From 730d2b54cdcf83263126ee7c2c935dbdcd2b9a3d Mon Sep 17 00:00:00 2001 From: Travis CI Date: Sat, 9 Sep 2017 00:47:37 +0000 Subject: [PATCH] Deploy to GitHub Pages: 99e3d1ec534b70246c6199072006c7ad4c3556ea --- .../doc/_sources/design/ops/dist_train.md.txt | 106 ++++++ develop/doc/design/ops/dist_train.html | 343 +++++++++++++++++ develop/doc/objects.inv | Bin 2528 -> 2552 bytes develop/doc/searchindex.js | 2 +- .../_sources/design/ops/dist_train.md.txt | 106 ++++++ develop/doc_cn/design/ops/dist_train.html | 351 ++++++++++++++++++ develop/doc_cn/objects.inv | Bin 3063 -> 3087 bytes develop/doc_cn/searchindex.js | 2 +- 8 files changed, 908 insertions(+), 2 deletions(-) create mode 100644 develop/doc/_sources/design/ops/dist_train.md.txt create mode 100644 develop/doc/design/ops/dist_train.html create mode 100644 develop/doc_cn/_sources/design/ops/dist_train.md.txt create mode 100644 develop/doc_cn/design/ops/dist_train.html diff --git a/develop/doc/_sources/design/ops/dist_train.md.txt b/develop/doc/_sources/design/ops/dist_train.md.txt new file mode 100644 index 0000000000..fa3c5d7990 --- /dev/null +++ b/develop/doc/_sources/design/ops/dist_train.md.txt @@ -0,0 +1,106 @@ +# Design Doc: Operation Graph Based Parameter Server + +## Abstract + +We propose an approach to implement the parameter server. In this +approach, there is no fundamental difference between the trainer and +the parameter server: they both run subgraphs, but subgraphs of +different purposes. + +## Background + +The previous implementations of the parameter server does not run a +subgraph. parameter initialization, optimizer computation, network +communication and checkpointing are implemented twice on both the +trainer and the parameter server. + +It would be great if we can write code once and use them on both the +trainer and the parameter server: reduces code duplication and +improves extensibility. Given that after the current refactor, we are +representing everything as a computing graph on the +trainer. Representing everything as a computing graph on the parameter +server becomes a natural extension. + +## Design + +### Graph Converter + +The *graph converter* converts the user-defined operation (OP) graph +into subgraphs to be scheduled on different nodes with the following +steps: + +1. OP placement: the OPs will be placed on different nodes according + to heuristic that minimizes estimated total computation + time. Currently we will use a simple heuristic that puts parameter + varable on parameter server workers and everything else on trainer + workers. + +1. Add communication OPs to enable the communication between nodes. + +We will need these OPs: *Send*, *Recv*, *Enqueue*, *Dequeue*. + +Below is an example of converting the user defined graph to the +subgraphs for the trainer and the parameter server: + + + +After converting: + + + +1. The parameter variable W and it's optimizer subgraph are placed on the parameter server. +1. Operators are added to the subgraphs. + - *Send* sends data to the connected *Recv* operator. The + scheduler on the receive node will only schedule *Recv* operator + to run when the *Send* operator has ran (the *Send* OP will mark + the *Recv* OP runnable automatically). + - *Enueue* enqueues the input variable, it can block until space + become available in the queue. + - *Dequeue* outputs configurable numbers of tensors from the + queue. It will block until the queue have the required number of + tensors. + + +### Benefits + +- 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's + placement functionality. + +- User-defined optimizer is easier to add - user can now express it as + a subgraph. + +- No more duplication logic inside the trainer and the parameter + server mentioned in the background section. + +### Challenges + +- It might be hard for the graph converter to cut a general graph + (without any hint for which subgraph is the optimizer). We may need + to label which subgraph inside the OP graph is the optimizer. + +- It's important to balance the parameter shards of on multiple + parameter server. If a single parameter is very big (some + word-embedding, fully connected, softmax layer), we need to + automatically partition the single parameter onto different + parameter servers when possible (only element-wise optimizer depends + on the parameter variable). + +### Discussion + +- 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. + +- 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) diff --git a/develop/doc/design/ops/dist_train.html b/develop/doc/design/ops/dist_train.html new file mode 100644 index 0000000000..7c133778e0 --- /dev/null +++ b/develop/doc/design/ops/dist_train.html @@ -0,0 +1,343 @@ + + + + + + + + + + + Design Doc: Operation Graph Based Parameter Server — PaddlePaddle documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
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+ Fork me on Github +
+ + English + + + + +
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  • Design Doc: Operation Graph Based Parameter Server
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Design Doc: Operation Graph Based Parameter Server

+
+

Abstract

+

We propose an approach to implement the parameter server. In this +approach, there is no fundamental difference between the trainer and +the parameter server: they both run subgraphs, but subgraphs of +different purposes.

