Search Results for "aws" - Page 4
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Showing 108 open source projects for "aws"
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Forever Free Full-Stack Observability | Grafana CloudBuilt on open standards like Prometheus and OpenTelemetry, Grafana Cloud includes Kubernetes Monitoring, Application Observability, Incident Response, plus the AI-powered Grafana Assistant. Get started with our generous free tier today.
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Opta
The next generation of Infrastructure-as-Code
Opta is an infrastructure-as-code framework. Rather than working with a low-level cloud configuration, Opta enables you to work with high-level constructs. Opta high-level constructs produce Terraform configuration files. This helps you avoid lock-in to Opta. You can write custom Terraform code or even take the Opta-generated Terraform and go your own way. Opta is a new kind of Infrastructure-as-Code (IaC) framework that lets engineers work with high-level constructs instead of getting lost... -
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Amazon Braket Ocean Plugin
A Python plugin for using Ocean with Amazon Braket
...Download and install Python 3.7.2 or greater from Python.org. If you are using Windows, choose Add Python to environment variables before you begin the installation. Make sure that your AWS account is onboarded to Amazon Braket. The Amazon Braket Ocean Plugin can be installed with pip. You can also install from source by cloning this repository and running a pip install command in the root directory of the repository. This package provides samplers which use Braket solvers. These samplers extend abstract base classes provided in Ocean's dimod and thus have the same interfaces as other samplers in Ocean. -
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SageMaker MXNet Inference Toolkit
Toolkit for allowing inference and serving with MXNet in SageMaker
...The AWS DLCs are used in Amazon SageMaker as the default vehicles for your SageMaker jobs such as training, inference, transforms etc. They've been tested for machine learning workloads on Amazon EC2, Amazon ECS and Amazon EKS services as well. -
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Apache MXNet (incubating)
A flexible and efficient library for deep learning
Apache MXNet is an open source deep learning framework designed for efficient and flexible research prototyping and production. It contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations. On top of this is a graph optimization layer, overall making MXNet highly efficient yet still portable, lightweight and scalable. -
Ship Agents FasterGemini Enterprise Agent Platform lets you rapidly build, scale, govern and optimize production-ready agents grounded in your organization's data. The platform enables developers to build custom or pre-built agents for virtually any use case. New customers get $300 in free credits.
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AWS Step Functions Data Science SDK
For building machine learning (ML) workflows and pipelines on AWS
The AWS Step Functions Data Science SDK is an open-source library that allows data scientists to easily create workflows that process and publish machine learning models using Amazon SageMaker and AWS Step Functions. You can create machine learning workflows in Python that orchestrate AWS infrastructure at scale, without having to provision and integrate the AWS services separately. -
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EB CLI Installer
Simplified EB CLI installation mechanism
...The ebcli_installer.py Python script will install the awsebcli package in a virtual environment to prevent potential conflicts with other Python packages. Even within a virtualenv, a developer might need to install multiple packages whose dependencies are in conflict. For example, at times the AWS CLI and the EB CLI have used conflicting versions of botocore. One such instance was particularly egregious. When there are conflicts, users have to manage separate virtualenvs for each of the conflicting packages, or find a combination of the packages without conflicts. -
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Zenoss Community Edition
Zenoss - Intelligent IT Operations Management
Zenoss provides software-defined IT operations for the world’s largest organizations. We deliver the ultimate level of IT service health with simplicity by providing the most granular and intelligent IT service modeling possible, at any scale, and sharing these unique insights with other IT operations management (ITOM) tools to make them more efficient. Zenoss Community Edition is not a “demo” or trial version of Zenoss Enterprise or Zenoss Cloud! Before You install Zenoss Community... -
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pyWhat
Identify emails, IP addresses, and more
...The tool is recursive: it can traverse files and directories to extract meaningful entities, which is useful when analyzing malware samples, network captures, or code repositories at scale. It offers powerful filters called “tags” and distributions that let you narrow results to specific categories like bug bounties, cryptocurrencies, or AWS-related artifacts. For automation and integration, pyWhat provides a CLI with options for rarity filtering, sorting, and JSON export, as well as an API that can be imported into other Python programs. -
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StreamAlert
StreamAlert is a serverless, realtime data analysis framework
...Ingested logs and generated alerts can be retroactively searched for compliance and research. Serverless design is cheaper, easier to maintain, and scales to terabytes per day. Deployment is automated, simple, safe and repeatable for any AWS account. Secure by design, least-privilege execution, containerized analysis, and encrypted data storage. -
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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PowerfulSeal
A powerful testing tool for Kubernetes clusters.
