AI large models are sailing towards the sea of ​​industry, requiring high-quality data 'rivers' to guide them

At an industry summit, an academician scientist from Tsinghua University revealed that our large AI model is trained on the Wanka cluster, and an error occurs every three hours. Although it sounds a bit ridiculous, this has reached the world's advanced level

The large AI models that are popular all over the world are undoubtedly the hot topic this year, and their number continues to grow, reaching an astonishing level. Amidst the “hundreds of rivals vying for power”, everyone often overlooks a key issue: the torrent of data brought by large AI models is more turbulent than imagined.

"An error occurs once in three hours", which sounds incredible, is actually the norm faced by practitioners of large models, even "top students". The current common practice in the industry is to write fault-tolerant checkpoints. Since an error is reported within three hours, we should stop every 2.5 hours, write checkpoints, save the data, and then start training again. Once a failure occurs, you can recover from the written checkpoints to avoid "starting from scratch" and doing everything in vain. The checkpoint needs to store a lot of data and will consume a lot of time. The academician team developed a large model based on the llama 2 architecture. It takes ten hours to store the data in the hardware once. The storage efficiency directly affects the development progress.

If large-scale heterogeneous data is a torrent that surges wantonly, the storage system is a river carrying data flow. Its width and solidity directly determines whether the data will be blocked or even stagnant, thus blocking the lifeline of the large AI model. . It can be said that the productivity and efficiency of the entire large model industry are "upper-limited" by storage.

This is why storage, as an AI data infrastructure, has received more and more attention.

On November 29, the “Digital Intelligence Innovation AI Future” 2023 China Data and Storage Summit was held in Beijing. Sugon Storage has released a storage solution for large AI models.

Let’s take this opportunity to learn about the carrying challenges brought to storage by the wave of AI large models, and how Sugon Storage is leading the way for the intelligent industry and boosting the success of AI large models.

AI large models are entering the deep water area of ​​the industry, and traditional storage methods are facing data challenges

I recently took a trip to Yunnan and found that not only the construction of large models is in full swing in science and technology hubs such as Beijing, Shanghai and Guangzhou, but also in second- and third-tier cities such as Kunming and Dali, and even in border areas, industry applications of large models are actively being explored.

All walks of life are moving toward intelligence, and almost all of them have ignited a burning interest in large models. At this time, a key issue also emerged: the industrialization trend of large AI models requires upgrading the storage infrastructure.

Every time model developers train, the data poses various challenges to the storage system:

1. The impact of the data flood. With the industrial implementation of large models, many industries have begun to train exclusive models. A large amount of industry data, proprietary data, and new annotation data are fed to large models. The huge amount of data poses a challenge to the storage system. A data technology company in Yunnan mentioned that large industry models need to be trained with high-quality data sets, documents, and customer private data. A separate annotation group is established for each project. The scale of data continues to increase, and storage requirements and costs also increase. Increase.

2. The shackles of data congestion. Very large-scale data preprocessing is slow and time-consuming. The collection, classification, relocation and other processes are time-consuming and laborious. Once the storage performance cannot keep up, the throughput of massive files is slow, more reading and less writing, and checkpoint waiting takes a long time. , will delay development progress and increase development costs.

3. The undercurrent of complex data. In addition, large AI models use a large amount of heterogeneous data, with complex file formats, diverse data set types, and a surge in data volume. Traditional storage is difficult to cope with the challenge of data complexity and is prone to indigestion problems, resulting in data access efficiency. Low, resulting in reduced model operating efficiency, increased training computing power consumption, and the inability to fully "squeeze" expensive GPU computing resources. For example, the local solar observatory in Yunnan uses AI scientific computing models to learn massive images to present the true appearance of the sun, generating 2TB of image data every day. The current storage throughput efficiency is low, which will lead to slow loading of training sets and long data processing cycles. Slow down the research process.

4. Data security concerns. At present, AI large models have deeply penetrated into various industries. In the process of training, development and application implementation, massive data support is required, including data containing sensitive industry or personal information. Without reasonable data desensitization and data hosting, mechanism, it may cause data leakage and cause losses to industries and individuals. At the same time, model security risks also need to be taken seriously. For example, plug-ins may be implanted with harmful content and become a tool for criminals to commit fraud and "poisoning", endangering social and industrial security.

