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@NitinKumar004 NitinKumar004 released this 14 Jun 17:21
· 1148 commits to master since this release
v1.8.0
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cloudemu Release Notes — v1.8.0

✨ Features

AWS SageMaker — in-memory emulation + SDK-compat server

Point the real aws-sdk-go-v2 SageMaker client at an in-memory backend: models and endpoints (deploy / predict), training, processing, tuning, and batch-transform jobs, notebooks and Studio, pipelines, the model registry, feature store, and HyperPod-style clusters — with auto-metrics to CloudWatch and a portable Go API carrying the usual recording / metrics / rate-limit / error-injection / latency wrappers.

GCP Vertex AI — in-memory emulation + SDK-compat REST server

Datasets, the model registry and endpoints (deploy / predict), Gemini generateContent / streamGenerateContent / countTokens, tuning, custom / batch / hyperparameter-tuning jobs, pipelines, feature store, vector search, and ML metadata — with long-running-operation machinery, served over a REST surface the Vertex client can target.

🔧 Enhancements

Azure Databricks — faithful data-plane round-trips

Cluster, instance-pool, and SQL-warehouse settings that previously dropped between create and read now round-trip — custom tags, Photon runtime engine, pool idle-autotermination, and cluster policy / instance pool / Azure availability / source — and an explicit "never auto-stop" warehouse is honored. Query history is now served end-to-end.

Azure Resource Graph — Databricks discovery & type filtering

Databricks workspaces now appear in Resource Graph results, and where type in~ (...) type filters narrow correctly — including returning nothing (not everything) for a type the emulator doesn't model.

Technical Details

AWS SageMaker
  • Portable Go API + driver + provider + SDK-compat handler (server/aws/sagemaker), AWS JSON 1.1 wire protocol.
  • Families: models, endpoints (+ deploy / weights / predict / rawPredict), training / processing / tuning / batch-transform jobs, notebooks, Studio, pipelines, model registry (+ versions / packages), feature store, clusters.
  • Auto-metrics to CloudWatch via SetMonitoring; chaos + cost wiring.
GCP Vertex AI
  • Portable Go API + driver + provider + SDK-compat REST handler (server/gcp/vertexai), aiplatform.googleapis.com shape.
  • Families: datasets, models (+ versions / evaluations), endpoints (+ deploy / predict), Model Garden generateContent / streamGenerateContent / countTokens, tuning + cached contents, custom / batch / HPO jobs, pipelines, feature store, vector search, metadata, schedules, notebook runtimes.
  • google.longrunning.Operation responses with typed results; auto-metrics to Cloud Monitoring; chaos + cost wiring.
Azure Databricks
  • Cluster: custom_tags, runtime_engine, policy_id, instance_pool_id, azure_attributes.availability, server-assigned cluster_source.
  • Instance pool: idle_instance_autotermination_minutes, custom_tags.
  • SQL warehouse: tags, and explicit auto_stop_mins = 0 honored (pointer-based, no default coercion).
  • New GET /api/2.0/sql/history/queries (QueryHistory) handler.
Azure Resource Graph
  • Databricks ARM workspaces fed into the cross-service discovery inventory via a dedicated walker.
  • KQL where type in (...) / in~ (...) parsing with an any-of type filter; microsoft.databricks/workspaces mapped both ways.
  • An all-unmapped / empty type filter now matches none instead of the whole inventory.
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