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compose_pipeline.yaml
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compose_pipeline.yaml
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# PIPELINE DEFINITION
# Name: compose
# Description: Compose of kubeflow, katib and spark
# Inputs:
# params_json_file_path: str [Default: '/mnt/params/params.json']
# params_pvc_name: str [Default: 'params-pvc']
# Outputs:
# parse-input-json-knn_input_metrics: system.Metrics
# parse-input-json-lr_input_metrics: system.Metrics
# parse-input-json-random_forest_input_metrics: system.Metrics
# parse-input-json-xgboost_input_metrics: system.Metrics
# run-knn-katib-experiment-best_params_metrics: system.Metrics
# run-lr-katib-experiment-best_params_metrics: system.Metrics
# run-random-forest-katib-experiment-best_params_metrics: system.Metrics
# run-xgboost-katib-experiment-best_params_metrics: system.Metrics
components:
comp-load-file-from-nas-to-minio:
executorLabel: exec-load-file-from-nas-to-minio
inputDefinitions:
parameters:
x_test_input_path:
parameterType: STRING
x_train_input_path:
parameterType: STRING
y_test_input_path:
parameterType: STRING
y_train_input_path:
parameterType: STRING
outputDefinitions:
artifacts:
x_test_output:
artifactType:
schemaTitle: system.Dataset
schemaVersion: 0.0.1
x_train_output:
artifactType:
schemaTitle: system.Dataset
schemaVersion: 0.0.1
y_test_output:
artifactType:
schemaTitle: system.Dataset
schemaVersion: 0.0.1
y_train_output:
artifactType:
schemaTitle: system.Dataset
schemaVersion: 0.0.1
comp-parse-input-json:
executorLabel: exec-parse-input-json
inputDefinitions:
parameters:
json_file_path:
parameterType: STRING
outputDefinitions:
artifacts:
knn_input_metrics:
artifactType:
schemaTitle: system.Metrics
schemaVersion: 0.0.1
lr_input_metrics:
artifactType:
schemaTitle: system.Metrics
schemaVersion: 0.0.1
random_forest_input_metrics:
artifactType:
schemaTitle: system.Metrics
schemaVersion: 0.0.1
xgboost_input_metrics:
artifactType:
schemaTitle: system.Metrics
schemaVersion: 0.0.1
comp-run-knn-katib-experiment:
executorLabel: exec-run-knn-katib-experiment
inputDefinitions:
artifacts:
input_params_metrics:
artifactType:
schemaTitle: system.Metrics
schemaVersion: 0.0.1
outputDefinitions:
artifacts:
best_params_metrics:
artifactType:
schemaTitle: system.Metrics
schemaVersion: 0.0.1
comp-run-knn-train:
executorLabel: exec-run-knn-train
inputDefinitions:
artifacts:
best_params_metrics:
artifactType:
schemaTitle: system.Metrics
schemaVersion: 0.0.1
x_test:
artifactType:
schemaTitle: system.Dataset
schemaVersion: 0.0.1
x_train:
artifactType:
schemaTitle: system.Dataset
schemaVersion: 0.0.1
y_test:
artifactType:
schemaTitle: system.Dataset
schemaVersion: 0.0.1
y_train:
artifactType:
schemaTitle: system.Dataset
schemaVersion: 0.0.1
outputDefinitions:
artifacts:
file:
artifactType:
schemaTitle: system.Artifact
schemaVersion: 0.0.1
model:
artifactType:
schemaTitle: system.Model
schemaVersion: 0.0.1
comp-run-lr-katib-experiment:
executorLabel: exec-run-lr-katib-experiment
inputDefinitions:
artifacts:
input_params_metrics:
artifactType:
schemaTitle: system.Metrics
schemaVersion: 0.0.1
outputDefinitions:
artifacts:
best_params_metrics:
artifactType:
schemaTitle: system.Metrics
schemaVersion: 0.0.1
comp-run-lr-train:
executorLabel: exec-run-lr-train
inputDefinitions:
artifacts:
best_params_metrics:
artifactType:
schemaTitle: system.Metrics
schemaVersion: 0.0.1
x_test:
artifactType:
schemaTitle: system.Dataset
schemaVersion: 0.0.1
x_train:
artifactType:
schemaTitle: system.Dataset
schemaVersion: 0.0.1
y_test:
artifactType:
schemaTitle: system.Dataset
schemaVersion: 0.0.1
y_train:
artifactType:
schemaTitle: system.Dataset
schemaVersion: 0.0.1
outputDefinitions:
artifacts:
file:
artifactType:
schemaTitle: system.Artifact
schemaVersion: 0.0.1
model:
artifactType:
schemaTitle: system.Model
schemaVersion: 0.0.1
comp-run-random-forest-katib-experiment:
executorLabel: exec-run-random-forest-katib-experiment
inputDefinitions:
artifacts:
input_params_metrics:
artifactType:
schemaTitle: system.Metrics
schemaVersion: 0.0.1
outputDefinitions:
artifacts:
best_params_metrics:
artifactType:
schemaTitle: system.Metrics
schemaVersion: 0.0.1
comp-run-random-forest-train:
executorLabel: exec-run-random-forest-train
inputDefinitions:
artifacts:
best_params_metrics:
artifactType:
schemaTitle: system.Metrics
schemaVersion: 0.0.1
x_test:
artifactType:
schemaTitle: system.Dataset
schemaVersion: 0.0.1
x_train:
artifactType:
schemaTitle: system.Dataset
schemaVersion: 0.0.1
y_test:
artifactType:
schemaTitle: system.Dataset
schemaVersion: 0.0.1
y_train:
artifactType:
schemaTitle: system.Dataset
schemaVersion: 0.0.1
outputDefinitions:
artifacts:
file:
artifactType:
schemaTitle: system.Artifact
schemaVersion: 0.0.1
model:
artifactType:
schemaTitle: system.Model
schemaVersion: 0.0.1
comp-run-xgboost-katib-experiment:
executorLabel: exec-run-xgboost-katib-experiment
inputDefinitions:
artifacts:
input_params_metrics:
artifactType:
schemaTitle: system.Metrics
schemaVersion: 0.0.1
outputDefinitions:
artifacts:
best_params_metrics:
artifactType:
schemaTitle: system.Metrics
schemaVersion: 0.0.1
comp-run-xgboost-train:
executorLabel: exec-run-xgboost-train
inputDefinitions:
artifacts:
best_params_metrics:
artifactType:
schemaTitle: system.Metrics
schemaVersion: 0.0.1
x_test:
artifactType:
schemaTitle: system.Dataset
schemaVersion: 0.0.1
x_train:
artifactType:
schemaTitle: system.Dataset
schemaVersion: 0.0.1
y_test:
artifactType:
schemaTitle: system.Dataset
schemaVersion: 0.0.1
y_train:
artifactType:
schemaTitle: system.Dataset
schemaVersion: 0.0.1
outputDefinitions:
artifacts:
file:
artifactType:
schemaTitle: system.Artifact
schemaVersion: 0.0.1
model:
artifactType:
schemaTitle: system.Model
schemaVersion: 0.0.1
deploymentSpec:
executors:
exec-load-file-from-nas-to-minio:
container:
args:
- --executor_input
- '{{$}}'
