From 1ff9360646215da9d0c83b8e45fdd1d112b0d0c9 Mon Sep 17 00:00:00 2001 From: Travis CI Date: Fri, 30 Jun 2017 20:37:47 +0000 Subject: [PATCH] Deploy to GitHub Pages: 45a78a4f2091d02e3bba709e5defbacdb9f35e08 --- .../design/cluster_train/save_model.md.txt | 110 ++++++ .../doc/design/cluster_train/save_model.html | 346 +++++++++++++++++ develop/doc/objects.inv | Bin 2334 -> 2351 bytes develop/doc/searchindex.js | 2 +- .../design/cluster_train/save_model.md.txt | 110 ++++++ .../design/cluster_train/save_model.html | 352 ++++++++++++++++++ develop/doc_cn/objects.inv | Bin 2819 -> 2839 bytes develop/doc_cn/searchindex.js | 2 +- 8 files changed, 920 insertions(+), 2 deletions(-) create mode 100644 develop/doc/_sources/design/cluster_train/save_model.md.txt create mode 100644 develop/doc/design/cluster_train/save_model.html create mode 100644 develop/doc_cn/_sources/design/cluster_train/save_model.md.txt create mode 100644 develop/doc_cn/design/cluster_train/save_model.html diff --git a/develop/doc/_sources/design/cluster_train/save_model.md.txt b/develop/doc/_sources/design/cluster_train/save_model.md.txt new file mode 100644 index 0000000000..b70f00176b --- /dev/null +++ b/develop/doc/_sources/design/cluster_train/save_model.md.txt @@ -0,0 +1,110 @@ +# Design Doc: Save Model + +## Overview + +The model is the output of the training process. There are two +ways from which user can obtain a model: + +- Save model triggered by user code: user code asks PaddlePaddle to + save a model. +- Convert model from the checkpoint: model being converted from + pservers' periodic checkpoint. In this way, the user can cancel a + job at any time, and still have a relatively fresh model (we + checkpoint around every 5 minutes). + +### Trainer Saving Model vs. Pservers Saving Model + +Both trainers and pservers have access to the model. So the model can +be saved from a trainer or pservers. We need to decide where the model +is saved from. + +#### Dense Update vs. Sparse Update + +There are two types of model update methods: dense update and sparse +update (when the model parameter is configured to be sparse). + +- Dense update + + Every trainer has it's own full copy of the model. Every model + update will update the entire model. + +- Sparse update + + The training input is sparse, and the trainer does not have the + entire model. It will only download the sub-model necessary related + to the input. When updating the model, only the sub-model related to + the training input is updated. + + +#### Pservers Saving Model + +The benefit of letting pservers save model is they have the entire +model all the time. However, since pservers are on different nodes, it +requires a merging process to merge model shards into the same +model. Thus requires the pservers to write models to a distributed +filesystem, making the checkpoint shards visible to the merge program. + +#### Trainer Saving Model + +The benefit of letting one trainer to save the model is it does not +require a distributed filesystem. And it's reusing the same save model +logic when training locally - except when doing sparse update, the +trainer needs to download the entire model during the saving process. + +#### Conclusion + +Given trainer saving model does not require a distributed filesystem, +and is an intuitive extension to trainer saving model when training +locally, we decide to let the trainer save the model when doing +distributed training. + + +### Convert Model from Checkpoint + +TODO + + +## Timeline + +We first implement trainer save the model. Converting the latest +snapshot to a model will be a TODO for future. + + +## Trainer Save Model + +### Trainer Election + +One trainer will be elected as the one to save the model. When using +etcd, trainer ID is a randomly generated UUID, we will utilize etcd to +elect one trainer. When not using etcd, unique trainer IDs will be +given by the administrator, the trainer whose ID is "0" is elected to +save the model. + +### Model Save Path + +Each trainer will be given the directory to save the model. The +elected trainer will save the model to +`given-directory/trainerID`. Since the trainer ID is unique, this +would prevent concurrent save to the same file when multiple trainers +are elected to save the model when split-brain problem happens. + +### What Happens When Model Is Saving + +It takes some time to save model, we need to define what will happen +when save model is taking place. + +When doing dense update, the trainer uses the local model. Pservers +does not need to pause model update. + +When doing sparse update. The trainer needs to download the entire +model while saving. To get the most accurate model, the model update +needs to be paused before the download starts and resumed after the +download finishes. Otherwise, the trainer gets a model that is +"polluted": some part of the model is old, some part of the model is +new. + +It's unclear that the "polluted" model will be inferior due to the +stochastic nature of deep learning, and pausing the model update will +add more complexity to the system. Since supporting sparse update is a +TODO item. We defer the evaluation of pause the model update or not +during saving model to the future. diff --git a/develop/doc/design/cluster_train/save_model.html b/develop/doc/design/cluster_train/save_model.html new file mode 100644 index 0000000000..90b73b058e --- /dev/null +++ b/develop/doc/design/cluster_train/save_model.html @@ -0,0 +1,346 @@ + + + + + + + + + + + Design Doc: Save Model — PaddlePaddle documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+ Folk me on Github +
+ + English + + + + +
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+ + + + +
+ + + + + + + + + + + +
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    + +
  • Design Doc: Save Model
    • +
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+ +
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+ +
+

