Basic Steps to Onboard a Machine as a Compute Provider on NuNet:

1. Download and install the Device Management Service (DMS): Install the DMS on your machine, which will enable you to connect to the NuNet platform and make your computing resources available for running ML jobs.

2. Download and install the Compute Provider Dashboard (CPD) : Install the Compute Provider Dashboard on your local machine, making it accessible for you to manage and monitor the ML jobs assigned to your machine and receive NTX tokens for your contributions.

3. Onboard your machine: Please refer to our CPD and NuNet CLI user guides for complete details on how to onboard your machine.

   1. Specify your Cardano address: During the DMS onboarding process, provide the Cardano address of the wallet connected to the Compute Provider Dashboard when using the NuNet CLI. This address will be used for receiving NTX token rewards for your deployment contributions. For Public Alpha we are using the PreProd Cardano Network.

   2. Allocate your Resources: Completing the onboarding process for your machine makes it available for receiving ML job requests while connected to the NuNet platform. Onboarding allows providing the necessary information about your machine to NuNet's Distributed Hash Table, or DHT, such as the available CPU or GPU resources, RAM, and other hardware details. This information will be used to match your machine with ML jobs requiring appropriate resources to deploy them.

4. Create Cardano testnet wallet: Make sure you choose the testnet network when setting up your wallet (Eternl or Nami wallet). The name of the specific Cardano testnet would be PreProd Cardano Network.

5. Set up your wallet: Connect your Cardano wallet to the Compute Provider Dashboard at localhost:9992. Ensure that your wallet has enough tADA for transaction fees. This wallet will be used to receive the NTX tokens on the PreProd Cardano Network as a reward for providing compute resources on NuNet. Make sure the wallet address on this wallet is the same as you specified in Step 3.1 when requesting your NTX tokens.

6. Receive ML jobs: Your machine will be automatically assigned ML jobs based on the resources needed and your machine's availability.

7. Receive NTX token rewards: Upon the successful completion of an ML job, you will receive NTX token rewards in your connected wallet. The amount of NTX tokens received will be based on the resources contributed and the terms set by the service provider.

8. Continue providing compute resources: Keep your machine connected to the NuNet platform and continue providing compute resources for ML jobs, earning NTX tokens as a reward for your contributions.

For a more comprehensive overview, you can always refer to our user guides in the Components Installation section. [Add YouTube video on running the Public Alpha as a Compute Provider.]

Basic Steps to Run an ML Job on NuNet as a Service Provider:

1. Download and install the Device Management Service (DMS): Install the DMS on your machine, which will enable you to request ML jobs by connecting you to compute provider machines that are onboarded on the NuNet platform.

2. Download and install the Service Provider Dashboard (SPD): Install the Service Provider Dashboard on your local machine, making it accessible for you to submit and monitor your ML jobs. No sign-up or NuNet account is needed.

3. Obtain compute resources: Open your preferred browser and visit localhost:9991 to specify the type of compute resources you require (CPU or GPU) and the amount of resources needed for your ML job.

4. Define your ML job: Specify your ML model URL, and provide any additional dependencies required for the execution of the ML job.

5. Set up your wallet: Connect your Cardano wallet (Eternl or Nami wallet) to the Service Provider Dashboard. Ensure that your wallet has enough ADA and NTX tokens for running the ML job and covering transaction fees. For Public Alpha we are using the PreProd Cardano Network.

6. Set a budget: Determine the maximum amount of NTX tokens you are willing to spend on the ML job. This budget will be locked in the smart contract on the PreProd Cardano Network as a guarantee for the compute providers.

7. Submit your ML job: Review your job configuration and submit the ML job to the NuNet platform. The platform will automatically match your job with suitable compute providers based on the resources needed.

8. Monitor your job: Track the progress of your ML job through the Service Provider Dashboard. You can check the log outputs of your job every 2 minutes since your job begins. Please wait for around 5 minutes for the first log to appear when deploying new ML jobs.

9. Review the results: Once the ML job is completed, you can download the output data and review the results. The locked NTX tokens will be released and distributed as a reward to the compute providers who contributed resources to your job.