- --function_to_execute
- load_file_from_nas_to_minio
command:
- sh
- -c
- "\nif ! [ -x \"$(command -v pip)\" ]; then\n python3 -m ensurepip ||\
\ python3 -m ensurepip --user || apt-get install python3-pip\nfi\n\nPIP_DISABLE_PIP_VERSION_CHECK=1\
\ python3 -m pip install --quiet --no-warn-script-location 'kfp==2.9.0'\
\ '--no-deps' 'typing-extensions>=3.7.4,<5; python_version<\"3.9\"' &&\
\ python3 -m pip install --quiet --no-warn-script-location 'pandas' &&\
\ \"$0\" \"$@\"\n"
- sh
- -ec
- 'program_path=$(mktemp -d)
printf "%s" "$0" > "$program_path/ephemeral_component.py"
_KFP_RUNTIME=true python3 -m kfp.dsl.executor_main --component_module_path "$program_path/ephemeral_component.py" "$@"
'
- "\nimport kfp\nfrom kfp import dsl\nfrom kfp.dsl import *\nfrom typing import\
\ *\n\ndef load_file_from_nas_to_minio(\n x_train_input_path: str, \n\
\ x_test_input_path: str, \n y_train_input_path: str, \n y_test_input_path:\
\ str, \n x_train_output: Output[Dataset], \n x_test_output: Output[Dataset],\
\ \n y_train_output: Output[Dataset], \n y_test_output: Output[Dataset]\n\
):\n import pandas as pd\n\n df = pd.read_csv(x_train_input_path)\n\
\ df.to_csv(x_train_output.path, index=False)\n\n df = pd.read_csv(x_test_input_path)\n\
\ df.to_csv(x_test_output.path, index=False)\n\n df = pd.read_csv(y_train_input_path)\n\
\ df.to_csv(y_train_output.path, index=False)\n\n df = pd.read_csv(y_test_input_path)\n\
\ df.to_csv(y_test_output.path, index=False)\n\n"
image: python:3.10-slim
exec-parse-input-json:
container:
args:
- --executor_input
- '{{$}}'
- --function_to_execute
- parse_input_json
command:
- sh
- -c
- "\nif ! [ -x \"$(command -v pip)\" ]; then\n python3 -m ensurepip ||\
\ python3 -m ensurepip --user || apt-get install python3-pip\nfi\n\nPIP_DISABLE_PIP_VERSION_CHECK=1\
\ python3 -m pip install --quiet --no-warn-script-location 'kfp==2.9.0'\
\ '--no-deps' 'typing-extensions>=3.7.4,<5; python_version<\"3.9\"' && \"\
$0\" \"$@\"\n"
- sh
- -ec
- 'program_path=$(mktemp -d)
printf "%s" "$0" > "$program_path/ephemeral_component.py"
_KFP_RUNTIME=true python3 -m kfp.dsl.executor_main --component_module_path "$program_path/ephemeral_component.py" "$@"
'
- "\nimport kfp\nfrom kfp import dsl\nfrom kfp.dsl import *\nfrom typing import\
\ *\n\ndef parse_input_json(\n json_file_path: str, \n xgboost_input_metrics:\
\ Output[Metrics], \n random_forest_input_metrics: Output[Metrics], \n\
\ knn_input_metrics: Output[Metrics],\n lr_input_metrics: Output[Metrics]\n\
):\n import json\n\n def log_metric(metrics: Metrics, input_dict:\
\ dict):\n for key in input_dict:\n if key == \"method\"\
:\n continue\n else:\n metrics.log_metric(key,\
\ input_dict.get(key))\n\n with open(file=json_file_path, mode='r', encoding='utf8')\
\ as file:\n input_dict_arr: list[dict] = json.load(file)\n\n \
\ for input_dict in input_dict_arr:\n if input_dict[\"method\"] ==\
\ \"xgboost\":\n log_metric(xgboost_input_metrics, input_dict)\n\
\ elif input_dict[\"method\"] == \"random_forest\":\n \
\ log_metric(random_forest_input_metrics, input_dict)\n elif input_dict[\"\
method\"] == \"knn\":\n log_metric(knn_input_metrics, input_dict)\n\
\ elif input_dict[\"method\"] == \"lr\":\n log_metric(lr_input_metrics,\
\ input_dict)\n else:\n continue\n\n"
image: python:3.10-slim
exec-run-knn-katib-experiment:
container:
args:
- --executor_input
- '{{$}}'
- --function_to_execute
- run_knn_katib_experiment
command:
- sh
- -c
- "\nif ! [ -x \"$(command -v pip)\" ]; then\n python3 -m ensurepip ||\
\ python3 -m ensurepip --user || apt-get install python3-pip\nfi\n\nPIP_DISABLE_PIP_VERSION_CHECK=1\
\ python3 -m pip install --quiet --no-warn-script-location 'kfp==2.9.0'\
\ '--no-deps' 'typing-extensions>=3.7.4,<5; python_version<\"3.9\"' &&\
\ python3 -m pip install --quiet --no-warn-script-location 'kubeflow-katib==0.17.0'\
\ && \"$0\" \"$@\"\n"
- sh
- -ec
- 'program_path=$(mktemp -d)
printf "%s" "$0" > "$program_path/ephemeral_component.py"
_KFP_RUNTIME=true python3 -m kfp.dsl.executor_main --component_module_path "$program_path/ephemeral_component.py" "$@"
'
- "\nimport kfp\nfrom kfp import dsl\nfrom kfp.dsl import *\nfrom typing import\
\ *\n\ndef run_knn_katib_experiment(\n input_params_metrics: Input[Metrics],\
\ \n best_params_metrics: Output[Metrics]\n):\n from kubeflow.katib\
\ import KatibClient\n from kubernetes.client import V1ObjectMeta\n \
\ from kubeflow.katib import V1beta1Experiment\n from kubeflow.katib\
\ import V1beta1AlgorithmSpec\n from kubeflow.katib import V1beta1ObjectiveSpec\n\
\ from kubeflow.katib import V1beta1FeasibleSpace\n from kubeflow.katib\
\ import V1beta1ExperimentSpec\n from kubeflow.katib import V1beta1ObjectiveSpec\n\
\ from kubeflow.katib import V1beta1ParameterSpec\n from kubeflow.katib\
\ import V1beta1TrialTemplate\n from kubeflow.katib import V1beta1TrialParameterSpec\n\
\n from datetime import datetime, timezone, timedelta\n\n dt_str =\
\ datetime.now(timezone(timedelta(hours=8))).strftime(\"%-Y-%m-%d-%H-%M-%S\"\
)\n\n experiment_name = \"knn-\" + dt_str.replace(\"_\", \"-\")\n \
\ experiment_namespace = input_params_metrics.metadata.get(\"experiment_namespace\"\
)\n\n if experiment_name is None or experiment_namespace is None:\n \
\ raise ValueError(\"Both experiment_name and experiment namespace\
\ needs to be a string!\")\n\n metadata = V1ObjectMeta(\n name=experiment_name,\
\ \n namespace=experiment_namespace\n )\n\n algorithm_spec\
\ = V1beta1AlgorithmSpec(\n algorithm_name=\"random\"\n )\n\n\
\ objective_spec = V1beta1ObjectiveSpec(\n type=\"maximize\",\n\
\ goal= 0.99,\n objective_metric_name=\"accuracy\",\n )\n\
\n n_neighbors_min = input_params_metrics.metadata.get(\"n_neighbors_min\"\
)\n n_neighbors_max = input_params_metrics.metadata.get(\"n_neighbors_max\"\
)\n n_neighbors_step = input_params_metrics.metadata.get(\"n_neighbors_step\"\
)\n\n if n_neighbors_min is None or n_neighbors_max is None or n_neighbors_step\
