From bf0f4a21ccb99c02e94e31acac13d563a1962182 Mon Sep 17 00:00:00 2001 From: Travis CI Date: Wed, 24 Jan 2018 02:29:55 +0000 Subject: [PATCH] Deploy to GitHub Pages: 7081f214d7e19db7b65187ac854499b1417e0622 --- .../dist_refactor/parameter_server.md.txt | 40 +++++++++---------- .../dist_refactor/parameter_server.html | 40 +++++++++---------- develop/doc/searchindex.js | 2 +- .../dist_refactor/parameter_server.md.txt | 40 +++++++++---------- .../dist_refactor/parameter_server.html | 40 +++++++++---------- develop/doc_cn/searchindex.js | 2 +- 6 files changed, 82 insertions(+), 82 deletions(-) diff --git a/develop/doc/_sources/design/dist_refactor/parameter_server.md.txt b/develop/doc/_sources/design/dist_refactor/parameter_server.md.txt index 1094f06d461..805dd13048d 100644 --- a/develop/doc/_sources/design/dist_refactor/parameter_server.md.txt +++ b/develop/doc/_sources/design/dist_refactor/parameter_server.md.txt @@ -9,16 +9,16 @@ different purposes. ## Background -The previous implementations of the parameter server does not run a +The previous implementations of the parameter server do not run a fluid sub-program. Parameter initialization, optimizer computation, network communication and checkpointing are implemented twice on both the -trainer and the parameter server. +trainer as well as 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 +It would be great if we can write code once and use them on both: the +trainer and the parameter server, since this reduces code duplication and +improves extensibility. Given that after the current refactoring, we are +representing everything as a computation graph on the +trainer. Representing everything as a computation graph on the parameter server becomes a natural extension. ## Design @@ -30,9 +30,9 @@ into sub-programs 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 + to a heuristic that minimizes the estimated total computation time. Currently we will use a simple heuristic that puts parameter - varable on parameter server workers and everything else on trainer + variable on parameter server workers and everything else on trainer workers. 1. Add communication OPs to enable the communication between nodes. @@ -47,22 +47,22 @@ After converting: -1. The parameter variable W and it's optimizer program are placed on the parameter server. +1. The parameter variable W and its optimizer program are placed on the parameter server. 1. Operators are added to the program. - *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 + - *Enqueue* 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 + queue. It will block until the queue has the required number of tensors. ### Benefits -- Model parallelism become easier to implement: it's an extension to +- Model parallelism becomes easier to implement: it is an extension to the trainer - parameter server approach. We can have several "Transpilers" to achieve different goals. - User-defined optimizer is easier to add - user can now express it as @@ -72,22 +72,22 @@ After converting: ### Challenges -- It's important to balance the parameter shards of on multiple - parameter server. If a single parameter is very big (some +- It is important to balance the parameter shards on multiple + parameter servers. If a single parameter is very big (for example: 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). -- In the "Aync SGD" figure, the "W" variable on the parameter server - could be read and wrote concurrently. See +- In the "Async SGD" figure, the "W" variable on the parameter server + could be read and written concurrently. See [here](https://github.com/PaddlePaddle/Paddle/pull/6394) for more - details about concurrent program in fluid. + details about concurrent program in Fluid. ### Discussion - 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 + (put the input tensor into the queue tensor)? +- *Dequeue* OP will have variable numbers of output (depending on the `min_count` attribute), does our current design support it? (similar question for the *Add* OP) diff --git a/develop/doc/design/dist_refactor/parameter_server.html b/develop/doc/design/dist_refactor/parameter_server.html index d81bc0c9eac..11ba8e09ef6 100644 --- a/develop/doc/design/dist_refactor/parameter_server.html +++ b/develop/doc/design/dist_refactor/parameter_server.html @@ -220,15 +220,15 @@ different purposes.

Background

-

The previous implementations of the parameter server does not run a +

The previous implementations of the parameter server do not run a fluid sub-program. 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 +trainer as well as the parameter server.

+

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

@@ -240,9 +240,9 @@ into sub-programs 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 +to a heuristic that minimizes the estimated total computation time. Currently we will use a simple heuristic that puts parameter -varable on parameter server workers and everything else on trainer +variable on parameter server workers and everything else on trainer workers.
  2. Add communication OPs to enable the communication between nodes.
@@ -253,16 +253,16 @@ subgraphs for the trainer and the parameter server:

After converting:

    -
  1. The parameter variable W and it’s optimizer program are placed on the parameter server.
    2. +
  2. The parameter variable W and its optimizer program are placed on the parameter server.
  3. Operators are added to the program.
    • 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 +
    • Enqueue 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 +queue. It will block until the queue has the required number of tensors.
    4. @@ -271,7 +271,7 @@ tensors.

    Benefits

      -
    • Model parallelism become easier to implement: it’s an extension to +
    • Model parallelism becomes easier to implement: it is an extension to the trainer - parameter server approach. We can have several “Transpilers” to achieve different goals.
    • User-defined optimizer is easier to add - user can now express it as @@ -283,24 +283,24 @@ server mentioned in the background section.

      • Challenges

        -
      • It’s important to balance the parameter shards of on multiple -parameter server. If a single parameter is very big (some +
      • It is important to balance the parameter shards on multiple +parameter servers. If a single parameter is very big (for example: 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).
        • -
      • In the “Aync SGD” figure, the “W” variable on the parameter server -could be read and wrote concurrently. See +
      • In the “Async SGD” figure, the “W” variable on the parameter server +could be read and written concurrently. See here for more -details about concurrent program in fluid.
        • +details about concurrent program in Fluid.

