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Remote-SSH, straight from VS Code or Cursor. There are no SSH keys to manage, no VMs to provision, and no local dependencies to maintain. The environment is a Snowflake Notebook service running on Snowpark Container Services, preloaded with Python, Jupyter, XGBoost, scikit-learn, and the Snowflake libraries, reachable from your editor as if it were localhost.\u003C/p\u003E\n","\u003Cp\u003EIn this Quickstart, we'll build an end-to-end ML workflow against Snowflake compute, entirely from a local editor. The scenario is the following: you're an ML engineer on the Tasty Bytes team. Tasty Bytes runs food trucks in cities across the globe, and you've been asked to forecast daily sales per truck location. Weather clearly moves food-truck sales – so we'll enrich internal orders with a Marketplace weather share, feature-engineer with help from CoCo, train an XGBoost regressor, and log it to the Snowflake Model Registry, all from VS Code.\u003C/p\u003E\n","\u003Cp\u003ELet's get started!\u003C/p\u003E\n","\u003Ch3\u003EWhat You'll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to create a Snowflake-backed remote development environment and connect to it over Remote-SSH from VS Code or Cursor\u003C/li\u003E\u003Cli\u003EHow to run a Jupyter notebook that mixes Python and SQL cells using the Snowflake Kernel (Python + SQL) – the same kernel that powers Notebooks in Workspaces\u003C/li\u003E\u003Cli\u003EHow to use Cortex Code (CoCo) inside the remote SSH session to accelerate feature engineering and analysis\u003C/li\u003E\u003Cli\u003EHow to run a plain Python script (.py file) against the remote environment, beyond notebook cells\u003C/li\u003E\u003Cli\u003EHow to train an XGBoost sales-forecasting model and log it to the Snowflake Model Registry\u003C/li\u003E\u003Cli\u003EHow to clone and work with a private GitHub repo from the remote environment – using your editor's GitHub sign-in, or a Snowflake secret for editor-independent auth\u003C/li\u003E\u003Cli\u003EHow to suspend and resume the remote environment while preserving cloned repos, installed packages, and trained artifacts\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA Snowflake account (Enterprise or higher) with Marketplace access and the \u003Ccode\u003EENABLE_NOTEBOOK_SERVICE_REMOTE_VS_CODE_ACCESS\u003C/code\u003E parameter set to \u003Ccode\u003ETRUE\u003C/code\u003E (on by default).\u003C/li\u003E\u003Cli\u003EA role with \u003Ccode\u003EUSAGE\u003C/code\u003E on a compute pool that allows the \u003Ccode\u003ENOTEBOOK\u003C/code\u003E workload type, plus permission to use external access integrations and secrets.\u003C/li\u003E\u003Cli\u003EVS Code or Cursor installed locally, and a GitHub account.\u003C/li\u003E\u003Cli\u003EWorking knowledge of Python, Jupyter notebooks, Git, and basic SQL.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Need\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA Snowflake account with a role that can create and run notebook services (\u003Ccode\u003EUSAGE\u003C/code\u003E on a compute pool that allows the \u003Ccode\u003ENOTEBOOK\u003C/code\u003E workload type; permission to use external access integrations and secrets). If you don't have one, \u003Ca href=\"https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides\"\u003Esign up for a free 30-day trial\u003C/a\u003E. Select Enterprise edition.\u003C/li\u003E\u003Cli\u003EThe account parameter \u003Ccode\u003EENABLE_NOTEBOOK_SERVICE_REMOTE_VS_CODE_ACCESS\u003C/code\u003E set to \u003Ccode\u003ETRUE\u003C/code\u003E. It's on by default; an account admin can confirm.\u003C/li\u003E\u003Cli\u003EAccess to the Snowflake Marketplace so you can acquire the free Pelmorex Weather Source: Frostbyte share (\u003Cstrong\u003Esetup.sql\u003C/strong\u003E acquires it programmatically – you need Marketplace access enabled on your account).\u003C/li\u003E\u003Cli\u003EVS Code or Cursor installed locally.\u003C/li\u003E\u003Cli\u003EThe Microsoft Remote - SSH extension: \u003Ca href=\"https://marketplace.visualstudio.com/items?itemName=ms-vscode-remote.remote-ssh\"\u003E\u003Ccode\u003Ems-vscode-remote.remote-ssh\u003C/code\u003E\u003C/a\u003E.\u003C/li\u003E\u003Cli\u003EThe Snowflake Extension for Visual Studio Code, version 1.39 or later (persistent storage and Git require v1.39+): \u003Ca href=\"https://marketplace.visualstudio.com/items?itemName=snowflake.snowflake-vsc\"\u003EMarketplace listing\u003C/a\u003E.\u003C/li\u003E\u003Cli\u003ESnowflake CLI (\u003Ccode\u003Esnow\u003C/code\u003E) installed with a configured connection. See \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-cli/installation/installation\"\u003EInstalling Snowflake CLI\u003C/a\u003E and \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-cli/connecting/configure-connections\"\u003EConfiguring Snowflake CLI connections\u003C/a\u003E.\u003C/li\u003E\u003Cli\u003ELocal \u003Ccode\u003Enc\u003C/code\u003E (netcat) on your \u003Ccode\u003EPATH\u003C/code\u003E. Preinstalled on macOS and most Linux distributions. On Windows you'll need to install a \u003Ccode\u003Enetcat\u003C/code\u003E-compatible executable and add it to \u003Ccode\u003EPATH\u003C/code\u003E.\u003C/li\u003E\u003Cli\u003EA GitHub account. You'll create a small private repo in step 8.\u003C/li\u003E\u003Cli\u003EComfort with Python, Jupyter notebooks, Git, and basic SQL. Familiarity with a gradient-boosted tree model (XGBoost or similar) is helpful for step 7.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Build\u003C/h3\u003E\n","\u003Cp\u003EBy the end of this guide, you'll have:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EA running Snowflake remote development environment, reachable from VS Code or Cursor over Remote-SSH.\u003C/li\u003E\u003Cli\u003E~1B rows of Tasty Bytes orders and their supporting dimensions ingested into a Snowflake database.\u003C/li\u003E\u003Cli\u003EA Marketplace weather share acquired and joined into a daily-per-location feature table.\u003C/li\u003E\u003Cli\u003EA trained XGBoost sales-forecasting model logged to the Snowflake Model Registry.\u003C/li\u003E\u003Cli\u003EA private Git repo cloned into \u003Cstrong\u003E/mnt/pd0\u003C/strong\u003E (persistent storage), authenticated via your editor's GitHub sign-in or a Snowflake secret.\u003C/li\u003E\u003Cli\u003EAn actual vs. predicted sales chart for a held-out week, rendered inline in the remote notebook.\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESet up your account\u003C/h2\u003E\n","\u003Cp\u003EDuration: 6\u003C/p\u003E\n","\u003Cp\u003ELet's set up everything you'll need on the Snowflake side: a warehouse for the bulk load, the Tasty Bytes database and schemas, an external stage against the public S3 bucket, all the raw tables, a compute pool for the notebook service, an external access integration for outbound GitHub and PyPI, and a Snowflake Workspace to mount into the remote environment.\u003C/p\u003E\n","\u003Cp\u003EWe'll do it all with one SQL script, run from your terminal via the Snowflake CLI.\u003C/p\u003E\n","\u003Ch3\u003EStep 2a – Run the setup script\u003C/h3\u003E\n","\u003Cp\u003EClone the companion repo locally and run \u003Cstrong\u003Esetup.sql\u003C/strong\u003E with the Snowflake CLI:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Egit clone https://github.com/Snowflake-Labs/sfguide-getting-started-with-remote-development-vscode-extension.git\ncd sfguide-getting-started-with-remote-development-vscode-extension\nsnow sql -f setup.sql\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis executes the whole script against your default \u003Ccode\u003Esnow\u003C/code\u003E connection.\u003C/p\u003E\n","\u003Cp\u003EHere's what the script does:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ECreates a Large warehouse (\u003Ccode\u003Etb_de_wh\u003C/code\u003E). Large lets the \u003Ccode\u003ECOPY INTO\u003C/code\u003E finish in ~1-2 minutes across all raw tables. It gets sized back down to XSmall at the end.\u003C/li\u003E\u003Cli\u003ECreates the \u003Ccode\u003Etb_101\u003C/code\u003E database plus the \u003Ccode\u003Eraw_pos\u003C/code\u003E, \u003Ccode\u003Eraw_customer\u003C/code\u003E, \u003Ccode\u003Eharmonized\u003C/code\u003E, \u003Ccode\u003Eanalytics\u003C/code\u003E, and \u003Ccode\u003Eml\u003C/code\u003E schemas.\u003C/li\u003E\u003Cli\u003ECreates a CSV file format and an external stage against \u003Cstrong\u003Es3://sfquickstarts/frostbyte_tastybytes/\u003C/strong\u003E – the canonical public Tasty Bytes bucket.\u003C/li\u003E\u003Cli\u003ECreates all eight raw tables: \u003Ccode\u003Ecountry\u003C/code\u003E, \u003Ccode\u003Efranchise\u003C/code\u003E, \u003Ccode\u003Elocation\u003C/code\u003E, \u003Ccode\u003Emenu\u003C/code\u003E, \u003Ccode\u003Etruck\u003C/code\u003E, \u003Ccode\u003Eorder_header\u003C/code\u003E, \u003Ccode\u003Eorder_detail\u003C/code\u003E, and \u003Ccode\u003Ecustomer_loyalty\u003C/code\u003E.\u003C/li\u003E\u003Cli\u003ECreates the \u003Ccode\u003Eharmonized.orders_v\u003C/code\u003E and \u003Ccode\u003Eanalytics.orders_v\u003C/code\u003E views that stitch orders, trucks, menus, franchises, locations, and customers into one queryable surface.\u003C/li\u003E\u003Cli\u003ERuns \u003Ccode\u003ECOPY INTO\u003C/code\u003E for every raw table. When this completes you'll have close to a billion rows across the fact tables.\u003C/li\u003E\u003Cli\u003ECreates a compute pool (\u003Ccode\u003Etb_remote_dev_pool\u003C/code\u003E, \u003Ccode\u003ECPU_X64_S\u003C/code\u003E) for the notebook service. The \u003Ccode\u003ENOTEBOOK\u003C/code\u003E workload type is allowed on all pools by default.\u003C/li\u003E\u003Cli\u003ECreates a network rule (\u003Ccode\u003Eegress_github_pypi\u003C/code\u003E) and an external access integration (\u003Ccode\u003Etb_remote_dev_eai\u003C/code\u003E) so the remote notebook service can reach \u003Ccode\u003Egithub.com\u003C/code\u003E and \u003Ccode\u003Epypi.org\u003C/code\u003E for cloning and \u003Ccode\u003Epip install\u003C/code\u003E.\u003C/li\u003E\u003Cli\u003EAcquires the Pelmorex Weather Source: Frostbyte Marketplace listing programmatically – requests it, accepts the legal terms, and installs it as the \u003Ccode\u003Efrostbyte_weathersource\u003C/code\u003E database.\u003C/li\u003E\u003Cli\u003ECreates a Snowflake Workspace (\u003Ccode\u003Etb_forecast_ws\u003C/code\u003E) – we'll mount this into the remote environment in step 4.\u003C/li\u003E\u003Cli\u003EConfirms \u003Ccode\u003EENABLE_NOTEBOOK_SERVICE_REMOTE_VS_CODE_ACCESS\u003C/code\u003E is on.