+
+
+

Background

+

The previous implementations of the parameter server does not run a +subgraph. parameter initialization, optimizer computation, network +communication and checkpointing are implemented twice on both the +trainer and the parameter server.

+

It would be great if we can write code once and use them on both the +trainer and the parameter server: reduces code duplication and +improves extensibility. Given that after the current refactor, we are +representing everything as a computing graph on the +trainer. Representing everything as a computing graph on the parameter +server becomes a natural extension.

+
+
+

Design

+
+

Graph Converter

+

The graph converter converts the user-defined operation (OP) graph +into subgraphs to be scheduled on different nodes with the following +steps:

+
    +
  1. OP placement: the OPs will be placed on different nodes according +to heuristic that minimizes estimated total computation +time. Currently we will use a simple heuristic that puts parameter +varable on parameter server workers and everything else on trainer +workers.
    2. +
  2. Add communication OPs to enable the communication between nodes.
    3. +
+

We will need these OPs: Send, Recv, Enqueue, Dequeue.

+

Below is an example of converting the user defined graph to the +subgraphs for the trainer and the parameter server:

+

+

After converting:

+

+
    +
  1. The parameter variable W and it’s optimizer subgraph are placed on the parameter server.
    2. +
  2. Operators are added to the subgraphs.
      +
    • Send sends data to the connected Recv operator. The +scheduler on the receive node will only schedule Recv operator +to run when the Send operator has ran (the Send OP will mark +the Recv OP runnable automatically).
      • +
    • Enueue enqueues the input variable, it can block until space +become available in the queue.
      • +
    • Dequeue outputs configurable numbers of tensors from the +queue. It will block until the queue have the required number of +tensors.
      • +
    +
    3. +
+
+
+

Benefits

+
    +
  • 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’s +placement functionality.
    • +
  • User-defined optimizer is easier to add - user can now express it as +a subgraph.
    • +
  • No more duplication logic inside the trainer and the parameter +server mentioned in the background section.
    • +
+
+
+

Challenges

+
    +
  • It might be hard for the graph converter to cut a general graph +(without any hint for which subgraph is the optimizer). We may need +to label which subgraph inside the OP graph is the optimizer.
    • +
  • It’s important to balance the parameter shards of on multiple +parameter server. If a single parameter is very big (some +word-embedding, fully connected, softmax layer), we need to +automatically partition the single parameter onto different +parameter servers when possible (only element-wise optimizer depends +on the parameter variable).
    • +
+
+
+

Discussion

+
    +
  • 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.
    • +
  • 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)
    • +
+
+ +
+
+ + +
+
+
+ + +
+ +
+

+ © Copyright 2016, PaddlePaddle developers. + +

+
+ Built with Sphinx using a theme provided by Read the Docs. + +
+ +
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+ +
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\ No newline at end of file diff --git a/develop/doc_cn/_sources/design/ops/dist_train.md.txt b/develop/doc_cn/_sources/design/ops/dist_train.md.txt new file mode 100644 index 0000000000..fa3c5d7990 --- /dev/null +++ b/develop/doc_cn/_sources/design/ops/dist_train.md.txt @@ -0,0 +1,106 @@ +# Design Doc: Operation Graph Based Parameter Server + +## Abstract + +We propose an approach to implement the parameter server. In this +approach, there is no fundamental difference between the trainer and +the parameter server: they both run subgraphs, but subgraphs of +different purposes. + +## Background + +The previous implementations of the parameter server does not run a +subgraph. parameter initialization, optimizer computation, network +communication and checkpointing are implemented twice on both the +trainer and the parameter server. + +It would be great if we can write code once and use them on both the +trainer and the parameter server: reduces code duplication and +improves extensibility. Given that after the current refactor, we are +representing everything as a computing graph on the +trainer. Representing everything as a computing graph on the parameter +server becomes a natural extension. + +## Design + +### Graph Converter + +The *graph converter* converts the user-defined operation (OP) graph +into subgraphs to be scheduled on different nodes with the following +steps: + +1. OP placement: the OPs will be placed on different nodes according + to heuristic that minimizes estimated total computation + time. Currently we will use a simple heuristic that puts parameter + varable on parameter server workers and everything else on trainer + workers. + +1. Add communication OPs to enable the communication between nodes. + +We will need these OPs: *Send*, *Recv*, *Enqueue*, *Dequeue*. + +Below is an example of converting the user defined graph to the +subgraphs for the trainer and the parameter server: + + + +After converting: + + + +1. The parameter variable W and it's optimizer subgraph are placed on the parameter server. +1. Operators are added to the subgraphs. + - *Send* sends data to the connected *Recv* operator. The + scheduler on the receive node will only schedule *Recv* operator + to run when the *Send* operator has ran (the *Send* OP will mark + the *Recv* OP runnable automatically). + - *Enueue* enqueues the input variable, it can block until space + become available in the queue. + - *Dequeue* outputs configurable numbers of tensors from the + queue. It will block until the queue have the required number of + tensors. + + +### Benefits + +- 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's + placement functionality. + +- User-defined optimizer is easier to add - user can now express it as + a subgraph. + +- No more duplication logic inside the trainer and the parameter + server mentioned in the background section. + +### Challenges + +- It might be hard for the graph converter to cut a general graph + (without any hint for which subgraph is the optimizer). We may need + to label which subgraph inside the OP graph is the optimizer. + +- It's important to balance the parameter shards of on multiple + parameter server. If a single parameter is very big (some + word-embedding, fully connected, softmax layer), we need to + automatically partition the single parameter onto different + parameter servers when possible (only element-wise optimizer depends + on the parameter variable). + +### Discussion + +- 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. + +- 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) diff --git a/develop/doc_cn/design/ops/dist_train.html b/develop/doc_cn/design/ops/dist_train.html new file mode 100644 index 0000000000..7adfce3827 --- /dev/null +++ b/develop/doc_cn/design/ops/dist_train.html @@ -0,0 +1,351 @@ + + + + + + + + + + + Design Doc: Operation Graph Based Parameter Server — PaddlePaddle 文档 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+ Fork me on Github +
+ + English + + + + +
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  • Design Doc: Operation Graph Based Parameter Server
    • +
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+

