diff --git a/develop/doc/_sources/design/refactor/distributed_architecture.md.txt b/develop/doc/_sources/design/dist_refactor/distributed_architecture.md.txt similarity index 52% rename from develop/doc/_sources/design/refactor/distributed_architecture.md.txt rename to develop/doc/_sources/design/dist_refactor/distributed_architecture.md.txt index d9fe7d6bbb0eeb73fcdca3ee749a4f10bcdda682..3a741f95866fb6c301ca9097af7916281f2278cf 100644 --- a/develop/doc/_sources/design/refactor/distributed_architecture.md.txt +++ b/develop/doc/_sources/design/dist_refactor/distributed_architecture.md.txt @@ -52,8 +52,9 @@ The IR for PaddlePaddle after refactoring is called a `Block`, it specifies the The user can not directly specify the parameter update rule for the parameter server in the Python module, since the parameter server does not use the same computation definition as the trainer. Instead, the update rule is baked inside the parameter server. The user can not specify the update rule explicitly. -This could be fixed by making the parameter server run the same computation definition as the trainer (the user's Python module). For a detailed explanation, refer to this document - -[Design Doc: Operation Graph Based Parameter Server](./parameter_server.md) +This could be fixed by making the parameter server also run an IR, which can be different to the trainer side +For a detailed explanation, refer to this document - +[Design Doc: Parameter Server](./parameter_server.md) ## Distributed Training Architecture @@ -61,68 +62,111 @@ The revamped distributed training architecture can address the above discussed l -The major components in the architecture are: *PaddlePaddle Python*, *PaddlePaddle converter* and *PaddlePaddle runtime*. +The major components are: *Python API*, *Distribute Transpiler* and *Remote Executor*. -### PaddlePaddle Python +### Python API -PaddlePaddle Python is the Python library that user's Python code invokes, to read the data. build the neural network topology, start training, etc. +Python API is the Python library that user's Python code invokes, to read the data, build the neural network topology, and start training, etc. ```Python -paddle.init() -input = paddle.op.recordIO("/home/data/mnist.recordio") # file stored on the cluster -img, label = input[0], input[1] -hidden = paddle.layer.fc(input=img, size=200, act=paddle.activation.Tanh()) -prediction = paddle.layer.fc(input=img, size=10, act=paddle.activation.Softmax()) -cost = paddle.layer.classification_cost(input=prediction, label=label) -optimizer = paddle.optimizer.SGD(cost, learning_rate=0.01) -session = paddle.session.NewRemote(num_trainer=3, num_ps=2, GPU_per_trainer=1) -for i in range(1000): - _, cost_val = session.eval(targets=[cost, optimizer]) - print cost_val +images = fluid.layers.data(name='pixel', shape=[1, 28, 28], dtype='float32') +label = fluid.layers.data(name='label', shape=[1], dtype='int64') +... +predict = fluid.layers.fc(input=conv_pool_2, size=10, act="softmax") +cost = fluid.layers.cross_entropy(input=predict, label=label) +avg_cost = fluid.layers.mean(x=cost) +optimizer = fluid.optimizer.Adam(learning_rate=0.01) +optimizer.minimize(avg_cost) + +train_reader = paddle.batch( + paddle.reader.shuffle( + paddle.dataset.mnist.train(), buf_size=500), + batch_size=BATCH_SIZE) + +place = fluid.CPUPlace() +exe = fluid.Executor(place) + +for pass_id in range(10): + for data in train_reader(): + loss, acc = exe.run(trainer_prog, + feed=feeder.feed(data), + fetch_list=[avg_cost]) ``` -The above code is what a typical Python trainer code is, the neural network topology is built using the helper functions such as `paddle.layer.fc`. Training is done by calling `session.eval` iteratively. - -#### session.eval - -As shown in the graph, `session.eval` sends the IR and the evaluation inputs or targets to the PaddlePaddle cluster for evaluation. -The targets can be any variable in the computation graph. When the target is say, the `optimizer` variable, the neural network will be optimized once. When the target is the `cost` variable, `session.eval` returns the cost value. Based on what the target is, an appropriate action is taken. - -The Python `session` is a wrapper of the C++ `Session` class. For more information about `Session`, refer to this document - [Design Doc: Session](./session.md). - -### PaddlePaddle Converter - -The PaddlePaddle converter automatically converts the IR in the request (IR and evaluation inputs/targets) from PaddlePaddle Python to partitioned IRs and dispatches the new IRs and evaluation inputs/targets to different PaddlePaddle runtimes. Below are the steps that are followed : - -1. Add a `feed` OP that feeds the eval inputs, and a `fetch` OP that fetches the eval targets to the IR. - -2. Extract a new computation (sub)graph with the `feed` and `fetch` OPs as the boundary. The runtime does not need to run the OP that is not dependent on the `fetch` OP. - -3. Optimize the computation graph. - -4. Place the OPs in the graph onto different devices on different PaddlePaddle runtime according to a placement algorithm and the device constraints specified by the user. - -5. Partition the graph according to runtime boundaries and add `send` / `recv` OP pair on the runtime boundaries. +The code above is a typical local training program, the "Training Program" is built using helper functions such as +`fluid.layer.fc`. The training is done by calling `Executor.run` +iteratively. + +For more details, the implementation of IR is [Program](../program.md), and `ProgramDesc` is the protobuf type. + +[Executor](../executor.md) simply runs the `ProgramDesc`. For local training you generally use +`Executor` to run the program locally. For any kind of distributed training, you can use +`RemoteExecutor` to specify desired distributed training method with some optional arguments. + +### Distributed Transpiler + +The Distributed Transpiler automatically converts the IR (in protobuf format) to partitioned IRs. Then +the Remote Executor dispatches the new IRs to Remote Executors across the cluster. +Below are the steps that are followed : + +1. User only need to change `Executor` to `RemoteExecutor` to change local program to distributed program. +1. `RemoteExecutor` calls `Distributed Transpiler` to "transpile" user's program to several IRs representing a + distributed training program: + 1. Parse configurations from `RemoteExecutor`. + 1. Determine the type of distributed program, can be DataParallelism, ModelParallelism or Streaming. + 1. Partition the `ProgramDesc` according to type and add `send` / `recv` OP pair on the boundaries. Take + DataParallelism type for example, it removes the optimization operators and add a `send` OP to the + "trainer" role, then add the optimization operators to the parameter server role within the `recv` OP. +1. Dispatch the partitioned graph to different `RemoteExecutor` in the cluster. +1. `RemoteExecutor` on each node run the received `ProgramDesc` utill the end. + + +### RemoteExecutor + +As shown in the graph, `RemoteExecutor.run` sends the IR to the cluster for Execution. +You can also use parameter `fetch_list` to interactively fetch variable back to local for +log printing. + +The Python `RemoteExecutor` is derived from `Executor` class. + +```python +exe = RemoteExecutor( + feed=feeder.feed(data), + fetch_list=[avg_cost], + job_desc=JobDesc( + jobname, + num_trainer, + num_pserver, + cpu_per_trainer, + gpu_per_trainer, + mem_per_trainer, + cpu_per_pserver, + mem_per_pserver + )) +for data in train_reader(): + loss, acc = exe.run(trainer_prog, + feed=feeder.feed(data), + fetch_list=[avg_cost]) +``` -6. Dispatch the partitioned graph to different PaddlePaddle runtimes. +`JobDesc` object describe the distributed job resource specification to run on +Cluster environment. -7. PaddlePaddle runtimes with the `fetch` OP reports evaluation results back to the converter, the converter reports the evaluation results back to the PaddlePaddle Python. + -The output IRs will be cached to optimize the conversion latency. +`RemoteExecutor.run` sends the `ProgramDesc` and +[TrainingJob](https://github.com/PaddlePaddle/cloud/blob/develop/doc/autoscale/README.md#training-job-resource) +to a server in the cluster which executes `RemoteExecutor.listen`. This server is responsible +to start the final Kubernetes Jobs to run the different role of `ProgramDesc`. -#### Placement Algorithm +### Placement Algorithm Our first implementation will only support "trainer-parameter server" placement: the parameters, initializers, and optimizers are all placed on the PaddlePaddle runtimes with the parameter server role. Everything else will be placed on the PaddlePaddle runtimes with the trainer role. This has the same functionality as the "trainer-parameter server" architecture of PaddlePaddle v0.10.0, but is more generic and flexible. In the future, a more general placement algorithm should be implemented, which makes placements according to the input IR, and a model of device computation time and device communication time. Model parallelism requires the generic placement algorithm. -### PaddlePaddle Runtime - -The PaddlePaddle runtime owns multiple devices (e.g., CPUs, GPUs) and runs the IR. The runtime does not need to do OP placement since it is already done by the converter. - - ### Local Training Architecture The local training architecture will be the same as the distributed training architecture, the difference is that everything runs locally, and there is just one PaddlePaddle runtime: @@ -132,9 +176,18 @@ The local training architecture will be the same as the distributed training arc ### Training Data -In PaddlePaddle v0.10.0, training data is typically read with a [data reader](../reader/README.md) from Python. This approach is no longer efficient when training in a distributed fashion since the Python process no longer runs on the same node with the trainer processes. The Python reader will need to read from the distributed filesystem (assuming it has the required access) and send to the trainers, doubling the network traffic. - -When doing distributed training, the user can still use Python data reader: the training data are sent with `session.eval`. However this should be used for debugging purpose only. The users are encouraged to use the