What are the differences in their usage and the main reason for development of PredictionIO?
From wikipedia:
Apache Mahout is a project of the Apache Software Foundation to
produce free implementations of distributed or otherwise scalable
machine learning algorithms focused primarily in the areas of
collaborative filtering, clustering and classification
From predictionio website:
PredictionIO is an open source Machine Learning Server built on top of
state-of-the-art open source stack for developers and data scientists
create predictive engines for any machine learning task. It is as a
full machine learning stack, bundled with Apache Spark, MLlib, HBase,
Spray and Elasticsearch, which simplifies and accelerates scalable
machine learning infrastructure management.
Apache Mahout is used to implement machine learning algorithms in a hadoop-based environment.
Predictionio is a full tech stack used to bring machine learning to a production environment. With predictionio, you can more easily build, train, deploy algorithms. It comes with an http server and database backend. PredictionIO actually used to be built on top of Apache Mahout but switched to Apache Spark.
related:
https://www.quora.com/What-is-the-difference-between-Prediction-io-and-apache-mahout
Related
I am building a multivariate LSTM to model longitudinal data with pytorch.
I have installed Graphcore pytorch (3.1, which includes poplar and popart) and tools from docker. Rather than installing an IPU immediately, can I develop the model on the CPU to start with before adding or migrating to an IPU? When I issue any gc-* command it reports no IPU available, which I know is true!
I generally prefer to run in bare metal [Ubuntu 20.04 LTS AMD 1950X Threadripper] rather than via VMs. Do I need a Graphcore account to do this, so I can sign the licence agreement etc? I guess that is implied in the docker application.
I would like to use stable diffusion as part of a side project, I have it currently deployed on a vm in google cloud, but its not scalable. How can I deploy it so that its scalable ?
I want to deploy skitlearn machine learning models and use them as a web service.I am using flask but it is not feasible for production ,what should i do,is there any other open source platforms available for deployment
Django is the best server side framework among all frameworks I used till now.
I use pandas, numpy and all other libraries with no issues at all.
The Question:
Does anyone know a stable framework which can be used to create a blockchain application, creating a server/node, creating a miner, a wallet, a blockchain inspector, etc?
Such a framework does not have to be in Node.js nor Ruby on Rails, but those are the two technologies I am most familiar with.
Some Background:
I have to craft an internship project based on blockchain technology.
I have been looking at Ethereum which seems nice. Ethereum's GETH command line interface allows me to create a blockchain and also mine that blockchain.
However, I need to be able to use a web-capable development platform such as Ruby on Rails, Node.js, or similar so I can have interns craft a UI to go along with a local blockchain.
I have looked at Toshi(RoR) and BitCoin.js(Node), but will need something that has better documentation.
Thanks for any and all your suggestions!
When I built Etheria, I chose the following:
Development techs:
Ubuntu Linux 14.04
Eclipse Mars 2 (get from web, not repo) (javascript formatting works
well, you can tell eclipse to format .sol files as javascript)
Solidity + chriseth's Solidity compiler at
https://ethereum.github.io/browser-solidity
And for deployment:
Digital Ocean
Ubuntu 14.04
geth (stable, not development)
node + async + express
Notes on choices:
Ubuntu 14.04 for development - As it is the Linux standard, many Ethereum docs assume it which streamlines things. Easy to install geth and keep it upgraded.
Eclipse Mars 2 - Ubuntu's packaged Eclipse is old.
Solidity - Was once (is possibly still) billed as the "official" ethereum language and is easy to learn. chriseth is the man.
Digital Ocean - cheap, easy hosting. My security needs were nil as I didn't need a wallet on the machine. If you plan on keeping wallets on your machine, your risk profile may be different and necessitate other options.
Ubuntu 14.04 for deployment - Easy to install geth and keep it upgraded.
geth - I'm sure pyeth and eth are equally valid. geth is more widely used
node - Seamless interaction with the indispensable and awesome web3.js library which is used to interact with your geth instance (which should be running with local-access-allowed IPC). Async for easy async calls, express for endpoint creation and organization.
My code:
Etheria contract: https://github.com/fivedogit/etheria
Etheria node: https://github.com/fivedogit/etheria_node
Is it possible to deploy an OpenCV application to windows azure?
Open CV is comes into client application category accessible through user interface and also can be used for backend processing. Windows Azure Cloud services is used for web application so Open CV does not fit in the application model. For backend processing you may think to use cloud service as worker but that need lots of work on your part and defeat the purpose.
For the sake of completeness and possibility, you sure can get a Windows Azure Virtual Machine, along with Windows OS and deploy OpenCV application there. Once ready you can Remote Desktop to the VM and use it. You may pay monthly cost to use the VM but you sure can do it. But I am sure that is not your objective either.
Yes, I'll say its possible to install OpenCV applications to Azure.
Check the following Deep Learning VM
It comes with pre-installed software. Most of the machine learning libraries along with the OpenCV project are pre-installed
You can also use APIs to host your models on the Windows Azure