PowerfulSeal injects failure into your Kubernetes clusters so that you can detect problems as early as possible. It allows for writing scenarios describing complete chaos experiments. -
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SageMaker TensorFlow Serving Container
A TensorFlow Serving solution for use in SageMaker
SageMaker TensorFlow Serving Container is an a open source project that builds docker images for running TensorFlow Serving on Amazon SageMaker. Some of the build and tests scripts interact with resources in your AWS account. Be sure to set your default AWS credentials and region using aws configure before using these scripts. Amazon SageMaker uses Docker containers to run all training jobs and inference endpoints. The Docker images are built from the Dockerfiles in docker/. The Dockerfiles are grouped based on the version of TensorFlow Serving they support. ... -
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FARM
Fast & easy transfer learning for NLP
...It's built upon transformers and provides additional features to simplify the life of developers: Parallelized preprocessing, highly modular design, multi-task learning, experiment tracking, easy debugging and close integration with AWS SageMaker. With FARM you can build fast proofs-of-concept for tasks like text classification, NER or question answering and transfer them easily into production. Easy fine-tuning of language models to your task and domain language. AMP optimizers (~35% faster) and parallel preprocessing (16 CPU cores => ~16x faster). Modular design of language models and prediction heads. ... -
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AWS IoT Greengrass Core SDK
SDK to use with functions running on Greengrass Core using Python
The AWS IoT Greengrass Core SDK is meant to be used by AWS Lambda functions running on an AWS IoT Greengrass Core. It will enable Lambda functions to invoke other Lambda functions deployed to the Greengrass Core, publish messages to the Greengrass Core and work with the local Shadow service. To use the AWS IoT Greengrass Core SDK, you must first import the AWS IoT Greengrass Core SDK in your Lambda function as you would with any other external libraries. ... -
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SageMaker MXNet Training Toolkit
Toolkit for running MXNet training scripts on SageMaker
SageMaker MXNet Training Toolkit is an open-source library for using MXNet to train models on Amazon SageMaker. For inference, see SageMaker MXNet Inference Toolkit. For the Dockerfiles used for building SageMaker MXNet Containers, see AWS Deep Learning Containers. For information on running MXNet jobs on Amazon SageMaker, please refer to the SageMaker Python SDK documentation. With the SDK, you can train and deploy models using popular deep learning frameworks Apache MXNet and TensorFlow. You can also train and deploy models with Amazon algorithms, which are scalable implementations of core machine learning algorithms that are optimized for SageMaker and GPU training. ... -
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Jeffy
Serverless Application Framework for Python AWS Lambda
...Decorators - Jeffy has an extensive range of decorators/ event handlers for implementing common things for Lambda functions. 3. Tracing - Events are traceable within related functions and AWS services with generating and passing correlation_id. -
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Streisand
Streisand sets up a new server running your choice
...This includes OpenVPN, WireGuard, Shadowsocks, OpenConnect, and Tor bridges, often with optional add-ons like obfuscation or stunnel to resist throttling and detection. The result is a private website that hosts client software downloads and setup instructions for easy access. Streisand supports major cloud providers like AWS, Azure, Google Cloud, Linode, DigitalOcean, and Rackspace, while also offering installation options for advanced users with their own servers. -
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Zappa
Serverless Python
...With Zappa, each request is given its own virtual HTTP "server" by Amazon API Gateway. AWS handles the horizontal scaling automatically, so no requests ever time out. Each request then calls your application from a memory cache in AWS Lambda and returns the response via Python's WSGI interface. After your app returns, the "server" dies. -