read data OPs. +In PaddlePaddle v0.10.0, training data is typically read +with [data reader](../reader/README.md) from Python. This approach is +no longer efficient when training distributedly since the Python +process no longer runs on the same node with the trainer processes, +the Python reader will need to read from the distributed filesystem +(assuming it has the access) and send to the trainers, doubling the +network traffic. + +When doing distributed training, the user can still use Python data +reader: the training data are sent with `Executor.run`. However, should +be used for debugging purpose only. The users are encouraged to use +the read data OPs. ## References: diff --git a/develop/doc/_sources/design/refactor/multi_cpu.md.txt b/develop/doc/_sources/design/dist_refactor/multi_cpu.md.txt similarity index 100% rename from develop/doc/_sources/design/refactor/multi_cpu.md.txt rename to develop/doc/_sources/design/dist_refactor/multi_cpu.md.txt diff --git a/develop/doc_cn/_sources/design/refactor/parameter_server.md.txt b/develop/doc/_sources/design/dist_refactor/parameter_server.md.txt similarity index 76% rename from develop/doc_cn/_sources/design/refactor/parameter_server.md.txt rename to develop/doc/_sources/design/dist_refactor/parameter_server.md.txt index fa3c5d7990213cf2b0d236e66e592dd2699da876..1094f06d461275a9ad4034d5e48b39856d967b71 100644 --- a/develop/doc_cn/_sources/design/refactor/parameter_server.md.txt +++ b/develop/doc/_sources/design/dist_refactor/parameter_server.md.txt @@ -1,4 +1,4 @@ -# Design Doc: Operation Graph Based Parameter Server +# Design Doc: Parameter Server ## Abstract @@ -10,7 +10,7 @@ different purposes. ## Background The previous implementations of the parameter server does not run a -subgraph. parameter initialization, optimizer computation, network +fluid sub-program. Parameter initialization, optimizer computation, network communication and checkpointing are implemented twice on both the trainer and the parameter server. @@ -23,10 +23,10 @@ server becomes a natural extension. ## Design -### Graph Converter +### Distributed Transpiler -The *graph converter* converts the user-defined operation (OP) graph -into subgraphs to be scheduled on different nodes with the following +The *Distributed Transpiler* converts the user-defined fluid program +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 @@ -34,7 +34,6 @@ steps: time. Currently we will use a simple heuristic that puts parameter varable on parameter server workers and everything else on trainer workers. - 1. Add communication OPs to enable the communication between nodes. We will need these OPs: *Send*, *Recv*, *Enqueue*, *Dequeue*. @@ -48,8 +47,8 @@ After converting: -1. The parameter variable W and it's optimizer subgraph are placed on the parameter server. -1. Operators are added to the subgraphs. +1. The parameter variable W and it's 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 @@ -64,39 +63,30 @@ After converting: ### Benefits - Model parallelism become easier to implement: it's an extension to - the trainer - parameter server approach. we already have the - communication OPs, but need to extend the graph converter's - placement functionality. - + 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 - a subgraph. - + a sub-program. - No more duplication logic inside the trainer and the parameter server mentioned in the background section. ### Challenges -- It might be hard for the graph converter to cut a general graph - (without any hint for which subgraph is the optimizer). We may need - to label which subgraph inside the OP graph is the optimizer. - - It's important to balance the parameter shards of on multiple parameter server. If a single parameter is very big (some word-embedding, fully connected, softmax layer), we need to automatically partition the single parameter onto different parameter servers when possible (only element-wise optimizer depends on the parameter variable). +- In the "Aync SGD" figure, the "W" variable on the parameter server + could be read and wrote concurrently. See + [here](https://github.com/PaddlePaddle/Paddle/pull/6394) for more + details about concurrent program in fluid. ### Discussion -- In the "Aync SGD" figure, the "W" variable on the parameter server - could be read and wrote concurrently, what is our locking strategy? - E.g., each variable have a lock cpp method to be invoked by every - OP, or, have a lock OP. - - Can the Enqueue OP be implemented under our current tensor design (puts the input tensor into the queue tensor)? - - *Dequeue* OP will have variable numbers of output (depends on the `min_count` attribute), does our current design support it? (similar question for the *Add* OP) diff --git a/develop/doc/_sources/design/refactor/session.md.txt b/develop/doc/_sources/design/refactor/session.md.txt deleted file mode 100644 index 1d9a26683c14f54e3b5fe41675cd03b5620646b8..0000000000000000000000000000000000000000 --- a/develop/doc/_sources/design/refactor/session.md.txt +++ /dev/null @@ -1,180 +0,0 @@ -# Design Doc: Session - -## Abstract - -The *session* object encapsulates the environment in which the -computation graph is executed. - -We will have the *local* session and *remote* session, they offer the -same [interface](#interface). The local session encapsulates the local -runtime environment and the remote session encapsulates the cluster -runtime environment. - -The local runtime environment contains: - -1. computation devices (i.e., CPU, GPU) handles, and -1. the [scope](../scope.md) which holds all variables. - -The remote runtime environment contains: - -1. computation devices (i.e., CPU and GPU on node 0, 1) in a cluster, - and -1. the distributed [scope](../scope.md) in a cluster which holds all - variables. - -The user can create a remote session on Paddle Cloud and evaluate the -computation graph with it. In this way, the user can control the -remote computation resource in a cluster from his local computer. - - -## Background - -The current design has an implicit global session in which -`paddle.eval()` is executed. The pain point is: - -Since the user is not able to explicitly switch between runtime -environments, the user cannot run a topology in two independent -environments. - -For example, in reinforcement learning, the user may want to have a -stale model for inference and a fresh model for training, and only -replace the stale model with the fresh model periodically. - -Furthermore, we have no concept that encapsulates a remote environment -that executes a computation graph. - -We need the session object to address above issues. - - -## Session - -A session is an object that owns the runtime environment. All -computations are executed through `session.eval()`. - - -### Interface - -```python -eval( - targets, - feed_dict=None, -) -``` - -Evaluates the target Operations or Variables in `targets`. - -- *targets*: the evaluation targets. Can be a single Operation or - Variable, or a list with the Operations or Variables as - elements. The value returned by `eval()` has the same shape as the - `target` argument. - - The PaddlePaddle program is represented by - the [ProgramDesc](../design/program.md), `eval()` will infer the - ProgramDesc from the given targets and run the PaddlePaddle - program. Please - see - [this graph](./distributed_architecture.md#local-training-architecture) for - the detailed illustration for the local session - and - [this graph](./distributed_architecture.md#distributed-training-architecture) for - the detailed illustration for the remote session. - -- *feed_dict*: a dictionary that contains the tensors which override - the edges of the computation graph. - - feed_dict not only can provide the input data, it can override any - OP's input as well: - - ```python - a = pd.constant(2.0, name="a") - b = pd.variable(name="b") - c = pd.mul(a,b) - sess.eval(targets=c, feed_dict={"b":3.0}) # returns 6.0 - ``` - -```python -close() -``` - -Closes the session and releases the scope that the session owns. - - -### Create a Local Session - -```python -session( - devices=None -) -``` - -Creates a new session. One session owns one global scope, so creating -multiple sessions will create different scopes. - -- *devices*: a single `string` or a list of `string` of device names, - the corresponding devices will be the computation devices for - `eval()`. If not specified, all available devices (e.g., all GPUs) - will be used. The user doesn't need to specify the CPU device since - it will be always used. Multiple sessions can use the same device. - - -#### Example - -```Python -a = paddle.constant(1.0) -b = paddle.constant(2.0) -c = a + b -sess = paddle.session(devices=["gpu:0", "gpu:1", "fpga:0"]) -sess.eval(c) -sess.close() -``` - -### Create a Remote Session - -```python -create_cloud_job( - name, - num_trainer, - mem_per_trainer, - gpu_per_trainer, - cpu_per_trainer, - num_ps, - mem_per_ps, - cpu_per_ps, -) -``` - -Creates a Paddle Cloud job. Fails if the job name exists. - -```python -get_cloud_job( - name -) -``` - -Gets a Paddle Cloud job. - -```python -remote_session( - job -) -``` - -- *job*: the Paddle Cloud job. - -#### Example - -```Python -reader = paddle.reader.recordio("/pfs/home/peter/mnist-train-*") # data stored on Paddle Cloud -image = reader.column(0) -label = reader.column(1) -fc1 = paddle.op.fc(image, size=256, act="sigmoid") -fc2 = paddle.op.fc(fc1, size=10, act="softmax") -cost = paddle.op.cross_entropy(fc2, label) -opt = paddle.optimizer.sgd(cost) - -job = paddle.create_cloud_job("test", 3, "1G", 1, 1, 2, "1G", 1) -sess = paddle.remote_ession(job) -for i in range(1000): - sess.eval(opt) -sess.close() -``` diff --git a/develop/doc/design/refactor/distributed_architecture.html b/develop/doc/design/dist_refactor/distributed_architecture.html similarity index 59% rename from develop/doc/design/refactor/distributed_architecture.html rename to develop/doc/design/dist_refactor/distributed_architecture.html index 0264f94bb10bf4f3a9193a40c91e5fe7c207b39d..741445ef1e5a2b2b624fd09434ec96de74541658 100644 --- a/develop/doc/design/refactor/distributed_architecture.html +++ b/develop/doc/design/dist_refactor/distributed_architecture.html @@ -251,62 +251,109 @@ computation is only specified in Python code which sits outside of PaddlePaddle,

