTTP persistent connections (HTTP keep-alive) – Alluxio now supports HTTP persistent connections, which maintain a single TCP connection for multiple requests. This reduces the overhead of opening new connections for each request and decreases latency by approximately 40% for 4KB S3 ReadObject requests.
- TLS encryption for enhanced security – Communication between the Alluxio S3 API and the Alluxio worker now supports TLS encryption, ensuring secure data transmission.
- Multipart upload (MPU) support – The Alluxio S3 API now supports multipart upload, which splits files into multiple parts and uploads each part separately. This feature simplifies the upload process and improves throughput for large files.
Other enhancements included in version 3.5 are:
- The Alluxio Index Service – A new caching service that improves the performance of directory listings for directories storing hundreds of millions of files and subdirectories. The Index Service ensures scalability and delivers 3–5x faster results by serving directory listing details from the cache, compared to listing directories on the UFS. This enhancement is experimental.
- UFS read rate limiter – Administrators can now set a rate limit to control the maximum bandwidth an individual Alluxio Worker can read from the UFS. By configuring the UFS Read Rate Limiter, administrators ensure optimized resource utilization while maintaining system stability. Alluxio supports rate limiting for various UFS types, including S3, HDFS, GCS, OSS, and COS. This enhancement is generally available.
- Support for heterogeneous worker nodes – Alluxio now supports clusters with worker nodes that have heterogeneous resource configurations (CPU, memory, disk, and network). This enhancement provides administrators greater flexibility in configuring clusters and offers improved opportunities to optimize resource allocation. This enhancement is generally available.
Availability
Alluxio Enterprise AI version 3.5 is available for download here: https://www.alluxio.io/demo
Supporting Resources
- Learn more about Alluxio Enterprise AI 3.5: www.alluxio.io/blog/new-features-in-alluxio-enterprise-ai-3-5
- Download a trial version: https://www.alluxio.io/demo