\ is None:\n raise ValueError(\"All n_neighbors_min, n_neighbors_max\
\ and n_neighbors_step cannot be null!\")\n\n try:\n n_neighbors_min\
\ = int(n_neighbors_min)\n n_neighbors_max = int(n_neighbors_max)\n\
\ n_neighbors_step = int(n_neighbors_step)\n except ValueError:\n\
\ raise ValueError(\"All n_neighbors_min, n_neighbors_max and n_neighbors_step\
\ needs to be a int!\")\n\n if n_neighbors_min % 2 != 1 or n_neighbors_max\
\ % 2 != 1 or n_neighbors_step % 2 != 0:\n raise ValueError(\"N neighbors\
\ needs to be an odd number!\")\n\n parameters = [\n V1beta1ParameterSpec(\n\
\ name=\"nn\",\n parameter_type=\"int\",\n \
\ feasible_space=V1beta1FeasibleSpace(\n min=str(n_neighbors_min),\n\
\ max=str(n_neighbors_max), \n step=str(n_neighbors_step)\n\
\ )\n )\n ]\n\n docker_image_name = input_params_metrics.metadata.get(\"\
docker_image_name\")\n if docker_image_name is None:\n raise ValueError(\"\
Docker image name cannot be null!\")\n\n random_state = input_params_metrics.metadata.get(\"\
random_state\")\n if random_state is None:\n random_state = 42\n\
\ else:\n try:\n random_state = int(random_state)\n\
\ except ValueError:\n raise ValueError(\"Random state\
\ needs to be an int!\")\n\n x_train_path = input_params_metrics.metadata.get(\"\
x_train_path\")\n x_test_path = input_params_metrics.metadata.get(\"\
x_test_path\")\n y_train_path = input_params_metrics.metadata.get(\"\
y_train_path\")\n y_test_path = input_params_metrics.metadata.get(\"\
y_test_path\")\n\n train_container = {\n \"name\": \"training-container\"\
,\n \"image\": f\"docker.io/{docker_image_name}\",\n \"command\"\
: [\n \"python3\",\n \"/opt/knn/train.py\",\n \
\ \"--nn=${trialParameters.nNeighbors}\",\n f\"--rs={random_state}\"\
,\n f\"--x_train_path={x_train_path}\",\n f\"--x_test_path={x_test_path}\"\
,\n f\"--y_train_path={y_train_path}\",\n f\"--y_test_path={y_test_path}\"\
,\n f\"--save_model=false\",\n f\"--model_folder_path=models\"\
\n ]\n }\n\n template_spec = {\n \"containers\": [\n\
\ train_container\n ],\n \"restartPolicy\": \"\
Never\"\n }\n\n volumes = []\n volumeMounts = []\n\n datasets_from_pvc\
\ = input_params_metrics.metadata.get(\"datasets_from_pvc\")\n datasets_pvc_name\
\ = input_params_metrics.metadata.get(\"datasets_pvc_name\")\n datasets_pvc_mount_path\
\ = input_params_metrics.metadata.get(\"datasets_pvc_mount_path\")\n\n \
\ if datasets_from_pvc is True:\n if datasets_pvc_name is None\
\ or datasets_pvc_mount_path is None:\n raise ValueError(\"Both\
\ datasets_pvc_name and datasets_pvc_mount_path cannot be null\")\n\n \
\ volumes.append({\n \"name\": \"datasets\", \n \
\ \"persistentVolumeClaim\": {\n \"claimName\": datasets_pvc_name\n\
\ }\n })\n volumeMounts.append({\n \"\
name\": \"datasets\", \n \"mountPath\": datasets_pvc_mount_path\n\
\ })\n\n '''\n if save_model is True:\n volumes.append({\n\
\ \"name\": \"models\", \n \"persistentVolumeClaim\"\
: {\n \"claimName\": models_pvc_name\n }\n \
\ })\n volumeMounts.append({\n \"name\": \"models\"\
, \n \"mountPath\": \"/opt/rfc/models\"\n })\n\n if\
\ datasets_from_pvc is True or save_model is True:\n train_container[\"\
volumeMounts\"] = volumeMounts\n template_spec[\"volumes\"] = volumes\n\
\ '''\n\n trial_spec={\n \"apiVersion\": \"batch/v1\",\n \
\ \"kind\": \"Job\",\n \"spec\": {\n \"template\"\
: {\n \"metadata\": {\n \"annotations\"\
: {\n \"sidecar.istio.io/inject\": \"false\"\n \
\ }\n },\n \"spec\": template_spec\n\
\ }\n }\n }\n\n trial_template=V1beta1TrialTemplate(\n\
\ primary_container_name=\"training-container\",\n trial_parameters=[\n\
\ V1beta1TrialParameterSpec(\n name=\"nNeighbors\"\
,\n description=\"N neighbors for the training model\",\n\
\ reference=\"nn\"\n )\n ],\n trial_spec=trial_spec,\n\
\ retain=True\n )\n\n max_trial_counts = input_params_metrics.metadata.get(\"\
max_trial_counts\")\n max_failed_trial_counts = input_params_metrics.metadata.get(\"\
max_failed_trial_counts\")\n parallel_trial_counts = input_params_metrics.metadata.get(\"\
parallel_trial_counts\")\n\n if max_failed_trial_counts is None or max_failed_trial_counts\
\ is None or parallel_trial_counts is None:\n raise ValueError(\"\
All max_trial_counts, max_failed_trial_counts and parallel_trial_counts\
\ cannot be null!\")\n\n try:\n max_trial_counts = int(max_trial_counts)\n\
\ max_failed_trial_counts = int(max_failed_trial_counts)\n \
\ parallel_trial_counts = int(parallel_trial_counts)\n except ValueError:\n\
\ raise ValueError(\"All max_trial_counts, max_failed_trial_counts\
\ and needs to be an int!\")\n\n experiment = V1beta1Experiment(\n \
\ api_version=\"kubeflow.org/v1beta1\",\n kind=\"Experiment\"\
,\n metadata=metadata,\n spec=V1beta1ExperimentSpec(\n \
\ max_trial_count=max_trial_counts,\n parallel_trial_count=parallel_trial_counts,\n\
\ max_failed_trial_count=max_failed_trial_counts,\n \
\ algorithm=algorithm_spec,\n objective=objective_spec,\n \
\ parameters=parameters,\n trial_template=trial_template,\n\
\ )\n )\n\n client_namespace = input_params_metrics.metadata.get(\"\
client_namespace\")\n if client_namespace is None:\n raise ValueError(\"\
Client namespace cannot be null!\")\n\n client = KatibClient(namespace=client_namespace)\n\
\ client.create_experiment(experiment=experiment)\n client.wait_for_experiment_condition(name=experiment_name,\
\ namespace=experiment_namespace, timeout=3600)\n\n result = client.get_optimal_hyperparameters(name=experiment_name,\
\ namespace=experiment_namespace).to_dict()\n\n best_params_list = result[\"\
parameter_assignments\"]\n\n for params in best_params_list:\n \
\ name = params[\"name\"]\n value = params[\"value\"]\n\n \
\ if name == \"nn\":\n value = int(value)\n\n best_params_metrics.log_metric(metric=name,\
\ value=value)\n\n"
image: python:3.10-slim
exec-run-knn-train:
container:
args:
- --executor_input
- '{{$}}'
- --function_to_execute
- run_knn_train
command:
- sh
- -c
