Launches an evaluator job.
    m1_path, m2_path: full gs:// paths to the .pb files to match up
    job_name: string, appended to the container, used to differentiate the job
    names (e.g. 'minigo-cc-evaluator-v5-123-v7-456')
    bucket_name: Where to write the sgfs, passed into the job as $BUCKET_NAME
    completions: the number of completions desired
    flags_path: the path to the eval flagfile to use (if any)

def launch_eval_job(m1_path, m2_path, job_name,
        bucket_name, completions=5, flags_path=None):
    """Launches an evaluator job.
    m1_path, m2_path: full gs:// paths to the .pb files to match up
    job_name: string, appended to the container, used to differentiate the job
    names (e.g. 'minigo-cc-evaluator-v5-123-v7-456')
    bucket_name: Where to write the sgfs, passed into the job as $BUCKET_NAME
    completions: the number of completions desired
    flags_path: the path to the eval flagfile to use (if any)
    """
    if not all([m1_path, m2_path, job_name, bucket_name]):
        print("Provide all of m1_path, m2_path, job_name, and bucket_name "
              "params")
        return
    api_instance = get_api()

    raw_job_conf = open("cluster/evaluator/cc-evaluator.yaml").read()

    if flags_path:
        os.environ['EVAL_FLAGS_PATH'] = flags_path
    else:
        os.environ['EVAL_FLAGS_PATH'] = ""
    os.environ['BUCKET_NAME'] = bucket_name
    os.environ['MODEL_BLACK'] = m1_path
    os.environ['MODEL_WHITE'] = m2_path
    os.environ['JOBNAME'] = job_name
    env_job_conf = os.path.expandvars(raw_job_conf)

    job_conf = yaml.load(env_job_conf)
    job_conf['spec']['completions'] = completions

    response = api_instance.create_namespaced_job('default', body=job_conf)
    return job_conf, response