diff --git a/.github/actions/free-up-disk-space/action.yml b/.github/actions/free-up-disk-space/action.yml index f8a0d42bd8a..04017503d46 100644 --- a/.github/actions/free-up-disk-space/action.yml +++ b/.github/actions/free-up-disk-space/action.yml @@ -1,5 +1,5 @@ name: 'Free-up disk space action' -description: 'Removes non-essential tools, libraries and cached files from GitHub action runner node' +description: 'Removes non-essential tools, libraries and cached files from GitHub action runner node and changes the docker data directory to /mnt/docker' runs: using: "composite" @@ -8,7 +8,7 @@ runs: shell: bash run: | echo "Disk usage before cleanup:" - df -h + df -hT # remove non-essential tools and libraries, see: # https://github.com/actions/runner-images/issues/2840#issuecomment-790492173 @@ -23,7 +23,7 @@ runs: sudo rm -rf /usr/share/swift echo "Disk usage after cleanup:" - df -h + df -hT - name: Prune docker images shell: bash @@ -31,4 +31,22 @@ runs: echo "Pruning docker images" docker image prune -a -f docker system df - df -h + df -hT + + - name: Move docker data directory + shell: bash + run: | + echo "Stopping docker service ..." + sudo systemctl stop docker + DOCKER_DEFAULT_ROOT_DIR=/var/lib/docker + DOCKER_ROOT_DIR=/mnt/docker + echo "Moving ${DOCKER_DEFAULT_ROOT_DIR} -> ${DOCKER_ROOT_DIR}" + sudo mv ${DOCKER_DEFAULT_ROOT_DIR} ${DOCKER_ROOT_DIR} + echo "Creating symlink ${DOCKER_DEFAULT_ROOT_DIR} -> ${DOCKER_ROOT_DIR}" + sudo ln -s ${DOCKER_ROOT_DIR} ${DOCKER_DEFAULT_ROOT_DIR} + echo "$(sudo ls -l ${DOCKER_DEFAULT_ROOT_DIR})" + echo "Starting docker service ..." + sudo systemctl daemon-reload + sudo systemctl start docker + echo "Docker service status:" + sudo systemctl --no-pager -l -o short status docker diff --git a/.github/workflows/huggingface-docker-publish.yml b/.github/workflows/huggingface-docker-publish.yml new file mode 100644 index 00000000000..07b5c41d3a7 --- /dev/null +++ b/.github/workflows/huggingface-docker-publish.yml @@ -0,0 +1,87 @@ +name: Huggingface Docker Publisher + +on: + push: + # Publish `master` as Docker `latest` image. + branches: + - master + + # Run tests for any PRs. + pull_request: + +env: + IMAGE_NAME: huggingfaceserver + +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + +jobs: + # Run tests. + # See also https://docs.docker.com/docker-hub/builds/automated-testing/ + test: + runs-on: ubuntu-latest + + steps: + - name: Checkout source + uses: actions/checkout@v2 + + - name: Free-up disk space + uses: ./.github/actions/free-up-disk-space + + - name: Run tests + run: | + if [ -f docker-compose.test.yml ]; then + docker-compose --file docker-compose.test.yml build + docker-compose --file docker-compose.test.yml run sut + else + cd python + docker buildx build . --file huggingface_server.Dockerfile + fi + + # Push image to GitHub Packages. + # See also https://docs.docker.com/docker-hub/builds/ + push: + # Ensure test job passes before pushing image. + needs: test + + runs-on: ubuntu-latest + if: github.event_name == 'push' + + steps: + - name: Checkout source + uses: actions/checkout@v2 + + - name: Free-up disk space + uses: ./.github/actions/free-up-disk-space + + - name: Build image + run: | + cd python + docker buildx build . --file huggingface_server.Dockerfile --tag $IMAGE_NAME + + - name: Log into registry + run: docker login -u ${{ secrets.DOCKER_USER }} -p ${{ secrets.DOCKER_PASSWORD }} + + - name: Push image + run: | + IMAGE_ID=kserve/$IMAGE_NAME + + # Change all uppercase to lowercase + IMAGE_ID=$(echo $IMAGE_ID | tr '[A-Z]' '[a-z]') + + # Strip git ref prefix from version + VERSION=$(echo "${{ github.ref }}" | sed -e 's,.*/\(.*\),\1,') + + # Strip "v" prefix from tag name + # [[ "${{ github.ref }}" == "refs/tags/"* ]] && VERSION=$(echo $VERSION | sed -e 's/^v//') + + # Use Docker `latest` tag convention + [ "$VERSION" == "master" ] && VERSION=latest + + echo IMAGE_ID=$IMAGE_ID + echo VERSION=$VERSION + + docker tag $IMAGE_NAME $IMAGE_ID:$VERSION + docker push $IMAGE_ID:$VERSION + diff --git a/.github/workflows/python-test.yml b/.github/workflows/python-test.yml index afe0fe825e2..982c2e22842 100644 --- a/.github/workflows/python-test.yml +++ b/.github/workflows/python-test.yml @@ -29,14 +29,19 @@ jobs: uses: py-actions/flake8@v1 with: max-line-length: "120" - exclude: "docs/samples/v1beta1/onnx/assets/*.py,python/kserve/test/test_v1*.py,python/kserve/kserve/__init__.py,python/kserve/test/test_knative*.py,python/kserve/kserve/protocol/grpc/grpc_predict_v2*.py" + exclude: "docs/samples/v1beta1/onnx/assets/*.py,python/kserve/kserve/*.py,python/kserve/test/test_v1*.py,python/kserve/kserve/__init__.py,python/kserve/test/__init__.py,python/kserve/test/test_knative*.py,python/kserve/kserve/protocol/grpc/grpc_predict_v2*.py" build: runs-on: ubuntu-latest strategy: matrix: python-version: ["3.8", "3.9", "3.10", "3.11"] steps: - - uses: actions/checkout@v2 + - name: Checkout source + uses: actions/checkout@v2 + + - name: Free-up disk space + uses: ./.github/actions/free-up-disk-space + - name: Set up Python ${{ matrix.python-version }} id: setup-python uses: actions/setup-python@v4 @@ -46,12 +51,19 @@ jobs: - name: Install Poetry and version plugin run: ./test/scripts/gh-actions/setup-poetry.sh + - name: Configure Poetry cache + run: | + sudo mkdir -p /mnt/poetry + # change permission so that poetry can install without sudo + sudo chown -R $USER /mnt/poetry + poetry config cache-dir /mnt/poetry + # load poetry cache if cache exists - name: Load poetry cache id: cached-poetry-dependencies uses: actions/cache@v3 with: - path: ~/.cache/pypoetry + path: /mnt/poetry key: poetry-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/poetry.lock') }} # ----------------------------------------Kserve Unit Tests-------------------------------------------------------- @@ -221,3 +233,41 @@ jobs: cd python source alibiexplainer/.venv/bin/activate pytest --cov=alibiexplainer ./alibiexplainer + + # ----------------------------------------Huggingface Server Unit Tests------------------------------------------------ + # load cached huggingface venv if cache exists + - name: Load cached huggingface venv + if: ${{ !startsWith(steps.setup-python.outputs.python-version, '3.8') }} + id: huggingface-dependencies + uses: actions/cache@v3 + with: + path: /mnt/python/huggingfaceserver-venv + key: huggingface-venv-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/kserve/poetry.lock', '**/huggingfaceserver/poetry.lock') }} + # install huggingface server dependencies if cache does not exist + - name: Configure poetry for huggingface server + if: ${{ !startsWith(steps.setup-python.outputs.python-version, '3.8') }} + run: | + poetry config virtualenvs.path /mnt/python/huggingfaceserver-venv + poetry config virtualenvs.in-project false + - name: Install huggingface dependencies + if: ${{ steps.cached-huggingface-dependencies.outputs.cache-hit != 'true' && !startsWith(steps.setup-python.outputs.python-version, '3.8') }} + run: | + sudo mkdir -p /mnt/python/huggingfaceserver-venv + # change permission so that poetry can install without sudo + sudo chown -R $USER /mnt/python/huggingfaceserver-venv + cd python/huggingfaceserver + make install_dependencies + - name: Install huggingface server + if: ${{ !startsWith(steps.setup-python.outputs.python-version, '3.8') }} + run: | + cd python/huggingfaceserver + make dev_install + - name: Test huggingfaceserver + if: ${{ !startsWith(steps.setup-python.outputs.python-version, '3.8') }} + run: | + cd python/huggingfaceserver + poetry run -- pytest --cov=huggingfaceserver + + - name: Free space after tests + run: | + df -hT diff --git a/README.md b/README.md index 0befcce6b96..432a5e324cb 100644 --- a/README.md +++ b/README.md @@ -3,7 +3,7 @@ [![Go Report Card](https://goreportcard.com/badge/github.com/opendatahub-io/kserve)](https://goreportcard.com/report/github.com/opendatahub-io/kserve) [![Releases](https://img.shields.io/github/release-pre/opendatahub-io/kserve.svg?sort=semver)](https://github.com/opendatahub-io/kserve/releases) [![LICENSE](https://img.shields.io/github/license/opendatahub-io/kserve.svg)](https://github.com/opendatahub-io/kserve/blob/master/LICENSE) -[![Slack Status](https://img.shields.io/badge/slack-join_chat-white.svg?logo=slack&style=social)](https://kubeflow.slack.com/archives/CH6E58LNP) +[![Slack Status](https://img.shields.io/badge/slack-join_chat-white.svg?logo=slack&style=social)](https://github.com/kserve/community/blob/main/README.md#questions-and-issues) KServe provides a Kubernetes [Custom Resource Definition](https://kubernetes.io/docs/concepts/extend-kubernetes/api-extension/custom-resources/) for serving machine learning (ML) models on arbitrary frameworks. It aims to solve production model serving use cases by providing performant, high abstraction interfaces for common ML frameworks like Tensorflow, XGBoost, ScikitLearn, PyTorch, and ONNX. diff --git a/charts/kserve-resources/templates/clusterservingruntimes.yaml b/charts/kserve-resources/templates/clusterservingruntimes.yaml index db508453906..69b6fcb4d88 100644 --- a/charts/kserve-resources/templates/clusterservingruntimes.yaml +++ b/charts/kserve-resources/templates/clusterservingruntimes.yaml @@ -344,3 +344,32 @@ spec: limits: cpu: "1" memory: 2Gi +--- +apiVersion: serving.kserve.io/v1alpha1 +kind: ClusterServingRuntime +metadata: + name: kserve-huggingfaceserver +spec: + annotations: + prometheus.kserve.io/port: '8080' + prometheus.kserve.io/path: "/metrics" + supportedModelFormats: + - name: huggingface + version: "1" + autoSelect: true + priority: 1 + protocolVersions: + - v2 + containers: + - name: kserve-container + image: "{{ .Values.kserve.servingruntime.huggingfaceserver.image }}:{{ .Values.kserve.servingruntime.huggingfaceserver.tag }}" + args: + - --model_id={{.Name}} + - --model_dir=/mnt/models + resources: + requests: + cpu: "1" + memory: 2Gi + limits: + cpu: "1" + memory: 2Gi diff --git a/charts/kserve-resources/values.yaml b/charts/kserve-resources/values.yaml index 9a5abd2abaf..95f71acc037 100644 --- a/charts/kserve-resources/values.yaml +++ b/charts/kserve-resources/values.yaml @@ -101,6 +101,9 @@ kserve: xgbserver: image: kserve/xgbserver tag: *defaultVersion + huggingfaceserver: + image: kserve/huggingfaceserver + tag: *defaultVersion tritonserver: image: nvcr.io/nvidia/tritonserver tag: 23.05-py3 diff --git a/config/configmap/inferenceservice.yaml b/config/configmap/inferenceservice.yaml index 56adf9da386..5232b65ce69 100644 --- a/config/configmap/inferenceservice.yaml +++ b/config/configmap/inferenceservice.yaml @@ -132,6 +132,7 @@ data: "s3Region": "", "s3VerifySSL": "", "s3UseVirtualBucket": "", + "s3UseAccelerate": "", "s3UseAnonymousCredential": "", "s3CABundle": "" } @@ -186,6 +187,9 @@ data: # s3UseVirtualBucket configures whether it is a virtual bucket or not. "s3UseVirtualBucket": "", + + # s3UseAccelerate configures whether to use transfer acceleration. + "s3UseAccelerate": "", # s3UseAnonymousCredential configures whether to use anonymous credentials to download the model or not. "s3UseAnonymousCredential": "", @@ -489,6 +493,7 @@ data: "s3Region": "", "s3VerifySSL": "", "s3UseVirtualBucket": "", + "s3UseAccelerate": "", "s3UseAnonymousCredential": "", "s3CABundle": "" } diff --git a/config/crd/patches/protocol.yaml b/config/crd/patches/protocol.yaml index a8464bb03ed..b98d2663a97 100644 --- a/config/crd/patches/protocol.yaml +++ b/config/crd/patches/protocol.yaml @@ -10,6 +10,10 @@ path: /spec/validation/openAPIV3Schema/properties/spec/properties/predictor/properties/onnx/properties/ports/items/required/1 value: protocol +- op: add + path: /spec/validation/openAPIV3Schema/properties/spec/properties/predictor/properties/huggingface/properties/ports/items/required/1 + value: protocol + - op: add path: /spec/validation/openAPIV3Schema/properties/spec/properties/predictor/properties/sklearn/properties/ports/items/required/1 value: protocol diff --git a/config/crd/serving.kserve.io_inferenceservices.yaml b/config/crd/serving.kserve.io_inferenceservices.yaml index f146ab445fc..5c5ebb0eadc 100644 --- a/config/crd/serving.kserve.io_inferenceservices.yaml +++ b/config/crd/serving.kserve.io_inferenceservices.yaml @@ -5009,6 +5009,633 @@ spec: type: boolean hostname: type: string + huggingface: + properties: + args: + items: + type: string + type: array + command: + items: + type: string + type: array + env: + items: + properties: + name: + type: string + value: + type: string + valueFrom: + properties: + configMapKeyRef: + properties: + key: + type: string + name: + type: string + optional: + type: boolean + required: + - key + type: object + x-kubernetes-map-type: atomic + fieldRef: + properties: + apiVersion: + type: string + fieldPath: + type: string + required: + - fieldPath + type: object + x-kubernetes-map-type: atomic + resourceFieldRef: + properties: + containerName: + type: string + divisor: + anyOf: + - type: integer + - type: string + pattern: ^(\+|-)?(([0-9]+(\.[0-9]*)?)|(\.[0-9]+))(([KMGTPE]i)|[numkMGTPE]|([eE](\+|-)?(([0-9]+(\.[0-9]*)?)|(\.[0-9]+))))?$ + x-kubernetes-int-or-string: true + resource: + type: string + required: + - resource + type: object + x-kubernetes-map-type: atomic + secretKeyRef: + properties: + key: + type: string + name: + type: string + optional: + type: boolean + required: + - key + type: object + x-kubernetes-map-type: atomic + type: object + required: + - name + type: object + type: array + envFrom: + items: + properties: + configMapRef: + properties: + name: + type: string + optional: + type: boolean + type: object + x-kubernetes-map-type: atomic + prefix: + type: string + secretRef: + properties: + name: + type: string + optional: + type: boolean + type: object + x-kubernetes-map-type: atomic + type: object + type: array + image: + type: string + imagePullPolicy: + type: string + lifecycle: + properties: + postStart: + properties: + exec: + properties: + command: + items: + type: string + type: array + type: object + httpGet: + properties: + host: + type: string + httpHeaders: + items: + properties: + name: + type: string + value: + type: string + required: + - name + - value + type: object + type: array + path: + type: string + port: + anyOf: + - type: integer + - type: string + x-kubernetes-int-or-string: true + scheme: + type: string + required: + - port + type: object + tcpSocket: + properties: + host: + type: string + port: + anyOf: + - type: integer + - type: string + x-kubernetes-int-or-string: true + required: + - port + type: object + type: object + preStop: + properties: + exec: + properties: + command: + items: + type: string + type: array + type: object + httpGet: + properties: + host: + type: string + httpHeaders: + items: + properties: + name: + type: string + value: + type: string + required: + - name + - value + type: object + type: array + path: + type: string + port: + anyOf: + - type: integer + - type: string + x-kubernetes-int-or-string: true + scheme: + type: string + required: + - port + type: object + tcpSocket: + properties: + host: + type: string + port: + anyOf: + - type: integer + - type: string + x-kubernetes-int-or-string: true + required: + - port + type: object + type: object + type: object + livenessProbe: + properties: + exec: + properties: + command: + items: + type: string + type: array + type: object + failureThreshold: + format: int32 + type: integer + grpc: + properties: + port: + format: int32 + type: integer + service: + type: string + required: + - port + type: object + httpGet: + properties: + host: + type: string + httpHeaders: + items: + properties: + name: + type: string + value: + type: string + required: + - name + - value + type: object + type: array + path: + type: string + port: + anyOf: + - type: integer + - type: string + x-kubernetes-int-or-string: true + scheme: + type: string + type: object + initialDelaySeconds: + format: int32 + type: integer + periodSeconds: + format: int32 + type: integer + successThreshold: + format: int32 + type: integer + tcpSocket: + properties: + host: + type: string + port: + anyOf: + - type: integer + - type: string + x-kubernetes-int-or-string: true + type: object + terminationGracePeriodSeconds: + format: int64 + type: integer + timeoutSeconds: + format: int32 + type: integer + type: object + name: + type: string + ports: + items: + properties: + containerPort: + format: int32 + type: integer + hostIP: + type: string + hostPort: + format: int32 + type: integer + name: + type: string + protocol: + default: TCP + type: string + required: + - containerPort + type: object + type: array + x-kubernetes-list-map-keys: + - containerPort + - protocol + x-kubernetes-list-type: map + protocolVersion: + type: string + readinessProbe: + properties: + exec: + properties: + command: + items: + type: string + type: array + type: object + failureThreshold: + format: int32 + type: integer + grpc: + properties: + port: + format: int32 + type: integer + service: + type: string + required: + - port + type: object + httpGet: + properties: + host: + type: string + httpHeaders: + items: + properties: + name: + type: string + value: + type: string + required: + - name + - value + type: object + type: array + path: + type: string + port: + anyOf: + - type: integer + - type: string + x-kubernetes-int-or-string: true + scheme: + type: string + type: object + initialDelaySeconds: + format: int32 + type: integer + periodSeconds: + format: int32 + type: integer + successThreshold: + format: int32 + type: integer + tcpSocket: + properties: + host: + type: string + port: + anyOf: + - type: integer + - type: string + x-kubernetes-int-or-string: true + type: object + terminationGracePeriodSeconds: + format: int64 + type: integer + timeoutSeconds: + format: int32 + type: integer + type: object + resizePolicy: + items: + properties: + resourceName: + type: string + restartPolicy: + type: string + required: + - resourceName + - restartPolicy + type: object + type: array + x-kubernetes-list-type: atomic + resources: + properties: + claims: + items: + properties: + name: + type: string + required: + - name + type: object + type: array + x-kubernetes-list-map-keys: + - name + x-kubernetes-list-type: map + limits: + additionalProperties: + anyOf: + - type: integer + - type: string + pattern: ^(\+|-)?(([0-9]+(\.[0-9]*)?)|(\.[0-9]+))(([KMGTPE]i)|[numkMGTPE]|([eE](\+|-)?(([0-9]+(\.[0-9]*)?)|(\.[0-9]+))))?$ + x-kubernetes-int-or-string: true + type: object + requests: + additionalProperties: + anyOf: + - type: integer + - type: string + pattern: ^(\+|-)?(([0-9]+(\.[0-9]*)?)|(\.[0-9]+))(([KMGTPE]i)|[numkMGTPE]|([eE](\+|-)?(([0-9]+(\.[0-9]*)?)|(\.[0-9]+))))?$ + x-kubernetes-int-or-string: true + type: object + type: object + restartPolicy: + type: string + runtimeVersion: + type: string + securityContext: + properties: + allowPrivilegeEscalation: + type: boolean + capabilities: + properties: + add: + items: + type: string + type: array + drop: + items: + type: string + type: array + type: object + privileged: + type: boolean + procMount: + type: string + readOnlyRootFilesystem: + type: boolean + runAsGroup: + format: int64 + type: integer + runAsNonRoot: + type: boolean + runAsUser: + format: int64 + type: integer + seLinuxOptions: + properties: + level: + type: string + role: + type: string + type: + type: string + user: + type: string + type: object + seccompProfile: + properties: + localhostProfile: + type: string + type: + type: string + required: + - type + type: object + windowsOptions: + properties: + gmsaCredentialSpec: + type: string + gmsaCredentialSpecName: + type: string + hostProcess: + type: boolean + runAsUserName: + type: string + type: object + type: object + startupProbe: + properties: + exec: + properties: + command: + items: + type: string + type: array + type: object + failureThreshold: + format: int32 + type: integer + grpc: + properties: + port: + format: int32 + type: integer + service: + type: string + required: + - port + type: object + httpGet: + properties: + host: + type: string + httpHeaders: + items: + properties: + name: + type: string + value: + type: string + required: + - name + - value + type: object + type: array + path: + type: string + port: + anyOf: + - type: integer + - type: string + x-kubernetes-int-or-string: true + scheme: + type: string + required: + - port + type: object + initialDelaySeconds: + format: int32 + type: integer + periodSeconds: + format: int32 + type: integer + successThreshold: + format: int32 + type: integer + tcpSocket: + properties: + host: + type: string + port: + anyOf: + - type: integer + - type: string + x-kubernetes-int-or-string: true + required: + - port + type: object + terminationGracePeriodSeconds: + format: int64 + type: integer + timeoutSeconds: + format: int32 + type: integer + type: object + stdin: + type: boolean + stdinOnce: + type: boolean + storage: + properties: + key: + type: string + parameters: + additionalProperties: + type: string + type: object + path: + type: string + schemaPath: + type: string + type: object + storageUri: + type: string + terminationMessagePath: + type: string + terminationMessagePolicy: + type: string + tty: + type: boolean + volumeDevices: + items: + properties: + devicePath: + type: string + name: + type: string + required: + - devicePath + - name + type: object + type: array + volumeMounts: + items: + properties: + mountPath: + type: string + mountPropagation: + type: string + name: + type: string + readOnly: + type: boolean + subPath: + type: string + subPathExpr: + type: string + required: + - mountPath + - name + type: object + type: array + workingDir: + type: string + type: object imagePullSecrets: items: properties: diff --git a/config/runtimes/kserve-huggingfaceserver.yaml b/config/runtimes/kserve-huggingfaceserver.yaml new file mode 100644 index 00000000000..4d8beeba6fb --- /dev/null +++ b/config/runtimes/kserve-huggingfaceserver.yaml @@ -0,0 +1,28 @@ +apiVersion: serving.kserve.io/v1alpha1 +kind: ClusterServingRuntime +metadata: + name: kserve-huggingfaceserver +spec: + annotations: + prometheus.kserve.io/port: '8080' + prometheus.kserve.io/path: "/metrics" + supportedModelFormats: + - name: huggingface + version: "1" + autoSelect: true + priority: 1 + protocolVersions: + - v2 + containers: + - name: kserve-container + image: huggingfaceserver:replace + args: + - --model_id={{.Name}} + - --model_dir=/mnt/models + resources: + requests: + cpu: "1" + memory: 2Gi + limits: + cpu: "1" + memory: 2Gi diff --git a/config/runtimes/kustomization.yaml b/config/runtimes/kustomization.yaml index 2a97d7c6dcb..48e6877b071 100644 --- a/config/runtimes/kustomization.yaml +++ b/config/runtimes/kustomization.yaml @@ -11,6 +11,7 @@ resources: - kserve-paddleserver.yaml - kserve-lgbserver.yaml - kserve-torchserve.yaml +- kserve-huggingfaceserver.yaml images: # SMS Only Runtimes @@ -49,3 +50,7 @@ images: - name: kserve-torchserve newName: pytorch/torchserve-kfs newTag: 0.9.0 + +- name: huggingfaceserver + newName: kserve/huggingfaceserver + newTag: latest diff --git a/go.mod b/go.mod index 4dd44fea0f0..05002385d22 100644 --- a/go.mod +++ b/go.mod @@ -5,7 +5,7 @@ go 1.21 require ( cloud.google.com/go/storage v1.35.1 github.com/aws/aws-sdk-go v1.48.0 - github.com/cloudevents/sdk-go v1.2.0 + github.com/cloudevents/sdk-go/v2 v2.14.0 github.com/fsnotify/fsnotify v1.7.0 github.com/getkin/kin-openapi v0.120.0 github.com/go-logr/logr v1.3.0 @@ -52,7 +52,6 @@ require ( github.com/blendle/zapdriver v1.3.1 // indirect github.com/census-instrumentation/opencensus-proto v0.4.1 // indirect github.com/cespare/xxhash/v2 v2.2.0 // indirect - github.com/cloudevents/sdk-go/v2 v2.14.0 github.com/davecgh/go-spew v1.1.1 // indirect github.com/emicklei/go-restful/v3 v3.11.0 // indirect github.com/evanphx/json-patch v5.7.0+incompatible // indirect diff --git a/go.sum b/go.sum index a8ca305abf9..871599901d6 100644 --- a/go.sum +++ b/go.sum @@ -663,6 +663,7 @@ go.opencensus.io v0.24.0 h1:y73uSU6J157QMP2kn2r30vwW1A2W2WFwSCGnAVxeaD0= go.opencensus.io v0.24.0/go.mod h1:vNK8G9p7aAivkbmorf4v+7Hgx+Zs0yY+0fOtgBfjQKo= go.opentelemetry.io/contrib/instrumentation/google.golang.org/grpc/otelgrpc v0.35.0/go.mod h1:h8TWwRAhQpOd0aM5nYsRD8+flnkj+526GEIVlarH7eY= go.opentelemetry.io/contrib/instrumentation/net/http/otelhttp v0.35.1/go.mod h1:9NiG9I2aHTKkcxqCILhjtyNA1QEiCjdBACv4IvrFQ+c= +go.opentelemetry.io/contrib/instrumentation/net/http/otelhttp v0.44.0/go.mod h1:SeQhzAEccGVZVEy7aH87Nh0km+utSpo1pTv6eMMop48= go.opentelemetry.io/otel v1.10.0/go.mod h1:NbvWjCthWHKBEUMpf0/v8ZRZlni86PpGFEMA9pnQSnQ= go.opentelemetry.io/otel/exporters/otlp/internal/retry v1.10.0/go.mod h1:78XhIg8Ht9vR4tbLNUhXsiOnE2HOuSeKAiAcoVQEpOY= go.opentelemetry.io/otel/exporters/otlp/otlptrace v1.10.0/go.mod h1:Krqnjl22jUJ0HgMzw5eveuCvFDXY4nSYb4F8t5gdrag= diff --git a/hack/python-release.sh b/hack/python-release.sh index 0e0707a1f37..7b74c906404 100755 --- a/hack/python-release.sh +++ b/hack/python-release.sh @@ -6,13 +6,15 @@ cd ./python echo "Installing poetry version plugin" pip install plugin/poetry-version-plugin -packages=$(find . -maxdepth 1 -type d) - -for folder in ${packages[@]} +pyproject_files=$(find . -type f -name "pyproject.toml") +for file in ${pyproject_files[@]} do + folder=$(dirname "${file}") echo "moving into folder ${folder}" - if [[ ${folder} == 'plugin' || ! -f "${folder}/pyproject.toml" ]]; then - echo -e "\033[33mskipping folder ${folder}\033[0m" + + # Check if the folder contains "plugin" or if it is "plugin" + if [[ ${folder} == *'plugin'* || ${folder} == 'plugin' ]]; then + echo -e "\033[33mSkipping folder ${folder}\033[0m" continue fi pushd "${folder}" diff --git a/hack/python-sdk/update_release_version.sh b/hack/python-sdk/update_release_version.sh deleted file mode 100755 index 51ccfe11d8c..00000000000 --- a/hack/python-sdk/update_release_version.sh +++ /dev/null @@ -1,45 +0,0 @@ -#!