- "\nif ! [ -x \"$(command -v pip)\" ]; then\n python3 -m ensurepip ||\
\ python3 -m ensurepip --user || apt-get install python3-pip\nfi\n\nPIP_DISABLE_PIP_VERSION_CHECK=1\
\ python3 -m pip install --quiet --no-warn-script-location 'kfp==2.9.0'\
\ '--no-deps' 'typing-extensions>=3.7.4,<5; python_version<\"3.9\"' &&\
\ python3 -m pip install --quiet --no-warn-script-location 'pandas' 'scikit-learn'\
\ 'joblib' && \"$0\" \"$@\"\n"
- sh
- -ec
- 'program_path=$(mktemp -d)
printf "%s" "$0" > "$program_path/ephemeral_component.py"
_KFP_RUNTIME=true python3 -m kfp.dsl.executor_main --component_module_path "$program_path/ephemeral_component.py" "$@"
'
- "\nimport kfp\nfrom kfp import dsl\nfrom kfp.dsl import *\nfrom typing import\
\ *\n\ndef run_knn_train(\n best_params_metrics: Input[Metrics], \n \
\ x_train: Input[Dataset], \n x_test: Input[Dataset], \n y_train:\
\ Input[Dataset], \n y_test: Input[Dataset], \n model: Output[Model],\
\ \n file: Output[Artifact]\n):\n import pandas as pd\n import\
\ joblib\n import json\n\n from sklearn.metrics import accuracy_score\n\
\ from sklearn.neighbors import KNeighborsClassifier\n\n n_neighbors\
\ = best_params_metrics.metadata.get(\"nn\")\n\n x_train_df = pd.read_csv(x_train.path)\n\
\ y_train_df = pd.read_csv(y_train.path)\n x_test_df = pd.read_csv(x_test.path)\n\
\ y_test_df = pd.read_csv(y_test.path)\n\n knn_model = KNeighborsClassifier(\n\
\ n_neighbors=n_neighbors\n )\n knn_model.fit(x_train_df.values,\
\ y_train_df.values.ravel())\n\n y_pred = knn_model.predict(x_test_df.values)\n\
\ accuracy = accuracy_score(y_test_df.values, y_pred)\n\n # Save the\
\ model\n joblib.dump(model, model.path)\n\n data = {}\n data['accuracy']\
\ = accuracy\n data['model_path'] = model.path\n\n with open(file=file.path,\
\ mode='w', encoding='utf8') as file:\n json.dump(data, file, indent=4)\n\
\n"
image: python:3.10-slim
exec-run-lr-katib-experiment:
container:
args:
- --executor_input
- '{{$}}'
- --function_to_execute
- run_lr_katib_experiment
command:
- sh
- -c
- "\nif ! [ -x \"$(command -v pip)\" ]; then\n python3 -m ensurepip ||\
\ python3 -m ensurepip --user || apt-get install python3-pip\nfi\n\nPIP_DISABLE_PIP_VERSION_CHECK=1\
\ python3 -m pip install --quiet --no-warn-script-location 'kfp==2.9.0'\
\ '--no-deps' 'typing-extensions>=3.7.4,<5; python_version<\"3.9\"' &&\
\ python3 -m pip install --quiet --no-warn-script-location 'kubeflow-katib==0.17.0'\
\ && \"$0\" \"$@\"\n"
- sh
- -ec
- 'program_path=$(mktemp -d)
printf "%s" "$0" > "$program_path/ephemeral_component.py"
_KFP_RUNTIME=true python3 -m kfp.dsl.executor_main --component_module_path "$program_path/ephemeral_component.py" "$@"
'
- "\nimport kfp\nfrom kfp import dsl\nfrom kfp.dsl import *\nfrom typing import\
\ *\n\ndef run_lr_katib_experiment(\n input_params_metrics: Input[Metrics],\
\ \n best_params_metrics: Output[Metrics]\n):\n from kubeflow.katib\
\ import KatibClient\n from kubernetes.client import V1ObjectMeta\n \
\ from kubeflow.katib import V1beta1Experiment\n from kubeflow.katib\
\ import V1beta1AlgorithmSpec\n from kubeflow.katib import V1beta1ObjectiveSpec\n\
\ from kubeflow.katib import V1beta1FeasibleSpace\n from kubeflow.katib\
\ import V1beta1ExperimentSpec\n from kubeflow.katib import V1beta1ObjectiveSpec\n\
\ from kubeflow.katib import V1beta1ParameterSpec\n from kubeflow.katib\
\ import V1beta1TrialTemplate\n from kubeflow.katib import V1beta1TrialParameterSpec\n\
\n from datetime import datetime, timezone, timedelta\n\n dt_str =\
\ datetime.now(timezone(timedelta(hours=8))).strftime(\"%-Y-%m-%d-%H-%M-%S\"\
)\n\n experiment_name = \"lr-\" + dt_str.replace(\"_\", \"-\")\n experiment_namespace\
\ = input_params_metrics.metadata.get(\"experiment_namespace\")\n\n if\
\ experiment_name is None or experiment_namespace is None:\n raise\
\ ValueError(\"Both experiment_name and experiment namespace needs to be\
\ a string!\")\n\n metadata = V1ObjectMeta(\n name=experiment_name,\
\ \n namespace=experiment_namespace\n )\n\n algorithm_spec\
\ = V1beta1AlgorithmSpec(\n algorithm_name=\"random\"\n )\n\n\
\ objective_spec = V1beta1ObjectiveSpec(\n type=\"maximize\",\n\
\ goal= 0.99,\n objective_metric_name=\"accuracy\",\n )\n\
\n iterators_min = input_params_metrics.metadata.get(\"iterators_min\"\
)\n iterators_max = input_params_metrics.metadata.get(\"iterators_max\"\
)\n iterators_step = input_params_metrics.metadata.get(\"iterators_step\"\
)\n\n if iterators_min is None or iterators_max is None or iterators_step\
\ is None:\n raise ValueError(\"All iterators_min, iterators_max\
\ and iterators_step cannot be null!\")\n\n try:\n iterators_min\
\ = int(iterators_min)\n iterators_max = int(iterators_max)\n \
\ iterators_step = int(iterators_step)\n except ValueError:\n \
\ raise ValueError(\"All iterators_min, iterators_max and iterators_step\
\ needs to be a int!\")\n\n parameters = [\n V1beta1ParameterSpec(\n\
\ name=\"it\",\n parameter_type=\"int\",\n \
\ feasible_space=V1beta1FeasibleSpace(\n min=str(iterators_min),\n\
\ max=str(iterators_max), \n step=str(iterators_step)\n\
\ )\n )\n ]\n\n docker_image_name = input_params_metrics.metadata.get(\"\
docker_image_name\")\n if docker_image_name is None:\n raise ValueError(\"\
Docker image name cannot be null!\")\n\n random_state = input_params_metrics.metadata.get(\"\
random_state\")\n if random_state is None:\n random_state = 42\n\
\ else:\n try:\n random_state = int(random_state)\n\
\ except ValueError:\n raise ValueError(\"Random state\
\ needs to be an int!\")\n\n x_train_path = input_params_metrics.metadata.get(\"\
x_train_path\")\n x_test_path = input_params_metrics.metadata.get(\"\
x_test_path\")\n y_train_path = input_params_metrics.metadata.get(\"\
y_train_path\")\n y_test_path = input_params_metrics.metadata.get(\"\
y_test_path\")\n\n train_container = {\n \"name\": \"training-container\"\
,\n \"image\": f\"docker.io/{docker_image_name}\",\n \"command\"\
