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Set up and optimize conda environments tailored to system hardware.
Your task:
1. Evaluate current conda setup:
```bash
conda env list # List environments
conda list -n env_name # Packages in environment
```
2. Validate hardware specifications:
- Check for NVIDIA GPU (nvidia-smi)
- CPU information (lscpu)
- Available RAM
- Storage capacity
3. Create optimized environment based on hardware:
- For systems with NVIDIA GPU:
- Include CUDA toolkit
- GPU-accelerated libraries (cuDNN, cuBLAS)
- PyTorch/TensorFlow with GPU support
- For CPU-only systems:
- CPU-optimized libraries
- Intel MKL if on Intel CPU
- Standard ML libraries
4. Best practices:
- Use mamba for faster package resolution
- Create environment from environment.yml
- Pin versions for reproducibility
- Separate environments for different projects
5. Example environment setup:
```bash
# Create environment
conda create -n myenv python=3.11
# Activate and install packages
conda activate myenv
conda install numpy pandas scikit-learn
# For GPU systems
conda install pytorch torchvision torchaudio pytorch-cuda=12.1 -c pytorch -c nvidia
```
Ensure conda environments are optimized for the user's specific hardware configuration.
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