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feat: federated learning use case implementation #70
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@arueth Can you please rebase |
PS: the only conflict is about a typo I fixed in |
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@arueth The build is failing because despite the KMS API being enabled, it likely needs time to set resources up. Did we already solve this issue in the core platform? It seems like that other APIs might have this problem too, but we didn't notice it until we used the same project for CI. If we didn't solve it yet, I can look into it. |
Previously I've been able to resolve these by forcing a dependency on the |
This probably wouldn't help in this case because the implicit dependency already makes it so that the CryptoKey resource is being created after the "enable API" call returns (
I think this would be the way forward. |
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@arueth I think the CI job is failing because of that issue with the Fleet API. Let's see if it works by restarting the job once I rebase the branch. |
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platforms/gke/base/use-cases/federated-learning/examples/nvflare-tff/README.md
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platforms/gke/base/use-cases/federated-learning/examples/nvflare-tff/teardown.sh
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This commit introduces the Federated Learning core platform use case. As a first step to get a feeling of how it is to integrate and existing use case with the platform, we provision only a simple resource (an Artifact Registry repository).
Configure Private Google Access for the federated learning use case
* chore: simplify fl scripts * chore: remove initialize terraservice in fl Remove the 'initialize' terraservice in the Federated Learning use case because the core platform 'initialize' terraservice takes care of configuring backends for use cases after #71 is merged. The only task implemented in the use case 'initialize' terraservice was to initialize backend configuration, so we don't need it anymore. Also, simplify provisioning and teardown scripts because we don't need two different terraform init commands anymore because now all the terraservices in the use case work with a remote backend.
Configure the GKE cluster for the federated learning use case
- configure firewall for federated learning - configure iam roles and service accounts - configure dedicated node pools - configure policy controller and policies - configure dedicated Kubernetes namespaces
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This commit introduces the Federated Learning core platform use case.