What is Streamlit and what are its top alternatives?
Top Alternatives to Streamlit
Dash is an API Documentation Browser and Code Snippet Manager. Dash stores snippets of code and instantly searches offline documentation sets for 150+ APIs. You can even generate your own docsets or request docsets to be included. ...
The Jupyter Notebook is a web-based interactive computing platform. The notebook combines live code, equations, narrative text, visualizations, interactive dashboards and other media. ...
Flask is intended for getting started very quickly and was developed with best intentions in mind. ...
Bokeh is an interactive visualization library for modern web browsers. It provides elegant, concise construction of versatile graphics, and affords high-performance interactivity over large or streaming datasets. ...
Django is a high-level Python Web framework that encourages rapid development and clean, pragmatic design. ...
TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. ...
Streamlit alternatives & related posts
- Dozens of API docs and Cheat-Sheets17
- Great for offline use12
- Works with Alfred8
- Excellent documentation8
- Quick API search8
- Good integration with Xcode and AppCode3
- Great for mobile dev work2
related Dash posts
- In-line code execution using blocks17
- In-line graphing support9
- Can be themed6
- Multiple kernel support5
- Best web-browser IDE for Python3
- Export to python code3
- LaTex Support2
- Can you provide me full list of companies Who uses Jupy1
- Multi-user with Kubernetes1
- HTML export capability1
related Jupyter posts
Jupyter Anaconda Pandas IPython
A great way to prototype your data analytic modules. The use of the package is simple and user-friendly and the migration from ipython to python is fairly simple: a lot of cleaning, but no more.
The negative aspect comes when you want to streamline your productive system or does CI with your anaconda environment: - most tools don't accept conda environments (as smoothly as pip requirements) - the conda environments (even with miniconda) have quite an overhead
I am learning Python coding and doing lots of hands on python problem. I like the feel of Jupyter notebook but I have concern will that slow my computer performance. Will PyCharm or Jupyter or Atom-IDE is good for python coding?
- Open source146
- Easy to use66
- Easy to setup and get it going54
- Well designed53
- Easy to develop and maintain applications48
- Easy to get started45
- Beautiful code18
- Rapid development17
- Simple to use11
- Get started quickly11
- Love it11
- Easy to integrate10
- Perfect for small to large projects with superb docs.9
- For it flexibility9
- Flexibilty and easy to use8
- User friendly6
- Not JS6
- Not JS10
- Not fast5
- Don't has many module as in spring1
related Flask posts
One of our top priorities at Pinterest is fostering a safe and trustworthy experience for all Pinners. As Pinterest’s user base and ads business grow, the review volume has been increasing exponentially, and more content types require moderation support. To solve greater engineering and operational challenges at scale, we needed a highly-reliable and performant system to detect, report, evaluate, and act on abusive content and users and so we created Pinqueue.
Pinqueue-3.0 serves as a generic platform for content moderation and human labeling. Under the hood, Pinqueue3.0 is a Flask + React app powered by Pinterest’s very own Gestalt UI framework. On the backend, Pinqueue3.0 heavily relies on PinLater, a Pinterest-built reliable asynchronous job execution system, to handle the requests for enqueueing and action-taking. Using PinLater has significantly strengthened Pinqueue3.0’s overall infra with its capability of processing a massive load of events with configurable retry policies.
Hundreds of millions of people around the world use Pinterest to discover and do what they love, and our job is to protect them from abusive and harmful content. We’re committed to providing an inspirational yet safe experience to all Pinners. Solving trust & safety problems is a joint effort requiring expertise across multiple domains. Pinqueue3.0 not only plays a critical role in responsively taking down unsafe content, it also has become an enabler for future ML/automation initiatives by providing high-quality human labels. Going forward, we will continue to improve the review experience, measure review quality and collaborate with our machine learning teams to solve content moderation beyond manual reviews at an even larger scale.
- R Compatibility8
- Highly customizable and extensible2
related Shiny posts
- Beautiful Interactive charts in seconds10
related Bokeh posts
Hi - I am looking to develop an app accessed by a browser that will display interactive networks (including adding or deleting nodes, edges, labels (or changing labels) based on user input. Look to use Django at the backend. Also need to manage graph versions if one person makes a graph change while another person is looking at it. Mainly tree networks for starters anyway. I probably will use the Networkx package. Not sure what the pros and cons are using Bokeh vs Matplotlib. I would be grateful for any comments or suggestions. Thanks.
