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# About | ||
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Seldon V2 APIs provide a state of the art solution for machine learning inference which | ||
can be run locally on a laptop as well as on Kubernetes for production. | ||
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{% embed url="https://www.youtube.com/watch?v=ar5lSG_idh4" %} | ||
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## Features | ||
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* A single platform for inference of wide range of standard and custom artifacts. | ||
* Deploy locally in Docker during development and testing of models. | ||
* Deploy at scale on Kubernetes for production. | ||
* Deploy single models to multi-step pipelines. | ||
* Save infrastructure costs by deploying multiple models transparently in inference servers. | ||
* Overcommit on resources to deploy more models than available memory. | ||
* Dynamically extended models with pipelines with a data-centric perspective backed by Kafka. | ||
* Explain individual models and pipelines with state of the art explanation techniques. | ||
* Deploy drift and outlier detectors alongside models. | ||
* Kubernetes Service mesh agnostic - use the service mesh of your choice. | ||
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## Core features and comparison to Seldon Core V1 APIs | ||
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Our V2 APIs separate out core tasks into separate resources allowing users to get started fast | ||
with deploying a Model and the progressing to more complex Pipelines, Explanations and Experiments. | ||
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![intro](images/intro.png) | ||
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## Multi-model serving | ||
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Seldon transparently will provision your model onto the correct inference server. | ||
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![mms1](images/multimodel1.png) | ||
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By packing multiple models onto a smaller set of servers users can save infrastructure costs and | ||
efficiently utilize their models. | ||
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![mms2](images/mms.png) | ||
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By allowing over-commit users can provision model models that available memory resources by | ||
allowing Seldon to transparently unload models that are not in use. | ||
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![mms3](images/overcommit.png) | ||
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## Inference Servers | ||
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Seldon V2 supports any V2 protocol inference server. At present we include Seldon's MLServer and NVIDIA's Triton inference server automatically on install. These servers cover a wide range of artifacts including custom python models. | ||
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![servers](images/servers.png) | ||
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## Service Mesh Agnostic | ||
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Seldon core v2 can be integrated with any Kubernetes service mesh. There are current examples with istio, Ambassador and Traefic. | ||
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![mesh](images/mesh.png) | ||
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## Publication | ||
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These features are influenced by our position paper on the next generation of ML model serving frameworks: | ||
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*Title*: [Desiderata for next generation of ML model serving](http://arxiv.org/abs/2210.14665) | ||
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*Workshop*: Challenges in deploying and monitoring ML systems workshop - NeurIPS 2022 |
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# Table of contents | ||
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* [Home](README.md) | ||
* [Getting Started](getting-started/README.md) | ||
* [Docker Installation](getting-started/docker-installation.md) | ||
* [Kubernetes Installation](getting-started/kubernetes-installation/README.md) | ||
* [Ansible](getting-started/kubernetes-installation/ansible.md) | ||
* [Helm](getting-started/kubernetes-installation/helm.md) | ||
* [Security](getting-started/kubernetes-installation/security/README.md) | ||
* [AWS MSK mTLS](getting-started/kubernetes-installation/security/aws-msk-mtls.md) | ||
* [AWS MSK SASL](getting-started/kubernetes-installation/security/aws-msk-sasl.md) | ||
* [Azure Event Hub SASL Example](getting-started/kubernetes-installation/security/azure-event-hub-sasl.md) | ||
* [Confluent Cloud Oauth 2.0 Example](getting-started/kubernetes-installation/security/confluent-oauth.md) | ||
* [Confluent Cloud SASL Example](getting-started/kubernetes-installation/security/confluent-sasl.md) | ||
* [Strimzi mTLS Example](getting-started/kubernetes-installation/security/strimzi-mtls.md) | ||
* [Strimzi SASL Example](getting-started/kubernetes-installation/security/strimzi-sasl.md) | ||
* [Reference](getting-started/kubernetes-installation/security/reference.md) | ||
* [Configuration](getting-started/configuration.md) | ||
* [Seldon CLI](getting-started/cli.md) | ||
* [APIs](apis/README.md) | ||
* [Internal](apis/internal/README.md) | ||
* [Chainer](apis/internal/chainer.md) | ||
* [Agent](apis/internal/agent.md) | ||
* [Inference](apis/inference/README.md) | ||
* [Open Inference Protocol](apis/inference/v2.md) | ||
* [Scheduler](apis/scheduler.md) | ||
* [Architecture](architecture/README.md) | ||
* [DataFlow](architecture/dataflow.md) | ||
* [Examples](examples/README.md) | ||
* [Local examples](examples/local-examples.md) | ||
* [Kubernetes examples](examples/k8s-examples.md) | ||