Limitation 3

The user can not directly specify the parameter update rule for the parameter server in the Python module, since the parameter server does not use the same computation definition as the trainer. Instead, the update rule is baked inside the parameter server. The user can not specify the update rule explicitly.

-

This could be fixed by making the parameter server run the same computation definition as the trainer (the user’s Python module). For a detailed explanation, refer to this document - -Design Doc: Operation Graph Based Parameter Server

+

This could be fixed by making the parameter server also run an IR, which can be different to the trainer side +For a detailed explanation, refer to this document - +Design Doc: Parameter Server

Distributed Training Architecture

The revamped distributed training architecture can address the above discussed limitations. Below is the illustration of how it does so:

-

The major components in the architecture are: PaddlePaddle Python, PaddlePaddle converter and PaddlePaddle runtime.

-
-

PaddlePaddle Python

-

PaddlePaddle Python is the Python library that user’s Python code invokes, to read the data. build the neural network topology, start training, etc.

-
paddle.init()
-input = paddle.op.recordIO("/home/data/mnist.recordio") # file stored on the cluster
-img, label = input[0], input[1]
-hidden = paddle.layer.fc(input=img, size=200, act=paddle.activation.Tanh())
-prediction = paddle.layer.fc(input=img, size=10, act=paddle.activation.Softmax())
-cost = paddle.layer.classification_cost(input=prediction, label=label)
-optimizer = paddle.optimizer.SGD(cost, learning_rate=0.01)
-session = paddle.session.NewRemote(num_trainer=3, num_ps=2, GPU_per_trainer=1)
-for i in range(1000):
-    _, cost_val = session.eval(targets=[cost, optimizer])
-    print cost_val
+
The major components are: Python API, Distribute Transpiler and Remote Executor.

+

+
Python API

+
Python API is the Python library that user’s Python code invokes, to read the data, build the neural network topology, and start training, etc.

+
images = fluid.layers.data(name='pixel', shape=[1, 28, 28], dtype='float32')
+label = fluid.layers.data(name='label', shape=[1], dtype='int64')
+...
+predict = fluid.layers.fc(input=conv_pool_2, size=10, act="softmax")
+cost = fluid.layers.cross_entropy(input=predict, label=label)
+avg_cost = fluid.layers.mean(x=cost)
+optimizer = fluid.optimizer.Adam(learning_rate=0.01)
+optimizer.minimize(avg_cost)
+
+train_reader = paddle.batch(
+    paddle.reader.shuffle(
+        paddle.dataset.mnist.train(), buf_size=500),
+    batch_size=BATCH_SIZE)
+
+place = fluid.CPUPlace()
+exe = fluid.Executor(place)
+
+for pass_id in range(10):
+    for data in train_reader():
+        loss, acc = exe.run(trainer_prog,
+                            feed=feeder.feed(data),
+                            fetch_list=[avg_cost])
 

 

-
The above code is what a typical Python trainer code is, the neural network topology is built using the helper functions such as paddle.layer.fc. Training is done by calling session.eval iteratively.

-

-
session.eval

-
As shown in the graph, session.eval sends the IR and the evaluation inputs or targets to the PaddlePaddle cluster for evaluation.
-The targets can be any variable in the computation graph. When the target is say, the optimizer variable, the neural network will be optimized once. When the target is the cost variable, session.eval returns the cost value. Based on what the target is, an appropriate action is taken.

