Anaconda

Create a dedicated Conda environment for PyTorch 2.x.

Install specific CUDA toolkit versions within the environment.

Run experiments without affecting global system Python installations.

Clone the environment for a teammate to ensure identical results.

Enable Anaconda Business policy filtering.

Define a policy to block any package with a CVSS score higher than 7.0.

Monitor developer install attempts through the admin dashboard.

Automatically replace vulnerable packages with patched versions.

Open Anaconda AI Navigator.

Search for a Mistral-7B GGUF model.

Download and run the model on local GPU/NPU.

Test prompts and RAG logic locally before deploying to the cloud.

Install the Anaconda Toolbox for Excel.

Write Python code directly in the formula bar to perform Monte Carlo simulations.

Use libraries like NumPy and Pandas on Excel data ranges.

Share the workbook with colleagues who can run the code via the Anaconda runtime.

Develop research code in a Conda environment.

Export the environment to a YAML file including exact build strings.

Include the YAML file in the GitHub repository.

Reviewers run 'conda env create' to replicate the exact development state.

Use Anaconda Cloud to store environment specifications.

Develop locally in VS Code connected to Anaconda.

Sync the environment to an AWS EC2 instance using the Anaconda CLI.

Deploy the model knowing the production environment matches the dev environment exactly.

Run 'anaconda-project lock' to freeze the dependency tree.

Generate a Software Bill of Materials (SBOM) using Anaconda's reporting tools.

Export the report for compliance officers.

Archive the specific package versions used for long-term auditability.