### Python 3.6 Compatible PyTorch Version For environments using Python 3.6, several versions of PyTorch are compatible depending on other dependencies such as CUDA and cuDNN versions. Specifically, when configuring an environment that includes Python 3.6 along with specific GPU support like NVIDIA GeForce RTX 3060 which requires a higher CUDA capability (sm_86), it's important to choose appropriate versions of these components. A suitable combination for this setup would be: - **Python**: 3.6 - **PyTorch**: 1.9.0 or earlier versions including 1.7.1 - **CUDA**: 11.1 or 11.0 based on compatibility requirements - **cuDNN**: v8.0.5 ensuring alignment with chosen CUDA version This configuration has been tested successfully in setups involving similar hardware and software specifications[^3]. For instance, switching from a lower CUDA version (e.g., 10.1) used previously with an older graphics card model (like RTX 1660) to a newer one (such as RTX 3060 supporting sm_86 architecture) necessitates updating both the CUDA toolkit and corresponding PyTorch build to maintain functionality without encountering errors related to unsupported architectures[^4]. To install PyTorch specifically tailored for Python 3.6 alongside CUDA 11.1, commands within a virtual environment can look something like this: ```bash conda create -n myenv python=3.6 conda activate myenv conda install pytorch torchvision torchaudio cudatoolkit=11.1 -c pytorch ``` The above command sequence ensures creation of a new conda environment named `myenv` configured around Python 3.6 while installing PyTorch built against CUDA 11.1, thus providing necessary compatibility between all elements involved[^2].