: offers the , a tool to help Data Scientists automate their weekly submission pipeline for the Numerai tournament. This CLI configures a in and automatically deploys your model to it. tl;dr Numerai numerai-cli Numerai Prediction Node Amazon Web Services (AWS) . 🚀 This guide describes how I set up my own weekly submission pipeline from scratch, using for free Microsoft Azure and python 🤖 Just give me the code: https://github.com/papaemman/azure-functions-with-python What is Numerai? is a quant hedge founded in San Francisco, CA, in 2015, built on thousands of crowdsourced machine learning models. Every week, Numerai gives away data so that Data Scientists worldwide have free, hedge-fund quality data to develop their Machine Learning (ML) / Deep Learning (DL) models — data they would otherwise not have access to. Numerai At the beginning of each round, competitors can stake (a cryptocurrency issued by Numerai) on the predictions they submit if they want to get paid (or "burnt") based on the predictive accuracy of their models. Staking on a model is fundamentally a type of "bet" competitors take towards the performance of their own model's predictions. NMR tokens Numerai Compute CLI Competing in Numerai is an ongoing, never-ending challenge because competitors need to download the new data every week and submit their predictions based on them. is a framework aiming to help participants automate their weekly submission workflow. Using the , participants can "provision" their cloud infrastructure in and deploy their pre-trained models as a Prediction Node that can Numerai Compute numerai-cli Amazon Web Services (AWS) be triggered by Numerai every weekend download new tournament data run the ML models and upload predictions to Numerai The configures a Numerai Prediction Node in Amazon Web Services (AWS). This solution is architected to cost less than on average, but actual costs may vary. numerai-cli $5/month You need 4 things to use Numerai CLI: , , , and . Docker Python Numerai API Keys AWS API Keys Why not just use Numerai Compute? 1. Cost Last week was tough for the crypto market. carried away the whole crypto market. Currently (May 2022), the NMR token has a price of around . Therefore, saving per month and investing them in buying more NMR to stake in your models is an appealing idea. The massive crash of LUNA 13$ 5$ 2. Learn something new When building something from scratch, you have the opportunity to understand how it works under the hood. Last year, I deep-dived into Microsoft Azure cloud technologies, and I wanted to test if my skills were enough to build a real-world solution. 4. Don't have an AWS account Many professionals prefer to work with other Cloud providers than AWS, such as or Google Cloud Platform. Setting up a new AWS account, adding billing information, and finding your way into the AWS console might seem like a lot of work. *, this guide is for you.* Microsoft Azure If you prefer to use Azure Goal Automate my weekly submission pipeline, for the Numerai Tournament, using Azure and python, with zero cost. Azure offers many services adequate to solve this task, but I decided to use Azure Functions after some research. is a cloud service available on-demand that provides all the continually updated infrastructure and resources needed to run your applications. You focus on the pieces of code that matter most to you, and Functions handles the rest. Functions provides serverless compute for Azure. Azure Functions Going for a is a great way to because you don't need to pay for the infrastructure you don't use. And if you already have the code you want to run, setting up an Azure function is . Also, Azure offers a which is more than enough for this project's scope. serverless architecture save money and time easy free tier, Thus my goal was to create an Azure Function that will: ⏲️ be triggered every Sunday 🔋interact with the Numerai API to download numerai_live_data and upload my predictions 💾 interact with Azure Storage to read and store and data pre-trained ML/DL models numerai_live_data numerai_predictions 📥 send a fun email notification after the submission 😊 allow me to enjoy my weekend MS Azure Architecture Design Technical Implementation Prerequisites Azure Account and VSCode Azure Extension Python 3.8 Azure Functions Core Tools Azure Storage Explorer Numerai API Keys Trained ML/DL models and inference python code Setup 1. Setup all prerequisites Download and install the required software Create your accounts in Azure and Numerai 2. Open VSCode and create a new local Azure function project While you don't have any workspace opened in VSCode, go to the extension, find and select _Create New Project…- S_elect , , and a to create a virtual environment- Select the template, give a name to the function, and set up the timer to run every [* 0 0 * * SUN]. You don't need to set up the function to run in short time intervals. Azure functions support " . Azure FUNCTIONS Directory Language (Python) python interpreter Time Trigger Sunday Execute Function now…" to trigger it manually within VSCode for testing purposes 3. Then, the VSCode will create a with a time-trigger function sample code. automatically Local project ## Azure function project codebase structure
.
├── .venv                     # Python Virtual environment
├── .vscode                   # Configuration options for VSCode
├── host.json                 # Configuration options that affect all functions in a function app instance
├── local.settings.json       # Maintains settings used when running functions locally.These settings aren't used when running in Azure
|
├── AzureFunction_1
│ ├── function.json           # Azure Function Settings
│ ├── __init__.py             # Python Code
│ └── readme.md               # Documentation
|
├── AzureFunction_2
│ ├── function.json           