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BytePS
A high performance and generic framework for distributed DNN training
...Each machine has 8 V100 GPUs (32GB memory) with NVLink-enabled. Machines are inter-connected with 100 Gbps RDMA network. This is the same hardware setup you can get on AWS. -
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Image Super-Resolution (ISR)
Super-scale your images and run experiments with Residual Dense
...This project contains Keras implementations of different Residual Dense Networks for Single Image Super-Resolution (ISR) as well as scripts to train these networks using content and adversarial loss components. Docker scripts and Google Colab notebooks are available to carry training and prediction. Also, we provide scripts to facilitate training on the cloud with AWS and Nvidia-docker with only a few commands. When training your own model, start with only PSNR loss (50+ epochs, depending on the dataset) and only then introduce GANS and feature loss. This can be controlled by the loss weights argument. The weights used to produce these images are available directly when creating the model object. ... -
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NoDB
NoDB isn't a database.. but it sort of looks like one
NoDB is a simple, lightweight, file-based database for Python applications that enables quick and easy data storage without requiring a separate database server. -
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BLESS
An SSH Certificate Authority that runs as an AWS Lambda function
...BLESS should be run as an AWS Lambda in an isolated AWS account. Because BLESS needs access to a private key that is trusted by your hosts, an isolated AWS account helps restrict who can access that private key, or modify the BLESS code you are running. AWS Lambda functions can use an AWS IAM Policy to limit which IAM Roles can invoke the Lambda Function. If properly configured, you can restrict which IAM Roles can request SSH Certificates. -
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Market Reporter
Automatic Generation of Brief Summaries of Time-Series Data
...This is an implementation of Murakami et al. This tool stores data to Amazon S3. Ask the manager to give you AmazonS3FullAccess and issue a credential file. For details, please read AWS Identity and Access Management. Install Docker and Docker Compose. Edit envs/docker-compose.yaml according to your environment. Then, launch containers by docker-compose. We recommend to use pipenv to make a Python environment for this project. Suppose you have a database named master on your local machine. Prediction submodule generates a single comment of a financial instrument at specified time by loading a trained model. -
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Security Monkey
Security Monkey monitors AWS, GCP, OpenStack, and GitHub orgs
Security Monkey monitors AWS, GCP, OpenStack, and GitHub orgs for assets and their changes over time. Security Monkey monitors your AWS and GCP accounts for policy changes and alerts on insecure configurations. Support is available for OpenStack public and private clouds. Security Monkey can also watch and monitor your GitHub organizations, teams, and repositories. -
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SAWS
A supercharged AWS command line interface (CLI)
Although the AWS CLI is a great resource to manage your AWS-powered services, it's tough to remember the usage of 70+ top-level commands, 2000+ subcommand, countless command-specific options, and resources such as instance tags and buckets. SAWS aims to supercharge the AWS CLI with features focusing on improving ease-of-use, and increasing productivity. -
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AWS IoT Arduino Yún SDK
SDK for connecting to AWS IoT from an Arduino Yún
The AWS-IoT-Arduino-Yún-SDK allows developers to connect their Arduino Yún compatible Board to AWS IoT. By connecting the device to the AWS IoT, users can securely work with the message broker, rules and the Thing Shadow provided by AWS IoT and with other AWS services like AWS Lambda, Amazon Kinesis, Amazon S3, etc. The AWS-IoT-Arduino-Yún-SDK consists of two parts, which take use of the resources of the two chips on Arduino Yún, one for native Arduino IDE API access and the other for functionality and connections to the AWS IoT built on top of AWS IoT Device SDK for Python. ...