MindMap Gallery How does Azure Blob storage support massive OpenAI training
Focusing on Microsoft's outstanding progress and innovation in Azure Blob storage, especially how it can effectively support the huge computing needs of OpenAI, which is the creator of ChatGPT. Jason Valerie of the Azure Blob Storage Product Management Team, worked with Jake and Deveraja to discuss the key role Azure Blob Storage plays in enabling OpenAI large-scale model training, processing data and storage up to exebbit level. The discussion involves the challenges facing scaling supercomputers for AI workloads and highlights architectural solutions such as data centers connecting regional network gateways, and the introduction of extended accounts to enable dynamic storage capacity expansion. Technical aspects cover checkpoint mechanisms, large-scale data processing, innovative blob views and hierarchical namespaces, and global data mobility capabilities, and strategically utilize Microsoft's global network infrastructure to achieve efficient data transmission. This conversation fully demonstrates Microsoft's commitment to providing powerful, scalable and efficient storage solutions for advanced AI research and development.
Edited at 2025-01-31 21:40:35Rumi: 10 dimensions of spiritual awakening. When you stop looking for yourself, you will find the entire universe because what you are looking for is also looking for you. Anything you do persevere every day can open a door to the depths of your spirit. In silence, I slipped into the secret realm, and I enjoyed everything to observe the magic around me, and didn't make any noise. Why do you like to crawl when you are born with wings? The soul has its own ears and can hear things that the mind cannot understand. Seek inward for the answer to everything, everything in the universe is in you. Lovers do not end up meeting somewhere, and there is no parting in this world. A wound is where light enters your heart.
Chronic heart failure is not just a problem of the speed of heart rate! It is caused by the decrease in myocardial contraction and diastolic function, which leads to insufficient cardiac output, which in turn causes congestion in the pulmonary circulation and congestion in the systemic circulation. From causes, inducement to compensation mechanisms, the pathophysiological processes of heart failure are complex and diverse. By controlling edema, reducing the heart's front and afterload, improving cardiac comfort function, and preventing and treating basic causes, we can effectively respond to this challenge. Only by understanding the mechanisms and clinical manifestations of heart failure and mastering prevention and treatment strategies can we better protect heart health.
Ischemia-reperfusion injury is a phenomenon that cellular function and metabolic disorders and structural damage will worsen after organs or tissues restore blood supply. Its main mechanisms include increased free radical generation, calcium overload, and the role of microvascular and leukocytes. The heart and brain are common damaged organs, manifested as changes in myocardial metabolism and ultrastructural changes, decreased cardiac function, etc. Prevention and control measures include removing free radicals, reducing calcium overload, improving metabolism and controlling reperfusion conditions, such as low sodium, low temperature, low pressure, etc. Understanding these mechanisms can help develop effective treatment options and alleviate ischemic injury.
Rumi: 10 dimensions of spiritual awakening. When you stop looking for yourself, you will find the entire universe because what you are looking for is also looking for you. Anything you do persevere every day can open a door to the depths of your spirit. In silence, I slipped into the secret realm, and I enjoyed everything to observe the magic around me, and didn't make any noise. Why do you like to crawl when you are born with wings? The soul has its own ears and can hear things that the mind cannot understand. Seek inward for the answer to everything, everything in the universe is in you. Lovers do not end up meeting somewhere, and there is no parting in this world. A wound is where light enters your heart.
Chronic heart failure is not just a problem of the speed of heart rate! It is caused by the decrease in myocardial contraction and diastolic function, which leads to insufficient cardiac output, which in turn causes congestion in the pulmonary circulation and congestion in the systemic circulation. From causes, inducement to compensation mechanisms, the pathophysiological processes of heart failure are complex and diverse. By controlling edema, reducing the heart's front and afterload, improving cardiac comfort function, and preventing and treating basic causes, we can effectively respond to this challenge. Only by understanding the mechanisms and clinical manifestations of heart failure and mastering prevention and treatment strategies can we better protect heart health.
Ischemia-reperfusion injury is a phenomenon that cellular function and metabolic disorders and structural damage will worsen after organs or tissues restore blood supply. Its main mechanisms include increased free radical generation, calcium overload, and the role of microvascular and leukocytes. The heart and brain are common damaged organs, manifested as changes in myocardial metabolism and ultrastructural changes, decreased cardiac function, etc. Prevention and control measures include removing free radicals, reducing calcium overload, improving metabolism and controlling reperfusion conditions, such as low sodium, low temperature, low pressure, etc. Understanding these mechanisms can help develop effective treatment options and alleviate ischemic injury.
