Windows GPU-Accelerated Conda Environment Setup -- 2

I need assistance in setting up my environment with GPU-accelerated libraries and frameworks. Here's what I need installed on my system using Conda:

**Core Libraries and Frameworks:**

- **ThunderSVM:** For GPU-accelerated support vector machines (SVM).
- **CuPy:** A NumPy-like library for GPU-accelerated operations.
- **RAPIDS:** Including cuML, cuDF, and cuGraph for machine learning, pandas-like operations, and graph analytics.
- **CUDA Toolkit:** Ensure the correct version compatible with all frameworks and my GPU.
- **cuBLAS / cuML:** For machine learning operations (part of RAPIDS).
- **Dask:** For scaling computations across multiple GPUs.
- **TensorFlow / PyTorch:** For deep learning with GPU acceleration.
- **NVIDIA Apex:** For optimizing PyTorch with mixed precision training.
- **NVIDIA NCCL:** For multi-GPU setups and communication.
- **nvidia-docker:** If containerization is used for deploying GPU-based workloads.

**Profiling and Monitoring Tools:**

- **Nsight Systems or nvprof:** For performance profiling of GPU workloads.
- **nvidia-smi:** For monitoring GPU usage and performance.

Please check the attached file to view the libraries currently installed in my Conda environment (bot-env). Make sure all dependencies are properly configured with Conda.

Also, I would prefer you to access my computer using Anydesk and install everything yourself without guiding me through the steps. Finally, ensure everything works properly in Visual Studio Code.

Let me know if you need any further details or access to my system.

Thank you!