Design Doc: Save Model

+
+

Overview

+

The model is the output of the training process. There are two +ways from which user can obtain a model:

+
    +
  • Save model triggered by user code: user code asks PaddlePaddle to +save a model.
    • +
  • Convert model from the checkpoint: model being converted from +pservers’ periodic checkpoint. In this way, the user can cancel a +job at any time, and still have a relatively fresh model (we +checkpoint around every 5 minutes).
    • +
+
+

Trainer Saving Model vs. Pservers Saving Model

+

Both trainers and pservers have access to the model. So the model can +be saved from a trainer or pservers. We need to decide where the model +is saved from.

+
+

Dense Update vs. Sparse Update

+

There are two types of model update methods: dense update and sparse +update (when the model parameter is configured to be sparse).

+
    +

  • Dense update

    +

    Every trainer has it’s own full copy of the model. Every model +update will update the entire model.

    +
    • +

  • Sparse update

    +

    The training input is sparse, and the trainer does not have the +entire model. It will only download the sub-model necessary related +to the input. When updating the model, only the sub-model related to +the training input is updated.

    +
    • +
+
+
+

Pservers Saving Model

+

The benefit of letting pservers save model is they have the entire +model all the time. However, since pservers are on different nodes, it +requires a merging process to merge model shards into the same +model. Thus requires the pservers to write models to a distributed +filesystem, making the checkpoint shards visible to the merge program.

+
+
+

Trainer Saving Model

+

The benefit of letting one trainer to save the model is it does not +require a distributed filesystem. And it’s reusing the same save model +logic when training locally - except when doing sparse update, the +trainer needs to download the entire model during the saving process.

+
+
+

Conclusion

+

Given trainer saving model does not require a distributed filesystem, +and is an intuitive extension to trainer saving model when training +locally, we decide to let the trainer save the model when doing +distributed training.

+
+
+
+

Convert Model from Checkpoint

+

TODO

+
+
+
+

Timeline

+

We first implement trainer save the model. Converting the latest +snapshot to a model will be a TODO for future.

+
+
+

Trainer Save Model

+
+

Trainer Election

+

One trainer will be elected as the one to save the model. When using +etcd, trainer ID is a randomly generated UUID, we will utilize etcd to +elect one trainer. When not using etcd, unique trainer IDs will be +given by the administrator, the trainer whose ID is “0” is elected to +save the model.

+
+
+

Model Save Path

+

Each trainer will be given the directory to save the model. The +elected trainer will save the model to +given-directory/trainerID. Since the trainer ID is unique, this +would prevent concurrent save to the same file when multiple trainers +are elected to save the model when split-brain problem happens.

+
+
+

What Happens When Model Is Saving

+

It takes some time to save model, we need to define what will happen +when save model is taking place.

+

When doing dense update, the trainer uses the local model. Pservers +does not need to pause model update.

+

When doing sparse update. The trainer needs to download the entire +model while saving. To get the most accurate model, the model update +needs to be paused before the download starts and resumed after the +download finishes. Otherwise, the trainer gets a model that is +“polluted”: some part of the model is old, some part of the model is +new.