: [\n \"python3\",\n \"/opt/lr/train.py\",\n \
\ \"--it=${trialParameters.iterators}\",\n f\"--rs={random_state}\"\
,\n f\"--x_train_path={x_train_path}\",\n f\"--x_test_path={x_test_path}\"\
,\n f\"--y_train_path={y_train_path}\",\n f\"--y_test_path={y_test_path}\"\
,\n f\"--save_model=false\",\n f\"--model_folder_path=models\"\
\n ]\n }\n\n template_spec = {\n \"containers\": [\n\
\ train_container\n ],\n \"restartPolicy\": \"\
Never\"\n }\n\n volumes = []\n volumeMounts = []\n\n datasets_from_pvc\
\ = input_params_metrics.metadata.get(\"datasets_from_pvc\")\n datasets_pvc_name\
\ = input_params_metrics.metadata.get(\"datasets_pvc_name\")\n datasets_pvc_mount_path\
\ = input_params_metrics.metadata.get(\"datasets_pvc_mount_path\")\n\n \
\ if datasets_from_pvc is True:\n if datasets_pvc_name is None\
\ or datasets_pvc_mount_path is None:\n raise ValueError(\"Both\
\ datasets_pvc_name and datasets_pvc_mount_path cannot be null\")\n\n \
\ volumes.append({\n \"name\": \"datasets\", \n \
\ \"persistentVolumeClaim\": {\n \"claimName\": datasets_pvc_name\n\
\ }\n })\n volumeMounts.append({\n \"\
name\": \"datasets\", \n \"mountPath\": datasets_pvc_mount_path\n\
\ })\n\n '''\n if save_model is True:\n volumes.append({\n\
\ \"name\": \"models\", \n \"persistentVolumeClaim\"\
: {\n \"claimName\": models_pvc_name\n }\n \
\ })\n volumeMounts.append({\n \"name\": \"models\"\
, \n \"mountPath\": \"/opt/lr/models\"\n })\n\n if\
\ datasets_from_pvc is True or save_model is True:\n train_container[\"\
volumeMounts\"] = volumeMounts\n template_spec[\"volumes\"] = volumes\n\
\ '''\n\n trial_spec={\n \"apiVersion\": \"batch/v1\",\n \
\ \"kind\": \"Job\",\n \"spec\": {\n \"template\"\
: {\n \"metadata\": {\n \"annotations\"\
: {\n \"sidecar.istio.io/inject\": \"false\"\n \
\ }\n },\n \"spec\": template_spec\n\
\ }\n }\n }\n\n trial_template=V1beta1TrialTemplate(\n\
\ primary_container_name=\"training-container\",\n trial_parameters=[\n\
\ V1beta1TrialParameterSpec(\n name=\"iterators\"\
,\n description=\"iterators for the training model\",\n \
\ reference=\"it\"\n )\n ],\n trial_spec=trial_spec,\n\
\ retain=True\n )\n\n max_trial_counts = input_params_metrics.metadata.get(\"\
max_trial_counts\")\n max_failed_trial_counts = input_params_metrics.metadata.get(\"\
max_failed_trial_counts\")\n parallel_trial_counts = input_params_metrics.metadata.get(\"\
parallel_trial_counts\")\n\n if max_failed_trial_counts is None or max_failed_trial_counts\
\ is None or parallel_trial_counts is None:\n raise ValueError(\"\
All max_trial_counts, max_failed_trial_counts and parallel_trial_counts\
\ cannot be null!\")\n\n try:\n max_trial_counts = int(max_trial_counts)\n\
\ max_failed_trial_counts = int(max_failed_trial_counts)\n \
\ parallel_trial_counts = int(parallel_trial_counts)\n except ValueError:\n\
\ raise ValueError(\"All max_trial_counts, max_failed_trial_counts\
\ and needs to be an int!\")\n\n experiment = V1beta1Experiment(\n \
\ api_version=\"kubeflow.org/v1beta1\",\n kind=\"Experiment\"\
,\n metadata=metadata,\n spec=V1beta1ExperimentSpec(\n \
\ max_trial_count=max_trial_counts,\n parallel_trial_count=parallel_trial_counts,\n\
\ max_failed_trial_count=max_failed_trial_counts,\n \
\ algorithm=algorithm_spec,\n objective=objective_spec,\n \
\ parameters=parameters,\n trial_template=trial_template,\n\
\ )\n )\n\n client_namespace = input_params_metrics.metadata.get(\"\
client_namespace\")\n if client_namespace is None:\n raise ValueError(\"\
Client namespace cannot be null!\")\n\n client = KatibClient(namespace=client_namespace)\n\
\ client.create_experiment(experiment=experiment)\n client.wait_for_experiment_condition(name=experiment_name,\
\ namespace=experiment_namespace, timeout=3600)\n\n result = client.get_optimal_hyperparameters(name=experiment_name,\
\ namespace=experiment_namespace).to_dict()\n\n best_params_list = result[\"\
parameter_assignments\"]\n\n for params in best_params_list:\n \
\ name = params[\"name\"]\n value = params[\"value\"]\n\n \
\ if name == \"it\":\n value = int(value)\n\n best_params_metrics.log_metric(metric=name,\
\ value=value)\n\n"
image: python:3.10-slim
exec-run-lr-train:
container:
args:
- --executor_input
- '{{$}}'
- --function_to_execute
- run_lr_train
command:
- sh
- -c
- "\nif ! [ -x \"$(command -v pip)\" ]; then\n python3 -m ensurepip ||\
\ python3 -m ensurepip --user || apt-get install python3-pip\nfi\n\nPIP_DISABLE_PIP_VERSION_CHECK=1\
\ python3 -m pip install --quiet --no-warn-script-location 'kfp==2.9.0'\
\ '--no-deps' 'typing-extensions>=3.7.4,<5; python_version<\"3.9\"' &&\
\ python3 -m pip install --quiet --no-warn-script-location 'pandas' 'scikit-learn'\
\ 'joblib' && \"$0\" \"$@\"\n"
- sh
- -ec
- 'program_path=$(mktemp -d)
printf "%s" "$0" > "$program_path/ephemeral_component.py"
_KFP_RUNTIME=true python3 -m kfp.dsl.executor_main --component_module_path "$program_path/ephemeral_component.py" "$@"
'
- "\nimport kfp\nfrom kfp import dsl\nfrom kfp.dsl import *\nfrom typing import\
\ *\n\ndef run_lr_train(\n best_params_metrics: Input[Metrics], \n \
\ x_train: Input[Dataset], \n x_test: Input[Dataset], \n y_train:\
\ Input[Dataset], \n y_test: Input[Dataset], \n model: Output[Model],\
\ \n file: Output[Artifact]\n):\n import pandas as pd\n import\
\ joblib\n import json\n\n from sklearn.metrics import accuracy_score\n\
\ from sklearn.linear_model import LogisticRegression\n\n iterators\
\ = best_params_metrics.metadata.get(\"it\")\n\n x_train_df = pd.read_csv(x_train.path)\n\
\ y_train_df = pd.read_csv(y_train.path)\n x_test_df = pd.read_csv(x_test.path)\n\
\ y_test_df = pd.read_csv(y_test.path)\n\n lr_model = LogisticRegression(\n\
\ random_state=0, \n max_iter=iterators\n )\n lr_model.fit(x_train_df.values,\
\ y_train_df.values.ravel())\n\n y_pred = lr_model.predict(x_test_df.values)\n\
\ accuracy = accuracy_score(y_test_df.values, y_pred)\n\n # Save the\
