Announcing DeepSeek-V3 on Azure AI Foundry and GitHub
We are pleased to announce the availability of DeepSeek-V3 on Azure AI Foundry model catalog with token-based billing. This latest iteration is part of our commitment to enable powerful, efficient, and accessible AI solutions through the breadth and diversity of choice in the model catalog.Discover the Azure AI Training Profiler: Transforming Large-Scale AI Jobs
Meet the AI Training Profiler Large-scale AI training can be complicated, especially in distributed environments like healthcare, finance, and e-commerce, where the need for accuracy, speed, and massive data processing is crucial. Efficiently managing hardware resources, ensuring smooth parallelism, and minimizing bottlenecks are crucial for optimal performance. The AI Training Profiler powered by PyTorch Profiler inAzure Machine Learning is here to help! By giving you detailed visibility into hardware and software metrics, this tool helps you spot inefficiencies, make the best use of resources, and scale your training workflows like a pro. Why Choose the AI Training Profiler? Running large AI training jobs on distributed infrastructure is inherently complex, and inefficiencies can quickly escalate into increased costs and delays in deploying models. The AI Training Profiler addresses these issues by providing a comprehensive breakdown of compute resource usage throughout the training lifecycle. This enables users to fine-tune and streamline their AI workflows, yielding several key benefits: Improved Performance: Identify bottlenecks and inefficiencies, such as slow data loading or underutilized GPUs, to enhance training throughput. Reduced Costs: Detect idle or underused resources, thereby minimizing compute time and hardware expenses. Faster Debugging: Leverage real-time monitoring and intuitive visualizations to troubleshoot performance issues swiftly. Key Features of the AI Training Profiler GPU Core and Tensor Core Utilization The profiler meticulously tracks GPU kernel execution, reporting utilization metrics such as time spent on forward and backward passes, tensor core operations, and other computation-heavy tasks. This detailed breakdown enables users to pinpoint under-utilized resources and optimize kernel execution patterns. Memory Profiling Memory Allocation and Peak Usage: Monitors GPU memory usage throughout the training process, offering insights into underutilized or over-allocated memory. CUDA Memory Footprint: Visualizes memory consumption during forward/backward propagation and optimizer steps to identify bottlenecks or fragmentation. Page Fault and Out-of-Memory Events: Detects critical events that could slow training or cause job failures due to insufficient memory allocation. Kernel Execution Metrics Kernel Execution Time: Provides per-kernel timing, breaking down execution into compute-bound and memory-bound operations, allowing users to discern whether performance bottlenecks stem from inefficient kernel launches or memory access patterns. Instruction-level Performance: Measures IPC (Instructions Per Cycle) to understand kernel-level performance and identify inefficient operations. Distributed Training Communication Primitives: Captures inter-GPU and inter-node communication patterns, focusing on the performance of primitives like AllReduce, AllGather, and Broadcast in multi-GPU training. This helps users identify communication bottlenecks such as imbalanced data distribution or excessive communication overhead. Synchronization Events: Measures the time spent on synchronization barriers between GPUs, highlighting where parallel execution is slowed by synchronization. Getting Started with the Profiling Process Using the AI Training Profiler is a breeze! Activate it when you launch a job, either through the CLI or our platform’s user-friendly interface. Here are the three environment variables you need to set: Enable/Disable the Profiler: ENABLE_AZUREML_TRAINING_PROFILER: 'true' Configure Trace Capture Duration: AZUREML_PROFILER_RUN_DURATION_MILLISECOND: '50000' Delay the Start of Trace Capturing: AZUREML_PROFILER_WAIT_DURATION_SECOND: '1200' Once your training job is running, the profiler collects metrics and stores them centrally. After the run, this data is analyzed to give you visual insights into critical metrics like kernel execution times. Use Cases The AI Training Profiler is a game-changer for fine-tuning large language models and other extensive architectures. By ensuring efficient GPU utilization and minimizing distributed training costs, this tool helps organizations get the most out of their infrastructure, whether they're working on cutting-edge models or refining existing workflows. In conclusion, the AI Training Profiler is a must-have for teams running large-scale AI training jobs. It offers the visibility and control needed to optimize resource utilization, reduce costs, and accelerate time to results. Embrace the future of AI training optimization with the AI Training Profiler and unlock the full potential of your AI endeavors. How to Get Started? The feature is available as a preview, you can just set up the environment variables and start using the profiler! Stay tuned for future repository with many samples that you can use as well!The Evolution of AI Frameworks: Understanding Microsoft's Latest Multi-Agent Systems