AI large models are heading into the deep water zone of the industry. What is gratifying is that this technological innovation is being deeply integrated into all walks of life to meet the needs of intelligence and is full of vitality. However, there are also some concerns. Data engineering plays an important role in the entire life cycle of large models, including data collection, cleaning, training, inference deployment, and feedback tuning, all of which require large amounts of data. However, the storage problem has become a bottleneck, which means that large AI models may face data congestion, failure and inefficiency at all stages, which will lead to a very high development cycle and comprehensive cost of large models, beyond the affordability of the industry

In order to avoid data siltation and support and cultivate the industrial development of large-scale models, we need to dredge the storage "river". Sugon Storage provides a new solution, which provides us with valuable reference cases

High-quality data "channel", Sugon Storage gives the large model industry an answer

After communicating with developers of large AI models, I came to a clear conclusion: building a new storage system that adapts to large AI models no longer requires discussion. The key is who can complete the solution upgrade first and provide Practical solutions

Having insight into the storage needs of the industry, Sugon Storage created an AI large model storage solution based on ParaStor large model dedicated storage, and wrote its own answer.

Sugon Storage AI large model storage cluster has three leading capabilities: heterogeneous fusion, ultimate performance and native security.

First of all, we can provide hundreds of billions of file storage services, and its expansion scale is close to unlimited. We have also specifically solved the problem of data access protocol diversity and support multiple storage protocols such as files and objects to avoid copying data between different storage systems

Secondly, in response to the high demand for data processing efficiency in the development process of AI large models, Sugon Storage AI large model storage cluster can provide multiple data IO performance optimization capabilities such as multi-level cache acceleration, XDS data acceleration and intelligent high-speed routing. .

In order to ensure the security of data throughout the entire process, Sugon storage nodes provide chip-level security capabilities and support national secret instruction sets. Through multi-level reliability, it can ensure that the storage cluster operates stably throughout the entire cycle of training and development, in line with policies and future security trends

Some people may ask, there are so many storage solutions on the market, and some also advertise to provide professional support for model development. What are the differentiated values ​​of Sugon Storage’s solutions?

If you are confused about the technical terms and product details of each company, you may wish to use a few words to remember the differentiated value of Sugon Storage AI large model storage cluster:

1.Advanced. Heterogeneous fusion, ultimate performance, and chip-level native security demonstrate the technological advancement of Sugon Storage and also specifically solve the problems of large data volume, complex and diverse data forms, low throughput efficiency, and storage and calculation time of large model development. The real pain point of waiting.

2. Reliable. High-performance AI data infrastructure is based on Sugon Storage’s self-developed innovation, which is more reliable and secure. It is in line with Xinchuang policy and future security trends. It can help domestic large-scale service providers avoid overseas supply chain risks, from supply chain security to data security. , model safety and other perspectives to protect the development of the large model industry.

3. Comprehensive. Sugon Storage has created a full-dimensional AI solution covering network, computing and platform, supporting stable operation throughout the training and development cycle, which can reduce overall costs and allow large model developers and industry customers to move forward worry-free.

To summarize, on the high-quality "channel" built by Sugon Storage, large-scale data can be efficiently processed and the development of large AI models can be accelerated. Therefore, industries and enterprises can be one step ahead and deeply integrate large models with vertical scenarios and businesses. , be the first to get the ticket to the intelligent era.

The new starting point of the fifth paradigm, observing the scene of many companies competing to advance and flourish

Turing Award winner Jim Gray once proposed the fourth paradigm, whose core is data-driven. With the "emergence of intelligence" in large language models, the fifth paradigm of "intelligence-driven" focuses more on the organic combination of data and intelligence, becoming the new underlying logic supporting scientific revolution and industrial revolution.

Everything in the past is prologue. This is true for artificial intelligence, and so is storage

At this conference, Hui Runhai, president of Sugon Storage Company, was awarded the title of "Storage Pioneer" with 20 years of industry experience and leading practices in AI storage technology breakthroughs, liquid cooling storage research and development and other fields. Under his leadership, Sugon distributed file storage has continued to lead the market for many years, ranking among the top in market share. Data storage solutions for AI large models have once again brought Sugon Storage to the forefront of the times.

Sugon Storage’s AI large model storage cluster is actively practicing paradigm shift to adapt to the new data paradigm and promote the vigorous development of large model industrialization through breakthroughs in data infrastructure

Next, in the new paradigm and new starting point of the storage industry, on the high-quality data "river" of Sugon Storage, we will see hundreds of industry large models vying for the stream, AI applications racing with thousands of sails, accelerating towards intelligence. China.

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