# Getting Started on GCP

Follow these instructions to get started with HEAVY.AI on Google Cloud Platform (GCP).

## Prerequisites

You must have a Google Cloud Platform account. If you do not have an account, follow [these instructions](https://console.cloud.google.com/freetrial?page=0) to sign up for one.

To launch HEAVY.AI on Google Cloud Platform, you select and configure an instance.

## Launching Your HEAVY.AI Instance

On the solution Launcher Page, click **Launch on Compute Engine** to begin configuring your deployment.

{% hint style="warning" %}
Before deploying a solution with a GPU machine type, avoid potential deployment failure by [checking your available quota for a project](https://cloud.google.com/compute/quotas#checking_your_quota) to make sure that you have not exceeded your limit.
{% endhint %}

To launch HEAVY.AI on Google Cloud Platform, you select and configure a GPU-enabled instance.

1. Search for HEAVY.AI on the [heavyai-launcher-public project on Google Cloud Platform](https://console.cloud.google.com/marketplace/partners/mapd-launcher-public?project=mapd-launcher-public\&folder\&organizationId=616933182455), and select a solution. HEAVY.AI has four instance types:
   * [HEAVY.AI Enterprise Edition (BYOL)](https://console.cloud.google.com/marketplace/details/omnisci/omnisci-enterprise-edition-byol?project=mapd-launcher-public\&folder\&organizationId=616933182455).
   * [HEAVY.AI Enterprise Edition for CPU (BYOL)](https://console.cloud.google.com/marketplace/details/omnisci/omnisci-enterprise-edition-cpu-byol?project=mapd-launcher-public\&folder\&organizationId=616933182455).
   * [HEAVY.AI Open Source Edition](https://console.cloud.google.com/marketplace/details/omnisci/omnisci-open-source-edition?project=mapd-launcher-public\&folder\&organizationId=616933182455).
   * [HEAVY.AI for CPU (Open Source)](https://console.cloud.google.com/marketplace/details/omnisci/omnisci-open-source-db-cpu?project=mapd-launcher-public\&folder\&organizationId=616933182455).
2. On the solution Launcher Page, click **Launch** to begin configuring your deployment.
3. On the new deployment page, configure the following:
   * **Deployment name**
   * **Zone**
   * **Machine type** - Click **Customize** and configure **Cores** and **Memory**, and select **Extend memory** if necessary.

     ![gcp\_machinetype](https://1128335264-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F6xvZtvY4UaNnTQRXqbwd%2Fuploads%2Fgit-blob-f26d37b4d705d4228a398a548c2a0013174f6b01%2F4_gcp_machinetype.png?alt=media)
   * **GPU type**. (Not applicable for CPU configurations.)
   * **Number of GPUs** - (Not applicable for CPU configurations.) Select the number of GPUs; subject to quota and GPU type by region. For more information about GPU-equipped instances and associated resources, see [GPU Models for Compute Engine](https://cloud.google.com/compute/docs/gpus/#gpus-list).
   * **Boot disk type**
   * **Boot disk size in GB**
   * **Networking** - Set the Network, Subnetwork, and External IP.
   * **Firewall** - Select the required ports to allow TCP-based connectivity to HEAVY.AI. Click **More** to set IP ranges for port traffic and IP forwarding.
4. Accept the GCP Marketplace Terms of Service and click **Deploy**.
5. In the Deployment Manager, click the instance that you deployed.
6. Launch the Heavy Immerse client:

   * Record the Admin password (Temporary).
   * Click the Site address link to go to the Heavy Immerse login page. Enter the password you recorded, and click **Connect**.
   * Copy your license key from the registration email message. If you have not received your license key, contact your Sales Representative or register for your 30-day trial [here](https://www.omnisci.com/platform/downloads/).
   * Connect to Immerse using a web browser connected to your host machine on port 6273. For example, `http://heavyai.mycompany.com:6273`.
   * When prompted, paste your license key in the text box and click **Apply**.
   * Click Connect to start using HEAVY.AI.

   On successful login, you see a list of sample dashboards loaded into your instance.


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