def _upload()

in gym/gym/scoreboard/api.py [0:0]

• 35 lines of code
• 12 McCabe index (conditional complexity)

def _upload(training_dir, algorithm_id=None, writeup=None, benchmark_run_id=None, api_key=None, ignore_open_monitors=False, skip_videos=False):
    if not ignore_open_monitors:
        open_monitors = monitoring._open_monitors()
        if len(open_monitors) > 0:
            envs = [m.env.spec.id if m.env.spec else '(unknown)' for m in open_monitors]
            raise error.Error("Still have an open monitor on {}. You must run 'env.close()' before uploading.".format(', '.join(envs)))

    env_info, training_episode_batch, training_video = upload_training_data(training_dir, api_key=api_key, skip_videos=skip_videos)
    env_id = env_info['env_id']
    training_episode_batch_id = training_video_id = None
    if training_episode_batch:
        training_episode_batch_id = training_episode_batch.id
    if training_video:
        training_video_id = training_video.id

    if logger.level <= logging.INFO:
        if training_episode_batch_id is not None and training_video_id is not None:
            logger.info('[%s] Creating evaluation object from %s with learning curve and training video', env_id, training_dir)
        elif training_episode_batch_id is not None:
            logger.info('[%s] Creating evaluation object from %s with learning curve', env_id, training_dir)
        elif training_video_id is not None:
            logger.info('[%s] Creating evaluation object from %s with training video', env_id, training_dir)
        else:
            raise error.Error("[%s] You didn't have any recorded training data in %s. Once you've used 'env = gym.wrappers.Monitor(env, directory)' to start recording, you need to actually run some rollouts. Please join the community chat on https://gym.openai.com if you have any issues."%(env_id, training_dir))

    evaluation = resource.Evaluation.create(
        training_episode_batch=training_episode_batch_id,
        training_video=training_video_id,
        env=env_info['env_id'],
        algorithm={
            'id': algorithm_id,
        },
        benchmark_run_id=benchmark_run_id,
        writeup=writeup,
        gym_version=env_info['gym_version'],
        api_key=api_key,
    )

    return evaluation