+

It’s unclear that the “polluted” model will be inferior due to the +stochastic nature of deep learning, and pausing the model update will +add more complexity to the system. Since supporting sparse update is a +TODO item. We defer the evaluation of pause the model update or not +during saving model to the future.

+
+
+
+ + +
+
+
+ + +
+ +
+

+ © Copyright 2016, PaddlePaddle developers. + +

+
+ Built with Sphinx using a theme provided by Read the Docs. + +
+ +
+
+ +
+ +
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\ No newline at end of file diff --git a/develop/doc_cn/_sources/design/cluster_train/save_model.md.txt b/develop/doc_cn/_sources/design/cluster_train/save_model.md.txt new file mode 100644 index 0000000000..b70f00176b --- /dev/null +++ b/develop/doc_cn/_sources/design/cluster_train/save_model.md.txt @@ -0,0 +1,110 @@ +# Design Doc: Save Model + +## Overview + +The model is the output of the training process. There are two +ways from which user can obtain a model: + +- Save model triggered by user code: user code asks PaddlePaddle to + save a model. +- Convert model from the checkpoint: model being converted from + pservers' periodic checkpoint. In this way, the user can cancel a + job at any time, and still have a relatively fresh model (we + checkpoint around every 5 minutes). + +### Trainer Saving Model vs. Pservers Saving Model + +Both trainers and pservers have access to the model. So the model can +be saved from a trainer or pservers. We need to decide where the model +is saved from. + +#### Dense Update vs. Sparse Update + +There are two types of model update methods: dense update and sparse +update (when the model parameter is configured to be sparse). + +- Dense update + + Every trainer has it's own full copy of the model. Every model + update will update the entire model. + +- Sparse update + + The training input is sparse, and the trainer does not have the + entire model. It will only download the sub-model necessary related + to the input. When updating the model, only the sub-model related to + the training input is updated. + + +#### Pservers Saving Model + +The benefit of letting pservers save model is they have the entire +model all the time. However, since pservers are on different nodes, it +requires a merging process to merge model shards into the same +model. Thus requires the pservers to write models to a distributed +filesystem, making the checkpoint shards visible to the merge program. + +#### Trainer Saving Model + +The benefit of letting one trainer to save the model is it does not +require a distributed filesystem. And it's reusing the same save model +logic when training locally - except when doing sparse update, the +trainer needs to download the entire model during the saving process. + +#### Conclusion + +Given trainer saving model does not require a distributed filesystem, +and is an intuitive extension to trainer saving model when training +locally, we decide to let the trainer save the model when doing +distributed training. + + +### Convert Model from Checkpoint + +TODO + + +## Timeline + +We first implement trainer save the model. Converting the latest +snapshot to a model will be a TODO for future. + + +## Trainer Save Model + +### Trainer Election + +One trainer will be elected as the one to save the model. When using +etcd, trainer ID is a randomly generated UUID, we will utilize etcd to +elect one trainer. When not using etcd, unique trainer IDs will be +given by the administrator, the trainer whose ID is "0" is elected to +save the model. + +### Model Save Path + +Each trainer will be given the directory to save the model. The +elected trainer will save the model to +`given-directory/trainerID`. Since the trainer ID is unique, this +would prevent concurrent save to the same file when multiple trainers +are elected to save the model when split-brain problem happens. + +### What Happens When Model Is Saving + +It takes some time to save model, we need to define what will happen +when save model is taking place. + +When doing dense update, the trainer uses the local model. Pservers +does not need to pause model update. + +When doing sparse update. The trainer needs to download the entire +model while saving. To get the most accurate model, the model update +needs to be paused before the download starts and resumed after the +download finishes. Otherwise, the trainer gets a model that is +"polluted": some part of the model is old, some part of the model is +new. + +It's unclear that the "polluted" model will be inferior due to the +stochastic nature of deep learning, and pausing the model update will +add more complexity to the system. Since supporting sparse update is a +TODO item. We defer the evaluation of pause the model update or not +during saving model to the future. diff --git a/develop/doc_cn/design/cluster_train/save_model.html b/develop/doc_cn/design/cluster_train/save_model.html new file mode 100644 index 0000000000..b97a85d638 --- /dev/null +++ b/develop/doc_cn/design/cluster_train/save_model.html @@ -0,0 +1,352 @@ + + + + + + + + + + + Design Doc: Save Model — PaddlePaddle 文档 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+ Folk me on Github +
+ + English + + + + +
+ +
+
+ + + + +
+
+ + + +
+
+
+
+
+ +
+ + + + +
+ + + + + + + + + + + +
+
    + +
  • Design Doc: Save Model
    • +
+
+ +
+
+
+
+ +
+

