From e4a8f8f6ccde01976a82084ac0d77d029cdcf401 Mon Sep 17 00:00:00 2001 From: Clive Cox Date: Fri, 4 Jan 2019 14:17:12 +0000 Subject: [PATCH 1/5] release notes 0.2.5 --- docs/articles/release-0.2.5.md | 46 ++++++++++++++++++++++++++++++++++ 1 file changed, 46 insertions(+) create mode 100644 docs/articles/release-0.2.5.md diff --git a/docs/articles/release-0.2.5.md b/docs/articles/release-0.2.5.md new file mode 100644 index 0000000000..411a5b80be --- /dev/null +++ b/docs/articles/release-0.2.5.md @@ -0,0 +1,46 @@ +# Seldon Core Release 0.2.5 + +A summary of the main contributions to the [Seldon Core release 0.2.5](https://github.com/SeldonIO/seldon-core/releases/tag/v0.2.5). + +## PyPy Python Module + +When packaging components to run under Seldon Core we provide easy integration via [S2I](https://github.com/openshift/source-to-image) builder images. The core functionality for our Python S2I image has now been packaged as a Python module which can be easily installed via pip with: + +``` +pip install seldon-core +``` + +The module contains: + + * The top level REST and gRPC wrapper code which can be tested with your component via the executable ```seldon-core-microservice```. + * An internal tester for the Seldon microservice REST and gRPC API accessible via the executable ```seldon-core-tester```. See the [documentation](https://github.com/SeldonIO/seldon-core/blob/master/docs/api-testing.md#microservice-api-tester) for further information. + * An external tester for the external Seldon REST and gRPC API accessible via the executable ```seldon-core-api-tester```. See the [documentation](https://github.com/SeldonIO/seldon-core/blob/master/docs/api-testing.md#seldon-core-api-tester) for further information. + +## Inference Graph Components +One of the aims of Seldon Core is to allow machine learning models to be deployed in production with the appropriate metrics and optimisation to give the required compliance and observability guarantees needed. We have recently extended Seldon Core with Outlier Detection and Multi-Armed Bandit components as discussed below. + +### Outlier Detection +The ability to identify unexpected input feature payloads to a machine learning model is an important feature for production deployments. As part of this release we have added outlier detection modules as a plug-and-play component in Seldon Core. The training and deployment of the implemented deep learning and tree based algorithms ([Variational Auto-Encoders](https://github.com/SeldonIO/seldon-core/tree/master/components/outlier-detection/vae) and [Isolation Forests](https://github.com/SeldonIO/seldon-core/tree/master/components/outlier-detection/isolation-forest) are illustrated by detecting computer network intrusions in real time. + +### Multi-Armed Bandits + +The problem of deciding how to route requests to competing machine learning model so that one determines which model is the best in the shortest amount of time can be treated as a [Multi-Armed Bandit Problem](https://en.wikipedia.org/wiki/Multi-armed_bandit). Seldon Core has extended the available components you can use with Thomson Sampling and a case study comparing it to the more basic Epsilon Greedy strategy. + +## Cluster Wide Operations +Seldon Core can now be installed in two ways: + + * Single-Namespace (default) : Manages and controls only Seldon Deployments created in that namespace. This only requires RBAC roles local to that namespace. + * Cluster-wide : Manages and controls Seldon Deployments in any namespace. This requires RBAC Cluster Roles. + +Cluster-wide operations have the advantage that only a single Seldon Core Operator needs to be installed and thus saves resources. This could be set up by the Kubernetes cluster manager. Single Namespace operations are best for multi-tenant use cases where you want to ensure everything is local to one namespace. See the [Install Docs](https://github.com/SeldonIO/seldon-core/blob/master/docs/install.md). + +## Extended API Payloads + +We have extended the Seldon Core API payload to include Tensorflow's [TensorProto](https://github.com/SeldonIO/seldon-core/blob/4149c6aeb11be518ec8589fd91599242c907e681/proto/prediction.proto#L29). This will allow utilisation of the fine grained type information available to most compactly send tensor based payloads as well as allow the use of [Tensorflow's library](https://www.tensorflow.org/api_docs/python/tf/make_tensor_proto) to construct the payload needed. + +We have ensured its now possible to use the ```binData``` and ```strData``` payload types to send arbitrary binary or string payloads via the Python wrapper. The python wrappers also allow more easy access to the low level payloads by allowing users to provide a ```predict_rest``` or ```predict_grpc``` methods to gain access directly to the underlying ```SeldonMessage```. + +## Custom Metrics + +The Seldon Core Service Orchestrator component that manages the request/response flow through a user's deployment graph already exposes Prometheus metrics for each of the API calls to the underling components in the graph (e.g. Models, Transformers etc). However, users can now pass back their own custom metrics in the returned ```SeldonMessage``` response from their components. Presently available are Counters, Gauges and Timers. Full documentation can be found [here](https://github.com/SeldonIO/seldon-core/blob/master/docs/custom_metrics.md). + From 57aead3e2499394cfa3c2cd16ecbb73d7b24c94e Mon Sep 17 00:00:00 2001 From: arnaudvl Date: Fri, 4 Jan 2019 14:38:06 +0000 Subject: [PATCH 2/5] fixing typos and adding links --- docs/articles/release-0.2.5.