### Python 3.6 Compatible PyTorch Version For compatibility between Python and PyTorch versions, specific combinations must be adhered to ensure stable operation of applications developed using these tools. For Python 3.6, several versions of PyTorch are compatible, but it's important to note that support for older Python versions like 3.6 has been gradually phased out as newer releases focus on more recent Python iterations. When configuring a development or deployment environment with Python 3.6, one can use PyTorch version 1.7.1 which supports CUDA 10.1 and Python 3.6[^4]. This combination ensures stability while working with GPU-accelerated tasks such as deep learning models training. However, for better performance especially when dealing with modern GPUs like NVIDIA GeForce RTX series, upgrading both the CUDA toolkit and PyTorch version might be necessary due to hardware advancements requiring updated software stacks. To install this particular configuration, including PyTorch along with its dependencies (such as `torchvision`), an appropriate command would look similar to: ```bash pip install torch==1.7.1+cu101 torchvision==0.8.2 -f https://download.pytorch.org/whl/torch_stable.html ``` This command installs PyTorch built against CUDA 10.1 specifically tailored for environments running Python 3.6. It also includes `torchvision`, essential libraries often used alongside PyTorch for handling datasets commonly utilized in machine learning projects. Additionally, another viable option exists by opting for PyTorch 1.9.1 paired with CUDA 11.1, ensuring broader compatibility across different systems and potentially offering enhanced features over earlier builds. The installation process involves executing commands designed to fetch binaries compiled under specified conditions from official repositories maintained by the PyTorch community[^5].