Search Results for "issues"
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Showing 45 open source projects for "issues"
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1
CleanVision
Automatically find issues in image datasets
...The quality of machine learning models hinges on the quality of the data used to train them, but it is hard to manually identify all of the low-quality data in a big dataset. CleanVision helps you automatically identify common types of data issues lurking in image datasets. This package currently detects issues in the raw images themselves, making it a useful tool for any computer vision task such as: classification, segmentation, object detection, pose estimation, keypoint detection, generative modeling, etc. -
2
Elementary
Open-source data observability for analytics engineers
...Uploading and modeling of dbt artifacts, run and test results to tables as part of your runs. Get informative notifications on data issues, schema changes, models and tests failures. Inspect upstream and downstream dependencies to understand impact and root cause of data issues. -
3
Arize Phoenix
Uncover insights, surface problems, monitor, and fine tune your LLM
...The toolset is designed to ingest model inference data for LLMs, CV, NLP and tabular datasets. It allows Data Scientists to quickly visualize their model data, monitor performance, track down issues & insights, and easily export to improve. Deep Learning Models (CV, LLM, and Generative) are an amazing technology that will power many of future ML use cases. A large set of these technologies are being deployed into businesses (the real world) in what we consider a production setting. -
4
RuntimeGeneratedFunctions.jl
Functions generated at runtime without world-age issues or overhead
RuntimeGeneratedFunctions are functions generated at runtime without world-age issues and with the full performance of a standard Julia anonymous function. This builds functions in a way that avoids eval. For technical reasons, RuntimeGeneratedFunctions needs to cache the function expression in a global variable within some module. This is normally transparent to the user, but if the RuntimeGeneratedFunction is evaluated during module precompilation, the cache module must be explicitly set to the module currently being precompiled. ... -
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5
Dagster
An orchestration platform for the development, production
...Centralize your metadata in one tool with built-in observability, diagnostics, cataloging, and lineage. Spot any issues and identify performance improvement opportunities. -
6
Cleanlab
The standard data-centric AI package for data quality and ML
...This package helps you find label issues and other data issues, so you can train reliable ML models. All features of cleanlab work with any dataset and any model. Yes, any model: PyTorch, Tensorflow, Keras, JAX, HuggingFace, OpenAI, XGBoost, scikit-learn, etc. If you use a sklearn-compatible classifier, all cleanlab methods work out-of-the-box. -
7
Luxor
Simple drawings using vector graphics; Cairo "for tourists!"
...The focus of Luxor is on simplicity and ease of use: it should be easier to use than plain Cairo.jl, with shorter names, fewer underscores, default contexts, and simplified functions. For more complex and sophisticated graphics in 2D and 3D, Makie.jl is the best choice. Luxor is thoroughly procedural and static: your code issues a sequence of simple graphics ‘commands’ until you’ve completed a drawing, and then the results are saved into a PDF, PNG, SVG, or EPS file. -
8
plotly.js
JavaScript charting library behind Plotly and Dash
...Built on top of d3.js and stack.gl, Plotly.js is a high-level, declarative charting library. plotly.js ships with over 40 chart types, including 3D charts, statistical graphs, and SVG maps. plotly.js is free and open source and you can view the source, report issues or contribute on GitHub. For plotly.js to build with Webpack you will need to install ify-loader@v1.1.0+ and add it to your webpack.config.json. This adds Browserify transform compatibility to Webpack which is necessary for some plotly.js dependencies. When users hover over a figure generated with plotly.js, a modebar appears in the top-right of the figure. ... -
9
Handsontable
JavaScript data grid with a spreadsheet look & feel
...Plus you get access to a comprehensive API, useful tutorials, and both community and commercial support. You can finally work with large volumes of data without worrying about performance issues. Large companies and startups across industries use Handsontable to build applications critical to their business. -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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10
ODD Platform
First open-source data discovery and observability platform
...Explore tags, ownership details, links to other sources and other information to shorten and simplify data discovery phase. Forget unnerved stakeholders and wasting too much time on digging the root cause of data issues when it fails. With ODD’s automatic company-wide ingestion-to-product lineage you’ll have answers in just seconds and stakeholders won’t need to wait. Sleep well, knowing all your data is in check. Forget manual testing, days of debugging, and weeks of worrying. Know the impact of each code change with automatic testing. Enjoy lineage and alerts powered with data quality information. -
11
Parsers.jl
fast parsing machinery for basic types in Julia
...Installation: at the Julia REPL, import Pkg; Pkg.add("Parsers") Parsers is maintained collectively by the JuliaData collaborators. Responsiveness to pull requests and issues can vary, depending on the availability of key collaborators. -
12
Personal Management System
Your web application for managing personal data
It's easier to understand this web application when you think about a CMS (WordPress) or CRM (SugarCRM); the logic behind this system is very similar to those two. My PMS may offer fewer possibilities than those systems above, but it just does what I want it to do. Additionally, writing extensions is not too hard, depending on the logic required. Anyone with development knowledge can pretty much write their own extensions for personal needs. Keep a track of your personal goals. You can use... -
13
Automated Tool for Optimized Modelling
Automated Tool for Optimized Modelling