/usr/bin/env bash - -# Copyright 2023 The KServe Contributors -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -# This script updates the release version of python toml files. -# Usage: ./hack/python-sdk/update_release_version.sh [VERSION] - -set -o errexit -set -o nounset -set -o pipefail - -VERSION=$1 -dirs=( - "python/aiffairness" - "python/alibiexplainer" - "python/artexplainer" - "python/custom_model" - "python/custom_transformer" - "python/lgbserver" - "python/paddleserver" - "python/pmmlserver" - "python/sklearnserver" - "python/xgbserver" -) - -hack/python-sdk/update_release_version_helper.py "${VERSION}" - -# update lock files of packages which depends on kserve sdk -for dir in "${dirs[@]}"; do - pushd "${dir}" - poetry lock --no-update - popd -done diff --git a/hack/python-sdk/update_release_version_helper.py b/hack/python-sdk/update_release_version_helper.py deleted file mode 100755 index 8bba0e476af..00000000000 --- a/hack/python-sdk/update_release_version_helper.py +++ /dev/null @@ -1,44 +0,0 @@ -#!/usr/bin/env python3 - -# Copyright 2023 The KServe Authors. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import tomlkit -import argparse - -parser = argparse.ArgumentParser(description="Update release version in python toml files") -parser.add_argument("version", type=str, help="release version") -args, _ = parser.parse_known_args() - -toml_files = [ - "python/kserve/pyproject.toml", - "python/aiffairness/pyproject.toml", - "python/alibiexplainer/pyproject.toml", - "python/artexplainer/pyproject.toml", - "python/custom_model/pyproject.toml", - "python/custom_transformer/pyproject.toml", - "python/lgbserver/pyproject.toml", - "python/paddleserver/pyproject.toml", - "python/pmmlserver/pyproject.toml", - "python/sklearnserver/pyproject.toml", - "python/xgbserver/pyproject.toml", -] - -for toml_file in toml_files: - with open(toml_file, "r") as file: - toml_config = tomlkit.load(file) - toml_config['tool']['poetry']['version'] = args.version - - with open(toml_file, "w") as file: - tomlkit.dump(toml_config, file) diff --git a/hack/telepresence-setup.sh b/hack/telepresence-setup.sh new file mode 100755 index 00000000000..ebbec745909 --- /dev/null +++ b/hack/telepresence-setup.sh @@ -0,0 +1,81 @@ +#!/bin/bash + +# Script for setting up telepresence to run the KServe controller +# locally, fully connected to the cluster (it works also for the +# incoming direction when used as a webhook) +# +# Usage: +# # Install everything. This command is idempotent and can +# # be called multiple times +# ./telepresence-setup.sh +# +# # Remove everything from telepresence +# ./telepresence-setup.sh uninstall +# +# More information for using telepresence for developing: +# +# * https://codefresh.io/blog/telepresence-2-local-development/ +# * https://ttt.io/kubernetes-admission-controller + +set -o pipefail +set +e + +if [[ "$1" == "uninstall" ]]; then + telepresence helm uninstall + kubectl delete ns ambassador + telepresence quit -s + [ -d "${TMPDIR}/k8s-webhook-server" ] && rm -rf "${TMPDIR}/k8s-webhook-server" + exit 0 +fi + +if ! type telepresence >/dev/null 2>&1; then + echo "Telepresence is not installed." + echo "Please install it, e.g. with \"brew install telepresence\" for Mac" + echo "or download it from https://www.getambassador.io/docs/telepresence/latest/install for all OS" + exit 1 +fi + +# Check if cluster is already setup for telepresence and install it if not +if ! kubectl get -n ambassador deploy traffic-manager >/dev/null 2>&1; then + echo "* Installing Telepresence in cluster" + set -e + telepresence helm install + set +e +else + echo "* Telepresence already installed in cluster" +fi + +# Connect to the cluster +set -e +echo "* Connecting to cluster (local root password might be required)" +telepresence connect +set +e + +# Intercept the kserve controller manager +if ! telepresence status --output json | jq -e '.user_daemon.intercepts[]? | select(.name == "kserve-controller-manager-kserve")' > /dev/null; then + echo "* Intercept kserve-webhook-server-service" + set -e + telepresence intercept kserve-controller-manager --service=kserve-webhook-server-service --port 9443 -n kserve --mount=false + set +e +else + echo "* Webhook service already intercepted" +fi + +# Copy of certs and ca from secret so that controller manager can run locally +# Get secret in JSON format +set -e +secret_json=$(kubectl get secret kserve-webhook-server-cert -n kserve -o json) +target_dir="${TMPDIR}/k8s-webhook-server/serving-certs" +echo "* Extracting Webhook certs to $target_dir" +mkdir -p $target_dir +for key in $(echo "$secret_json" | jq -r '.data | keys[]'); do + # Decode value + value=$(echo "$secret_json" | jq -r ".data[\"$key\"]" | base64 -d) + echo "$value" > "$target_dir/$key" +done + +cat <=4.0,<5.0)"] http2 = ["h2 (>=3,<5)"] socks = ["socksio (==1.*)"] +trio = ["trio (>=0.22.0,<0.23.0)"] [[package]] name = "httptools" @@ -873,24 +848,25 @@ test = ["Cython (>=0.29.24,<0.30.0)"] [[package]] name = "httpx" -version = "0.23.3" +version = "0.26.0" description = "The next generation HTTP client." optional = false -python-versions = ">=3.7" +python-versions = ">=3.8" files = [ - {file = "httpx-0.23.3-py3-none-any.whl", hash = "sha256:a211fcce9b1254ea24f0cd6af9869b3d29aba40154e947d2a07bb499b3e310d6"}, - {file = "httpx-0.23.3.tar.gz", hash = "sha256:9818458eb565bb54898ccb9b8b251a28785dd4a55afbc23d0eb410754fe7d0f9"}, + {file = 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POETRY_VERSION=1.7.1 + +RUN apt-get update -y && apt-get install gcc python3.10-venv python3-dev -y +RUN python3 -m venv ${POETRY_HOME} && ${POETRY_HOME}/bin/pip3 install poetry==${POETRY_VERSION} +ENV PATH="$PATH:${POETRY_HOME}/bin" + +# Activate virtual env +ARG VENV_PATH +ENV VIRTUAL_ENV=${VENV_PATH} +RUN python3 -m venv $VIRTUAL_ENV +ENV PATH="$VIRTUAL_ENV/bin:$PATH" + +COPY kserve/pyproject.toml kserve/poetry.lock kserve/ +RUN cd kserve && poetry install --no-root --no-interaction --no-cache +COPY kserve kserve +RUN cd kserve && poetry install --no-interaction --no-cache + +COPY huggingfaceserver/pyproject.toml huggingfaceserver/poetry.lock huggingfaceserver/ +RUN cd huggingfaceserver && poetry install --no-root --no-interaction --no-cache +COPY huggingfaceserver huggingfaceserver +RUN cd huggingfaceserver && poetry install --no-interaction --no-cache + + +FROM nvidia/cuda:12.1.0-base-ubuntu22.04 as prod + +RUN apt-get update -y && apt-get install python3.10-venv -y + +COPY third_party third_party + +# Activate virtual env +ARG VENV_PATH +ENV VIRTUAL_ENV=${VENV_PATH} +ENV PATH="$VIRTUAL_ENV/bin:$PATH" + +RUN useradd kserve -m -u 1000 -d /home/kserve + +COPY --from=builder --chown=kserve:kserve $VIRTUAL_ENV $VIRTUAL_ENV +COPY --from=builder kserve kserve +COPY --from=builder huggingfaceserver huggingfaceserver + +USER 1000 +ENTRYPOINT ["python3", "-m", "huggingfaceserver"] + diff --git a/python/huggingfaceserver/Makefile b/python/huggingfaceserver/Makefile new file mode 100644 index 00000000000..92cf90452a8 --- /dev/null +++ b/python/huggingfaceserver/Makefile @@ -0,0 +1,11 @@ +dev_install: + poetry install --with test + +install_dependencies: + poetry install --with=test --no-interaction --no-root + +test: type_check + pytest -W ignore + +type_check: + mypy --ignore-missing-imports huggingfaceserver diff --git a/python/huggingfaceserver/README.md b/python/huggingfaceserver/README.md new file mode 100644 index 00000000000..c615fc10e1c --- /dev/null +++ b/python/huggingfaceserver/README.md @@ -0,0 +1,104 @@ +# Huggingface Serving Runtime + +The Huggingface serving runtime implements a runtime that can serve huggingface transformer based model out of the box. +The preprocess and post-process handlers are implemented based on different ML tasks, for example text classification, +token-classification, text-generation, text2text generation. Based on the performance requirement, you can choose to perform +the inference on a more optimized inference engine like triton inference server and vLLM for text generation. + + +## Run Huggingface Server Locally + +```bash +python -m huggingfaceserver --model_id=bert-base-uncased --model_name=bert + +INFO:kserve:successfully loaded tokenizer for task: 5 +Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForMaskedLM: ['cls.seq_relationship.weight', 'bert.pooler.dense.bias', 'cls.seq_relationship.bias', 'bert.pooler.dense.weight'] +- This IS expected if you are initializing BertForMaskedLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model). +- This IS NOT expected if you are initializing BertForMaskedLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model). +INFO:kserve:successfully loaded huggingface model from path bert-base-uncased +INFO:kserve:Registering model: model +INFO:kserve:Setting max asyncio worker threads as 16 +INFO:kserve:Starting uvicorn with 1 workers +2024-01-08 06:32:08.801 uvicorn.error INFO: Started server process [75012] +2024-01-08 06:32:08.801 uvicorn.error INFO: Waiting for application startup. +2024-01-08 06:32:08.804 75012 kserve INFO [start():62] Starting gRPC server on [::]:8081 +2024-01-08 06:32:08.804 uvicorn.error INFO: Application startup complete. +2024-01-08 06:32:08.805 uvicorn.error INFO: Uvicorn running on http://0.0.0.0:8080 (Press CTRL+C to quit) +``` + +Perform the inference + +```bash +curl -H "content-type:application/json" -v localhost:8080/v1/models/bert:predict -d '{"instances": ["The capital of france is [MASK]."] }' + +{"predictions":["paris"]} +``` + +## Deploy Huggingface Server on KServe + +1. Serve the huggingface model using KServe python runtime for both preprocess(tokenization)/postprocess and inference. +```yaml +apiVersion: serving.kserve.io/v1beta1 +kind: InferenceService +metadata: + name: huggingface +spec: + predictor: + containers: + - args: + - --model_name=bert + - --model_id=bert-base-uncased + - --tensor_input_names=input_ids + image: kserve/huggingfaceserver:latest + name: kserve-container + resources: + limits: + cpu: "1" + memory: 2Gi + requests: + cpu: 100m + memory: 2Gi + nvidia.com/gpu: "1" +``` + +1. Serve the huggingface model using triton inference runtime and KServe transformer for the preprocess(tokenization) and postprocess. +```yaml +apiVersion: serving.kserve.io/v1beta1 +kind: InferenceService +metadata: + name: huggingface-triton +spec: + predictor: + model: + args: + - --log-verbose=1 + modelFormat: + name: triton + protocolVersion: v2 + resources: + limits: + cpu: "1" + memory: 8Gi + requests: + cpu: "1" + memory: 8Gi + nvidia.com/gpu: "1" + runtimeVersion: 23.10-py3 + storageUri: gs://kfserving-examples/models/triton/huggingface/model_repository + transformer: + containers: + - args: + - --model_name=bert + - --model_id=bert-base-uncased + - --predictor_protocol=v2 + - --tensor_input_names=input_ids + image: kserve/huggingfaceserver:latest + name: kserve-container + resources: + limits: + cpu: "1" + memory: 2Gi + requests: + cpu: 100m + memory: 2Gi +``` diff --git a/python/huggingfaceserver/huggingfaceserver/__init__.py b/python/huggingfaceserver/huggingfaceserver/__init__.py new file mode 100644 index 00000000000..a1a146f5c4e --- /dev/null +++ b/python/huggingfaceserver/huggingfaceserver/__init__.py @@ -0,0 +1,16 @@ +# Copyright 2023 The KServe Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from .model import HuggingfaceModel # noqa # pylint: disable=unused-import +from .huggingface_model_repository import HuggingfaceModelRepository # noqa # pylint: disable=unused-import diff --git a/python/huggingfaceserver/huggingfaceserver/__main__.py b/python/huggingfaceserver/huggingfaceserver/__main__.py new file mode 100644 index 00000000000..8789e63c15b --- /dev/null +++ b/python/huggingfaceserver/huggingfaceserver/__main__.py @@ -0,0 +1,72 @@ +# Copyright 2024 The KServe Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import argparse +import logging + +from kserve.model import PredictorConfig +from . import HuggingfaceModel, HuggingfaceModelRepository +import kserve +from kserve.errors import ModelMissingError + + +def list_of_strings(arg): + return arg.split(',') + + +parser = argparse.ArgumentParser(parents=[kserve.model_server.parser]) + +parser.add_argument('--model_dir', required=False, default=None, + help='A URI pointer to the model binary') +parser.add_argument('--model_id', required=False, + help='Huggingface model id') +parser.add_argument('--tensor_parallel_degree', type=int, default=-1, + help='tensor parallel degree') +parser.add_argument('--max_length', type=int, default=None, + help='max sequence length for the tokenizer') +parser.add_argument('--do_lower_case', type=bool, default=True, + help='do lower case for the tokenizer') +parser.add_argument('--add_special_tokens', type=bool, default=True, + help='the sequences will be encoded with the special tokens relative to their model') +parser.add_argument('--tensor_input_names', type=list_of_strings, default=None, + help='the tensor input names passed to the model') +parser.add_argument('--task', required=False, help="The ML task name") + +try: + from vllm.engine.arg_utils import AsyncEngineArgs + + parser = AsyncEngineArgs.add_cli_args(parser) + _vllm = True +except ImportError: + _vllm = False +args, _ = parser.parse_known_args() + +if __name__ == "__main__": + engine_args = AsyncEngineArgs.from_cli_args(args) if _vllm else None + predictor_config = PredictorConfig(args.predictor_host, args.predictor_protocol, + args.predictor_use_ssl, + args.predictor_request_timeout_seconds) + model = HuggingfaceModel(args.model_name, + predictor_config=predictor_config, + kwargs=vars(args), engine_args=engine_args) + try: + model.load() + except ModelMissingError: + logging.error(f"fail to locate model file for model {args.model_name} under dir {args.model_dir}," + f"trying loading from model repository.") + if not args.model_id: + kserve.ModelServer(registered_models=HuggingfaceModelRepository(args.model_dir)).start( + [model] if model.ready else []) + else: + kserve.ModelServer().start([model] if model.ready else []) diff --git a/python/huggingfaceserver/huggingfaceserver/async_generate_stream.py b/python/huggingfaceserver/huggingfaceserver/async_generate_stream.py new file mode 100644 index 00000000000..ce27ce0681b --- /dev/null +++ b/python/huggingfaceserver/huggingfaceserver/async_generate_stream.py @@ -0,0 +1,48 @@ +# Copyright 2024 The KServe Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from asyncio import Queue +from typing import Optional + +from transformers import TextStreamer, AutoTokenizer + + +class AsyncGenerateStream(TextStreamer): + """A stream of GenerateOutputs for a request that can be + iterated over asynchronously.""" + + def __init__( + self, tokenizer: AutoTokenizer, skip_prompt: bool = False, timeout: Optional[float] = None, + **decode_kwargs + ): + super().__init__(tokenizer, skip_prompt, **decode_kwargs) + self.text_queue = Queue() + self.stop_signal = None + self.timeout = timeout + + def on_finalized_text(self, text: str, stream_end: bool = False): + """Put the new text in the queue. If the stream is ending, also put a stop signal in the queue.""" + self.text_queue.put_nowait(text) + if stream_end: + self.text_queue.put_nowait(self.stop_signal) + + def __aiter__(self): + return self + + def __anext__(self): + value = self.text_queue.get() + if value == self.stop_signal: + raise StopIteration() + else: + return value diff --git a/python/huggingfaceserver/huggingfaceserver/huggingface_model_repository.py b/python/huggingfaceserver/huggingfaceserver/huggingface_model_repository.py new file mode 100644 index 00000000000..ab96a012448 --- /dev/null +++ b/python/huggingfaceserver/huggingfaceserver/huggingface_model_repository.py @@ -0,0 +1,33 @@ +# Copyright 2024 The KServe Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +from kserve.model_repository import ModelRepository, MODEL_MOUNT_DIRS +from . import HuggingfaceModel + + +class HuggingfaceModelRepository(ModelRepository): + + def __init__(self, model_dir: str = MODEL_MOUNT_DIRS): + super().__init__(model_dir) + self.load_models() + + async def load(self, name: str) -> bool: + return self.load_model(name) + + def load_model(self, name: str) -> bool: + model = HuggingfaceModel(name, os.path.join(self.models_dir, name)) + if model.load(): + self.update(model) + return model.ready diff --git a/python/huggingfaceserver/huggingfaceserver/model.py b/python/huggingfaceserver/huggingfaceserver/model.py new file mode 100644 index 00000000000..78baebcf0de --- /dev/null +++ b/python/huggingfaceserver/huggingfaceserver/model.py @@ -0,0 +1,251 @@ +# Copyright 2024 The KServe Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import uuid +from threading import Thread + +from torch import Tensor + +from kserve.model import PredictorConfig +from kserve.protocol.rest.v2_datamodels import GenerateRequest, GenerateResponse, Token, Details +from .async_generate_stream import AsyncGenerateStream +from .task import ARCHITECTURES_2_TASK, MLTask +from kserve.logging import logger +import pathlib +from typing import Dict, Union, Any, AsyncIterator, Optional + +from kserve.errors import InferenceError +from kserve.storage import Storage + +from kserve.protocol.infer_type import InferRequest, InferResponse, InferInput +from kserve.utils.utils import get_predict_response, get_predict_input, from_np_dtype +from kserve import Model +import torch + +try: + from vllm.sampling_params import SamplingParams + from vllm.vllm_async_engine import AsyncLLMEngine + + _vllm = True +except ImportError: + _vllm = False +from transformers import AutoModelForCausalLM, AutoModelForSeq2SeqLM, AutoTokenizer, \ + AutoConfig, \ + AutoModelForSequenceClassification, AutoModelForTokenClassification, AutoModelForQuestionAnswering, \ + AutoModelForMaskedLM, BatchEncoding, TensorType + + +class HuggingfaceModel(Model): # pylint:disable=c-extension-no-member + def __init__(self, model_name: str, kwargs, + engine_args=None, predictor_config: Optional[PredictorConfig] = None): + super().__init__(model_name, predictor_config) + if kwargs is None: + kwargs = {} + self.kwargs = {} + tp_degree = kwargs.get('tensor_parallel_degree', -1) + self.device = torch.device( + "cuda" if torch.cuda.is_available() else "cpu" + ) + if "device_map" in kwargs: + self.kwargs["device_map"] = kwargs['device_map'] + elif tp_degree > 0: + self.kwargs["device_map"] = "auto" + world_size = torch.cuda.device_count() + assert world_size == tp_degree, f"TP degree ({tp_degree}) doesn't match available GPUs ({world_size})" + self.model_id = kwargs.get('model_id', None) + self.model_dir = kwargs.get('model_dir', None) + self.do_lower_case = kwargs.get('do_lower_case', True) + self.add_special_tokens = kwargs.get('add_special_tokens', True) + self.max_length = kwargs.get('max_length', None) + self.tensor_input_names = kwargs.get('tensor_input_names', None) + self.task = kwargs.get('task', None) + self.tokenizer = None + self.model = None + self.mapping = None + self.engine = AsyncLLMEngine.from_engine_args(engine_args) if _vllm else None + self.ready = False + + @staticmethod + def infer_task_from_model_architecture(model_config_path: str): + model_config = AutoConfig.from_pretrained(model_config_path) + architecture = model_config.architectures[0] + task = None + for arch_options in ARCHITECTURES_2_TASK: + if architecture.endswith(arch_options): + return ARCHITECTURES_2_TASK[arch_options] + + if task is None: + raise ValueError(f"Task couldn't be inferred from {architecture}. Please manually set `task` option.") + + def load(self) -> bool: + model_id_or_path = self.model_id + if self.model_dir: + model_id_or_path = pathlib.Path(Storage.download(self.model_dir)) + # TODO Read the mapping file, index to object name + if not self.task: + self.task = self.infer_task_from_model_architecture(model_id_or_path) + # load huggingface tokenizer + self.tokenizer = AutoTokenizer.from_pretrained( + model_id_or_path, do_lower_case=self.do_lower_case) + logger.info(f"successfully loaded tokenizer for task: {self.task}") + # load huggingface model using from_pretrained for inference mode + if not self.predictor_host: + if self.task == MLTask.sequence_classification.value: + self.model = AutoModelForSequenceClassification.from_pretrained( + model_id_or_path) + elif self.task == MLTask.question_answering.value: + self.model = AutoModelForQuestionAnswering.from_pretrained(model_id_or_path) + elif self.task == MLTask.token_classification.value: + self.model = AutoModelForTokenClassification.from_pretrained(model_id_or_path) + elif self.task == MLTask.fill_mask.value: + self.model = AutoModelForMaskedLM.from_pretrained(model_id_or_path) + elif self.task == MLTask.text_generation.value: + self.model = AutoModelForCausalLM.from_pretrained(model_id_or_path) + elif self.task == MLTask.text2text_generation.value: + self.model = AutoModelForSeq2SeqLM.from_pretrained(model_id_or_path) + else: + raise ValueError(f"Unsupported task {self.task}. Please check the supported `task` option.") + self.model.eval() + self.model.to(self.device) + logger.info(f"successfully loaded huggingface model from path {model_id_or_path}") + self.ready = True + return self.ready + + def preprocess(self, payload: Union[Dict, InferRequest], context: Dict[str, Any] = None) -> \ + Union[BatchEncoding, InferRequest]: + instances = get_predict_input(payload) + + # Serialize to tensor + if self.predictor_host: + inputs = self.tokenizer( + instances, + max_length=self.max_length, + add_special_tokens=self.add_special_tokens, + return_tensors=TensorType.NUMPY, + ) + context["payload"] = payload + context["input_ids"] = inputs["input_ids"] + infer_inputs = [] + for key, input_tensor in inputs.items(): + if (not self.tensor_input_names) or (key in self.tensor_input_names): + infer_input = InferInput(name=key, datatype=from_np_dtype(input_tensor.dtype), + shape=list(input_tensor.shape), data=input_tensor) + infer_inputs.append(infer_input) + infer_request = InferRequest(infer_inputs=infer_inputs, model_name=self.name) + return infer_request + else: + inputs = self.tokenizer( + instances, + max_length=self.max_length, + add_special_tokens=self.add_special_tokens, + return_tensors=TensorType.PYTORCH, + ) + context["payload"] = payload + context["input_ids"] = inputs["input_ids"] + return inputs + + async def generate(self, generate_request: GenerateRequest, headers: Dict[str, str] = None) \ + -> Union[GenerateResponse, AsyncIterator[Any]]: + parameters = generate_request.parameters + prompt = generate_request.text_input + request_id = str(uuid.uuid4()) + if _vllm: + sampling_params = SamplingParams(**parameters) + results_generator = self.engine.generate(prompt, sampling_params=sampling_params, + request_id=request_id) + return results_generator + else: + input_batch = self.tokenizer( + prompt, + max_length=self.max_length, + add_special_tokens=self.add_special_tokens, + return_tensors="pt", + ) + input_batch = input_batch.to(self.device) + if headers.get("streaming", "false") == "true": + streamer = AsyncGenerateStream(self.tokenizer) + generation_kwargs = dict(**input_batch, streamer=streamer) + if parameters: + generation_kwargs = dict(**input_batch, **parameters, streamer=streamer) + # TODO change to use thread pool executor + thread = Thread(target=self.model.generate, kwargs=generation_kwargs) + thread.start() + return streamer + else: + if parameters: + output_ids = self.model.generate(**input_batch, **parameters) + else: + output_ids = self.model.generate(**input_batch) + outputs = self.tokenizer.batch_decode(output_ids, skip_special_tokens=True) + token_outputs = [Token(id=output_id, special=False, logprob=0, # TODO set logprob + text=self.tokenizer.decode(output_id, skip_special_tokens=True)) + for output_id in output_ids[0]] + generate_details = Details(finish_reason="length", logprobs=token_outputs) + return GenerateResponse(text_output=outputs[0], model_name=self.name, details=generate_details) + + async def predict(self, input_batch: Union[BatchEncoding, InferRequest], context: Dict[str, Any] = None) \ + -> Union[Tensor, InferResponse]: + if self.predictor_host: + # when predictor_host is provided, serialize the tensor and send to optimized model serving runtime + # like NVIDIA triton inference server + return await super().predict(input_batch, context) + else: + input_batch = input_batch.to(self.device) + try: + with torch.no_grad(): + if self.task == MLTask.text2text_generation.value or self.task == MLTask.text_generation: + outputs = self.model.generate(**input_batch) + else: + outputs = self.model(**input_batch).logits + return outputs + except Exception as e: + raise InferenceError(str(e)) + + def postprocess(self, outputs: Union[Tensor, InferResponse], context: Dict[str, Any] = None) \ + -> Union[Dict, InferResponse]: + input_ids = context["input_ids"] + request = context["payload"] + if isinstance(outputs, InferResponse): + shape = torch.Size(outputs.outputs[0].shape) + data = torch.Tensor(outputs.outputs[0].data) + outputs = data.view(shape) + input_ids = torch.Tensor(input_ids) + inferences = [] + if self.task == MLTask.sequence_classification.value: + num_rows, num_cols = outputs.shape + for i in range(num_rows): + out = outputs[i].unsqueeze(0) + predicted_idx = out.argmax().item() + inferences.append(predicted_idx) + return get_predict_response(request, inferences, self.name) + elif self.task == MLTask.fill_mask.value: + num_rows = outputs.shape[0] + for i in range(num_rows): + mask_pos = (input_ids == self.tokenizer.mask_token_id)[i] + mask_token_index = mask_pos.nonzero(as_tuple=True)[0] + predicted_token_id = outputs[i, mask_token_index].argmax(axis=-1) + inferences.append(self.tokenizer.decode(predicted_token_id)) + return get_predict_response(request, inferences, self.name) + elif self.task == MLTask.token_classification.value: + num_rows = outputs.shape[0] + for i in range(num_rows): + output = outputs[i].unsqueeze(0) + predictions = torch.argmax(output, dim=2) + inferences.append(predictions.tolist()) + return get_predict_response(request, inferences, self.name) + elif self.task == MLTask.text_generation.value or self.task == MLTask.text2text_generation.value: + outputs = self.tokenizer.batch_decode(outputs, skip_special_tokens=True) + return get_predict_response(request, outputs, self.name) + else: + raise ValueError(f"Unsupported task {self.task}. Please check the supported `task` option.") diff --git a/python/huggingfaceserver/huggingfaceserver/task.py b/python/huggingfaceserver/huggingfaceserver/task.py new file mode 100644 index 00000000000..ed742485649 --- /dev/null +++ b/python/huggingfaceserver/huggingfaceserver/task.py @@ -0,0 +1,51 @@ +# Copyright 2024 The KServe Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from enum import auto, Enum + + +class MLTask(str, Enum): + """ + Task defines the common ML tasks using Huggingface Transformer Models + """ + table_question_answering = auto() + question_answering = auto() + token_classification = auto() + sequence_classification = auto() + fill_mask = auto() + text_generation = auto() + text2text_generation = auto() + multiple_choice = auto() + + @classmethod + def _missing_(cls, value: str): + value = value.lower() + for member in cls: + if member.value == value: + return member + return None + + +ARCHITECTURES_2_TASK = { + "TapasForQuestionAnswering": MLTask.table_question_answering.value, + "ForQuestionAnswering": MLTask.question_answering.value, + "ForTokenClassification": MLTask.token_classification.value, + "ForSequenceClassification": MLTask.sequence_classification.value, + "ForMultipleChoice": MLTask.multiple_choice.value, + "ForMaskedLM": MLTask.fill_mask.value, + "ForCausalLM": MLTask.text_generation.value, + "ForConditionalGeneration": MLTask.text2text_generation.value, + "MTModel": MLTask.text2text_generation.value, + "EncoderDecoderModel": MLTask.text2text_generation.value +} diff --git a/python/huggingfaceserver/huggingfaceserver/test_model.py b/python/huggingfaceserver/huggingfaceserver/test_model.py new file mode 100644 index 00000000000..0cf957d0696 --- /dev/null +++ b/python/huggingfaceserver/huggingfaceserver/test_model.py @@ -0,0 +1,93 @@ +# Copyright 2024 The KServe Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import asyncio +import unittest +from pytest_httpx import HTTPXMock + +from kserve.model import PredictorConfig +from kserve.protocol.rest.v2_datamodels import GenerateRequest +from .task import MLTask +from .model import HuggingfaceModel + + +def test_t5(): + model = HuggingfaceModel("t5-small", {"model_id": "t5-small"}) + model.load() + + request = "translate this to germany" + response = asyncio.run(model({"instances": [request, request]}, headers={})) + assert response == {"predictions": ['Das ist für Deutschland', 'Das ist für Deutschland']} + + +def test_bert(): + model = HuggingfaceModel("bert-base-uncased", {"model_id": "bert-base-uncased", "do_lower_case": True}) + model.load() + + response = asyncio.run(model({"instances": ["The capital of France is [MASK].", + "The capital of [MASK] is paris."]}, headers={})) + assert response == {"predictions": ["paris", "france"]} + + +def test_bert_predictor_host(httpx_mock: HTTPXMock): + httpx_mock.add_response(json={"outputs": [{"name": "OUTPUT__0", "shape": [1, 9, 758], + "data": [1] * 9 * 758, + "datatype": "INT64"}]}) + + model = HuggingfaceModel("bert", {"model_id": "bert-base-uncased", + "tensor_input_names": "input_ids", + "do_lower_case": True}, predictor_config=PredictorConfig( + predictor_host="localhost:8081", predictor_protocol="v2")) + model.load() + + response = asyncio.run(model({"instances": ["The capital of France is [MASK]."]}, headers={})) + assert response == {"predictions": ["[PAD]"]} + + +def test_bert_sequence_classification(): + model = HuggingfaceModel("bert-base-uncased-yelp-polarity", + {"model_id": "textattack/bert-base-uncased-yelp-polarity", + "task": MLTask.sequence_classification.value}) + model.load() + + request = "Hello, my dog is cute." + response = asyncio.run(model({"instances": [request, request]}, headers={})) + assert response == {"predictions": [1, 1]} + + +def test_bert_token_classification(): + model = HuggingfaceModel("bert-large-cased-finetuned-conll03-english", + {"model_id": "dbmdz/bert-large-cased-finetuned-conll03-english", + "add_special_tokens": False}) + model.load() + + request = "HuggingFace is a company based in Paris and New York" + response = asyncio.run(model({"instances": [request, request]}, headers={})) + assert response == {"predictions": [[[0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], + [[0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]]} + + +def test_bloom(): + model = HuggingfaceModel("bloom-560m", + {"model_id": "bigscience/bloom-560m", + "add_special_tokens": False}) + model.load() + + request = "Hello, my dog is cute" + response = asyncio.run(model.generate(generate_request=GenerateRequest(text_input=request), headers={})) + assert response.text_output == "Hello, my dog is cute.\n- Hey, my dog is cute.\n- Hey, my dog" + + +if __name__ == '__main__': + unittest.main() diff --git a/python/huggingfaceserver/poetry.lock b/python/huggingfaceserver/poetry.lock new file mode 100644 index 00000000000..e101125fba0 --- /dev/null +++ b/python/huggingfaceserver/poetry.lock @@ -0,0 +1,4133 @@ +# This file is automatically @generated by Poetry 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+version = "0.12.0rc1" +description = "Model Server implementation for huggingface. Not intended for use outside KServe Frameworks Images." +authors = ["Dan Sun "] +license = "https://github.com/kserve/kserve/blob/master/LICENSE" +readme = "README.md" +packages = [ + { include = "huggingfaceserver" }, +] + +[tool.poetry.dependencies] +python = ">=3.9,<3.12" +kserve = { path = "../kserve", extras = ["storage"], develop = true } +transformers = "~4.36.0" +torch = "~2.1.2" +vllm = { version = "^0.2.6", optional = true } + +[tool.poetry.extras] +vllm = [ + "vllm", +] + +[tool.poetry.group.test] +optional = true + +[tool.poetry.group.test.dependencies] +pytest = "^7.2.0" +pytest-cov = "^4.0.0" +mypy = "^0.991" +pytest-asyncio = "^0.20.3" +pytest_httpx = "^v0.28.0" + +[tool.poetry-version-plugin] +source = "file" +file_path = "../VERSION" + +[build-system] +requires = ["poetry-core>=1.0.0"] +build-backend = "poetry.core.masonry.api" diff --git a/python/kserve/docs/V1beta1HuggingFaceRuntimeSpec.md b/python/kserve/docs/V1beta1HuggingFaceRuntimeSpec.md new file mode 100644 index 00000000000..6bd41ab0dc2 --- /dev/null +++ b/python/kserve/docs/V1beta1HuggingFaceRuntimeSpec.md @@ -0,0 +1,38 @@ +# V1beta1HuggingFaceRuntimeSpec + +HuggingFaceRuntimeSpec defines arguments for configuring HuggingFace model serving. +## Properties +Name | Type | Description | Notes +------------ | ------------- | ------------- | ------------- +**args** | **list[str]** | Arguments to the entrypoint. The container image's CMD is used if this is not provided. Variable references $(VAR_NAME) are expanded using the container's environment. If a variable cannot be resolved, the reference in the input string will be unchanged. Double $$ are reduced to a single $, which allows for escaping the $(VAR_NAME) syntax: i.e. \"$$(VAR_NAME)\" will produce the string literal \"$(VAR_NAME)\". Escaped references will never be expanded, regardless of whether the variable exists or not. Cannot be updated. More info: https://kubernetes.io/docs/tasks/inject-data-application/define-command-argument-container/#running-a-command-in-a-shell | [optional] +**command** | **list[str]** | Entrypoint array. Not executed within a shell. The container image's ENTRYPOINT is used if this is not provided. Variable references $(VAR_NAME) are expanded using the container's environment. If a variable cannot be resolved, the reference in the input string will be unchanged. Double $$ are reduced to a single $, which allows for escaping the $(VAR_NAME) syntax: i.e. \"$$(VAR_NAME)\" will produce the string literal \"$(VAR_NAME)\". Escaped references will never be expanded, regardless of whether the variable exists or not. Cannot be updated. More info: https://kubernetes.io/docs/tasks/inject-data-application/define-command-argument-container/#running-a-command-in-a-shell | [optional] +**env** | [**list[V1EnvVar]**](https://github.com/kubernetes-client/python/blob/master/kubernetes/docs/V1EnvVar.md) | List of environment variables to set in the container. Cannot be updated. | [optional] +**env_from** | [**list[V1EnvFromSource]**](https://github.com/kubernetes-client/python/blob/master/kubernetes/docs/V1EnvFromSource.md) | List of sources to populate environment variables in the container. The keys defined within a source must be a C_IDENTIFIER. All invalid keys will be reported as an event when the container is starting. When a key exists in multiple sources, the value associated with the last source will take precedence. Values defined by an Env with a duplicate key will take precedence. Cannot be updated. | [optional] +**image** | **str** | Container image name. More info: https://kubernetes.io/docs/concepts/containers/images This field is optional to allow higher level config management to default or override container images in workload controllers like Deployments and StatefulSets. | [optional] +**image_pull_policy** | **str** | Image pull policy. One of Always, Never, IfNotPresent. Defaults to Always if :latest tag is specified, or IfNotPresent otherwise. Cannot be updated. More info: https://kubernetes.io/docs/concepts/containers/images#updating-images | [optional] +**lifecycle** | [**V1Lifecycle**](https://github.com/kubernetes-client/python/blob/master/kubernetes/docs/V1Lifecycle.md) | | [optional] +**liveness_probe** | [**V1Probe**](https://github.com/kubernetes-client/python/blob/master/kubernetes/docs/V1Probe.md) | | [optional] +**name** | **str** | Name of the container specified as a DNS_LABEL. Each container in a pod must have a unique name (DNS_LABEL). Cannot be updated. | [optional] [default to ''] +**ports** | [**list[V1ContainerPort]**](https://github.com/kubernetes-client/python/blob/master/kubernetes/docs/V1ContainerPort.md) | List of ports to expose from the container. Not specifying a port here DOES NOT prevent that port from being exposed. Any port which is listening on the default \"0.0.0.0\" address inside a container will be accessible from the network. Modifying this array with strategic merge patch may corrupt the data. For more information See https://github.com/kubernetes/kubernetes/issues/108255. Cannot be updated. | [optional] +**protocol_version** | **str** | Protocol version to use by the predictor (i.e. v1 or v2 or grpc-v1 or grpc-v2) | [optional] +**readiness_probe** | [**V1Probe**](https://github.com/kubernetes-client/python/blob/master/kubernetes/docs/V1Probe.md) | | [optional] +**resize_policy** | [**list[V1ContainerResizePolicy]**](V1ContainerResizePolicy.md) | Resources resize policy for the container. | [optional] +**resources** | [**V1ResourceRequirements**](https://github.com/kubernetes-client/python/blob/master/kubernetes/docs/V1ResourceRequirements.md) | | [optional] +**restart_policy** | **str** | RestartPolicy defines the restart behavior of individual containers in a pod. This field may only be set for init containers, and the only allowed value is \"Always\". For non-init containers or when this field is not specified, the restart behavior is defined by the Pod's restart policy and the container type. Setting the RestartPolicy as \"Always\" for the init container will have the following effect: this init container will be continually restarted on exit until all regular containers have terminated. Once all regular containers have completed, all init containers with restartPolicy \"Always\" will be shut down. This lifecycle differs from normal init containers and is often referred to as a \"sidecar\" container. Although this init container still starts in the init container sequence, it does not wait for the container to complete before proceeding to the next init container. Instead, the next init container starts immediately after this init container is started, or after any startupProbe has successfully completed. | [optional] +**runtime_version** | **str** | Runtime version of the predictor docker image | [optional] +**security_context** | [**V1SecurityContext**](https://github.com/kubernetes-client/python/blob/master/kubernetes/docs/V1SecurityContext.md) | | [optional] +**startup_probe** | [**V1Probe**](https://github.com/kubernetes-client/python/blob/master/kubernetes/docs/V1Probe.md) | | [optional] +**stdin** | **bool** | Whether this container should allocate a buffer for stdin in the container runtime. If this is not set, reads from stdin in the container will always result in EOF. Default is false. | [optional] +**stdin_once** | **bool** | Whether the container runtime should close the stdin channel after it has been opened by a single attach. When stdin is true the stdin stream will remain open across multiple attach sessions. If stdinOnce is set to true, stdin is opened on container start, is empty until the first client attaches to stdin, and then remains open and accepts data until the client disconnects, at which time stdin is closed and remains closed until the container is restarted. If this flag is false, a container processes that reads from stdin will never receive an EOF. Default is false | [optional] +**storage** | [**V1beta1StorageSpec**](V1beta1StorageSpec.md) | | [optional] +**storage_uri** | **str** | This field points to the location of the trained model which is mounted onto the pod. | [optional] +**termination_message_path** | **str** | Optional: Path at which the file to which the container's termination message will be written is mounted into the container's filesystem. Message written is intended to be brief final status, such as an assertion failure message. Will be truncated by the node if greater than 4096 bytes. The total message length across all containers will be limited to 12kb. Defaults to /dev/termination-log. Cannot be updated. | [optional] +**termination_message_policy** | **str** | Indicate how the termination message should be populated. File will use the contents of terminationMessagePath to populate the container status message on both success and failure. FallbackToLogsOnError will use the last chunk of container log output if the termination message file is empty and the container exited with an error. The log output is limited to 2048 bytes or 80 lines, whichever is smaller. Defaults to File. Cannot be updated. | [optional] +**tty** | **bool** | Whether this container should allocate a TTY for itself, also requires 'stdin' to be true. Default is false. | [optional] +**volume_devices** | [**list[V1VolumeDevice]**](https://github.com/kubernetes-client/python/blob/master/kubernetes/docs/V1VolumeDevice.md) | volumeDevices is the list of block devices to be used by the container. | [optional] +**volume_mounts** | [**list[V1VolumeMount]**](https://github.com/kubernetes-client/python/blob/master/kubernetes/docs/V1VolumeMount.md) | Pod volumes to mount into the container's filesystem. Cannot be updated. | [optional] +**working_dir** | **str** | Container's working directory. If not specified, the container runtime's default will be used, which might be configured in the container image. Cannot be updated. | [optional] + +[[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md) + + diff --git a/python/kserve/docs/V1beta1PredictorSpec.md b/python/kserve/docs/V1beta1PredictorSpec.md index 9c8fff37661..21af0d1d9a6 100644 --- a/python/kserve/docs/V1beta1PredictorSpec.md +++ b/python/kserve/docs/V1beta1PredictorSpec.md @@ -22,6 +22,7 @@ Name | Type | Description | Notes **host_pid** | **bool** | Use the host's pid namespace. Optional: Default to false. | [optional] **host_users** | **bool** | Use the host's user namespace. Optional: Default to true. If set to true or not present, the pod will be run in the host user namespace, useful for when the pod needs a feature only available to the host user namespace, such as loading a kernel module with CAP_SYS_MODULE. When set to false, a new userns is created for the pod. Setting false is useful for mitigating container breakout vulnerabilities even allowing users to run their containers as root without actually having root privileges on the host. This field is alpha-level and is only honored by servers that enable the UserNamespacesSupport feature. | [optional] **hostname** | **str** | Specifies the hostname of the Pod If not specified, the pod's hostname will be set to a system-defined value. | [optional] +**huggingface** | [**V1beta1HuggingFaceRuntimeSpec**](V1beta1HuggingFaceRuntimeSpec.md) | | [optional] **image_pull_secrets** | [**list[V1LocalObjectReference]**](https://github.com/kubernetes-client/python/blob/master/kubernetes/docs/V1LocalObjectReference.md) | ImagePullSecrets is an optional list of references to secrets in the same namespace to use for pulling any of the images used by this PodSpec. If specified, these secrets will be passed to individual puller implementations for them to use. For example, in the case of docker, only DockerConfig type secrets are honored. More info: https://kubernetes.io/docs/concepts/containers/images#specifying-imagepullsecrets-on-a-pod | [optional] **init_containers** | [**list[V1Container]**](https://github.com/kubernetes-client/python/blob/master/kubernetes/docs/V1Container.md) | List of initialization containers belonging to the pod. Init containers are executed in order prior to containers being started. If any init container fails, the pod is considered to have failed and is handled according to its restartPolicy. The name for an init container or normal container must be unique among all containers. Init containers may not have Lifecycle actions, Readiness probes, Liveness probes, or Startup probes. The resourceRequirements of an init container are taken into account during scheduling by finding the highest request/limit for each resource type, and then using the max of of that value or the sum of the normal containers. Limits are applied to init containers in a similar fashion. Init containers cannot currently be added or removed. Cannot be updated. More info: https://kubernetes.io/docs/concepts/workloads/pods/init-containers/ | [optional] **labels** | **dict(str, str)** | Labels that will be add to the component pod. More info: http://kubernetes.io/docs/user-guide/labels | [optional] diff --git a/python/kserve/kserve/api/kserve_client.py b/python/kserve/kserve/api/kserve_client.py index 82fb2863eeb..8fe11069fca 100644 --- a/python/kserve/kserve/api/kserve_client.py +++ b/python/kserve/kserve/api/kserve_client.py @@ -422,6 +422,8 @@ def wait_model_ready(self, service_name, model_name, isvc_namespace=None, # pyl time.sleep(polling_interval) # Check model health API url = f"http://{cluster_ip}/{protocol_version}/models/{model_name}" + if protocol_version.lower() == "v2": + url = f"http://{cluster_ip}/{protocol_version}/models/{model_name}/ready" response = requests.get(url, headers=headers).status_code if response == 200: return diff --git a/python/kserve/kserve/api_client.py b/python/kserve/kserve/api_client.py index d716ea8e7cb..d1298accf2d 100644 --- a/python/kserve/kserve/api_client.py +++ b/python/kserve/kserve/api_client.py @@ -304,7 +304,7 @@ def __deserialize(self, data, klass): if data is None: return None - if isinstance(klass, str): + if type(klass) == str: if klass.startswith('list['): sub_kls = re.match(r'list\[(.*)\]', klass).group(1) return [self.