│ ├── __init__.py             
│ └── readme.md               
|
└── requirements.txt          # Package dependencies (Open VSCode and Press F5 or 4. Run the Function locally Run →Start Debugging) To debug, you must select a storage account for internal use by the Azure Functions runtime: Select a , create a , create a and select a for resources. Keep in mind that this is a . subscription new storage account new resource group, location temporary resource group Go to extension, find and go to . Find the function, right-click, and select Azure FUNCTIONS Local Project Execute Function now… Examine functions' logs Stop the function (ctrl+c) Delete the resource group Important note ⚠️ ”, update the extensionBundle “version”: “[2.*, 4.0.0)” If you get an error “connect ECONNREFUSED 127.0.0.1:9091 to version , in host.json file. 5. Publish the project to Azure Go to extension_,_ find and select Azure FUNCTIONS, Deploy to Function App… Select a and subscription create a new function App Select a and for the new resource runtime stack location This will create a new resource group with an App Service Plan, a Function App, a Storage Account, and an Application Insights service. 6. Run the Function in Azure Go to extension, find , open the related and find the function app. Azure FUNCTIONS subscription Open the function app, find the specific function and select This will run the function to the Azure environment. Execute Function now…. You can see the function logs in the Live metrics tab using the Application Insights resource. 7. Download the function app settings Open VSCode and download remote settings in the Local environment for the function App ( and select Azure Functions: Download Remote Settings) F1 This will update the file with info about App service Plan, Function App, Storage Account, and Application Insights. local.settings.json 8. Setup the Azure Storage account. Open the connect with your Azure account and find the related storage account inside the newly created resource group. Azure Storage Explorer, Create a new Blob container called and upload all your pre-trained ML models there. production-models Create a new Blob container called to store numerai_live_data for every round. live-data Create a new Blob container called to store numerai_predictions for every round. predictions-files 9. Purchase the Twilio SendGrid SaaS offering from Microsoft Azure Marketplace To send a notification email every time the function triggered and submitted predictions, I decided to use the Twilio SendGrid's Email API Purchase the Twilio SendGrid offer (free tier) from Microsoft Azure Marketplace and configure your account on their platform. Get API keys from Twilio SendGrid 10. Configure Function App Having the and hardcoded in the source code is a terrible practice. Azure Functions offers a dedicated Function's App configuration to store environmental variables and secrets. Numerai API Keys Twillio Seng Grid API keys Open the , find the and go to . The complete set of Environment Variables is stored in the Application settings by default. This helps developers define input/output bindings with other resources in Azure. Azure portal Function App service Configuration Create 2 new application settings and store Numeai API Keys and SendGridKeys NumeaiAPIKeys →secret_key=***;public_id=***” SendGridKeyss →*** Open VSCode and re-download remote settings to the local environment ( and ) to get the new Application Settings into the file. F1 Azure Functions: Download Remote Settings local.settings.json 11. Write the Python code Now it's time to write (or copy-paste) the python code responsible for reading the numerai live data & pre-trained ML/DL models and submitting predictions using Numerai API. The input and output configurations (configurations and paths to load and store files) are the only thing you have to modify in your previous python code (running locally) to run it as an Azure Function. Instead of local paths, you should use the newly created Storage Account. For this, you need to use the . Azure Storage Python SDK Go to the file and replace your python code. .py init 12. Run the Function locally and verify that it is working Run the locally ( F5 or Run → Start Debugging), go to A_zure extension,_ find the Function in , and select ` Function Local Project ExecutFunctionon now… Check Function's logs and the connection with the Azure Storage Account Stop the Function (ctrl+c) 13. Redeploy and verify the updated App Go to extension, find , and select Azure FUNCTIONS Deploy to function App… After the deployment completes, find the Function under the respective subscription in VSCode, right-click to function, and select Execute Function now… Check Function's logs in the Metrics tab for this Function. Conclusions Last Sunday, I woke up to this fun email from myself, letting me know that everything went as planned. The Azure Function was triggered on Sunday at 00:00:00 UTC, the Numerai submissions were successful, and I was able to enjoy my day! 🌞 Email Notification for successful submission PS. Check the awesome quote at the end. I added a random quote generator inside the function creating the email notification just to spark things up ✨ Get in touch I hope you liked this guide. If you have any questions or just want to introduce yourself, don't hesitate to reach out! Panagiotis Papaemmanouil 📧 papaemman.pan@gmail.com 💬 https://www.linkedin.com/in/panagiotis-papaemmanouil/ 🌐 https://papaemman.com/ 🔥 Check my under the name Numerai Models PAPAEMMAN https://numer.ai/papaemman1 Also Published here

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Automate Submissions for the Numerai Tournament Using Azure Functions and Python

Automate Submissions for the Numerai Tournament Using Azure Functions and Python

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