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    Simudyne SDK on Windows Server 2022

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    Sold by: Simudyne 
    Deployed on AWS
    This product has charges associated with it for the Simudyne SDK software license, usage of the Microsoft Windows operating system - as well as AWS machine costs. Simudyne is a Software Development Kit (SDK) that allows users to build agent-based models. These models empower users to reflect the complexity of the real world. The SDK can build tiny details and general concepts into its models, unifying macro and micro modelling.

    Overview

    This is a repackaged software product wherein additional charges apply for the proprietary Simudyne SDK software libraries on-top of existing AWS instance costs including usage of a Microsoft Windows Server 2022 license.

    The Simudyne SDK can model a wide variety of situations and concepts, including the increase of complexity over time, the emergence of new entities, the way a system responds to feedback, and the process of contagion.

    The Simudyne SDK is running on a Microsoft Windows Windows 2022 Server.

    Other software has been installed included Java, Apache Maven, and JetBrains IntelliJ in order to support the ability to develop and run Simudyne SDK simulations.

    Highlights

    • The modelling core uses objects called agents to mirror the real world at every level of detail. These agents communicate with each other by sending messages. The agents and messages mimic real world interactions, as well as the effects of those interactions over time.
    • Models built with the Simudyne SDK are optimized to run quickly on either single machines or distributed systems. They can be distributed over any number of nodes in larger systems. These nodes can be local hardware or on a cloud, as necessary. When many copies of the same model are running on nodes, the SDK returns a distribution of outcomes combining all successful runs.
    • The Simudyne AMI includes the supplykit module which provides a set of classes for quickly defining and building a network-based simulation of a supply chain. The API sits on top of the core abm modules allowing you to implement supply chain models characterized by distribution facilities, the products and goods they process, and the transport methods involved in driving the flow throughout the network.

    Details

    Delivery method

    Delivery option
    64-bit (x86) Amazon Machine Image (AMI)

    Latest version

    Operating system
    Win2022 21H2

    Deployed on AWS

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    Pricing

    Simudyne SDK on Windows Server 2022

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    Pricing is based on a fixed subscription cost and actual usage of the product. You pay the same amount each billing period for access, plus an additional amount according to how much you consume. The fixed subscription cost is prorated, so you're only charged for the number of days you've been subscribed. Subscriptions have no end date and may be canceled any time.
    Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator  to estimate your infrastructure costs.

    Fixed subscription cost

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    $1,250.00/month

    Usage costs (43)

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    Dimension
    Cost/hour
    r6i.2xlarge
    Recommended
    $1.00
    r6a.32xlarge
    $1.00
    r6in.4xlarge
    $1.00
    r6i.8xlarge
    $1.00
    r7i.8xlarge
    $1.00
    r6i.16xlarge
    $1.00
    r6in.2xlarge
    $1.00
    r7a.24xlarge
    $1.00
    r7a.12xlarge
    $1.00
    r6in.8xlarge
    $1.00

    Vendor refund policy

    No Refunds. All sales are final, and Simudyne does not offer any money-back guarantees. You recognize and agree that you shall not be entitled to a refund for any purchase of the Software and Services under any circumstances.

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    Legal

    Vendor terms and conditions

    Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA) .

    Content disclaimer

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    Usage information

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    Delivery details

    64-bit (x86) Amazon Machine Image (AMI)

    Amazon Machine Image (AMI)

    An AMI is a virtual image that provides the information required to launch an instance. Amazon EC2 (Elastic Compute Cloud) instances are virtual servers on which you can run your applications and workloads, offering varying combinations of CPU, memory, storage, and networking resources. You can launch as many instances from as many different AMIs as you need.

    Additional details

    Usage instructions

    For a full guide: https://docs.simudyne.com/reference/run_deploy/aws 

    1. The first step is to make a few changes to your pom.xml. You'll add the maven and docker shade plugin which allows you to easily package for deployment. For an example of this pom file please refer to: https://docs.simudyne.com/reference/run_deploy/aws#packaging-your-model 

    2. Once your pom has been updated you can the package everything into a single jar (allinone) by running 'mvn clean compile package -s settings.xml' inside the project's folder.

    Next we'll create our instance. We'll outline the steps below, but you can always refer to the Official AWS Documentation for guidance on setting up a new instance.

    1. Open the Amazon EC2 console at https://console.aws.amazon.com/ec2/  and select the orange Launch instance button.

    2. Set a name for your instance, and then select Windows instance and then search for the Simudyne Amazon Machine Image (AMI).

