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67eff9fa
编写于
8月 31, 2017
作者:
H
Helin Wang
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@@ -65,7 +65,8 @@ After converting:
-
Model parallelism become easier to implement: it's an extension to
the trainer - parameter server approach. we already have the
communication OPs, but need to extend the graph converter.
communication OPs, but need to extend the graph converter's
placement functionality.
-
User-defined optimizer is easier to add - user can now express it as
a subgraph.
...
...
@@ -90,14 +91,16 @@ After converting:
-
In the "Aync SGD" figure, the "W" variable on the parameter server
could be read and wrote concurrently, what is our locking strategy?
E.g., each variable have a lock cpp method to be invoked by every
OP, or, have a lock OP.
-
Does our current tensor design supports enqueue (put the input tensor
into the queue tensor)?
-
Can the Enqueue OP be implemented under our current tensor design
(puts the input tensor
into the queue tensor)?
-
*Dequeue*
OP will have variable numbers of output (depends on the
`min_count`
attribute), does our current design support it? (similar
question for the
*Add*
OP)
References:
[1]
(TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems)[https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/45166.pdf]
###
References:
[
1]
[TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
](
https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/45166.pdf
)
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