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 0000000000000000000000000000000000000000..b70f00176b6701ef487ef88ac0933b9b227037ea --- /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/_sources/design/simple_op_design.md.txt b/develop/doc/_sources/design/simple_op_design.md.txt new file mode 100644 index 0000000000000000000000000000000000000000..49ca5db5da9e400fd2c54eb8903b0dd2eb832d44 --- /dev/null +++ b/develop/doc/_sources/design/simple_op_design.md.txt @@ -0,0 +1,201 @@ +## Interaction between C++ and Python + +Users employ API in Python to describe their own network, however, the network construction actually happens in C++. so Protobuf is introduced to send the message between Python and C++. + +The Interaction between Python and C++ can be simplified as two steps: + +1. C++ tells Python how many Ops there are, and what parameter do users need to offer to initialize a new Op. Python then builds API for each Op at compile time. + +2. Users invoke APIs built by Python and provide necessary parameters. These parameters will be sent to C++ fo finish Op construction task. + +### Message form C++ to Python + +We define a Protobuf message class `OpProto` to hold message needed in the first step. What should an `OpProto` contain? This question is equivalent to “What message do we need to offer, to build a Python API which is legal and user oriented and can use to describe a whole Op.” + +Following message are necessary: + +1. Op's name, and its simple comment. +2. Input and output variable number; each variable's name, type, and comment. +3. Op's attributes; each attribute includes name, type, comment, **default value** and **value range**. + +So `OpProto` can be defined as follows: + +```proto +enum AttrType { + INT = 1; + FLOAT = 2; + STRING = 3; + INTS = 4; + FLOATS = 5; + STRINGS = 6; +}; + +message AttrValue { + AttrType type = 1; + optional int iv = 2; + optional float fv = 3; + optional string sv = 4; + repeated int ivs = 5; + repeated float fvs = 6; + repeated string svs = 7; +}; + +message AttrProto { + required string name = 1; + required string comment = 2; + required AttrType type = 3; +}; + +message VarProto { + required string name = 1; + required string comment = 2; +}; + +message OpProto { + repeated VarProto inputs = 1; + repeated VarProto outputs = 2; + repeated AttrProto attrs = 3; + required string type = 4; + required string comment = 5; +}; +``` + +To generate Python code automatically: + +```python +def create_python_ops_creatation_functions(): + op_protos = paddle.framework.OpRegistry.get_all_op_proto() + for type_name in op_protos: + op_proto = op_protos[type_name] + def __impl__(**kwargs): # User must use key word args in Paddle API + inputs = [kwargs.get(ipt.name, "") for ipt in op_proto.inputs] + outputs = [kwargs.get(opt.name, "") for opt in op_proto.outputs] + attrs = [cast_to_op_attr(attr, kwargs.get(attr.name, None)) for attr in op_proto.attrs] + opdesc = (input, outputs, type_name, attrs) + return paddle.framework.OpRegistry.CreateOp(opdesc) + __impl__.__doc__ = create_doc_string(op_proto) + globals()[type_name] = __impl__ + +create_python_ops_creatation_functions() +``` + +### Message from Python to C++ + +To hold message needed in the above second step, we define Protobuf message class `OpDesc`. It is used to hold user-specified parameters in Op describing. + +```proto +message OpDesc { + required string type = 1; + repeated string inputs = 2; + repeated string outputs = 3; + map attrs = 4; +}; +``` + +## OpProto Register + +Every Op has its own `OpProto`. For using convenience, we need to register them and record all their messages. For each `Op` class, we define a corresponding `OpMaker` class, in whose constructor we implement the `OpProto`'s building process. `OpMaker`'s constructor will be invoked by another function `OpRegistry::RegisterOp()`. + +```cpp +class OpProtoMaker { +public: + OpProtoMaker(OpProto* proto): proto_(proto) {} +protected: + OpProto* proto_; + void AddInput(const std::string& name, const std::string& desc) {...} + void AddAttr(const std::string& name, const std::string& desc, TypeId type) {...} + void AddComment(const std::string& comment) { ... } +}; + +class OpRegistry { +public: + using OpCreator = std::function; + + template + static void RegisterOp(const std::string& name) { + gCreators_[name] = [](const OpDesc& desc) { + return new OpType(desc); + }; + OpProto& opProto = gProtos_[name]; + OpMaker()(&opProto); + } + + static map gCreators_; + static map gProtos_; +}; + +template +class OpRegister { + public: + OpRegister(std::string type) { + OpRegistry::RegisterOp(type); + } +}; + +#define REGISTER_OP(op_class, op_maker_class, type_name) \ + class op_class##Register { \ + private: \ + const static OpRegister<#op_class, #op_maker_class> reg; \ + }; \ + const Register op_class##Register::reg(#type_name); + +class CosineOp { +// ... +} + +struct CosineOpProtoMaker : public OpProtoMaker { + CosineOpProtoMaker(OpProto* proto) : OpProtoMaker(proto) { + AddInput("input", "input of cosine op"); + AddAttr("scale", "scale of cosine op", float).Default(1.0).LargerThan(0.0); + AddType("cos"); + AddComment("This is cos op"); + } +} + +REGISTER_OP(CosineOp, CosineOpProtoMaker, cos); +``` + +In `REGISTER_OP(CosineOp, CosineOpProtoMaker, cos)`, we register not only `CosineOp` but also `CosineOpProto`. As fields of `CosineOpProto`, the default value and value range of `scale` are also registered here. + +## Python API + +Python APIs are divided into two types, high-level API and low-level API. + +### High-Level API + +High-level API is called by users directly, so it should keep its style consistent with existing V2 APIs. + +Here is a sample about how a define a fc layer: + +```python +hd = fc_layer(input=data, size=56, with_bias=True, activation="sigmoid"); +``` + +`hd` is the output of `fc_layer` and it's a `variable`. It can be further sent into other layers as input. + +The definition of `fc_layer()`: + +```python +def fc_layer(input, size, with_bias, activation): + attr_map = {"size":size} + check_attrs(attr_map) + w = make_variable('w') + if with_bias: + b = make_variable('b') + else: + b = None + fc_output = make_variable('fc_output'); + fc_op(input, w, b, fc_output, attr_map) + act_output = make_variable('sigmod_output'); + if activation == "sigmod": + sigmod_op(fc_output, act_output); + elif: + # ... + return act_output; +``` + +### Low Leval API + +In above sample, `fc_op` and `sigmod_op` are low-level API. They build `OpDesc` and invoke corresponding C++ code. + +*TODO* 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 0000000000000000000000000000000000000000..90b73b058ea74df568602d1c3b4e1db443f36c10 --- /dev/null +++ b/develop/doc/design/cluster_train/save_model.html @@ -0,0 +1,346 @@ + + + + + + + + + + + Design Doc: Save Model — PaddlePaddle documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + + + + + + + +
+
    + +
  • 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. + +
+ +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/develop/doc/design/simple_op_design.html b/develop/doc/design/simple_op_design.html new file mode 100644 index 0000000000000000000000000000000000000000..95a8fb84ba304119b0b68088d566a8ef8d320238 --- /dev/null +++ b/develop/doc/design/simple_op_design.html @@ -0,0 +1,427 @@ + + + + + + + + + + + Interaction between C++ and Python — PaddlePaddle documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + + + + + + + +
+
    + +
  • Interaction between C++ and Python
  • +
+
+ +
+
+
+
+ +
+

