### Install Protocol Buffer Compiler

Source: https://github.com/kubeflow/pipelines/blob/master/frontend/CONTRIBUTING.md

Example command to install the Protocol Buffer compiler (protoc) on a Debian-based system. Ensure the correct version is installed.

```bash
# Example:
apt install -y protobuf-compiler=3.15.8
```

--------------------------------

### Install bmizerany/assert

Source: https://github.com/kubeflow/pipelines/blob/master/third_party/minio/license.txt

Install the bmizerany/assert library using the go get command. This is a prerequisite for using the assertion functions in Go tests.

```bash
$ go get github.com/bmizerany/assert
```

--------------------------------

### Install kfp-server-api via Setuptools

Source: https://github.com/kubeflow/pipelines/blob/master/backend/api/v1beta1/python_http_client/README.md

Install the Python package using Setuptools. This method is an alternative to pip installation.

```sh
python setup.py install --user
(or `sudo python setup.py install` to install the package for all users)
```

--------------------------------

### Local Development Environment Setup

Source: https://github.com/kubeflow/pipelines/blob/master/CLAUDE.md

Sets up a Python virtual environment and installs KFP packages in editable mode for local development. Ensure you have Make, pip, setuptools, and wheel installed.

```bash
python3 -m venv .venv
source .venv/bin/activate
python -m pip install -U pip setuptools wheel

make -C api python-dev
make -C kubernetes_platform python-dev

pip install -e api/v2alpha1/python --config-settings editable_mode=strict
pip install -e sdk/python --config-settings editable_mode=strict
pip install -e kubernetes_platform/python --config-settings editable_mode=strict
```

--------------------------------

### Create Experiment

Source: https://github.com/kubeflow/pipelines/blob/master/backend/api/v2beta1/python_http_client/docs/ExperimentServiceApi.md

Creates a new experiment. This example shows the initial setup for API client configuration with bearer token authentication.

```python
from __future__ import print_function
import time
import kfp_server_api
from kfp_server_api.rest import ApiException
from pprint import pprint
# Defining the host is optional and defaults to http://localhost
# See configuration.py for a list of all supported configuration parameters.
configuration = kfp_server_api.Configuration(
    host = "http://localhost"
)

# The client must configure the authentication and authorization parameters
# in accordance with the API server security policy.
# Examples for each auth method are provided below, use the example that
# satisfies your auth use case.


```

--------------------------------

### First-Time Setup for UI Smoke Test Tool

Source: https://github.com/kubeflow/pipelines/blob/master/frontend/scripts/ui-smoke-test/README.md

Installs tool dependencies, Playwright, and captures screenshots for visual regression testing. Ensure Docker is running for the compare workflow.

```bash
cd frontend/scripts/ui-smoke-test
npm install
npx playwright install chromium
open -a Docker
node smoke-test-runner.js --compare master
```

--------------------------------

### Install Argo CLI

Source: https://github.com/kubeflow/pipelines/wiki/Tests

Installs the Argo command-line interface. This is a one-time setup step for GKE testing.

```bash
argo install
```

--------------------------------

### Install and Build Frontend Dependencies

Source: https://github.com/kubeflow/pipelines/blob/master/frontend/README.md

Navigate to the frontend directory, install NPM dependencies, and build the project for local development.

```bash
cd ${WORKING_DIRECTORY}/frontend
npm ci
npm run build
```

--------------------------------

### Example DEV_IMAGE_PREFIX Configuration

Source: https://github.com/kubeflow/pipelines/blob/master/backend/src/v2/README.md

An example of how to configure the DEV_IMAGE_PREFIX in the .env file for pushing built development images.

```makefile
export DEV_IMAGE_PREFIX=ghcr.io/kubeflow-test/kfp-/kfp-
```

--------------------------------

### Go Prompt Example

Source: https://github.com/kubeflow/pipelines/blob/master/third_party/minio/license.txt

Demonstrates how to use the prompt.Choose function to ask the user a question and select from a list of predefined options. The selected option's index is returned.

```go
package main

import "github.com/segmentio/go-prompt"

var langs = []string{
  "c",
  "c++",
  "lua",
  "go",
  "js",
  "ruby",
  "python",
}

func main() {
  i := prompt.Choose("What's your favorite language?", langs)
  println("picked: " + langs[i])
}
```

