Open Source

Forum

American Data Science

API Reference

Dashboard

Support

The Data Visualization environments are designed for various data visualization tools and libraries. These include environments for matplotlib, seaborn, plotly, hvplot, datashader, dash, and more. Each environment is equipped with the necessary tools for efficient data visualization tasks, providing a streamlined experience for creating interactive, publication-quality graphs and web analytic applications.

Data Visualization

American Data Science helps AI/ML Engineers and Data Scientists accelerate their notebook development workflow with pre-built GPU environments, compute, and AI agents.

Introduction

Getting started with powerful notebook servers on CPUs or H100s with a click of the button.

Quickstart

Our environments are GPU-enabled and support PyTorch, Jax, RAPIDS, Transformers, and much more.

Overview

The AI environments are each tailored for specific AI tools and SDKs. They include tools for general AI development like PyTorch, as well as those specifically designed for working with AI models and tools from Anthropic, Cohere, Fireworks, Langchain, Mistral AI, OpenAI, Perplexity, and Replicate. Each environment provides the necessary clients, tools, and libraries for efficient development and interaction with the respective AI models and APIs.

The Computer Vision environments are designed for various computer vision tools and libraries. These include environments for general computer vision tasks, PyTorch Image Models (TIMM), OpenCV, and Pillow. Each environment is equipped with the necessary tools for efficient image processing, model training, and real-time computer vision tasks.

Computer Vision

The Data Science and Machine Learning environments are designed for a variety of data science tools and machine learning libraries. These include environments for xgboost, lightgbm, scikit-learn, scipy, pandas, numpy, matplotlib, seaborn, numba, and cupy. Each environment is equipped with the necessary tools for efficient data analysis, machine learning model training, and numerical computations.

Data Science and Machine Learning

The Deep Learning and Tensors environments are designed for specific deep learning libraries and tools. These include environments for PyTorch, JAX, TensorFlow, Hugging Face, and Fast.ai, each equipped with the necessary tools for efficient deep learning and tensor computations. They provide a streamlined experience for tasks such as training neural network models, high-performance numerical computing, and large-scale machine learning.

Deep Learning and Tensors

The Model Deployment environments are designed for various model deployment tools and libraries. These include environments for Gradio, Modelbit, and Streamlit, each equipped with the necessary tools for efficient model deployment and user interface creation. They provide a streamlined experience for tasks such as creating customizable UI components around your models, working with the Modelbit package and ecosystem, and using Streamlit, an open-source app framework for Machine Learning and Data Science teams.

Model Deployment

The Vector Databases environments are tailored for specific vector database tools and clients, including vectordb, pinecone, milvus, weaviate, raft, faiss, chroma, and qdrant. Each environment is equipped with the necessary libraries and tools for efficient interaction with the respective vector databases. They facilitate tasks such as vector search, analytics, and clustering of dense vectors.

Vector Databases

The Natural Language Processing environments are designed for various NLP tools and libraries. These include environments for spacy, nltk, gensim, and flair, each equipped with the necessary tools for efficient natural language processing tasks. They provide a streamlined experience for tasks such as text analysis, language understanding, and topic modeling.

Natural Language Processing

Other

Launch your notebook servers on GPUs with cloud providers like Lambda Labs and DataCrunch at no additional cost.

Compute

A powerful AI assistant that generate and run code directly in your notebook servers

Agent Alph

Easily share your applications and services straight from your notebook servers.

Environment	Description	Quickstart
datavisualization	Environment equipped with tools for data visualization like matplotlib, seaborn, plotly, hvplot, datashader, and dash.
hvplot	Environment equipped with the tools and SDKs for working with hvPlot, a high-level plotting API for the PyData ecosystem built on HoloViews.
datashader	Environment equipped with the tools and SDKs for working with Datashader, a data rasterization pipeline for automating the process of creating meaningful representations of large amounts of data.
dash	Environment equipped with the tools and SDKs for working with Dash, a productive Python framework for building web analytic applications.
plotly	Environment for Plotly, a graphing library makes interactive, publication-quality graphs.
cuxfilter	Environment equipped with the tools and SDKs for working with cuXfilter, a RAPIDS framework to connect web-based visualization with GPU-accelerated crossfiltering.
bokeh	Environment equipped with the tools and SDKs for working with Bokeh, an interactive visualization library for modern web browsers.
seaborn	Environment for Seaborn, a Python data visualization library based on Matplotlib.
matplotlib	Environment for Matplotlib, a plotting library for the Python programming language and its numerical mathematics extension NumPy.

Get Started

Guides

Demos

Data Visualization