> For the complete documentation index, see [llms.txt](https://docs.zerve.ai/zerve-docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.zerve.ai/zerve-docs/environments/environment-presets.md).

# Environment presets

Ready-made environments for common kinds of work in Python and R.

**Presets** are ready-made environments for common kinds of work. Start from one, then add any other packages you need.

## Python presets

| Preset                                                         | What it includes                                                                                                                     |
| -------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------ |
| **Default**                                                    | Plotly, scikit-learn, SciPy, seaborn, NLTK, Hugging Face Transformers and Datasets, PyArrow, Matplotlib, Beautiful Soup and Snowpark |
| **Minimal**                                                    | Nothing extra, for when you want full control                                                                                        |
| **ML Starter**                                                 | NumPy, pandas, Polars, XGBoost, LightGBM, Optuna and category encoders                                                               |
| **Deep Learning (CPU)**                                        | PyTorch and TensorFlow for CPUs, Keras, ONNX, TensorBoard and Transformers                                                           |
| **Deep Learning (CUDA 12.6)**                                  | PyTorch and TensorFlow for GPUs, DeepSpeed, Accelerate, bitsandbytes, Triton and Transformers                                        |
| **Natural Language Processing**                                | Transformers, Datasets, tokenizers, spaCy, NLTK, PEFT and Evaluate                                                                   |
| **Computer Vision**                                            | OpenCV, Albumentations, Ultralytics (YOLO), MMCV, Tesseract and Supervision                                                          |
| **Geospatial**                                                 | GeoPandas, Shapely, Rasterio, Fiona, Folium and Kepler.gl, with the GDAL, GEOS and PROJ system libraries                             |
| **Spark Connect**                                              | The Databricks SQL connector and the PySpark client                                                                                  |
| **FastAPI**, **Streamlit**, **Gradio**, **Dash** and **Flask** | Each framework plus common data libraries, for building [apps and APIs](/zerve-docs/ship-your-work/shipping.md)                      |

## R presets

| Preset                          | What it includes                                                                                                                      |
| ------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------- |
| **Default**                     | A broad set, including the tidyverse, tidymodels, Shiny, forecasting (forecast, Prophet), modeling, text mining and plotting packages |
| **Minimal**                     | Nothing extra                                                                                                                         |
| **ML Starter**                  | tidyverse, caret, mlr3, XGBoost, LightGBM, randomForest and glmnet                                                                    |
| **Natural Language Processing** | tidytext, quanteda, text2vec, udpipe and others                                                                                       |
| **Computer Vision**             | magick, imager and tesseract                                                                                                          |
| **Geospatial**                  | sf, terra, tmap, leaflet and others                                                                                                   |

## Choosing a preset

| For                                            | Start with                                                                     |
| ---------------------------------------------- | ------------------------------------------------------------------------------ |
| **Quantitative research and general analysis** | Python **Default** or **ML Starter**                                           |
| **Model training on a GPU**                    | **Deep Learning (CUDA 12.6)**, with [GPU compute](/zerve-docs/compute/gpus.md) |
| **Working with text, such as filings or news** | **Natural Language Processing**                                                |
| **Querying Databricks**                        | **Spark Connect**                                                              |
| **Building a dashboard or API**                | The preset for your framework, such as **Streamlit** or **FastAPI**            |
| **Statistical modeling and forecasting in R**  | R **Default**                                                                  |


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation by asking a question.

Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters:

```
GET https://docs.zerve.ai/zerve-docs/environments/environment-presets.md?ask=<question>&goal=<user_goal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.