Hyperconverged cloud-edge native database. Contribute to matrixorigin/matrixone development by creating an account on GitHub.
githubawslambdascalableserverlessterraformself-hostedcicdhacktoberfestgithub-actionsactions-runneraction-runner UpdatedMay 6, 2025 HCL jonico/awesome-runners Sponsor Star793 Code Issues Pull requests A curated list of awesome self-hosted GitHub Action runners in a large comparison matrix ...
GitHub Actions 是一个持续集成和持续交付 (CI/CD) 平台,可用于自动执行构建、测试和部署管道。 您可以创建工作流程来构建和测试存储库的每个拉取请求,或将合并的拉取请求部署到生产环境。 GitHub Actions 不仅仅是 DevOps,还允许您在存储库中发生其他事件时运行工作流程。 例如,您可以运行工作流程,以便在有人在您...
CuMFis a CUDA-based matrix factorization library that optimizes alternate least square (ALS) method to solve very large-scale MF. CuMF uses a set of techniques to maximize the performance on single and multiple GPUs. These techniques include smart access of sparse data leveraging GPU memory hier...
使用矩阵创建不同的测试配置matrix 在运行器上安装nodeactions/setup-node 缓存依赖项actions/cache 示例工作流 GitHub Docs 工程团队创建了以下工作流。 工作流针对拉取请求中的代码运行测试。 若要查看github/docs存储库中此文件的最新版本,请参阅test.yml。
iFEM is a MATLAB software package containing robust, efficient, and easy-following codes for the main building blocks of adaptive finite element methods on unstructured simplicial grids in both two and three dimensions. Besides the simplicity and readability, sparse matrixlization, an innovative programm...
使用矩阵创建不同的测试配置 matrix 在运行器上安装 node actions/setup-node 缓存依赖项 actions/cache 在运行器上运行测试 npm test 示例工作流 GitHub Docs 工程团队创建了以下工作流。 工作流针对拉取请求中的代码运行测试。 若要查看 github/docs 存储库中此文件的最新版本,请参阅 test.yml。 YAML...
Updated import of NMF for compatibility with scikit-learn versions >22 Colorbar for heatmaps included with consensus matrix plot New in version 1.1 Now operates by default on sparse matrices. Use --densify option in prepare step if data is not sparse ...
A comprehensive workbench for single cell ATAC-seq data processing, analysis and visualization Workflow scATAC-pro consists of two units, the data processing unit and the downstream analysis unit. The data processing unit takes raw fastq files as input and outputs peak-by-cell count matrix, QC re...
conv2 = GCNConv(hidden_channels, out_channels) def forward(self, x: Tensor, edge_index: Tensor) -> Tensor: # x: Node feature matrix of shape [num_nodes, in_channels] # edge_index: Graph connectivity matrix of shape [2, num_edges] x = self.conv1(x, edge_index).relu() x = ...