10. Repeat the process (optional): If you have more ML jobs to run, you can follow the same steps to execute them on the NuNet platform, utilizing its decentralized computing resources.

[Add YouTube video on running the Public Alpha as a Service Provider.]

Here's a step-by-step guide to setting up a new Nami Wallet (Chrome extension), setting it to the preprod Ada network, noting the receiving address, and backing up your mnemonics:

Step 1: Install the Nami Wallet browser extension

1. Open your preferred browser.

2. Visit the official Nami Wallet website at https://namiwallet.io/.

3. Click on the "Download" or "Get Started" button (or similar, depending on the site's design).

4. You will be redirected to the official download page for the Nami Wallet extension. Click on the download button for your specific browser (Chrome, Firefox, etc.).

5. Confirm the installation by following your browser's prompts.

Step 2: Set up a new Nami Wallet

1. Click on the Nami Wallet icon in the top-right corner of your Chrome browser.

2. Read and accept the terms and conditions.

3. You will be presented with two options: "Create new wallet" and "Restore wallet." Click on "Create new wallet."

4. Set a strong password for your wallet and click "Next."

Step 3: Backup your mnemonics (recovery phrase)

1. The wallet will now generate a 24-word recovery phrase. Write it down and store it in a safe place, as you will need it to restore your wallet if needed. Click "Next."

2. Verify your recovery phrase by selecting the words in the correct order, and then click "Confirm."

3. For extra security, consider backing up your recovery phrase in multiple secure locations, such as on paper, in a password manager, or in an encrypted file stored on a secure device or cloud storage service.

Step 4: Switch to the preprod Ada network

1. Click on the round icon on the top-right corner of your Nami Wallet to access the settings.

2. Under "Network," click on the dropdown menu and select "Preprod."

3. Click "Save" to confirm your selection.

Step 5: Note the receiving address

1. Click on the "Receive" tab in your Nami Wallet.

2. Your receiving address will be displayed in the form of a QR code and a text address.

3. Click on the "Copy" button next to the text address to copy it to your clipboard.

4. Save the receiving address in a secure location, as you will need it to receive Ada on the preprod network.

Step 6: Add a Collateral Amount

1. Make sure you have atleast 5 tADA

2. Select the round icon on the top right corner

3. Select Collateral from the drop-down menu

4. Add the collateral amount (around 5 tADA) in order to interact with the NTX smart contract on Cardano

You have now successfully set up a new Nami Wallet, connected it to the preprod Ada network, backed up your mnemonics, and noted the receiving address. You can now use this address to receive and send transactions on the preprod network.

This step-by-step guide will walk you through the process of setting up a new Eternl wallet as a Chrome extension, backing up your mnemonics, setting it to single address mode, connecting it to the preprod Ada network, and noting the receiving address.

Step 1: Install the Eternl Wallet Chrome Extension

1. Open your preferred web browser on your computer.

2. Navigate to the official Eternl Wallet website by visiting https://eternl.io/app/mainnet/welcome.

3. Follow the prompts on the page to install the Eternl Wallet.

4. Once the wallet is installed, you should see the Eternl Wallet icon on your browser's extension bar.

5. Click the icon to open the Eternl wallet.

Step 2: Connect to the preprod Ada network

• In the main page, locate the "mainnet" option in purple at the bottom right corner.

• Click on it and switch the network to select "Pre-Production Testnet".

Step 3: Set up a new wallet

1. Click on the Eternl Wallet icon in your browser to open the extension.

2. Select "Create a new wallet."

3. Enter a strong and unique password to encrypt your wallet. Make sure to remember this password, as it will be required to access your wallet.

4. Click "Next" and follow the on-screen instructions to complete the wallet creation process.

Step 4: Backup your mnemonics

1. After creating your wallet, you'll be presented with a 24-word mnemonic phrase. This phrase is essential for recovering your wallet in case you lose access to your device or need to restore your wallet on another device.

2. Write down the mnemonic (seed) phrase on a piece of paper and store it in a secure location, such as a safe deposit box. Alternatively, you can store it digitally in a password-protected file or encrypted storage medium.

3. Confirm that you've safely stored your mnemonic (seed) phrase by selecting "I have written it down" and clicking "Next."