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EYou should see a final \u003Ccode\u003Enote\u003C/code\u003E row that reads: \u003Ccode\u003ESetup complete.\u003C/code\u003E.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote:\u003C/strong\u003E the Tasty Bytes S3 bucket is public, so it does NOT need to be in the external access integration. \u003Ccode\u003ECOPY INTO\u003C/code\u003E runs on Snowflake compute against the stage – the EAI governs outbound traffic from the notebook container, not stage access.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EStep 2b – Verify the weather share\u003C/h3\u003E\n","\u003Cp\u003EWeather is one of the strongest predictors of food-truck sales, so \u003Cstrong\u003Esetup.sql\u003C/strong\u003E already acquired the free Pelmorex Weather Source: Frostbyte share from the Snowflake Marketplace and installed it as the \u003Ccode\u003Efrostbyte_weathersource\u003C/code\u003E database. Confirm it's ready with one command:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Esnow sql -q "SELECT date_valid_std, city_name, avg_temperature_air_2m_f, tot_precipitation_in FROM frostbyte_weathersource.onpoint_id.history_day WHERE date_valid_std BETWEEN '2024-01-01' AND '2024-01-07' AND country = 'US' LIMIT 10;" --database frostbyte_weathersource --schema onpoint_id\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should see weather observations for a US city across the first week of January 2024. That's the data we'll join to daily orders in step 6.\u003C/p\u003E\n","\u003Cp\u003EGreat job – the Snowflake side is ready. Now let's get your editor set up.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EInstall the extension\u003C/h2\u003E\n","\u003Cp\u003EDuration: 3\u003C/p\u003E\n","\u003Cp\u003ENow we'll install the Snowflake Extension for VS Code (or Cursor) plus the companion Remote - SSH extension, and sign in to your Snowflake account.\u003C/p\u003E\n","\u003Ch3\u003EVS Code\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003EOpen VS Code.\u003C/li\u003E\u003Cli\u003EIn the Extensions view, search for Snowflake and install the extension published by Snowflake Inc. – make sure the version is 1.39.0 or later.\u003C/li\u003E\u003Cli\u003EIn the Extensions view again, search for \u003Ccode\u003Ems-vscode-remote.remote-ssh\u003C/code\u003E. The top results, called Remote - SSH, is the extension to install. Install it.\u003C/li\u003E\u003Cli\u003EOpen the Snowflake extension from the Activity Bar. Sign in to your Snowflake account.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003ECursor is built on the same VS Code extension model. Follow the same instructions above for installing the Remote - SSH extension in Cursor.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote:\u003C/strong\u003E if you had an older Snowflake extension installed, reload your editor after upgrading. The \u003Cstrong\u003ERemote Environments\u003C/strong\u003E panel that we'll use next only appears in v1.38+.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/get-started-with-remote-development-snowflake-vscode-extension/remote-ssh.png\" alt=\"remote ssh extension\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECreate your remote environment\u003C/h2\u003E\n","\u003Cp\u003EDuration: 5\u003C/p\u003E\n","\u003Cp\u003EWe're ready to spin up the remote environment. From the Snowflake extension in your local editor, we'll create a notebook service running on the compute pool we provisioned earlier, and enable persistent storage so cloned Git repos and installed packages survive suspend/resume.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EIn the Snowflake extension sidebar, expand the \u003Cstrong\u003ERemote Environments\u003C/strong\u003E panel.\u003C/li\u003E\u003Cli\u003EClick the \u003Cstrong\u003E+\u003C/strong\u003E sign - hovering over it should read: \u003Cstrong\u003ESnowflake: Create Remote Development Environment\u003C/strong\u003E.\u003C/li\u003E\u003Cli\u003EFill in the form:\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EService name:\u003C/strong\u003E \u003Ccode\u003Etb_forecast_env\u003C/code\u003E (or any Snowflake identifier; avoid collisions with existing SSH aliases in your \u003Cstrong\u003E~/.ssh/config\u003C/strong\u003E).\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EWorkspaces:\u003C/strong\u003E select \u003Ccode\u003Etb_forecast_ws\u003C/code\u003E.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EExternal access integrations:\u003C/strong\u003E select \u003Ccode\u003Etb_remote_dev_eai\u003C/code\u003E.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESecrets:\u003C/strong\u003E leave this empty. (Only needed for the optional Snowflake-secret auth path in step 8, which recreates the service with a secret attached.)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECompute pool:\u003C/strong\u003E select \u003Ccode\u003Etb_remote_dev_pool\u003C/code\u003E.\u003C/li\u003E\u003Cli\u003EExpand the \u003Cstrong\u003EService settings\u003C/strong\u003E section:\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ECompute type:\u003C/strong\u003E CPU\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ERuntime version:\u003C/strong\u003E accept the default (includes XGBoost, scikit-learn, and the Snowflake ML libraries this guide uses).\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EEnable persistent storage:\u003C/strong\u003E check this box.\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003ECreate\u003C/strong\u003E.\u003C/li\u003E\u003C/ol\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EImportant:\u003C/strong\u003E \u003Cstrong\u003EEnable persistent storage\u003C/strong\u003E can only be set at create time – you cannot add it to an existing service. If you skip it now, you'll have to delete this service and create a new one. Persistent storage mounts SPCS block storage at \u003Cstrong\u003E/mnt/pd0\u003C/strong\u003E in the remote container. Cloned repos and pip installs live there across suspend and resume.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EThe service enters PENDING status while Snowflake provisions the container. This takes a few minutes. The panel refreshes every 30 seconds; you can also refresh manually. When the status flips to RUNNING, you're ready to connect.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/get-started-with-remote-development-snowflake-vscode-extension/create-env.png\" alt=\"create env\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConnect and open the notebook\u003C/h2\u003E\n","\u003Cp\u003EDuration: 8\u003C/p\u003E\n","\u003Cp\u003ELet's connect over SSH.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EIn the \u003Cstrong\u003ERemote Environments\u003C/strong\u003E panel, find your \u003Ccode\u003Etb_forecast_env\u003C/code\u003E service.\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003EConnect to Remove Service: SSH\u003C/strong\u003E.\u003C/li\u003E\u003Cli\u003EWhen prompted, select the \u003Ccode\u003Etb_forecast_ws\u003C/code\u003E workspace to mount into the remote environment. Press Enter.\u003C/li\u003E\u003Cli\u003EA new editor window opens, connected over SSH to the remote container at \u003Cstrong\u003E/root\u003C/strong\u003E.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/get-started-with-remote-development-snowflake-vscode-extension/connect.png\" alt=\"connect\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/get-started-with-remote-development-snowflake-vscode-extension/root.png\" alt=\"root\"\u003E\u003C/p\u003E\n","\u003Cp\u003EBehind the scenes, the extension started a local proxy on \u003Ccode\u003E127.0.0.1\u003C/code\u003E that forwards SSH traffic to Snowflake over a secure WebSocket, wrote a \u003Ccode\u003EHost <service-name>\u003C/code\u003E entry to your \u003Cstrong\u003E~/.ssh/config\u003C/strong\u003E, installed the required extensions on the remote host, and opened the folder. You didn't have to do any of this manually.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote:\u003C/strong\u003E The very first connection installs the Python, Jupyter, and Snowflake extensions on the remote host. This can take a couple of minutes. If the Snowflake Kernel option doesn't appear when you try to run a cell, wait for the installs to finish and reload the remote window (\u003Cstrong\u003EDeveloper: Reload Window\u003C/strong\u003E from the Command Palette).\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EImportant:\u003C/strong\u003E The remote window opens at \u003Cstrong\u003E/root\u003C/strong\u003E, which is ephemeral – anything you write there is wiped when the service suspends. Do all of your work under \u003Cstrong\u003E/mnt/pd0\u003C/strong\u003E (the persistent drive you enabled at create time). The next step clones the companion repo into \u003Cstrong\u003E/mnt/pd0\u003C/strong\u003E for exactly this reason.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EClone the companion repo into persistent storage\u003C/h3\u003E\n","\u003Cp\u003ELet's pull the notebook and helper Python modules we'll be using. In the remote window, open a terminal (from the \u003Cstrong\u003ETerminal\u003C/strong\u003E menu, choose \u003Cstrong\u003ENew Terminal\u003C/strong\u003E) and clone the repo into \u003Cstrong\u003E/mnt/pd0\u003C/strong\u003E:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Ecd /mnt/pd0\ngit clone https://github.com/Snowflake-Labs/sfguide-getting-started-with-remote-development-vscode-extension.git\ncd sfguide-getting-started-with-remote-development-vscode-extension\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAdd the folder to your workspace: from the \u003Cstrong\u003EFile\u003C/strong\u003E menu, choose \u003Cstrong\u003EAdd Folder to Workspace...\u003C/strong\u003E and pick \u003Cstrong\u003E/mnt/pd0/sfguide-getting-started-with-remote-development-vscode-extension\u003C/strong\u003E. You should see \u003Cstrong\u003EREADME.md\u003C/strong\u003E, \u003Cstrong\u003Esetup.sql\u003C/strong\u003E, \u003Cstrong\u003Ecleanup.sql\u003C/strong\u003E, \u003Cstrong\u003Eforecast.ipynb\u003C/strong\u003E, and \u003Cstrong\u003Etrain.py\u003C/strong\u003E in the Explorer.\u003C/p\u003E\n","\u003Ch3\u003EOpen the notebook\u003C/h3\u003E\n","\u003Cp\u003EOpen \u003Cstrong\u003Eforecast.ipynb\u003C/strong\u003E. In the search bar, type \u003Cstrong\u003ESnowflake: Start Notebook Kernel\u003C/strong\u003E, then select \u003Cstrong\u003ESnowflake Kernel (Python + SQL)\u003C/strong\u003E from the kernel picker above the notebook.