parameter server strategy#8
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| class ParameterServerStrategy(BaseStrategy): | ||
| """Strategy that trains a model via collective AllReduce. |
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change this docstring summary ?
| training_operator_cls, | ||
| operator_config=None, | ||
| initialization_hook=None, | ||
| num_workers=1, |
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| assert num_ps | ||
| self.num_ps = num_ps | ||
| self.num_workers = num_workers |
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same here... don't use the plural form
| assert num_ps | ||
| self.num_ps = num_ps | ||
| self.num_workers = num_workers | ||
| self.num_cpus_per_server = num_cpus_per_server |
| ray.get([server.set_params.remote(this_shard_ref)]) | ||
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| def _start_workers(self): | ||
| """Create worker(actor), maybe need worker group to manager these workers. |
| """ | ||
| # TODO (Hao): infer the per-replica batch size here... | ||
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| # so here we get two set of params that will be passed around: |
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you can clean this comment as it is redundent with those I left in AllReduceStrategy
| } | ||
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| # Should we make two groups for worker and server? | ||
| self.worker_group = DataParallelGroup(**workergroup_init_args) |
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This is strange. Is this the same DataParallelGroup with the one in AllReduceStrategy?
If yes -- then fine
If not -- is there any way we can share the same class? If it is hard then we should at least use a different class name?
| self.server_group.start_actors( | ||
| self.num_ps) # server at the last num_ps processes. | ||
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| worker_rets = self.worker_group.test_connection() |
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Are testing connection necessary? if not, probably move it to DEBUG mode.
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| def setup_operator(self): | ||
| # figure out the signature of training_operator_cls later. | ||
| self.training_operator = self.training_operator_cls( |
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I am not sure whether we should setup the whole operator on the server side? One drawback is that this will take a lot of GPU memory?
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