\ model\n joblib.dump(model, model.path)\n\n data = {}\n data['accuracy']\
\ = accuracy\n data['model_path'] = model.path\n\n with open(file=file.path,\
\ mode='w', encoding='utf8') as file:\n json.dump(data, file, indent=4)\n\
\n"
image: python:3.10-slim
exec-run-random-forest-katib-experiment:
container:
args:
- --executor_input
- '{{$}}'
- --function_to_execute
- run_random_forest_katib_experiment
command:
- sh
- -c
- "\nif ! [ -x \"$(command -v pip)\" ]; then\n python3 -m ensurepip ||\
\ python3 -m ensurepip --user || apt-get install python3-pip\nfi\n\nPIP_DISABLE_PIP_VERSION_CHECK=1\
\ python3 -m pip install --quiet --no-warn-script-location 'kfp==2.9.0'\
\ '--no-deps' 'typing-extensions>=3.7.4,<5; python_version<\"3.9\"' &&\
\ python3 -m pip install --quiet --no-warn-script-location 'kubeflow-katib==0.17.0'\
\ && \"$0\" \"$@\"\n"
- sh
- -ec
- 'program_path=$(mktemp -d)
printf "%s" "$0" > "$program_path/ephemeral_component.py"
_KFP_RUNTIME=true python3 -m kfp.dsl.executor_main --component_module_path "$program_path/ephemeral_component.py" "$@"
'
- "\nimport kfp\nfrom kfp import dsl\nfrom kfp.dsl import *\nfrom typing import\
\ *\n\ndef run_random_forest_katib_experiment(\n input_params_metrics:\
\ Input[Metrics], \n best_params_metrics: Output[Metrics]\n):\n from\
\ kubeflow.katib import KatibClient\n from kubernetes.client import V1ObjectMeta\n\
\ from kubeflow.katib import V1beta1Experiment\n from kubeflow.katib\
\ import V1beta1AlgorithmSpec\n from kubeflow.katib import V1beta1ObjectiveSpec\n\
\ from kubeflow.katib import V1beta1FeasibleSpace\n from kubeflow.katib\
\ import V1beta1ExperimentSpec\n from kubeflow.katib import V1beta1ObjectiveSpec\n\
\ from kubeflow.katib import V1beta1ParameterSpec\n from kubeflow.katib\
\ import V1beta1TrialTemplate\n from kubeflow.katib import V1beta1TrialParameterSpec\n\
\n from datetime import datetime, timezone, timedelta\n\n dt_str =\
\ datetime.now(timezone(timedelta(hours=8))).strftime(\"%-Y-%m-%d-%H-%M-%S\"\
)\n\n experiment_name = \"random-forest-\" + dt_str.replace(\"_\", \"\
-\")\n experiment_namespace = input_params_metrics.metadata.get(\"experiment_namespace\"\
)\n\n if experiment_name is None or experiment_namespace is None:\n \
\ raise ValueError(\"Both experiment_name and experiment namespace\
\ needs to be a string!\")\n\n metadata = V1ObjectMeta(\n name=experiment_name,\
\ \n namespace=experiment_namespace\n )\n\n algorithm_spec\
\ = V1beta1AlgorithmSpec(\n algorithm_name=\"random\"\n )\n\n\
\ objective_spec = V1beta1ObjectiveSpec(\n type=\"maximize\",\n\
\ goal= 0.99,\n objective_metric_name=\"accuracy\",\n )\n\
\n n_estimators_min = input_params_metrics.metadata.get(\"n_estimators_min\"\
)\n n_estimators_max = input_params_metrics.metadata.get(\"n_estimators_max\"\
)\n n_estimators_step = input_params_metrics.metadata.get(\"n_estimators_step\"\
)\n\n if n_estimators_min is None or n_estimators_max is None or n_estimators_step\
\ is None:\n raise ValueError(\"All n_estimators_min, n_estimators_max\
\ and n_estimators_step cannot be null!\")\n\n try:\n n_estimators_min\
\ = int(n_estimators_min)\n n_estimators_max = int(n_estimators_max)\n\
\ n_estimators_step = int(n_estimators_step)\n except ValueError:\n\
\ raise ValueError(\"All n_estimators_min, n_estimators_max and n_estimators_step\
\ needs to be a float!\")\n\n parameters = [\n V1beta1ParameterSpec(\n\
\ name=\"ne\",\n parameter_type=\"int\",\n \
\ feasible_space=V1beta1FeasibleSpace(\n min=str(n_estimators_min),\n\
\ max=str(n_estimators_max), \n step=str(n_estimators_step)\n\
\ ),\n )\n ]\n\n docker_image_name = input_params_metrics.metadata.get(\"\
docker_image_name\")\n if docker_image_name is None:\n raise ValueError(\"\
Docker image name cannot be null!\")\n\n random_state = input_params_metrics.metadata.get(\"\
random_state\")\n if random_state is None:\n random_state = 42\n\
\ else:\n try:\n random_state = int(random_state)\n\
\ except ValueError:\n raise ValueError(\"Random state\
\ needs to be an int!\")\n\n x_train_path = input_params_metrics.metadata.get(\"\
x_train_path\")\n x_test_path = input_params_metrics.metadata.get(\"\
x_test_path\")\n y_train_path = input_params_metrics.metadata.get(\"\
y_train_path\")\n y_test_path = input_params_metrics.metadata.get(\"\
y_test_path\")\n\n train_container = {\n \"name\": \"training-container\"\
,\n \"image\": f\"docker.io/{docker_image_name}\",\n \"command\"\
: [\n \"python3\",\n \"/opt/rfc/train.py\",\n \
\ \"--ne=${trialParameters.nEstimators}\",\n f\"--rs={random_state}\"\
,\n f\"--x_train_path={x_train_path}\",\n f\"--x_test_path={x_test_path}\"\
,\n f\"--y_train_path={y_train_path}\",\n f\"--y_test_path={y_test_path}\"\
,\n f\"--save_model=false\",\n f\"--model_folder_path=models\"\
\n ]\n }\n\n template_spec = {\n \"containers\": [\n\
\ train_container\n ],\n \"restartPolicy\": \"\
Never\"\n }\n\n volumes = []\n volumeMounts = []\n\n datasets_from_pvc\
\ = input_params_metrics.metadata.get(\"datasets_from_pvc\")\n datasets_pvc_name\
\ = input_params_metrics.metadata.get(\"datasets_pvc_name\")\n datasets_pvc_mount_path\
\ = input_params_metrics.metadata.get(\"datasets_pvc_mount_path\")\n\n \
\ if datasets_from_pvc is True:\n if datasets_pvc_name is None\
\ or datasets_pvc_mount_path is None:\n raise ValueError(\"Both\
\ datasets_pvc_name and datasets_pvc_mount_path cannot be null\")\n\n \
\ volumes.append({\n \"name\": \"datasets\", \n \
\ \"persistentVolumeClaim\": {\n \"claimName\": datasets_pvc_name\n\
\ }\n })\n volumeMounts.append({\n \"\
name\": \"datasets\", \n \"mountPath\": datasets_pvc_mount_path\n\
\ })\n\n '''\n if save_model is True:\n volumes.append({\n\
\ \"name\": \"models\", \n \"persistentVolumeClaim\"\
: {\n \"claimName\": models_pvc_name\n }\n \
\ })\n volumeMounts.append({\n \"name\": \"models\"\
, \n \"mountPath\": \"/opt/rfc/models\"\n })\n\n if\
\ datasets_from_pvc is True or save_model is True:\n train_container[\"\