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote:\u003C/strong\u003E Run \u003Cstrong\u003ESnowflake: Start Notebook Kernel\u003C/strong\u003E from the remote window (the editor window connected over SSH), not your local window – the kernel lives on the remote host. If the action doesn't appear, the remote extensions are still installing: wait for Snowflake, Python, and Jupyter to finish, then run \u003Cstrong\u003EDeveloper: Reload Window\u003C/strong\u003E and reopen the notebook.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EThis is the same kernel that powers Snowflake Notebooks in Snowsight Workspaces. Python and SQL cells run side by side without switching kernels – Python cells run in the container's Python interpreter, and SQL cells run against your Snowflake account.\u003C/p\u003E\n","\u003Cp\u003ERun the first SQL cell to confirm the raw tables loaded:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT 'order_header' AS tbl, COUNT(*) AS row_count FROM tb_101.raw_pos.order_header\nUNION ALL SELECT 'order_detail', COUNT(*) FROM tb_101.raw_pos.order_detail\nUNION ALL SELECT 'location',     COUNT(*) FROM tb_101.raw_pos.location\nUNION ALL SELECT 'truck',        COUNT(*) FROM tb_101.raw_pos.truck\nUNION ALL SELECT 'menu',         COUNT(*) FROM tb_101.raw_pos.menu\nUNION ALL SELECT 'customer_loyalty', COUNT(*) FROM tb_101.raw_customer.customer_loyalty\nORDER BY 2 DESC;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should see \u003Ccode\u003Eorder_detail\u003C/code\u003E at the top with the largest row count – hundreds of millions of line items. Together with \u003Ccode\u003Eorder_header\u003C/code\u003E this is close to a billion rows. You're querying that volume from your local editor, and the query runs on Snowflake compute.\u003C/p\u003E\n","\u003Cp\u003EThe remote environment isn't limited to notebooks – you can open and run plain Python files against the same remote interpreter. We'll do this in a later step.\u003C/p\u003E\n","\u003Cp\u003EGreat job. You're now running Snowflake-backed compute from your local editor. Let's put it to work.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/get-started-with-remote-development-snowflake-vscode-extension/kernel.png\" alt=\"kernel\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EEnrich and feature-engineer with CoCo\u003C/h2\u003E\n","\u003Cp\u003EDuration: 8\u003C/p\u003E\n","\u003Cp\u003EWe have 1B rows of raw orders on one side and a weather share on the other. Let's turn them into a modeling-ready feature table.\u003C/p\u003E\n","\u003Ch3\u003EAggregate orders to daily-per-location grain\u003C/h3\u003E\n","\u003Cp\u003ERun the next cell in the notebook:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE TABLE tb_101.ml.daily_sales AS\nSELECT\n    DATE(order_ts)             AS date,\n    location_id,\n    ANY_VALUE(location_city)   AS city,\n    ANY_VALUE(location_region) AS region,\n    ANY_VALUE(country)         AS country,\n    SUM(price)                 AS daily_sales,\n    COUNT(DISTINCT order_id)   AS order_count\nFROM tb_101.harmonized.orders_v\nGROUP BY 1, 2;\n\nSELECT COUNT(*) AS rows_in_daily_sales FROM tb_101.ml.daily_sales;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EHere's what the code does:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EReads from \u003Ccode\u003Eharmonized.orders_v\u003C/code\u003E, the view that joins orders, line items, trucks, menus, and locations.\u003C/li\u003E\u003Cli\u003EGroups by \u003Ccode\u003E(date, location_id)\u003C/code\u003E to get one row per truck location per day.\u003C/li\u003E\u003Cli\u003EMaterializes the result to \u003Ccode\u003Etb_101.ml.daily_sales\u003C/code\u003E – this is now a small table (tens of thousands of rows) that we can work with in memory.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EJoin the Marketplace weather\u003C/h3\u003E\n","\u003Cp\u003ENow let's bring in weather. The weather view is keyed at postal-code + date grain, so joining directly on city fans every city out to all of its postal codes (Denver alone has ~74) and explodes the row count. We first pre-aggregate weather to one row per city per date, then join. We also programmatically bump the warehouse to MEDIUM for this step and size it back down right after. Run the next cell (note that it may take about 3 minutes to complete):\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EALTER WAREHOUSE tb_de_wh SET WAREHOUSE_SIZE = 'MEDIUM';\n\nCREATE OR REPLACE TABLE tb_101.ml.daily_sales_weather AS\nWITH weather_by_city AS (\n    SELECT\n        date_valid_std,\n        UPPER(city_name)              AS city_upper,\n        AVG(avg_temperature_air_2m_f) AS avg_temperature_air_2m_f,\n        AVG(tot_precipitation_in)     AS tot_precipitation_in,\n        AVG(avg_wind_speed_100m_mph)  AS avg_wind_speed_100m_mph\n    FROM frostbyte_weathersource.onpoint_id.history_day\n    WHERE date_valid_std BETWEEN (SELECT MIN(date) FROM tb_101.ml.daily_sales)\n                             AND (SELECT MAX(date) FROM tb_101.ml.daily_sales)\n      AND UPPER(city_name) IN (SELECT DISTINCT UPPER(city) FROM tb_101.ml.daily_sales)\n    GROUP BY date_valid_std, UPPER(city_name)\n)\nSELECT\n    ds.date,\n    ds.location_id,\n    ds.city,\n    ds.daily_sales,\n    ds.order_count,\n    w.avg_temperature_air_2m_f                     AS temp_f,\n    (w.avg_temperature_air_2m_f - 32.0) * 5.0/9.0  AS temp_c,\n    w.tot_precipitation_in * 25.4                  AS precip_mm,\n    w.avg_wind_speed_100m_mph * 1.60934            AS wind_kph\nFROM tb_101.ml.daily_sales ds\nJOIN weather_by_city w\n  ON w.date_valid_std = ds.date\n AND w.city_upper = UPPER(ds.city)\nWHERE ds.daily_sales IS NOT NULL;\n\nALTER WAREHOUSE tb_de_wh SET WAREHOUSE_SIZE = 'XSMALL';\n\nSELECT COUNT(*) AS joined_rows FROM tb_101.ml.daily_sales_weather;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EHere's what the code does:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EBumps \u003Ccode\u003Etb_de_wh\u003C/code\u003E to MEDIUM for the weather scan, then back to XSMALL when done.\u003C/li\u003E\u003Cli\u003EPre-aggregates the weather view to one row per (\u003Ccode\u003Edate\u003C/code\u003E, \u003Ccode\u003Ecity\u003C/code\u003E) in \u003Ccode\u003Eweather_by_city\u003C/code\u003E, averaging across the postal codes in each city. This is what avoids the postal-code fan-out that would otherwise multiply every sales row.\u003C/li\u003E\u003Cli\u003EFilters the weather scan to the date range and cities present in \u003Ccode\u003Edaily_sales\u003C/code\u003E.\u003C/li\u003E\u003Cli\u003EJoins the pre-aggregated weather to \u003Ccode\u003Edaily_sales\u003C/code\u003E on \u003Ccode\u003Edate\u003C/code\u003E + \u003Ccode\u003Ecity\u003C/code\u003E, converts imperial units to metric, and drops rows where the join failed or sales are null.\u003C/li\u003E\u003Cli\u003EMaterializes \u003Ccode\u003Etb_101.ml.daily_sales_weather\u003C/code\u003E – the base for feature engineering.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EFeature-engineer with CoCo\u003C/h3\u003E\n","\u003Cp\u003ENow the fun part. We need calendar, lag, and rolling features on the joined data – the standard patterns for time-series forecasting. We'll let CoCo generate them.\u003C/p\u003E\n","\u003Cp\u003EOpen the CoCo panel in the remote window from the Activity Bar and try prompts like:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E"Add day-of-week, month, and is_weekend features to a daily sales DataFrame."\u003C/li\u003E\u003Cli\u003E"Add lag-1 and lag-7 daily-sales features per location_id."\u003C/li\u003E\u003Cli\u003E"Add a 7-day rolling mean of precip_mm per location_id."\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003ECoCo runs inside the remote SSH session with full access to Snowflake compute and data. By default it shows the generated code in its panel for you to read or copy – it won't change the notebook on its own. If you attach the notebook as context with \u003Ccode\u003E@\u003C/code\u003E, CoCo will instead try to apply its code as edits to the file.\u003C/p\u003E\n","\u003Cp\u003EEither way, \u003Cstrong\u003Eyou don't need to accept any edits\u003C/strong\u003E: the two cells below are already in the notebook – one defines the transforms, one applies them. Run them as-is to continue, and use CoCo alongside to see how you'd generate them yourself.\u003C/p\u003E\n","\u003Cp\u003EThe first cell defines three small pandas transforms:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimport pandas as pd\n\ndef add_calendar_features(df, date_col='date'):\n    df = df.copy()\n    df[date_col] = pd.to_datetime(df[date_col])\n    df['day_of_week'] = df[date_col].dt.dayofweek\n    df['month'] = df[date_col].dt.month\n    df['is_weekend'] = df['day_of_week'].isin([5, 6]).astype(int)\n    return df\n\ndef add_lag_features(df, group_cols, target_col='daily_sales', lags=(1, 7)):\n    df = df.copy().sort_values(group_cols + ['date'])\n    for lag in lags:\n        df[f'{target_col}_lag_{lag}'] = df.groupby(group_cols)[target_col].shift(lag)\n    return df\n\ndef add_rolling_precip(df, group_cols, precip_col='precip_mm', window=7):\n    df = df.copy().sort_values(group_cols + ['date'])\n    df[f'{precip_col}_roll_{window}'] = (\n        df.groupby(group_cols)[precip_col]\n        .rolling(window=window, min_periods=1)\n        .mean()\n        .reset_index(level=list(range(len(group_cols))), drop=True)\n    )\n    return df\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe second cell loads the joined table and chains the transforms via \u003Ccode\u003E.pipe()\u003C/code\u003E:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom snowflake.snowpark.context import get_active_session\n\nsession = get_active_session()\nraw = session.table('tb_101.ml.daily_sales_weather').to_pandas()\nraw.columns = [c.lower() for c in raw.columns]\n\nfeatures_df = (\n    raw\n    .pipe(add_calendar_features)\n    .pipe(add_rolling_precip, group_cols=['location_id'])\n    .pipe(add_lag_features, group_cols=['location_id'])\n    .dropna()\n    .reset_index(drop=True)\n)\nfeatures_df.head()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EHere's what the code does:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EGets the active Snowflake session; the remote container is already authenticated.\u003C/li\u003E\u003Cli\u003EMaterializes the joined table to a pandas DataFrame in the container.\u003C/li\u003E\u003Cli\u003EChains the three transforms with \u003Ccode\u003E.pipe()\u003C/code\u003E: calendar features first, then a 7-day rolling precipitation feature, then lag-1 and lag-7 sales features.\u003C/li\u003E\u003Cli\u003EDrops the initial rows containing NaN lag values.