__deserialize(sub_data, sub_kls) diff --git a/python/kserve/kserve/model.py b/python/kserve/kserve/model.py index c0ef160ff88..c503faf95c5 100644 --- a/python/kserve/kserve/model.py +++ b/python/kserve/kserve/model.py @@ -15,7 +15,7 @@ import inspect import time from enum import Enum -from typing import Dict, List, Union, Optional +from typing import Dict, List, Union, Optional, AsyncIterator, Any import grpc import httpx @@ -32,6 +32,7 @@ from .protocol.grpc.grpc_predict_v2_pb2 import (ModelInferRequest, ModelInferResponse) from .protocol.infer_type import InferRequest, InferResponse +from .protocol.rest.v2_datamodels import GenerateRequest, GenerateResponse PREDICTOR_URL_FORMAT = "{0}://{1}/v1/models/{2}:predict" EXPLAINER_URL_FORMAT = "{0}://{1}/v1/models/{2}:explain" @@ -39,9 +40,10 @@ EXPLAINER_V2_URL_FORMAT = "{0}://{1}/v2/models/{2}/explain" -class ModelType(Enum): - EXPLAINER = 1 - PREDICTOR = 2 +class InferenceVerb(Enum): + EXPLAIN = 1 + PREDICT = 2 + GENERATE = 3 class PredictorProtocol(Enum): @@ -103,17 +105,17 @@ def __init__(self, name: str, predictor_config: Optional[PredictorConfig] = None self.enable_latency_logging = False async def __call__(self, body: Union[Dict, CloudEvent, InferRequest], - model_type: ModelType = ModelType.PREDICTOR, - headers: Dict[str, str] = None) -> Dict: + verb: InferenceVerb = InferenceVerb.PREDICT, + headers: Dict[str, str] = None) -> Union[Dict, InferResponse, List[str]]: """Method to call predictor or explainer with the given input. Args: body: Request body. - model_type: ModelType enum: `ModelType.PREDICTOR` or `ModelType.EXPLAINER`. + verb: The inference verb for predict/generate/explain headers: Request headers. Returns: - Dict: Response output from preprocess -> predictor/explainer -> postprocess + Response output from preprocess -> predict/generate/explain -> postprocess """ request_id = headers.get("x-request-id", "N.A.") if headers else "N.A." @@ -130,13 +132,13 @@ async def __call__(self, body: Union[Dict, CloudEvent, InferRequest], else self.preprocess(body, headers) preprocess_ms = get_latency_ms(start, time.time()) payload = self.validate(payload) - if model_type == ModelType.EXPLAINER: + if verb == InferenceVerb.EXPLAIN: with EXPLAIN_HIST_TIME.labels(**prom_labels).time(): start = time.time() response = (await self.explain(payload, headers)) if inspect.iscoroutinefunction(self.explain) \ else self.explain(payload, headers) explain_ms = get_latency_ms(start, time.time()) - elif model_type == ModelType.PREDICTOR: + elif verb == InferenceVerb.PREDICT: with PREDICT_HIST_TIME.labels(**prom_labels).time(): start = time.time() response = (await self.predict(payload, headers)) if inspect.iscoroutinefunction(self.predict) \ @@ -322,6 +324,13 @@ async def predict(self, payload: Union[Dict, InferRequest, ModelInferRequest], # return an InferResponse if this is REST V2, otherwise just return the dictionary return InferResponse.from_rest(self.name, res) if is_v2(PredictorProtocol(self.protocol)) else res + async def generate(self, payload: GenerateRequest, + headers: Dict[str, str] = None) -> Union[GenerateResponse, AsyncIterator[Any]]: + """`generate` handler can be overridden to implement text generation. + + """ + raise NotImplementedError("generate is not implemented") + async def explain(self, payload: Dict, headers: Dict[str, str] = None) -> Dict: """`explain` handler can be overridden to implement the model explanation. The default implementation makes call to the explainer if ``explainer_host`` is specified. diff --git a/python/kserve/kserve/model_server.py b/python/kserve/kserve/model_server.py index 31844bea70b..3e04883a103 100644 --- a/python/kserve/kserve/model_server.py +++ b/python/kserve/kserve/model_server.py @@ -20,7 +20,7 @@ import signal import socket from multiprocessing import Process -from typing import Dict, List, Optional, Union +from typing import Dict, List, Optional, Union, Callable, Any from ray import serve as rayserve from ray.serve.api import Deployment @@ -140,6 +140,7 @@ def __init__(self, http_port: int = args.http_port, self.log_config = None self.access_log_format = access_log_format + self._custom_exception_handler = None def start(self, models: Union[List[Model], Dict[str, Deployment]]) -> None: """ Start the model server with a set of registered models. @@ -187,6 +188,10 @@ async def serve(): loop.add_signal_handler( sig, lambda s=sig: asyncio.create_task(self.stop(sig=s)) ) + if self._custom_exception_handler is None: + loop.set_exception_handler(self.default_exception_handler) + else: + loop.set_exception_handler(self._custom_exception_handler) if self.workers == 1: self._rest_server = UvicornServer(self.http_port, [], self.dataplane, self.model_repository_extension, @@ -231,6 +236,31 @@ async def stop(self, sig: Optional[int] = None): logger.info("Stopping the grpc server") await self._grpc_server.stop(sig) + def register_exception_handler(self, handler: Callable[[asyncio.events.AbstractEventLoop, Dict[str, Any]], None]): + """ + Add a custom handler as the event loop exception handler. + + If a handler is not provided, the default exception handler will be set. + + handler should be a callable object, it should have a signature matching '(loop, context)', where 'loop' + will be a reference to the active event loop, 'context' will be a dict object (see `call_exception_handler()` + documentation for details about context). + """ + self._custom_exception_handler = handler + + def default_exception_handler(self, loop: asyncio.events.AbstractEventLoop, context: Dict[str, Any]): + """ + Default exception handler for event loop. + + This is called when an exception occurs and no exception handler is set. + By default, this will shut down the server gracefully. + + This can be called by a custom exception handler that wants to defer to the default handler behavior. + """ + # gracefully shutdown the server + loop.run_until_complete(self.stop()) + loop.default_exception_handler(context) + def register_model_handle(self, name: str, model_handle: RayServeHandle): self.registered_models.update_handle(name, model_handle) logger.info("Registering model handle: %s", name) diff --git a/python/kserve/kserve/models/__init__.py b/python/kserve/kserve/models/__init__.py index bc9c448daa2..897a8feb7b4 100644 --- a/python/kserve/kserve/models/__init__.py +++ b/python/kserve/kserve/models/__init__.py @@ -66,6 +66,7 @@ from kserve.models.v1beta1_explainer_spec import V1beta1ExplainerSpec from kserve.models.v1beta1_explainers_config import V1beta1ExplainersConfig from kserve.models.v1beta1_failure_info import V1beta1FailureInfo +from kserve.models.v1beta1_hugging_face_runtime_spec import V1beta1HuggingFaceRuntimeSpec from kserve.models.v1beta1_inference_service import V1beta1InferenceService from kserve.models.v1beta1_inference_service_list import V1beta1InferenceServiceList from kserve.models.v1beta1_inference_service_spec import V1beta1InferenceServiceSpec diff --git a/python/kserve/kserve/models/v1beta1_hugging_face_runtime_spec.py b/python/kserve/kserve/models/v1beta1_hugging_face_runtime_spec.py new file mode 100644 index 00000000000..46a429bca60 --- /dev/null +++ b/python/kserve/kserve/models/v1beta1_hugging_face_runtime_spec.py @@ -0,0 +1,878 @@ +# Copyright 2023 The KServe Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# coding: utf-8 + +""" + KServe + + Python SDK for KServe # noqa: E501 + + The version of the OpenAPI document: v0.1 + Generated by: https://openapi-generator.tech +""" + + +import pprint +import re # noqa: F401 + +import six + +from kserve.configuration import Configuration + + +class V1beta1HuggingFaceRuntimeSpec(object): + """NOTE: This class is auto generated by OpenAPI Generator. + Ref: https://openapi-generator.tech + + Do not edit the class manually. + """ + + """ + Attributes: + openapi_types (dict): The key is attribute name + and the value is attribute type. + attribute_map (dict): The key is attribute name + and the value is json key in definition. + """ + openapi_types = { + 'args': 'list[str]', + 'command': 'list[str]', + 'env': 'list[V1EnvVar]', + 'env_from': 'list[V1EnvFromSource]', + 'image': 'str', + 'image_pull_policy': 'str', + 'lifecycle': 'V1Lifecycle', + 'liveness_probe': 'V1Probe', + 'name': 'str', + 'ports': 'list[V1ContainerPort]', + 'protocol_version': 'str', + 'readiness_probe': 'V1Probe', + 'resize_policy': 'list[V1ContainerResizePolicy]', + 'resources': 'V1ResourceRequirements', + 'restart_policy': 'str', + 'runtime_version': 'str', + 'security_context': 'V1SecurityContext', + 'startup_probe': 'V1Probe', + 'stdin': 'bool', + 'stdin_once': 'bool', + 'storage': 'V1beta1StorageSpec', + 'storage_uri': 'str', + 'termination_message_path': 'str', + 'termination_message_policy': 'str', + 'tty': 'bool', + 'volume_devices': 'list[V1VolumeDevice]', + 'volume_mounts': 'list[V1VolumeMount]', + 'working_dir': 'str' + } + + attribute_map = { + 'args': 'args', + 'command': 'command', + 'env': 'env', + 'env_from': 'envFrom', + 'image': 'image', + 'image_pull_policy': 'imagePullPolicy', + 'lifecycle': 'lifecycle', + 'liveness_probe': 'livenessProbe', + 'name': 'name', + 'ports': 'ports', + 'protocol_version': 'protocolVersion', + 'readiness_probe': 'readinessProbe', + 'resize_policy': 'resizePolicy', + 'resources': 'resources', + 'restart_policy': 'restartPolicy', + 'runtime_version': 'runtimeVersion', + 'security_context': 'securityContext', + 'startup_probe': 'startupProbe', + 'stdin': 'stdin', + 'stdin_once': 'stdinOnce', + 'storage': 'storage', + 'storage_uri': 'storageUri', + 'termination_message_path': 'terminationMessagePath', + 'termination_message_policy': 'terminationMessagePolicy', + 'tty': 'tty', + 'volume_devices': 'volumeDevices', + 'volume_mounts': 'volumeMounts', + 'working_dir': 'workingDir' + } + + def __init__(self, args=None, command=None, env=None, env_from=None, image=None, image_pull_policy=None, lifecycle=None, liveness_probe=None, name='', ports=None, protocol_version=None, readiness_probe=None, resize_policy=None, resources=None, restart_policy=None, runtime_version=None, security_context=None, startup_probe=None, stdin=None, stdin_once=None, storage=None, storage_uri=None, termination_message_path=None, termination_message_policy=None, tty=None, volume_devices=None, volume_mounts=None, working_dir=None, local_vars_configuration=None): # noqa: E501 + """V1beta1HuggingFaceRuntimeSpec - a model defined in OpenAPI""" # noqa: E501 + if local_vars_configuration is None: + local_vars_configuration = Configuration() + self.local_vars_configuration = local_vars_configuration + + self._args = None + self._command = None + self._env = None + self._env_from = None + self._image = None + self._image_pull_policy = None + self._lifecycle = None + self._liveness_probe = None + self._name = None + self._ports = None + self._protocol_version = None + self._readiness_probe = None + self._resize_policy = None + self._resources = None + self._restart_policy = None + self._runtime_version = None + self._security_context = None + self._startup_probe = None + self._stdin = None + self._stdin_once = None + self._storage = None + self._storage_uri = None + self._termination_message_path = None + self._termination_message_policy = None + self._tty = None + self._volume_devices = None + self._volume_mounts = None + self._working_dir = None + self.discriminator = None + + if args is not None: + self.args = args + if command is not None: + self.command = command + if env is not None: + self.env = env + if env_from is not None: + self.env_from = env_from + if image is not None: + self.image = image + if image_pull_policy is not None: + self.image_pull_policy = image_pull_policy + if lifecycle is not None: + self.lifecycle = lifecycle + if liveness_probe is not None: + self.liveness_probe = liveness_probe + if name is not None: + self.name = name + if ports is not None: + self.ports = ports + if protocol_version is not None: + self.protocol_version = protocol_version + if readiness_probe is not None: + self.readiness_probe = readiness_probe + if resize_policy is not None: + self.resize_policy = resize_policy + if resources is not None: + self.resources = resources + if restart_policy is not None: + self.restart_policy = restart_policy + if runtime_version is not None: + self.runtime_version = runtime_version + if security_context is not None: + self.security_context = security_context + if startup_probe is not None: + self.startup_probe = startup_probe + if stdin is not None: + self.stdin = stdin + if stdin_once is not None: + self.stdin_once = stdin_once + if storage is not None: + self.storage = storage + if storage_uri is not None: + self.storage_uri = storage_uri + if termination_message_path is not None: + self.termination_message_path = termination_message_path + if termination_message_policy is not None: + self.termination_message_policy = termination_message_policy + if tty is not None: + self.tty = tty + if volume_devices is not None: + self.volume_devices = volume_devices + if volume_mounts is not None: + self.volume_mounts = volume_mounts + if working_dir is not None: + self.working_dir = working_dir + + @property + def args(self): + """Gets the args of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + + Arguments to the entrypoint. The container image's CMD is used if this is not provided. Variable references $(VAR_NAME) are expanded using the container's environment. If a variable cannot be resolved, the reference in the input string will be unchanged. Double $$ are reduced to a single $, which allows for escaping the $(VAR_NAME) syntax: i.e. \"$$(VAR_NAME)\" will produce the string literal \"$(VAR_NAME)\". Escaped references will never be expanded, regardless of whether the variable exists or not. Cannot be updated. More info: https://kubernetes.io/docs/tasks/inject-data-application/define-command-argument-container/#running-a-command-in-a-shell # noqa: E501 + + :return: The args of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + :rtype: list[str] + """ + return self._args + + @args.setter + def args(self, args): + """Sets the args of this V1beta1HuggingFaceRuntimeSpec. + + Arguments to the entrypoint. The container image's CMD is used if this is not provided. Variable references $(VAR_NAME) are expanded using the container's environment. If a variable cannot be resolved, the reference in the input string will be unchanged. Double $$ are reduced to a single $, which allows for escaping the $(VAR_NAME) syntax: i.e. \"$$(VAR_NAME)\" will produce the string literal \"$(VAR_NAME)\". Escaped references will never be expanded, regardless of whether the variable exists or not. Cannot be updated. More info: https://kubernetes.io/docs/tasks/inject-data-application/define-command-argument-container/#running-a-command-in-a-shell # noqa: E501 + + :param args: The args of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + :type: list[str] + """ + + self._args = args + + @property + def command(self): + """Gets the command of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + + Entrypoint array. Not executed within a shell. The container image's ENTRYPOINT is used if this is not provided. Variable references $(VAR_NAME) are expanded using the container's environment. If a variable cannot be resolved, the reference in the input string will be unchanged. Double $$ are reduced to a single $, which allows for escaping the $(VAR_NAME) syntax: i.e. \"$$(VAR_NAME)\" will produce the string literal \"$(VAR_NAME)\". Escaped references will never be expanded, regardless of whether the variable exists or not. Cannot be updated. More info: https://kubernetes.io/docs/tasks/inject-data-application/define-command-argument-container/#running-a-command-in-a-shell # noqa: E501 + + :return: The command of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + :rtype: list[str] + """ + return self._command + + @command.setter + def command(self, command): + """Sets the command of this V1beta1HuggingFaceRuntimeSpec. + + Entrypoint array. Not executed within a shell. The container image's ENTRYPOINT is used if this is not provided. Variable references $(VAR_NAME) are expanded using the container's environment. If a variable cannot be resolved, the reference in the input string will be unchanged. Double $$ are reduced to a single $, which allows for escaping the $(VAR_NAME) syntax: i.e. \"$$(VAR_NAME)\" will produce the string literal \"$(VAR_NAME)\". Escaped references will never be expanded, regardless of whether the variable exists or not. Cannot be updated. More info: https://kubernetes.io/docs/tasks/inject-data-application/define-command-argument-container/#running-a-command-in-a-shell # noqa: E501 + + :param command: The command of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + :type: list[str] + """ + + self._command = command + + @property + def env(self): + """Gets the env of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + + List of environment variables to set in the container. Cannot be updated. # noqa: E501 + + :return: The env of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + :rtype: list[V1EnvVar] + """ + return self._env + + @env.setter + def env(self, env): + """Sets the env of this V1beta1HuggingFaceRuntimeSpec. + + List of environment variables to set in the container. Cannot be updated. # noqa: E501 + + :param env: The env of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + :type: list[V1EnvVar] + """ + + self._env = env + + @property + def env_from(self): + """Gets the env_from of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + + List of sources to populate environment variables in the container. The keys defined within a source must be a C_IDENTIFIER. All invalid keys will be reported as an event when the container is starting. When a key exists in multiple sources, the value associated with the last source will take precedence. Values defined by an Env with a duplicate key will take precedence. Cannot be updated. # noqa: E501 + + :return: The env_from of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + :rtype: list[V1EnvFromSource] + """ + return self._env_from + + @env_from.setter + def env_from(self, env_from): + """Sets the env_from of this V1beta1HuggingFaceRuntimeSpec. + + List of sources to populate environment variables in the container. The keys defined within a source must be a C_IDENTIFIER. All invalid keys will be reported as an event when the container is starting. When a key exists in multiple sources, the value associated with the last source will take precedence. Values defined by an Env with a duplicate key will take precedence. Cannot be updated. # noqa: E501 + + :param env_from: The env_from of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + :type: list[V1EnvFromSource] + """ + + self._env_from = env_from + + @property + def image(self): + """Gets the image of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + + Container image name. More info: https://kubernetes.io/docs/concepts/containers/images This field is optional to allow higher level config management to default or override container images in workload controllers like Deployments and StatefulSets. # noqa: E501 + + :return: The image of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + :rtype: str + """ + return self._image + + @image.setter + def image(self, image): + """Sets the image of this V1beta1HuggingFaceRuntimeSpec. + + Container image name. More info: https://kubernetes.io/docs/concepts/containers/images This field is optional to allow higher level config management to default or override container images in workload controllers like Deployments and StatefulSets. # noqa: E501 + + :param image: The image of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + :type: str + """ + + self._image = image + + @property + def image_pull_policy(self): + """Gets the image_pull_policy of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + + Image pull policy. One of Always, Never, IfNotPresent. Defaults to Always if :latest tag is specified, or IfNotPresent otherwise. Cannot be updated. More info: https://kubernetes.io/docs/concepts/containers/images#updating-images # noqa: E501 + + :return: The image_pull_policy of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + :rtype: str + """ + return self._image_pull_policy + + @image_pull_policy.setter + def image_pull_policy(self, image_pull_policy): + """Sets the image_pull_policy of this V1beta1HuggingFaceRuntimeSpec. + + Image pull policy. One of Always, Never, IfNotPresent. Defaults to Always if :latest tag is specified, or IfNotPresent otherwise. Cannot be updated. More info: https://kubernetes.io/docs/concepts/containers/images#updating-images # noqa: E501 + + :param image_pull_policy: The image_pull_policy of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + :type: str + """ + + self._image_pull_policy = image_pull_policy + + @property + def lifecycle(self): + """Gets the lifecycle of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + + + :return: The lifecycle of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + :rtype: V1Lifecycle + """ + return self._lifecycle + + @lifecycle.setter + def lifecycle(self, lifecycle): + """Sets the lifecycle of this V1beta1HuggingFaceRuntimeSpec. + + + :param lifecycle: The lifecycle of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + :type: V1Lifecycle + """ + + self._lifecycle = lifecycle + + @property + def liveness_probe(self): + """Gets the liveness_probe of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + + + :return: The liveness_probe of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + :rtype: V1Probe + """ + return self._liveness_probe + + @liveness_probe.setter + def liveness_probe(self, liveness_probe): + """Sets the liveness_probe of this V1beta1HuggingFaceRuntimeSpec. + + + :param liveness_probe: The liveness_probe of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + :type: V1Probe + """ + + self._liveness_probe = liveness_probe + + @property + def name(self): + """Gets the name of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + + Name of the container specified as a DNS_LABEL. Each container in a pod must have a unique name (DNS_LABEL). Cannot be updated. # noqa: E501 + + :return: The name of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + :rtype: str + """ + return self._name + + @name.setter + def name(self, name): + """Sets the name of this V1beta1HuggingFaceRuntimeSpec. + + Name of the container specified as a DNS_LABEL. Each container in a pod must have a unique name (DNS_LABEL). Cannot be updated. # noqa: E501 + + :param name: The name of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + :type: str + """ + + self._name = name + + @property + def ports(self): + """Gets the ports of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + + List of ports to expose from the container. Not specifying a port here DOES NOT prevent that port from being exposed. Any port which is listening on the default \"0.0.0.0\" address inside a container will be accessible from the network. Modifying this array with strategic merge patch may corrupt the data. For more information See https://github.com/kubernetes/kubernetes/issues/108255. Cannot be updated. # noqa: E501 + + :return: The ports of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + :rtype: list[V1ContainerPort] + """ + return self._ports + + @ports.setter + def ports(self, ports): + """Sets the ports of this V1beta1HuggingFaceRuntimeSpec. + + List of ports to expose from the container. Not specifying a port here DOES NOT prevent that port from being exposed. Any port which is listening on the default \"0.0.0.0\" address inside a container will be accessible from the network. Modifying this array with strategic merge patch may corrupt the data. For more information See https://github.com/kubernetes/kubernetes/issues/108255. Cannot be updated. # noqa: E501 + + :param ports: The ports of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + :type: list[V1ContainerPort] + """ + + self._ports = ports + + @property + def protocol_version(self): + """Gets the protocol_version of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + + Protocol version to use by the predictor (i.e. v1 or v2 or grpc-v1 or grpc-v2) # noqa: E501 + + :return: The protocol_version of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + :rtype: str + """ + return self._protocol_version + + @protocol_version.setter + def protocol_version(self, protocol_version): + """Sets the protocol_version of this V1beta1HuggingFaceRuntimeSpec. + + Protocol version to use by the predictor (i.e. v1 or v2 or grpc-v1 or grpc-v2) # noqa: E501 + + :param protocol_version: The protocol_version of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + :type: str + """ + + self._protocol_version = protocol_version + + @property + def readiness_probe(self): + """Gets the readiness_probe of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + + + :return: The readiness_probe of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + :rtype: V1Probe + """ + return self._readiness_probe + + @readiness_probe.setter + def readiness_probe(self, readiness_probe): + """Sets