@component(base_image='gcr.io/foo/robocode-mlflow-run')
def record_video(record_steps: int, output_path: OutputPath(Artifact)):
    import pathlib
    import os
    import gym
    import gym_robocode
    import string
    from stable_baselines3.common.vec_env import VecVideoRecorder, DummyVecEnv
    import random
    import boto3
    from stable_baselines3 import A2C, PPO
    import logging
    import shutil

    video_length = record_steps

    video_folder = './videos'
    env_id = 'RobocodeDownSample-v2'
    env = DummyVecEnv([lambda: gym.make(env_id)])

    model = PPO('CnnPolicy', env, verbose=1)

    obs = env.reset()
    # Record the video starting at the first step
    os.mkdir(output_path)
    env = VecVideoRecorder(env, video_folder,
                           record_video_trigger=lambda x: x == 0, video_length=video_length,
                           name_prefix=f"{output_path}/videos" + "-{}".format(env_id))
    env.reset()
    for _ in range(video_length + 1):
        action, states = model.predict(obs)
        obs, , ,  = env.step(action)
    # Save the video
    env.close()
    for file in os.listdir(f"{output_path}/videos"):
        print(f"Trying to list file {file}")

In reply to@_slack_kubeflow_U03974MNYLB:matrix.org
Hello guys! New to kubeflow-pipeline, and struggling to set up a pipeline_root. I have a full kubeflow installation on minikube on local machine. I have followed a tutorial [1] that creates a simple component that needs to write to pipeline_root but I cannot get it to work. I have tried: • Using a Google Cloud Storage bucket, but this fails because I don't know how to give kubeflow access to it. I can specify a kubernetes service account when creating the run, but that doesn't help. • Using an existing Volume, but I get errors Not being able to write to disk at all prevents me from using kubeflow locally. How do you guys do it? (Pipelines not depending on pipieline_root work fine btw) [1] https://www.kubeflow.org/docs/components/pipelines/sdk-v2/build-pipeline/