assertions.assertthat
时间: 2023-06-05 10:48:07 浏览: 192
assertions.assertthat是一个Java中的断言库,用于编写测试代码时进行断言判断。它可以帮助开发人员快速、简便地编写测试用例,并且可以提高代码的可读性和可维护性。使用assertions.assertthat可以方便地进行各种类型的断言判断,例如判断两个值是否相等、判断一个值是否为null、判断一个集合是否包含某个元素等等。总之,assertions.assertthat是Java开发中非常实用的一个工具库。
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assertions.assertequals
assertions.assertequals是Java中的一个断言方法,用于比较两个对象是否相等。如果两个对象不相等,该方法会抛出AssertionError异常,否则程序会继续执行。该方法常用于单元测试中,用于验证程序的正确性。
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.
This is a compile-time flag for PyTorch that enables device-side assertions (DSA) when running CUDA code. Device-side assertions are a debugging tool that allows developers to check for errors that occur on the GPU during runtime.
To compile PyTorch with `TORCH_USE_CUDA_DSA` flag, you need to rebuild PyTorch from source. Here are the steps to do that:
1. Clone the PyTorch repository from GitHub:
```
git clone --recursive https://github.com/pytorch/pytorch
```
2. Checkout the branch or tag you want to build:
```
cd pytorch
git checkout <branch or tag>
```
3. Install the dependencies needed to build PyTorch. You can find the list of dependencies in the PyTorch documentation.
4. Set the environment variables needed to build PyTorch:
```
export CMAKE_PREFIX_PATH=/path/to/libtorch
export CUDA_HOME=/usr/local/cuda
```
`CMAKE_PREFIX_PATH` should point to the location where you installed the C++ API for PyTorch. `CUDA_HOME` should point to the location where CUDA is installed on your system.
5. Build PyTorch with the `TORCH_USE_CUDA_DSA` flag:
```
python setup.py build --cmake --define TORCH_USE_CUDA_DSA=ON
```
This will build PyTorch with device-side assertions enabled. Once the build is complete, you can install PyTorch by running:
```
python setup.py install
```
Note that enabling device-side assertions can significantly slow down your code, so it is recommended to only use them for debugging purposes.
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