    3. You'll then select the instance type. If you are just trying to deploy for your first time to ensure that everything works, you can use the the r6i selected by default based on your region.

    4. Under Key pair (login) select 'Create new key pair' (unlesss you have already created or associated a key pair, or one has been assigned to you)

    5. Follow the steps to create a key pair, and make sure to select .pem. You will use this file and follow the steps here from AWS: https://docs.aws.amazon.com/AWSEC2/latest/WindowsGuide/connecting_to_windows_instance.html  to create an RDP file and associated password. You will connect by opening this file on Windows after downloading and using the created password,

    6. Under Network Settings unless you have already created a Security Group you will likely want to proceed with the default which will create one for you.

    7. If your goal with this deployment is to access the SDK either via the console, or via a dashboard created with the REST API you will then want to select the boxes allowing for HTTP/HTTPS traffic. However if you are running directly in the command line, then you will not need to choose these options.

    8. Keep the default selections for the other configuration settings for your instance.

    9. Finally from the summary panel on the right when you're ready, choose Launch instance. This will take a few minutes to complete.

    New we'll start adding our files

    1. Once you have created your instance you'll then want to find it's IP Address or Public DNS. If you followed the above steps and were taken to your new instance you should see this on the summary screen for the instance. Otherwise go to https://console.aws.amazon.com/ec2/  and select instances, and finally your desired instance.

    2. First we'll connect to our instance directly via RDP per above. Once you have connected directly via RDP we now will want to move our relevant files to the instance. There are multiple ways you could do this either usage of an online File Share (Dropbox/Sharepoint/etc), or alternatively uploading/downloading via an S3 Bucket

    The files we'll need to move are

    a packaged FatJAR file that you created above a Simudyne license file a simudyneSDK.properties file

    14. Once everything has been moved and you are connected to machine running is as simple as java -jar NAMEOFYOURJARHERE.jar. Of course if this is meant to be a long running process (aka not a batch style run, but something accessible via the web) you will want to use tools to keep the process running after your close your login session. For Windows you can use the javaw instead of java to maintain a long-running process.

    Support

    Vendor support

    The best option for getting support is to email support@simudyne.com 

    Currently there are 2 tiers of support for the Simudyne SDK.

    • Limited to no support is available for those using a free or trial license.
    • Dedicated support-team and developers will be assigned to any tickets created via our email system above if you have purchased a license.
    • During contract discussions businesses may request "modeling support" which involves assigning a member of our team or more likely a 3rd-party partner to said contract to provide support beyond technical, development, or deployment issues in order to help build the underlying model.

    AWS infrastructure support

    AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.

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    Overview

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    AI generated from product descriptions
    Agent-Based Modeling
    Modeling core uses objects called agents to mirror real world interactions and complexity at multiple detail levels
    Distributed Simulation
    Models can be optimized to run quickly on single machines or distributed systems across multiple nodes with combined outcome distribution
    Simulation Modularity
    Includes supply chain simulation module with classes for defining network-based simulations involving distribution facilities, products, and transport methods
    Development Environment
    Integrated development environment with Java, Apache Maven, and JetBrains IntelliJ for simulation development and execution
    Interaction Modeling
    Agents communicate through message-passing mechanisms that simulate real-world interactions and their temporal effects
    Simulation Modeling
    Advanced predictive simulation capabilities for comprehensive product engineering and performance analysis
    Systems Engineering
    Performance-based systems engineering approach for full product lifecycle modeling from concept to operational use
    AI-Driven Engineering
    AI-powered engineering workflows with automated design exploration and optimization techniques
    Cloud-Based Processing
    Scalable cloud-based computational infrastructure for complex engineering simulations
    Digital Thread Integration
    Comprehensive digital thread integration connecting simulation models with product development processes
    Machine Learning Operations (MLOps)
    Integrated workflows and automation for enterprise-level model development, deployment, and monitoring across diverse computational environments
    Tool and Infrastructure Ecosystem
    Comprehensive support for open-source and commercial tools including Jupyter, RStudio, SAS, Anaconda, MATLAB, and distributed computing frameworks like Spark, Ray, Dask, and MPI
    Cloud Deployment Flexibility
    Supports hybrid and multi-cloud deployment options, including public cloud, on-premises, and seamless integration with Amazon SageMaker
    Collaborative Knowledge Management
    Centralized platform for cross-functional collaboration, knowledge sharing, and reproducible data science work across enterprise teams
    Governance and Compliance Framework
    Built-in audit-ready controls, model governance, monitoring, and reproducibility mechanisms for enterprise-level AI risk management

    Contract

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    Standard contract
    No
    No

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