the readiness_probe of this V1beta1HuggingFaceRuntimeSpec. + + + :param readiness_probe: The readiness_probe of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + :type: V1Probe + """ + + self._readiness_probe = readiness_probe + + @property + def resize_policy(self): + """Gets the resize_policy of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + + Resources resize policy for the container. # noqa: E501 + + :return: The resize_policy of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + :rtype: list[V1ContainerResizePolicy] + """ + return self._resize_policy + + @resize_policy.setter + def resize_policy(self, resize_policy): + """Sets the resize_policy of this V1beta1HuggingFaceRuntimeSpec. + + Resources resize policy for the container. # noqa: E501 + + :param resize_policy: The resize_policy of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + :type: list[V1ContainerResizePolicy] + """ + + self._resize_policy = resize_policy + + @property + def resources(self): + """Gets the resources of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + + + :return: The resources of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + :rtype: V1ResourceRequirements + """ + return self._resources + + @resources.setter + def resources(self, resources): + """Sets the resources of this V1beta1HuggingFaceRuntimeSpec. + + + :param resources: The resources of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + :type: V1ResourceRequirements + """ + + self._resources = resources + + @property + def restart_policy(self): + """Gets the restart_policy of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + + RestartPolicy defines the restart behavior of individual containers in a pod. This field may only be set for init containers, and the only allowed value is \"Always\". For non-init containers or when this field is not specified, the restart behavior is defined by the Pod's restart policy and the container type. Setting the RestartPolicy as \"Always\" for the init container will have the following effect: this init container will be continually restarted on exit until all regular containers have terminated. Once all regular containers have completed, all init containers with restartPolicy \"Always\" will be shut down. This lifecycle differs from normal init containers and is often referred to as a \"sidecar\" container. Although this init container still starts in the init container sequence, it does not wait for the container to complete before proceeding to the next init container. Instead, the next init container starts immediately after this init container is started, or after any startupProbe has successfully completed. # noqa: E501 + + :return: The restart_policy of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + :rtype: str + """ + return self._restart_policy + + @restart_policy.setter + def restart_policy(self, restart_policy): + """Sets the restart_policy of this V1beta1HuggingFaceRuntimeSpec. + + RestartPolicy defines the restart behavior of individual containers in a pod. This field may only be set for init containers, and the only allowed value is \"Always\". For non-init containers or when this field is not specified, the restart behavior is defined by the Pod's restart policy and the container type. Setting the RestartPolicy as \"Always\" for the init container will have the following effect: this init container will be continually restarted on exit until all regular containers have terminated. Once all regular containers have completed, all init containers with restartPolicy \"Always\" will be shut down. This lifecycle differs from normal init containers and is often referred to as a \"sidecar\" container. Although this init container still starts in the init container sequence, it does not wait for the container to complete before proceeding to the next init container. Instead, the next init container starts immediately after this init container is started, or after any startupProbe has successfully completed. # noqa: E501 + + :param restart_policy: The restart_policy of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + :type: str + """ + + self._restart_policy = restart_policy + + @property + def runtime_version(self): + """Gets the runtime_version of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + + Runtime version of the predictor docker image # noqa: E501 + + :return: The runtime_version of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + :rtype: str + """ + return self._runtime_version + + @runtime_version.setter + def runtime_version(self, runtime_version): + """Sets the runtime_version of this V1beta1HuggingFaceRuntimeSpec. + + Runtime version of the predictor docker image # noqa: E501 + + :param runtime_version: The runtime_version of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + :type: str + """ + + self._runtime_version = runtime_version + + @property + def security_context(self): + """Gets the security_context of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + + + :return: The security_context of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + :rtype: V1SecurityContext + """ + return self._security_context + + @security_context.setter + def security_context(self, security_context): + """Sets the security_context of this V1beta1HuggingFaceRuntimeSpec. + + + :param security_context: The security_context of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + :type: V1SecurityContext + """ + + self._security_context = security_context + + @property + def startup_probe(self): + """Gets the startup_probe of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + + + :return: The startup_probe of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + :rtype: V1Probe + """ + return self._startup_probe + + @startup_probe.setter + def startup_probe(self, startup_probe): + """Sets the startup_probe of this V1beta1HuggingFaceRuntimeSpec. + + + :param startup_probe: The startup_probe of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + :type: V1Probe + """ + + self._startup_probe = startup_probe + + @property + def stdin(self): + """Gets the stdin of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + + Whether this container should allocate a buffer for stdin in the container runtime. If this is not set, reads from stdin in the container will always result in EOF. Default is false. # noqa: E501 + + :return: The stdin of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + :rtype: bool + """ + return self._stdin + + @stdin.setter + def stdin(self, stdin): + """Sets the stdin of this V1beta1HuggingFaceRuntimeSpec. + + Whether this container should allocate a buffer for stdin in the container runtime. If this is not set, reads from stdin in the container will always result in EOF. Default is false. # noqa: E501 + + :param stdin: The stdin of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + :type: bool + """ + + self._stdin = stdin + + @property + def stdin_once(self): + """Gets the stdin_once of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + + Whether the container runtime should close the stdin channel after it has been opened by a single attach. When stdin is true the stdin stream will remain open across multiple attach sessions. If stdinOnce is set to true, stdin is opened on container start, is empty until the first client attaches to stdin, and then remains open and accepts data until the client disconnects, at which time stdin is closed and remains closed until the container is restarted. If this flag is false, a container processes that reads from stdin will never receive an EOF. Default is false # noqa: E501 + + :return: The stdin_once of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + :rtype: bool + """ + return self._stdin_once + + @stdin_once.setter + def stdin_once(self, stdin_once): + """Sets the stdin_once of this V1beta1HuggingFaceRuntimeSpec. + + Whether the container runtime should close the stdin channel after it has been opened by a single attach. When stdin is true the stdin stream will remain open across multiple attach sessions. If stdinOnce is set to true, stdin is opened on container start, is empty until the first client attaches to stdin, and then remains open and accepts data until the client disconnects, at which time stdin is closed and remains closed until the container is restarted. If this flag is false, a container processes that reads from stdin will never receive an EOF. Default is false # noqa: E501 + + :param stdin_once: The stdin_once of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + :type: bool + """ + + self._stdin_once = stdin_once + + @property + def storage(self): + """Gets the storage of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + + + :return: The storage of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + :rtype: V1beta1StorageSpec + """ + return self._storage + + @storage.setter + def storage(self, storage): + """Sets the storage of this V1beta1HuggingFaceRuntimeSpec. + + + :param storage: The storage of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + :type: V1beta1StorageSpec + """ + + self._storage = storage + + @property + def storage_uri(self): + """Gets the storage_uri of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + + This field points to the location of the trained model which is mounted onto the pod. # noqa: E501 + + :return: The storage_uri of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + :rtype: str + """ + return self._storage_uri + + @storage_uri.setter + def storage_uri(self, storage_uri): + """Sets the storage_uri of this V1beta1HuggingFaceRuntimeSpec. + + This field points to the location of the trained model which is mounted onto the pod. # noqa: E501 + + :param storage_uri: The storage_uri of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + :type: str + """ + + self._storage_uri = storage_uri + + @property + def termination_message_path(self): + """Gets the termination_message_path of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + + Optional: Path at which the file to which the container's termination message will be written is mounted into the container's filesystem. Message written is intended to be brief final status, such as an assertion failure message. Will be truncated by the node if greater than 4096 bytes. The total message length across all containers will be limited to 12kb. Defaults to /dev/termination-log. Cannot be updated. # noqa: E501 + + :return: The termination_message_path of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + :rtype: str + """ + return self._termination_message_path + + @termination_message_path.setter + def termination_message_path(self, termination_message_path): + """Sets the termination_message_path of this V1beta1HuggingFaceRuntimeSpec. + + Optional: Path at which the file to which the container's termination message will be written is mounted into the container's filesystem. Message written is intended to be brief final status, such as an assertion failure message. Will be truncated by the node if greater than 4096 bytes. The total message length across all containers will be limited to 12kb. Defaults to /dev/termination-log. Cannot be updated. # noqa: E501 + + :param termination_message_path: The termination_message_path of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + :type: str + """ + + self._termination_message_path = termination_message_path + + @property + def termination_message_policy(self): + """Gets the termination_message_policy of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + + Indicate how the termination message should be populated. File will use the contents of terminationMessagePath to populate the container status message on both success and failure. FallbackToLogsOnError will use the last chunk of container log output if the termination message file is empty and the container exited with an error. The log output is limited to 2048 bytes or 80 lines, whichever is smaller. Defaults to File. Cannot be updated. # noqa: E501 + + :return: The termination_message_policy of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + :rtype: str + """ + return self._termination_message_policy + + @termination_message_policy.setter + def termination_message_policy(self, termination_message_policy): + """Sets the termination_message_policy of this V1beta1HuggingFaceRuntimeSpec. + + Indicate how the termination message should be populated. File will use the contents of terminationMessagePath to populate the container status message on both success and failure. FallbackToLogsOnError will use the last chunk of container log output if the termination message file is empty and the container exited with an error. The log output is limited to 2048 bytes or 80 lines, whichever is smaller. Defaults to File. Cannot be updated. # noqa: E501 + + :param termination_message_policy: The termination_message_policy of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + :type: str + """ + + self._termination_message_policy = termination_message_policy + + @property + def tty(self): + """Gets the tty of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + + Whether this container should allocate a TTY for itself, also requires 'stdin' to be true. Default is false. # noqa: E501 + + :return: The tty of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + :rtype: bool + """ + return self._tty + + @tty.setter + def tty(self, tty): + """Sets the tty of this V1beta1HuggingFaceRuntimeSpec. + + Whether this container should allocate a TTY for itself, also requires 'stdin' to be true. Default is false. # noqa: E501 + + :param tty: The tty of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + :type: bool + """ + + self._tty = tty + + @property + def volume_devices(self): + """Gets the volume_devices of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + + volumeDevices is the list of block devices to be used by the container. # noqa: E501 + + :return: The volume_devices of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + :rtype: list[V1VolumeDevice] + """ + return self._volume_devices + + @volume_devices.setter + def volume_devices(self, volume_devices): + """Sets the volume_devices of this V1beta1HuggingFaceRuntimeSpec. + + volumeDevices is the list of block devices to be used by the container. # noqa: E501 + + :param volume_devices: The volume_devices of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + :type: list[V1VolumeDevice] + """ + + self._volume_devices = volume_devices + + @property + def volume_mounts(self): + """Gets the volume_mounts of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + + Pod volumes to mount into the container's filesystem. Cannot be updated. # noqa: E501 + + :return: The volume_mounts of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + :rtype: list[V1VolumeMount] + """ + return self._volume_mounts + + @volume_mounts.setter + def volume_mounts(self, volume_mounts): + """Sets the volume_mounts of this V1beta1HuggingFaceRuntimeSpec. + + Pod volumes to mount into the container's filesystem. Cannot be updated. # noqa: E501 + + :param volume_mounts: The volume_mounts of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + :type: list[V1VolumeMount] + """ + + self._volume_mounts = volume_mounts + + @property + def working_dir(self): + """Gets the working_dir of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + + Container's working directory. If not specified, the container runtime's default will be used, which might be configured in the container image. Cannot be updated. # noqa: E501 + + :return: The working_dir of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + :rtype: str + """ + return self._working_dir + + @working_dir.setter + def working_dir(self, working_dir): + """Sets the working_dir of this V1beta1HuggingFaceRuntimeSpec. + + Container's working directory. If not specified, the container runtime's default will be used, which might be configured in the container image. Cannot be updated. # noqa: E501 + + :param working_dir: The working_dir of this V1beta1HuggingFaceRuntimeSpec. # noqa: E501 + :type: str + """ + + self._working_dir = working_dir + + def to_dict(self): + """Returns the model properties as a dict""" + result = {} + + for attr, _ in six.iteritems(self.openapi_types): + value = getattr(self, attr) + if isinstance(value, list): + result[attr] = list(map( + lambda x: x.to_dict() if hasattr(x, "to_dict") else x, + value + )) + elif hasattr(value, "to_dict"): + result[attr] = value.to_dict() + elif isinstance(value, dict): + result[attr] = dict(map( + lambda item: (item[0], item[1].to_dict()) + if hasattr(item[1], "to_dict") else item, + value.items() + )) + else: + result[attr] = value + + return result + + def to_str(self): + """Returns the string representation of the model""" + return pprint.pformat(self.to_dict()) + + def __repr__(self): + """For `print` and `pprint`""" + return self.to_str() + + def __eq__(self, other): + """Returns true if both objects are equal""" + if not isinstance(other, V1beta1HuggingFaceRuntimeSpec): + return False + + return self.to_dict() == other.to_dict() + + def __ne__(self, other): + """Returns true if both objects are not equal""" + if not isinstance(other, V1beta1HuggingFaceRuntimeSpec): + return True + + return self.to_dict() != other.to_dict() diff --git a/python/kserve/kserve/models/v1beta1_predictor_spec.py b/python/kserve/kserve/models/v1beta1_predictor_spec.py index d62337c7fa0..0c7d531dcdf 100644 --- a/python/kserve/kserve/models/v1beta1_predictor_spec.py +++ b/python/kserve/kserve/models/v1beta1_predictor_spec.py @@ -65,6 +65,7 @@ class V1beta1PredictorSpec(object): 'host_pid': 'bool', 'host_users': 'bool', 'hostname': 'str', + 'huggingface': 'V1beta1HuggingFaceRuntimeSpec', 'image_pull_secrets': 'list[V1LocalObjectReference]', 'init_containers': 'list[V1Container]', 'labels': 'dict(str, str)', @@ -128,6 +129,7 @@ class V1beta1PredictorSpec(object): 'host_pid': 'hostPID', 'host_users': 'hostUsers', 'hostname': 'hostname', + 'huggingface': 'huggingface', 'image_pull_secrets': 'imagePullSecrets', 'init_containers': 'initContainers', 'labels': 'labels', @@ -172,7 +174,7 @@ class V1beta1PredictorSpec(object): 'xgboost': 'xgboost' } - def __init__(self, active_deadline_seconds=None, affinity=None, annotations=None, automount_service_account_token=None, batcher=None, canary_traffic_percent=None, container_concurrency=None, containers=None, dns_config=None, dns_policy=None, enable_service_links=None, ephemeral_containers=None, host_aliases=None, host_ipc=None, host_network=None, host_pid=None, host_users=None, hostname=None, image_pull_secrets=None, init_containers=None, labels=None, lightgbm=None, logger=None, max_replicas=None, min_replicas=None, model=None, node_name=None, node_selector=None, onnx=None, os=None, overhead=None, paddle=None, pmml=None, preemption_policy=None, priority=None, priority_class_name=None, pytorch=None, readiness_gates=None, resource_claims=None, restart_policy=None, runtime_class_name=None, scale_metric=None, scale_target=None, scheduler_name=None, scheduling_gates=None, security_context=None, service_account=None, service_account_name=None, set_hostname_as_fqdn=None, share_process_namespace=None, sklearn=None, subdomain=None, tensorflow=None, termination_grace_period_seconds=None, timeout=None, tolerations=None, topology_spread_constraints=None, triton=None, volumes=None, xgboost=None, local_vars_configuration=None): # noqa: E501 + def __init__(self, active_deadline_seconds=None, affinity=None, annotations=None, automount_service_account_token=None, batcher=None, canary_traffic_percent=None, container_concurrency=None, containers=None, dns_config=None, dns_policy=None, enable_service_links=None, ephemeral_containers=None, host_aliases=None, host_ipc=None, host_network=None, host_pid=None, host_users=None, hostname=None, huggingface=None, image_pull_secrets=None, init_containers=None, labels=None, lightgbm=None, logger=None, max_replicas=None, min_replicas=None, model=None, node_name=None, node_selector=None, onnx=None, os=None, overhead=None, paddle=None, pmml=None, preemption_policy=None, priority=None, priority_class_name=None, pytorch=None, readiness_gates=None, resource_claims=None, restart_policy=None, runtime_class_name=None, scale_metric=None, scale_target=None, scheduler_name=None, scheduling_gates=None, security_context=None, service_account=None, service_account_name=None, set_hostname_as_fqdn=None, share_process_namespace=None, sklearn=None, subdomain=None, tensorflow=None, termination_grace_period_seconds=None, timeout=None, tolerations=None, topology_spread_constraints=None, triton=None, volumes=None, xgboost=None, local_vars_configuration=None): # noqa: E501 """V1beta1PredictorSpec - a model defined in OpenAPI""" # noqa: E501 if local_vars_configuration is None: local_vars_configuration = Configuration() @@ -196,6 +198,7 @@ def __init__(self, active_deadline_seconds=None, affinity=None, annotations=None self._host_pid = None self._host_users = None self._hostname = None + self._huggingface = None self._image_pull_secrets = None self._init_containers = None self._labels = None @@ -276,6 +279,8 @@ def __init__(self, active_deadline_seconds=None, affinity=None, annotations=None self.host_users = host_users if hostname is not None: self.hostname = hostname + if huggingface is not None: + self.huggingface = huggingface if image_pull_secrets is not None: self.image_pull_secrets = image_pull_secrets if init_containers is not None: @@ -769,6 +774,27 @@ def hostname(self, hostname): self._hostname = hostname + @property + def huggingface(self): + """Gets the huggingface of this V1beta1PredictorSpec. # noqa: E501 + + + :return: The huggingface of this V1beta1PredictorSpec. # noqa: E501 + :rtype: V1beta1HuggingFaceRuntimeSpec + """ + return self._huggingface + + @huggingface.setter + def huggingface(self, huggingface): + """Sets the huggingface of this V1beta1PredictorSpec. + + + :param huggingface: The huggingface of this V1beta1PredictorSpec. # noqa: E501 + :type: V1beta1HuggingFaceRuntimeSpec + """ + + self._huggingface = huggingface + @property def image_pull_secrets(self): """Gets the image_pull_secrets of this V1beta1PredictorSpec. # noqa: E501 diff --git a/python/kserve/kserve/protocol/dataplane.py b/python/kserve/kserve/protocol/dataplane.py index 31d7fd32fe3..5c3881c8073 100644 --- a/python/kserve/kserve/protocol/dataplane.py +++ b/python/kserve/kserve/protocol/dataplane.py @@ -13,7 +13,7 @@ # limitations under the License. from importlib import metadata -from typing import Dict, Union, Tuple, Optional +from typing import Dict, Union, Tuple, Optional, Any, AsyncIterator import cloudevents.exceptions as ce import orjson @@ -22,9 +22,10 @@ from cloudevents.sdk.converters.util import has_binary_headers from ray.serve.handle import RayServeHandle, RayServeSyncHandle, DeploymentHandle +from .rest.v2_datamodels import GenerateRequest, GenerateResponse from ..model import Model from ..errors import InvalidInput, ModelNotFound -from ..model import ModelType +from ..model import InferenceVerb from ..model_repository import ModelRepository from ..utils.utils import create_response_cloudevent, is_structured_cloudevent from .infer_type import InferRequest, InferResponse @@ -323,6 +324,30 @@ async def infer( response = await model(request, headers=headers) return response, headers + async def generate( + self, + model_name: str, + request: Union[Dict, GenerateRequest], + headers: Optional[Dict[str, str]] = None + ) -> Tuple[Union[GenerateResponse, AsyncIterator[Any]], Dict[str, str]]: + """Generate the text with the provided text prompt. + + Args: + model_name (str): Model name. + request (bytes|GenerateRequest): Generate Request body data. + headers: (Optional[Dict[str, str]]): Request headers. + + Returns: + response: The generated output or output stream. + response_headers: Headers to construct the HTTP response. + + Raises: + InvalidInput: An error when the body bytes can't be decoded as JSON. + """ + model = self.get_model(model_name) + response = await model.generate(request, headers=headers) + return response, headers + async def explain(self, model_name: str, request: Union[bytes, Dict, InferRequest], headers: Optional[Dict[str, str]] = None @@ -343,9 +368,9 @@ async def explain(self, model_name: str, # call model locally or remote model workers model = self.get_model(model_name) if isinstance(model, RayServeSyncHandle): - response = ray.get(model.remote(request, model_type=ModelType.EXPLAINER)) + response = ray.get(model.remote(request, verb=InferenceVerb.EXPLAIN)) elif isinstance(model, (RayServeHandle, DeploymentHandle)): - response = await model.remote(request, model_type=ModelType.EXPLAINER) + response = await model.remote(request, verb=InferenceVerb.EXPLAIN) else: - response = await model(request, model_type=ModelType.EXPLAINER) + response = await model(request, verb=InferenceVerb.EXPLAIN) return response, headers diff --git a/python/kserve/kserve/protocol/infer_type.py b/python/kserve/kserve/protocol/infer_type.py index 7250345ebc0..b5da94df013 100644 --- a/python/kserve/kserve/protocol/infer_type.py +++ b/python/kserve/kserve/protocol/infer_type.py @@ -12,6 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. +import struct from typing import Optional, List, Dict, Union import numpy @@ -20,7 +21,6 @@ import uuid from google.protobuf.internal.containers import MessageMap -from tritonclient.utils import serialize_byte_tensor from ..constants.constants import GRPC_CONTENT_DATATYPE_MAPPINGS from ..errors import InvalidInput, InferenceError @@ -29,6 +29,44 @@ from ..utils.numpy_codec import to_np_dtype, from_np_dtype +def serialize_byte_tensor(input_tensor: numpy.ndarray): + """ + Serializes a bytes tensor into a flat numpy array of length prepended + bytes. The numpy array should use dtype of np.object_. For np.bytes_, + numpy will remove trailing zeros at the end of byte sequence and because + of this it should be avoided. + Args: + input_tensor : np.array of the bytes tensor to serialize. + Returns: + serialized_bytes_tensor : The 1-D numpy array of type uint8 containing the serialized bytes in 'C' order. + """ + + if input_tensor.size == 0: + return () + + # If the input is a tensor of string/bytes objects, then must flatten those + # into a 1-dimensional array containing the 4-byte byte size followed by the + # actual element bytes. All elements are concatenated together in "C" order. + if (input_tensor.dtype == np.object_) or (input_tensor.dtype.type == np.bytes_): + flattened_ls = [] + for obj in np.nditer(input_tensor, flags=["refs_ok"], order="C"): + # If directly passing bytes to BYTES type, + # don't convert it to str as Python will encode the + # bytes which may distort the meaning + if input_tensor.dtype == np.object_: + if type(obj.item()) == bytes: + s = obj.item() + else: + s = str(obj.item()).encode("utf-8") + else: + s = obj.item() + flattened_ls.append(struct.pack(" List[int]: return self._shape @property - def parameters(self) -> Union[Dict, MessageMap[str, InferParameter]]: + def parameters(self) -> Union[Dict, MessageMap[str, InferParameter], None]: """Get the parameters of the inference input associated with this object. Returns: @@ -111,6 +148,12 @@ def set_shape(self, shape: List[int]): """ self._shape = shape + def as_string(self) -> List[List[str]]: + if self.datatype == "BYTES": + return [s.decode("utf-8") for li in self._data for s in li] + else: + raise InvalidInput(f"invalid datatype {self.datatype} in the input") + def as_numpy(self) -> np.ndarray: """ Decode the inference input data as numpy array. @@ -141,11 +184,11 @@ def set_data_from_numpy(self, input_tensor: np.ndarray, binary_data: bool = True InferenceError if failed to set data for the tensor. """ if not isinstance(input_tensor, (np.ndarray,)): - InferenceError("input_tensor must be a numpy array") + raise InferenceError("input_tensor must be a numpy array") dtype = from_np_dtype(input_tensor.dtype) if self._datatype != dtype: - InferenceError( + raise InferenceError( "got unexpected datatype {} from numpy array, expected {}".format(dtype, self._datatype)) valid_shape = True if len(self._shape) != len(input_tensor.shape): @@ -155,13 +198,14 @@ def set_data_from_numpy(self, input_tensor: np.ndarray, binary_data: bool = True if self._shape[i] != input_tensor.shape[i]: valid_shape = False if not valid_shape: - InferenceError( + raise InferenceError( "got unexpected numpy array shape [{}], expected [{}]".format( str(input_tensor.shape)[1:-1], str(self._shape)[1:-1])) if not binary_data: - self._parameters.pop('binary_data_size', None) + if self._parameters: + self._parameters.pop('binary_data_size', None) self._raw_data = None if self._datatype == "BYTES": self._data = [] @@ -183,7 +227,7 @@ def set_data_from_numpy(self, input_tensor: np.ndarray, binary_data: bool = True self._data.append( str(obj.item(), encoding='utf-8')) except UnicodeDecodeError: - InferenceError( + raise InferenceError( f'Failed to encode "{obj.item()}" using UTF-8. Please use binary_data=True, if' ' you want to pass a byte array.') else: @@ -198,7 +242,25 @@ def set_data_from_numpy(self, input_tensor: np.ndarray, binary_data: bool = True self._raw_data = b'' else: self._raw_data = input_tensor.tobytes() - self._parameters['binary_data_size'] = len(self._raw_data) + if self._parameters is None: + self._parameters = {'binary_data_size': len(self._raw_data)} + else: + self._parameters['binary_data_size'] = len(self._raw_data) + + def __eq__(self, other): + if not isinstance(other, InferInput): + return False + if self.name != other.name: + return False + if self.shape != other.shape: + return False + if self.datatype != other.datatype: + return False + if self.parameters != other.parameters: + return False + if self.data != other.data: + return False + return True def get_content(datatype: str, data: InferTensorContents): @@ -244,8 +306,7 @@ def __init__(self, model_name: str, infer_inputs: List[InferInput], from_grpc: Indicate if the data model is constructed from gRPC request. parameters: The additional inference parameters. """ - if parameters is None: - parameters = {} + self.id = request_id self.model_name = model_name self.inputs = infer_inputs @@ -276,11 +337,16 @@ def to_rest(self) -> Dict: """ infer_inputs = [] for infer_input in self.inputs: + datatype = infer_input.datatype + if isinstance(infer_input.datatype, numpy.dtype): + datatype = from_np_dtype(infer_input.datatype) infer_input_dict = { "name": infer_input.name, "shape": infer_input.shape, - "datatype": infer_input.datatype + "datatype": datatype } + if infer_input.parameters: + infer_input_dict["parameters"] = to_http_parameters(infer_input.parameters) if isinstance(infer_input.data, numpy.ndarray): infer_input.set_data_from_numpy(infer_input.data, binary_data=False) infer_input_dict["data"] = infer_input.data @@ -291,6 +357,8 @@ def to_rest(self) -> Dict: 'id': self.id if self.id else str(uuid.uuid4()), 'inputs': infer_inputs } + if self.parameters: + infer_request['parameters'] = to_http_parameters(self.parameters) return infer_request def to_grpc(self) -> ModelInferRequest: @@ -309,6 +377,8 @@ def to_grpc(self) -> ModelInferRequest: "shape": infer_input.shape, "datatype": infer_input.datatype, } + if infer_input.parameters: + infer_input_dict["parameters"] = to_grpc_parameters(infer_input.parameters) if infer_input._raw_data is not None: raw_input_contents.append(infer_input._raw_data) else: @@ -326,7 +396,8 @@ def to_grpc(self) -> ModelInferRequest: infer_inputs.append(infer_input_dict) return ModelInferRequest(id=self.id, model_name=self.model_name, inputs=infer_inputs, - raw_input_contents=raw_input_contents) + raw_input_contents=raw_input_contents, + parameters=to_grpc_parameters(self.parameters) if self.parameters else None) def as_dataframe(self) -> pd.DataFrame: """ Decode the tensor inputs as pandas dataframe. @@ -343,6 +414,21 @@ def as_dataframe(self) -> pd.DataFrame: dfs.append(pd.DataFrame(input_data, columns=[input.name])) return pd.concat(dfs, axis=1) + def __eq__(self, other): + if not isinstance(other, InferRequest): + return False + if self.model_name != other.model_name: + return False + if self.id != other.id: + return False + if self.from_grpc != other.from_grpc: + return False + if self.parameters != other.parameters: + return False + if self.inputs != other.inputs: + return False + return True + class InferOutput: def __init__(self, name: str, shape: List[int], datatype: str, @@ -358,11 +444,10 @@ def __init__(self, name: str, shape: List[int], datatype: str, raw_data is used for gRPC with numpy array bytes by calling set_data_from_numpy. parameters : The additional inference parameters. """ - if parameters is None: - parameters = {} + self._name = name self._shape = shape - self._datatype = datatype + self._datatype = datatype.upper() self._parameters = parameters self._data = data self._raw_data = None @@ -404,7 +489,7 @@ def shape(self) -> List[int]: return self._shape @property - def parameters(self) -> Union[Dict, MessageMap[str, InferParameter]]: + def parameters(self) -> Union[Dict, MessageMap[str, InferParameter], None]: """Get the parameters of inference output associated with this object. Returns: @@ -412,6 +497,10 @@ def parameters(self) -> Union[Dict, MessageMap[str, InferParameter]]: """ return self._parameters + @parameters.setter + def parameters(self, params: Union[Dict, MessageMap[str, InferParameter]]): + self._parameters = params + def set_shape(self, shape: List[int]): """Set the shape of inference output. @@ -450,11 +539,11 @@ def set_data_from_numpy(self, output_tensor: np.ndarray, binary_data: bool = Tru InferenceError if failed to set data for the output tensor. """ if not isinstance(output_tensor, (np.ndarray,)): - InferenceError("input_tensor must be a numpy array") + raise InferenceError("input_tensor must be a numpy array") dtype = from_np_dtype(output_tensor.dtype) if self._datatype != dtype: - InferenceError( + raise InferenceError( "got unexpected datatype {} from numpy array, expected {}".format(dtype, self._datatype)) valid_shape = True if len(self._shape) != len(output_tensor.shape): @@ -464,13 +553,14 @@ def set_data_from_numpy(self, output_tensor: np.ndarray, binary_data: bool = Tru if self._shape[i] != output_tensor.shape[i]: valid_shape = False if not valid_shape: - InferenceError( + raise InferenceError( "got unexpected numpy array shape [{}], expected [{}]".format( str(output_tensor.shape)[1:-1], str(self._shape)[1:-1])) if not binary_data: - self._parameters.pop('binary_data_size', None) + if self._parameters: + self._parameters.pop('binary_data_size', None) self._raw_data = None if self._datatype == "BYTES": self._data = [] @@ -492,7 +582,7 @@ def set_data_from_numpy(self, output_tensor: np.ndarray, binary_data: bool = Tru self._data.append( str(obj.item(), encoding='utf-8')) except UnicodeDecodeError: - InferenceError( + raise InferenceError( f'Failed to encode "{obj.item()}" using UTF-8. Please use binary_data=True, if' ' you want to pass a byte array.') else: @@ -507,7 +597,25 @@ def set_data_from_numpy(self, output_tensor: np.ndarray, binary_data: bool = Tru self._raw_data = b'' else: self._raw_data = output_tensor.tobytes() - self._parameters['binary_data_size'] = len(self._raw_data) + if self._parameters is None: + self._parameters = {'binary_data_size': len(self._raw_data)} + else: + self._parameters['binary_data_size'] = len(self._raw_data) + + def __eq__(self, other): + if not isinstance(other, InferOutput): + return False + if self.name != other.name: + return False + if self.shape != other.shape: + return False + if self.datatype != other.datatype: + return False + if self.parameters != other.parameters: + return False + if self.data != other.data: + return False + return True class InferResponse: @@ -530,8 +638,7 @@ def __init__(self, response_id: str, model_name: str, infer_outputs: List[InferO from_grpc: Indicate if the InferResponse is constructed from a gRPC response. parameters: The additional inference parameters. """ - if parameters is None: - parameters = {} + self.id = response_id self.model_name = model_name self.outputs = infer_outputs @@ -562,11 +669,11 @@ def from_rest(cls, model_name: str, response: Dict) -> 'InferResponse': shape=list(output['shape']), datatype=output['datatype'], data=output['data'], - parameters=output.get('parameters', {})) + parameters=output.get('parameters', None)) for output in response['outputs']] return cls(model_name=model_name, response_id=response.get('id', None), - parameters=response.get('parameters', {}), + parameters=response.get('parameters', None), infer_outputs=infer_outputs) def to_rest(self) -> Dict: @@ -582,6 +689,8 @@ def to_rest(self) -> Dict: "shape": infer_output.shape, "datatype": infer_output.datatype } + if infer_output.parameters: + infer_output_dict["parameters"] = to_http_parameters(infer_output.parameters) if isinstance(infer_output.data, numpy.ndarray): infer_output.set_data_from_numpy(infer_output.data, binary_data=False) infer_output_dict["data"] = infer_output.data @@ -595,6 +704,8 @@ def to_rest(self) -> Dict: 'model_name': self.model_name, 'outputs': infer_outputs } + if self.parameters: + res['parameters'] = to_http_parameters(self.parameters) return res def to_grpc(self) -> ModelInferResponse: @@ -613,6 +724,8 @@ def to_grpc(self) -> ModelInferResponse: "shape": infer_output.shape, "datatype": infer_output.datatype, } + if infer_output.parameters: + infer_output_dict["parameters"] = to_grpc_parameters(infer_output.parameters) if infer_output._raw_data is not None: raw_output_contents.append(infer_output._raw_data) else: @@ -630,4 +743,65 @@ def to_grpc(self) -> ModelInferResponse: infer_outputs.append(infer_output_dict) return ModelInferResponse(id=self.id, model_name=self.model_name, outputs=infer_outputs, - raw_output_contents=raw_output_contents) + raw_output_contents=raw_output_contents, + parameters=to_grpc_parameters(self.parameters) if self.parameters else None) + + def __eq__(self, other): + if not isinstance(other, InferResponse): + return False + if self.model_name != other.model_name: + return False + if self.id != other.id: + return False + if self.from_grpc != other.from_grpc: + return False + if self.parameters != other.parameters: + return False + if self.outputs != other.outputs: + return False + return True + + +def to_grpc_parameters(parameters: Union[Dict[str, Union[str, bool, int]], MessageMap[str, InferParameter]]) \ + -> Dict[str, InferParameter]: + """ + Converts REST parameters to GRPC InferParameter objects + + :param parameters: parameters to be converted. + :return: converted parameters as Dict[str, InferParameter] + :raises InvalidInput: if the parameter type is not supported. + """ + grpc_params: Dict[str, InferParameter] = {} + for key, val in parameters.items(): + if isinstance(val, str): + grpc_params[key] = InferParameter(string_param=val) + elif isinstance(val, bool): + grpc_params[key] = InferParameter(bool_param=val) + elif isinstance(val, int): + grpc_params[key] = InferParameter(int64_param=val) + elif isinstance(val, InferParameter): + grpc_params[key] = val + else: + raise InvalidInput(f"to_grpc: invalid parameter value: {val}") + return grpc_params + + +def to_http_parameters(parameters: Union[dict, MessageMap[str, InferParameter]]) -> Dict[str, Union[str, bool, int]]: + """ + Converts GRPC InferParameter parameters to REST parameters + + :param parameters: parameters to be converted. + :return: converted parameters as Dict[str, Union[str, bool, int]] + """ + http_params: Dict[str, Union[str, bool, int]] = {} + for key, val in parameters.items(): + if isinstance(val, InferParameter): + if val.HasField("bool_param"): + http_params[key] = val.bool_param + elif val.HasField("int64_param"): + http_params[key] = val.int64_param + elif val.HasField("string_param"): + http_params[key] = val.string_param + else: + http_params[key] = val + return http_params diff --git a/python/kserve/kserve/protocol/rest/server.py b/python/kserve/kserve/protocol/rest/server.py index 5896ed5b083..d732632d13b 100644 --- a/python/kserve/kserve/protocol/rest/server.py +++ b/python/kserve/kserve/protocol/rest/server.py @@ -113,6 +113,10 @@ def create_application(self) -> FastAPI: v2_endpoints.model_ready, response_model=ModelReadyResponse, tags=["V2"]), FastAPIRoute(r"/v2/models/{model_name}/infer", v2_endpoints.infer, methods=["POST"], response_model=InferenceResponse, tags=["V2"]), + FastAPIRoute(r"/v2/models/{model_name}/generate", + v2_endpoints.generate, methods=["POST"], tags=["V2"]), + FastAPIRoute(r"/v2/models/{model_name}/generate_stream", + v2_endpoints.generate_stream, methods=["POST"], tags=["V2"]), FastAPIRoute(r"/v2/models/{model_name}/versions/{model_version}/infer", v2_endpoints.infer, methods=["POST"], tags=["V2"], include_in_schema=False), FastAPIRoute(r"/v2/repository/models/{model_name}/load", diff --git a/python/kserve/kserve/protocol/rest/v2_datamodels.py b/python/kserve/kserve/protocol/rest/v2_datamodels.py index 4def1c6fba6..48255d54494 100644 --- a/python/kserve/kserve/protocol/rest/v2_datamodels.py +++ b/python/kserve/kserve/protocol/rest/v2_datamodels.py @@ -14,7 +14,7 @@ from typing import Optional, List, Union, Dict import orjson -from pydantic import BaseModel +from pydantic import BaseModel, StrictBool, StrictInt, StrictFloat # TODO: in the future, this file can be auto generated # https://pydantic-docs.helpmanual.io/datamodel_code_generator/ @@ -22,7 +22,7 @@ # Reference: https://github.com/kserve/kserve/blob/master/docs/predict-api/v2/rest_predict_v2.yaml -InferParameter = Union[int, float, bool, str] +InferParameter = Union[StrictFloat, StrictInt, StrictBool, str] Parameters = Dict[str, InferParameter] @@ -248,3 +248,166 @@ class Config: ] } } + + +class GenerateRequest(BaseModel): + """GenerateRequest Model + + $generate_request = + { + "text_input" : $string, + "parameters" : $string #optional, + } + """ + text_input: str + parameters: Optional[Parameters] = None + + class Config: + json_loads = orjson.loads + schema_extra = { + "example": { + "text_input": "Tell me about the AI", + "parameters": + { + "temperature": 0.8, + "top_p": 0.9, + } + } + } + + +class Token(BaseModel): + """Token Data Model + + """ + id: int + logprob: float + special: bool + text: str + + class Config: + json_loads = orjson.loads + schema_extra = { + "example": { + "id": 267, + "logprob": -2.0723474, + "special": False, + "text": " a", + } + } + + +class Details(BaseModel): + """Generate response details + + """ + finish_reason: str + logprobs: List[Token] + + class Config: + json_loads = orjson.loads + schema_extra = { + "example": { + "finish_reason": "stop", + "logprobs": [{ + "id": 267, + "logprob": -2.0723474, + "special": False, + "text": " a", + }] + } + } + + +class StreamingDetails(BaseModel): + """Generate response details + + """ + finish_reason: str + logprobs: Token + + class Config: + json_loads = orjson.loads + schema_extra = { + "example": { + "finish_reason": "stop", + "logprobs": { + "id": 267, + "logprob": -2.0723474, + "special": False, + "text": " a", + } + } + } + + +class GenerateResponse(BaseModel): + """GenerateResponse Model + + $generate_response = + { + "text_output" : $string, + "model_name" : $string, + "model_version" : $string #optional, + "details": $Details #optional + } + """ + text_output: str + model_name: str + model_version: Optional[str] + details: Optional[Details] + + class Config: + json_loads = orjson.loads + schema_extra = { + "example": { + "text_output": "Tell me about the AI", + "model_name": "bloom7b1", + "details": { + "finish_reason": "stop", + "logprobs": [ + { + "id": "267", + "logprob": -2.0723474, + "special": False, + "text": " a", + } + ] + } + } + } + + +class GenerateStreamingResponse(BaseModel): + """GenerateStreamingResponse Model + + $generate_response = + { + "text_output" : $string, + "model_name" : $string, + "model_version" : $string #optional, + "details": $Details #optional + } + """ + text_output: str + model_name: str + model_version: Optional[str] + details: Optional[StreamingDetails] + + class Config: + json_loads = orjson.loads + schema_extra = { + "example": { + "text_output": "Tell me about the AI", + "model_name": "bloom7b1", + "details": { + "finish_reason": "stop", + "logprobs": { + "id": "267", + "logprob": -2.0723474, + "special": False, + "text": " a", + } + } + } + } diff --git a/python/kserve/kserve/protocol/rest/v2_endpoints.py b/python/kserve/kserve/protocol/rest/v2_endpoints.py index c22612de863..33682209e60 100644 --- a/python/kserve/kserve/protocol/rest/v2_endpoints.py +++ b/python/kserve/kserve/protocol/rest/v2_endpoints.py @@ -11,16 +11,17 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - -from typing import Optional, Dict +import json +from typing import Optional, Dict, AsyncGenerator from fastapi.requests import Request from fastapi.responses import Response +from starlette.responses import StreamingResponse from ..infer_type import InferInput, InferRequest from .v2_datamodels import ( InferenceRequest, ServerMetadataResponse, ServerLiveResponse, ServerReadyResponse, - ModelMetadataResponse, InferenceResponse, ModelReadyResponse, ListModelsResponse + ModelMetadataResponse, InferenceResponse, ModelReadyResponse, ListModelsResponse, GenerateRequest, GenerateResponse ) from ..dataplane import DataPlane from ..model_repository_extension import ModelRepositoryExtension @@ -157,6 +158,109 @@ async def infer( res = InferenceResponse.parse_obj(response) return res + async def generate( + self, + raw_request: Request, + raw_response: Response, + model_name: str, + request_body: GenerateRequest, + model_version: Optional[str] = None + ) -> GenerateResponse: + """Generate handler. + + Args: + raw_request (Request): fastapi request object, + raw_response (Response): fastapi response object, + model_name (str): Model name. + request_body (GenerateRequest): Generate request body. + model_version (Optional[str]): Model version (optional). + + Returns: + Response: Generate response object. + """ + # TODO: support model_version + if model_version: + raise NotImplementedError("Model versioning not supported yet.") + + model_ready = self.dataplane.model_ready(model_name) + + if not model_ready: + raise ModelNotReady(model_name) + + request_headers = dict(raw_request.headers) + results, response_headers = await self.dataplane.generate(model_name=model_name, + request=request_body, + headers=request_headers) + + if isinstance(results, AsyncGenerator): + final_output = None + async for request_output in results: + if await raw_request.is_disconnected(): + # Abort the request if the client disconnects. + # await engine.abort(request_id) + return Response(status_code=499) + final_output = request_output + + assert final_output is not None + prompt = request_body.text_input + text_outputs = [prompt + output.text for output in final_output.outputs] + return GenerateResponse(text_output=text_outputs[0], model_name=model_name) + else: + return results + + async def generate_stream( + self, + raw_request: Request, + raw_response: Response, + model_name: str, + request_body: GenerateRequest, + model_version: Optional[str] = None + ) -> Response: + """Generate handler. + + Args: + raw_request (Request): fastapi request object, + raw_response (Response): fastapi response object, + model_name (str): Model name. + request_body (GenerateRequest): Generate request body. + model_version (Optional[str]): Model version (optional). + + Returns: + InferenceResponse: Inference response object. + """ + # TODO: support model_version + if model_version: + raise NotImplementedError("Model versioning not supported yet.") + + model_ready = self.dataplane.model_ready(model_name) + + if not model_ready: + raise ModelNotReady(model_name) + + request_headers = dict(raw_request.headers) + request_headers["streaming"] = "true" + results_generator, response_headers = await self.dataplane.generate(model_name=model_name, + request=request_body, + headers=request_headers) + + async def stream_results() -> AsyncGenerator[bytes, None]: + async for request_output in results_generator: + prompt = request_body.text_input + if hasattr(request_output, "outputs"): + # process GenerateResponse + text_outputs = [ + prompt + output.text for output in request_output.outputs + ] + ret = GenerateResponse(text_output=text_outputs[0], model_name=model_name, + outputs=request_output.outputs) + else: + if request_output is None: + request_output = "" + ret = GenerateResponse(text_output=request_output, model_name=model_name) + yield (json.dumps(ret.json()) + "\0").encode("utf-8") + + return StreamingResponse(stream_results()) + async def load(self, model_name: str) -> Dict: """Model load handler. diff --git a/python/kserve/kserve/storage/storage.py b/python/kserve/kserve/storage/storage.py index 0cb1f8d98e1..c614d27d37d 100644 --- a/python/kserve/kserve/storage/storage.py +++ b/python/kserve/kserve/storage/storage.py @@ -149,11 +149,16 @@ def get_S3_config(): # S3UseVirtualBucket environment variable defined in s3_secret.go # use virtual hosted-style URLs if enabled virtual = ("true" == os.getenv("S3_USER_VIRTUAL_BUCKET", "false").lower()) + # S3UseAccelerate environment variable defined in s3_secret.go + # use transfer acceleration if enabled + accelerate = ("true" == os.getenv("S3_USE_ACCELERATE", "false").lower()) if anon: c = c.merge(Config(signature_version=UNSIGNED)) if virtual: c = c.merge(Config(s3={"addressing_style": "virtual"})) + if accelerate: + c = c.merge(Config(s3={"use_accelerate_endpoint": accelerate})) return c diff --git a/python/kserve/kserve/storage/test/test_s3_storage.py b/python/kserve/kserve/storage/test/test_s3_storage.py index c0544feb050..101aeb6e009 100644 --- a/python/kserve/kserve/storage/test/test_s3_storage.py +++ b/python/kserve/kserve/storage/test/test_s3_storage.py @@ -183,6 +183,7 @@ def test_get_S3_config(): DEFAULT_CONFIG = Config() ANON_CONFIG = Config(signature_version=UNSIGNED) VIRTUAL_CONFIG = Config(s3={"addressing_style": "virtual"}) + USE_ACCELERATE_CONFIG = Config(s3={"use_accelerate_endpoint": True}) with mock.patch.dict(os.environ, {}): config1 = Storage.get_S3_config() @@ -214,6 +215,14 @@ def test_get_S3_config(): config7 = Storage.get_S3_config() assert config7.s3["addressing_style"] == VIRTUAL_CONFIG.s3["addressing_style"] + with mock.patch.dict(os.environ, {"S3_USE_ACCELERATE": "False"}): + config6 = Storage.get_S3_config() + assert vars(config6) == vars(DEFAULT_CONFIG) + + with mock.patch.dict(os.environ, {"S3_USE_ACCELERATE": "True"}): + config7 = Storage.get_S3_config() + assert config7.s3["use_accelerate_endpoint"] == USE_ACCELERATE_CONFIG.s3["use_accelerate_endpoint"] + def test_update_with_storage_spec_s3(monkeypatch): # save the environment and restore it after the test to avoid mutating it diff --git a/python/kserve/kserve/utils/utils.py b/python/kserve/kserve/utils/utils.py index 6aa4f4cf91f..c9af995fc42 100644 --- a/python/kserve/kserve/utils/utils.py +++ b/python/kserve/kserve/utils/utils.py @@ -15,6 +15,7 @@ import os import sys import uuid + from kserve.protocol.grpc.grpc_predict_v2_pb2 import InferParameter from typing import Dict, Union, List @@ -26,6 +27,7 @@ from cloudevents.http import CloudEvent from grpc import ServicerContext from kserve.protocol.infer_type import InferOutput, InferRequest, InferResponse +from ..errors import InvalidInput def is_running_in_k8s(): @@ -146,13 +148,14 @@ def get_predict_input(payload: Union[Dict, InferRequest], columns: List = None) if isinstance(instances[0], Dict) or ( isinstance(instances[0], List) and len(instances[0]) != 0 and isinstance(instances[0][0], Dict)): dfs = [] - for input in instances: - dfs.append(pd.DataFrame(input, columns=columns)) + for instance in instances: + dfs.append(pd.DataFrame(instance, columns=columns)) inputs = pd.concat(dfs, axis=0) return inputs else: + if isinstance(instances[0], str): + return instances return np.array(instances) - elif isinstance(payload, InferRequest): content_type = '' parameters = payload.parameters @@ -171,7 +174,7 @@ def get_predict_input(payload: Union[Dict, InferRequest], columns: List = None) return input.as_numpy() -def get_predict_response(payload: Union[Dict, InferRequest], result: Union[np.ndarray, pd.DataFrame], +def get_predict_response(payload: Union[Dict, InferRequest], result: Union[np.ndarray, List, pd.DataFrame], model_name: str) -> Union[Dict, InferResponse]: if isinstance(payload, Dict): infer_outputs = result @@ -206,6 +209,8 @@ def get_predict_response(payload: Union[Dict, InferRequest], result: Union[np.nd infer_outputs=infer_outputs, response_id=payload.id if payload.id else generate_uuid() ) + else: + raise InvalidInput(f"unsupported payload type {type(payload)}") def strtobool(val: str) -> bool: diff --git a/python/kserve/poetry.lock b/python/kserve/poetry.lock index 8bb949d35e2..f30f36626ff 100644 --- a/python/kserve/poetry.lock +++ b/python/kserve/poetry.lock @@ -338,17 +338,17 @@ wcwidth = ">=0.1.4" [[package]] name = "boto3" -version = "1.34.14" +version = "1.34.15" description = "The AWS SDK for Python" optional = true python-versions = ">= 3.8" files = [ - {file = "boto3-1.34.14-py3-none-any.whl", hash = "sha256:1f94042f4efb5133b6b9b8b3243afc01143a81d21b3197a3afadf5780f97b05d"}, - {file = "boto3-1.34.14.tar.gz", hash = "sha256:5c1bb487c68120aae236354d81b8a1a55d0aa3395d30748a01825ef90891921e"}, + {file = "boto3-1.34.15-py3-none-any.whl", hash = "sha256:f8f16c2d0ec1dca291857f1c138d5c30e01e40f653443cc2679e2f6ae71b05a6"}, + {file = "boto3-1.34.15.tar.gz", hash = "sha256:2b74c58f475ff0dcf2f3637da9367a9465d29fad971ff5d8dc54ac39554e9022"}, ] [package.dependencies] -botocore = ">=1.34.14,<1.35.0" +botocore = ">=1.34.15,<1.35.0" jmespath = ">=0.7.1,<2.0.0" s3transfer = ">=0.10.0,<0.11.0" @@ -357,13 +357,13 @@ crt = ["botocore[crt] (>=1.21.0,<2.0a0)"] [[package]] name = "botocore" -version = "1.34.14" +version = "1.34.15" description = "Low-level, data-driven core of boto 3." optional = true python-versions = ">= 3.8" files = [ - {file = "botocore-1.34.14-py3-none-any.whl", hash = "sha256:3b592f50f0406e236782a3a0a9ad1c3976060fdb2e04a23d18c3df5b7dfad3e0"}, - {file = "botocore-1.34.14.tar.gz", hash = "sha256:041bed0852649cab7e4dcd4d87f9d1cc084467fb846e5b60015e014761d96414"}, + {file = "botocore-1.34.15-py3-none-any.whl", hash = "sha256:16bcf871e67ef0177593f06e9e5bae4db51c9a9a2e953cb14feeb42d53441a85"}, + {file = "botocore-1.34.15.tar.gz", hash = "sha256:c3c3404962a6d9d5e1634bd70ed53b8eff1ff17ee9d7a6240e9e8c94db48ad6f"}, ] [package.dependencies] @@ -971,23 +971,25 @@ grpc = ["grpcio (>=1.38.0,<2.0dev)", "grpcio-status (>=1.38.0,<2.0.dev0)"] [[package]] name = "google-cloud-storage" -version = "1.44.0" +version = "2.14.0" description = "Google Cloud Storage API client library" optional = true -python-versions = ">=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*" +python-versions = ">=3.7" files = [ - {file = "google-cloud-storage-1.44.0.tar.gz", hash = "sha256:29edbfeedd157d853049302bf5d104055c6f0cb7ef283537da3ce3f730073001"}, - {file = "google_cloud_storage-1.44.0-py2.py3-none-any.whl", hash = "sha256:cd4a223e9c18d771721a85c98a9c01b97d257edddff833ba63b7b1f0b9b4d6e9"}, + {file = "google-cloud-storage-2.14.0.tar.gz", hash = "sha256:2d23fcf59b55e7b45336729c148bb1c464468c69d5efbaee30f7201dd90eb97e"}, + {file = "google_cloud_storage-2.14.0-py2.py3-none-any.whl", hash = "sha256:8641243bbf2a2042c16a6399551fbb13f062cbc9a2de38d6c0bb5426962e9dbd"}, ] [package.dependencies] -google-api-core = {version = ">=1.29.0,<3.0dev", markers = "python_version >= \"3.6\""} -google-auth = {version = ">=1.25.0,<3.0dev", markers = "python_version >= \"3.6\""} -google-cloud-core = {version = ">=1.6.0,<3.0dev", markers = "python_version >= \"3.6\""} -google-resumable-media = {version = ">=1.3.0,<3.0dev", markers = "python_version >= \"3.6\""} -protobuf = {version = "*", markers = "python_version >= \"3.6\""} +google-api-core = ">=1.31.5,<2.0.dev0 || >2.3.0,<3.0.0dev" +google-auth = ">=2.23.3,<3.0dev" +google-cloud-core = ">=2.3.0,<3.0dev" +google-crc32c = ">=1.0,<2.0dev" +google-resumable-media = ">=2.6.0" requests = ">=2.18.0,<3.0.0dev" -six = "*" + +[package.extras] +protobuf = ["protobuf (<5.0.0dev)"] [[package]] name = "google-crc32c" @@ -1202,24 +1204,24 @@ files = [ [[package]] name = "httpcore" -version = "0.16.3" +version = "1.0.2" description = "A minimal low-level HTTP client." optional = false -python-versions = ">=3.7" +python-versions = ">=3.8" files = [ - {file = "httpcore-0.16.3-py3-none-any.whl", hash = "sha256:da1fb708784a938aa084bde4feb8317056c55037247c787bd7e19eb2c2949dc0"}, - {file = "httpcore-0.16.3.tar.gz", hash = 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hash = "sha256:40df9b996c2b73138957fe23a16a4f0ba614f4c0efce1e9406a184b6d07fa3a9"}, + {file = "PyYAML-6.0.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a08c6f0fe150303c1c6b71ebcd7213c2858041a7e01975da3a99aed1e7a378ef"}, {file = "PyYAML-6.0.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6c22bec3fbe2524cde73d7ada88f6566758a8f7227bfbf93a408a9d86bcc12a0"}, {file = "PyYAML-6.0.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:8d4e9c88387b0f5c7d5f281e55304de64cf7f9c0021a3525bd3b1c542da3b0e4"}, {file = "PyYAML-6.0.1-cp312-cp312-win32.whl", hash = "sha256:d483d2cdf104e7c9fa60c544d92981f12ad66a457afae824d146093b8c294c54"}, @@ -2684,23 +2618,6 @@ requests = ">=2.0.0" [package.extras] rsa = ["oauthlib[signedtoken] (>=3.0.0)"] -[[package]] -name = "rfc3986" -version = "1.5.0" -description = "Validating URI References per RFC 3986" -optional = false -python-versions = "*" -files = [ - {file = "rfc3986-1.5.0-py2.py3-none-any.whl", hash = "sha256:a86d6e1f5b1dc238b218b012df0aa79409667bb209e58da56d0b94704e712a97"}, - {file = "rfc3986-1.5.0.tar.gz", hash = "sha256:270aaf10d87d0d4e095063c65bf3ddbc6ee3d0b226328ce21e036f946e421835"}, -] - -[package.dependencies] -idna = {version = "*", optional = true, markers = "extra == \"idna2008\""} - -[package.extras] -idna2008 = ["idna"] - [[package]] name = "rpds-py" version = "0.16.2" @@ -2948,28 +2865,6 @@ files = [ {file = "tomlkit-0.11.8.tar.gz", hash = "sha256:9330fc7faa1db67b541b28e62018c17d20be733177d290a13b24c62d1614e0c3"}, ] -[[package]] -name = "tritonclient" -version = "2.41.1" -description = "Python client library and utilities for communicating with Triton Inference Server" -optional = false -python-versions = "*" -files = [ - {file = "tritonclient-2.41.1-py3-none-any.whl", hash = "sha256:91cb234331a7145c407cea605caf9eecbd4276ddc5f085ddd5a6dcab64e5e70b"}, - {file = "tritonclient-2.41.1-py3-none-manylinux1_x86_64.whl", hash = "sha256:22ad56ae5ab25518862dec85af0a8246a32a1e14e2ee1d86f1444ce432c254e1"}, - {file = "tritonclient-2.41.1-py3-none-manylinux2014_aarch64.whl", hash = "sha256:6545055add115e9bd07ca540af95db5ceda0c783009ad41df6a7f35a79d57474"}, -] - -[package.dependencies] -numpy = ">=1.19.1" -python-rapidjson = ">=0.9.1" - -[package.extras] -all = ["aiohttp (>=3.8.1,<4.0.0)", "cuda-python", "geventhttpclient (>=1.4.4,<=2.0.2)", "grpcio (>=1.41.0)", "numpy (>=1.19.1)", "packaging (>=14.1)", "protobuf (>=3.5.0,<5)", "python-rapidjson (>=0.9.1)"] -cuda = ["cuda-python"] -grpc = ["grpcio (>=1.41.0)", "numpy (>=1.19.1)", "packaging (>=14.1)", "protobuf (>=3.5.0,<5)", "python-rapidjson (>=0.9.1)"] -http = ["aiohttp (>=3.8.1,<4.0.0)", "geventhttpclient (>=1.4.4,<=2.0.2)", "numpy (>=1.19.1)", "python-rapidjson (>=0.9.1)"] - [[package]] name = "typing-extensions" version = "4.9.0" @@ -3417,4 +3312,4 @@ storage = ["azure-identity", "azure-storage-blob", "azure-storage-file-share", " [metadata] lock-version = "2.0" python-versions = ">=3.8,<3.12" -content-hash = "432c49cda1757e6ea615070057c64bcc8e12fcb7bf473fc475ef5617bab21dff" +content-hash = "57f2522d4aa14809989e8ee4a04b864b5371a5a0c6559d27bc745483f4c6d2c7" diff --git a/python/kserve/pyproject.toml b/python/kserve/pyproject.toml index 4ebdb5f052e..1b149f32c8c 100644 --- a/python/kserve/pyproject.toml +++ b/python/kserve/pyproject.toml @@ -42,22 +42,21 @@ kubernetes = ">=23.3.0" python-dateutil = "^2.8.0" numpy = "^1.23.5" psutil = "^5.9.0" -ray = { version = ">=2.8.1", extras = ["serve"] } +ray = { version = "^2.8.1", extras = ["serve"] } async-timeout = { version = "^4.0.3", python = "==3.11.*" } grpcio = "^1.49.1" protobuf = "^3.19.0" prometheus-client = "^0.13.1" orjson = "^3.8.0" -httpx = "^0.23.0" +httpx = "^0.26.0" timing-asgi = "^0.3.0" -tritonclient = "^2.18.0" tabulate = "^0.9.0" pandas = ">=1.3.5" # Storage dependencies. They can be opted into by apps. urllib3 = { version = "^1.26.8", optional = true } requests = { version = "^2.20.0", optional = true } -google-cloud-storage = { version = "^1.20.0", optional = true } +google-cloud-storage = { version = "^2.3.0", optional = true } azure-storage-blob = { version = "^12.10.0", optional = true } azure-storage-file-share = { version = "^12.7.0", optional = true } azure-identity = { version = "^1.8.0", optional = true } diff --git a/python/kserve/test/__init__.py b/python/kserve/test/__init__.py index 6135a95fa7d..40badc12c61 100644 --- a/python/kserve/test/__init__.py +++ b/python/kserve/test/__init__.py @@ -11,3 +11,4 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. + diff --git a/python/kserve/test/test_infer_type.py b/python/kserve/test/test_infer_type.py new file mode 100644 index 00000000000..43a4010dfbe --- /dev/null +++ b/python/kserve/test/test_infer_type.py @@ -0,0 +1,258 @@ +# Copyright 2024 The KServe Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from kserve import InferRequest, InferInput, InferResponse, InferOutput +from kserve.protocol.grpc.grpc_predict_v2_pb2 import ModelInferRequest, InferParameter, ModelInferResponse + + +class TestInferRequest: + def test_to_rest(self): + infer_req = InferRequest(model_name="TestModel", request_id="123", + parameters={ + "test-str": InferParameter(string_param="dummy"), + "test-bool": InferParameter(bool_param=True), + "test-int": InferParameter(int64_param=100) + }, + infer_inputs=[ + InferInput(name="input-0", datatype="INT32", shape=[1, 2], data=[1, 2], + parameters={ + "test-str": InferParameter(string_param="dummy"), + "test-bool": InferParameter(bool_param=True), + "test-int": InferParameter(int64_param=100) + })] + ) + # model_name should not be present for rest + expected = { + "id": "123", + "inputs": [ + { + "name": "input-0", + "shape": [1, 2], + "datatype": "INT32", + "data": [1, 2], + "parameters": { + "test-str": "dummy", + "test-bool": True, + "test-int": 100 + } + } + ], + "parameters": { + "test-str": "dummy", + "test-bool": True, + "test-int": 100 + } + } + res = infer_req.to_rest() + assert res == expected + + def test_to_grpc(self): + infer_req = InferRequest(model_name="TestModel", request_id="123", + parameters={ + "test-str": "dummy", + "test-bool": True, + "test-int": 100 + }, + infer_inputs=[ + InferInput(name="input-0", datatype="INT32", shape=[1, 2], data=[1, 2], + parameters={ + "test-str": "dummy", + "test-bool": True, + "test-int": 100 + })] + ) + expected = ModelInferRequest(model_name="TestModel", id="123", + parameters={ + "test-str": InferParameter(string_param="dummy"), + "test-bool": InferParameter(bool_param=True), + "test-int": InferParameter(int64_param=100) + }, + inputs=[ + { + "name": "input-0", + "shape": [1, 2], + "datatype": "INT32", + "contents": { + "int_contents": [1, 2] + }, + "parameters": { + "test-str": InferParameter(string_param="dummy"), + "test-bool": InferParameter(bool_param=True), + "test-int": InferParameter(int64_param=100) + }, + }] + ) + res = infer_req.to_grpc() + assert res == expected + + def test_from_grpc(self): + infer_req = ModelInferRequest(model_name="TestModel", id="123", + parameters={ + "test-str": InferParameter(string_param="dummy"), + "test-bool": InferParameter(bool_param=True), + "test-int": InferParameter(int64_param=100) + }, + inputs=[ + { + "name": "input-0", + "shape": [1, 2], + "datatype": "INT32", + "contents": { + "int_contents": [1, 2] + }, + "parameters": { + "test-str": InferParameter(string_param="dummy"), + "test-bool": InferParameter(bool_param=True), + "test-int": InferParameter(int64_param=100) + }, + }] + ) + expected = InferRequest(model_name="TestModel", request_id="123", + parameters={ + "test-str": InferParameter(string_param="dummy"), + "test-bool": InferParameter(bool_param=True), + "test-int": InferParameter(int64_param=100) + }, + infer_inputs=[ + InferInput(name="input-0", datatype="INT32", shape=[1, 2], data=[1, 2], + parameters={ + "test-str": InferParameter(string_param="dummy"), + "test-bool": InferParameter(bool_param=True), + "test-int": InferParameter(int64_param=100) + })], + from_grpc=True + ) + res = InferRequest.from_grpc(infer_req) + assert res == expected + + class TestInferResponse: + def test_to_rest(self): + infer_res = InferResponse(model_name="TestModel", response_id="123", + parameters={ + "test-str": InferParameter(string_param="dummy"), + "test-bool": InferParameter(bool_param=True), + "test-int": InferParameter(int64_param=100) + }, + infer_outputs=[ + InferOutput(name="output-0", datatype="INT32", shape=[1, 2], data=[1, 2], + parameters={ + "test-str": InferParameter(string_param="dummy"), + "test-bool": InferParameter(bool_param=True), + "test-int": InferParameter(int64_param=100) + })] + ) + expected = { + "id": "123", + "model_name": "TestModel", + "outputs": [ + { + "name": "output-0", + "shape": [1, 2], + "datatype": "INT32", + "data": [1, 2], + "parameters": { + "test-str": "dummy", + "test-bool": True, + "test-int": 100 + } + } + ], + "parameters": { + "test-str": "dummy", + "test-bool": True, + "test-int": 100 + } + } + res = infer_res.to_rest() + assert res == expected + + def test_to_grpc(self): + infer_res = InferResponse(model_name="TestModel", response_id="123", + parameters={ + "test-str": "dummy", + "test-bool": True, + "test-int": 100 + }, + infer_outputs=[ + InferOutput(name="output-0", datatype="INT32", shape=[1, 2], data=[1, 2], + parameters={ + "test-str": "dummy", + "test-bool": True, + "test-int": 100 + })] + ) + expected = ModelInferResponse(model_name="TestModel", id="123", + parameters={ + "test-str": InferParameter(string_param="dummy"), + "test-bool": InferParameter(bool_param=True), + "test-int": InferParameter(int64_param=100) + }, + outputs=[ + { + "name": "output-0", + "shape": [1, 2], + "datatype": "INT32", + "contents": { + "int_contents": [1, 2] + }, + "parameters": { + "test-str": InferParameter(string_param="dummy"), + "test-bool": InferParameter(bool_param=True), + "test-int": InferParameter(int64_param=100) + }, + }] + ) + res = infer_res.to_grpc() + assert res == expected + + def test_from_grpc(self): + infer_res = ModelInferResponse(model_name="TestModel", id="123", + parameters={ + "test-str": InferParameter(string_param="dummy"), + "test-bool": InferParameter(bool_param=True), + "test-int": InferParameter(int64_param=100) + }, + outputs=[ + { + "name": "output-0", + "shape": [1, 2], + "datatype": "INT32", + "contents": { + "int_contents": [1, 2] + }, + "parameters": { + "test-str": InferParameter(string_param="dummy"), + "test-bool": InferParameter(bool_param=True), + "test-int": InferParameter(int64_param=100) + }, + }] + ) + expected = InferResponse(model_name="TestModel", response_id="123", + parameters={ + "test-str": InferParameter(string_param="dummy"), + "test-bool": InferParameter(bool_param=True), + "test-int": InferParameter(int64_param=100) + }, + infer_outputs=[ + InferOutput(name="output-0", datatype="INT32", shape=[1, 2], data=[1, 2], + parameters={ + "test-str": InferParameter(string_param="dummy"), + "test-bool": InferParameter(bool_param=True), + "test-int": InferParameter(int64_param=100) + })], + from_grpc=True + ) + res = InferResponse.from_grpc(infer_res) + assert res == expected diff --git a/python/kserve/test/test_server.py b/python/kserve/test/test_server.py index 3b13d3616a3..222fd651910 100644 --- a/python/kserve/test/test_server.py +++ b/python/kserve/test/test_server.py @@ -32,7 +32,7 @@ from kserve.model import PredictorProtocol from kserve.protocol.rest.server import RESTServer -from kserve.protocol.infer_type import InferRequest +from kserve.protocol.infer_type import InferRequest, InferInput, InferResponse, InferOutput from kserve.utils.utils import get_predict_input, get_predict_response test_avsc_schema = ''' @@ -81,6 +81,10 @@ async def predict(self, request, headers=None): if isinstance(request, InferRequest): inputs = get_predict_input(request) infer_response = get_predict_response(request, inputs, self.name) + if request.parameters: + infer_response.parameters = request.parameters + if request.inputs[0].parameters: + infer_response.outputs[0].parameters = request.inputs[0].parameters return infer_response else: return {"predictions": request["instances"]} @@ -200,6 +204,7 @@ async def test_validate(self): with pytest.raises(InvalidInput): model.validate(bad_request) + # Separate out v1 and v2 endpoint unit tests in # https://github.com/kserve/kserve/blob/master/python/kserve/test/test_server.py. @@ -240,14 +245,14 @@ def test_list_models_v1(self, http_server_client): def test_predict_v1(self, http_server_client): resp = http_server_client.post('/v1/models/TestModel:predict', - data=b'{"instances":[[1,2]]}') + content=b'{"instances":[[1,2]]}') assert resp.status_code == 200 assert resp.content == b'{"predictions":[[1,2]]}' assert resp.headers['content-type'] == "application/json" def test_explain_v1(self, http_server_client): resp = http_server_client.post('/v1/models/TestModel:explain', - data=b'{"instances":[[1,2]]}') + content=b'{"instances":[[1,2]]}') assert resp.status_code == 200 assert resp.content == b'{"predictions":[[1,2]]}' assert resp.headers['content-type'] == "application/json" @@ -286,7 +291,7 @@ def test_list_models_v2(self, http_server_client): def test_infer_v2(self, http_server_client): input_data = b'{"inputs": [{"name": "input-0","shape": [1, 2],"datatype": "INT32","data": [[1,2]]}]}' resp = http_server_client.post('/v2/models/TestModel/infer', - data=input_data) + content=input_data) result = json.loads(resp.content) assert resp.status_code == 200 @@ -295,11 +300,53 @@ def test_infer_v2(self, http_server_client): def test_explain_v2(self, http_server_client): resp = http_server_client.post('/v1/models/TestModel:explain', - data=b'{"instances":[[1,2]]}') + content=b'{"instances":[[1,2]]}') assert resp.status_code == 200 assert resp.content == b'{"predictions":[[1,2]]}' assert resp.headers['content-type'] == "application/json" + def test_infer_parameters_v2(self, http_server_client): + model_name = "TestModel" + req = InferRequest(model_name=model_name, request_id="123", + parameters={ + "test-str": "dummy", + "test-bool": True, + "test-int": 100, + "test-float": 1.3, + }, + infer_inputs=[ + InferInput(name="input-0", datatype="INT32", shape=[1, 2], data=[1, 2], + parameters={ + "test-str": "dummy", + "test-bool": True, + "test-int": 100, + "test-float": 1.3, + })]) + + input_data = json.dumps(req.to_rest()).encode('utf-8') + expected_res = InferResponse(model_name=model_name, response_id="123", + parameters={ + "test-str": "dummy", + "test-bool": True, + "test-int": 100, + "test-float": 1.3, + }, + infer_outputs=[ + InferOutput(name="output-0", + datatype="INT32", + shape=[1, 2], data=[1, 2], + parameters={ + "test-str": "dummy", + "test-bool": True, + "test-int": 100, + "test-float": 1.3, + })]) + resp = http_server_client.post('/v2/models/TestModel/infer', content=input_data) + assert resp.status_code == 200 + assert resp.headers['content-type'] == "application/json" + result = InferResponse.from_rest(model_name=model_name, response=json.loads(resp.content)) + assert result == expected_res + class TestRayServer: @pytest.fixture(scope="class") @@ -338,7 +385,7 @@ def test_health_handler(self, http_server_client): def test_predict(self, http_server_client): resp = http_server_client.post('/v1/models/TestModel:predict', - data=b'{"instances":[[1,2]]}') + content=b'{"instances":[[1,2]]}') assert resp.status_code == 200 assert resp.content == b'{"predictions":[[1,2]]}' assert resp.headers['content-type'] == "application/json" @@ -346,7 +393,7 @@ def test_predict(self, http_server_client): def test_infer(self, http_server_client): input_data = b'{"inputs": [{"name": "input-0","shape": [1, 2],"datatype": "INT32","data": [[1,2]]}]}' resp = http_server_client.post('/v2/models/TestModel/infer', - data=input_data) + content=input_data) result = json.loads(resp.content) assert resp.status_code == 200 @@ -355,7 +402,7 @@ def test_infer(self, http_server_client): def test_explain(self, http_server_client): resp = http_server_client.post('/v1/models/TestModel:explain', - data=b'{"instances":[[1,2]]}') + content=b'{"instances":[[1,2]]}') assert resp.status_code == 200 assert resp.content == b'{"predictions":[[1,2]]}' assert resp.headers['content-type'] == "application/json" @@ -398,7 +445,7 @@ def test_predict_ce_structured(self, http_server_client): event = dummy_cloud_event({"instances": [[1, 2]]}) headers, body = to_structured(event) - resp = http_server_client.post('/v1/models/TestModel:predict', headers=headers, data=body) + resp = http_server_client.post('/v1/models/TestModel:predict', headers=headers, content=body) body = json.loads(resp.content) assert resp.status_code == 200 @@ -417,7 +464,7 @@ def test_predict_custom_ce_attributes(self, http_server_client): event = dummy_cloud_event({"instances": [[1, 2]]}) headers, body = to_structured(event) - resp = http_server_client.post('/v1/models/TestModel:predict', headers=headers, data=body) + resp = http_server_client.post('/v1/models/TestModel:predict', headers=headers, content=body) body = json.loads(resp.content) assert resp.status_code == 200 @@ -433,7 +480,7 @@ def test_predict_merge_structured_ce_attributes(self, http_server_client): event = dummy_cloud_event({"instances": [[1, 2]]}, add_extension=True) headers, body = to_structured(event) - resp = http_server_client.post('/v1/models/TestModel:predict', headers=headers, data=body) + resp = http_server_client.post('/v1/models/TestModel:predict', headers=headers, content=body) body = json.loads(resp.content) assert resp.status_code == 200 @@ -451,7 +498,7 @@ def test_predict_merge_binary_ce_attributes(self, http_server_client): event = dummy_cloud_event({"instances": [[1, 2]]}, set_contenttype=True, add_extension=True) headers, body = to_binary(event) - resp = http_server_client.post('/v1/models/TestModel:predict', headers=headers, data=body) + resp = http_server_client.post('/v1/models/TestModel:predict', headers=headers, content=body) assert resp.status_code == 200 assert resp.headers['content-type'] == "application/json" @@ -468,7 +515,7 @@ def test_predict_ce_binary_dict(self, http_server_client): event = dummy_cloud_event({"instances": [[1, 2]]}, set_contenttype=True) headers, body = to_binary(event) - resp = http_server_client.post('/v1/models/TestModel:predict', headers=headers, data=body) + resp = http_server_client.post('/v1/models/TestModel:predict', headers=headers, content=body) assert resp.status_code == 200 assert resp.headers['content-type'] == "application/json" @@ -482,7 +529,7 @@ def test_predict_ce_binary_dict(self, http_server_client): def test_predict_ce_binary_bytes(self, http_server_client): event = dummy_cloud_event(b'{"instances":[[1,2]]}', set_contenttype=True) headers, body = to_binary(event) - resp = http_server_client.post('/v1/models/TestModel:predict', headers=headers, data=body) + resp = http_server_client.post('/v1/models/TestModel:predict', headers=headers, content=body) assert resp.status_code == 200 assert resp.headers['content-type'] == "application/json" @@ -497,7 +544,7 @@ def test_predict_ce_bytes_bad_format_exception(self, http_server_client): event = dummy_cloud_event(b'{', set_contenttype=True) headers, body = to_binary(event) - resp = http_server_client.post('/v1/models/TestModel:predict', headers=headers, data=body) + resp = http_server_client.post('/v1/models/TestModel:predict', headers=headers, content=body) assert resp.status_code == 400 error_regex = re.compile("Failed to decode or parse binary json cloudevent: " @@ -509,7 +556,7 @@ def test_predict_ce_bytes_bad_hex_format_exception(self, http_server_client): event = dummy_cloud_event(b'0\x80\x80\x06World!\x00\x00', set_contenttype=True) headers, body = to_binary(event) - resp = http_server_client.post('/v1/models/TestModel:predict', headers=headers, data=body) + resp = http_server_client.post('/v1/models/TestModel:predict', headers=headers, content=body) assert resp.status_code == 400 error_regex = re.compile("Failed to decode or parse binary json cloudevent: " @@ -546,7 +593,7 @@ def test_predict_ce_avro_binary(self, http_server_client): event = dummy_cloud_event(data, set_contenttype=True, contenttype="application/avro") # Creates the HTTP request representation of the CloudEvent in binary content mode headers, body = to_binary(event) - resp = http_server_client.post('/v1/models/TestModel:predict', headers=headers, data=body) + resp = http_server_client.post('/v1/models/TestModel:predict', headers=headers, content=body) assert resp.status_code == 200 assert resp.headers['content-type'] == "application/json" @@ -571,12 +618,12 @@ def http_server_client(self, app): return TestClient(app) def test_load(self, http_server_client): - resp = http_server_client.post('/v2/repository/models/model/load', data=b'') + resp = http_server_client.post('/v2/repository/models/model/load', content=b'') assert resp.status_code == 200 assert resp.content == b'{"name":"model","load":true}' def test_unload(self, http_server_client): - resp = http_server_client.post('/v2/repository/models/model/unload', data=b'') + resp = http_server_client.post('/v2/repository/models/model/unload', content=b'') assert resp.status_code == 200 assert resp.content == b'{"name":"model","unload":true}' @@ -593,11 +640,11 @@ def http_server_client(self, app): return TestClient(app) def test_load_fail(self, http_server_client): - resp = http_server_client.post('/v2/repository/models/model/load', data=b'') + resp = http_server_client.post('/v2/repository/models/model/load', content=b'') assert resp.status_code == 503 def test_unload_fail(self, http_server_client): - resp = http_server_client.post('/v2/repository/models/model/unload', data=b'') + resp = http_server_client.post('/v2/repository/models/model/unload', content=b'') assert resp.status_code == 404 @@ -624,16 +671,16 @@ def test_model_not_ready_v2(self, http_server_client): def test_predict(self, http_server_client): resp = http_server_client.post('/v1/models/TestModel:predict', - data=b'{"instances":[[1,2]]}') + content=b'{"instances":[[1,2]]}') assert resp.status_code == 503 def test_infer(self, http_server_client): input_data = b'{"inputs": [{"name": "input-0","shape": [1, 2],"datatype": "INT32","data": [[1,2]]}]}' resp = http_server_client.post('/v2/models/TestModel/infer', - data=input_data) + content=input_data) assert resp.status_code == 503 def test_explain(self, http_server_client): resp = http_server_client.post('/v1/models/TestModel:explain', - data=b'{"instances":[[1,2]]}') + content=b'{"instances":[[1,2]]}') assert resp.status_code == 503 diff --git a/python/kserve/test/test_v1beta1_hugging_face_runtime_spec.py b/python/kserve/test/test_v1beta1_hugging_face_runtime_spec.py new file mode 100644 index 00000000000..be8dae09988 --- /dev/null +++ b/python/kserve/test/test_v1beta1_hugging_face_runtime_spec.py @@ -0,0 +1,108 @@ +# Copyright 2023 The KServe Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# coding: utf-8 + +""" + KServe + + Python SDK for KServe # noqa: E501 + + The version of the OpenAPI document: v0.1 + Generated by: https://openapi-generator.tech +""" + + +from __future__ import absolute_import + +import unittest +import datetime + +import kserve +from kserve.models.v1beta1_hugging_face_runtime_spec import V1beta1HuggingFaceRuntimeSpec # noqa: E501 +from kserve.rest import ApiException + +class TestV1beta1HuggingFaceRuntimeSpec(unittest.TestCase): + """V1beta1HuggingFaceRuntimeSpec unit test stubs""" + + def setUp(self): + pass + + def tearDown(self): + pass + + def make_instance(self, include_optional): + """Test V1beta1HuggingFaceRuntimeSpec + include_option is a boolean, when False only required + params are included, when True both required and + optional params are included """ + # model = kserve.models.v1beta1_hugging_face_runtime_spec.V1beta1HuggingFaceRuntimeSpec() # noqa: E501 + if include_optional : + return V1beta1HuggingFaceRuntimeSpec( + args = [ + '0' + ], + command = [ + '0' + ], + env = [ + None + ], + env_from = [ + None + ], + image = '0', + image_pull_policy = '0', + lifecycle = None, + liveness_probe = None, + name = '0', + ports = [ + None + ], + protocol_version = '0', + readiness_probe = None, + resize_policy = [ + None + ], + resources = None, + restart_policy = '0', + runtime_version = '0', + security_context = None, + startup_probe = None, + stdin = True, + stdin_once = True, + storage_uri = '0', + termination_message_path = '0', + termination_message_policy = '0', + tty = True, + volume_devices = [ + None + ], + volume_mounts = [ + None + ], + working_dir = '0' + ) + else : + return V1beta1HuggingFaceRuntimeSpec( + ) + + def testV1beta1HuggingFaceRuntimeSpec(self): + """Test V1beta1HuggingFaceRuntimeSpec""" + inst_req_only = self.make_instance(include_optional=False) + inst_req_and_optional = self.make_instance(include_optional=True) + + +if __name__ == '__main__': + unittest.main() diff --git a/python/lgbserver/poetry.lock b/python/lgbserver/poetry.lock index 384769e697b..f1e46d3acd8 100644 --- a/python/lgbserver/poetry.lock +++ b/python/lgbserver/poetry.lock @@ -682,31 +682,6 @@ ssh = ["bcrypt (>=3.1.5)"] test = ["pretend", "pytest (>=6.2.0)", "pytest-benchmark", "pytest-cov", "pytest-xdist"] test-randomorder = ["pytest-randomly"] -[[package]] -name = "cuda-python" -version = "12.3.0" -description = "Python bindings for CUDA" -optional = false -python-versions = "*" -files = [ - {file = 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"colorful", "fastapi", "gpustat (>=1.0.0)", "grpcio (>=1.32.0)", "grpcio (>=1.42.0)", "opencensus", "prometheus-client (>=0.7.1)", "py-spy (>=0.2.0)", "pydantic (<2)", "requests", "smart-open", "starlette", "uvicorn[standard]", "virtualenv (>=20.0.24,<20.21.1)", "watchfiles"] +serve = ["aiohttp (>=3.7)", "aiohttp-cors", "aiorwlock", "colorful", "fastapi", "gpustat (>=1.0.0)", "grpcio (>=1.32.0)", "grpcio (>=1.42.0)", "opencensus", "prometheus-client (>=0.7.1)", "py-spy (>=0.2.0)", "pydantic (<2.0.dev0 || >=2.5.dev0,<3)", "requests", "smart-open", "starlette", "uvicorn[standard]", "virtualenv (>=20.0.24,!=20.21.1)", "watchfiles"] +serve-grpc = ["aiohttp (>=3.7)", "aiohttp-cors", "aiorwlock", "colorful", "fastapi", "gpustat (>=1.0.0)", "grpcio (>=1.32.0)", "grpcio (>=1.42.0)", "opencensus", "prometheus-client (>=0.7.1)", "py-spy (>=0.2.0)", "pydantic (<2.0.dev0 || >=2.5.dev0,<3)", "requests", "smart-open", "starlette", "uvicorn[standard]", "virtualenv (>=20.0.24,!=20.21.1)", "watchfiles"] train = ["fsspec", "pandas", "pyarrow (>=6.0.1)", "requests", "tensorboardX (>=1.9)"] tune = ["fsspec", "pandas", "pyarrow (>=6.0.1)", "requests", "tensorboardX (>=1.9)"] @@ -2544,23 +2453,6 @@ requests = ">=2.0.0" [package.extras] rsa = ["oauthlib[signedtoken] (>=3.0.0)"] -[[package]] -name = "rfc3986" -version = "1.5.0" -description = "Validating URI References per RFC 3986" -optional = false -python-versions = "*" -files = [ - {file = "rfc3986-1.5.0-py2.py3-none-any.whl", hash = "sha256:a86d6e1f5b1dc238b218b012df0aa79409667bb209e58da56d0b94704e712a97"}, - {file = "rfc3986-1.5.0.tar.gz", hash = "sha256:270aaf10d87d0d4e095063c65bf3ddbc6ee3d0b226328ce21e036f946e421835"}, -] - -[package.dependencies] -idna = {version = "*", optional = true, markers = "extra == \"idna2008\""} - -[package.extras] -idna2008 = ["idna"] - [[package]] name = "rpds-py" version = "0.13.1" @@ -2888,27 +2780,6 @@ files = [ {file = "tomli-2.0.1.tar.gz", hash = "sha256:de526c12914f0c550d15924c62d72abc48d6fe7364aa87328337a31007fe8a4f"}, ] -[[package]] -name = "tritonclient" -version = "2.39.0" -description = "Python client library and utilities for communicating with Triton Inference Server" -optional = false -python-versions = "*" -files = [ - {file = "tritonclient-2.39.0-py3-none-any.whl", hash = "sha256:69d0bcd35e2aedb758b92dfe798914717a5276714d2997f2c26f6dc0db205581"}, - {file = "tritonclient-2.39.0-py3-none-manylinux1_x86_64.whl", hash = "sha256:9bbbf5731e62da943ab16929960f57189bfe59700c20a865fdb287abfb1baf33"}, -] - -[package.dependencies] -cuda-python = "*" -numpy = ">=1.19.1" -python-rapidjson = ">=0.9.1" - -[package.extras] -all = ["aiohttp (>=3.8.1,<4.0.0)", "geventhttpclient (>=1.4.4,<=2.0.2)", "grpcio (>=1.41.0)", "numpy (>=1.19.1)", "packaging (>=14.1)", "protobuf (>=3.5.0,<4)", "python-rapidjson (>=0.9.1)"] -grpc = ["grpcio (>=1.41.0)", "numpy (>=1.19.1)", "packaging (>=14.1)", "protobuf (>=3.5.0,<4)", "python-rapidjson (>=0.9.1)"] -http = ["aiohttp (>=3.8.1,<4.0.0)", "geventhttpclient (>=1.4.4,<=2.0.2)", "numpy (>=1.19.1)", "python-rapidjson (>=0.9.1)"] - [[package]] name = "typing-extensions" version = "4.8.0" diff --git a/test/crds/serving.kserve.io_inferenceservices.yaml b/test/crds/serving.kserve.io_inferenceservices.yaml index ece77316688..331eda7e785 100644 --- a/test/crds/serving.kserve.io_inferenceservices.yaml +++ b/test/crds/serving.kserve.io_inferenceservices.yaml @@ -8098,6 +8098,633 @@ spec: type: boolean hostname: type: string + huggingface: + properties: + args: + items: + type: string + type: array + command: + items: + type: string + type: array + env: + items: + properties: + name: + type: string + value: + type: string + valueFrom: + properties: + configMapKeyRef: + properties: + key: + type: string + name: + type: string + optional: + type: boolean + required: + - key + type: object + x-kubernetes-map-type: atomic + fieldRef: + properties: + apiVersion: + type: string + fieldPath: + type: string + required: + - fieldPath + type: object + x-kubernetes-map-type: atomic + resourceFieldRef: + properties: + containerName: + type: string + divisor: + anyOf: + - type: integer + - type: string + pattern: ^(\+|-)?(([0-9]+(\.[0-9]*)?)|(\.[0-9]+))(([KMGTPE]i)|[numkMGTPE]|([eE](\+|-)?(([0-9]+(\.[0-9]*)?)|(\.[0-9]+))))?$ + x-kubernetes-int-or-string: true + resource: + type: string + required: + - resource + type: object + x-kubernetes-map-type: atomic + secretKeyRef: + properties: + key: + type: string + name: + type: string + optional: + type: boolean + required: + - key + type: object + x-kubernetes-map-type: atomic + type: object + required: + - name + type: object + type: array + envFrom: + items: + properties: + configMapRef: + properties: + name: + type: string + optional: + type: boolean + type: object + x-kubernetes-map-type: atomic + prefix: + type: string + secretRef: + properties: + name: + type: string + optional: + type: boolean + type: object + x-kubernetes-map-type: atomic + type: object + type: array + image: + type: string + imagePullPolicy: + type: string + lifecycle: + properties: + postStart: + properties: + exec: + properties: + command: + items: + type: string + type: array + type: object + httpGet: + properties: + host: + type: string + httpHeaders: + items: + properties: + name: + type: string + value: + type: string + required: + - name + - value + type: object + type: array + path: + type: string + port: + anyOf: + - type: integer + - type: string + x-kubernetes-int-or-string: true + scheme: + type: string + required: + - port + type: object + tcpSocket: + properties: + host: + type: string + port: + anyOf: + - type: integer + - type: string + x-kubernetes-int-or-string: true + required: + - port + type: object + type: object + preStop: + properties: + exec: + properties: + command: + items: + type: string + type: array + type: object + httpGet: + properties: + host: + type: string + httpHeaders: + items: + properties: + name: + type: string + value: + type: string + required: + - name + - value + type: object + type: array + path: + type: string + port: + anyOf: + - type: integer + - type: string + x-kubernetes-int-or-string: true + scheme: + type: string + required: + - port + type: object + tcpSocket: + properties: + host: + type: string + port: + anyOf: + - type: integer + - type: string + x-kubernetes-int-or-string: true + required: + - port + type: object + type: object + type: object + livenessProbe: + properties: + exec: + properties: + command: + items: + type: string + type: array + type: object + failureThreshold: + format: int32 + type: integer + grpc: + properties: + port: + format: int32 + type: integer + service: + type: string + required: + - port + type: object + httpGet: + properties: + host: + type: string + httpHeaders: + items: + properties: + name: + type: string + value: + type: string + required: + - name + - value + type: object + type: array + path: + type: string + port: + anyOf: + - type: integer + - type: string + x-kubernetes-int-or-string: true + scheme: + type: string + type: object + initialDelaySeconds: + format: int32 + type: integer + periodSeconds: + format: int32 + type: integer + successThreshold: + format: int32 + type: integer + tcpSocket: + properties: + host: + type: string + port: + anyOf: + - type: integer + - type: string + x-kubernetes-int-or-string: true + type: object + terminationGracePeriodSeconds: + format: int64 + type: integer + timeoutSeconds: + format: int32 + type: integer + type: object + name: + type: string + ports: + items: + properties: + containerPort: + format: int32 + type: integer + hostIP: + type: string + hostPort: + format: int32 + type: integer + name: + type: string + protocol: + default: TCP + type: string + required: + - containerPort + type: object + type: array + x-kubernetes-list-map-keys: + - containerPort + - protocol + x-kubernetes-list-type: map + protocolVersion: + type: string + readinessProbe: + properties: + exec: + properties: + command: + items: + type: string + type: array + type: object + failureThreshold: + format: int32 + type: integer + grpc: + properties: + port: + format: int32 + type: integer + service: + type: string + required: + - port + type: object + httpGet: + properties: + host: + type: string + httpHeaders: + items: + properties: + name: + type: string + value: + type: string + required: + - name + - value + type: object + type: array + path: + type: string + port: + anyOf: + - type: integer + - type: string + x-kubernetes-int-or-string: true + scheme: + type: string + type: object + initialDelaySeconds: + format: int32 + type: integer + periodSeconds: + format: int32 + type: integer + successThreshold: + format: int32 + type: integer + tcpSocket: + properties: + host: + type: string + port: + anyOf: + - type: integer + - type: string + x-kubernetes-int-or-string: true + type: object + terminationGracePeriodSeconds: + format: int64 + type: integer + timeoutSeconds: + format: int32 + type: integer + type: object + resizePolicy: + items: + properties: + resourceName: + type: string + restartPolicy: + type: string + required: + - resourceName + - restartPolicy + type: object + type: array + x-kubernetes-list-type: atomic + resources: + properties: + claims: + items: + properties: + name: + type: string + required: + - name + type: object + type: array + x-kubernetes-list-map-keys: + - name + x-kubernetes-list-type: map + limits: + additionalProperties: + anyOf: + - type: integer + - type: string + pattern: ^(\+|-)?(([0-9]+(\.[0-9]*)?)|(\.[0-9]+))(([KMGTPE]i)|[numkMGTPE]|([eE](\+|-)?(([0-9]+(\.[0-9]*)?)|(\.[0-9]+))))?$ + x-kubernetes-int-or-string: true + type: object + requests: + additionalProperties: + anyOf: + - type: integer + - type: string + pattern: ^(\+|-)?(([0-9]+(\.[0-9]*)?)|(\.[0-9]+))(([KMGTPE]i)|[numkMGTPE]|([eE](\+|-)?(([0-9]+(\.[0-9]*)?)|(\.[0-9]+))))?$ + x-kubernetes-int-or-string: true + type: object + type: object + restartPolicy: + type: string + runtimeVersion: + type: string + securityContext: + properties: + allowPrivilegeEscalation: + type: boolean + capabilities: + properties: + add: + items: + type: string + type: array + drop: + items: + type: string + type: array + type: object + privileged: + type: boolean + procMount: + type: string + readOnlyRootFilesystem: + type: boolean + runAsGroup: + format: int64 + type: integer + runAsNonRoot: + type: boolean + runAsUser: + format: int64 + type: integer + seLinuxOptions: + properties: + level: + type: string + role: + type: string + type: + type: string + user: + type: string + type: object + seccompProfile: + properties: + localhostProfile: + type: string + type: + type: string + required: + - type + type: object + windowsOptions: + properties: + gmsaCredentialSpec: + type: string + gmsaCredentialSpecName: + type: string + hostProcess: + type: boolean + runAsUserName: + type: string + type: object + type: object + startupProbe: + properties: + exec: + properties: + command: + items: + type: string + type: array + type: object + failureThreshold: + format: int32 + type: integer + grpc: + properties: + port: + format: int32 + type: integer + service: + type: string + required: + - port + type: object + httpGet: + properties: + host: + type: string + httpHeaders: + items: + properties: + name: + type: string + value: + type: string + required: + - name + - value + type: object + type: array + path: + type: string + port: + anyOf: + - type: integer + - type: string + x-kubernetes-int-or-string: true + scheme: + type: string + required: + - port + type: object + initialDelaySeconds: + format: int32 + type: integer + periodSeconds: + format: int32 + type: integer + successThreshold: + format: int32 + type: integer + tcpSocket: + properties: + host: + type: string + port: + anyOf: + - type: integer + - type: string + x-kubernetes-int-or-string: true + required: + - port + type: object + terminationGracePeriodSeconds: + format: int64 + type: integer + timeoutSeconds: + format: int32 + type: integer + type: object + stdin: + type: boolean + stdinOnce: + type: boolean + storage: + properties: + key: + type: string + parameters: + additionalProperties: + type: string + type: object + path: + type: string + schemaPath: + type: string + type: object + storageUri: + type: string + terminationMessagePath: + type: string + terminationMessagePolicy: + type: string + tty: + type: boolean + volumeDevices: + items: + properties: + devicePath: + type: string + name: + type: string + required: + - devicePath + - name + type: object + type: array + volumeMounts: + items: + properties: + mountPath: + type: string + mountPropagation: + type: string + name: + type: string + readOnly: + type: boolean + subPath: + type: string + subPathExpr: + type: string + required: + - mountPath + - name + type: object + type: array + workingDir: + type: string + type: object imagePullSecrets: items: properties: diff --git a/test/e2e/data/custom_model_input_v2.json b/test/e2e/data/custom_model_input_v2.json index b469dcbc9ef..7559180ffea 100644 --- a/test/e2e/data/custom_model_input_v2.json +++ b/test/e2e/data/custom_model_input_v2.json @@ -9,4 +9,4 @@ ] } ] -} \ No newline at end of file +} diff --git a/test/e2e/logger/test_raw_logger.py b/test/e2e/logger/test_raw_logger.py index fbe296f536a..7bd0ac5fdda 100644 --- a/test/e2e/logger/test_raw_logger.py +++ b/test/e2e/logger/test_raw_logger.py @@ -49,7 +49,8 @@ def test_kserve_logger(): isvc = V1beta1InferenceService(api_version=constants.KSERVE_V1BETA1, kind=constants.KSERVE_KIND, metadata=client.V1ObjectMeta( - name=msg_dumper, namespace=KSERVE_TEST_NAMESPACE), + name=msg_dumper, namespace=KSERVE_TEST_NAMESPACE, + annotations=annotations), spec=V1beta1InferenceServiceSpec(predictor=predictor)) kserve_client.create(isvc) @@ -60,7 +61,7 @@ def test_kserve_logger(): min_replicas=1, logger=V1beta1LoggerSpec( mode="all", - url="http://"+msg_dumper+"."+KSERVE_TEST_NAMESPACE+".svc.cluster.local" + url="http://"+msg_dumper+"-predictor."+KSERVE_TEST_NAMESPACE+".svc.cluster.local" ), sklearn=V1beta1SKLearnSpec( storage_uri='gs://kfserving-examples/models/sklearn/1.0/model', diff --git a/test/scripts/gh-actions/status-check.sh b/test/scripts/gh-actions/status-check.sh index a45a98796f6..5f7a1a7fe4d 100755 --- a/test/scripts/gh-actions/status-check.sh +++ b/test/scripts/gh-actions/status-check.sh @@ -2,7 +2,7 @@ sleep 10 echo "::group::Free Space" -df -T +df -hT echo "::endgroup::" echo "::group::List Docker Images"