--------------------------------

### Install Ginkgo CLI with Go Install

Source: https://github.com/kubeflow/pipelines/blob/master/AGENTS.md

Installs the Ginkgo CLI tool directly into a project-local bin directory using 'go install'. Ensures the binary is added to the system's PATH.

```bash
GOBIN=$PWD/bin go install github.com/onsi/ginkgo/v2/ginkgo@latest
export PATH="$PWD/bin:$PATH"
```

--------------------------------

### Install Kubeflow Pipelines SDK

Source: https://github.com/kubeflow/pipelines/blob/master/samples/tutorials/Data passing in python components.ipynb

Install the Kubeflow Pipelines SDK. Use the --user argument if you encounter permission errors during installation.

```python
# Install Kubeflow Pipelines SDK. Add the --user argument if you get permission errors.
!PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install 'kfp>=1.4.0' --quiet --user
```

--------------------------------

### Install Kubernetes Platform Package

Source: https://github.com/kubeflow/pipelines/blob/master/sdk/CONTRIBUTING.md

Install the kfp kubernetes_platform package. Use 'make python-dev' for development or omit '-dev' for a regular installation.

```bash
pushd kubernetes_platform
make python-dev # omit -dev for regular install
popd
```

--------------------------------

### List Objects

Source: https://github.com/kubeflow/pipelines/blob/master/third_party/minio/license.txt

Initializes an OSS client, gets a bucket handle, and lists all objects within the bucket.

```go
    client, err := oss.New("Endpoint", "AccessKeyId", "AccessKeySecret")
    if err != nil {
        // HandleError(err)
    }
    
    bucket, err := client.Bucket("my-bucket")
    if err != nil {
        // HandleError(err)
    }
    
    lsRes, err := bucket.ListObjects()
    if err != nil {
        // HandleError(err)
    }
    
    for _, object := range lsRes.Objects {
        fmt.Println("Objects:", object.Key)
    }
```

--------------------------------

### Install Kubeflow Pipelines SDK

Source: https://github.com/kubeflow/pipelines/blob/master/samples/core/kfp_env_validation/kfp_env_validation.ipynb

Installs the KFP SDK using pip. This command should be run once in the environment.

```python
# Install Pipeline SDK - This only needs to be run once in the environment. 
!python3 -m pip install 'kfp>=0.1.31' --user --quiet
```

--------------------------------

### Install and Use Node.js Version

Source: https://github.com/kubeflow/pipelines/blob/master/CLAUDE.md

Installs and switches to the Node.js version specified in frontend/.nvmrc using fnm or nvm.

```bash
# With fnm (faster)
fnm install "$(cat frontend/.nvmrc)" && fnm use "$(cat frontend/.nvmrc)"
# With nvm
nvm install "$(cat frontend/.nvmrc)" && nvm use "$(cat frontend/.nvmrc)"
```

--------------------------------

### Install Pre-commit Hooks

Source: https://github.com/kubeflow/pipelines/blob/master/sdk/CONTRIBUTING.md

Install the pre-commit framework to automatically check code quality before commits. This is recommended for all contributors.

```sh
pre-commit install
```

--------------------------------

### Get Object To File

Source: https://github.com/kubeflow/pipelines/blob/master/third_party/minio/license.txt

Initializes an OSS client, gets a bucket handle, and downloads an object from the bucket to a local file.

```go
    client, err := oss.New("Endpoint", "AccessKeyId", "AccessKeySecret")
    if err != nil {
        // HandleError(err)
    }
    
    bucket, err := client.Bucket("my-bucket")
    if err != nil {
        // HandleError(err)
    }
    
    err = bucket.GetObjectToFile("my-object", "LocalFile")
    if err != nil {
        // HandleError(err)
    }
```

--------------------------------

### Put Object From File

Source: https://github.com/kubeflow/pipelines/blob/master/third_party/minio/license.txt

Initializes an OSS client, gets a bucket handle, and uploads a local file to a specified object key within the bucket.

```go
    client, err := oss.New("Endpoint", "AccessKeyId", "AccessKeySecret")
    if err != nil {
        // HandleError(err)
    }
    
    bucket, err := client.Bucket("my-bucket")
    if err != nil {
        // HandleError(err)
    }
    
    err = bucket.PutObjectFromFile("my-object", "LocalFile")
    if err != nil {
        // HandleError(err)
    }
```

--------------------------------

### Delete Object

Source: https://github.com/kubeflow/pipelines/blob/master/third_party/minio/license.txt