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ELand the feature table back in Snowflake\u003C/h3\u003E\n","\u003Cp\u003EPush the DataFrame back to Snowflake so \u003Cstrong\u003Etrain.py\u003C/strong\u003E can consume it:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Esession.write_pandas(\n    features_df,\n    table_name='FEATURE_TABLE',\n    database='TB_101',\n    schema='ML',\n    auto_create_table=True,\n    overwrite=True,\n)\nsession.table('tb_101.ml.feature_table').count()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENow the feature table lives in Snowflake, ready for training.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ETrain and register the model\u003C/h2\u003E\n","\u003Cp\u003EDuration: 6\u003C/p\u003E\n","\u003Cp\u003EThis is the step where we ship a model beyond a notebook. We'll train an XGBoost regressor on the feature table, evaluate it on the last week of data, chart actual vs predicted, and log the model to the Snowflake Model Registry – where it becomes discoverable by other teammates and downstream jobs.\u003C/p\u003E\n","\u003Ch3\u003ETrain from a .py file\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003Etrain.py\u003C/strong\u003E in the repo is a plain Python script – no notebook required. Let's run it against the remote environment. In the notebook, execute this cell (use the full path so it runs regardless of the notebook's working directory):\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E!python /mnt/pd0/sfguide-getting-started-with-remote-development-vscode-extension/train.py \\\n    --source-table tb_101.ml.feature_table \\\n    --database tb_101 \\\n    --schema ml \\\n    --model-name tb_sales_forecaster \\\n    --version v1\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou can also run it directly in the remote terminal (\u003Ccode\u003Epython /mnt/pd0/sfguide-getting-started-with-remote-development-vscode-extension/train.py --source-table ...\u003C/code\u003E). Either way it executes on the Snowflake-hosted container. Because a script run this way is a separate process from the notebook kernel, \u003Cstrong\u003Etrain.py\u003C/strong\u003E builds its own Snowflake session from the container's credentials (falling back from the kernel's active session), so no extra auth setup is needed.\u003C/p\u003E\n","\u003Cp\u003EHere's what \u003Cstrong\u003Etrain.py\u003C/strong\u003E does:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EBuilds a Snowflake session – reusing the notebook's active session in-kernel, or creating one from the container's OAuth token when run standalone.\u003C/li\u003E\u003Cli\u003ELoads the feature table into a pandas DataFrame using \u003Ccode\u003Esession.table(...).to_pandas()\u003C/code\u003E.\u003C/li\u003E\u003Cli\u003EDoes a time-based train/test split – the last 7 days are held out. Never a random split for forecasting.\u003C/li\u003E\u003Cli\u003EFits an \u003Ccode\u003EXGBRegressor\u003C/code\u003E with sensible defaults (400 trees, depth 6, learning rate 0.05).\u003C/li\u003E\u003Cli\u003EEvaluates on the holdout: MAPE and RMSE.\u003C/li\u003E\u003Cli\u003ELogs the fitted model to the Snowflake Model Registry via \u003Ccode\u003ERegistry.log_model()\u003C/code\u003E, along with a sample input, the version, and a comment carrying the holdout metrics. It logs for both \u003Ccode\u003EWAREHOUSE\u003C/code\u003E and \u003Ccode\u003ESNOWPARK_CONTAINER_SERVICES\u003C/code\u003E so you can run inference either way – without this, a Container Runtime model defaults to SPCS-only and \u003Ccode\u003Emodel_ref.run()\u003C/code\u003E on a pandas frame fails.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EYou should see output ending in something like (your exact metrics will vary):\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-text\"\u003ELoading features from tb_101.ml.feature_table ...\n  327,515 rows across 9968 locations\n  Train: 322,766 rows | Test: 4,749 rows\nHoldout MAPE: 0.40  |  RMSE: 8,621.66\nLogging model to registry: tb_101.ml.tb_sales_forecaster (v1) ...\nDone. The model is now discoverable from the Model Registry.\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote:\u003C/strong\u003E A couple of \u003Ccode\u003EFailed to get kernel ID for per-kernel logging\u003C/code\u003E lines may print at the top. They are benign and won't impact any of the work we're doing.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EYour model is now live in the Model Registry. You can list it from any session:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESHOW MODELS IN SCHEMA tb_101.ml;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EGreat job – you shipped a model. From here, other teammates can retrieve it, run predictions, or promote it to a downstream inference job – all without ever seeing your notebook.\u003C/p\u003E\n","\u003Ch3\u003EChart actual vs predicted\u003C/h3\u003E\n","\u003Cp\u003ELet's visualize the holdout. Run the final cell of the notebook:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom snowflake.ml.registry import Registry\n\nregistry = Registry(session=session, database_name='TB_101', schema_name='ML')\nmodel_ref = registry.get_model('tb_sales_forecaster').version('v1')\n\nholdout = features_df[features_df['date'] > features_df['date'].max() - pd.Timedelta(days=7)].copy()\none_loc = holdout[holdout['location_id'] == holdout['location_id'].value_counts().idxmax()].copy()\n\nFEATURE_COLS = [\n    'day_of_week', 'month', 'is_weekend',\n    'temp_c', 'precip_mm', 'wind_kph',\n    'precip_mm_roll_7', 'daily_sales_lag_1', 'daily_sales_lag_7',\n]\n# model_ref.run returns a DataFrame; the prediction is the last column.\none_loc['predicted'] = model_ref.run(one_loc[FEATURE_COLS]).iloc[:, -1].to_numpy()\n\nfig, ax = plt.subplots(figsize=(10, 4))\nax.plot(one_loc['date'], one_loc['daily_sales'], marker='o', label='Actual')\nax.plot(one_loc['date'], one_loc['predicted'], marker='x', label='Predicted')\nax.set_title(f"Location {int(one_loc['location_id'].iloc[0])} – actual vs predicted (holdout week)")\nax.set_ylabel('Daily sales (USD)')\nax.legend()\nplt.xticks(rotation=45)\nplt.tight_layout()\nplt.show()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EHere's what the code does:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ERetrieves the model from the registry – we don't reload the Python object, we ask Snowflake for it.\u003C/li\u003E\u003Cli\u003EPicks the single location with the most rows in the holdout.\u003C/li\u003E\u003Cli\u003ERuns inference through the registered model via \u003Ccode\u003Emodel_ref.run(...)\u003C/code\u003E.\u003C/li\u003E\u003Cli\u003ECharts actual vs predicted sales for that location across the holdout week.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EYou should see the two lines tracking each other reasonably closely. The fit has room to tune, and you've built a working forecaster end to end in a fraction of an hour.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/get-started-with-remote-development-snowflake-vscode-extension/chart.png\" alt=\"chart\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EWork with private repos\u003C/h2\u003E\n","\u003Cp\u003EDuration: 5\u003C/p\u003E\n","\u003Cp\u003EData science and machine learning work often involves the use of source control and git repositories. The suggested path for working with repos using remote development is to clone them into the \u003Cstrong\u003E/mnt/pd0\u003C/strong\u003E directory. This is the persisted storage directory, and cloning into it will ensure that the repo will be available to you in subsequent SSH sessions.\u003C/p\u003E\n","\u003Cp\u003EGit authentication is typically handled by your editor's GitHub sign-in – no secret or credential helper needed. If your remote VS Code (or Cursor) session is signed into GitHub, many git operations will work with little to no configuration.\u003C/p\u003E\n","\u003Cp\u003ETo quickly check if you are logged into GitHub, do the following:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003EClick on the \u003Cstrong\u003EAccounts\u003C/strong\u003E icon in VS Code (or Cursor) \u003Cstrong\u003Ein the remote window\u003C/strong\u003E.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EIf you see something like "your-username (GitHub)", then you're signed into GitHub.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EIf you're signed in, then the typical \u003Ccode\u003Egit clone\u003C/code\u003E workflow applies and automatically works for repos that you own or have been added to as a collaborator:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Ecd /mnt/pd0\ngit clone https://github.com/<your_github_username>/<repo-name>.git # Path to repo\ncd <repo-name>\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIf you are running git under a service identity instead of your own, store an access token in a \u003Cstrong\u003ESnowflake secret\u003C/strong\u003E, mount it into the service, and point git at it with a credential helper. This is the common pattern for team and production environments. For details, see \u003Ca href=\"https://docs.snowflake.com/en/user-guide/vscode-ext-remote-development\"\u003ERemote Development with the Snowflake Extension for Visual Studio Code\u003C/a\u003E.\u003C/p\u003E\n","\u003Ch3\u003ESet your git identity at the local level to persist it\u003C/h3\u003E\n","\u003Cp\u003EBefore your first commit in this repo, git needs an author identity. Set it at the local repo level using \u003Ccode\u003E--local\u003C/code\u003E to persist your git identity from session to session. Using \u003Ccode\u003E--global\u003C/code\u003E will write the config to \u003Cstrong\u003E/root\u003C/strong\u003E, which is wiped on session suspend. Local config lives in the repo on \u003Cstrong\u003E/mnt/pd0\u003C/strong\u003E and persists:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Ecd /mnt/pd0/tb-forecast-private\ngit config --local user.name "Your Name"\ngit config --local user.email "you@example.com"\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EImportant:\u003C/strong\u003E If you commit before setting this, git raises \u003Ccode\u003EAuthor identity unknown\u003C/code\u003E and suggests \u003Ccode\u003Egit config --global user.email ...\u003C/code\u003E. Don't follow that suggestion here – \u003Ccode\u003E--global\u003C/code\u003E writes to \u003Cstrong\u003E/root/.gitconfig\u003C/strong\u003E, which is wiped on suspend, so your identity vanishes on the next resume. Use \u003Ccode\u003E--local\u003C/code\u003E (as above) so it persists with the repo on \u003Cstrong\u003E/mnt/pd0\u003C/strong\u003E.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESuspend and resume\u003C/h2\u003E\n","\u003Cp\u003EDuration: 2\u003C/p\u003E\n","\u003Cp\u003ELet's confirm your work survives a suspend.