volumeMounts\"] = volumeMounts\n template_spec[\"volumes\"] = volumes\n\
\ '''\n\n trial_spec={\n \"apiVersion\": \"batch/v1\",\n \
\ \"kind\": \"Job\",\n \"spec\": {\n \"template\"\
: {\n \"metadata\": {\n \"annotations\"\
: {\n \"sidecar.istio.io/inject\": \"false\"\n \
\ }\n },\n \"spec\": template_spec\n\
\ }\n }\n }\n\n trial_template=V1beta1TrialTemplate(\n\
\ primary_container_name=\"training-container\",\n trial_parameters=[\n\
\ V1beta1TrialParameterSpec(\n name=\"nEstimators\"\
,\n description=\"N estimators for the training model\",\n\
\ reference=\"ne\"\n )\n ],\n trial_spec=trial_spec,\n\
\ retain=True\n )\n\n max_trial_counts = input_params_metrics.metadata.get(\"\
max_trial_counts\")\n max_failed_trial_counts = input_params_metrics.metadata.get(\"\
max_failed_trial_counts\")\n parallel_trial_counts = input_params_metrics.metadata.get(\"\
parallel_trial_counts\")\n\n if max_failed_trial_counts is None or max_failed_trial_counts\
\ is None or parallel_trial_counts is None:\n raise ValueError(\"\
All max_trial_counts, max_failed_trial_counts and parallel_trial_counts\
\ cannot be null!\")\n\n try:\n max_trial_counts = int(max_trial_counts)\n\
\ max_failed_trial_counts = int(max_failed_trial_counts)\n \
\ parallel_trial_counts = int(parallel_trial_counts)\n except ValueError:\n\
\ raise ValueError(\"All max_trial_counts, max_failed_trial_counts\
\ and needs to be an int!\")\n\n experiment = V1beta1Experiment(\n \
\ api_version=\"kubeflow.org/v1beta1\",\n kind=\"Experiment\"\
,\n metadata=metadata,\n spec=V1beta1ExperimentSpec(\n \
\ max_trial_count=max_trial_counts,\n parallel_trial_count=parallel_trial_counts,\n\
\ max_failed_trial_count=max_failed_trial_counts,\n \
\ algorithm=algorithm_spec,\n objective=objective_spec,\n \
\ parameters=parameters,\n trial_template=trial_template,\n\
\ )\n )\n\n client_namespace = input_params_metrics.metadata.get(\"\
client_namespace\")\n if client_namespace is None:\n raise ValueError(\"\
Client namespace cannot be null!\")\n\n client = KatibClient(namespace=client_namespace)\n\
\ client.create_experiment(experiment=experiment)\n client.wait_for_experiment_condition(name=experiment_name,\
\ namespace=experiment_namespace, timeout=3600)\n\n result = client.get_optimal_hyperparameters(name=experiment_name,\
\ namespace=experiment_namespace).to_dict()\n\n best_params_list = result[\"\
parameter_assignments\"]\n\n for params in best_params_list:\n \
\ name = params[\"name\"]\n value = params[\"value\"]\n\n \
\ if name == \"ne\":\n value = int(value)\n\n best_params_metrics.log_metric(metric=name,\
\ value=value)\n\n"
image: python:3.10-slim
exec-run-random-forest-train:
container:
args:
- --executor_input
- '{{$}}'
- --function_to_execute
- run_random_forest_train
command:
- sh
- -c
- "\nif ! [ -x \"$(command -v pip)\" ]; then\n python3 -m ensurepip ||\
\ python3 -m ensurepip --user || apt-get install python3-pip\nfi\n\nPIP_DISABLE_PIP_VERSION_CHECK=1\
\ python3 -m pip install --quiet --no-warn-script-location 'kfp==2.9.0'\
\ '--no-deps' 'typing-extensions>=3.7.4,<5; python_version<\"3.9\"' &&\
\ python3 -m pip install --quiet --no-warn-script-location 'pandas' 'scikit-learn'\
\ 'joblib' && \"$0\" \"$@\"\n"
- sh
- -ec
- 'program_path=$(mktemp -d)
printf "%s" "$0" > "$program_path/ephemeral_component.py"
_KFP_RUNTIME=true python3 -m kfp.dsl.executor_main --component_module_path "$program_path/ephemeral_component.py" "$@"
'
- "\nimport kfp\nfrom kfp import dsl\nfrom kfp.dsl import *\nfrom typing import\
\ *\n\ndef run_random_forest_train(\n best_params_metrics: Input[Metrics],\
\ \n x_train: Input[Dataset], \n x_test: Input[Dataset], \n y_train:\
\ Input[Dataset], \n y_test: Input[Dataset], \n model: Output[Model],\
\ \n file: Output[Artifact]\n):\n import pandas as pd\n import\
\ joblib\n import json\n\n from sklearn.metrics import accuracy_score\n\
\ from sklearn.ensemble import RandomForestClassifier\n\n n_estimators\
\ = best_params_metrics.metadata.get(\"ne\")\n\n x_train_df = pd.read_csv(x_train.path)\n\
\ y_train_df = pd.read_csv(y_train.path)\n x_test_df = pd.read_csv(x_test.path)\n\
\ y_test_df = pd.read_csv(y_test.path)\n\n rfc = RandomForestClassifier(n_estimators=n_estimators)\n\
\ rfc.fit(x_train_df.values, y_train_df.values.ravel())\n\n rfc.predict(x_test_df.values)\n\
\ rfc_accuracy = rfc.score(x_test_df.values, y_test_df.values)\n\n \
\ # Save the model\n joblib.dump(rfc, model.path)\n\n data = {}\n\
\ data['accuracy'] = rfc_accuracy\n data['model_path'] = model.path\n\
\n with open(file=file.path, mode='w', encoding='utf8') as file:\n \
\ json.dump(data, file, indent=4)\n\n"
image: python:3.10-slim
exec-run-xgboost-katib-experiment:
container:
args:
- --executor_input
- '{{$}}'
- --function_to_execute
- run_xgboost_katib_experiment
command:
- sh
- -c
- "\nif ! [ -x \"$(command -v pip)\" ]; then\n python3 -m ensurepip ||\
\ python3 -m ensurepip --user || apt-get install python3-pip\nfi\n\nPIP_DISABLE_PIP_VERSION_CHECK=1\
\ python3 -m pip install --quiet --no-warn-script-location 'kfp==2.9.0'\
\ '--no-deps' 'typing-extensions>=3.7.4,<5; python_version<\"3.9\"' &&\
\ python3 -m pip install --quiet --no-warn-script-location 'kubeflow-katib==0.17.0'\
\ && \"$0\" \"$@\"\n"
- sh
- -ec
- 'program_path=$(mktemp -d)
printf "%s" "$0" > "$program_path/ephemeral_component.py"
_KFP_RUNTIME=true python3 -m kfp.dsl.executor_main --component_module_path "$program_path/ephemeral_component.py" "$@"
'
- "\nimport kfp\nfrom kfp import dsl\nfrom kfp.dsl import *\nfrom typing import\
\ *\n\ndef run_xgboost_katib_experiment(\n input_params_metrics: Input[Metrics],\
\ \n best_params_metrics: Output[Metrics]\n):\n from kubeflow.katib\
\ import KatibClient\n from kubernetes.client import V1ObjectMeta\n \
\ from kubeflow.katib import V1beta1Experiment\n from kubeflow.katib\
\ import V1beta1AlgorithmSpec\n from kubeflow.katib import V1beta1ObjectiveSpec\n\
