---
title: "Deployment"
description: "An introduction to the local Research Environment. For more comprehensive information on research, see the Research Environment documentation."
canonical: https://www.quantconnect.com/docs/v2/local-platform/research/deployment
markdown: https://www.quantconnect.com/docs/v2/local-platform/research/deployment.md
language: python
source: https://github.com/QuantConnect/Documentation/tree/master/02%20Local%20Platform/11%20Research/02%20Deployment
modified: 2026-10-28
index: https://www.quantconnect.com/llms.txt
---

# Deployment

[Documentation](https://www.quantconnect.com/docs/v2.md) > [Local Platform](https://www.quantconnect.com/docs/v2/local-platform.md) > Research > Deployment

> Code examples are shown in Python. Append `?lang=csharp` to this URL for the C# version of this page.

## Introduction

This page is an introduction to the Research Environment for the Local Platform Lab. For more comprehensive information on using research notebooks, see the [Research Environment](https://www.quantconnect.com/docs/v2/research-environment.md) documentation product.

## Nodes

A **node** is a term to describe the compute hardware when your notebook runs. We create "virtual nodes", which enable you to spin up multiple research notebook with your on-premises hardware. When you run multiple notebooks, each one runs in a separate container on the same host machine. To view all your virtual nodes, see the [Resources panel](https://www.quantconnect.com/docs/v2/local-platform/development-environment/resource-management.md).

![](https://cdn.quantconnect.com/i/tu/lean-img.png)

## Concurrent Research

Concurrent research is the process of running multiple notebooks at the same time. Concurrent research speeds up your research process because you don't have to wait while a cell from a notebook finishes executing. You can run as many concurrent local notebooks as your CPU and RAM can handle.

## Deployment Targets

Local Platform provides multiple [deployment targets](https://www.quantconnect.com/docs/v2/local-platform/key-concepts/deployment-targets.md) to enable you to [open notebooks](https://www.quantconnect.com/docs/v2/local-platform/research/getting-started.md#03-Open-Notebooks) on-premises and in QuantConnect Cloud. When you open a notebook, it uses the hardware and data that's available on the deployment target machine.

## Select Kernel

When you [open a notebook](https://www.quantconnect.com/docs/v2/local-platform/research/getting-started.md#03-Open-Notebooks), it automatically tries to connect to the correct Jupyter server and select the correct kernel. If it doesn't correctly select the kernel, the top-right corner of the notebook displays a **Select Kernel** button and the notebook won't let you run any of the cells. If this occurs, follow these steps to fix the issue:

1. In the top-right corner of the notebook, click **Select Kernel**.
2. In the Select Kernel window, click **QuantConnect Research Server Collection**.
3. In the Select a Jupyter Server window, click the first option.
4. In the Select a Kernel window, click **Foundation-Py-Default**.

## Save Notebooks

To save notebooks, press **CTRL+S**.

When you save a notebook, it saves the content of the cells. If you [stop the Research Environment node](https://www.quantconnect.com/docs/v2/local-platform/research/getting-started.md#05-Stop-Nodes) or even just [close the notebook](https://www.quantconnect.com/docs/v2/local-platform/projects/files.md#07-Close-Files), when you [open the notebook](https://www.quantconnect.com/docs/v2/local-platform/research/getting-started.md#03-Open-Notebooks) again, you'll see the cell output. However, if you stop the Research Environment node and [close the project](https://www.quantconnect.com/docs/v2/local-platform/projects/getting-started.md#05-Close-Projects), you'll need to run the cells again to generate the output.