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003ESave any unsaved files. In the Remote Environments panel, stop the \u003Ccode\u003Etb_forecast_env\u003C/code\u003E environment and close the remote window. The environment status will flip to SUSPENDED.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EResume the service and pick the same workspace when connecting.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EIn the terminal on the remote, confirm your files are intact:\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Els /mnt/pd0/<name-of-repo-you-cloned>\nls /root\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EEverything under \u003Cstrong\u003E/mnt/pd0\u003C/strong\u003E – the repo, your pip installs – is exactly as you left it. \u003Cstrong\u003E/root\u003C/strong\u003E is empty: it's the ephemeral filesystem, wiped on suspend. The registered model persists in Snowflake regardless (run \u003Ccode\u003ESHOW MODELS IN SCHEMA tb_101.ml;\u003C/code\u003E for example).\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EClean up\u003C/h2\u003E\n","\u003Cp\u003EDuration: 2\u003C/p\u003E\n","\u003Cp\u003EWhen you're done, tear everything down. Do it in this order, because the cleanup script drops the compute pool the remote service runs on – so the service has to go first.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003EDelete the remote service. In VS Code, in the Remote Environments panel: stop the remote environment and then delete it.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EDrop the Snowflake objects. From the companion repo, run \u003Cstrong\u003Ecleanup.sql\u003C/strong\u003E with the Snowflake CLI (or paste its contents into a Snowsight worksheet):\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Esnow sql -f cleanup.sql\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThat script:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EDrops the \u003Ccode\u003Etb_101\u003C/code\u003E database, which cascades to everything under it – tables, views, feature tables, model registry entries, and the Snowflake Workspace.\u003C/li\u003E\u003Cli\u003EDrops the compute pool and external access integration.\u003C/li\u003E\u003Cli\u003EDrops the Large warehouse.\u003C/li\u003E\u003Cli\u003EDrops the \u003Ccode\u003Efrostbyte_weathersource\u003C/code\u003E database acquired from the Marketplace.\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion and Resources\u003C/h2\u003E\n","\u003Cp\u003EDuration: 1\u003C/p\u003E\n","\u003Cp\u003ECongratulations! You built and shipped an end-to-end ML workflow – data ingest, feature engineering, model training, and registry logging – entirely against Snowflake compute, from your local editor, without provisioning a VM, managing an SSH key, or moving a single row down to your laptop.\u003C/p\u003E\n","\u003Cp\u003EThe same remote environment that ran this notebook can host your team's other ML projects. The same Model Registry entry can be picked up by inference services, scheduled tasks, or downstream teammates. And the same editor you already work in – VS Code or Cursor – is now a first-class interface to Snowflake's compute plane.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to create a Snowflake-backed remote development environment and connect over Remote-SSH from VS Code or Cursor.\u003C/li\u003E\u003Cli\u003EHow to ingest ~1B rows of Tasty Bytes data from a public S3 stage into Snowflake in a couple of minutes on a Large warehouse.\u003C/li\u003E\u003Cli\u003EHow to enrich internal data with a Marketplace weather share – internal facts + external reference data, the way real DS/ML work looks.\u003C/li\u003E\u003Cli\u003EHow to use the Snowflake Kernel (Python + SQL) in a Jupyter notebook, with Python and SQL cells side by side.\u003C/li\u003E\u003Cli\u003EHow to work with CoCo inside the remote SSH session to accelerate feature engineering.\u003C/li\u003E\u003Cli\u003EHow to run plain .py files against the remote environment.\u003C/li\u003E\u003Cli\u003EHow to train an XGBoost model and log it to the Snowflake Model Registry.\u003C/li\u003E\u003Cli\u003EHow to clone and work with a private Git repo in the remote environment – via your editor's GitHub sign-in, or a Snowflake secret for editor-independent auth.\u003C/li\u003E\u003Cli\u003EHow persistent storage at \u003Cstrong\u003E/mnt/pd0\u003C/strong\u003E survives suspend and resume, while \u003Cstrong\u003E/root\u003C/strong\u003E doesn't.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/vscode-ext-remote-development\"\u003ERemote Development with the Snowflake Extension for Visual Studio Code\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/vscode-ext\"\u003ESnowflake Extension for Visual Studio Code\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-ml/notebooks-on-spcs\"\u003ESnowflake Notebooks (Container Runtime)\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-ml/model-registry/overview\"\u003ESnowflake ML – Model Registry\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code\"\u003ECortex Code (CoCo) in your code editor\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-getting-started-with-remote-development-vscode-extension\"\u003ECompanion repo: sfguide-getting-started-with-remote-development-vscode-extension\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://app.snowflake.com/marketplace/listing/GZSOZ1LLEL\"\u003EPelmorex Weather Source: Frostbyte – Snowflake Marketplace\u003C/a\u003E\u003C/li\u003E\u003Cli\u003ERelated Quickstart: \u003Ca href=\"https://www.snowflake.com/en/developers/guides/get-started-coco-vscode-extension/\"\u003EGetting Started with CoCo in the Snowflake VS Code Extension\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"description":"","title":"Base Quickstart CF",":type":"snowflake-site/components/contentfragment","elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\n## Overview\n\nDuration: 3\n\nRemote Development in the Snowflake Extension for VS Code provides you with a Snowflake-backed development environment you connect to over Remote-SSH, straight from VS Code or Cursor. There are no SSH keys to manage, no VMs to provision, and no local dependencies to maintain. The environment is a Snowflake Notebook service running on Snowpark Container Services, preloaded with Python, Jupyter, XGBoost, scikit-learn, and the Snowflake libraries, reachable from your editor as if it were localhost.\n\nIn this Quickstart, we'll build an end-to-end ML workflow against Snowflake compute, entirely from a local editor. The scenario is the following: you're an ML engineer on the Tasty Bytes team. Tasty Bytes runs food trucks in cities across the globe, and you've been asked to forecast daily sales per truck location. Weather clearly moves food-truck sales – so we'll enrich internal orders with a Marketplace weather share, feature-engineer with help from CoCo, train an XGBoost regressor, and log it to the Snowflake Model Registry, all from VS Code.\n\nLet's get started!\n\n### What You'll Learn\n\n- How to create a Snowflake-backed remote development environment and connect to it over Remote-SSH from VS Code or Cursor\n- How to run a Jupyter notebook that mixes Python and SQL cells using the Snowflake Kernel (Python + SQL) – the same kernel that powers Notebooks in Workspaces\n- How to use Cortex Code (CoCo) inside the remote SSH session to accelerate feature engineering and analysis\n- How to run a plain Python script (.py file) against the remote environment, beyond notebook cells\n- How to train an XGBoost sales-forecasting model and log it to the Snowflake Model Registry\n - How to clone and work with a private GitHub repo from the remote environment – using your editor's GitHub sign-in, or a Snowflake secret for editor-independent auth\n- How to suspend and resume the remote environment while preserving cloned repos, installed packages, and trained artifacts\n\n### Prerequisites\n\n- A Snowflake account (Enterprise or higher) with Marketplace access and the `ENABLE_NOTEBOOK_SERVICE_REMOTE_VS_CODE_ACCESS` parameter set to `TRUE` (on by default).\n- A role with `USAGE` on a compute pool that allows the `NOTEBOOK` workload type, plus permission to use external access integrations and secrets.\n- VS Code or Cursor installed locally, and a GitHub account.\n- Working knowledge of Python, Jupyter notebooks, Git, and basic SQL.\n\n### What You'll Need\n\n- A Snowflake account with a role that can create and run notebook services (`USAGE` on a compute pool that allows the `NOTEBOOK` workload type; permission to use external access integrations and secrets). If you don't have one, [sign up for a free 30-day trial](https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides). Select Enterprise edition.\n- The account parameter `ENABLE_NOTEBOOK_SERVICE_REMOTE_VS_CODE_ACCESS` set to `TRUE`. It's on by default; an account admin can confirm.\n- Access to the Snowflake Marketplace so you can acquire the free Pelmorex Weather Source: Frostbyte share (**setup.sql** acquires it programmatically – you need Marketplace access enabled on your account).\n- VS Code or Cursor installed locally.\n- The Microsoft Remote - SSH extension: [`ms-vscode-remote.remote-ssh`](https://marketplace.visualstudio.com/items?itemName=ms-vscode-remote.remote-ssh).\n- The Snowflake Extension for Visual Studio Code, version 1.39 or later (persistent storage and Git require v1.39+): [Marketplace listing](https://marketplace.visualstudio.com/items?itemName=snowflake.snowflake-vsc).\n- Snowflake CLI (`snow`) installed with a configured connection. See [Installing Snowflake CLI](https://docs.snowflake.com/en/developer-guide/snowflake-cli/installation/installation) and [Configuring Snowflake CLI connections](https://docs.snowflake.com/en/developer-guide/snowflake-cli/connecting/configure-connections).\n- Local `nc` (netcat) on your `PATH`. Preinstalled on macOS and most Linux distributions. On Windows you'll need to install a `netcat`-compatible executable and add it to `PATH`.\n- A GitHub account. You'll create a small private repo in step 8.\n- Comfort with Python, Jupyter notebooks, Git, and basic SQL. Familiarity with a gradient-boosted tree model (XGBoost or similar) is helpful for step 7.\n\n### What You'll Build\n\nBy the end of this guide, you'll have:\n\n- A running Snowflake remote development environment, reachable from VS Code or Cursor over Remote-SSH.\n- ~1B rows of Tasty Bytes orders and their supporting dimensions ingested into a Snowflake database.\n- A Marketplace weather share acquired and joined into a daily-per-location feature table.\n- A trained XGBoost sales-forecasting model logged to the Snowflake Model Registry.