-
The Python session is a wrapper of the C++ Session class. For more information about Session, refer to this document - Design Doc: Session.

+
The code above is a typical local training program, the “Training Program” is built using helper functions such as
+fluid.layer.fc. The training is done by calling Executor.run
+iteratively.

+
For more details, the implementation of IR is Program, and ProgramDesc is the protobuf type.

+
Executor simply runs the ProgramDesc. For local training you generally use
+Executor to run the program locally. For any kind of distributed training, you can use
+RemoteExecutor to specify desired distributed training method with some optional arguments.

 

-

-

-
PaddlePaddle Converter

-
The PaddlePaddle converter automatically converts the IR in the request (IR and evaluation inputs/targets) from PaddlePaddle Python to partitioned IRs and dispatches the new IRs and evaluation inputs/targets to different PaddlePaddle runtimes. Below are the steps that are followed :

+

+
Distributed Transpiler

+
The Distributed Transpiler automatically converts the IR (in protobuf format) to partitioned IRs. Then
+the Remote Executor dispatches the new IRs to Remote Executors across the cluster.
+Below are the steps that are followed :

 
    
-
  1. Add a feed OP that feeds the eval inputs, and a fetch OP that fetches the eval targets to the IR.
    2. 
-
  2. Extract a new computation (sub)graph with the feed and fetch OPs as the boundary. The runtime does not need to run the OP that is not dependent on the fetch OP.
    3. 
-
  3. Optimize the computation graph.
    4. 
-
  4. Place the OPs in the graph onto different devices on different PaddlePaddle runtime according to a placement algorithm and the device constraints specified by the user.
    5. 
-
  5. Partition the graph according to runtime boundaries and add send / recv OP pair on the runtime boundaries.
    6. 
-
  6. Dispatch the partitioned graph to different PaddlePaddle runtimes.
    7. 
-
  7. PaddlePaddle runtimes with the fetch OP reports evaluation results back to the converter, the converter reports the evaluation results back to the PaddlePaddle Python.
    8. 
+
  8. User only need to change Executor to RemoteExecutor to change local program to distributed program.
    9. 
+
  9. RemoteExecutor calls Distributed Transpiler to “transpile” user’s program to several IRs representing a
+distributed training program:
      
+
    1. Parse configurations from RemoteExecutor.
      2. 
+
    2. Determine the type of distributed program, can be DataParallelism, ModelParallelism or Streaming.
      3. 
+
    3. Partition the ProgramDesc according to type and add send / recv OP pair on the boundaries. Take
+DataParallelism type for example, it removes the optimization operators and add a send OP to the
+“trainer” role, then add the optimization operators to the parameter server role within the recv OP.
      4. 
+
    
+
    10. 
+
  10. Dispatch the partitioned graph to different RemoteExecutor in the cluster.
    11. 
+
  11. RemoteExecutor on each node run the received ProgramDesc utill the end.
    12. 
 

-
The output IRs will be cached to optimize the conversion latency.

+

+

+
RemoteExecutor

+
As shown in the graph, RemoteExecutor.run sends the IR to the cluster for Execution.
+You can also use parameter fetch_list to interactively fetch variable back to local for
+log printing.

+
The Python RemoteExecutor is derived from Executor class.

+
exe = RemoteExecutor(
+    feed=feeder.feed(data),
+    fetch_list=[avg_cost],
+    job_desc=JobDesc(
+      jobname,
+      num_trainer,
+      num_pserver,
+      cpu_per_trainer,
+      gpu_per_trainer,
+      mem_per_trainer,
+      cpu_per_pserver,
+      mem_per_pserver
+    ))
+for data in train_reader():
+    loss, acc = exe.run(trainer_prog,
+                        feed=feeder.feed(data),
+                        fetch_list=[avg_cost])
+

+

+
JobDesc object describe the distributed job resource specification to run on
+Cluster environment.

+

+
RemoteExecutor.run sends the ProgramDesc and
+TrainingJob
+to a server in the cluster which executes RemoteExecutor.listen. This server is responsible
+to start the final Kubernetes Jobs to run the different role of ProgramDesc.

+

 

-
Placement Algorithm

+
Placement Algorithm

 
Our first implementation will only support “trainer-parameter server” placement: the parameters, initializers, and optimizers are all placed on the PaddlePaddle runtimes with the parameter server role. Everything else will be placed on the PaddlePaddle runtimes with the trainer role. This has the same functionality as the “trainer-parameter server” architecture of PaddlePaddle v0.10.0, but is more generic and flexible.

 
In the future, a more general placement algorithm should be implemented, which makes placements according to the input IR, and a model of device computation time and device communication time. Model parallelism requires the generic placement algorithm.

 

-

-

-
PaddlePaddle Runtime

-
The PaddlePaddle runtime owns multiple devices (e.g., CPUs, GPUs) and runs the IR. The runtime does not need to do OP placement since it is already done by the converter.

-

 

 
Local Training Architecture

 
The local training architecture will be the same as the distributed training architecture, the difference is that everything runs locally, and there is just one PaddlePaddle runtime:

@@ -314,8 +361,17 @@ The targets can be any variable in the computation graph. When the target is say
 

 

 
Training Data

-
In PaddlePaddle v0.10.0, training data is typically read with a data reader from Python. This approach is no longer efficient when training in a distributed fashion since the Python process no longer runs on the same node with the trainer processes. The Python reader will need to read from the distributed filesystem (assuming it has the required access) and send to the trainers, doubling the network traffic.

-
When doing distributed training, the user can still use Python data reader: the training data are sent with session.eval. However this should be used for debugging purpose only. The users are encouraged to use the read data OPs.

+
In PaddlePaddle v0.10.0, training data is typically read
+with data reader from Python. This approach is
+no longer efficient when training distributedly since the Python
+process no longer runs on the same node with the trainer processes,
+the Python reader will need to read from the distributed filesystem
+(assuming it has the access) and send to the trainers, doubling the
+network traffic.

+
When doing distributed training, the user can still use Python data
+reader: the training data are sent with Executor.run. However, should
+be used for debugging purpose only. The users are encouraged to use
+the read data OPs.
diff --git a/develop/doc/design/refactor/multi_cpu.html b/develop/doc/design/dist_refactor/multi_cpu.html similarity index 100% rename from develop/doc/design/refactor/multi_cpu.html rename to develop/doc/design/dist_refactor/multi_cpu.html diff --git a/develop/doc/design/refactor/parameter_server.html b/develop/doc/design/dist_refactor/parameter_server.html similarity index 91% rename from develop/doc/design/refactor/parameter_server.html rename to develop/doc/design/dist_refactor/parameter_server.html index 535ef3275560e2aaca409ad07b1c905eca2a03cd..64d6c890640a6dae7064884a6c440f16d5f8d85f 100644 --- a/develop/doc/design/refactor/parameter_server.html +++ b/develop/doc/design/dist_refactor/parameter_server.html @@ -8,7 +8,7 @@ - Design Doc: Operation Graph Based Parameter Server — PaddlePaddle documentation + Design Doc: Parameter Server — PaddlePaddle documentation @@ -199,7 +199,7 @@
    -
  • Design Doc: Operation Graph Based Parameter Server
    • +
  • Design Doc: Parameter Server
@@ -208,8 +208,8 @@
-
-

Design Doc: Operation Graph Based Parameter Server

+
+

Design Doc: Parameter Server

Abstract

We propose an approach to implement the parameter server. In this @@ -220,7 +220,7 @@ different purposes.