Interaction between C++ and Python

+

Users employ API in Python to describe their own network, however, the network construction actually happens in C++. so Protobuf is introduced to send the message between Python and C++.

+

The Interaction between Python and C++ can be simplified as two steps:

+
    +
  1. C++ tells Python how many Ops there are, and what parameter do users need to offer to initialize a new Op. Python then builds API for each Op at compile time.
  2. +
  3. Users invoke APIs built by Python and provide necessary parameters. These parameters will be sent to C++ fo finish Op construction task.
  4. +
+
+

Message form C++ to Python

+

We define a Protobuf message class OpProto to hold message needed in the first step. What should an OpProto contain? This question is equivalent to “What message do we need to offer, to build a Python API which is legal and user oriented and can use to describe a whole Op.”

+

Following message are necessary:

+
    +
  1. Op’s name, and its simple comment.
  2. +
  3. Input and output variable number; each variable’s name, type, and comment.
  4. +
  5. Op’s attributes; each attribute includes name, type, comment, default value and value range.
  6. +
+

So OpProto can be defined as follows:

+
enum AttrType {
+    INT = 1;
+    FLOAT = 2;
+    STRING = 3;
+    INTS = 4;
+    FLOATS = 5;
+    STRINGS = 6;
+};
+
+message AttrValue {
+    AttrType type = 1;
+    optional int iv = 2;
+    optional float fv = 3;
+    optional string sv = 4;
+    repeated int ivs = 5;
+    repeated float fvs = 6;
+    repeated string svs = 7;
+};
+
+message AttrProto {
+    required string name = 1;
+    required string comment = 2;
+    required AttrType type = 3;
+};
+
+message VarProto {
+    required string name = 1;
+    required string comment = 2;
+};
+
+message OpProto {
+    repeated VarProto inputs = 1;
+    repeated VarProto outputs = 2;
+    repeated AttrProto attrs = 3;
+    required string type = 4;
+    required string comment = 5;
+};
+
+
+

To generate Python code automatically:

+
def create_python_ops_creatation_functions():
+    op_protos = paddle.framework.OpRegistry.get_all_op_proto()
+    for type_name in op_protos:
+        op_proto = op_protos[type_name]
+        def __impl__(**kwargs):  # User must use key word args in Paddle API
+            inputs = [kwargs.get(ipt.name, "") for ipt in op_proto.inputs]
+            outputs = [kwargs.get(opt.name, "") for opt in op_proto.outputs]
+            attrs = [cast_to_op_attr(attr, kwargs.get(attr.name, None)) for attr in op_proto.attrs]
+            opdesc = (input, outputs, type_name, attrs)
+            return paddle.framework.OpRegistry.CreateOp(opdesc)
+        __impl__.__doc__ = create_doc_string(op_proto)
+        globals()[type_name] = __impl__
+
+create_python_ops_creatation_functions()
+
+
+
+
+

Message from Python to C++

+

To hold message needed in the above second step, we define Protobuf message class OpDesc. It is used to hold user-specified parameters in Op describing.