\n- A private Git repo cloned into **/mnt/pd0** (persistent storage), authenticated via your editor's GitHub sign-in or a Snowflake secret.\n- An actual vs. predicted sales chart for a held-out week, rendered inline in the remote notebook.\n\n\u003C!-- ------------------------ --\u003E\n## Set up your account\n\nDuration: 6\n\nLet's set up everything you'll need on the Snowflake side: a warehouse for the bulk load, the Tasty Bytes database and schemas, an external stage against the public S3 bucket, all the raw tables, a compute pool for the notebook service, an external access integration for outbound GitHub and PyPI, and a Snowflake Workspace to mount into the remote environment.\n\nWe'll do it all with one SQL script, run from your terminal via the Snowflake CLI.\n\n### Step 2a – Run the setup script\n\nClone the companion repo locally and run **setup.sql** with the Snowflake CLI:\n\n```bash\ngit clone https://github.com/Snowflake-Labs/sfguide-getting-started-with-remote-development-vscode-extension.git\ncd sfguide-getting-started-with-remote-development-vscode-extension\nsnow sql -f setup.sql\n```\n\nThis executes the whole script against your default `snow` connection.\n\nHere's what the script does:\n\n- Creates a Large warehouse (`tb_de_wh`). Large lets the `COPY INTO` finish in ~1-2 minutes across all raw tables. It gets sized back down to XSmall at the end.\n- Creates the `tb_101` database plus the `raw_pos`, `raw_customer`, `harmonized`, `analytics`, and `ml` schemas.\n- Creates a CSV file format and an external stage against **s3://sfquickstarts/frostbyte_tastybytes/** – the canonical public Tasty Bytes bucket.\n- Creates all eight raw tables: `country`, `franchise`, `location`, `menu`, `truck`, `order_header`, `order_detail`, and `customer_loyalty`.\n- Creates the `harmonized.orders_v` and `analytics.orders_v` views that stitch orders, trucks, menus, franchises, locations, and customers into one queryable surface.\n- Runs `COPY INTO` for every raw table. When this completes you'll have close to a billion rows across the fact tables.\n- Creates a compute pool (`tb_remote_dev_pool`, `CPU_X64_S`) for the notebook service. The `NOTEBOOK` workload type is allowed on all pools by default.\n- Creates a network rule (`egress_github_pypi`) and an external access integration (`tb_remote_dev_eai`) so the remote notebook service can reach `github.com` and `pypi.org` for cloning and `pip install`.\n- Acquires the Pelmorex Weather Source: Frostbyte Marketplace listing programmatically – requests it, accepts the legal terms, and installs it as the `frostbyte_weathersource` database.\n- Creates a Snowflake Workspace (`tb_forecast_ws`) – we'll mount this into the remote environment in step 4.\n- Confirms `ENABLE_NOTEBOOK_SERVICE_REMOTE_VS_CODE_ACCESS` is on.\n\nYou should see a final `note` row that reads: `Setup complete.`.\n\n\u003E **Note:** the Tasty Bytes S3 bucket is public, so it does NOT need to be in the external access integration. `COPY INTO` runs on Snowflake compute against the stage – the EAI governs outbound traffic from the notebook container, not stage access.\n\n### Step 2b – Verify the weather share\n\nWeather is one of the strongest predictors of food-truck sales, so **setup.sql** already acquired the free Pelmorex Weather Source: Frostbyte share from the Snowflake Marketplace and installed it as the `frostbyte_weathersource` database. Confirm it's ready with one command:\n\n```bash\nsnow sql -q \"SELECT date_valid_std, city_name, avg_temperature_air_2m_f, tot_precipitation_in FROM frostbyte_weathersource.onpoint_id.history_day WHERE date_valid_std BETWEEN '2024-01-01' AND '2024-01-07' AND country = 'US' LIMIT 10;\" --database frostbyte_weathersource --schema onpoint_id\n```\n\nYou should see weather observations for a US city across the first week of January 2024. That's the data we'll join to daily orders in step 6.\n\nGreat job – the Snowflake side is ready. Now let's get your editor set up.\n\n\u003C!-- ------------------------ --\u003E\n## Install the extension\n\nDuration: 3\n\nNow we'll install the Snowflake Extension for VS Code (or Cursor) plus the companion Remote - SSH extension, and sign in to your Snowflake account.\n\n### VS Code\n\n1. Open VS Code.\n2. In the Extensions view, search for Snowflake and install the extension published by Snowflake Inc. – make sure the version is 1.39.0 or later.\n3. In the Extensions view again, search for `ms-vscode-remote.remote-ssh`. The top results, called Remote - SSH, is the extension to install. Install it.\n4. Open the Snowflake extension from the Activity Bar. Sign in to your Snowflake account.\n\nCursor is built on the same VS Code extension model. Follow the same instructions above for installing the Remote - SSH extension in Cursor.\n\n\u003E **Note:** if you had an older Snowflake extension installed, reload your editor after upgrading. The **Remote Environments** panel that we'll use next only appears in v1.38+.\n\n![remote ssh extension](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/get-started-with-remote-development-snowflake-vscode-extension/remote-ssh.png)\n\n\u003C!-- ------------------------ --\u003E\n## Create your remote environment\n\nDuration: 5\n\nWe're ready to spin up the remote environment. From the Snowflake extension in your local editor, we'll create a notebook service running on the compute pool we provisioned earlier, and enable persistent storage so cloned Git repos and installed packages survive suspend/resume.\n\n1. In the Snowflake extension sidebar, expand the **Remote Environments** panel.\n2. Click the **+** sign - hovering over it should read: **Snowflake: Create Remote Development Environment**.\n3. Fill in the form:\n   - **Service name:** `tb_forecast_env` (or any Snowflake identifier; avoid collisions with existing SSH aliases in your **~/.ssh/config**).\n   - **Workspaces:** select `tb_forecast_ws`.\n   - **External access integrations:** select `tb_remote_dev_eai`.\n   - **Secrets:** leave this empty. (Only needed for the optional Snowflake-secret auth path in step 8, which recreates the service with a secret attached.)\n   - **Compute pool:** select `tb_remote_dev_pool`.\n   - Expand the **Service settings** section:\n     - **Compute type:** CPU\n     - **Runtime version:** accept the default (includes XGBoost, scikit-learn, and the Snowflake ML libraries this guide uses).\n     - **Enable persistent storage:** check this box.\n4. Click **Create**.\n\n\u003E **Important:** **Enable persistent storage** can only be set at create time – you cannot add it to an existing service. If you skip it now, you'll have to delete this service and create a new one. Persistent storage mounts SPCS block storage at **/mnt/pd0** in the remote container. Cloned repos and pip installs live there across suspend and resume.\n\nThe service enters PENDING status while Snowflake provisions the container. This takes a few minutes. The panel refreshes every 30 seconds; you can also refresh manually. When the status flips to RUNNING, you're ready to connect.\n\n![create env](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/get-started-with-remote-development-snowflake-vscode-extension/create-env.png)\n\n\u003C!-- ------------------------ --\u003E\n## Connect and open the notebook\n\nDuration: 8\n\nLet's connect over SSH.\n\n1. In the **Remote Environments** panel, find your `tb_forecast_env` service.\n2. Click **Connect to Remove Service: SSH**.\n3. When prompted, select the `tb_forecast_ws` workspace to mount into the remote environment. Press Enter.\n4. A new editor window opens, connected over SSH to the remote container at **/root**.\n\n![connect](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/get-started-with-remote-development-snowflake-vscode-extension/connect.png)\n\n![root](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/get-started-with-remote-development-snowflake-vscode-extension/root.png)\n\nBehind the scenes, the extension started a local proxy on `127.0.0.1` that forwards SSH traffic to Snowflake over a secure WebSocket, wrote a `Host \u003Cservice-name\u003E` entry to your **~/.ssh/config**, installed the required extensions on the remote host, and opened the folder. You didn't have to do any of this manually.\n\n\u003E **Note:** The very first connection installs the Python, Jupyter, and Snowflake extensions on the remote host. This can take a couple of minutes. If the Snowflake Kernel option doesn't appear when you try to run a cell, wait for the installs to finish and reload the remote window (**Developer: Reload Window** from the Command Palette).\n\n\u003E **Important:** The remote window opens at **/root**, which is ephemeral – anything you write there is wiped when the service suspends. Do all of your work under **/mnt/pd0** (the persistent drive you enabled at create time). The next step clones the companion repo into **/mnt/pd0** for exactly this reason.\n\n### Clone the companion repo into persistent storage\n\nLet's pull the notebook and helper Python modules we'll be using. In the remote window, open a terminal (from the **Terminal** menu, choose **New Terminal**) and clone the repo into **/mnt/pd0**:\n\n```bash\ncd /mnt/pd0\ngit clone https://github.com/Snowflake-Labs/sfguide-getting-started-with-remote-development-vscode-extension.git\ncd sfguide-getting-started-with-remote-development-vscode-extension\n```\n\nAdd the folder to your workspace: from the **File** menu, choose **Add Folder to Workspace...** and pick **/mnt/pd0/sfguide-getting-started-with-remote-development-vscode-extension**. You should see **README.md**, **setup.sql**, **cleanup.sql**, **forecast.ipynb**, and **train.py** in the Explorer.\n\n### Open the notebook\n\nOpen **forecast.ipynb**. In the search bar, type **Snowflake: Start Notebook Kernel**, then select **Snowflake Kernel (Python + SQL)** from the kernel picker above the notebook.\n\n\u003E **Note:** Run **Snowflake: Start Notebook Kernel** from the remote window (the editor window connected over SSH), not your local window – the kernel lives on the remote host. If the action doesn't appear, the remote extensions are still installing: wait for Snowflake, Python, and Jupyter to finish, then run **Developer: Reload Window** and reopen the notebook.\n\nThis is the same kernel that powers Snowflake Notebooks in Snowsight Workspaces. Python and SQL cells run side by side without switching kernels – Python cells run in the container's Python interpreter, and SQL cells run against your Snowflake account.