\ from kubeflow.katib import V1beta1FeasibleSpace\n from kubeflow.katib\
\ import V1beta1ExperimentSpec\n from kubeflow.katib import V1beta1ObjectiveSpec\n\
\ from kubeflow.katib import V1beta1ParameterSpec\n from kubeflow.katib\
\ import V1beta1TrialTemplate\n from kubeflow.katib import V1beta1TrialParameterSpec\n\
\n from datetime import datetime, timezone, timedelta\n\n dt_str =\
\ datetime.now(timezone(timedelta(hours=8))).strftime(\"%-Y-%m-%d-%H-%M-%S\"\
)\n\n experiment_name = \"xgboost-\" + dt_str.replace(\"_\", \"-\")\n\
\ experiment_namespace = input_params_metrics.metadata.get(\"experiment_namespace\"\
)\n\n if experiment_name is None or experiment_namespace is None:\n \
\ raise ValueError(\"Both experiment_name and experiment namespace\
\ needs to be a string!\")\n\n metadata = V1ObjectMeta(\n name=experiment_name,\
\ \n namespace=experiment_namespace\n )\n\n algorithm_spec\
\ = V1beta1AlgorithmSpec(\n algorithm_name=\"random\"\n )\n\n\
\ objective_spec = V1beta1ObjectiveSpec(\n type=\"maximize\",\n\
\ goal= 0.99,\n objective_metric_name=\"accuracy\",\n )\n\
\n learning_rate_min = input_params_metrics.metadata.get(\"learning_rate_min\"\
)\n learning_rate_max = input_params_metrics.metadata.get(\"learning_rate_max\"\
)\n learning_rate_step = input_params_metrics.metadata.get(\"learning_rate_step\"\
)\n\n if learning_rate_min is None or learning_rate_max is None or learning_rate_step\
\ is None:\n raise ValueError(\"All learning_rate_min, learning_rate_max\
\ and learning_rate_step cannot be null!\")\n\n try:\n learning_rate_min\
\ = float(learning_rate_min)\n learning_rate_max = float(learning_rate_max)\n\
\ learning_rate_step = float(learning_rate_step)\n except ValueError:\n\
\ raise ValueError(\"All learning_rate_min, learning_rate_max and\
\ learning_rate_step needs to be a float!\")\n\n n_estimators_min = input_params_metrics.metadata.get(\"\
n_estimators_min\")\n n_estimators_max = input_params_metrics.metadata.get(\"\
n_estimators_max\")\n n_estimators_step = input_params_metrics.metadata.get(\"\
n_estimators_step\")\n\n if n_estimators_min is None or n_estimators_max\
\ is None or n_estimators_step is None:\n raise ValueError(\"All\
\ n_estimators_min, n_estimators_max and n_estimators_step cannot be null!\"\
)\n\n try:\n n_estimators_min = int(n_estimators_min)\n \
\ n_estimators_max = int(n_estimators_max)\n n_estimators_step =\
\ int(n_estimators_step)\n except ValueError:\n raise ValueError(\"\
All n_estimators_min, n_estimators_max and n_estimators_step needs to be\
\ a float!\")\n\n parameters = [\n V1beta1ParameterSpec(\n \
\ name=\"lr\",\n parameter_type=\"double\",\n \
\ feasible_space=V1beta1FeasibleSpace(\n min=str(learning_rate_min),\n\
\ max=str(learning_rate_max), \n step=str(learning_rate_step)\n\
\ ),\n ), \n V1beta1ParameterSpec(\n \
\ name=\"ne\",\n parameter_type=\"int\",\n feasible_space=V1beta1FeasibleSpace(\n\
\ min=str(n_estimators_min),\n max=str(n_estimators_max),\
\ \n step=str(n_estimators_step)\n ),\n \
\ )\n ]\n\n docker_image_name = input_params_metrics.metadata.get(\"\
docker_image_name\")\n if docker_image_name is None:\n raise ValueError(\"\
Docker image name cannot be null!\")\n\n random_state = input_params_metrics.metadata.get(\"\
random_state\")\n if random_state is None:\n random_state = 42\n\
\ else:\n try:\n random_state = int(random_state)\n\
\ except ValueError:\n raise ValueError(\"Random state\
\ needs to be an int!\")\n\n x_train_path = input_params_metrics.metadata.get(\"\
x_train_path\")\n x_test_path = input_params_metrics.metadata.get(\"\
x_test_path\")\n y_train_path = input_params_metrics.metadata.get(\"\
y_train_path\")\n y_test_path = input_params_metrics.metadata.get(\"\
y_test_path\")\n\n train_container = {\n \"name\": \"training-container\"\
,\n \"image\": f\"docker.io/{docker_image_name}\",\n \"command\"\
: [\n \"python3\",\n \"/opt/xgboost/train.py\",\n\
\ \"--lr=${trialParameters.learningRate}\",\n \"--ne=${trialParameters.nEstimators}\"\
,\n f\"--rs={random_state}\",\n f\"--esp=100000\"\
,\n f\"--booster=gbtree\",\n f\"--x_train_path={x_train_path}\"\
,\n f\"--x_test_path={x_test_path}\",\n f\"--y_train_path={y_train_path}\"\
,\n f\"--y_test_path={y_test_path}\",\n f\"--save_model=false\"\
,\n f\"--model_folder_path=models\"\n ]\n }\n\n \
\ template_spec = {\n \"containers\": [\n train_container\n\
\ ],\n \"restartPolicy\": \"Never\"\n }\n\n volumes\
\ = []\n volumeMounts = []\n\n datasets_from_pvc = input_params_metrics.metadata.get(\"\
datasets_from_pvc\")\n datasets_pvc_name = input_params_metrics.metadata.get(\"\
datasets_pvc_name\")\n datasets_pvc_mount_path = input_params_metrics.metadata.get(\"\
datasets_pvc_mount_path\")\n\n if datasets_from_pvc is True:\n \
\ if datasets_pvc_name is None or datasets_pvc_mount_path is None:\n \
\ raise ValueError(\"Both datasets_pvc_name and datasets_pvc_mount_path\
\ cannot be null\")\n\n volumes.append({\n \"name\": \"\
datasets\", \n \"persistentVolumeClaim\": {\n \
\ \"claimName\": datasets_pvc_name\n }\n })\n volumeMounts.append({\n\
\ \"name\": \"datasets\", \n \"mountPath\": datasets_pvc_mount_path\n\
\ })\n\n '''\n if save_model is True:\n volumes.append({\n\
\ \"name\": \"models\", \n \"persistentVolumeClaim\"\
: {\n \"claimName\": models_pvc_name\n }\n \
\ })\n volumeMounts.append({\n \"name\": \"models\"\
, \n \"mountPath\": \"/opt/xgboost/models\"\n })\n\n \
\ if datasets_from_pvc is True or save_model is True:\n train_container[\"\
volumeMounts\"] = volumeMounts\n template_spec[\"volumes\"] = volumes\n\
\ '''\n\n trial_spec={\n \"apiVersion\": \"batch/v1\",\n \
\ \"kind\": \"Job\",\n \"spec\": {\n \"template\"\
: {\n \"metadata\": {\n \"annotations\"\
: {\n \"sidecar.istio.io/inject\": \"false\"\n \
\ }\n },\n \"spec\": template_spec\n\
\ }\n }\n }\n\n trial_template=V1beta1TrialTemplate(\n\
\ primary_container_name=\"training-container\",\n trial_parameters=[\n\