Design Doc: Save Model

+
+

Overview

+

The model is the output of the training process. There are two +ways from which user can obtain a model:

+
    +
  • Save model triggered by user code: user code asks PaddlePaddle to +save a model.
    • +
  • Convert model from the checkpoint: model being converted from +pservers’ periodic checkpoint. In this way, the user can cancel a +job at any time, and still have a relatively fresh model (we +checkpoint around every 5 minutes).
    • +
+
+

Trainer Saving Model vs. Pservers Saving Model

+

Both trainers and pservers have access to the model. So the model can +be saved from a trainer or pservers. We need to decide where the model +is saved from.

+
+

Dense Update vs. Sparse Update

+

There are two types of model update methods: dense update and sparse +update (when the model parameter is configured to be sparse).

+
    +

  • Dense update

    +

    Every trainer has it’s own full copy of the model. Every model +update will update the entire model.

    +
    • +

  • Sparse update

    +

    The training input is sparse, and the trainer does not have the +entire model. It will only download the sub-model necessary related +to the input. When updating the model, only the sub-model related to +the training input is updated.

    +
    • +
+
+
+

Pservers Saving Model

+

The benefit of letting pservers save model is they have the entire +model all the time. However, since pservers are on different nodes, it +requires a merging process to merge model shards into the same +model. Thus requires the pservers to write models to a distributed +filesystem, making the checkpoint shards visible to the merge program.

+
+
+

Trainer Saving Model

+

The benefit of letting one trainer to save the model is it does not +require a distributed filesystem. And it’s reusing the same save model +logic when training locally - except when doing sparse update, the +trainer needs to download the entire model during the saving process.

+
+
+

Conclusion

+

Given trainer saving model does not require a distributed filesystem, +and is an intuitive extension to trainer saving model when training +locally, we decide to let the trainer save the model when doing +distributed training.

+
+
+
+

Convert Model from Checkpoint

+

TODO

+
+
+
+

Timeline

+

We first implement trainer save the model. Converting the latest +snapshot to a model will be a TODO for future.

+
+
+

Trainer Save Model

+
+

Trainer Election

+

One trainer will be elected as the one to save the model. When using +etcd, trainer ID is a randomly generated UUID, we will utilize etcd to +elect one trainer. When not using etcd, unique trainer IDs will be +given by the administrator, the trainer whose ID is “0” is elected to +save the model.

+
+
+

Model Save Path

+

Each trainer will be given the directory to save the model. The +elected trainer will save the model to +given-directory/trainerID. Since the trainer ID is unique, this +would prevent concurrent save to the same file when multiple trainers +are elected to save the model when split-brain problem happens.

+
+
+

What Happens When Model Is Saving

+

It takes some time to save model, we need to define what will happen +when save model is taking place.

+

When doing dense update, the trainer uses the local model. Pservers +does not need to pause model update.

+

When doing sparse update. The trainer needs to download the entire +model while saving. To get the most accurate model, the model update +needs to be paused before the download starts and resumed after the +download finishes. Otherwise, the trainer gets a model that is +“polluted”: some part of the model is old, some part of the model is +new.

+

It’s unclear that the “polluted” model will be inferior due to the +stochastic nature of deep learning, and pausing the model update will +add more complexity to the system. Since supporting sparse update is a +TODO item. We defer the evaluation of pause the model update or not +during saving model to the future.

+
+
+
+ + +
+
+
+ + +
+ +
+

+ © Copyright 2016, PaddlePaddle developers. + +

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