我python3.8对应pytorch2.01行不行

可以通过以下步骤在 Python 3.8 中安装 PyTorch： 1. 确保已安装 pip 包管理器。如果没有安装，可以通过以下命令在终端中安装： ``` $ curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py $ python get-pip.py ``` 2. 访问 PyTorch 官网（https://pytorch.org/）并选择适合你的操作系统和 Python 版本的安装包。在本例中，我们选择 Linux、Python 3.8、CPU版本的安装包。 3. 在终端中执行以下命令安装 PyTorch： ``` $ pip install torch==1.6.0+cpu -f https://download.pytorch.org/whl/torch_stable.html ``` 这将安装 PyTorch 1.6.0 的 CPU 版本。 4. 验证 PyTorch 是否已正确安装。在 Python 终端中输入以下命令： ``` >>> import torch >>> print(torch.__version__) ``` 如果 PyTorch 成功安装，将会输出版本号。

### Python 3.8 Compatible PyTorch Version and Installation Guide For a system with specific requirements such as Python 3.8, finding the correct version of PyTorch that is both compatible and meets other hardware or software constraints like CUDA versions can be crucial. When considering compatibility between Python and PyTorch versions, it's important to note that not all combinations are supported. For instance, when using Python 3.8, one should look into versions of PyTorch released after support for Python 3.8 was introduced[^2]. According to official documentation, newer releases generally offer better stability and feature sets compared to older ones which might have been marked as deprecated or no longer maintained. Given an environment setup involving Conda where environments can be listed through `conda env list` and activated via commands similar to `conda activate`, ensuring the chosen PyTorch version aligns well within this ecosystem becomes essential[^1]. Considering the requirement for CUDA compatibility up to version 11.4 due to existing NVIDIA driver configurations, selecting a suitable PyTorch release involves checking against these parameters too. The recommendation would lean towards choosing relatively recent stable builds available from previous versions sections on the official site while adhering closely to what matches best with installed drivers and desired Python interpreter version (in this case, Python 3.8). To install a compatible version of PyTorch for Python 3.8 along with CUDA 11.4 support, visiting the [official PyTorch website](https://pytorch.org/) provides detailed instructions tailored based on user input regarding platform specifics including OS type, package manager preference (`pip` vs `conda`), language bindings, and compute platforms (CPU/GPU). Following those guidelines ensures obtaining a properly configured installation command fitting individual needs without encountering issues related to dependency mismatches or unsupported features. ```bash # Example Command Using Conda Based On Provided Specifications And Compatibility Requirements conda install pytorch torchvision torchaudio cudatoolkit=11.4 -c pytorch -c nvidia ``` This example assumes usage of Conda as specified earlier; adjustments may apply depending upon exact preferences or additional libraries required beyond core components mentioned here.