Search Results for "graphs"
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Showing 9 open source projects for "graphs"
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Seldon Core
An MLOps framework to package, deploy, monitor and manage models
...Seldon Core serves models built in any open-source or commercial model building framework. You can make use of powerful Kubernetes features like custom resource definitions to manage model graphs. And then connect your continuous integration and deployment (CI/CD) tools to scale and update your deployment. Built on Kubernetes, runs on any cloud and on-premises. Framework agnostic, supports top ML libraries, toolkits and languages. Advanced deployments with experiments, ensembles and transformers. Our open-source framework makes it easier and faster to deploy your machine learning models and experiments at scale on Kubernetes. ... -
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Dash
Build beautiful web-based analytic apps, no JavaScript required
...Built on top of Plotly.js, React and Flask, Dash easily achieves what an entire team of designers and engineers normally would. It ties modern UI controls and displays such as dropdown menus, sliders and graphs directly to your analytical Python code, and creates exceptional, interactive analytics apps. Dash apps are very lightweight, requiring only a limited number of lines of Python or R code; and every aesthetic element can be customized and rendered in the web. It’s also not just for dashboards. You have full control over the look and feel of your apps, so you can style them to look any way you want. -
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Optuna
A hyperparameter optimization framework
...Thanks to our define-by-run API, the code written with Optuna enjoys high modularity, and the user of Optuna can dynamically construct the search spaces for the hyperparameters. Optuna Dashboard is a real-time web dashboard for Optuna. You can check the optimization history, hyperparameter importances, etc. in graphs and tables. You don't need to create a Python script to call Optuna's visualization functions. Automated search for optimal hyperparameters using Python conditionals, loops, and syntax. Efficiently search large spaces and prune unpromising trials for faster results. Parallelize hyperparameter searches over multiple threads or processes without modifying code. -
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SageMaker Spark Container
Docker image used to run data processing workloads
Apache Spark™ is a unified analytics engine for large-scale data processing. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing. The SageMaker Spark Container is a Docker image used to run batch data processing workloads on Amazon SageMaker using the Apache Spark framework. ... -
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Django Rules
Awesome Django authorization, without the database
...At its core, it is a generic framework for building rule-based systems, similar to decision trees. It can also be used as a standalone library in other contexts and frameworks. Versatile. Decorate callables to build complex graphs of predicates. Predicates can be any type of callable -- simple functions, lambdas, methods, callable class objects, partial functions, decorated functions, anything really. A good Django citizen. Seamless integration with Django views, templates and the Admin for testing for object-level permissions. Efficient and smart. No need to mess around with a database to figure out whether John really wrote that book. ... -
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Miasm
Reverse engineering framework in Python
...Among them, the SSA/Out-of-SSA transformation, expression propagation and high-level operators can be joined to “lift” Miasm IR to a more human-readable language. We use graphviz to illustrate some graphs. Its layout does not always totally conform with a reverse engineering “ideal view”, so please be tolerant of those odd graphs. Miasm is not the first tool to implement this feature. But, well, as the tool already had everything needed to implement DSE, it was just a matter of time before these features landed in the main branch. -
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Python Taint
Static Analysis Tool for Detecting Security Vulnerabilities in Python
Static analysis of Python web applications based on theoretical foundations (Control flow graphs, fixed point, dataflow analysis) Detect command injection, SSRF, SQL injection, XSS, directory traveral etc. A lot of customization is possible. For functions from builtins or libraries, e.g. url_for or os.path.join, use the -m option to specify whether or not they return tainted values given tainted inputs, by default this file is used. -
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Gin Config
Gin provides a lightweight configuration framework for Python
Gin Config is a lightweight and flexible configuration framework for Python built around dependency injection. It enables developers to manage complex parameter hierarchies—particularly common in machine learning experiments—without relying on boilerplate configuration classes or protos. By decorating functions and classes with @gin.configurable, Gin allows their parameters to be overridden using simple configuration files (.gin) or command-line bindings. Users can define default parameter... -
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Mantikhor is an *information representation* language. Like RDF, it models information as directed-edge arc-node graphs, although Mantikhor's use of such constructs is more structured and constrained.
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