- Rapid development641
- Open source473
- Great community406
- Easy to learn357
- Beautiful code217
- Great packages194
- Great libraries182
- Comes with auth and crud admin panel67
- Great documentation64
- Great for web61
- Great orm38
- Great for api36
- All included27
- Web Apps22
- Used by top startups19
- Easy setup16
- Convention over configuration13
- Allows for very rapid development with great libraries9
- The Django community9
- Great MVC and templating engine7
- King of backend world7
- Its elegant and practical7
- Full stack6
- Fast prototyping6
- Have not found anything that it can't do6
- Batteries included5
- Very quick to get something up and running5
- Easy Structure , useful inbuilt library5
- Easy to develop end to end AI Models5
- Python community4
- Great peformance4
- Easy to use4
- Many libraries4
- Full-Text Search3
- Zero code burden to change databases3
- Just the right level of abstraction3
- Easy to change database manager2
- Node js1
- Underpowered templating25
- Autoreload restarts whole server21
- Underpowered ORM20
- URL dispatcher ignores HTTP method15
- Internal subcomponents coupling10
- Not nodejs7
- Configuration hell7
- Not as clean and nice documentation like Laravel5
- Bloated admin panel included3
- Not typed3
- Overwhelming folder structure2
- InEffective Multithreading2
related Django posts
Simple controls over complex technologies, as we put it, wouldn't be possible without neat UIs for our user areas including start page, dashboard, settings, and docs.
Initially, there was Django. Back in 2011, considering our Python-centric approach, that was the best choice. Later, we realized we needed to iterate on our website more quickly. And this led us to detaching Django from our front end. That was when we decided to build an SPA.
For building user interfaces, we're currently using React as it provided the fastest rendering back when we were building our toolkit. It’s worth mentioning Uploadcare is not a front-end-focused SPA: we aren’t running at high levels of complexity. If it were, we’d go with Ember.js.
However, there's a chance we will shift to the faster Preact, with its motto of using as little code as possible, and because it makes more use of browser APIs. One of our future tasks for our front end is to configure our Webpack bundler to split up the code for different site sections. For styles, we use PostCSS along with its plugins such as cssnano which minifies all the code.
All that allows us to provide a great user experience and quickly implement changes where they are needed with as little code as possible.
- Bindings to popular languages like Python, Node, R, etc16
- Integrated zoom and filter-out tools in charts and maps10
- Great support for complex and multiple axes9
- Powerful out-of-the-box featureset8
- Beautiful visualizations6
- Active user base4
- Webgl chart types are extremely performant3
- Impressive support for webgl 3D charts3
- Charts are easy to share with a cloud account3
- Publication quality image export2
- Easy to use online editor for creating plotly.js charts2
- Interactive charts2
- Terrible document16
related Plotly.js posts
If you've tried using D3.js, it's a pretty poor developer experience, and that translates to spending a bunch of time getting the graphs one wants even for things that are conceptually pretty basic. Plotly isn't amazing (it's decent), but it's way better than than D3 unless you have very specialized needs.
Here is my stack on #Visualization. @FusionCharts and Highcharts are easy to use but only free for non-commercial. Chart.js and Plotly are two lovely tools for commercial use under the MIT license. And D3.js would be my last choice only if a complex customized plot is needed.
- High Performance26
- Connect Research and Production16
- Deep Flexibility13
- True Portability9
- High level abstraction3
- Easy to use2
- Hard to debug6
- Documentation not very helpful1
related TensorFlow posts
Why we built an open source, distributed training framework for TensorFlow , Keras , and PyTorch:
At Uber, we apply deep learning across our business; from self-driving research to trip forecasting and fraud prevention, deep learning enables our engineers and data scientists to create better experiences for our users.
TensorFlow has become a preferred deep learning library at Uber for a variety of reasons. To start, the framework is one of the most widely used open source frameworks for deep learning, which makes it easy to onboard new users. It also combines high performance with an ability to tinker with low-level model details—for instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA’s CUDA toolkit.
Uber has introduced Michelangelo (https://eng.uber.com/michelangelo/), an internal ML-as-a-service platform that democratizes machine learning and makes it easy to build and deploy these systems at scale. In this article, we pull back the curtain on Horovod, an open source component of Michelangelo’s deep learning toolkit which makes it easier to start—and speed up—distributed deep learning projects with TensorFlow:
(Direct GitHub repo: https://github.com/uber/horovod)
In mid-2015, Uber began exploring ways to scale ML across the organization, avoiding ML anti-patterns while standardizing workflows and tools. This effort led to Michelangelo.
Michelangelo consists of a mix of open source systems and components built in-house. The primary open sourced components used are HDFS, Spark, Samza, Cassandra, MLLib, XGBoost, and TensorFlow.