      Discussion

      • 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 +(put the input tensor into the queue tensor)?
        • +
      • Dequeue OP will have variable numbers of output (depending on the min_count attribute), does our current design support it? (similar question for the Add OP)
      diff --git a/develop/doc/searchindex.js b/develop/doc/searchindex.js index dc3517d1e54..02533a3bfc1 100644 --- a/develop/doc/searchindex.js +++ b/develop/doc/searchindex.js @@ -1 +1 @@ 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\ No newline at end of file diff --git a/develop/doc_cn/_sources/design/dist_refactor/parameter_server.md.txt b/develop/doc_cn/_sources/design/dist_refactor/parameter_server.md.txt index 1094f06d461..805dd13048d 100644 --- a/develop/doc_cn/_sources/design/dist_refactor/parameter_server.md.txt +++ b/develop/doc_cn/_sources/design/dist_refactor/parameter_server.md.txt @@ -9,16 +9,16 @@ different purposes. ## Background -The previous implementations of the parameter server does not run a +The previous implementations of the parameter server do not run a fluid sub-program. Parameter initialization, optimizer computation, network communication and checkpointing are implemented twice on both the -trainer and the parameter server. +trainer as well as 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 +It would be great if we can write code once and use them on both: the +trainer and the parameter server, since this reduces code duplication and +improves extensibility. Given that after the current refactoring, we are +representing everything as a computation graph on the +trainer. Representing everything as a computation graph on the parameter server becomes a natural extension. ## Design @@ -30,9 +30,9 @@ into sub-programs 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 + to a heuristic that minimizes the estimated total computation time. Currently we will use a simple heuristic that puts parameter - varable on parameter server workers and everything else on trainer + variable on parameter server workers and everything else on trainer workers. 1. Add communication OPs to enable the communication between nodes. @@ -47,22 +47,22 @@ After converting: -1. The parameter variable W and it's optimizer program are placed on the parameter server. +1. The parameter variable W and its optimizer program are placed on the parameter server. 1. Operators are added to the program. - *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 + - *Enqueue* 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 + queue. It will block until the queue has the required number of tensors. ### Benefits -- Model parallelism become easier to implement: it's an extension to +- Model parallelism becomes easier to implement: it is an extension to the trainer - parameter server approach. We can have several "Transpilers" to achieve different goals. - User-defined optimizer is easier to add - user can now express it as @@ -72,22 +72,22 @@ After converting: ### Challenges -- It's important to balance the parameter shards of on multiple - parameter server. If a single parameter is very big (some +- It is important to balance the parameter shards on multiple + parameter servers. If a single parameter is very big (for example: 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). -- In the "Aync SGD" figure, the "W" variable on the parameter server - could be read and wrote concurrently. See +- In the "Async SGD" figure, the "W" variable on the parameter server + could be read and written concurrently. See [here](https://github.com/PaddlePaddle/Paddle/pull/6394) for more - details about concurrent program in fluid. + details about concurrent program in Fluid. ### Discussion - 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 + (put the input tensor into the queue tensor)? +- *Dequeue* OP will have variable numbers of output (depending on the `min_count` attribute), does our current design support it? (similar question for the *Add* OP) diff --git a/develop/doc_cn/design/dist_refactor/parameter_server.html b/develop/doc_cn/design/dist_refactor/parameter_server.html index 741ca0af91f..18f1debdab4 100644 --- a/develop/doc_cn/design/dist_refactor/parameter_server.html +++ b/develop/doc_cn/design/dist_refactor/parameter_server.html @@ -239,15 +239,15 @@ different purposes.

      Background

      -

      The previous implementations of the parameter server does not run a +

      The previous implementations of the parameter server do not run a fluid sub-program. 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 +trainer as well as the parameter server.

      +

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

      @@ -259,9 +259,9 @@ into sub-programs 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 +to a heuristic that minimizes the estimated total computation time. Currently we will use a simple heuristic that puts parameter -varable on parameter server workers and everything else on trainer +variable on parameter server workers and everything else on trainer workers.
      2. Add communication OPs to enable the communication between nodes.
      @@ -272,16 +272,16 @@ subgraphs for the trainer and the parameter server:

      After converting:

        -
      1. The parameter variable W and it’s optimizer program are placed on the parameter server.
        2. +
      2. The parameter variable W and its optimizer program are placed on the parameter server.
      3. Operators are added to the program.
        • 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 +
        • Enqueue 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 +queue. It will block until the queue has the required number of tensors.
        4. @@ -290,7 +290,7 @@ tensors.

        Benefits

          -
        • Model parallelism become easier to implement: it’s an extension to +
        • Model parallelism becomes easier to implement: it is an extension to the trainer - parameter server approach. We can have several “Transpilers” to achieve different goals.
        • User-defined optimizer is easier to add - user can now express it as @@ -302,24 +302,24 @@ server mentioned in the background section.

          • Challenges

            -
          • It’s important to balance the parameter shards of on multiple -parameter server. If a single parameter is very big (some +
          • It is important to balance the parameter shards on multiple +parameter servers. If a single parameter is very big (for example: 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).
            • -
          • In the “Aync SGD” figure, the “W” variable on the parameter server -could be read and wrote concurrently. See +
          • In the “Async SGD” figure, the “W” variable on the parameter server +could be read and written concurrently. See here for more -details about concurrent program in fluid.
            • +details about concurrent program in Fluid.

          Discussion

          • 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 +(put the input tensor into the queue tensor)?
            • +
          • Dequeue OP will have variable numbers of output (depending on the min_count attribute), does our current design support it? (similar question for the Add OP)
          diff --git a/develop/doc_cn/searchindex.js b/develop/doc_cn/searchindex.js index 40b74ea62fd..926e495da19 100644 --- a/develop/doc_cn/searchindex.js +++ b/develop/doc_cn/searchindex.js @@ -1 +1 @@ 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