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test: initial support for testing kubernetes support
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‎.github/workflows/kind.yml

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name: Kind
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on:
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push:
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branches: [ main ]
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pull_request:
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branches: [ main ]
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jobs:
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kind:
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runs-on: ${{ matrix.os }}
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strategy:
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matrix:
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os: [ubuntu-latest]
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node-version: [16.x]
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profile: [keep-it-simple, ray-autoscaler]
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steps:
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- uses: actions/checkout@v3
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- name: Create k8s Kind Cluster
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uses: helm/[email protected]
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with:
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install_only: true
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- name: Use Node.js ${{ matrix.node-version }}
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uses: actions/setup-node@v3
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with:
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node-version: ${{ matrix.node-version }}
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cache: 'npm'
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- name: Setup
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env:
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TERM: xterm
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run: ./tests/kind/setup-ci.sh
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- name: Run Test with profile ${{ matrix.profile }}
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env:
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EXECUTABLE_PATH: github-actions-production
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DEBUG_KUBERNETES: true
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RAY_STARTUP_PROBE_INITIAL_DELAY_SECONDS: 30
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run: ./tests/kind/run.sh ${{ matrix.profile }}

‎.github/workflows/test.yml renamed to ‎.github/workflows/ui.yml

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# This workflow will do a clean installation of node dependencies, cache/restore them, build the source code and run tests across different versions of node
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# For more information see: https://help.github.com/actions/language-and-framework-guides/using-nodejs-with-github-actions
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name: Tests
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name: UI
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on:
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push:
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branches: [ main ]
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jobs:
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test-with-prod-build:
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production:
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runs-on: ${{ matrix.os }}
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strategy:

‎package-lock.json

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‎package.json

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"pty:electron": "npm run pty:rebuild electron",
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"pty:nodejs": "npm run pty:rebuild node",
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"prepare": "cross-env husky install",
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"build:docker:cli": "npm run build:docker1 && docker build -f deploy/cli/Dockerfile -t ghcr.io/project-codeflare/codeflare-cli .",
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"build:docker:logs": "npm run build:docker1 && docker build -f deploy/log-aggregator/Dockerfile -t ghcr.io/project-codeflare/codeflare-log-aggregator .",
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"build:docker1": "if [ ! -n \"$FAST\" ]; then npm run build:docker0; fi",
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"build:docker0": "CLIENT_HOME=$PWD webpack-cli --mode=${MODE-production} --config node_modules/@kui-shell/webpack/headless-webpack.config.js",
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"build:docker:cli": "npm run build:headless-maybe && docker build -f deploy/cli/Dockerfile -t ghcr.io/project-codeflare/codeflare-cli .",
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"build:docker:logs": "npm run build:headless-maybe && docker build -f deploy/log-aggregator/Dockerfile -t ghcr.io/project-codeflare/codeflare-log-aggregator .",
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"build:headless-maybe": "if [ ! -n \"$FAST\" ]; then npm run build:headless; fi",
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"build:headless": "CLIENT_HOME=$PWD webpack-cli --mode=${MODE-production} --config node_modules/@kui-shell/webpack/headless-webpack.config.js",
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"docker:debug": "ENTRYPOINT=bash ./bin/codeflare -d",
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"build:electron:mac:amd64": "KUI_HEADLESS_WEBPACK=true KUI_LAUNCHER=$PWD/bin/codeflare PLATFORM=mac ARCH=x64 kui-build-electron",
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"build:electron:mac:arm64": "KUI_HEADLESS_WEBPACK=true KUI_LAUNCHER=$PWD/bin/codeflare PLATFORM=mac ARCH=arm64 kui-build-electron",
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"typescript": "4.7.4"
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},
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"dependencies": {
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"@guidebooks/store": "^0.0.1",
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"@guidebooks/store": "^0.1.1",
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"@kui-shell/client": "file:./plugins/plugin-client-default",
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"@kui-shell/core": "11.5.0-dev-20220807-191654",
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"@kui-shell/plugin-bash-like": "11.5.0-dev-20220807-191654",

‎tests/kind/config.yaml

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kind: Cluster
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apiVersion: kind.x-k8s.io/v1alpha4
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nodes:
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- role: control-plane
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- role: worker

