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Showing 17 open source projects for "include"
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Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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SDGym
Benchmarking synthetic data generation methods
...The SDGym library integrates with the Synthetic Data Vault ecosystem. You can use any of its synthesizers, datasets or metrics for benchmarking. You also customize the process to include your own work. Select any of the publicly available datasets from the SDV project, or input your own data. Choose from any of the SDV synthesizers and baselines. Or write your own custom machine learning model. In addition to performance and memory usage, you can also measure synthetic data quality and privacy through a variety of metrics. ... -
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SageMaker Training Toolkit
Train machine learning models within Docker containers
...Amazon SageMaker is a fully managed service for data science and machine learning (ML) workflows. You can use Amazon SageMaker to simplify the process of building, training, and deploying ML models. To train a model, you can include your training script and dependencies in a Docker container that runs your training code. A container provides an effectively isolated environment, ensuring a consistent runtime and reliable training process. The SageMaker Training Toolkit can be easily added to any Docker container, making it compatible with SageMaker for training models. ... -
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Avogadro
An intuitive molecular editor and visualization tool
Avogadro is an advanced molecular editor designed for cross-platform use in computational chemistry, molecular modeling, bioinformatics, materials science and related areas. It offers a flexible rendering framework and a powerful plugin architecture. -
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MCPower
MCPower — simple Monte Carlo power analysis for complex models
...It guides users through the full workflow across three tabs: Model setup (formula input with live parsing, CSV data upload with auto-detected variable types, effect size sliders, and correlation editing), Analysis configuration (find power for a given sample size or find the minimum sample size for a target power, with multiple testing correction and scenario analysis), and Results (interactive charts, exportable tables, and auto-generated Python replication scripts). Supports both standard linear models and mixed-effects models. Additional features include analysis history, configurable scenarios, and built-in documentation. -
Cut Data Warehouse Costs by 54%BigQuery delivers 54% lower TCO with exabyte scale and flexible pricing. Free migration tools handle the SQL translation automatically.
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SageMaker Inference Toolkit
Serve machine learning models within a Docker container
...Amazon SageMaker is a fully managed service for data science and machine learning (ML) workflows. You can use Amazon SageMaker to simplify the process of building, training, and deploying ML models. Once you have a trained model, you can include it in a Docker container that runs your inference code. A container provides an effectively isolated environment, ensuring a consistent runtime regardless of where the container is deployed. Containerizing your model and code enables fast and reliable deployment of your model. The SageMaker Inference Toolkit implements a model serving stack and can be easily added to any Docker container, making it deployable to SageMaker. ... -
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StellarGraph
Machine Learning on Graphs
...It can solve many machine learning tasks. Graph-structured data represent entities as nodes (or vertices) and relationships between them as edges (or links), and can include data associated with either as attributes. For example, a graph can contain people as nodes and friendships between them as links, with data like a person’s age and the date a friendship was established. StellarGraph supports the analysis of many kinds of graphs. StellarGraph is built on TensorFlow 2 and its Keras high-level API, as well as Pandas and NumPy. ... -
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SageMaker Containers
Create SageMaker-compatible Docker containers
Amazon SageMaker is a fully managed service for data science and machine learning (ML) workflows. You can use Amazon SageMaker to simplify the process of building, training, and deploying ML models. To train a model, you can include your training script and dependencies in a Docker container that runs your training code. A container provides an effectively isolated environment, ensuring a consistent runtime and reliable training process. The SageMaker Training Toolkit can be easily added to any Docker container, making it compatible with SageMaker for training models. ... -
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The TRANSIMS Studio application is an integrated development environment for the TRansportation ANalysis and SIMulation System (TRANSIMS). Components include a run time environment to execute TRANSIMS in parallel, as well as a full featured GUI.
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GeoNotebook
A Jupyter notebook extension for geospatial visualization and analysis
...GeoNotebook bridges the gap between data science workflows and GIS exploration by combining the flexibility of interactive notebooks with browser-based map display driven by a Python backend and WebGL/Canvas tools. It supports workflows that include map reprojection, layer interaction, and tile serving, which are essential for real world geoscience and environmental analysis. -
Fully Managed MySQL, PostgreSQL, and SQL ServerCloud SQL handles your database ops end to end, so you can focus on your app.
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fooltrader
Quant framework for stock
...Its goal is to provide a unified framework for the whole market (stock, futures, bonds, foreign exchange, digital currency, macroeconomics, etc.) for research, backtesting, forecasting, and trading. Its applicable objects include quantitative traders, teachers, and students majoring in finance, people interested in economic data, programmers, and people who like freedom and the spirit of exploration. You could write the Strategy using an event-driven or time walkway and view and analyze the performance in a uniform way. -
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Matplotlib is a python library for making publication quality plots using a syntax familiar to MATLAB users. Matplotlib uses numpy for numerics. Output formats include PDF, Postscript, SVG, and PNG, as well as screen display. As of matplotlib version 1.5, we are no longer making file releases available on SourceForge. Please visit http://matplotlib.org/users/installing.html for help obtaining matplotlib.
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TinkerCell is a software for synthetic biology. The visual interface allows users to design networks using various biological "parts". Models can include modules and multiple cells. Users can program new functions using C or Python. www.tinkercell.
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NanoCap
Carbon Fullerene and Capped Nanotube Generator
...The implementation involves a standalone application which includes a GUI and allows for dynamic visual inspection through 3D rendering. In addition, the NanoCap core libraries can be used in custom Python scripts that enabled the user to produce structures in bulk or to include the structure generation routines into pre-existing code. -
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Portable Linux
Portable Ubuntu Linux for Scientific Computing
...Install any flavor of Ubuntu. 3. Swap out the root.disk with the ones provided here. Overview LAMP stack running on localhost (127.0.0.1). Scientific, productivity & media packages include R (Rattle Data Miner), GridEngine, Condor, cooperative computing tools, WINE, LibreOffice, Evolution, Clinica, Neuro Debian Desktop, PsycoPy, OpenVibe, 3DSlicer, Paraview, Openshot. Cheers, Gregory Remington -
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CollectD-CSV Plotter
Web interface for visualizing CSV data from CollectD monitoring system
...The plotter is actually a front-end for collectd-csv module (http://pypi.python.org/pypi/collectd-csv/), which it uses for fetching the data. Technologies CollectD-CSV Plotter uses include CGI/Python, HTML5/JavaScript, jQuery, jQuery-UI and Gnuplot. -
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--UPDATE-- New Version is now integrated in the official PyMOL plugin repository! --UPDATE-- The BNI (Beyond Normal Interaction)- Tools is a plug in for the PyMOL molecular visualization system which adds additional functionalities and presets to the PyMOL GUI and also adds useful extended commands.
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The Scientific Visualization Artist Tools (SVAT) include converters from the Visualization Toolkit (VTK) to RenderMan Interface Bytestream (RIB) (ASCII and binary files supported), Blender to RenderMan, compilable RenderMan shaders and other utilities.
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