+
message OpDesc {
+    required string type = 1;   
+    repeated string inputs = 2;
+    repeated string outputs = 3;
+    map<string, AttrValue> attrs = 4;
+};
+
+
+
+
+
+

OpProto Register

+

Every Op has its own OpProto. For using convenience, we need to register them and record all their messages. For each Op class, we define a corresponding OpMaker class, in whose constructor we implement the OpProto‘s building process. OpMaker‘s constructor will be invoked by another function OpRegistry::RegisterOp().

+
class OpProtoMaker {
+public:
+    OpProtoMaker(OpProto* proto): proto_(proto) {}
+protected:
+    OpProto* proto_;
+    void AddInput(const std::string& name, const std::string& desc) {...}
+    void AddAttr(const std::string& name, const std::string& desc, TypeId type) {...}
+    void AddComment(const std::string& comment) { ... }
+};
+
+class OpRegistry {
+public:
+    using OpCreator = std::function<OperatorBase* (OpDesc& desc)>;
+    
+    template <typename OpType, typename OpMaker>
+    static void RegisterOp(const std::string& name) {
+        gCreators_[name] = [](const OpDesc& desc) {
+            return new OpType(desc);
+        };
+        OpProto& opProto = gProtos_[name];
+        OpMaker()(&opProto);
+    }
+
+    static map<string, OpCreator> gCreators_;
+    static map<string, OpProto> gProtos_;
+};
+
+template <typename OpType, typename OpMaker>
+class OpRegister {
+  public:
+    OpRegister(std::string type) {
+        OpRegistry::RegisterOp<OpType, OpMaker>(type);
+    }
+};
+
+#define REGISTER_OP(op_class, op_maker_class, type_name)         \
+    class op_class##Register {                                   \
+      private:                                                   \
+        const static OpRegister<#op_class, #op_maker_class> reg; \
+    };                                                           \
+    const Register op_class##Register::reg(#type_name);
+    
+class CosineOp {
+// ...
+}
+
+struct CosineOpProtoMaker : public OpProtoMaker {
+    CosineOpProtoMaker(OpProto* proto) : OpProtoMaker(proto) {
+        AddInput("input", "input of cosine op");
+        AddAttr("scale", "scale of cosine op", float).Default(1.0).LargerThan(0.0);
+        AddType("cos");
+        AddComment("This is cos op");
+    }
+}
+
+REGISTER_OP(CosineOp, CosineOpProtoMaker, cos);
+
+
+

In REGISTER_OP(CosineOp, CosineOpProtoMaker, cos), we register not only CosineOp but also CosineOpProto. As fields of CosineOpProto, the default value and value range of scale are also registered here.

+
+
+

Python API

+

Python APIs are divided into two types, high-level API and low-level API.

+
+

High-Level API

+

High-level API is called by users directly, so it should keep its style consistent with existing V2 APIs.

+

Here is a sample about how a define a fc layer:

+
hd = fc_layer(input=data, size=56, with_bias=True, activation="sigmoid");
+
+
+

hd is the output of fc_layer and it’s a variable. It can be further sent into other layers as input.

+

The definition of fc_layer():

+
def fc_layer(input, size, with_bias, activation):
+    attr_map = {"size":size}
+    check_attrs(attr_map)
+    w = make_variable('w')
+    if with_bias:
+        b = make_variable('b')
+    else:
+        b = None
+    fc_output = make_variable('fc_output');
+    fc_op(input, w, b, fc_output, attr_map)
+    act_output = make_variable('sigmod_output');
+    if activation == "sigmod":
+        sigmod_op(fc_output, act_output);
+    elif:
+        # ...
+    return act_output;
+
+
+
+
+

Low Leval API

+

In above sample, fc_op and sigmod_op are low-level API. They build OpDesc and invoke corresponding C++ code.

