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update docker container and aws config
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docs/guides/python/ai-podcast-part-1.mdx

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@@ -32,7 +32,7 @@ In this first part we'll using the [suno/bark](https://huggingface.co/suno/bark)
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- [uv](https://docs.astral.sh/uv/#getting-started) - for simplified dependency management
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- The [Nitric CLI](/get-started/installation)
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- _(optional)_ Your choice of an [AWS](https://aws.amazon.com) or [GCP](https://cloud.google.com) account
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- _(optional)_ An [AWS](https://aws.amazon.com) account
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## Getting started
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@@ -535,6 +535,10 @@ We'll also add a dockerignore file to try and keep the image size down.
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.nitric/
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.venv/
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.model/
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nitric-spec.json
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nitric.yaml
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README.md
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model.zip
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```
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We'll also need to update the `python.dockerfile` to ignore the `.model` directory.
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.nitric/
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.venv/
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.model/
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nitric-spec.json
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nitric.yaml
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README.md
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model.zip
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```
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Let's also update the `nitric.yaml` to add the new dockerfile to our runtimes.
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This will generate a nitric stack file called `test` which defines how we want to deploy a stack to AWS. We can update this stack file with settings to configure our batch service and the AWS Compute environment it will run in.
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```yaml title: nitric.test.yaml
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# The nitric provider to use
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provider: nitric/[email protected]
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provider: nitric/[email protected]
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# The target aws region to deploy to
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# See available regions:
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# https://docs.aws.amazon.com/general/latest/gr/lambda-service.html
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region: ap-southeast-2
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region: <enter-your-region>
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# specify the compute environment for the batch service
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batch-compute-env:
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min-cpus: 0
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# Allow a maximum of 4 CPUs to be used
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max-cpus: 4
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instance-types:
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# Allow use of G5 instances
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- g5
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- g5 # Or use another instance type that supports GPUs
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- optimal
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# Customize the ECS launch template for this compute environment
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launch-template:
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# Increase the default docker system volume size
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# This is so large models can be downloaded and cached
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block-device-mappings:
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- device-name: /dev/xvda
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ebs:
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volume-size: 50
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volume-type: gp2
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delete-on-termination: 'true'
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```
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<Note>

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