dependencies

问题 My question: How do I set a module path for gradle build ? I've become comfortable working with Java modules from the command line. I do a frequent exercise in Powershell which results in these source files. └───src ├───appMod │ │ module-info.java │ │ │ └───appPack │ Entry.java │ └───greetMod │ module-info.java │ └───greetPack Hello.java appMod/module-info module appMod { requires greetMod; } appMod/appPack.Entry package appPack; import greetPack.Hello; public class Entry { public static void

问题 Are there any other processor registers (e.g. flags) besides the architectural registers (eax, ebx,.) in x86 for which RAW dependencies need to be enforced by the scoreboard in pipelined processors? 回答1: Literally every register guarantees that if you write it, later instructions will read the new value. x86 is defined in terms of serial execution; pipelining and out-of-order exec need to preserve that illusion for everything , including segment registers, FP rounding modes, control and debug

问题 Hello I am new to gradle and it is a little bit confusing for me. How should I add a dependency in my gradle configuration to have access to B1.java in projectA1 ? Project B is gradle project and project A is just a folder with another gradle projects. Here is my structure: Workspace: ProjectA projectA1 ... here I want to have access to B1.java build.gradle projectA2 ... build.gradle ProjectB projectB1 B1.java ... build.gradle projectB2 ... build.gradle build.gradle I tried to read gradle

问题 Package managers for JavaScript like npm and yarn use a package.json to specify 'top-level' dependencies, and create a lock-file to keep track of the specific versions of all packages (i.e. top-level and sub-level dependencies) that are installed as a result. In addition, the package.json allows us to make a distinction between types of top-level dependencies, such as production and development . For Python , on the other hand, we have pip . I suppose the pip equivalent of a lock -file would

问题 Package managers for JavaScript like npm and yarn use a package.json to specify 'top-level' dependencies, and create a lock-file to keep track of the specific versions of all packages (i.e. top-level and sub-level dependencies) that are installed as a result. In addition, the package.json allows us to make a distinction between types of top-level dependencies, such as production and development . For Python , on the other hand, we have pip . I suppose the pip equivalent of a lock -file would

问题 Package managers for JavaScript like npm and yarn use a package.json to specify 'top-level' dependencies, and create a lock-file to keep track of the specific versions of all packages (i.e. top-level and sub-level dependencies) that are installed as a result. In addition, the package.json allows us to make a distinction between types of top-level dependencies, such as production and development . For Python , on the other hand, we have pip . I suppose the pip equivalent of a lock -file would

问题 I have written a Gradle Plugin that contains a bunch of common setup configuration so that all of our projects just need to apply that plugin and a set of dependencies. It uses the Spring Dependency Management Plugin to setup the BOM imports for Spring as shown in the code snippet below: trait ConfigureDependencyManagement { void configureDependencyManagement(final Project project) { assert project != null project.apply(plugin: "io.spring.dependency-management") final

问题 In the Docker image for Tensorflow with GPU support (for example: tensorflow/tensorflow:2.2.0-gpu ) the installed python package is tensorflow-gpu (as shown in pip freeze ). Installing any python package that depends on tensorflow triggers the installation of tensorflow itself, although it's already installed under a different name (because -- correctly -- tensorflow-gpu != tensorflow ). Is there a way to avoid this? 回答1: You can add an instruction to install a fake tensorflow "package" that

问题 In the Docker image for Tensorflow with GPU support (for example: tensorflow/tensorflow:2.2.0-gpu ) the installed python package is tensorflow-gpu (as shown in pip freeze ). Installing any python package that depends on tensorflow triggers the installation of tensorflow itself, although it's already installed under a different name (because -- correctly -- tensorflow-gpu != tensorflow ). Is there a way to avoid this? 回答1: You can add an instruction to install a fake tensorflow "package" that

问题 I have a pod that depends on a static lib (precompiled, source code not available). In this pod project, i drag and drop the folder containing the ".a" file and some other C, Obj-c files. When i try to include this pod in an application, Cocoapods copy all that pod files but not the ".a" file. In the Podspec, im using this to include the precompiled library to my pod s.ios.vendored_frameworks = 'path/a_staticLib.a' The line above copy the file in frameworks (Pods folder), this is normal as it