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/articles/release-0.2.5.md b/docs/articles/release-0.2.5.md index 411a5b80be..1ba903c42a 100644 --- a/docs/articles/release-0.2.5.md +++ b/docs/articles/release-0.2.5.md @@ -2,7 +2,7 @@ A summary of the main contributions to the [Seldon Core release 0.2.5](https://github.com/SeldonIO/seldon-core/releases/tag/v0.2.5). -## PyPy Python Module +## PyPI Python Module When packaging components to run under Seldon Core we provide easy integration via [S2I](https://github.com/openshift/source-to-image) builder images. The core functionality for our Python S2I image has now been packaged as a Python module which can be easily installed via pip with: @@ -20,11 +20,11 @@ The module contains: One of the aims of Seldon Core is to allow machine learning models to be deployed in production with the appropriate metrics and optimisation to give the required compliance and observability guarantees needed. We have recently extended Seldon Core with Outlier Detection and Multi-Armed Bandit components as discussed below. ### Outlier Detection -The ability to identify unexpected input feature payloads to a machine learning model is an important feature for production deployments. As part of this release we have added outlier detection modules as a plug-and-play component in Seldon Core. The training and deployment of the implemented deep learning and tree based algorithms ([Variational Auto-Encoders](https://github.com/SeldonIO/seldon-core/tree/master/components/outlier-detection/vae) and [Isolation Forests](https://github.com/SeldonIO/seldon-core/tree/master/components/outlier-detection/isolation-forest) are illustrated by detecting computer network intrusions in real time. +The ability to identify unexpected input feature payloads to a machine learning model is an important feature for production deployments. As part of this release we have added outlier detection modules as a plug-and-play component in Seldon Core. The training and deployment of the implemented deep learning and tree based algorithms ([Variational Auto-Encoders](https://github.com/SeldonIO/seldon-core/tree/master/components/outlier-detection/vae) and [Isolation Forests](https://github.com/SeldonIO/seldon-core/tree/master/components/outlier-detection/isolation-forest)) are illustrated by detecting computer network intrusions in real time. ### Multi-Armed Bandits -The problem of deciding how to route requests to competing machine learning model so that one determines which model is the best in the shortest amount of time can be treated as a [Multi-Armed Bandit Problem](https://en.wikipedia.org/wiki/Multi-armed_bandit). Seldon Core has extended the available components you can use with Thomson Sampling and a case study comparing it to the more basic Epsilon Greedy strategy. +The problem of deciding how to route requests to competing machine learning model so that one determines which model is the best in the shortest amount of time can be treated as a [Multi-Armed Bandit Problem](https://en.wikipedia.org/wiki/Multi-armed_bandit). Seldon Core has extended the available components you can use with [Thompson Sampling](https://github.com/SeldonIO/seldon-core/tree/master/components/routers/thompson-sampling) and a [case study](https://github.com/SeldonIO/seldon-core/blob/master/components/routers/case_study/credit_card_default.ipynb) comparing it to the more basic [Epsilon Greedy](https://github.com/SeldonIO/seldon-core/tree/master/components/routers/epsilon-greedy) strategy. ## Cluster Wide Operations Seldon Core can now be installed in two ways: From 56fe49d9a4705ad97db0e5a07a424333e4337f80 Mon Sep 17 00:00:00 2001 From: Clive Cox Date: Fri, 4 Jan 2019 14:50:09 +0000 Subject: [PATCH 3/5] add outlier image --- docs/articles/outlier-detection-dashboard.png | Bin 0 -> 51301 bytes docs/articles/release-0.2.5.md | 2 ++ readme.md | 1 + 3 files changed, 3 insertions(+) create mode 100644 docs/articles/outlier-detection-dashboard.png diff --git a/docs/articles/outlier-detection-dashboard.png 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As part of this release we have added outlier detection modules as a plug-and-play component in Seldon Core. The training and deployment of the implemented deep learning and tree based algorithms ([Variational Auto-Encoders](https://github.com/SeldonIO/seldon-core/tree/master/components/outlier-detection/vae) and [Isolation Forests](https://github.com/SeldonIO/seldon-core/tree/master/components/outlier-detection/isolation-forest)) are illustrated by detecting computer network intrusions in real time. -![outlier-detection-dashboard](./outlier-detection-dashboard.png) +

+ +

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b/docs/articles/release-0.2.5.md index e7884af6df..ce40fde2ee 100644 --- a/docs/articles/release-0.2.5.md +++ b/docs/articles/release-0.2.5.md @@ -30,6 +30,10 @@ The ability to identify unexpected input feature payloads to a machine learning The problem of deciding how to route requests to competing machine learning model so that one determines which model is the best in the shortest amount of time can be treated as a [Multi-Armed Bandit Problem](https://en.wikipedia.org/wiki/Multi-armed_bandit). Seldon Core has extended the available components you can use with [Thompson Sampling](https://github.com/SeldonIO/seldon-core/tree/master/components/routers/thompson-sampling) and a [case study](https://github.com/SeldonIO/seldon-core/blob/master/components/routers/case_study/credit_card_default.ipynb) comparing it to the more basic [Epsilon Greedy](https://github.com/SeldonIO/seldon-core/tree/master/components/routers/epsilon-greedy) strategy. +

+ +

+ ## Cluster Wide Operations Seldon Core can now be installed in two ways: