Building Data Science Solutions With Anaconda Page

conda env remove -n old-env

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: building data science solutions with anaconda

conda env list

jupyter notebook Your notebook automatically uses the correct kernel. import pandas as pd from sklearn.ensemble import RandomForestClassifier import joblib df = pd.read_csv("data/raw/churn.csv") X = df.drop("churn", axis=1) y = df["churn"] conda env remove -n old-env conda install tensorflow-gpu

conda create -n project-name python=3.10 conda activate project-name conda install jupyter pandas scikit-learn matplotlib Then commit your environment.yml alongside your code. Your future self — and your team — will thank you. : Explore conda build for packaging your own libraries, or anaconda-project for automating multi-step workflows. The foundation you build with Anaconda today enables the production-grade solutions of tomorrow. Your future self — and your team — will thank you