4. You will be prompted to re-enter your mnemonic (seed) phrase.

5. Click "Save" to apply the changes.

Step 5: Note the receiving address

1. Click on the "Receive" tab within the Eternl Wallet extension.

2. You'll see your wallet's receiving address displayed as both a string of characters and a QR code. This is the address you'll use to receive funds on the preprod Ada network.

3. Copy the address by clicking on the copy button next to it or take a screenshot of the QR code.

Step 6: Set wallet to single address mode

1. Click on the gear icon in the top right corner of the Eternl Wallet name to access the settings menu.

2. Locate the "Single address mode" option and toggle this option to enable it (by default, it's disabled).

3. Enter your receiving address and click on "Save".

Step 7: Enable dApp connection

1. Toggle the dApp button as you see on the Eternl screen

Step 8: Add a Collateral Amount

1. Make sure you have at least 5 tADA

2. Select the gear icon on the right-side of your wallet

3. Select Collateral from the Settings menu

4. Set Collateral amount

5. Toggle to Enable Collateral

Your Eternl Wallet is now set up with the single address mode, connected to the preprod Ada network, and you have noted the receiving address. Remember to store your mnemonic phrase and password securely, as they will be required to access your wallet and recover it if necessary.

NuNet is building a globally decentralized computing framework that combines latent computing power of independently owned compute devices across the globe into a dynamic marketplace of compute resources, individually rewarded via a multi chain Tokenomic ecosystem based on the NuNet Utility Token (NTX).

Being a multi-sided platform, NuNet will support a variety of decentralized services that are defined by use cases. These use cases will be decided on by both community and partnerships, they currently range from distributed machine learning jobs to decentralized Cardano nodes. The requirements of the use cases will drive the development of the core platform, this will ensure features that support actual use cases are prioritized and add utility to the network.

NuNet is part of the SingularityNET eco system and was originally intended to support the decentralized operation of their global AI market place. NuNet has also sought to integrate with other decentralized platforms, the diagram below shows some of the platforms we could integrate with as we build out our vision. As with the use cases, as partnerships are formed with other platforms the core infrastructure will be developed in order to support interoperability.

This is the description of the current NuNet architecture for testing the ML use case. The business goal of implementing this use case is to allow users to onboard both latent GPU and CPU resources to be used by service providers to run compute jobs (in this case, ML jobs) on the NuNet platform and be compensated in NTX (NuNet’s Utility Token) for the job. For Public Alpha we are using the PreProd Cardano Network.

These are the NuNet core elements for the Public Alpha:

Decentralized Components:

• In NuNet, the term service provider refers to the individual or group that wants to run a job on NuNet’s decentralized community hardware.

• The term compute provider refers to an individual on the community who has on-boarded their devices onto the NuNet platform.

• The term DMS refers to Device Management Service, which is essentially the NuNet platform itself. It is the lightweight peer-to-peer connection between services and providers.

• Service Provider Dashboard is the application that service providers use to deploy jobs on NuNet and to lock funds on the smart contract.

• Compute Provider Dashboard is the application that compute providers use to claim their tokens for work carried out on their devices.

Centralized Components:

• ​​NuNet Oracle and Haskell Server are components responsible for locking funds on the smart contract and validating results of compute jobs prior to the withdraw transaction. For Public Alpha we implemented a smart contract on the PreProd Cardano Network.

• Control Server is responsible to any control functionalities necessary on NuNet. On this use case, it servers the captcha functionality.

• NuNet Network Status displays real-time statistics about all computational processes executed on the network and their telemetry information.

Estimating Resource Types

In order to effectively allocate resources for machine learning/computational tasks on the NuNet platform, it is essential to categorize the different resource types available. We have classified resource types into three main categories based on the capabilities of the machines and GPUs:

1. Low Resource Usage: This category represents low-end machines with low-end GPUs.

2. Moderate Resource Usage: This category represents medium-end machines with medium-end GPUs.

3. High Resource Usage: This category represents high-end machines with high-end GPUs.

The outlines a function called estimate_resource, which is designed to estimate the resource parameters for different categories of machines based on their resource usage type. The function accepts a single input, resource_usage, which can take on one of three possible values: "Low", "Moderate", or "High".

