Cortex Versions Save

Production infrastructure for machine learning at scale

v0.42.1

1 year ago

v0.42.1

New features

Bug fixes

Misc

v0.42.0

2 years ago

v0.42.0

New features

Bug fixes

Misc

v0.41.0

2 years ago

v0.41.0

New features

Misc

Bug fixes

Nucleus Model Server

We have released v0.1.0 of the Nucleus model server!

Nucleus is a model server for TensorFlow and generic Python models. It is compatible with Cortex clusters, Kubernetes clusters, and any other container-based deployment platforms. Nucleus can also be run locally via Docker compose.

Some of Nucleus's features include:

  • Generic Python models (PyTorch, ONNX, Sklearn, MLFlow, Numpy, Pandas, etc)
  • TensorFlow models
  • CPU and GPU support
  • Serve models directly from S3 paths
  • Configurable multiprocessing and multithreadding
  • Multi-model endpoints
  • Dynamic server-side request batching
  • Automatic model reloading when new model versions are uploaded to S3
  • Model caching based on LRU policy (on disk and memory)
  • HTTP and gRPC support

v0.40.0

2 years ago

v0.40.0

New features

Misc

v0.39.1

2 years ago

v0.39.1

Bug fixes

v0.39.0

2 years ago

v0.39.0

New features

Reliability

Bug fixes

Docs

Misc

v0.38.0

2 years ago

v0.38.0

New features

Bug fixes

Misc

v0.37.0

2 years ago

v0.37.0

New features

Breaking changes

  • The cortex cluster scale command has been replaced by the cortex cluster configure command.

Bug fixes

Docs

Misc

v0.36.0

2 years ago

v0.36.0

New features

Breaking changes

  • With this release, we have generalized Cortex to exclusively support running arbitrary Docker containers for all workload types (Realtime, Async, Batch, and Task). This enables the use of any model server, programming language, etc. As a result, the API configuration has been updated: the predictor section has been removed, the pod section has been added, and the autoscaling parameters have been modified slightly (depending on the workload type). See updated docs for Realtime, Async, Batch, and Task. If you'd like to to see examples of Dockerizing Python applications, see our test/apis folder.
  • The cortex prepare-debug command has been removed; Cortex now exclusively runs Docker containers, which can be run locally via docker run.
  • The cortex patch command as been removed; its behavior is now identical to cortex deploy.
  • The cortex logs command now prints a CloudWatch Insights URL with a pre-populated query which can be executed to show logs from your workloads, since this is the recommended approach in production. If you wish to stream logs from a pod at random, you can use cortex logs --random-pod (keep in mind that these logs will not include some system logs related to your workload).
  • gRPC support has been temporarily removed; we are working on adding it back in v0.37.

Bug fixes

Docs

Misc

v0.35.0

3 years ago

v0.35.0

New features

Breaking changes

  • The Python client methods which deploy Python classes have been separated from the deploy() method. Now, deploy() is used only to deploy project folders, and deploy_realtime_api(), deploy_async_api(), deploy_batch_api(), and deploy_task_api() are for deploying Python classes. (docs)
  • The name of the bucket that Cortex uses for internal purposes is no longer configurable. During cluster creation, Cortex will auto-generate the bucket name (and create the bucket if it doesn't exist). During cluster deletion, the bucket will be emptied (unless the --keep-aws-resources flag is provided to cortex cluster down). Users' files should not be stored in the Cortex internal bucket.

Bug fixes

Misc