The landscape of artificial intelligence is undergoing a fundamental transformation in late 2024. Microsoft has unveiled three groundbreaking frameworks—AutoGen 0.4, Magentic-One, and TinyTroupe—that are revolutionizing how we approach AI development. Moving beyond single-model systems, these frameworks represent a shift toward collaborative AI, where multiple specialized agents work together to solve complex problems. Think of these frameworks as different but complementary systems, much like how a city needs infrastructure, service providers, and community organizations to function effectively. AutoGen 0.4 provides the robust foundation, Magentic-One orchestrates complex tasks through specialized agents, and TinyTroupe simulates human behavior for business insights. Together, they form a comprehensive ecosystem for building the next generation of intelligent systems. As we explore each framework in detail, we'll see how this coordinated approach is opening new possibilities in AI development, from enterprise-scale applications to sophisticated business simulations. Framework Comparison: A Deep Dive Before we explore each framework in detail, let's understand how they compare across key dimensions. These comparisons will help us understand where each framework excels and how they complement each other. Core Capabilities and Design Focus Aspect AutoGen 0.4 Magentic-One TinyTroupe Primary Architecture Layered & Event-driven Orchestrator-based Persona-based Core Strength Infrastructure & Scalability Task Orchestration Human Simulation Development Stage Beta Preview Early Release Target Users Enterprise Developers Automation Teams Business Analysts Key Innovation Cross-language Support Dual-loop Orchestration Persona Modeling Deployment Model Cloud/On-premise Container-based Local Main Use Case Enterprise Systems Task Automation Business Insights AutoGen 0.4: The Digital Infrastructure Builder Imagine building a modern city. Before any services can operate, you need robust infrastructure – roads, power grids, water systems, and communication networks. AutoGen 0.4 serves a similar foundational role in the AI ecosystem. It provides the essential infrastructure that allows Agentic systems to operate at enterprise scale. The framework's brilliance lies in its three-layer architecture: The Core Layer acts as the fundamental infrastructure, handling basic communication and resource management, much like a city's utility systems. The AgentChat Layer provides high-level interaction capabilities, similar to how city services interface with residents. The Extensions Layer enables specialized functionalities, comparable to how cities can add new services based on specific needs. What truly sets AutoGen 0.4 apart is its understanding of real-world enterprise needs. Modern organizations rarely operate with a single technology stack – they might use Python for data science, .NET for backend services, and other languages for specific needs. AutoGen 0.4 embraces this reality through its multi-language support, ensuring different components can communicate effectively while maintaining strict type safety to prevent errors. from autogen_agentchat.agents import AssistantAgent from autogen_agentchat.task import Console from autogen_ext.models import OpenAIChatCompletionClient async def enterprise_example(): # Create an enterprise agent with specific configuration agent = AssistantAgent( name="enterprise_system", model_client=OpenAIChatCompletionClient( model="gpt-4o-2024-08-06", api_key="YOUR_API_KEY" ) ) # Define a complex enterprise task task = { "objective": "Analyze sales data and generate insights", "data_source": "sales_database", "output_format": "report" } # Execute task with streaming output stream = agent.run_stream(task=task) await Console(stream) # Example usage: # asyncio.run(enterprise_example()) Magentic-One: The Master Orchestra Conductor If AutoGen 0.4 builds the city's infrastructure, Magentic-One acts as its management system. Think of it as a highly skilled orchestra conductor, coordinating various musicians (specialized agents) to create a harmonious performance (completed tasks). The framework's innovative dual-loop architecture demonstrates this orchestration: The Task Ledger works like a conductor's score, planning out what needs to be done. The Progress Ledger functions as the conductor's real-time monitoring, ensuring each section performs its part correctly. Magentic-One's specialized agents exemplify this orchestra metaphor: WebSurfer: Like the string section, handling intricate web interactions FileSurfer: Similar to the percussion section, managing rhythmic file operations Coder: Comparable to the brass section, producing powerful code outputs ComputerTerminal: Like the woodwinds, executing precise commands This specialization has proven its worth through impressive benchmark performances across GAIA, AssistantBench, and WebArena, showing that specialized expertise, when properly coordinated, produces superior results. from magentic_one import ( Orchestrator, WebSurfer, FileSurfer, Coder, ComputerTerminal ) def automation_example(): # Initialize specialized agents agents = { 'web': WebSurfer(), 'file': FileSurfer(), 'code': Coder(), 'terminal': ComputerTerminal() } # Create orchestrator with task and progress ledgers orchestrator = Orchestrator(agents) # Define complex automation task task = { "type": "web_automation", "steps": [ {"action": "browse", "url": "example.com"}, {"action": "extract", "data": "pricing_info"}, {"action": "save", "format": "csv"} ] } # Execute orchestrated task result = orchestrator.execute_task(task) return result # Example usage: # result = automation_example() TinyTroupe: The Social Behavior Laboratory TinyTroupe takes a fundamentally different approach, more akin to a sophisticated social simulation laboratory than a traditional AI framework. Instead of focusing on task completion, it seeks to understand and replicate human behavior, much like how social scientists study human interactions and decision-making. The framework creates detailed artificial personas (TinyPersons) with rich backgrounds, personalities, and behaviors. Think of it as creating a miniature society where researchers can observe how different personality types interact with products, services, or each other. These personas exist within controlled environments (TinyWorlds), allowing for systematic observation and analysis. Consider a real-world parallel: When automotive companies design new vehicles, they often create detailed driver personas to understand different user needs. TinyTroupe automates and scales this approach, allowing businesses to simulate thousands of interactions with different personality types, providing insights that would be impractical or impossible to gather through traditional focus groups. The beauty of TinyTroupe lies in its ability to capture the nuances of human behavior. Just as no two people are exactly alike, each TinyPerson brings its unique perspective, shaped by its programmed background, experiences, and preferences. This diversity enables more realistic and valuable insights for business decision-making. from tinytroupe import TinyPerson, TinyWorld, TinyPersonFactory from tinytroupe.utils import ResultsExtractor def simulation_example(): # Create simulation environment world = TinyWorld("E-commerce Platform") # Generate diverse personas factory = TinyPersonFactory() personas = [ factory.generate_person( "Create a tech-savvy professional who values efficiency" ), factory.generate_person( "Create a budget-conscious parent who prioritizes safety" ), factory.generate_person( "Create a senior citizen who prefers simplicity" ) ] # Add personas to simulation world for persona in personas: world.add_person(persona) # Define simulation scenario scenario = { "type": "product_evaluation", "product": "Smart Home Device", "interaction_points": ["discovery", "purchase", "setup"] } # Run simulation and extract insights results = world.run_simulation(scenario) insights = ResultsExtractor().analyze(results) return insights # Example usage: # insights = simulation_example() Framework Selection Guide To help you make an informed decision, here's a comprehensive selection matrix based on specific needs: Need Best Choice Reason Alternative Enterprise Scale AutoGen 0.4 Built for distributed systems Magentic-One Task Automation Magentic-One Specialized agents AutoGen 0.4 User Research TinyTroupe Persona simulation None High Performance AutoGen 0.4 Optimized architecture Magentic-One Quick Deployment TinyTroupe Minimal setup Magentic-One Complex Workflows Magentic-One Strong orchestration AutoGen 0.4 Practical Implications For organizations looking to implement these frameworks, consider the following guidance: For Enterprise Applications: Use AutoGen 0.4 as your foundation. Its robust infrastructure and cross-language support make it ideal for building scalable, production-ready systems. For Complex Automation: Implement Magentic-One for tasks requiring sophisticated orchestration. Its specialized agents and safety features make it perfect for automated workflows. For Business Intelligence: Deploy TinyTroupe for market research and user behavior analysis. Its unique simulation capabilities provide valuable insights for business decision-making. Conclusion Microsoft's three-pronged approach to multi-agent AI systems represents a significant leap forward in artificial intelligence. By addressing different aspects of the AI development landscape – infrastructure (AutoGen 0.4), task execution (Magentic-One), and human simulation (TinyTroupe) – these frameworks provide a comprehensive toolkit for building the next generation of AI applications. As these frameworks continue to evolve, we can expect to see even more sophisticated capabilities and tighter integration between them. Organizations that understand and leverage the strengths of each framework will be well-positioned to build powerful, scalable, and intelligent systems that drive real business value. Appendix Technical Implementation Details Feature AutoGen 0.4 Magentic-One TinyTroupe Language Support Python, .NET Python Python State Management Distributed Centralized Environment-based Message Passing Async Event-driven Task-based Simulation-based Error Handling Comprehensive Task-specific Simulation-bound Monitoring Enterprise-grade Task-focused Analysis-oriented Extensibility High Medium Framework-bound Performance and Scalability Metrics Metric AutoGen 0.4 Magentic-One TinyTroupe Response Time Milliseconds Seconds Variable Concurrent Users Thousands Hundreds Dozens Resource Usage Optimized Task-dependent Simulation-dependent Horizontal Scaling Yes Limited No State Persistence Distributed Cache Container Storage Local Files Recovery Capabilities Advanced Basic Manual Security and Safety Features Security Aspect AutoGen 0.4 Magentic-One TinyTroupe Access Control Role-based Container-based Environment-based Content Filtering Enterprise-grade Active Monitoring Simulation Bounds Audit Logging Comprehensive Action-based Simulation Logs Isolation Level Service Container Process Risk Assessment Dynamic Pre-execution Scenario-based Recovery Options Automated Semi-automated Manual Integration and Ecosystem Support Integration Type AutoGen 0.4 Magentic-One TinyTroupe API Support REST, gRPC REST Python API External Services Extensive Web-focused Limited Database Support Multiple Basic Simulation Only Cloud Services Full Support Container Services Local Only Custom Extensions Yes Limited Framework-bound Third-party Tools Wide Support Moderate MinimalIntroducing Meta Llama 3 Models on Azure AI Model Catalog