\n\nRun the first SQL cell to confirm the raw tables loaded:\n\n```sql\nSELECT 'order_header' AS tbl, COUNT(*) AS row_count FROM tb_101.raw_pos.order_header\nUNION ALL SELECT 'order_detail', COUNT(*) FROM tb_101.raw_pos.order_detail\nUNION ALL SELECT 'location',     COUNT(*) FROM tb_101.raw_pos.location\nUNION ALL SELECT 'truck',        COUNT(*) FROM tb_101.raw_pos.truck\nUNION ALL SELECT 'menu',         COUNT(*) FROM tb_101.raw_pos.menu\nUNION ALL SELECT 'customer_loyalty', COUNT(*) FROM tb_101.raw_customer.customer_loyalty\nORDER BY 2 DESC;\n```\n\nYou should see `order_detail` at the top with the largest row count – hundreds of millions of line items. Together with `order_header` this is close to a billion rows. You're querying that volume from your local editor, and the query runs on Snowflake compute.\n\nThe remote environment isn't limited to notebooks – you can open and run plain Python files against the same remote interpreter. We'll do this in a later step.\n\nGreat job. You're now running Snowflake-backed compute from your local editor. Let's put it to work.\n\n![kernel](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/get-started-with-remote-development-snowflake-vscode-extension/kernel.png)\n\n\n\u003C!-- ------------------------ --\u003E\n## Enrich and feature-engineer with CoCo\n\nDuration: 8\n\nWe have 1B rows of raw orders on one side and a weather share on the other. Let's turn them into a modeling-ready feature table.\n\n### Aggregate orders to daily-per-location grain\n\nRun the next cell in the notebook:\n\n```sql\nCREATE OR REPLACE TABLE tb_101.ml.daily_sales AS\nSELECT\n    DATE(order_ts)             AS date,\n    location_id,\n    ANY_VALUE(location_city)   AS city,\n    ANY_VALUE(location_region) AS region,\n    ANY_VALUE(country)         AS country,\n    SUM(price)                 AS daily_sales,\n    COUNT(DISTINCT order_id)   AS order_count\nFROM tb_101.harmonized.orders_v\nGROUP BY 1, 2;\n\nSELECT COUNT(*) AS rows_in_daily_sales FROM tb_101.ml.daily_sales;\n```\n\nHere's what the code does:\n\n- Reads from `harmonized.orders_v`, the view that joins orders, line items, trucks, menus, and locations.\n- Groups by `(date, location_id)` to get one row per truck location per day.\n- Materializes the result to `tb_101.ml.daily_sales` – this is now a small table (tens of thousands of rows) that we can work with in memory.\n\n### Join the Marketplace weather\n\nNow let's bring in weather. The weather view is keyed at postal-code + date grain, so joining directly on city fans every city out to all of its postal codes (Denver alone has ~74) and explodes the row count. We first pre-aggregate weather to one row per city per date, then join. We also programmatically bump the warehouse to MEDIUM for this step and size it back down right after. Run the next cell (note that it may take about 3 minutes to complete):\n\n```sql\nALTER WAREHOUSE tb_de_wh SET WAREHOUSE_SIZE = 'MEDIUM';\n\nCREATE OR REPLACE TABLE tb_101.ml.daily_sales_weather AS\nWITH weather_by_city AS (\n    SELECT\n        date_valid_std,\n        UPPER(city_name)              AS city_upper,\n        AVG(avg_temperature_air_2m_f) AS avg_temperature_air_2m_f,\n        AVG(tot_precipitation_in)     AS tot_precipitation_in,\n        AVG(avg_wind_speed_100m_mph)  AS avg_wind_speed_100m_mph\n    FROM frostbyte_weathersource.onpoint_id.history_day\n    WHERE date_valid_std BETWEEN (SELECT MIN(date) FROM tb_101.ml.daily_sales)\n                             AND (SELECT MAX(date) FROM tb_101.ml.daily_sales)\n      AND UPPER(city_name) IN (SELECT DISTINCT UPPER(city) FROM tb_101.ml.daily_sales)\n    GROUP BY date_valid_std, UPPER(city_name)\n)\nSELECT\n    ds.date,\n    ds.location_id,\n    ds.city,\n    ds.daily_sales,\n    ds.order_count,\n    w.avg_temperature_air_2m_f                     AS temp_f,\n    (w.avg_temperature_air_2m_f - 32.0) * 5.0/9.0  AS temp_c,\n    w.tot_precipitation_in * 25.4                  AS precip_mm,\n    w.avg_wind_speed_100m_mph * 1.60934            AS wind_kph\nFROM tb_101.ml.daily_sales ds\nJOIN weather_by_city w\n  ON w.date_valid_std = ds.date\n AND w.city_upper = UPPER(ds.city)\nWHERE ds.daily_sales IS NOT NULL;\n\nALTER WAREHOUSE tb_de_wh SET WAREHOUSE_SIZE = 'XSMALL';\n\nSELECT COUNT(*) AS joined_rows FROM tb_101.ml.daily_sales_weather;\n```\n\nHere's what the code does:\n\n- Bumps `tb_de_wh` to MEDIUM for the weather scan, then back to XSMALL when done.\n- Pre-aggregates the weather view to one row per (`date`, `city`) in `weather_by_city`, averaging across the postal codes in each city. This is what avoids the postal-code fan-out that would otherwise multiply every sales row.\n- Filters the weather scan to the date range and cities present in `daily_sales`.\n- Joins the pre-aggregated weather to `daily_sales` on `date` + `city`, converts imperial units to metric, and drops rows where the join failed or sales are null.\n- Materializes `tb_101.ml.daily_sales_weather` – the base for feature engineering.\n\n### Feature-engineer with CoCo\n\nNow the fun part. We need calendar, lag, and rolling features on the joined data – the standard patterns for time-series forecasting. We'll let CoCo generate them.\n\nOpen the CoCo panel in the remote window from the Activity Bar and try prompts like:\n\n- \"Add day-of-week, month, and is_weekend features to a daily sales DataFrame.\"\n- \"Add lag-1 and lag-7 daily-sales features per location_id.\"\n- \"Add a 7-day rolling mean of precip_mm per location_id.\"\n\nCoCo runs inside the remote SSH session with full access to Snowflake compute and data. By default it shows the generated code in its panel for you to read or copy – it won't change the notebook on its own. If you attach the notebook as context with `@`, CoCo will instead try to apply its code as edits to the file.\n\nEither way, **you don't need to accept any edits**: the two cells below are already in the notebook – one defines the transforms, one applies them. Run them as-is to continue, and use CoCo alongside to see how you'd generate them yourself.\n\nThe first cell defines three small pandas transforms:\n\n```python\nimport pandas as pd\n\ndef add_calendar_features(df, date_col='date'):\n    df = df.copy()\n    df[date_col] = pd.to_datetime(df[date_col])\n    df['day_of_week'] = df[date_col].dt.dayofweek\n    df['month'] = df[date_col].dt.month\n    df['is_weekend'] = df['day_of_week'].isin([5, 6]).astype(int)\n    return df\n\ndef add_lag_features(df, group_cols, target_col='daily_sales', lags=(1, 7)):\n    df = df.copy().sort_values(group_cols + ['date'])\n    for lag in lags:\n        df[f'{target_col}_lag_{lag}'] = df.groupby(group_cols)[target_col].shift(lag)\n    return df\n\ndef add_rolling_precip(df, group_cols, precip_col='precip_mm', window=7):\n    df = df.copy().sort_values(group_cols + ['date'])\n    df[f'{precip_col}_roll_{window}'] = (\n        df.groupby(group_cols)[precip_col]\n        .rolling(window=window, min_periods=1)\n        .mean()\n        .reset_index(level=list(range(len(group_cols))), drop=True)\n    )\n    return df\n```\n\nThe second cell loads the joined table and chains the transforms via `.pipe()`:\n\n```python\nfrom snowflake.snowpark.context import get_active_session\n\nsession = get_active_session()\nraw = session.table('tb_101.ml.daily_sales_weather').to_pandas()\nraw.columns = [c.lower() for c in raw.columns]\n\nfeatures_df = (\n    raw\n    .pipe(add_calendar_features)\n    .pipe(add_rolling_precip, group_cols=['location_id'])\n    .pipe(add_lag_features, group_cols=['location_id'])\n    .dropna()\n    .reset_index(drop=True)\n)\nfeatures_df.head()\n```\n\nHere's what the code does:\n\n- Gets the active Snowflake session; the remote container is already authenticated.\n- Materializes the joined table to a pandas DataFrame in the container.\n- Chains the three transforms with `.pipe()`: calendar features first, then a 7-day rolling precipitation feature, then lag-1 and lag-7 sales features.\n- Drops the initial rows containing NaN lag values.\n\n### Land the feature table back in Snowflake\n\nPush the DataFrame back to Snowflake so **train.py** can consume it:\n\n```python\nsession.write_pandas(\n    features_df,\n    table_name='FEATURE_TABLE',\n    database='TB_101',\n    schema='ML',\n    auto_create_table=True,\n    overwrite=True,\n)\nsession.table('tb_101.ml.feature_table').count()\n```\n\nNow the feature table lives in Snowflake, ready for training.\n\n\u003C!-- ------------------------ --\u003E\n## Train and register the model\n\nDuration: 6\n\nThis is the step where we ship a model beyond a notebook. We'll train an XGBoost regressor on the feature table, evaluate it on the last week of data, chart actual vs predicted, and log the model to the Snowflake Model Registry – where it becomes discoverable by other teammates and downstream jobs.\n\n### Train from a .py file\n\n**train.py** in the repo is a plain Python script – no notebook required. Let's run it against the remote environment. In the notebook, execute this cell (use the full path so it runs regardless of the notebook's working directory):\n\n```python\n!python /mnt/pd0/sfguide-getting-started-with-remote-development-vscode-extension/train.py \\\n    --source-table tb_101.ml.feature_table \\\n    --database tb_101 \\\n    --schema ml \\\n    --model-name tb_sales_forecaster \\\n    --version v1\n```\n\nYou can also run it directly in the remote terminal (`python /mnt/pd0/sfguide-getting-started-with-remote-development-vscode-extension/train.py --source-table ...`). Either way it executes on the Snowflake-hosted container. Because a script run this way is a separate process from the notebook kernel, **train.py** builds its own Snowflake session from the container's credentials (falling back from the kernel's active session), so no extra auth setup is needed.\n\nHere's what **train.py** does:\n\n- Builds a Snowflake session – reusing the notebook's active session in-kernel, or creating one from the container's OAuth token when run standalone.\n- Loads the feature table into a pandas DataFrame using `session.table(...).to_pandas()`.\n- Does a time-based train/test split – the last 7 days are held out. Never a random split for forecasting.\n- Fits an `XGBRegressor` with sensible defaults (400 trees, depth 6, learning rate 0.05).\n- Evaluates on the holdout: MAPE and RMSE.\n- Logs the fitted model to the Snowflake Model Registry via `Registry.log_model()`, along with a sample input, the version, and a comment carrying the holdout metrics. It logs for both `WAREHOUSE` and `SNOWPARK_CONTAINER_SERVICES` so you can run inference either way – without this, a Container Runtime model defaults to SPCS-only and `model_ref.run()` on a pandas frame fails.