This guide will show you how to set up continuous integration and deployment for your Cog model, so you can safely and easily publish new versions of your model as part of your GitHub-based development workflow. ![Use GitHub, Anthropic, and Replicate to build a CI/CD pipeline for your Cog model.](/_content/assets/openai-anthropic-replicate.B4ZhQOYb_Z2tNfmX.webp) Use GitHub, Anthropic, and Replicate to build a CI/CD pipeline for your Cog model. [](#what-is-cicd)What is CI/CD? ------------------------------- CI/CD stands for continuous integration and deployment. It’s a well-known process for building, testing, and deploying software. It has been a popular practice in the software industry for a long time, but it’s just as applicable when developing machine learning models. After all, [AI models are just software](https://replicate.com/blog/machine-learning-needs-better-tools#machine-learning-is-just-software). CI/CD has many benefits: * Catch errors early. This helps reduce the risk of breaking things for your users. * Test your model in a consistent and repeatable way. * Automate the release process so you can ship new versions of your model more frequently. * Collaborate with others. CI/CD workflows live as code in your repository, so they’re easy to discover and understand. [](#what-are-we-building)What are we building? ---------------------------------------------- In this guide, you’ll set up a CI/CD pipeline for your Cog model using GitHub Actions and [github.com/replicate/cog-safe-push](https://github.com/replicate/cog-safe-push), an open-source project we use internally at Replicate to test and deploy our production models. Your pipeline will: * Lint your Python model code. * Create a private test model on Replicate. * Push your local Cog model to the test model on Replicate. * Lint the model schema, making sure all inputs have descriptions, etc. * Run predictions against the test model and compare the outputs to the upstream model. * "Fuzz" the test model for five minutes by throwing a bunch of different inputs at it and making sure it doesn’t throw any errors. * Push your changes to the production model once the test model passes all the tests. [](#what-is-fuzz-testing)What is fuzz testing? ---------------------------------------------- Fuzz testing is a technique for finding bugs in software by passing invalid, unexpected, or random data as inputs to the software and seeing if it crashes. In this guide, we’ll use Anthropic’s [Claude API](https://docs.anthropic.com/en/api/introduction) to generate random inputs to test your model. Language models are great for generating plain text, but they can also be used to generate JSON data. We’ll take advantage of that here to generate structured data that matches the input schema of your model. [](#prerequisites)Prerequisites ------------------------------- Before starting this guide, you’ll need the following: * An existing [Cog](https://cog.run/) model. If you’ve never pushed a Cog model before, check out the [guide to pushing your first model](/docs/guides/push-a-model). * A [Replicate](https://replicate.com/) account for publishing your Cog model. * A [GitHub](https://github.com/) repository containing the source code for your model. * An [Anthropic](https://console.anthropic.com/) account for generating JSON data to test your model. [](#step-1-create-a-github-actions-workflow)Step 1: Create a GitHub Actions workflow ------------------------------------------------------------------------------------ GitHub Actions is a feature built into GitHub that makes it easy to add scripts to your repository, and run those scripts on GitHub’s servers. You can use GitHub Actions to do all kinds of automation, like running tests, deploying your model, and more. To use GitHub Actions, you define workflows using YAML files stored in the `.github/workflows` directory of your GitHub repository. Create a new file in your repository called `.github/workflows/cog-safe-push.yml`. Then paste the following code into the file: ```yml name: Cog Safe Push on: workflow_dispatch: inputs: model: description: 'The name of the model to push, in the format owner/model-name' type: string jobs: cog-safe-push: # Tip: Create custom runners in your GitHub organization for faster builds runs-on: ubuntu-latest steps: - uses: actions/checkout@v3 - name: Set up Python uses: actions/setup-python@v4 with: python-version: "3.12" - name: Install Cog run: | sudo curl -o /usr/local/bin/cog -L https://github.com/replicate/cog/releases/latest/download/cog_`uname -s`_`uname -m` sudo chmod +x /usr/local/bin/cog - name: cog login run: | echo ${{ secrets.COG_TOKEN }} | cog login --token-stdin - name: Install cog-safe-push run: | pip install git+https://github.com/replicate/cog-safe-push.git - name: Push selected models env: ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }} REPLICATE_API_TOKEN: ${{ secrets.REPLICATE_API_TOKEN }} run: | cog-safe-push ${{ inputs.model }} ``` Commit this file to your Git repository and push it to GitHub. [](#step-2-set-a-replicate-api-token)Step 2: Set a Replicate API token ---------------------------------------------------------------------- Before you can run your GitHub Actions workflow, you’ll need to set a Replicate API token as a secret in your GitHub repository. 