Initializes an OSS client, gets a bucket handle, and deletes a specified object from the bucket.

```go
    client, err := oss.New("Endpoint", "AccessKeyId", "AccessKeySecret")
    if err != nil {
        // HandleError(err)
    }
    
    bucket, err := client.Bucket("my-bucket")
    if err != nil {
        // HandleError(err)
    }
    
    err = bucket.DeleteObject("my-object")
    if err != nil {
        // HandleError(err)
    }
```

--------------------------------

### Start Proxy for Client UI Development

Source: https://github.com/kubeflow/pipelines/blob/master/frontend/CONTRIBUTING.md

Starts the development server with proxying enabled for client UI development against a single-user KFP instance.

```bash
npm run start:proxy
```

--------------------------------

### Install NPM Dependencies

Source: https://github.com/kubeflow/pipelines/blob/master/frontend/CONTRIBUTING.md

Installs exact NPM dependency versions based on package-lock.json. Recommended for initial setup, after package.json changes, and in CI/CD.

```bash
npm ci
```

--------------------------------

### Upload Pipeline Example

Source: https://github.com/kubeflow/pipelines/blob/master/backend/api/v1beta1/python_http_client/docs/PipelineUploadServiceApi.md

Demonstrates how to upload a pipeline using the PipelineUploadServiceApi. It shows configuration for API key authentication and how to pass pipeline details.

```python
from __future__ import print_function
import time
import kfp_server_api
from kfp_server_api.rest import ApiException
from pprint import pprint
# Defining the host is optional and defaults to http://localhost
# See configuration.py for a list of all supported configuration parameters.
configuration = kfp_server_api.Configuration(
    host = "http://localhost"
)

# The client must configure the authentication and authorization parameters
# in accordance with the API server security policy.
# Examples for each auth method are provided below, use the example that
# satisfies your auth use case.

# Configure API key authorization: Bearer
configuration = kfp_server_api.Configuration(
    host = "http://localhost",
    api_key = {
        'authorization': 'YOUR_API_KEY'
    }
)
# Uncomment below to setup prefix (e.g. Bearer) for API key, if needed
# configuration.api_key_prefix['authorization'] = 'Bearer'

# Enter a context with an instance of the API client
with kfp_server_api.ApiClient(configuration) as api_client:
    # Create an instance of the API class
    api_instance = kfp_server_api.PipelineUploadServiceApi(api_client)
    uploadfile = '/path/to/file' # file | The pipeline to upload. Maximum size of 32MB is supported.
name = 'name_example' # str |  (optional)
description = 'description_example' # str |  (optional)
namespace = 'namespace_example' # str |  (optional)

    try:
        api_response = api_instance.upload_pipeline(uploadfile, name=name, description=description, namespace=namespace)
        pprint(api_response)
    except ApiException as e:
        print("Exception when calling PipelineUploadServiceApi->upload_pipeline: %s\n" % e)
```

--------------------------------

### Install Google Cloud Pipeline Components

Source: https://github.com/kubeflow/pipelines/blob/master/components/google-cloud/README.md

Install the latest release of Google Cloud Pipeline Components from PyPI. Use this command to get the most recent stable version.

```shell
pip install -U google-cloud-pipeline-components
```

--------------------------------

### Start Local Development with Proxy and Server

Source: https://github.com/kubeflow/pipelines/blob/master/AGENTS.md

Starts the local development server, proxying to the cluster and enabling hot reload for full integration testing.

```bash
npm run start:proxy-and-server
```

--------------------------------

### Set up a Virtual Environment

Source: https://github.com/kubeflow/pipelines/blob/master/sdk/CONTRIBUTING.md

Create and activate a Python virtual environment to isolate project dependencies. Replace 'env' with your preferred environment name.

```bash
# optional, replace with your tool of choice
python -m venv env && source ./env/bin/activate
```

--------------------------------

### Setup and Check Python SDK Imports

Source: https://github.com/kubeflow/pipelines/blob/master/CLAUDE.md

Installs development dependencies for the Python SDK and checks for import order and unused imports using pycln and isort. Ensure you have the correct requirements installed before running.

```bash
pip install -r sdk/python/requirements-dev.txt
pycln --check sdk/python
isort --check --profile google sdk/python
```

--------------------------------

### Get Experiment

Source: https://github.com/kubeflow/pipelines/blob/master/backend/api/v1beta1/python_http_client/docs/ExperimentServiceApi.md