The function then checks the value of resource_usage and, depending on the category, sets the minimum and maximum values for the CPU, RAM, and GPU VRAM, as well as the estimated levels of GPU power and GPU usage. These values are assigned based on the specific resource usage category:

1. For "Low" resource usage, the function sets lower values for CPU, RAM, and GPU VRAM, as well as lower GPU power and usage levels. This category represents low-end machines with low-end GPUs.

2. For "Moderate" resource usage, the function sets medium values for CPU, RAM, and GPU VRAM, as well as medium GPU power and usage levels. This category represents medium-end machines with medium-end GPUs.

3. For "High" resource usage, the function sets higher values for CPU, RAM, and GPU VRAM, as well as higher GPU power and usage levels. This category represents high-end machines with high-end GPUs.

Finally, the function returns a dictionary containing all the estimated parameters for the given resource usage category. By using this function, the NuNet platform can estimate resource parameters for machines in different categories, helping to efficiently allocate resources for machine learning tasks based on the specific requirements of each job.

Calculating Resource Prices

For each resource type, resource prices are calculated in the NuNet ML on GPU API. These functions help estimate the cost of using different types of machines for executing machine learning tasks. The process involves the Estimated Static NTX.

1. Estimated Static NTX is calculated using the user's input for the estimated execution time of the task and the chosen resource type (Low, Moderate, or High). The function Calculate_Static_NTX_GPU(resource_usage) calculates this value based on these inputs and returns the Estimated NTX.

Reporting Task Results

1. Upload_Compute_Job_Result() is a function that regularly updates the task's progress. It runs in a loop and performs the following steps every 2 minutes until the job is completed or the off-chain transaction of the Estimated Static NTX is done:

   • Wait for 2 minutes.

   • Save the machine learning log output as a file that can be appended with new information.

   • Upload the file to the cloud, making sure that only an authenticated user can access it.

   This function allows users to keep track of their tasks and view intermediate results during the execution.

2. Send_Answer_Message() is a function that provides a unique link to the WebApp, which helps users track their tasks. It performs the following steps:

   • Retrieve a unique URL (permalink) for each machine learning job.

   • Send the permalink from the Decentralized Management System (DMS) to the WebApp as an answer message.

   This function enables users to access their task's progress updates and results using the provided link.

In summary, these functions work together to ensure users can monitor their machine learning tasks' progress and access the results in a user-friendly and organized manner.

1. Download and install a testnet wallet: You can choose from Nami or Eternl. Make sure to select the testnet network option during the installation process.

2. Get testnet ADA: You can obtain testnet ADA from a testnet faucet, which is a service that provides free testnet tokens. You can learn more about .

3. Explore the testnet: Once you have your testnet wallet set up and testnet ADA in your account, you can start exploring the testnet network. You can send and receive transactions, create test tokens, and interact with smart contracts.

4. Join a testnet community: You can join the Cardano testnet community on forums like Reddit or Discord to connect with other developers and users, ask questions, and share your experiences.

5. Test our service/compute provider dashboards: The Cardano testnet is an ideal environment to test the dashboards before deploying them on the mainnet. You can use the testnet to identify bugs, test functionalities, and optimize our applications' performance.

6. Keep your testnet wallet safe: Although testnet tokens have no real-world value, you should still keep your testnet wallet secure. Make sure to use a strong password and never share your private keys or seed phrases with anyone.

Before running the tests, consider the following:

• If you are a compute provider, please make sure to backup any important data you might have before onboarding your machine as this is a testing phase.

• We use centralized components (Oracle, Control Server, Stats Network) running on our servers. Check if any configuration changes are needed to use these components.

• Use Eternl or Nami wallets for these tests. Make sure you have enabled single address mode if using Eternl.

• Ensure you have mNTX and tADA in your wallet. To get mNTX on the PreProd Cardano network, follow the mNTX Faucet Guide. For tADA, follow the tADA Faucet Guide.

• For a basic outline on how to use a Cardano testnet, you can use the Cardano Testnet Guide. The Cardano address provided during DMS onboarding should match the wallet connected to the dashboard.