+

TODO

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

+ © Copyright 2016, PaddlePaddle developers. + +

+
+ Built with Sphinx using a theme provided by Read the Docs. + +
+ +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/develop/doc/objects.inv b/develop/doc/objects.inv index 36ebc059b574787fe255769f1bb7bc0d65c18f5c..b302cc6224fca55bfc395c207e19946b231942f6 100644 Binary files a/develop/doc/objects.inv and b/develop/doc/objects.inv differ diff --git a/develop/doc/searchindex.js b/develop/doc/searchindex.js index fbc200e1b827ccad5d232a8dd7526e19c55ad207..b5eeabd000e81a9906da8867ce4c7afc52dd461b 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/cluster_train/save_model.md.txt b/develop/doc_cn/_sources/design/cluster_train/save_model.md.txt new file mode 100644 index 0000000000000000000000000000000000000000..b70f00176b6701ef487ef88ac0933b9b227037ea --- /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/_sources/design/simple_op_design.md.txt b/develop/doc_cn/_sources/design/simple_op_design.md.txt new file mode 100644 index 0000000000000000000000000000000000000000..49ca5db5da9e400fd2c54eb8903b0dd2eb832d44 --- /dev/null +++ b/develop/doc_cn/_sources/design/simple_op_design.md.txt @@ -0,0 +1,201 @@ +## Interaction between C++ and Python + +Users employ API in Python to describe their own network, however, the network construction actually happens in C++. so Protobuf is introduced to send the message between Python and C++. + +The Interaction between Python and C++ can be simplified as two steps: + +1. C++ tells Python how many Ops there are, and what parameter do users need to offer to initialize a new Op. Python then builds API for each Op at compile time. + +2. Users invoke APIs built by Python and provide necessary parameters. These parameters will be sent to C++ fo finish Op construction task. + +### Message form C++ to Python + +We define a Protobuf message class `OpProto` to hold message needed in the first step. What should an `OpProto` contain? This question is equivalent to “What message do we need to offer, to build a Python API which is legal and user oriented and can use to describe a whole Op.” + +Following message are necessary: + +1. Op's name, and its simple comment. +2. Input and output variable number; each variable's name, type, and comment. +3. Op's attributes; each attribute includes name, type, comment, **default value** and **value range**. + +So `OpProto` can be defined as follows: + +```proto +enum AttrType { + INT = 1; + FLOAT = 2; + STRING = 3; + INTS = 4; + FLOATS = 5; + STRINGS = 6; +}; + +message AttrValue { + AttrType type = 1; + optional int iv = 2; + optional float fv = 3; + optional string sv = 4; + repeated int ivs = 5; + repeated float fvs = 6; + repeated string svs = 7; +}; + +message AttrProto { + required string name = 1; + required string comment = 2; + required AttrType type = 3; +}; + +message VarProto { + required string name = 1; + required string comment = 2; +}; + +message OpProto { + repeated VarProto inputs = 1; + repeated VarProto outputs = 2; + repeated AttrProto attrs = 3; + required string type = 4; + required string comment = 5; +}; +``` + +To generate Python code automatically: + +```python +def create_python_ops_creatation_functions(): + op_protos = paddle.framework.OpRegistry.get_all_op_proto() + for type_name in op_protos: + op_proto = op_protos[type_name] + def __impl__(**kwargs): # User must use key word args in Paddle API + inputs = [kwargs.get(ipt.name, "") for ipt in op_proto.inputs] + outputs = [kwargs.get(opt.name, "") for opt in op_proto.outputs] + attrs = [cast_to_op_attr(attr, kwargs.get(attr.name, None)) for attr in op_proto.attrs] + opdesc = (input, outputs, type_name, attrs) + return paddle.framework.OpRegistry.CreateOp(opdesc) + __impl__.__doc__ = create_doc_string(op_proto) + globals()[type_name] = __impl__ + +create_python_ops_creatation_functions() +``` + +### Message from Python to C++ + +To hold message needed in the above second step, we define Protobuf message class `OpDesc`. It is used to hold user-specified parameters in Op describing. + +```proto +message OpDesc { + required string type = 1; + repeated string inputs = 2; + repeated string outputs = 3; + map attrs = 4; +}; +``` + +## OpProto Register + +Every Op has its own `OpProto`. For using convenience, we need to register them and record all their messages. For each `Op` class, we define a corresponding `OpMaker` class, in whose constructor we implement the `OpProto`'s building process. `OpMaker`'s constructor will be invoked by another function `OpRegistry::RegisterOp()`. + +```cpp +class OpProtoMaker { +public: + OpProtoMaker(OpProto* proto): proto_(proto) {} +protected: + OpProto* proto_; + void AddInput(const std::string& name, const std::string& desc) {...} + void AddAttr(const std::string& name, const std::string& desc, TypeId type) {...} + void AddComment(const std::string& comment) { ... } +}; + +class OpRegistry { +public: + using OpCreator = std::function; + + template + static void RegisterOp(const std::string& name) { + gCreators_[name] = [](const OpDesc& desc) { + return new OpType(desc); + }; + OpProto& opProto = gProtos_[name]; + OpMaker()(&opProto); + } + + static map gCreators_; + static map gProtos_; +}; + +template +class OpRegister { + public: + OpRegister(std::string type) { + OpRegistry::RegisterOp(type); + } +}; + +#define REGISTER_OP(op_class, op_maker_class, type_name) \ + class op_class##Register { \ + private: \ + const static OpRegister<#op_class, #op_maker_class> reg; \ + }; \ + const Register op_class##Register::reg(#type_name); + +class CosineOp { +// ... +} + +struct CosineOpProtoMaker : public OpProtoMaker { + CosineOpProtoMaker(OpProto* proto) : OpProtoMaker(proto) { + AddInput("input", "input of cosine op"); + AddAttr("scale", "scale of cosine op", float).Default(1.0).LargerThan(0.0); + AddType("cos"); + AddComment("This is cos op"); + } +} + +REGISTER_OP(CosineOp, CosineOpProtoMaker, cos); +``` + +In `REGISTER_OP(CosineOp, CosineOpProtoMaker, cos)`, we register not only `CosineOp` but also `CosineOpProto`. As fields of `CosineOpProto`, the default value and value range of `scale` are also registered here. + +## Python API + +Python APIs are divided into two types, high-level API and low-level API. + +### High-Level API + +High-level API is called by users directly, so it should keep its style consistent with existing V2 APIs. + +Here is a sample about how a define a fc layer: + +```python +hd = fc_layer(input=data, size=56, with_bias=True, activation="sigmoid"); +``` + +`hd` is the output of `fc_layer` and it's a `variable`. It can be further sent into other layers as input. + +The definition of `fc_layer()`: + +```python +def fc_layer(input, size, with_bias, activation): + attr_map = {"size":size} + check_attrs(attr_map) + w = make_variable('w') + if with_bias: + b = make_variable('b') + else: + b = None + fc_output = make_variable('fc_output'); + fc_op(input, w, b, fc_output, attr_map) + act_output = make_variable('sigmod_output'); + if activation == "sigmod": + sigmod_op(fc_output, act_output); + elif: + # ... + return act_output; +``` + +### Low Leval API + +In above sample, `fc_op` and `sigmod_op` are low-level API. They build `OpDesc` and invoke corresponding C++ code. + +*TODO* 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 0000000000000000000000000000000000000000..b97a85d6385a1453be59b5367ed132f64fc3e643 --- /dev/null +++ b/develop/doc_cn/design/cluster_train/save_model.html @@ -0,0 +1,352 @@ + + + + + + + + + + + Design Doc: Save Model — PaddlePaddle 文档 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + + + + + + + +
+
    + +
  • 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. + +
+ +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/develop/doc_cn/design/simple_op_design.html b/develop/doc_cn/design/simple_op_design.html new file mode 100644 index 0000000000000000000000000000000000000000..ae435907e5d77fde431ab62ad98ee912a3010261 --- /dev/null +++ b/develop/doc_cn/design/simple_op_design.html @@ -0,0 +1,433 @@ + + + + + + + + + + + Interaction between C++ and Python — PaddlePaddle 文档 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + + + + + + + +
+
    + +
  • Interaction between C++ and Python
  • +
+
+ +
+
+
+
+ +
+