Retrieve a specific experiment by its ID using this code example. Ensure the API client is properly configured.

```python
from __future__ import print_function
import time
import kfp_server_api
from kfp_server_api.rest import ApiException
from pprint import pprint
# Defining the host is optional and defaults to http://localhost

```

--------------------------------

### Example Usage of prsha Function

Source: https://github.com/kubeflow/pipelines/blob/master/RELEASE.md

Demonstrates how to use the `prsha` function to get the commit SHA for a specific pull request URL.

```bash
> prsha https://github.com/kubeflow/pipelines/pull/13344                                                       
67a73cf809f7c76fee33c749f302f2bceedd8fab
```

--------------------------------

### List Buckets

Source: https://github.com/kubeflow/pipelines/blob/master/third_party/minio/license.txt

Initializes an OSS client and lists all buckets associated with the provided credentials.

```go
    client, err := oss.New("Endpoint", "AccessKeyId", "AccessKeySecret")
    if err != nil {
        // HandleError(err)
    }
    
    lsRes, err := client.ListBuckets()
    if err != nil {
        // HandleError(err)
    }
    
    for _, bucket := range lsRes.Buckets {
        fmt.Println("Buckets:", bucket.Name)
    }
```

--------------------------------

### List Pipeline Versions

Source: https://github.com/kubeflow/pipelines/blob/master/backend/api/v1beta1/python_http_client/docs/PipelineServiceApi.md

Lists all available pipeline versions for a given pipeline. This example shows the setup for API key authentication.

```python
from __future__ import print_function
import time
import kfp_server_api
from kfp_server_api.rest import ApiException
from pprint import pprint
# Defining the host is optional and defaults to http://localhost
# See configuration.py for a list of all supported configuration parameters.
configuration = kfp_server_api.Configuration(
    host = "http://localhost"
)

# The client must configure the authentication and authorization parameters
# in accordance with the API server security policy.

```

--------------------------------

### Install Protobuf Compiler and gRPC Go Plugins

Source: https://github.com/kubeflow/pipelines/blob/master/third_party/ml-metadata/README.md

Install specific versions of the protobuf-compiler, protoc-gen-go, and protoc-gen-go-grpc required for building the Go gRPC client.

```bash
apt install -y protobuf-compiler=3.15.8
go install google.golang.org/protobuf/cmd/protoc-gen-go@v1.26
go install google.golang.org/grpc/cmd/protoc-gen-go-grpc@v1.1
```

--------------------------------

### Kubeflow Pipelines Benchmark Setup

Source: https://github.com/kubeflow/pipelines/blob/master/tools/benchmarks/run_service_api.ipynb

Sets up the necessary imports and configurations for running Kubeflow Pipelines benchmarks. This includes defining the KFP host, the pipeline file URL, and the desired number of runs.

```python
# This benchmark measures the performance of run related operations in Kubeflow pipelines, including run durations and latencies of creating/getting/deleting runs.

import random
import kfp
import kfp_server_api
import os
import string
import time
from google.cloud import storage
from kfp.components import create_component_from_func
from datetime import datetime, timezone, timedelta

import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from scipy import stats

# CHANGE necessary paramters here
# host is your KFP endpoint
host = 'http://127.0.0.1:3001'
# Use the pipeline you prefer
pipeline_file_url = 'https://storage.googleapis.com/jingzhangjz-project-pipelines/benchmarks/taxi.yaml'
# number of runs you want to create
num_runs = 5

```

--------------------------------

### Install Sample Test Dependencies

Source: https://github.com/kubeflow/pipelines/blob/master/backend/src/v2/README.md

Installs Python dependencies required for running sample tests. Navigate to the 'test' directory before running.

```bash
cd test
pip install -r requirements.txt
```

--------------------------------

### Multi-user Setup Environment Variable

Source: https://github.com/kubeflow/pipelines/blob/master/frontend/CONTRIBUTING.md

Set the VITE_NAMESPACE environment variable for multi-user Kubeflow Pipelines installations. This is typically done before building the frontend.

```bash
export VITE_NAMESPACE=kubeflow-user-example-com
npm run build
```

--------------------------------

### Install Documentation Generation Tools

Source: https://github.com/kubeflow/pipelines/blob/master/backend/api/README.md

Install the necessary npm packages for generating API reference documentation. This includes 'bootprint' and 'bootprint-openapi' for processing OpenAPI specifications and 'html-inline' for creating self-contained HTML files.

```bash
npm install -g bootprint
npm install -g bootprint-openapi
npm -g install html-inline
```