Background

The previous implementations of the parameter server does not run a -subgraph. parameter initialization, optimizer computation, network +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 @@ -232,10 +232,10 @@ server becomes a natural extension.

Design

-
-

Graph Converter

-

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

+

Distributed Transpiler

+

The Distributed Transpiler converts the user-defined fluid program +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 @@ -252,8 +252,8 @@ subgraphs for the trainer and the parameter server:

    After converting:

      -
    1. The parameter variable W and it’s optimizer subgraph are placed on the parameter server.
      2. -
    2. Operators are added to the subgraphs.
        +
      • The parameter variable W and it’s optimizer program are placed on the parameter server.
        • +
      • 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 @@ -271,11 +271,10 @@ tensors.

          • Benefits

          • Model parallelism become easier to implement: it’s an extension to -the trainer - parameter server approach. we already have the -communication OPs, but need to extend the graph converter’s -placement functionality.
            • +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 -a subgraph.
            • +a sub-program.
          • No more duplication logic inside the trainer and the parameter server mentioned in the background section.
          @@ -283,24 +282,21 @@ server mentioned in the background section.

          Challenges

            -
          • It might be hard for the graph converter to cut a general graph -(without any hint for which subgraph is the optimizer). We may need -to label which subgraph inside the OP graph is the optimizer.
          • It’s important to balance the parameter shards of on multiple parameter server. If a single parameter is very big (some word-embedding, fully connected, softmax layer), we need to automatically partition the single parameter onto different parameter servers when possible (only element-wise optimizer depends on the parameter variable).
            • +
          • In the “Aync SGD” figure, the “W” variable on the parameter server +could be read and wrote concurrently. See +here for more +details about concurrent program in fluid.

          Discussion

            -
          • In the “Aync SGD” figure, the “W” variable on the parameter server -could be read and wrote concurrently, what is our locking strategy? -E.g., each variable have a lock cpp method to be invoked by every -OP, or, have a lock OP.
          • Can the Enqueue OP be implemented under our current tensor design (puts the input tensor into the queue tensor)?
          • Dequeue OP will have variable numbers of output (depends on the diff --git a/develop/doc/design/refactor/session.html b/develop/doc/design/refactor/session.html deleted file mode 100644 index 1b193b01c8a5398bc5d6197849137f99544149ea..0000000000000000000000000000000000000000 --- a/develop/doc/design/refactor/session.html +++ /dev/null @@ -1,432 +0,0 @@ - - - - - - - - - - - Design Doc: Session — PaddlePaddle documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
            - -
            -
            - Fork me on Github -
            - - English - - - - -
            - -
            -
            - - - - -
            -
            - - - -
            -
            -
            -
            -
            - -
            - - - - -
            - - - - - - - - - - - -
            -
              - -
            • Design Doc: Session
              • -
            -
            - -
            -
            -
            -
            - -
            -

            Design Doc: Session

            -
            -

            Abstract

            -

            The session object encapsulates the environment in which the -computation graph is executed.

            -

            We will have the local session and remote session, they offer the -same interface. The local session encapsulates the local -runtime environment and the remote session encapsulates the cluster -runtime environment.

            -

            The local runtime environment contains:

            -
              -
            1. computation devices (i.e., CPU, GPU) handles, and
              2. -
            2. the scope which holds all variables.
              3. -
            -

            The remote runtime environment contains:

            -
              -
            1. computation devices (i.e., CPU and GPU on node 0, 1) in a cluster, -and
              2. -
            2. the distributed scope in a cluster which holds all -variables.
              3. -
            -

            The user can create a remote session on Paddle Cloud and evaluate the -computation graph with it. In this way, the user can control the -remote computation resource in a cluster from his local computer.

            -
            -
            -

            Background

            -

            The current design has an implicit global session in which -paddle.eval() is executed. The pain point is:

            -

            Since the user is not able to explicitly switch between runtime -environments, the user cannot run a topology in two independent -environments.

            -

            For example, in reinforcement learning, the user may want to have a -stale model for inference and a fresh model for training, and only -replace the stale model with the fresh model periodically.

            -

            Furthermore, we have no concept that encapsulates a remote environment -that executes a computation graph.

            -

            We need the session object to address above issues.

            -
            -
            -

            Session

            -

            A session is an object that owns the runtime environment. All -computations are executed through session.eval().

            -
            -

            Interface

            -
            eval(
-    targets,
-    feed_dict=None,
-)
-
            -
            -

            Evaluates the target Operations or Variables in targets.

            -
              -

            • targets: the evaluation targets. Can be a single Operation or -Variable, or a list with the Operations or Variables as -elements. The value returned by eval() has the same shape as the -target argument.

              -

              The PaddlePaddle program is represented by -the ProgramDesc, eval() will infer the -ProgramDesc from the given targets and run the PaddlePaddle -program. Please -see -this graph for -the detailed illustration for the local session -and -this graph for -the detailed illustration for the remote session.

              -
              • -

            • feed_dict: a dictionary that contains the tensors which override -the edges of the computation graph.

              -

              feed_dict not only can provide the input data, it can override any -OP’s input as well:

              -
              a = pd.constant(2.0, name="a")
-b = pd.variable(name="b")
-c = pd.mul(a,b)
-sess.eval(targets=c, feed_dict={"b":3.0}) # returns 6.0
-
              -
              -
              • -
            -
            close()
-
            -
            -

            Closes the session and releases the scope that the session owns.

            -
            -
            -

            Create a Local Session

            -
            session(
-    devices=None
-)
-
            -
            -

            Creates a new session. One session owns one global scope, so creating -multiple sessions will create different scopes.

            -
              -
            • devices: a single string or a list of string of device names, -the corresponding devices will be the computation devices for -eval(). If not specified, all available devices (e.g., all GPUs) -will be used. The user doesn’t need to specify the CPU device since -it will be always used. Multiple sessions can use the same device.
              • -
            -
            -

            Example

            -
            a = paddle.constant(1.0)
-b = paddle.constant(2.0)
-c = a + b
-sess = paddle.session(devices=["gpu:0", "gpu:1", "fpga:0"])
-sess.eval(c)
-sess.close()
-
            -
            -
            -
            -
            -

            Create a Remote Session

            -
            create_cloud_job(
-    name,
-    num_trainer,
-    mem_per_trainer,
-    gpu_per_trainer,
-    cpu_per_trainer,
-    num_ps,
-    mem_per_ps,
-    cpu_per_ps,
-)
-
            -
            -

            Creates a Paddle Cloud job. Fails if the job name exists.

            -
            get_cloud_job(
-    name
-)
-
            -
            -

            Gets a Paddle Cloud job.

