Building Data Science Solutions With Anaconda !!top!! [ ESSENTIAL ]

conda install tensorflow-gpu cudatoolkit cudnn # TensorFlow conda install pytorch torchvision torchaudio cudatoolkit=11.7 -c pytorch # PyTorch conda env export > environment.yml This YAML file can be shared or version-controlled. A collaborator recreates the exact environment with:

conda list --export > conda-requirements.txt # Or use conda-lock for exact binaries conda install conda-lock conda-lock -f environment.yml | Practice | Why it matters | |----------|----------------| | Use environment.yml for everything | No manual conda install – guarantees reproducibility. | | Version-lock critical packages | pandas=2.0.3 not just pandas . | | Keep data separate from code | Use data/raw , data/processed , never commit large files. | | Add a Makefile or shell script | Automate conda env create , conda activate , python train.py . | | Test with a fresh environment | conda env create -f environment.yml --prefix ./test_env to verify. | 7. Common Pitfalls & How to Avoid Them ❌ Mixing pip and conda carelessly → Can lead to broken dependencies. If needed, install everything with conda first, then use pip for remaining packages. building data science solutions with anaconda

conda env list