repo_name,start_date,end_date,issues_created,open_discussion,closed_discussion,open_no_discussion,closed_no_discussion,open_questions,executive_summary,sentiment_count_negative,sentiment_count_neutral,sentiment_count_positive,severity_count_major,severity_count_minor,severity_count_critical,op_expertise_count_advanced,op_expertise_count_intermediate,themes_count_model_loading,themes_count_model_fine_tuning_and_training,themes_count_model_inference,themes_count_distributed_training_and_multi_gpu,themes_count_performance_and_optimization,themes_count_quantization_and_mixed_precision,themes_count_documentation,themes_count_cuda_compatibility,themes_count_model_evaluation_and_benchmarking,themes_count_miscellaneous
pytorch/pytorch,2024-08-28,2024-08-28,26,8,0,18,0,"['What is the root cause of the performance regression in AMP static shape default wrapper with multiple threads on CPU?', 'How can we improve the naming and commenting scheme for triton fusion ops with custom triton kernel?', 'What is the impact of the security vulnerabilities in the protobuf version used by PyTorch?']","The PyTorch repository is facing various challenges, including performance regressions, compatibility issues, and security vulnerabilities. These issues are affecting different aspects of the project, such as tensor parallelism, autograd, and CUDA support. To address these challenges, the team needs to prioritize and tackle the root causes, which include missing optimized ops, incompatible backend combinations, and insufficient testing coverage.",17,7,2,21,4,1,21,5,3,3,3,3,5,1,1,2,1,14