* [Huggingface models](examples/huggingface.md) | ||
* [Model zoo](examples/model-zoo.md) | ||
* [Artifact versions](examples/multi-version.md) | ||
* [Pipeline examples](examples/pipeline-examples.md) | ||
* [Pipeline to pipeline examples](examples/pipeline-to-pipeline.md) | ||
* [Explainer examples](examples/explainer-examples.md) | ||
* [Custom Servers](examples/custom-servers.md) | ||
* [Local experiments](examples/local-experiments.md) | ||
* [Experiment version examples](examples/experiment-versions.md) | ||
* [Inference examples](examples/inference.md) | ||
* [Tritonclient examples](examples/tritonclient-examples.md) | ||
* [Batch Inference examples (kubernetes)](examples/batch-examples-k8s.md) | ||
* [Batch Inference examples (local)](examples/batch-examples-local.md) | ||
* [Checking Pipeline readiness](examples/pipeline-ready-and-metadata.md) | ||
* [Multi-Namespace Kubernetes](examples/k8s-clusterwide.md) | ||
* [Huggingface speech to sentiment with explanations pipeline](examples/speech-to-sentiment.md) | ||
* [Production image classifier with drift and outlier monitoring](examples/cifar10.md) | ||
* [Production income classifier with drift, outlier and explanations](examples/income.md) | ||
* [Conditional pipeline with pandas query model](examples/pandasquery.md) | ||
* [Kubernetes Server with PVC](examples/k8s-pvc.md) | ||
* [Local Overcommit](examples/k8s-pvc.md) | ||
* [Kubernetes](kubernetes/README.md) | ||
* [Scaling](kubernetes/scaling.md) | ||
* [Autoscaling](kubernetes/autoscaling.md) | ||
* [HPA Autoscaling in single-model serving](kubernetes/hpa-rps-autoscaling.md) | ||
* [Tracing](kubernetes/tracing.md) | ||
* [Storage Secrets](kubernetes/storage-secrets.md) | ||
* [Kafka](kubernetes/kafka.md) | ||
* [Metrics](kubernetes/metrics.md) | ||
* [Resources](kubernetes/resources/README.md) | ||
* [Model](kubernetes/resources/model.md) | ||
* [Experiment](kubernetes/resources/experiment.md) | ||
* [Pipeline](kubernetes/resources/pipeline.md) | ||
* [Server](kubernetes/resources/server.md) | ||
* [Server Config](kubernetes/resources/serverconfig.md) | ||
* [Server Runtime](kubernetes/resources/seldonruntime.md) | ||
* [Seldon Config](kubernetes/resources/seldonconfig.md) | ||
* [Service Meshes](kubernetes/service-meshes/README.md) | ||
* [Ambassador](kubernetes/service-meshes/ambassador.md) | ||
* [Istio](kubernetes/service-meshes/istio.md) | ||
* [Traefik](kubernetes/service-meshes/traefik.md) | ||
* [Resource allocation](resource-allocation/README.md) | ||
* [Example: Serving models on dedicated GPU nodes](resource-allocation/example-serving-models-on-dedicated-gpu-nodes.md) | ||
* [Models](models/README.md) | ||
* [Multi-Model Serving](models/mms.md) | ||
* [Inference Artifacts](models/inference-artifacts.md) | ||
* [rClone](models/rclone.md) | ||
* [Parameterized Models](models/parameterized-models/README.md) | ||
* [Pandas Query](models/parameterized-models/pandasquery.md) | ||
* [Metrics](metrics/README.md) | ||
* [Usage](metrics/usage.md) | ||
* [Operational](metrics/operational.md) | ||
* [Local Metrics](metrics/local-metrics-test.md) | ||
* [Development](development/README.md) | ||
* [License](development/licenses.md) | ||
* [Release](development/release.md) | ||
* [CLI](cli/README.md) | ||
* [Seldon](cli/seldon.md) | ||
* [Config](cli/seldon\_config.md) | ||
* [Config Activate](cli/seldon\_config\_activate.md) | ||
* [Config Deactivate](cli/seldon\_config\_deactivate.md) | ||
* [Config Add](cli/seldon\_config\_add.md) | ||
* [Config List](cli/seldon\_config\_list.md) | ||
* [Config Remove](cli/seldon\_config\_remove.md) | ||
* [Experiment](cli/seldon\_experiment.md) | ||
* [Experiment Start](cli/seldon\_experiment\_start.md) | ||
* [Experiment Status](cli/seldon\_experiment\_status.md) | ||
* [Experiment List](cli/seldon\_experiment\_list.md) | ||
* [Experiment Stop](cli/seldon\_experiment\_stop.md) | ||
* [Model](cli/seldon\_model.md) | ||
* [Model Status](cli/seldon\_model\_status.md) | ||
* [Model Load](cli/seldon\_model\_load.md) | ||
* [Model List](cli/seldon\_model\_list.md) | ||
* [Model Infer](cli/seldon\_model\_infer.md) | ||
* [Model Metadata](cli/seldon\_model\_metadata.md) | ||
* [Model Unload](cli/seldon\_model\_unload.md) | ||
* [Pipeline](cli/seldon\_pipeline.md) | ||
* [Pipeline Load](cli/seldon\_pipeline\_load.md) | ||
* [Pipeline Status](cli/seldon\_pipeline\_status.md) | ||
* [Pipeline List](cli/seldon\_pipeline\_list.md) | ||
* [Pipeline Inspect](cli/seldon\_pipeline\_inspect.md) | ||
* [Pipeline Infer](cli/seldon\_pipeline\_infer.md) | ||
* [Pipeline Unload](cli/seldon\_pipeline\_unload.md) | ||
* [Server](cli/seldon\_server.md) | ||
* [Server List](cli/seldon\_server\_list.md) | ||
* [Server Status](cli/seldon\_server\_status.md) | ||
* [Pipelines](pipelines.md) | ||
* [Experiments](experiments.md) | ||
* [Servers](servers.md) | ||
* [Inference](inference.md) | ||
* [Outlier Detection](outlier.md) | ||
* [Drift Detection](drift.md) | ||
* [Explainers](explainers.md) | ||
* [Performance Tests](performance-tests.md) | ||
* [Upgrading](upgrading.md) | ||
* [FAQ](faqs.md) |
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# APIs | ||
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Seldon provides APIs for management and inference. | ||
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* [API for inference](./inference/README.md) | ||
* [Scheduler API for management](./scheduler/README.md) (Advanced) | ||
* [Internal APIs](./internal/README.md) (Reference) |
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# Inference API | ||
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Seldon inference servers must respect the following API specification. | ||
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* [Seldon, KServe, NVIDIA V2 Inference API Spec](./v2.md) | ||
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In future, Seldon may provide extensions for use with Pipelines, Experiments and Explainers. |
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