Failure prediction is crucial for ensuring the stability of cloud computing systems and has garnered extensive attention from both academia and industry. Generally, data used for prediction includes two modalities: 1) Text data, such as logs; and, 2) Numerical data, such as error counts and monitoring metrics. However, most existing failure prediction algorithms for cloud computing only focus on a single modality. The lack of high-quality multimodal datasets from real-world production environments constrains academic research on multimodal failure prediction. To fill this gap, this paper releases a large multimodal dataset of operation data from the Alibaba cloud computing platform, namely, Logs and Metrics Integration Dataset (LMID). It consists of 100 million pieces of logs (textual data) and 37 dimensions of monitoring metrics (numerical data) from 220,000 physical machines. To our knowledge, it is the first multimodal dataset for cloud computing system failure prediction, and is expected to greatly benefit the community. This paper provides a detailed introduction to the construction of LMID, its contents, and the performance of state-of-the-art algorithms on it. It also conducts extensive experiments to reveal a new insight that cross-modality connections are effective for failure prediction. LMID is now available at https://huggingface.co/datasets/AliyunECSAlgos/LMID.