...How many times have you conducted the same action to pre-process a raw dataset? How many times have you copy-and-pasted code from an old repository to re-use it in a new use case? ATOM is here to help solve these common issues. The package acts as a wrapper of the whole machine learning pipeline, helping the data scientist to rapidly find a good model for his problem. -
14
Checkmate
Checkmate is an open-source, self-hosted tool
...It supports detailed infrastructure monitoring through an optional agent called Capture, which collects hardware metrics such as CPU, memory, disk usage, and temperature from remote machines. The system is designed to scale, having been stress-tested with more than a thousand active monitors without major performance issues. Checkmate also includes incident tracking and notification capabilities so teams can quickly respond to outages or anomalies. Overall, the platform serves as a comprehensive, developer-friendly alternative to commercial uptime and infrastructure monitoring tools. -
15
Infiltrator.jl
No-overhead breakpoints in Julia
...Use an actual debugger if you need that level of flexibility. Running code that ends up triggering the @infiltrate REPL mode via inline evaluation in VS Code or Juno can cause issues, so it's recommended to always use the REPL directly. When the infiltration point is hit, it will drop you into an interactive REPL session that lets you inspect local variables and the call stack as well as execute arbitrary statements in the context of the current local and global scope. -
16
Great Expectations
Always know what to expect from your data
...Great Expectations brings the same confidence, integrity, and acceleration to data science and data engineering teams. Expectations are assertions for data. They are the workhorse abstraction in Great Expectations, covering all kinds of common data issues. Expectations are a great start, but it takes more to get to production-ready data validation. Where are Expectations stored? How do they get updated? How do you securely connect to production data systems? How do you notify team members and triage when data validation fails? Great Expectations supports all of these use cases out of the box. ... -
17
SciML Style Guide for Julia
A style guide for stylish Julia developers
...Instead, the principle is to be as correct as possible to begin with, and grow the generic support over time. All recommended functionality should be tested, and any known generality issues should be documented in an issue (and with a @test_broken test when possible). However, a function that is known to not be GPU-compatible is not grounds to block merging, rather it is encouraged for a follow-up PR to improve the general type support. -
18
LinearSolve.jl
High-Performance Unified Interface for Linear Solvers in Julia
...Performance is key: the current methods are made to be highly performant on scalar and statically sized small problems, with options for large-scale systems. If you run into any performance issues, please file an issue. -
19
Superstruct
A simple and composable way to validate data in JavaScript
...Its API is intentionally familiar if you’ve used TypeScript, Flow, Go structs, or GraphQL schemas, but the output is oriented around runtime correctness. When validation fails, it can throw or return detailed errors that are useful both for debugging and for communicating issues back to end users or API clients. The library is designed to be composable, so you can build larger validators from small pieces rather than rewriting similar checks in multiple places. It’s especially effective in REST or GraphQL servers, but it’s equally useful for protecting internal boundaries where untrusted or loosely typed data can slip in. -
20
Pandas Profiling
Create HTML profiling reports from pandas DataFrame objects
...Mostly global details about the dataset (number of records, number of variables, overall missigness and duplicates, memory footprint). Comprehensive and automatic list of potential data quality issues (high correlation, skewness, uniformity, zeros, missing values, constant values, between others). -
21
Viking GPS data editor and analyzer
Viking is a free/open source program to manage GPS data
...Viking is currently dual managed - with a corresponding Github presence at: https://github.com/viking-gps/viking Where you may find it easier to view the code and raise issues there too. -
22
Msc-generator
Draws signalling charts, block diagrams and graphs from text input.
NOTE! We have moved to https://gitlab.com/msc-generator/msc-generator All development happens there. Also, download new releases & submit issues there. A tool to draw various charts from textual descriptions. Currently, three types of charts are supported: Message Sequence Charts, generic Graphs, and Block Diagrams, with more to be added in the future. There is a command-line version for Linux and Mac (replacing mscgen), which now sports a GUI, as well. Msc-generator allows fine control over the appearance and has a rich feature set complete with detailed documentation. ... -
23
Caesar.jl
Robust robotic localization and mapping
A multimodal/non-Gaussian robotic toolkit for localization and mapping -- reducing the barrier of entry for sensor/data fusion tasks, including Simultaneous Localization and Mapping (SLAM). Code changes are currently tracked via Github's integrated Milestone/Issues/PR system. -
24
data-diff
Efficiently diff rows across two different databases
...Replicating data at scale, across hundreds of tables, with low latency and at a reasonable infrastructure cost is a hard problem, and most data teams we’ve talked to, have faced data quality issues in their replication processes. The hard truth is that the quality of the replication is the quality of the data. Since copying entire datasets in batch is often infeasible at the modern data scale, businesses rely on the Change Data Capture (CDC) approach of replicating data using a continuous stream of updates. -
25
re_data
re_data - fix data issues before your users & CEO would discover them
re_data is an open-source data reliability framework for the modern data stack. Currently, re_data focuses on observing the dbt project (together with underlying data warehouse - Postgres, BigQuery, Snowflake, Redshift). Gather all relevant outputs about your data in one place using our cloud. Invite your team and debug it easily from there. Go back in time, and see your past metadata. Set up Slack notifications to always know when a new report is produced or an existing one got updated.
