New vitality of Su River! 2023 Shanghai Suzhou River Half Marathon was held.

Transfer from: People’s Daily Client Shanghai Channel

Cao Lingjuan

At 7: 00 a.m. on April 22nd, in front of the Tianan Qianshu, a landmark building on the Suzhou River in Putuo District, the 2023 Shanghai Suzhou River Half Marathon started, and the road runners shared a sports feast in the early morning sunshine.

On the same day, Mari took the lead in crossing the finish line and won the championship in 1 hour, 03 minutes and 46 seconds. Liu Min won the first place in the women’s team in 1 hour, 13 minutes and 19 seconds. After waiting for 3 hours and 15 minutes, the first Shanghai Suzhou River Half Marathon ushered in the "closing time", and the race completion rate reached 98.78%.

The wind blows thousands of trees and waters in a chain, half of the Masuhe River and 18 bays. In recent years, Putuo District has achieved a comprehensive connection of the coastline of the Putuo section of Suzhou River for 21 kilometers, and made every effort to build a "semi-Masuhe" world-class waterfront. The Suzhou Creek in Putuo District witnessed the rapid changes of Shanghai’s history and culture. The 2023 Shanghai Suzhou Creek Half Marathon was held here, which made people feel the fireworks on the banks of the Suzhou Creek.

2023 Shanghai Suzhou River Half Marathon

The theme of this year’s competition is "Shanghai-style fireworks land, new vitality of Suzhou River", which connects industrial civilization and modern creative buildings spanning over a hundred years in series. The starting point of the competition is located in Tian ‘an Qianshu (Moganshan Road) in Dayang Jingdian, and the end point is Banmashu Park (Yunling East Road). The track passes through Putuo landmark buildings such as Tian ‘an Qianshu, Shanghai Mint Museum and Banmashu Park, with a total length of 21.0975 kilometers and a total of 4,000 participants. Runners can feel the red genes, industrial civilization, development vitality, colorful life and pleasant ecology along the "semi-Masu River".

2023 Shanghai Suzhou River Half Marathon

The 2023 Shanghai Suzhou River Half Marathon is the first time to be held. As one of the stops in the Shangma series, the "Suzhou River Half Horse" puts the safety of runners in the first place. At the start and end of the track and along the way, in addition to street security personnel, 917 volunteers from Putuo District and universities in Shanghai, together with more than 130 referees, woven a safety net. During the competition, the public security traffic management department took traffic control measures on some roads involving the track. The police of the traffic police detachment of Putuo Public Security Bureau rode motorcycles to clear the way, and the police of the special police detachment rode police bicycles to escort the whole competition.

The event was hosted by Shanghai Sports Federation, Putuo District People’s Government and Donghao Lansheng (Group) Co., Ltd.

Infrastructure for training AI to solve common problems

In order to train artificial intelligence models that can solve common problems, infrastructure is needed to provide support. These infrastructures are usually composed of hardware, software and tools to improve the efficiency and accuracy of model training. This article will introduce the infrastructure for training AI to solve common problems.

I. Hardware infrastructure

When training artificial intelligence models, it is usually necessary to use high-performance computing hardware to provide support. The following are several common hardware infrastructures:

  1. CPU: The central processing unit (CPU) is a general-purpose computing hardware, which can be used to run various types of software, including artificial intelligence models. Although the performance of CPU is relatively low, it is still useful in training small models or debugging.

  2. GPU: A graphics processor is a special computing hardware, which is usually used to process images and videos. Because of its highly parallel structure, GPU can provide higher computing performance than CPU when training artificial intelligence models, so it is widely used.

  3. TPU: Tensor processor is a kind of hardware specially used for artificial intelligence computing, developed by Google. The performance of TPU is higher than that of GPU, and it is suitable for large-scale artificial intelligence model training and reasoning.

Second, the software infrastructure

In addition to hardware infrastructure, some software tools are needed to support the training of artificial intelligence model. The following are some common software infrastructures:

  1. Operating system: Artificial intelligence models usually need to run on an operating system, such as Linux, Windows or macOS.

  2. Development environment: Development environment usually includes programming language, editor and integrated development environment (IDE) for writing and testing artificial intelligence models. Common development environments include Python, TensorFlow, PyTorch and Jupyter Notebook.

  3. Frames and libraries: Frames and libraries provide some common artificial intelligence model algorithms and data processing tools, making model development and training more convenient. Common frameworks and libraries include TensorFlow, PyTorch, Keras and Scikit-Learn.

Third, the tool infrastructure

In addition to the hardware and software infrastructure, some tools are needed to support the training of artificial intelligence models. The following are several common tool infrastructures:

Dataset tool: Dataset tool is used to process and prepare training datasets, such as data cleaning, preprocessing, format conversion, etc. Common data set tools include Pandas, NumPy and SciPy.

2 Visualization tools: Visualization tools are used to visualize the training process and results to help users better understand the performance and behavior of the model. Common visualization tools include Matplotlib, Seaborn and Plotly.

Automatic parameter tuning tool: The automatic parameter tuning tool is used to optimize the parameters of the model to improve the performance and accuracy of the model. Common automatic parameter tuning tools include Optuna, Hyperopt and GridSearchCV.

In short, training artificial intelligence models to solve common problems requires the use of a variety of infrastructures, including hardware, software and tools. These infrastructures are designed to improve the efficiency and accuracy of model training, so that the model can better solve various practical problems. In practical application, users need to choose the appropriate infrastructure according to specific requirements and data characteristics, and design and implement it accordingly.