\n\nYou should see output ending in something like (your exact metrics will vary):\n\n```text\nLoading features from tb_101.ml.feature_table ...\n  327,515 rows across 9968 locations\n  Train: 322,766 rows | Test: 4,749 rows\nHoldout MAPE: 0.40  |  RMSE: 8,621.66\nLogging model to registry: tb_101.ml.tb_sales_forecaster (v1) ...\nDone. The model is now discoverable from the Model Registry.\n```\n\n\u003E **Note:** A couple of `Failed to get kernel ID for per-kernel logging` lines may print at the top. They are benign and won't impact any of the work we're doing.\n\nYour model is now live in the Model Registry. You can list it from any session:\n\n```sql\nSHOW MODELS IN SCHEMA tb_101.ml;\n```\n\nGreat job – you shipped a model. From here, other teammates can retrieve it, run predictions, or promote it to a downstream inference job – all without ever seeing your notebook.\n\n### Chart actual vs predicted\n\nLet's visualize the holdout. Run the final cell of the notebook:\n\n```python\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom snowflake.ml.registry import Registry\n\nregistry = Registry(session=session, database_name='TB_101', schema_name='ML')\nmodel_ref = registry.get_model('tb_sales_forecaster').version('v1')\n\nholdout = features_df[features_df['date'] \u003E features_df['date'].max() - pd.Timedelta(days=7)].copy()\none_loc = holdout[holdout['location_id'] == holdout['location_id'].value_counts().idxmax()].copy()\n\nFEATURE_COLS = [\n    'day_of_week', 'month', 'is_weekend',\n    'temp_c', 'precip_mm', 'wind_kph',\n    'precip_mm_roll_7', 'daily_sales_lag_1', 'daily_sales_lag_7',\n]\n# model_ref.run returns a DataFrame; the prediction is the last column.\none_loc['predicted'] = model_ref.run(one_loc[FEATURE_COLS]).iloc[:, -1].to_numpy()\n\nfig, ax = plt.subplots(figsize=(10, 4))\nax.plot(one_loc['date'], one_loc['daily_sales'], marker='o', label='Actual')\nax.plot(one_loc['date'], one_loc['predicted'], marker='x', label='Predicted')\nax.set_title(f\"Location {int(one_loc['location_id'].iloc[0])} – actual vs predicted (holdout week)\")\nax.set_ylabel('Daily sales (USD)')\nax.legend()\nplt.xticks(rotation=45)\nplt.tight_layout()\nplt.show()\n```\n\nHere's what the code does:\n\n- Retrieves the model from the registry – we don't reload the Python object, we ask Snowflake for it.\n- Picks the single location with the most rows in the holdout.\n- Runs inference through the registered model via `model_ref.run(...)`.\n- Charts actual vs predicted sales for that location across the holdout week.\n\nYou should see the two lines tracking each other reasonably closely. The fit has room to tune, and you've built a working forecaster end to end in a fraction of an hour.\n\n![chart](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/get-started-with-remote-development-snowflake-vscode-extension/chart.png)\n\n\u003C!-- ------------------------ --\u003E\n## Work with private repos\n\nDuration: 5\n\nData science and machine learning work often involves the use of source control and git repositories. The suggested path for working with repos using remote development is to clone them into the **/mnt/pd0** directory. This is the persisted storage directory, and cloning into it will ensure that the repo will be available to you in subsequent SSH sessions.\n\nGit authentication is typically handled by your editor's GitHub sign-in – no secret or credential helper needed. If your remote VS Code (or Cursor) session is signed into GitHub, many git operations will work with little to no configuration. \n\nTo quickly check if you are logged into GitHub, do the following:\n\n1. Click on the **Accounts** icon in VS Code (or Cursor) **in the remote window**. \n\n2. If you see something like \"your-username (GitHub)\", then you're signed into GitHub. \n\nIf you're signed in, then the typical `git clone` workflow applies and automatically works for repos that you own or have been added to as a collaborator:\n\n```bash\ncd /mnt/pd0\ngit clone https://github.com/\u003Cyour_github_username\u003E/\u003Crepo-name\u003E.git # Path to repo\ncd \u003Crepo-name\u003E\n```\n\nIf you are running git under a service identity instead of your own, store an access token in a **Snowflake secret**, mount it into the service, and point git at it with a credential helper. This is the common pattern for team and production environments. For details, see [Remote Development with the Snowflake Extension for Visual Studio Code](https://docs.snowflake.com/en/user-guide/vscode-ext-remote-development).\n\n### Set your git identity at the local level to persist it\n\nBefore your first commit in this repo, git needs an author identity. Set it at the local repo level using `--local` to persist your git identity from session to session. Using `--global` will write the config to **/root**, which is wiped on session suspend. Local config lives in the repo on **/mnt/pd0** and persists:\n\n```bash\ncd /mnt/pd0/tb-forecast-private\ngit config --local user.name \"Your Name\"\ngit config --local user.email \"you@example.com\"\n```\n\n\u003E **Important:** If you commit before setting this, git raises `Author identity unknown` and suggests `git config --global user.email ...`. Don't follow that suggestion here – `--global` writes to **/root/.gitconfig**, which is wiped on suspend, so your identity vanishes on the next resume. Use `--local` (as above) so it persists with the repo on **/mnt/pd0**.\n\n\n\u003C!-- ------------------------ --\u003E\n## Suspend and resume\n\nDuration: 2\n\nLet's confirm your work survives a suspend.\n\n1. Save any unsaved files. In the Remote Environments panel, stop the `tb_forecast_env` environment and close the remote window. The environment status will flip to SUSPENDED.\n\n2. Resume the service and pick the same workspace when connecting.\n\n3. In the terminal on the remote, confirm your files are intact:\n\n```bash\nls /mnt/pd0/\u003Cname-of-repo-you-cloned\u003E\nls /root\n```\n\nEverything under **/mnt/pd0** – the repo, your pip installs – is exactly as you left it. **/root** is empty: it's the ephemeral filesystem, wiped on suspend. The registered model persists in Snowflake regardless (run `SHOW MODELS IN SCHEMA tb_101.ml;` for example).\n\n\u003C!-- ------------------------ --\u003E\n## Clean up\n\nDuration: 2\n\nWhen you're done, tear everything down. Do it in this order, because the cleanup script drops the compute pool the remote service runs on – so the service has to go first.\n\n1. Delete the remote service. In VS Code, in the Remote Environments panel: stop the remote environment and then delete it.\n\n2. Drop the Snowflake objects. From the companion repo, run **cleanup.sql** with the Snowflake CLI (or paste its contents into a Snowsight worksheet):\n\n```bash\nsnow sql -f cleanup.sql\n```\n\nThat script:\n\n- Drops the `tb_101` database, which cascades to everything under it – tables, views, feature tables, model registry entries, and the Snowflake Workspace.\n- Drops the compute pool and external access integration.\n- Drops the Large warehouse.\n- Drops the `frostbyte_weathersource` database acquired from the Marketplace.\n\n\u003C!-- ------------------------ --\u003E\n## Conclusion and Resources\n\nDuration: 1\n\nCongratulations! You built and shipped an end-to-end ML workflow – data ingest, feature engineering, model training, and registry logging – entirely against Snowflake compute, from your local editor, without provisioning a VM, managing an SSH key, or moving a single row down to your laptop.\n\nThe same remote environment that ran this notebook can host your team's other ML projects. The same Model Registry entry can be picked up by inference services, scheduled tasks, or downstream teammates. And the same editor you already work in – VS Code or Cursor – is now a first-class interface to Snowflake's compute plane.\n\n### What You Learned\n\n- How to create a Snowflake-backed remote development environment and connect over Remote-SSH from VS Code or Cursor.\n- How to ingest ~1B rows of Tasty Bytes data from a public S3 stage into Snowflake in a couple of minutes on a Large warehouse.\n- How to enrich internal data with a Marketplace weather share – internal facts + external reference data, the way real DS/ML work looks.\n- How to use the Snowflake Kernel (Python + SQL) in a Jupyter notebook, with Python and SQL cells side by side.\n- How to work with CoCo inside the remote SSH session to accelerate feature engineering.\n- How to run plain .py files against the remote environment.\n- How to train an XGBoost model and log it to the Snowflake Model Registry.\n- How to clone and work with a private Git repo in the remote environment – via your editor's GitHub sign-in, or a Snowflake secret for editor-independent auth.\n- How persistent storage at **/mnt/pd0** survives suspend and resume, while **/root** doesn't.\n\n### Related Resources\n\n- [Remote Development with the Snowflake Extension for Visual Studio Code](https://docs.snowflake.com/en/user-guide/vscode-ext-remote-development)\n- [Snowflake Extension for Visual Studio Code](https://docs.snowflake.com/en/user-guide/vscode-ext)\n- [Snowflake Notebooks (Container Runtime)](https://docs.snowflake.com/en/developer-guide/snowflake-ml/notebooks-on-spcs)\n- [Snowflake ML – Model Registry](https://docs.snowflake.com/en/developer-guide/snowflake-ml/model-registry/overview)\n- [Cortex Code (CoCo) in your code editor](https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code)\n- [Companion repo: sfguide-getting-started-with-remote-development-vscode-extension](https://github.com/Snowflake-Labs/sfguide-getting-started-with-remote-development-vscode-extension)\n- [Pelmorex Weather Source: Frostbyte – Snowflake Marketplace](https://app.snowflake.com/marketplace/listing/GZSOZ1LLEL)\n- Related Quickstart: [Getting Started with CoCo in the Snowflake VS Code Extension](https://www.snowflake.com/en/developers/guides/get-started-coco-vscode-extension/)\n","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],":items":{},":itemsOrder":[],"isDeveloperGuidesPage":false,"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-5fd6bad4e2","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"none","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-0a8406b5b3",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-a0d9c93bc7","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-09-28",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-3c62760a2d","additionalClasses":"qs-disclaimer-text","text":"\u003Cp\u003E\u003Cspan style=\"color: #666;\"\u003EThis content is provided as is, and is not maintained on an ongoing basis. 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