我的TFCONFIG就是这样

def parse_argument():
if flaGS.job_name is None or flaGS.job_name == "":
    raise ValueError("Must specify an explicit `job_name`")
if flaGS.task_index is None or flaGS.task_index == "":
    raise ValueError("Must specify an explicit `task_index`")

print("job name = %s" % flaGS.job_name)
print("task index = %d" % flaGS.task_index)
os.environ["TF_ROLE"] = flaGS.job_name
os.environ["TF_INDEX"] = str(flaGS.task_index)

# Construct the cluster and start the server
ps_spec = flaGS.ps_hosts.split(",")
worker_spec = flaGS.worker_hosts.split(",")
cluster = {"worker": worker_spec,"ps": ps_spec}
os.environ["TF_CLUSTER_DEF"] = json.dumps(cluster)

def set_tfconfig_environ():
if "TF_CLUSTER_DEF" in os.environ:
    cluster = json.loads(os.environ["TF_CLUSTER_DEF"])
    task_index = int(os.environ["TF_INDEX"])
    task_type = os.environ["TF_ROLE"]

    tf_config = dict()
    worker_num = len(cluster["worker"])

    if task_type == "ps":
        tf_config["task"] = {"index": task_index,"type": task_type}
        flaGS.job_name = "ps"
        flaGS.task_index = task_index
    else:
        if task_index == 0:
            tf_config["task"] = {"index": 0,"type": "chief"}
        else:
            tf_config["task"] = {"index": task_index - 1,"type": task_type}
        flaGS.job_name = "worker"
        flaGS.task_index = task_index

    if worker_num == 1:
        cluster["chief"] = cluster["worker"]
        del cluster["worker"]
    else:
        cluster["chief"] = [cluster["worker"][0]]
        del cluster["worker"][0] 
        del cluster["worker"][worker_num-2]

    tf_config["cluster"] = cluster
    os.environ["TF_CONFIG"] = json.dumps(tf_config)
    print("TF_CONFIG",json.loads(os.environ["TF_CONFIG"]))

执行分布训练时，无法在hdfs modeldir下获得eval文件夹和事件文件，因此张量板仅显示火车曲线，而没有eval曲线。

现在，我对Tensorflow Distribution API感到困惑。我无法获得正确的解决方案，有人可以和我遇到同样的问题吗？ 我发现同一问题与我有相同的常见问题，但没有得到答案。

Distributed Tensorflow Estimator execution does not trigger evaluation or export

https://github.com/tensorflow/tensorflow/issues/30121

希望有人可以提供帮助，谢谢。