Interaction between C++ and Python

+

Users employ API in Python to describe their own network, however, the network construction actually happens in C++. so Protobuf is introduced to send the message between Python and C++.

+

The Interaction between Python and C++ can be simplified as two steps:

+
    +
  1. C++ tells Python how many Ops there are, and what parameter do users need to offer to initialize a new Op. Python then builds API for each Op at compile time.
  2. +
  3. Users invoke APIs built by Python and provide necessary parameters. These parameters will be sent to C++ fo finish Op construction task.
  4. +
+
+

Message form C++ to Python

+

We define a Protobuf message class OpProto to hold message needed in the first step. What should an OpProto contain? This question is equivalent to “What message do we need to offer, to build a Python API which is legal and user oriented and can use to describe a whole Op.”

+

Following message are necessary:

+
    +
  1. Op’s name, and its simple comment.
  2. +
  3. Input and output variable number; each variable’s name, type, and comment.
  4. +
  5. Op’s attributes; each attribute includes name, type, comment, default value and value range.
  6. +
+

So OpProto can be defined as follows:

+
enum AttrType {
+    INT = 1;
+    FLOAT = 2;
+    STRING = 3;
+    INTS = 4;
+    FLOATS = 5;
+    STRINGS = 6;
+};
+
+message AttrValue {
+    AttrType type = 1;
+    optional int iv = 2;
+    optional float fv = 3;
+    optional string sv = 4;
+    repeated int ivs = 5;
+    repeated float fvs = 6;
+    repeated string svs = 7;
+};
+
+message AttrProto {
+    required string name = 1;
+    required string comment = 2;
+    required AttrType type = 3;
+};
+
+message VarProto {
+    required string name = 1;
+    required string comment = 2;
+};
+
+message OpProto {
+    repeated VarProto inputs = 1;
+    repeated VarProto outputs = 2;
+    repeated AttrProto attrs = 3;
+    required string type = 4;
+    required string comment = 5;
+};
+
+
+

To generate Python code automatically:

+
def create_python_ops_creatation_functions():
+    op_protos = paddle.framework.OpRegistry.get_all_op_proto()
+    for type_name in op_protos:
+        op_proto = op_protos[type_name]
+        def __impl__(**kwargs):  # User must use key word args in Paddle API
+            inputs = [kwargs.get(ipt.name, "") for ipt in op_proto.inputs]
+            outputs = [kwargs.get(opt.name, "") for opt in op_proto.outputs]
+            attrs = [cast_to_op_attr(attr, kwargs.get(attr.name, None)) for attr in op_proto.attrs]
+            opdesc = (input, outputs, type_name, attrs)
+            return paddle.framework.OpRegistry.CreateOp(opdesc)
+        __impl__.__doc__ = create_doc_string(op_proto)
+        globals()[type_name] = __impl__
+
+create_python_ops_creatation_functions()
+
+
+
+
+

Message from Python to C++

+

To hold message needed in the above second step, we define Protobuf message class OpDesc. It is used to hold user-specified parameters in Op describing.

+
message OpDesc {
+    required string type = 1;   
+    repeated string inputs = 2;
+    repeated string outputs = 3;
+    map<string, AttrValue> attrs = 4;
+};
+
+
+
+
+
+

OpProto Register

+

Every Op has its own OpProto. For using convenience, we need to register them and record all their messages. For each Op class, we define a corresponding OpMaker class, in whose constructor we implement the OpProto‘s building process. OpMaker‘s constructor will be invoked by another function OpRegistry::RegisterOp().