• Note that compute providers would have add a collateral amount of around 5 tADA to interact with the NTX smart contract when using the Computer Provider Dashboard (CPD).

• To check if one machine can see the other, follow the instructions in the respective component documentation.

What is the Device Management Service (DMS)?

A device management service or DMS is a program that helps users run various computational services, including machine learning (ML) jobs, on a compute provider machine, based on an NTX token request system on NuNet. In simple terms, it connects users who want to perform computational tasks to powerful CPU/GPU enabled computers that can handle these tasks. The purpose of the DMS is to connect users on NuNet, allow them to run any service (not only ML jobs) and be rewarded for it.

The NTX token is a digital cryptographic asset available on the Cardano and Ethereum blockchain as a smart contract. However, for the current use case of running machine learning jobs, only the Cardano blockchain is being used. Users request and allocate resources for computational jobs through a Service Provider Dashboard. Compute providers receive the NTX tokens based on the jobs through a Compute Provider Dashboard.

Please note that the dashboards are not components of NuNet's core architecture. Both these components have been developed to perform the current use case that is to run ML jobs on compute providers machines.

Here's a step-by-step explanation:

1. Users have computational services they want to run. These services often require a lot of computing power, which may not be available on their personal devices.

2. Compute provider machines are powerful computers designed to handle resource-intensive tasks like machine learning jobs.

3. The device management service acts as a bridge, connecting users with these compute provider machines.

4. Users specify resources and job requirements through a webapp interface, and request access to the compute provider machines by sending mNTX tokens. mNTX acts as a digital ticket, granting users access to the resources they need.

5. The device management service receives the job request after verifying the authenticity of the mNTX transaction through an Oracle.

6. Once received, the DMS allocates the necessary resources on the compute provider machine to run the user's job.

7. The user's job is executed on the provider's machine, and the results are sent back to the user.

In summary, a device management service simplifies the process of running machine learning jobs on powerful computers. Users can easily request access to these resources with NTX tokens, allowing them to complete their tasks efficiently and effectively.

How to Install the Device Management Service?

Before going through the installation process, let's take a quick look at the system requirements and other things to keep in mind.

Installing via Virtual Machines or Windows Subsystem (WSL) for Linux

When using a VM or WSL, using Ubuntu 20.04 is highly recommended.

Things to keep in mind for VMs

• Skip doing an unattended installation for the new Ubuntu VM as it might not add the user with administrative privileges.

• Enable Guest Additions when installing the VM (VirtualBox only).

• Always change the default NAT network setting to Bridged before booting the VM.

• Install Extension Pack if on VirtualBox (recommended)

• Install VMware Tools if on VMware (recommended)

• ML on GPU jobs on VMs are not supported

Things to keep in mind for WSLs

• Install WSL through the Windows Store.

• Install the Update KB5020030 (Windows 10 only)

• Install Ubuntu 20.04 through WSL

• Enable systemd on Ubuntu WSL

• ML Jobs begun on Linux cannot be resumed on WSL

• Make sure virtualization is enabled in the system BIOS

Though it is possible to run ML jobs on Windows machines with WSL, using Ubuntu 20.04 natively is highly recommended. The reason being our development is completely based around the Linux operating system. Also, the system requirements when using WSL would increase by at least around 25%.

If you are using a dual boot machine, make sure you use the wsl --shutdown command before shutting down Windows and running Linux for ML jobs. Also, ensure your Windows machine is not in a hibernated state when you reboot into Linux.

CPU-only machines

Minimum System requirements

We only require for you to specify CPU (MHz x no. of cores) and RAM but your system must meet at least the following set of requirements before you decide to onboard it:

• CPU - 2 GHz

• RAM - 4 GB

• Free Disk Space - 10 GB

• Internet Download/Upload Speed - 4 Mbps / 0.5 MBps

If the above CPU has 4 cores, your available CPU would be around 8000 MHz. So if you want to onboard half your CPU and RAM on NuNet, you can specify 4000 MHz CPU and 2000 MB RAM.