            -
            remote_session(
-    job
-)
-
            -
            -
              -
            • job: the Paddle Cloud job.
              • -
            -
            -

            Example

            -
            reader = paddle.reader.recordio("/pfs/home/peter/mnist-train-*") # data stored on Paddle Cloud
-image = reader.column(0)
-label = reader.column(1)
-fc1 = paddle.op.fc(image, size=256, act="sigmoid")
-fc2 = paddle.op.fc(fc1, size=10, act="softmax")
-cost = paddle.op.cross_entropy(fc2, label)
-opt = paddle.optimizer.sgd(cost)
-
-job = paddle.create_cloud_job("test", 3, "1G", 1, 1, 2, "1G", 1)
-sess = paddle.remote_ession(job)
-for i in range(1000):
-    sess.eval(opt)
-sess.close()
-
            -
            -
            -
            -
            -
            - - -
            -
            -
            - - -
            - -
            -

            - © 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 864a46fe691f207fd2b80363f799f99ce80abce2..429bf82eb6d377204174de82c56575fe5e441105 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 d8df908f42e3af5e2e5daeca5ca885c38ddb7951..b615673bfcf8d0372229482ee389e27086053a00 100644 --- a/develop/doc/searchindex.js +++ b/develop/doc/searchindex.js @@ -1 +1 @@ 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Parameters","Introduction","Design Doc: PaddlePaddle Programs","Prune","Design Doc: Python API","Python Data Reader Design Doc","Design Doc: Refactorization Overview","Design Doc: Gradient Operators Registration","Regularization in PaddlePaddle","PaddlePaddle\u53d1\u884c\u89c4\u8303","Design of Scope in Paddle","Design Doc: Selected Rows","Interaction between C++ and Python","Design Doc: Supporting new Device/Library","Background","Design for TensorArray","Background","Build from Sources","Run in Docker Containers","Install and Build","Install Using pip","GET STARTED","RNN Models","RNN Configuration","Build using Docker","Contribute Code","Write New Layers","How to write a new operator","How to use Eigen in Paddle","Contribute Documentation","HOW TO","Profiling the Python Code","Tune GPU Performance","PaddlePaddle Fluid Source Code Overview","Distributed Training","Cluster Training Using Fabric","Distributed PaddlePaddle Training on AWS with Kubernetes","PaddlePaddle On 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\ No newline at end of file diff --git a/develop/doc_cn/_sources/design/refactor/distributed_architecture.md.txt b/develop/doc_cn/_sources/design/dist_refactor/distributed_architecture.md.txt similarity index 52% rename from develop/doc_cn/_sources/design/refactor/distributed_architecture.md.txt rename to develop/doc_cn/_sources/design/dist_refactor/distributed_architecture.md.txt index d9fe7d6bbb0eeb73fcdca3ee749a4f10bcdda682..3a741f95866fb6c301ca9097af7916281f2278cf 100644 --- a/develop/doc_cn/_sources/design/refactor/distributed_architecture.md.txt +++ b/develop/doc_cn/_sources/design/dist_refactor/distributed_architecture.md.txt @@ -52,8 +52,9 @@ The IR for PaddlePaddle after refactoring is called a `Block`, it specifies the The user can not directly specify the parameter update rule for the parameter server in the Python module, since the parameter server does not use the same computation definition as the trainer. Instead, the update rule is baked inside the parameter server. The user can not specify the update rule explicitly. -This could be fixed by making the parameter server run the same computation definition as the trainer (the user's Python module). For a detailed explanation, refer to this document - -[Design Doc: Operation Graph Based Parameter Server](./parameter_server.md) +This could be fixed by making the parameter server also run an IR, which can be different to the trainer side +For a detailed explanation, refer to this document - +[Design Doc: Parameter Server](./parameter_server.md) ## Distributed Training Architecture @@ -61,68 +62,111 @@ The revamped distributed training architecture can address the above discussed l -The major components in the architecture are: *PaddlePaddle Python*, *PaddlePaddle converter* and *PaddlePaddle runtime*. +The major components are: *Python API*, *Distribute Transpiler* and *Remote Executor*. -### PaddlePaddle Python +### Python API -PaddlePaddle Python is the Python library that user's Python code invokes, to read the data. build the neural network topology, start training, etc. +Python API is the Python library that user's Python code invokes, to read the data, build the neural network topology, and start training, etc. ```Python -paddle.init() -input = paddle.op.recordIO("/home/data/mnist.recordio") # file stored on the cluster -img, label = input[0], input[1] -hidden = paddle.layer.fc(input=img, size=200, act=paddle.activation.Tanh()) -prediction = paddle.layer.fc(input=img, size=10, act=paddle.activation.Softmax()) -cost = paddle.layer.classification_cost(input=prediction, label=label) -optimizer = paddle.optimizer.SGD(cost, learning_rate=0.01) -session = paddle.session.NewRemote(num_trainer=3, num_ps=2, GPU_per_trainer=1) -for i in range(1000): - _, cost_val = session.eval(targets=[cost, optimizer]) - print cost_val +images = fluid.layers.data(name='pixel', shape=[1, 28, 28], dtype='float32') +label = fluid.layers.data(name='label', shape=[1], dtype='int64') +... +predict = fluid.layers.fc(input=conv_pool_2, size=10, act="softmax") +cost = fluid.layers.cross_entropy(input=predict, label=label) +avg_cost = fluid.layers.mean(x=cost) +optimizer = fluid.optimizer.Adam(learning_rate=0.01) +optimizer.minimize(avg_cost) + +train_reader = paddle.batch( + paddle.reader.shuffle( + paddle.dataset.mnist.train(), buf_size=500), + batch_size=BATCH_SIZE) + +place = fluid.CPUPlace() +exe = fluid.Executor(place) + +for pass_id in range(10): + for data in train_reader(): + loss, acc = exe.run(trainer_prog, + feed=feeder.feed(data), + fetch_list=[avg_cost]) ``` -The above code is what a typical Python trainer code is, the neural network topology is built using the helper functions such as `paddle.layer.fc`. Training is done by calling `session.eval` iteratively. - -#### session.eval - -As shown in the graph, `session.eval` sends the IR and the evaluation inputs or targets to the PaddlePaddle cluster for evaluation. -The targets can be any variable in the computation graph. When the target is say, the `optimizer` variable, the neural network will be optimized once. When the target is the `cost` variable, `session.eval` returns the cost value. Based on what the target is, an appropriate action is taken. - -The Python `session` is a wrapper of the C++ `Session` class. For more information about `Session`, refer to this document - [Design Doc: Session](./session.md). - -### PaddlePaddle Converter - -The PaddlePaddle converter automatically converts the IR in the request (IR and evaluation inputs/targets) from PaddlePaddle Python to partitioned IRs and dispatches the new IRs and evaluation inputs/targets to different PaddlePaddle runtimes. Below are the steps that are followed : - -1. Add a `feed` OP that feeds the eval inputs, and a `fetch` OP that fetches the eval targets to the IR. - -2. Extract a new computation (sub)graph with the `feed` and `fetch` OPs as the boundary. The runtime does not need to run the OP that is not dependent on the `fetch` OP. - -3. Optimize the computation graph. - -4. Place the OPs in the graph onto different devices on different PaddlePaddle runtime according to a placement algorithm and the device constraints specified by the user. - -5. Partition the graph according to runtime boundaries and add `send` / `recv` OP pair on the runtime boundaries. +The code above is a typical local training program, the "Training Program" is built using helper functions such as +`fluid.layer.fc`. The training is done by calling `Executor.run` +iteratively. + +For more details, the implementation of IR is [Program](../program.md), and `ProgramDesc` is the protobuf type. + +[Executor](../executor.md) simply runs the `ProgramDesc`. For local training you generally use +`Executor` to run the program locally. For any kind of distributed training, you can use +`RemoteExecutor` to specify desired distributed training method with some optional arguments. + +### Distributed Transpiler + +The Distributed Transpiler automatically converts the IR (in protobuf format) to partitioned IRs. Then +the Remote Executor dispatches the new IRs to Remote Executors across the cluster. +Below are the steps that are followed : + +1. User only need to change `Executor` to `RemoteExecutor` to change local program to distributed program. +1. `RemoteExecutor` calls `Distributed Transpiler` to "transpile" user's program to several IRs representing a + distributed training program: + 1. Parse configurations from `RemoteExecutor`. + 1. Determine the type of distributed program, can be DataParallelism, ModelParallelism or Streaming. + 1. Partition the `ProgramDesc` according to type and add `send` / `recv` OP pair on the boundaries. Take + DataParallelism type for example, it removes the optimization operators and add a `send` OP to the + "trainer" role, then add the optimization operators to the parameter server role within the `recv` OP. +1. Dispatch the partitioned graph to different `RemoteExecutor` in the cluster. +1. `RemoteExecutor` on each node run the received `ProgramDesc` utill the end. + + +### RemoteExecutor + +As shown in the graph, `RemoteExecutor.run` sends the IR to the cluster for Execution. +You can also use parameter `fetch_list` to interactively fetch variable back to local for +log printing. + +The Python `RemoteExecutor` is derived from `Executor` class. + +```python +exe = RemoteExecutor( + feed=feeder.feed(data), + fetch_list=[avg_cost], + job_desc=JobDesc( + jobname, + num_trainer, + num_pserver, + cpu_per_trainer, + gpu_per_trainer, + mem_per_trainer, + cpu_per_pserver, + mem_per_pserver + )) +for data in train_reader(): + loss, acc = exe.run(trainer_prog, + feed=feeder.feed(data), + fetch_list=[avg_cost]) +``` -6. Dispatch the partitioned graph to different PaddlePaddle runtimes. +`JobDesc` object describe the distributed job resource specification to run on +Cluster environment. -7. PaddlePaddle runtimes with the `fetch` OP reports evaluation results back to the converter, the converter reports the evaluation results back to the PaddlePaddle Python. + -The output IRs will be cached to optimize the conversion latency. +`RemoteExecutor.run` sends the `ProgramDesc` and +[TrainingJob](https://github.com/PaddlePaddle/cloud/blob/develop/doc/autoscale/README.md#training-job-resource) +to a server in the cluster which executes `RemoteExecutor.listen`. This server is responsible +to start the final Kubernetes Jobs to run the different role of `ProgramDesc`. -#### Placement Algorithm +### Placement Algorithm Our first implementation will only support "trainer-parameter server" placement: the parameters, initializers, and optimizers are all placed on the PaddlePaddle runtimes with the parameter server role. Everything else will be placed on the PaddlePaddle runtimes with the trainer role. This has the same functionality as the "trainer-parameter server" architecture of PaddlePaddle v0.10.0, but is more generic and flexible. In the future, a more general placement algorithm should be implemented, which makes placements according to the input IR, and a model of device computation time and device communication time. Model parallelism requires the generic placement algorithm. -### PaddlePaddle