# Quick Start ## Prerequisites To begin using Gradient, follow these preliminary steps: 1. [Create a Paperspace account ](https://console.paperspace.com/signup?gradient=true) 2. Optional: [Create a team ](https://support.paperspace.com/hc/en-us/articles/360010359213-Creating-and-Managing-Paperspace-Teams)to invite collaborators Now you can create Notebooks, Workflows, Deployments, and more! {% embed url="" %} ## Logging in for the first time When you first log into the Paperspace Console, you can select Gradient from the product dropdown: ![](/files/-MYbdsyLINABf1-rLkRn) ## First Create a Project Projects help organize your work. To get started, just choose a name and click create. ![](/files/-MkrlryiZjN9Z-N80zb2) ## Create a Notebook After creating a Project, Notebooks can be created on the Notebooks tab within the Project. Just select a [template](/gradient/explore-train-deploy/notebooks/notebook-containers.md), choose your [instance type](/gradient/more/instance-types.md), and then click create. {% hint style="success" %} Check out the [FREE GPU](/gradient/more/instance-types/free-instances.md) option when launching Notebooks! {% endhint %} ![](/files/-MkxdAl_RIJYmM3tU4YD) {% hint style="success" %} Check out the [ML Showcase](https://ml-showcase.paperspace.com/) for a list of projects you can fork into your own account {% endhint %} You can stop, start, fork, and swap out the instance type anytime. Choose from a wide selection of pre-configured templates or bring your own. ## Create a Workflow You can automate machine learning tasks using Workflows. You can define a workflow once and use it repeatedly to perform simple or complex MLOps activities, such as pre-processing data, training models, and creating or updating deployments. ![](/files/-MkwzmTHv5RfUmiyV6In) ### Run a Workflow from a GitHub trigger (recommended) This requires the project to be linked to a GitHub repository. Follow the instructions on the page to install the Gradient **Github App** into your Github account, and select the git repo you want to associate with project. Alternatively you can select one of the **sample repos.** If you choose to use your own git repo, you will be prompted to add a YAML file to your repo that defines the Workflow steps. ### **Run a Workflow from the CLI (advanced)** 1. [**Install the Gradient CLI**](/gradient/get-started/quick-start/install-the-cli.md) 2. [**Connect your account**](/gradient/get-started/quick-start/install-the-cli.md#connecting-your-account) 3. **Create a Workflow** This step is only needed if you didn't already create a default `demo workflow` in the web interface. Specify a name for the Workflow and a `projectId`. Use the `projectId` from the project you created earlier. ```bash gradient workflows create \ --name \ --projectId ``` To see a list of your projects and `projectIds` run `gradient projects list`. For a list of Workflows within a project run `gradient workflows list --projectId `. 4. **Download or copy the sample Workflow Spec to your computer** Here is the Workflow Spec for reference: ```yaml jobs: CloneRepo: resources: instance-type: C5 outputs: repo: type: volume uses: git-checkout@v1 with: url: https://github.com/NVlabs/stylegan2.git StyleGan2: resources: instance-type: P4000 needs: - CloneRepo inputs: repo: CloneRepo.outputs.repo outputs: generatedFaces: type: dataset with: ref: demo-dataset uses: script@v1 with: script: |- pip install scipy==1.3.3 pip install requests==2.22.0 pip install Pillow==6.2.1 cp -R /inputs/repo /stylegan2 cd /stylegan2 python run_generator.py generate-images \ --network=gdrive:networks/stylegan2-ffhq-config-f.pkl \ --seeds=6600-6605 \ --truncation-psi=0.5 \ --result-dir=/outputs/generatedFaces image: tensorflow/tensorflow:1.14.0-gpu-py3 ``` Place the contents in a file named `workflow.yaml`. 5. **Run the Workflow from the CLI** The following command will run an instance of the Workflow in your project. Be sure to replace `` with your **Workflow ID**. ```bash gradient workflows run \ --id \ --path ./workflow.yaml ``` {% hint style="info" %} **Note:** We recommend stashing your API key with `gradient apiKey XXXXXXXXXXXXX` or you can add your API key as an option on each command. See [Connecting Your Account](/gradient/get-started/quick-start/install-the-cli.md#connecting-your-account). {% endhint %} Behind the scenes, your Workflow will be executed on the Gradient public cluster. Congratulations! You ran your first Workflow on Gradient 🚀 ## Explore the rest of the platform From [Models](/gradient/data/models.md) to [Deployments](https://docs.paperspace.com/gradient/explore-train-deploy/deployments-preview), there's a lot more to the Gradient platform. We recommend using the Web UI to explore the primary components, including Notebooks, and basic usage of Workflows & Deployments. For more advanced non-GUI-based usage, be sure to install the [CLI](/gradient/get-started/quick-start/install-the-cli.md) and check out the [SDK](/gradient/more/gradient-python-sdk-1.md). --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://paperspace.gitbook.io/gradient/get-started/quick-start.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.