Recommended System requirements

• CPU - 3.5 GHz

• RAM - 8-16 GB

• Free Disk Space - 20 GB

• Internet Download/Upload Speed - 10 Mbps / 1.25 MBps

GPU Machines

Minimum System Requirements

• CPU - 3 GHz

• RAM - 8 GB

• NVIDIA GPU - 4 GB VRAM

• Free Disk Space - 50 GB

• Internet Download/Upload Speed - 50 Mbps

Recommended System requirements

• CPU - 4 GHz

• RAM - 16-32 GB

• NVIDIA GPU - 8-12 GB VRAM

• Free Disk Space - 100 GB

• Internet Download/Upload Speed - 100 Mbps

Here's a step by step process to install the device management service (DMS) on a compute provider machine:

1. Download the DMS package:

   Download the latest version with this command: (note please use the second option for now until we fix the auto linked latest version)

# do not use currently wget https://d.nunet.io/nunet-dms-latest.deb -O nunet-dms-latest.deb

wget https://d.nunet.io/nunet-dms_0.4.159_amd64.deb -O nunet-dms-latest.deb

1. Install DMS:

   DMS has some dependencies, but they'll be installed automatically during the installation process.

​ Open a terminal and navigate to the directory where you downloaded the DMS package (skip this step if you used the wget command above). Install the DMS with this command:

sudo apt update && sudo apt install ./nunet-dms-latest.deb -y

​ If the installation fails, try these commands instead:

sudo dpkg -i nunet-dms-latest.deb
sudo apt -f install -y

If you see a "Permission denied" error, don't worry, it's just a notice. Proceed to the next step.

Check if DMS is running: Look for "/usr/bin/nunet-dms" in the output of this command:

ps aux | grep nunet-dms

If it's not running, submit a bug report with the error messages. Here are the contribution guidelines.

1. Uninstall DMS (if needed):

To remove DMS, use this command:

sudo apt remove nunet-dms

To download and install a new DMS package, repeat steps 1 and 2.

1. Completely remove DMS (if needed):

To fully uninstall and stop DMS, use either of these commands:

sudo apt purge nunet-dms

or

sudo dpkg --purge nunet-dms

1. Update DMS:

To update the DMS to the latest version, follow these steps in the given sequence:

​ a. Uninstall the current DMS (Step 3)

b. Download the latest DMS package (Step 1) ​

c. Install the new DMS package (Step 2)

Public Alpha is released with the ML use case, which allows users to run simple open source machine learning training on NuNet on-boarded computers and pay for the compute in NTX. We can use widely-used machine learning libraries, such as TensorFlow, PyTorch, and scikit-learn, ensuring that users can effortlessly integrate their preferred tools and frameworks. Moreover, the platform provides the flexibility to run jobs on either CPUs or GPUs, catering to various computational needs and budget constraints.

Designed with a user-centric approach, the Service Provider Dashboard has a simple interface that allows users to easily submit their ML models, define resource usage based on their job requirements, and keep track of their job's progress in real-time. This level of transparency and control empowers users to manage their machine learning jobs effectively and efficiently, ultimately facilitating and accelerating the development & deployment of innovative AI solutions.

For Public Alpha we implemented a smart contract on the PreProd Cardano Network to lock service provider NTX funds and reward compute provider users for the use of their resources.

You need to choose one role to play on this use case: you can be a that will requires to run a ML job on NuNet’s decentralized community hardware or you can be a who has on-boarded their devices onto the NuNet platform and will be compensated in NTX (NuNet’s Utility Token) for running the ML job requested by some service provider.

During this testing you can contribute in NuNet's development by reporting bugs and suggesting improvements. Please, refer to this documentation about the contribution guidelines:

You can also connect with us on Discord at:

To get mNTX (mock NTX) on the PreProd Cardano network, follow the below instructions on how to use the mNTX faucet:

• To use the NTX faucet on the Cardano Blockchain, follow these basic instructions:

  1. Create a Cardano wallet: If you don't have a Cardano wallet already on the PreProd Cardano network, create one using Eternl or Nami wallet applications. Make sure to securely store your seed phrase, as it is crucial for recovering your wallet.

  2. Find your wallet address: After creating your wallet double check you are on the preprod network, locate your Cardano wallet address. It typically starts with "addr_test1" and is followed by a long string of alphanumeric characters. in most wallets you find it by clicking on receive. Copy this address to use it in the next step.