Runtime - -The PaddlePaddle runtime owns multiple devices (e.g., CPUs, GPUs) and runs the IR. The runtime does not need to do OP placement since it is already done by the converter. - - ### Local Training Architecture The local training architecture will be the same as the distributed training architecture, the difference is that everything runs locally, and there is just one PaddlePaddle runtime: @@ -132,9 +176,18 @@ The local training architecture will be the same as the distributed training arc ### Training Data -In PaddlePaddle v0.10.0, training data is typically read with a [data reader](../reader/README.md) from Python. This approach is no longer efficient when training in a distributed fashion since the Python process no longer runs on the same node with the trainer processes. The Python reader will need to read from the distributed filesystem (assuming it has the required access) and send to the trainers, doubling the network traffic. - -When doing distributed training, the user can still use Python data reader: the training data are sent with `session.eval`. However this should be used for debugging purpose only. The users are encouraged to use the read data OPs. +In PaddlePaddle v0.10.0, training data is typically read +with [data reader](../reader/README.md) from Python. This approach is +no longer efficient when training distributedly since the Python +process no longer runs on the same node with the trainer processes, +the Python reader will need to read from the distributed filesystem +(assuming it has the access) and send to the trainers, doubling the +network traffic. + +When doing distributed training, the user can still use Python data +reader: the training data are sent with `Executor.run`. However, should +be used for debugging purpose only. The users are encouraged to use +the read data OPs. ## References: diff --git a/develop/doc_cn/_sources/design/refactor/multi_cpu.md.txt b/develop/doc_cn/_sources/design/dist_refactor/multi_cpu.md.txt similarity index 100% rename from develop/doc_cn/_sources/design/refactor/multi_cpu.md.txt rename to develop/doc_cn/_sources/design/dist_refactor/multi_cpu.md.txt diff --git a/develop/doc/_sources/design/refactor/parameter_server.md.txt b/develop/doc_cn/_sources/design/dist_refactor/parameter_server.md.txt similarity index 76% rename from develop/doc/_sources/design/refactor/parameter_server.md.txt rename to develop/doc_cn/_sources/design/dist_refactor/parameter_server.md.txt index fa3c5d7990213cf2b0d236e66e592dd2699da876..1094f06d461275a9ad4034d5e48b39856d967b71 100644 --- a/develop/doc/_sources/design/refactor/parameter_server.md.txt +++ b/develop/doc_cn/_sources/design/dist_refactor/parameter_server.md.txt @@ -1,4 +1,4 @@ -# Design Doc: Operation Graph Based Parameter Server +# Design Doc: Parameter Server ## Abstract @@ -10,7 +10,7 @@ different purposes. ## Background The previous implementations of the parameter server does not run a -subgraph. parameter initialization, optimizer computation, network +fluid sub-program. Parameter initialization, optimizer computation, network communication and checkpointing are implemented twice on both the trainer and the parameter server. @@ -23,10 +23,10 @@ server becomes a natural extension. ## Design -### Graph Converter +### Distributed Transpiler -The *graph converter* converts the user-defined operation (OP) graph -into subgraphs to be scheduled on different nodes with the following +The *Distributed Transpiler* converts the user-defined fluid program +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 @@ -34,7 +34,6 @@ steps: time. Currently we will use a simple heuristic that puts parameter varable on parameter server workers and everything else on trainer workers. - 1. Add communication OPs to enable the communication between nodes. We will need these OPs: *Send*, *Recv*, *Enqueue*, *Dequeue*. @@ -48,8 +47,8 @@ After converting: -1. The parameter variable W and it's optimizer subgraph are placed on the parameter server. -1. Operators are added to the subgraphs. +1. The parameter variable W and it's 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 @@ -64,39 +63,30 @@ After converting: ### Benefits - Model parallelism become easier to implement: it's an extension to - the trainer - parameter server approach. we already have the - communication OPs, but need to extend the graph converter's - placement functionality. - + 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 - a subgraph. - + a sub-program. - No more duplication logic inside the trainer and the parameter server mentioned in the background section. ### Challenges -- It might be hard for the graph converter to cut a general graph - (without any hint for which subgraph is the optimizer). We may need - to label which subgraph inside the OP graph is the optimizer. - - It's important to balance the parameter shards of on multiple parameter server. If a single parameter is very big (some word-embedding, fully connected, softmax layer), we need to automatically partition the single parameter onto different parameter servers when possible (only element-wise optimizer depends on the parameter variable). +- In the "Aync SGD" figure, the "W" variable on the parameter server + could be read and wrote concurrently. See + [here](https://github.com/PaddlePaddle/Paddle/pull/6394) for more + details about concurrent program in fluid. ### Discussion -- In the "Aync SGD" figure, the "W" variable on the parameter server - could be read and wrote concurrently, what is our locking strategy? - E.g., each variable have a lock cpp method to be invoked by every - OP, or, have a lock OP. - - Can the Enqueue OP be implemented under our current tensor design (puts the input tensor into the queue tensor)? - - *Dequeue* OP will have variable numbers of output (depends on the `min_count` attribute), does our current design support it? (similar question for the *Add* OP) diff --git a/develop/doc_cn/_sources/design/refactor/session.md.txt b/develop/doc_cn/_sources/design/refactor/session.md.txt deleted file mode 100644 index 1d9a26683c14f54e3b5fe41675cd03b5620646b8..0000000000000000000000000000000000000000 --- a/develop/doc_cn/_sources/design/refactor/session.md.txt +++ /dev/null @@ -1,180 +0,0 @@ -# Design Doc: Session - -## Abstract - -The *session* object encapsulates the environment in which the -computation graph is executed. - -We will have the *local* session and *remote* session, they offer the -same [interface](#interface). The local session encapsulates the local -runtime environment and the remote session encapsulates the cluster -runtime environment. - -The local runtime environment contains: - -1. computation devices (i.e., CPU, GPU) handles, and -1. the [scope](../scope.md) which holds all variables. - -The remote runtime environment contains: - -1. computation devices (i.e., CPU and GPU on node 0, 1) in a cluster, - and -1. the distributed [scope](../scope.md) in a cluster which holds all - variables. - -The user can create a remote session on Paddle Cloud and evaluate the -computation graph with it. In this way, the user can control the -remote computation resource in a cluster from his local computer. - - -## Background - -The current design has an implicit global session in which -`paddle.eval()` is executed. The pain point is: - -Since the user is not able to explicitly switch between runtime -environments, the user cannot run a topology in two independent -environments. - -For example, in reinforcement learning, the user may want to have a -stale model for inference and a fresh model for training, and only -replace the stale model with the fresh model periodically. - -Furthermore, we have no concept that encapsulates a remote environment -that executes a computation graph. - -We need the session object to address above issues. - - -## Session - -A session is an object that owns the runtime environment. All -computations are executed through `session.eval()`. - - -### Interface - -```python -eval( - targets, - feed_dict=None, -) -``` - -Evaluates the target Operations or Variables in `targets`. - -- *targets*: the evaluation targets. Can be a single Operation or - Variable, or a list with the Operations or Variables as - elements. The value returned by `eval()` has the same shape as the - `target` argument. - - The PaddlePaddle program is represented by - the [ProgramDesc](../design/program.md), `eval()` will infer the - ProgramDesc from the given targets and run the PaddlePaddle - program. Please - see - [this graph](./distributed_architecture.md#local-training-architecture) for - the detailed illustration for the local session - and - [this graph](./distributed_architecture.md#distributed-training-architecture) for - the detailed illustration for the remote session. - -- *feed_dict*: a dictionary that contains the tensors which override - the edges of the computation graph. - - feed_dict not only can provide the input data, it can override any - OP's input as well: - - ```python - a = pd.constant(2.0, name="a") - b = pd.variable(name="b") - c = pd.mul(a,b) - sess.eval(targets=c, feed_dict={"b":3.0}) # returns 6.0 - ``` - -```python -close() -``` - -Closes the session and releases the scope that the session owns. - - -### Create a Local Session - -```python -session( - devices=None -) -``` - -Creates a new session. One session owns one global scope, so creating -multiple sessions will create different scopes. - -- *devices*: a single `string` or a list of `string` of device names, - the corresponding devices will be the computation devices for - `eval()`. If not specified, all available devices (e.g., all GPUs) - will be used. The user doesn't need to specify the CPU device since - it will be always used. Multiple sessions can use the same device. - - -#### Example - -```Python -a = paddle.constant(1.0) -b = paddle.constant(2.0) -c = a + b -sess = paddle.session(devices=["gpu:0", "gpu:1", "fpga:0"]) -sess.eval(c) -sess.close() -``` - -### Create a Remote Session - -```python -create_cloud_job( - name, - num_trainer, - mem_per_trainer, - gpu_per_trainer, - cpu_per_trainer, - num_ps, - mem_per_ps, - cpu_per_ps, -) -``` - -Creates a Paddle Cloud job. Fails if the job name exists. - -```python -get_cloud_job( - name -) -``` - -Gets a Paddle Cloud job. - -```python -remote_session( - job -) -``` - -- *job*: the Paddle Cloud job. - -#### Example - -```Python -reader = paddle.reader.recordio("/pfs/home/peter/mnist-train-*") # data stored on Paddle Cloud -image = reader.column(0) -label = reader.column(1) -fc1 = paddle.op.fc(image, size=256, act="sigmoid") -fc2 = paddle.op.fc(fc1, size=10, act="softmax") -cost = paddle.op.cross_entropy(fc2, label) -opt = paddle.optimizer.sgd(cost) - -job = paddle.create_cloud_job("test", 3, "1G", 1, 1, 2, "1G", 1) -sess = paddle.remote_ession(job) -for i in range(1000): - sess.eval(opt) -sess.close() -``` diff --git a/develop/doc_cn/design/refactor/distributed_architecture.html b/develop/doc_cn/design/dist_refactor/distributed_architecture.html similarity index 61% rename from develop/doc_cn/design/refactor/distributed_architecture.html rename to develop/doc_cn/design/dist_refactor/distributed_architecture.html index cdcec8e2ffc420b14900c6dba9a562b90abff7a4..646d8b612db867b3297b609b0dc4056922bc1124 100644 --- a/develop/doc_cn/design/refactor/distributed_architecture.html +++ b/develop/doc_cn/design/dist_refactor/distributed_architecture.html @@ -264,62 +264,109 @@ computation is only specified in Python code which sits outside of PaddlePaddle,

