docs: introduction doc (#77)

added introduction doc content

Co-authored-by: Rares Gaia <[email protected]>

docs/source/sections/introduction.rst

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 **Introduction**
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+In the rapidly evolving landscape of machine learning and artificial intelligence, the need for large-scale distributed ML serving has become more pressing than ever before. Modern deep learning models, which are often comprised of billions of parameters, require substantial computational resources that typically exceed the capacity of a single machine. This necessitates the use of distributed computing environments, where multiple nodes work in concert to serve a model. However, with this distribution comes a new set of challenges, particularly in the realm of fault tolerance and efficient communication between nodes.
 
+Collective communication libraries, such as NVIDIA's NCCL (NVIDIA Collective Communications Library), play a pivotal role in facilitating the data exchange required for distributed serving. These libraries are designed to optimize performance, ensuring that data is transferred swiftly and effectively across the network. However, the increasing complexity and scale of distributed systems expose them to various faults, including hardware failures, network disruptions, and software bugs. These faults, if not managed properly, can lead to significant downtime, and hamper the quality of service in serving ML models.
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+To address these challenges, the multiworld framework has been developed. multiworld is an advanced, fault-tolerant framework built atop PyTorch, specifically designed to enhance the robustness of collective communication operations in distributed ML serving environments. The framework provides a layer of fault management that can detect, isolate, and mitigate the effects of node failures and communication errors in real-time.
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+In a typical distributed ML serving scenario, the failure of a single node or a communication link could force the entire serving pipeline to restart, leading to substantial delays. multiworld aims to minimize this impact by ensuring that even in the presence of faults, the ML serving can continue with minimal disruption. Furthermore, multiworld is designed with scalability in mind. Its API and mechanisms make it easier for developers and researchers to build advanced ML serving systems that can elastically manage (e.g., scale out and in) expensive GPU resources in a fine-grained manner (e.g., stage-level replication) as the serving demands (i.e., requests) change over time.
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+In summary, multiworld addresses the critical need for fault tolerance and scalability in distributed ML serving, ensuring the high efficiency and availability, even in the face of hardware and communication failures. Through its integration with PyTorch and support for collective communication libraries like NCCL, multiworld offers a powerful tool set for developers and researchers looking to push the boundaries of what is possible in distributed ML serving.