  3. Join and Whitelist: and head to the relevant testing stage. Fill out the Google Form linked with your wallet address, Discord Name and GitLab name. We will use the wallet address provided to reward contributions both on Discord and GitLab. *Please note this may take some hours to receive mNTX to discourage spamming.

  4. Access the NTX faucet: Visit the NTX faucet's website provided by the NuNet team. . Note only whitelisted addresses will be able to receive tokens.

  5. Connect your Cardano wallet: click the connect wallet button and select the wallet you would like to use from the dropdown then click the mint mNTX button. (NOTE: please doublecheck this is the wallet you entered in the google form and that it is on the cardano preprod testnet)

  6. Sign the transaction: When you click the mint mNTX button it should open your wallet and ask you to sign a transaction. (note you should have some test ADA in your wallet before you do this if you dont have it already go )

  7. Faucet response: if it all went well then you should see a transaction hash, you can copy this and check on you may also receive a message saying you are not whitelisted, if this is the case please ensure you submitted the google form and have received notification that you were successfully whitelisted in discord. If you are still getting some sort of error message please go to discord for help.

  8. Check your wallet balance: After submitting your request, wait for a few minutes and then check your wallet application to confirm the receipt of mNTX tokens. It might take some time for the transaction to be processed, depending on the network's congestion.

  9. Use your mNTX tokens: You can now use the mNTX tokens in your wallet for testing or participating in the NuNet platform's services.

  Keep in mind that the mNTX tokens received from the faucet are intended for testing and development purposes. They may not hold any real-world value outside of the test environment or PreProd Cardano network.

Here's a basic step-by-step guide on how to use a tADA (testnet ADA) faucet and obtain tADA tokens:

1. Go to the Cardano Developer Portal: The Cardano Developer Portal is the official resource for Cardano developers and contains information on how to use the Cardano testnet, including a list of tADA faucets.

2. Get tADA from a faucet: You can obtain tADA from the Cardano Developer Portal that includes a Cardano Faucet. For more instructions on how to use one, see our Cardano Testnet Guide.

3. Enter your testnet wallet address: Copy and paste your testnet wallet address into the field provided on the tADA faucet website. Make sure you are using a testnet wallet address and not a mainnet address, as the two are not interchangeable.

4. Solve the captcha: Some tADA faucets require you to solve a captcha or complete a task to prove you are a real person and not a bot.

5. Request tADA: Click on the "Request tADA" button on the faucet website. Your tADA tokens should be sent to your testnet wallet address within a few minutes.

6. Check your testnet wallet balance: Open your testnet wallet and check your balance to make sure the tADA tokens have been successfully deposited.

Remember, tADA tokens are not real ADA tokens and have no real-world value. They are only intended for testing purposes on the Cardano testnet. Additionally, some tADA faucets have limits on how many tokens you can request per day or per IP address, so be sure to check the faucet's rules and guidelines before requesting tokens.

NuNet Network Status is live at , displaying real-time statistics about all computational processes executed on the network and their telemetry information. It tracks successful and failed processes, registered consumers, compute devices, and the amount of NuNet tokens paid by service providers to compute providers.

Dials on the right show available resources and current utilization percentages. Graphs on the left display resource availability over time.

The table at the bottom on the left displays the "heartbeats" (these are updated every minute) from currently active DMS nodes. You can expand the event to see the details.

The hearbeats list the onboarded ram and cpu for each dms so can be used to calculate the currenty available resources.

• RAM: Memory used by a process, measured in megabyte seconds (MBs). The metric is calculated by adding spot RAM usages sampled every second for the entire time of execution, indicating actual memory consumption.

• CPU: Processor work used to complete a process, measured in MTicks (million ticks), showing CPU time used during execution.

• ID: lists the peer id for the heartbeat. This can be used to filter the dashboard to show events from a specific peer, see below.

The table at the bottom on the right hand side displays telemetry metrics for each process running on NuNet. You can expand these events to reveal the details.

You can see job status, time it took to run, ram, cpu and network used and the amount of ntx.