--------------------------------

### Retry a Run

Source: https://github.com/kubeflow/pipelines/blob/master/backend/api/v2beta1/python_http_client/docs/RunServiceApi.md

Re-initiates a failed or terminated run using its ID. This example demonstrates the basic setup for authentication and API client configuration.

```python
from __future__ import print_function
import time
import kfp_server_api
from kfp_server_api.rest import ApiException
from pprint import pprint

# Defining the host is optional and defaults to http://localhost
# See configuration.py for a list of all supported configuration parameters.
configuration = kfp_server_api.Configuration(
    host = "http://localhost"
)

# The client must configure the authentication and authorization parameters
# in accordance with the API server security policy.
# Examples for each auth method are provided below, use the example that
# satisfies your auth use case.

# Configure API key authorization: Bearer
configuration = kfp_server_api.Configuration(
    host = "http://localhost",
    api_key = {
        'authorization': 'YOUR_API_KEY'
    }
)
# Uncomment below to setup prefix (e.g. Bearer) for API key, if needed
# configuration.api_key_prefix['authorization'] = 'Bearer'

# Example for retry_run (assuming api_instance is already created and configured)
# run_id = 'run_id_example' # str | The ID of the run to retry.
# experiment_id = 'experiment_id_example'  # str | The ID of the experiment that the run belongs to. (optional)
# try:
#     api_response = api_instance.run_service_retry_run(run_id, experiment_id=experiment_id)
#     pprint(api_response)
# except ApiException as e:
#     print("Exception when calling RunServiceApi->run_service_retry_run: %s\n" % e)

```

--------------------------------

### Start Interactive Shell in a Docker Image

Source: https://github.com/kubeflow/pipelines/blob/master/third_party/README.md

Run an interactive shell within a Docker container created from a specified image. This requires the image to have a shell installed.

```bash
docker run -it --entrypoint sh $IMAGE_NAME
```

--------------------------------

### Get Pipeline Details using Python HTTP Client

Source: https://github.com/kubeflow/pipelines/blob/master/backend/api/v2beta1/python_http_client/docs/PipelineServiceApi.md

Retrieves details for a specific pipeline using its ID. This example uses API key authentication.

```python
from __future__ import print_function
import time
import kfp_server_api
from kfp_server_api.rest import ApiException
from pprint import pprint
# Defining the host is optional and defaults to http://localhost
# See configuration.py for a list of all supported configuration parameters.
configuration = kfp_server_api.Configuration(
    host = "http://localhost"
)

# The client must configure the authentication and authorization parameters
# in accordance with the API server security policy.
# Examples for each auth method are provided below, use the example that
# satisfies your auth use case.

# Configure API key authorization: Bearer
configuration = kfp_server_api.Configuration(
    host = "http://localhost",
    api_key = {
        'authorization': 'YOUR_API_KEY'
    }
)
# Uncomment below to setup prefix (e.g. Bearer) for API key, if needed
# configuration.api_key_prefix['authorization'] = 'Bearer'

# Enter a context with an instance of the API client
with kfp_server_api.ApiClient(configuration) as api_client:
    # Create an instance of the API class
    api_instance = kfp_server_api.PipelineServiceApi(api_client)
    pipeline_id = 'pipeline_id_example' # str | Required input. The ID of the pipeline to be retrieved.

    try:
        # Finds a specific pipeline by ID.
        api_response = api_instance.pipeline_service_get_pipeline(pipeline_id)
        pprint(api_response)
    except ApiException as e:
        print("Exception when calling PipelineServiceApi->pipeline_service_get_pipeline: %s\n" % e)
```

--------------------------------

### Start Frontend Development Server

Source: https://github.com/kubeflow/pipelines/blob/master/CLAUDE.md

Starts the frontend development server using npm. This command is used for local frontend development and testing.

```bash
cd frontend && npm start
```

--------------------------------

### Get Pipeline Template by ID

Source: https://github.com/kubeflow/pipelines/blob/master/backend/api/v1beta1/python_http_client/docs/PipelineServiceApi.md