Design Doc: Operation Graph Based Parameter Server

+
+

Abstract

+

We propose an approach to implement the parameter server. In this +approach, there is no fundamental difference between the trainer and +the parameter server: they both run subgraphs, but subgraphs of +different purposes.

+
+
+

Background

+

The previous implementations of the parameter server does not run a +subgraph. parameter initialization, optimizer computation, network +communication and checkpointing are implemented twice on both the +trainer and the parameter server.

+

It would be great if we can write code once and use them on both the +trainer and the parameter server: reduces code duplication and +improves extensibility. Given that after the current refactor, we are +representing everything as a computing graph on the +trainer. Representing everything as a computing graph on the parameter +server becomes a natural extension.

+
+
+

Design

+
+

Graph Converter

+

The graph converter converts the user-defined operation (OP) graph +into subgraphs to be scheduled on different nodes with the following +steps:

+
    +
  1. OP placement: the OPs will be placed on different nodes according +to heuristic that minimizes estimated total computation +time. Currently we will use a simple heuristic that puts parameter +varable on parameter server workers and everything else on trainer +workers.
    2. +
  2. Add communication OPs to enable the communication between nodes.
    3. +
+

We will need these OPs: Send, Recv, Enqueue, Dequeue.

+

Below is an example of converting the user defined graph to the +subgraphs for the trainer and the parameter server:

+

+

After converting:

+

+
    +
  1. The parameter variable W and it’s optimizer subgraph are placed on the parameter server.
    2. +
  2. Operators are added to the subgraphs.
      +
    • Send sends data to the connected Recv operator. The +scheduler on the receive node will only schedule Recv operator +to run when the Send operator has ran (the Send OP will mark +the Recv OP runnable automatically).
      • +
    • Enueue enqueues the input variable, it can block until space +become available in the queue.
      • +
    • Dequeue outputs configurable numbers of tensors from the +queue. It will block until the queue have the required number of +tensors.
      • +
    +
    3. +
+
+
+

Benefits

+
    +
  • 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’s +placement functionality.
    • +
  • User-defined optimizer is easier to add - user can now express it as +a subgraph.
    • +
  • No more duplication logic inside the trainer and the parameter +server mentioned in the background section.
    • +
+
+
+

Challenges

+
    +
  • It might be hard for the graph converter to cut a general graph +(without any hint for which subgraph is the optimizer). We may need +to label which subgraph inside the OP graph is the optimizer.
    • +
  • It’s important to balance the parameter shards of on multiple +parameter server. If a single parameter is very big (some +word-embedding, fully connected, softmax layer), we need to +automatically partition the single parameter onto different +parameter servers when possible (only element-wise optimizer depends +on the parameter variable).
    • +
+
+
+

Discussion

+
    +
  • 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.
    • +
  • 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)
    • +
+
+ +
+
+ + +
+
+
+ + +
+ +
+

+ © Copyright 2016, PaddlePaddle developers. + +

+
+ Built with Sphinx using a theme provided by Read the Docs. + +
+ +
+
+ +
+ +
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