‎tests/kind/inputs/qiskit/main.py

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import ray
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import numpy as np
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from qiskit.opflow import Z, I, X
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from qiskit.providers.basicaer import QasmSimulatorPy
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from qiskit.utils import QuantumInstance
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from qiskit.algorithms import VQE
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from qiskit.circuit.library import TwoLocal
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from qiskit.algorithms.optimizers import SLSQP
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from qiskit.providers import Backend
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@ray.remote
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def prep_problem():
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"""Prepare demo problem."""
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op = (
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(-1.052373245772859 * I ^ I)
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+ (0.39793742484318045 * I ^ Z)
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+ (-0.39793742484318045 * Z ^ I)
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+ (-0.01128010425623538 * Z ^ Z)
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+ (0.18093119978423156 * X ^ X)
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)
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initial_state = np.random.random(8)
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var_form = TwoLocal(
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rotation_blocks="ry",
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entanglement_blocks="cz"
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)
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return (op, var_form, initial_state)
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@ray.remote
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def optimize(problem):
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"""Optimization demo routine."""
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backend = QasmSimulatorPy()
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op, var_form, init_point = problem
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qi = QuantumInstance(backend, seed_transpiler=42, seed_simulator=42)
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ansatz = TwoLocal(rotation_blocks="ry", entanglement_blocks="cz")
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slsqp = SLSQP(maxiter=100)
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vqe = VQE(
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var_form,
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optimizer=slsqp,
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quantum_instance=qi,
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initial_point=init_point
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)
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result = vqe.compute_minimum_eigenvalue(op)
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print(f"Optimization result:")
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print(f" - eigenvalue: {result.eigenvalue}")
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print(f" - optimal value: {result.optimal_value}")
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print(f" - optinal parameters: {result.optimal_parameters}")
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return result
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@ray.remote
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def analyze(results):
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"""Analyzing demo results."""
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return min(results, key=lambda r: r.eigenvalue.real)
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if __name__ == "__main__":
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with ray.init():
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number_of_trials = 30
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workflow_graph = analyze.remote(
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results=ray.get([
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optimize.remote(
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problem=prep_problem.remote()
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)
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for _ in range(number_of_trials)
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])
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)
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result = ray.get(workflow_graph)
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print(f"Final result:")
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print(f" - eigenvalue: {result.eigenvalue}")
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print(f" - optimal value: {result.optimal_value}")
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print(f" - optinal parameters: {result.optimal_parameters}")

‎tests/kind/inputs/qiskit/requirements.txt

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qiskit==0.37.0

‎tests/kind/profiles/keep-it-simple

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{
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"name": "keep-it-simple",
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"creationTime": 1660657756574,
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"lastModifiedTime": 1660658307175,
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"lastUsedTime": 1660742079201,
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"choices": {
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"madwizard/apriori/use-gpu": "don't use gpus",
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"madwizard/apriori/arch": "x64",
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"madwizard/apriori/platform": "darwin",
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"madwizard/apriori/mac-installer": "Homebrew",
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"madwizard/apriori/in-terminal": "HTML",
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"Start a new Run####Connect Dashboard to an existing Run####Boot up a Cloud Computer####Shut down a Cloud Computer": "Start a new Run",
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"Training Demos####Fine Tuning Demos####Bring Your Own Code": "Bring Your Own Code",
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"BERT####MLFlow Demo####Tensorboard Demo": "MLFlow Demo",
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"Location of your working directory": "{\"Location of your working directory\":\"tests/kind/inputs/qiskit\"}",
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"Provide custom base image, if any": "{\"Provide custom base image, if any\":\"rayproject/ray:1.13.1-py37\"}",
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"AWS####IBM####My data is not stored in S3": "My data is not stored in S3",
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"Run Locally####Run on a Kubernetes Cluster": "Run on a Kubernetes Cluster",
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"expand(kubectl config get-contexts -o name, Kubernetes contexts)": "kind-codeflare-test",
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"expand([ -z ${KUBE_CONTEXT} ] && exit 1 || X=$(kubectl ${KUBE_CONTEXT_ARG} get ns -o name || oc ${KUBE_CONTEXT_ARG} get projects -o name); echo \"$X\" | sed -E 's#(namespace|project.project.openshift.io)/##' | grep -Ev 'openshift|kube-', Kubernetes namespaces)####Create a namespace": "default",
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"Number of CPUs####Number of GPUs####Minimum Workers####Maximum Workers####Worker Memory####Head Memory": "{\"Number of CPUs\":\"500m\",\"Number of GPUs\":\"0\",\"Minimum Workers\":\"1\",\"Maximum Workers\":\"1\",\"Worker Memory\":\"1Gi\",\"Head Memory\":\"1Gi\"}",
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"Keep It Simple####Use the Ray Autoscaler####Use the Multi-user Enhanced Kubernetes Scheduler": "Keep It Simple"
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}
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}