Unveiling the next generation of Meta Llama models on Azure AI: Meta Llama 3 is here! With new capabilities, including improved reasoning and Azure AI Studio integrations, Microsoft and Meta are pushing the frontiers of innovation. Dive into enhanced contextual understanding, tokenizer efficiency and a diverse model ecosystem—ready for you to build and deploy generative AI models and applications across your organization. Explore Meta Llama 3 now through Azure AI Models as a Service and Azure AI Model Catalog, where next generation models scale with Azure's trusted, sustainable and AI-optimized high-performance infrastructure.LLM Load Test on Azure (Serverless & Managed-Compute)
In the ever-evolving landscape of artificial intelligence, the ability to efficiently load test large language models (LLMs) is crucial for ensuring optimal performance and scalability. llm-load-test-azure is a powerful tool designed to facilitate load testing of LLMs running in various Azure endpoint deployment settings (Serverless and Managed).Empowering Snowflake users with Azure Machine Learning
Azure Machine Learning (Azure ML) offers a preferred platform for Snowflake, a cloud-based data warehouse, that is increasingly becoming the go-to choice for many organizations to store their data. Data scientists from organizations that have adopted Snowflake as their data warehouse solution can now explore Azure ML capabilities without relying on third-party libraries or engaging data engineering teams. Now, with a seamless and native integration between Snowflake and Azure Machine Learning, data scientists can import their data from Snowflake to Azure ML with a single command and kick-start their machine learning projects. We are thrilled to announce the public preview of Azure Machine Learning (Azure ML) data import CLI & SDK which is designed to bring data effortlessly from data repositories that are not part of Azure platform for training in Azure ML. This includes databases like Snowflake and cloud storage services like AWS S3. This blog post will outline the advantages and steps to get started with Azure Machine Learning for Snowflake users without any external dependencies. Advantages of Snowflake and Azure Machine Learning Integration Enhanced Collaboration: This integration empowers data scientists to directly import data from Snowflake, eliminating the need for constant communication with data engineering teams. Time Efficiency: By removing the need for third-party libraries or additional data pipeline development, the data scientists can save time and focus on developing their machine learning models. Simplified Workflow: Leveraging native connectivity between Snowflake and Azure Machine Learning results in a more streamlined and user-friendly workflow. Flexibility: Leveraging schedules or on-demand options, data scientists can decide when and what data needs to be imported. With certain configurations the data expirations can also be managed, providing them complete flexibility on the datasets. Traceability: Each import, whether scheduled or not, creates a unique version of the dataset which is in turn used in training jobs, giving data scientists the required traceability in scenarios that need retraining or for model audits. How to get started? A connection is where it all begins, this is where the endpoint details of the Snowflake instance including the server, database, warehouse and role information is entered as target along with valid the credentials to access data. Typically, an admin is the one who would create a connection. It is easy for a data scientist to use an existing connection if the query to pull the required data that is intended to be used in training is known. In one single step, one can import data and register it as a Azure ML data asset that can be readily referenced in training jobs. If the scenario demands to import data on a schedule, one can use popular cron or recurrence patterns to define the frequency of import. We are also excited to introduce the public preview of lifecycle management on Azure ML managed datastore (a “Hosted On Behalf Of/HOBO datastore). This offering from Azure ML is exclusively for data import scenarios available on CLI and SDK. On choosing HOBO datastore as their preferred destination to import data, one gets the capability of lifecycle management or as we call it “auto delete settings” on the imported data assets. A policy to automatically delete an imported data asset if unused for 30 days by any job is set on every imported data asset in the AzureML managed datastore. All that one has to do is to set “azureml://datastores/workspacemanagedstore” as the path when defining their import as shown in the snippet below and rest will be handled by AzureML. Once the data is imported, one can update the auto delete settings to lengthen or shorten the time duration or even change the condition to be based on created time instead of last used time using CLI or SDK commands. Quick recap – Customers having data in Snowflake can now utilize the power of Azure ML for training directly from our platform. They can import data on-demand or on a schedule and can also set “auto-delete” policies to manage their imported data in Azure ML managed datastore from cost and compliance point of view. Try it for yourself – To get started with Azure Machine Learning data import, please visit Azure ML documentation and GitHub repo, where you can find detailed instructions to setup connections for external sources in Azure ML workspace , and train or deploy models with a variety of Azure ML examples. Learn More – To stay updated on Azure Machine Learning announcements, watch our breakout sessions from Microsoft Build. Build and maintain your company Copilot with Azure Machine Learning and GPT-4 Practical deep-dive into machine learning techniques and MLOps Building and using AI models responsibly