+
class OpProtoMaker {
+public:
+    OpProtoMaker(OpProto* proto): proto_(proto) {}
+protected:
+    OpProto* proto_;
+    void AddInput(const std::string& name, const std::string& desc) {...}
+    void AddAttr(const std::string& name, const std::string& desc, TypeId type) {...}
+    void AddComment(const std::string& comment) { ... }
+};
+
+class OpRegistry {
+public:
+    using OpCreator = std::function<OperatorBase* (OpDesc& desc)>;
+    
+    template <typename OpType, typename OpMaker>
+    static void RegisterOp(const std::string& name) {
+        gCreators_[name] = [](const OpDesc& desc) {
+            return new OpType(desc);
+        };
+        OpProto& opProto = gProtos_[name];
+        OpMaker()(&opProto);
+    }
+
+    static map<string, OpCreator> gCreators_;
+    static map<string, OpProto> gProtos_;
+};
+
+template <typename OpType, typename OpMaker>
+class OpRegister {
+  public:
+    OpRegister(std::string type) {
+        OpRegistry::RegisterOp<OpType, OpMaker>(type);
+    }
+};
+
+#define REGISTER_OP(op_class, op_maker_class, type_name)         \
+    class op_class##Register {                                   \
+      private:                                                   \
+        const static OpRegister<#op_class, #op_maker_class> reg; \
+    };                                                           \
+    const Register op_class##Register::reg(#type_name);
+    
+class CosineOp {
+// ...
+}
+
+struct CosineOpProtoMaker : public OpProtoMaker {
+    CosineOpProtoMaker(OpProto* proto) : OpProtoMaker(proto) {
+        AddInput("input", "input of cosine op");
+        AddAttr("scale", "scale of cosine op", float).Default(1.0).LargerThan(0.0);
+        AddType("cos");
+        AddComment("This is cos op");
+    }
+}
+
+REGISTER_OP(CosineOp, CosineOpProtoMaker, cos);
+
+
+

In REGISTER_OP(CosineOp, CosineOpProtoMaker, cos), we register not only CosineOp but also CosineOpProto. As fields of CosineOpProto, the default value and value range of scale are also registered here.

+
+
+

Python API

+

Python APIs are divided into two types, high-level API and low-level API.

+
+

High-Level API

+

High-level API is called by users directly, so it should keep its style consistent with existing V2 APIs.

+

Here is a sample about how a define a fc layer:

+
hd = fc_layer(input=data, size=56, with_bias=True, activation="sigmoid");
+
+
+

hd is the output of fc_layer and it’s a variable. It can be further sent into other layers as input.

+

The definition of fc_layer():

+
def fc_layer(input, size, with_bias, activation):
+    attr_map = {"size":size}
+    check_attrs(attr_map)
+    w = make_variable('w')
+    if with_bias:
+        b = make_variable('b')
+    else:
+        b = None
+    fc_output = make_variable('fc_output');
+    fc_op(input, w, b, fc_output, attr_map)
+    act_output = make_variable('sigmod_output');
+    if activation == "sigmod":
+        sigmod_op(fc_output, act_output);
+    elif:
+        # ...
+    return act_output;
+
+
+
+
+

Low Leval API

+

In above sample, fc_op and sigmod_op are low-level API. They build OpDesc and invoke corresponding C++ code.