‎tests/kind/profiles/ray-autoscaler

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{
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"name": "ray-autoscaler",
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"creationTime": 1660657756574,
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"lastModifiedTime": 1660675440396,
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"lastUsedTime": 1660743373674,
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"choices": {
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"madwizard/apriori/use-gpu": "don't use gpus",
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"madwizard/apriori/arch": "x64",
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"madwizard/apriori/platform": "darwin",
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"madwizard/apriori/mac-installer": "Homebrew",
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"madwizard/apriori/in-terminal": "HTML",
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"Start a new Run####Connect Dashboard to an existing Run####Boot up a Cloud Computer####Shut down a Cloud Computer": "Start a new Run",
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"Training Demos####Fine Tuning Demos####Bring Your Own Code": "Bring Your Own Code",
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"BERT####MLFlow Demo####Tensorboard Demo": "MLFlow Demo",
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"Location of your working directory": "{\"Location of your working directory\":\"tests/kind/inputs/qiskit\"}",
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"Provide custom base image, if any": "{\"Provide custom base image, if any\":\"rayproject/ray:1.13.1-py37\"}",
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"AWS####IBM####My data is not stored in S3": "My data is not stored in S3",
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"Run Locally####Run on a Kubernetes Cluster": "Run on a Kubernetes Cluster",
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"expand(kubectl config get-contexts -o name, Kubernetes contexts)": "kind-codeflare-test",
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"expand([ -z ${KUBE_CONTEXT} ] && exit 1 || X=$(kubectl ${KUBE_CONTEXT_ARG} get ns -o name || oc ${KUBE_CONTEXT_ARG} get projects -o name); echo \"$X\" | sed -E 's#(namespace|project.project.openshift.io)/##' | grep -Ev 'openshift|kube-', Kubernetes namespaces)####Create a namespace": "default",
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"Number of CPUs####Number of GPUs####Minimum Workers####Maximum Workers####Worker Memory####Head Memory": "{\"Number of CPUs\":\"350m\",\"Number of GPUs\":\"0\",\"Minimum Workers\":\"1\",\"Maximum Workers\":\"1\",\"Worker Memory\":\"1Gi\",\"Head Memory\":\"1Gi\"}",
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"Keep It Simple####Use the Ray Autoscaler####Use the Multi-user Enhanced Kubernetes Scheduler": "Use the Ray Autoscaler"
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}
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}

‎tests/kind/run.sh

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#!/usr/bin/env bash
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SCRIPTDIR=$(cd $(dirname "$0") && pwd)
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. "$SCRIPTDIR"/values.sh
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export KUBECONFIG=$("$SCRIPTDIR"/setup.sh)
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echo "Using KUBECONFIG=$KUBECONFIG"
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echo "================NODE INFO================="
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kubectl get node -o custom-columns=CAPACITY:.status.capacity
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echo "=========================================="
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while getopts "ab:f:s:" opt
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do
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case $opt in
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a) FORCE_ALL=true; continue;;
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f) FORCE=$OPTARG; continue;;
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s) export GUIDEBOOK_STORE=$OPTARG; continue;;
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b) GUIDEBOOK=$OPTARG; continue;;
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*) continue;;
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esac
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done
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shift $((OPTIND-1))
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mkdir -p "$MWPROFILES_PATH"
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function run {
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local profile=$1
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local guidebook=${2-$GUIDEBOOK}
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local yes=$([ -z "$FORCE_ALL" ] && [ "$FORCE" != "$profile" ] && [ -f "$MWPROFILES_PATH/$profile" ] && echo "--yes" || echo "")
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echo "Running with profile $profile"
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"$ROOT"/bin/codeflare -p $profile $yes $guidebook
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}
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function logpoller {
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local type=$1
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sleep 10
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while true; do
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kubectl logs -l ray-node-type=$type -f
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sleep 3
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done
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}
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if [ -n "$DEBUG_KUBERNETES" ]; then
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logpoller head &
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HEAD_POLLER_PID=$!
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logpoller worker &
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WORKER_POLLER_PID=$!
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fi
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if [ -n "$1" ]; then
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OUTPUT=$(mktemp)
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run "$1" | tee $OUTPUT
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grep succeeded $OUTPUT
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else
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idx=1
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for i in "$SCRIPTDIR"/profiles/*; do
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profile=$(basename $i)
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if [ $idx -gt 1 ]; then
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echo "Tearing down previous setup"
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run $profile ml/ray/stop/kubernetes
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fi
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OUTPUT=$(mktemp)
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run $profile | tee $OUTPUT
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grep succeeded $OUTPUT
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idx=$((idx + 1))
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done
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fi
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if [ -n "$HEAD_POLLER_PID" ]; then
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kill $HEAD_POLLER_PID
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fi
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if [ -n "$WORKER_POLLER_PID" ]; then
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kill $WORKER_POLLER_PID
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fi

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