Cloud storage and artificial intelligence applications
Evolution of object storage
Origin: Ocean Store Paper (Mid-late 1990s) - Basic Concepts of Modern Object Storage Systems
Early 21st century: AWS S3 pioneers the modern era of object storage
Mid-2010s: Cloud service providers develop mature object storage services
Transformation of analytical big data applications: increased throughput requests, analytic-driven data access, strong consistency requirements
Recent developments: pure cloud storage services (Wasabi, Backblaze B2, Cloudflare R2)
AI workload and storage requirements
Data acquisition and preparation: original data set, cleaning, annotation
Training: Checkpoint generation, model complexity challenges
Fine-tuning: Data integration in specific domains
Inference: model distribution, loading time, data management
Responsible Artificial Intelligence: Data Lineage, Source, Bias/Alchemy Tracking
Search for enhanced generation (RAG) mode
Prompt project: Situational learning through prompts
Fine-tuning: Update model weights
RAG: Learn new facts through searching and enhancement
Pipe Components:
Data ingestion and preprocessing: breaking data into small chunks
Embed generation: convert data into vectors
Vector database integration: Embedded storage and retrieval
Quick enhancement: Retrieve relevant data to enhance queries
Inferred: Send enhancement prompts to LLM
Storage service requirements for AI
Intake engine: efficiently process massive data
Low latency access: Ensure timely response to queries
Vector database integration: seamless interaction of search
Data freshness: timely update vector index
Security and Access Control: Data Governance and Unauthorized Access Prevention
Demo Highlights
Multi-protocol access: compatibility with various storage systems
SSD Support Storage: Advanced Blobs for Low Latency Access
Vector database integration: pinecone, Azure AI search
Real-time update: Blob Change Feed for on-demand index updates
Security: Integrate with Azure Active Directory, attribute-based access control
The future of object storage in artificial intelligence
Extensible access: Exabyte-level capacity, PB-level throughput
Retrieval and multi-modal data processing: Processing large amounts of unstructured data
System and user context: storing complex data and user interaction history
Performance storage: SSD-supported solutions to improve performance
Indexing and search: Tag-based or metadata-based indexing enables efficient search
Security: Identity-based security, policy-based management
in conclusion
What is Object Storage: Great for AI training, fine-tuning and RAG-based applications
Continuous evolution: Respond to challenges in the artificial intelligence pipeline and enhance the capabilities of future LLM operating systems.
How does Azure Blob storage support massive OpenAI training?
Introduction to Azure Blob Storage and OpenAI Collaboration
The role of Azure Blob storage in supporting OpenAI supercomputer training pipelines
Emphasize the scale and complexity of processing AI training data and checkpoints
Artificial Intelligence Training Pipeline
Data ingestion: artificially generated content, carefully planned Internet data, private data sets, synthetic data
Processing: Cleaning, Aggregating, Vectorization
Training data is piped to supercomputer for modeling
Checkpoints for model development and hardware failure recovery
Final model deployment
Artificial Intelligence Supercomputers: Ultra-large-scale clusters
GPU capacity continues to grow
Unprecedented single-purpose facility
Integration of GPU racks, Blob storage racks and network equipment
Logic aggregates into a single supercomputer and storage namespace
Regional Network Gateway (RNG) that connects between data centers through RDMA
Check for workload challenges
Frequent checkpoints to mitigate hardware failures and enable job switching
Synchronous process affecting GPU idle time
Large sequential reading and writing using Python pickle files
Capacity management through retention policies based on last access and modification time
Blob storage architecture and scalability
Logical grouping of 20 rack units for scale-out
Original architecture limitations: single physical storage cluster, limited throughput, TPS and capacity
Introduce extended accounts for horizontal scalability and overcome limitations
The front-end layer is used for throughput expansion, the partition layer is used for TPS and IOPS expansion, and the flow layer is used for capacity expansion
Scale Account and Flexibility
Dynamic and elastic scaling of clusters to meet workload requirements
Load balancing across clusters for elastic and dynamic resource allocation
Supports various redundancy types in Azure storage
Private Link technology for secure and scalable access to Blob storage
Artificial Intelligence Data Platform and Intake Challenges
Exabyte-level data from different sources
Efficient stream-based uploads and packages with the unique features of Blob storage
Hierarchical namespaces for exebbit file and folder semantics
Blob Fuse is used to cache and manage local resources on GPU hosts
Global GPU deployment and data mobility
Efficient data transfer between regions using Microsoft's global wide area network
Proxy WAN function to optimize data transmission using low priority traffic tags - Brokered WAN Traffic
Put Blobs from URL and Put Blocks from URL APIs for multi-TB data streams
Key Points
The ability to dynamically expand horizontally in scale statistics capacity
Unique Blob API functionality for stream-based uploads and packaging
Hierarchical namespaces for exebbit file and folder semantics
Dynamic partition splitting for high IOPS data processing workloads
Take advantage of Microsoft's global WAN's digital terabyte data mobility capabilities
Highlights of the Q&A session
Discussion on Blobs, checkpointing policies, and GPU-storage interactions
Deeply understand infrastructure wish lists, network components, and hardware improvements
Clarifications on Blob Fuse, Prefetch and Storage Account Scalability
The open source features of Blob Fuse and its differences from internal Blob caching technology