1. Go to [replicate.com/account/api-tokens](https://replicate.com/account/api-tokens?new-token-name=continuous-deployment) 2. Create a new token with a name like "GitHub Actions CI/CD workflow for my-username/my-model". 3. Copy the token to your clipboard. 4. In your GitHub repository, go to the **Settings**, click **Secrets and variables**, then click **Actions**. 5. Click **New repository secret**. 6. In the **Name** field, enter `REPLICATE_API_TOKEN`. 7. In the **Value** field, paste the token you copied from Replicate. 8. Click **Add secret**. [](#step-3-set-a-cog-api-token)Step 3: Set a Cog API token ---------------------------------------------------------- You’ll also need to set a Cog API token as a secret in your GitHub repository, so the `cog-safe-push` script can push your model to Replicate. 1. Go to [replicate.com/auth/token](https://replicate.com/auth/token) and copy your CLI auth token. 2. In your GitHub repository, go to the **Settings**, click **Secrets and variables**, then click **Actions**. 3. Click **New repository secret**. 4. In the **Name** field, enter `COG_TOKEN`. 5. In the **Value** field, paste the token you copied from Replicate. 6. Click **Add secret**. [](#step-4-set-an-anthropic-api-key)Step 4: Set an Anthropic API key -------------------------------------------------------------------- You’ll also need to set an Anthropic API key as a secret in your GitHub repository, so the `cog-safe-push` script can generate structured JSON inputs to test your model. 1. Go to [console.anthropic.com/settings/keys](https://console.anthropic.com/settings/keys) 2. Create a new API key with a name like "fuzz testing with cog-safe-push for my-username/my-model". 3. Copy the key to your clipboard. 4. In your GitHub repository, go to the **Settings**, click **Secrets and variables**, then click **Actions**. 5. Click **New repository secret**. 6. In the **Name** field, enter `ANTHROPIC_API_KEY`. 7. In the **Value** field, paste the key you copied from Anthropic. 8. Click **Add secret**. [](#step-5-run-the-workflow)Step 5: Run the workflow ---------------------------------------------------- Now that you’ve created your workflow and configured the necessary secrets, you can run the workflow manually. If you’ve configured your workflow correctly, you should see a green checkmark next to the workflow run, and output like the following: 1. In your GitHub repository, go to the **Actions** tab. 2. Click **Cog Safe Push**. 3. In the **Model** field, enter the name of your model in the format `owner/model-name`. 4. Click **Run workflow**. ![Screenshot of the completed GitHub Actions workflow.](/_content/assets/actions.DNJDa6Ua_2cxMxY.webp) Screenshot of the completed GitHub Actions workflow. Success! You’ve just run your CI/CD pipeline. If all the steps passed, you’ve now got a newly published [version](https://replicate.com/docs/topics/models/versions) of your model on Replicate. [](#step-6-optional-customize-cog-safe-push)Step 6 (Optional): Customize cog-safe-push -------------------------------------------------------------------------------------- The `cog-safe-push` command has sensible defaults and will work without any specific configuration, but you’ll probably want to customize it to your needs. If you create a file named `cog-safe-push.yaml` in your Cog directory, it will be used automatically. Here’s an example configuration that specifies a model, a test model, hardware, and a set of test cases: ```yaml # This is the model that the deployment will use model: owner/my-model # A private model to run the test against before pushing test_model: owner/my-model-test test_hardware: gpu-l40s # Define the predict section to run predictions against the test model predict: compare_outputs: false predict_timeout: 700 test_cases: - inputs: prompt: A formula one car seed: 1 - inputs: prompt: A cat duration: 4 negative_prompt: Ginger cat frame_image_url: https://tjzk.replicate.delivery/models_models_featured_image/afce5fa4-5f8a-45db-95c0-bf62ec6958e7/output-59.webp seed: 2 ``` To learn more about the available options, see the [cog-safe-push README](https://github.com/replicate/cog-safe-push/blob/main/README.md). [](#next-steps)Next steps ------------------------- Now that you have a CI/CD pipeline for your Cog model, you can use it to publish new versions of your model as part of your GitHub-based development workflow. The Actions workflow you created is triggered manually. This is a good starting point, as it lets you run the workflow repeatedly until you get everything working the way you want. Once you’ve got things configured just right, consider updating the workflow to run automatically when you push to a branch of your repository, or whenever you open a pull request. Remember that CI/CD is an iterative process, and you’ll need to customize the workflow and testing parameters to match your needs. The investment is worth it though: CI/CD will help you ship new versions of your model more frequently, and your team will enjoy faster development velocity and fewer operational headaches. Ship it!

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