Comments (1)
poson
commented on May 18, 2024
hash bucket size 和embedding dimension可以设置小一些，节约内存。
worker的内存超过40G，可能是要等待比较久，也可能分配不到资源。默认30G，一般不超过40G
worker 可以设置9个，8个计算，1一个evaluation
ps 在能装下模型基础上越少越好，cpu多一点，通信快一点。
elif FLAGS.task_index == 1: os.environ['TF_CONFIG'] = json.dumps( {'cluster': cluster, 'task': {'type': "evaluator", 'index': 0}})
应该是worker1 单独做evaluation的。 当然test数据太多，每次evaluation会很慢
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