Retrieves the YAML template for a pipeline, including its description, parameters, and metadata. This example demonstrates API key authentication and error handling.

```python
from __future__ import print_function
import time
import kfp_server_api
from kfp_server_api.rest import ApiException
from pprint import pprint
# Defining the host is optional and defaults to http://localhost
# See configuration.py for a list of all supported configuration parameters.
configuration = kfp_server_api.Configuration(
    host = "http://localhost"
)

# The client must configure the authentication and authorization parameters
# in accordance with the API server security policy.
# Examples for each auth method are provided below, use the example that
# satisfies your auth use case.

# Configure API key authorization: Bearer
configuration = kfp_server_api.Configuration(
    host = "http://localhost",
    api_key = {
        'authorization': 'YOUR_API_KEY'
    }
)
# Uncomment below to setup prefix (e.g. Bearer) for API key, if needed
# configuration.api_key_prefix['authorization'] = 'Bearer'

# Enter a context with an instance of the API client
with kfp_server_api.ApiClient(configuration) as api_client:
    # Create an instance of the API class
    api_instance = kfp_server_api.PipelineServiceApi(api_client)
    id = 'id_example' # str | The ID of the pipeline whose template is to be retrieved. 

    try:
        # Returns a single YAML template that contains the description, parameters, and metadata associated with the pipeline provided.
        api_response = api_instance.pipeline_service_get_template(id)
        pprint(api_response)
    except ApiException as e:
        print("Exception when calling PipelineServiceApi->pipeline_service_get_template: %s\n" % e)
```
```

--------------------------------

### Install Development Requirements

Source: https://github.com/kubeflow/pipelines/blob/master/sdk/CONTRIBUTING.md

Install essential tools like pip, wheel, and setuptools. Ensure your package management tools are up-to-date before proceeding.

```bash
python -m pip install -U pip wheel setuptools
```

--------------------------------

### Build All Backend Images

Source: https://github.com/kubeflow/pipelines/blob/master/third_party/argo/UPGRADE.md

Run 'make image_all' from the 'backend' directory to verify that backend images still build after an upgrade.

```bash
make image_all
```

--------------------------------

### Accessing UI Variables in Kubeflow Pipelines

Source: https://github.com/kubeflow/pipelines/blob/master/backend/src/apiserver/visualization/README.md

Shows how to retrieve custom variables passed from the Kubeflow Pipelines UI to a visualization script. Includes examples for getting values with and without default fallbacks, and checking for their existence.

```python
# Get a value for a specified key
key = variables.get("key")
# Get a value for a specified key with a default
key = variables.get("key", "default_value")
# Check if a value for a specified key exists
if variables.get("key", "default_value") is "default_value":
    # Value for a specified key does not exist
    pass
else:
    # Value for a specified key does exist
    pass
```

--------------------------------

### Report Run Metrics V1 Example

Source: https://github.com/kubeflow/pipelines/blob/master/backend/api/v1beta1/python_http_client/docs/RunServiceApi.md

Demonstrates how to report metrics for a specific run using the RunServiceApi. Includes setup for API key authentication and error handling. The API accepts partial failures and ignores duplicate reporting.

```python
from __future__ import print_function
import time
import kfp_server_api
from kfp_server_api.rest import ApiException
from pprint import pprint
# Defining the host is optional and defaults to http://localhost
# See configuration.py for a list of all supported configuration parameters.
configuration = kfp_server_api.Configuration(
    host = "http://localhost"
)

# The client must configure the authentication and authorization parameters
# in accordance with the API server security policy.
# Examples for each auth method are provided below, use the example that
# satisfies your auth use case.

# Configure API key authorization: Bearer
configuration = kfp_server_api.Configuration(
    host = "http://localhost",
    api_key = {
        'authorization': 'YOUR_API_KEY'
    }
)
# Uncomment below to setup prefix (e.g. Bearer) for API key, if needed
# configuration.api_key_prefix['authorization'] = 'Bearer'

# Enter a context with an instance of the API client
with kfp_server_api.ApiClient(configuration) as api_client:
    # Create an instance of the API class
    api_instance = kfp_server_api.RunServiceApi(api_client)
    run_id = 'run_id_example' # str | Required. The parent run ID of the metric. 
body = kfp_server_api.RunServiceReportRunMetricsV1Body() # RunServiceReportRunMetricsV1Body | 

    try:
        # ReportRunMetrics reports metrics of a run. Each metric is reported in its own transaction, so this API accepts partial failures. Metric can be uniquely identified by (run_id, node_id, name). Duplicate reporting will be ignored by the API. First reporting wins.
        api_response = api_instance.run_service_report_run_metrics_v1(run_id, body)
        pprint(api_response)
    except ApiException as e:
        print("Exception when calling RunServiceApi->run_service_report_run_metrics_v1: %s\n" % e)

```