+

TODO

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

+ © Copyright 2016, PaddlePaddle developers. + +

+
+ Built with Sphinx using a theme provided by Read the Docs. + +
+ +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/develop/doc_cn/getstarted/concepts/use_concepts_cn.html b/develop/doc_cn/getstarted/concepts/use_concepts_cn.html index 3391e5d857706951ceeb98303c23a4d831ced0be..d3b2aa62d86f045c561e9805172fb25705ffb907 100644 --- a/develop/doc_cn/getstarted/concepts/use_concepts_cn.html +++ b/develop/doc_cn/getstarted/concepts/use_concepts_cn.html @@ -413,7 +413,7 @@ trainer.train( # define training dataset reader def train_reader(): train_x = np.array([[1, 1], [1, 2], [3, 4], [5, 2]]) - train_y = np.array([-2, -3, -7, -7]) + train_y = np.array([[-2], [-3], [-7], [-7]]) def reader(): for i in xrange(train_y.shape[0]): diff --git a/develop/doc_cn/objects.inv b/develop/doc_cn/objects.inv index d06f6b86bbc0a42730bb8dabcf8fd351683e2bcb..6fd0d70b0e1522e01281105d7ef1d7c0b1dba001 100644 Binary files a/develop/doc_cn/objects.inv and b/develop/doc_cn/objects.inv differ diff --git a/develop/doc_cn/searchindex.js 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url(fonts/icomoon.woff) format('woff'), url(fonts/icomoon.svg#icomoon) format('svg'); + font-weight: normal; + font-style: normal; +} +[class^="icon-"], +[class*=" icon-"] { + /* use !important to prevent issues with browser extensions that change fonts */ + font-family: 'icomoon' !important; + speak: none; + font-style: normal; + font-weight: normal; + font-variant: normal; + text-transform: none; + line-height: 1; + /* Better Font Rendering =========== */ + -webkit-font-smoothing: antialiased; + -moz-osx-font-smoothing: grayscale; +} +.icon-correct:before { + content: "\E901"; +} +.icon-down-arrow:before { + content: "\E900"; +} +.icon-menu:before { + content: "\E9BD"; +} +.icon-checkmark:before { + content: "\EA10"; +} +html, +body { + height: 100%; + margin: 0; + font-size: 12px; + -webkit-font-smoothing: antialiased; + -moz-osx-font-smoothing: grayscale; + overflow: hidden; + font-family: tahoma, arial, "Hiragino Sans GB", "Microsoft YaHei", sans-serif; +} +@media (max-width: 374px) { +html, + body { + font-size: 9px; +} +} +a { + text-decoration: none; +} +.ai-top-nav { + position: relative; + height: 50px; + line-height: 50px; + background-color: #000; + padding: 0 1.04rem; + -webkit-box-align: center; + -webkit-align-items: center; + align-items: center; +} +.ai-top-nav-logo { + max-width: 10.54rem; + max-height: 1.5rem; +} +.ai-top-nav-menu { + position: absolute; + right: 1.04rem; + top: 50%; + width: 28px; + height: 50%; + background-size: 50%; + background-position: center; + background-repeat: no-repeat; + background-image: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADkAAAA5CAYAAACMGIOFAAAAAXNSR0IArs4c6QAAAO5JREFUaAXt2D0OgkAQhmGWmut4ChDv6s8ldK3xHtqIxfptS0GycRJmzUsyAUKYfPtMN03DhQAC7gRSSr3qpnqr/uGadYioGjN20EOv+9mdvF2gMR/yqn47u57uOsV8yKdide6i2QV6ter1sOvnstOUJ7lXtKPLeDahhjaEcFKvg+qu+tj03bzLrARRNeh8l83TEAABBBBAAAEEEEAAAQQWAnkFomLHs3Cp6ZUdT03TWsvKjmdNp6Zv7HhqmhZZEUAAAQQQQAABBBBAAAFvAmzrvE2kPA/bunIzn3+wrfM5l/JUbOvKzfgDAQR+FfgCtgrYzmLEak8AAAAASUVORK5CYII=); + -webkit-transform: translateY(-50%); + transform: translateY(-50%); +} +.ai-top-nav-mask { + position: absolute; + top: 0; + bottom: 0; + left: 0; + right: 0; + z-index: 4; + background-color: rgba(0, 0, 0, 0.8); +} +.ai-app { + position: relative; + height: 100%; + overflow: hidden; +} +.ai-main { + position: absolute; + top: 50px; + bottom: 0; + left: 0; + right: 0; + padding: 0 1.06rem; + overflow: auto; + -webkit-overflow-scrolling: touch; +} +.ai-main .mint-swipe { + margin: 0 -1.06rem; +} +.ai-slide-nav { + position: absolute; + display: -webkit-box; + display: -webkit-flex; + display: flex; + -webkit-box-orient: vertical; + -webkit-box-direction: normal; + -webkit-flex-direction: column; + flex-direction: column; + top: 0; + bottom: 0; + right: 0; + width: 70%; + padding-left: 1.25rem; + background: rgba(30, 30, 30, 0.9) 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) top left; + background-size: 100%; + background-repeat: no-repeat; + -webkit-transform: translateX(100%); + transform: translateX(100%); + -webkit-transition: .2s transform linear; + transition: .2s transform linear; + z-index: 5; + overflow: hidden; +} +.ai-slide-nav-visible { + -webkit-transform: translateX(0); + transform: translateX(0); +} +.ai-slide-nav-content { + width: 100%; + overflow: auto; + -webkit-transition: .2s all linear; + transition: .2s all linear; + -webkit-overflow-scrolling: touch; + height: 0; + -webkit-box-flex: 0; + -webkit-flex: 0; + flex: 0; +} +.ai-slide-nav-content-active { + -webkit-box-flex: 1; + -webkit-flex: 1; + flex: 1; +} +.ai-slide-nav-top-border { + position: absolute; + top: 0; + left: 0; + right: 0; + height: 1px; + background-color: rgba(255, 255, 255, 0.3); + -webkit-transform: scaleY(0.5); + transform: scaleY(0.5); +} +.ai-slide-nav-title { + position: relative; + box-sizing: border-box; + width: 100%; + height: 4.1875rem; + line-height: 4.1875rem; + font-size: 1.5rem; + color: #fff; +} +.ai-slide-nav-title:before { + content: ""; + position: absolute; + left: 0; + right: 0; + height: 1px; + background-color: rgba(255, 255, 255, 0.3); + -webkit-transform: scaleY(0.5); + transform: scaleY(0.5); +} +.ai-slide-nav-title:before { + bottom: 0; +} +.ai-slide-nav-title:after { + top: 0; +} +.ai-slide-nav-title:first-child:after { + display: none; +} +.ai-slide-nav-title.top-shadow:after { + display: block; + content: ''; + position: absolute; + top: -3.5rem; + left: -1.2rem; + right: 0; + height: 3.5rem; + z-index: 9; + background-image: -webkit-linear-gradient(top, rgba(27, 27, 27, 0) 0%, #1b1b1b 99%); + background-image: linear-gradient(-180deg, rgba(27, 27, 27, 0) 0%, #1b1b1b 99%); +} +.ai-slide-nav-title-icon:after { + position: absolute; + content: "\E900"; + right: 1.25rem; + color: #7e7e7e; + font-family: 'icomoon'; + font-size: 1.2rem; + -webkit-transition: .2s transform linear; + transition: .2s transform linear; + -webkit-transform: rotate(0); + transform: rotate(0); +} +.ai-slide-nav-title-icon-active:after { + -webkit-transform: rotate(180deg); + transform: rotate(180deg); +} +.ai-slide-nav-item { + padding: .9375rem 0 .9375rem 1.25rem; + font-size: 1.3rem; + color: #fff; +} +.ai-footer { + margin: 0 -1.06rem; + height: 4rem; + line-height: 4rem; + text-align: center; + background-color: #1e1e1e; + color: #747474; +} + +.ai-top-nav { + position: relative; + height: 50px; + line-height: 50px; + background-color: #000; + padding: 0 1.04rem; + display: -webkit-box; + display: -webkit-flex; + display: flex; + -webkit-box-align: center; + -webkit-align-items: center; + align-items: center; +} +.ai-top-nav-title { + color: #fff; +} +.ai-top-nav-logo { + max-width: 10.54rem; + max-height: 1.5rem; + margin: 0 .4rem; +} +.ai-top-nav-item { + color: #fff; + position: absolute; + right: 1.04rem; + margin: 0 .4rem; +} +.ai-top-nav-item a { + color: #fff; +} +.ai-top-nav-mask { + position: absolute; + top: 0; + bottom: 0; + left: 0; + right: 0; + z-index: 4; + background-color: rgba(0, 0, 0, 0.8); +} + +.ai-heading { + box-sizing: border-box; + width: 100%; + padding: 3.77rem 0 1.3125rem; + font-size: 1.7rem; + line-height: 1; + letter-spacing: .2rem; + text-align: center; + font-weight: bold; + color: #333; +} +.ai-heading-paddle { + font-size: 1.3rem; +} + +.ai-head-more { + box-sizing: border-box; + width: 100%; + padding: 3.77rem 0 1.3125rem; + line-height: 1; + letter-spacing: .0rem; + text-align: center; + font-family: PingFangSC-Regular; +} +.ai-head-more-heading { + /*font-size: 1.5rem;*/ + font-size: 1.6875rem; + margin-bottom: .5rem; +} +.ai-head-more-intro { + /*font-size: 1.0rem;*/ + font-size: 1.1818rem; + color: #999999; +} +.ai-head-more-more { + font-size: 1.0rem; + color: #006FEF; +} + +.ai-head-poster { + position: relative; + display: -webkit-box; + display: -webkit-flex; + display: flex; + margin: 0 -1.06rem; + -webkit-box-orient: vertical; + -webkit-box-direction: 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PaddlePaddle English
易学易用的分布式深度学习平台
正在为100+项产品提供深度学习算法支持
丰富的算法服务
易用、高效、灵活、扩展性好
机器视觉
卷积神经网络可以识别图像中的主要对象,并输出分类结果
自然语言理解
利用LSTM网络从IMDB电影评论的中分析出评论者情绪的正面和负面
搜索引擎排序
分析用户特征、电影特征、点评分数,预测新用户对不同电影的点评分数
Technology and Service Anvantages
+ 易用性 +
+ 为用户提供了直观、灵活的数据接口和模型配置接口 +
+ 灵活性 +
+ 支持CNN、RNN等多种神经网络结构和优化算法。简单书写配置文件即可实现复杂模型 +
+ 高效性 +
+ 在计算、存储、通信、架构等方面都做了高效优化,充分发挥各种资源的性能 +
+ 扩展性 +
+ 全面支持多核、多GPU、多机环境。轻松应对大规模数据训练需求 +
现在开始使用PaddlePaddle
易学易用的分布式深度学习平台
+ + + + diff --git a/mobile/index.html b/mobile/index.html new file mode 100644 index 0000000000000000000000000000000000000000..7ace2db37c3ef7ba3fef1042e48709f75a8fd77b --- /dev/null +++ b/mobile/index.html @@ -0,0 +1,60 @@ + + + + + + + PaddlePaddle + + + + + + +
PaddlePaddle 中文版
Easy to Learn and Use Distributed Deep Learning Platform
Providing deep learning algorithms for 100+ products
Extensive Algorithmic Service
Easy to use, efficient, flexible, and scalable
Mechine Vision
The convoluted neural network can identify the main object in the image and output the classification result
Natural Language Understanding
Using the LSTM network to analyze the positive and negative aspects of the commenter's emotions from IMDB film review
Search Engine Ranking
Analyze user characteristics, movie features, rating scores, predict new users' ratings for different movies
Technology and Service Anvantages
+ Ease of use +
+ Provids an intuitive and flexible interface for loading data and specifying model structure. +
+ Flexibility +
+ Supports CNN, RNN and other neural network. Easy to configure complex models. +
+ Efficiency +
+ Efficient optimization of computing, memory, communications and architecture. +
+ Scalability +
+ Easy to use many CPUs/GPUs and machines to speed up your training and handle large-scale data easily. +
Start Using PaddlePaddle
Just go to www.paddlepaddle.org on computer
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