            Limitation 3

            The user can not directly specify the parameter update rule for the parameter server in the Python module, since the parameter server does not use the same computation definition as the trainer. Instead, the update rule is baked inside the parameter server. The user can not specify the update rule explicitly.

            -

            This could be fixed by making the parameter server run the same computation definition as the trainer (the user’s Python module). For a detailed explanation, refer to this document - -Design Doc: Operation Graph Based Parameter Server

            +

            This could be fixed by making the parameter server also run an IR, which can be different to the trainer side +For a detailed explanation, refer to this document - +Design Doc: Parameter Server

          Distributed Training Architecture

          The revamped distributed training architecture can address the above discussed limitations. Below is the illustration of how it does so:

          -

          The major components in the architecture are: PaddlePaddle Python, PaddlePaddle converter and PaddlePaddle runtime.

          -
          -

          PaddlePaddle Python

          -

          PaddlePaddle Python is the Python library that user’s Python code invokes, to read the data. build the neural network topology, start training, etc.

          -
          paddle.init()
-input = paddle.op.recordIO("/home/data/mnist.recordio") # file stored on the cluster
-img, label = input[0], input[1]
-hidden = paddle.layer.fc(input=img, size=200, act=paddle.activation.Tanh())
-prediction = paddle.layer.fc(input=img, size=10, act=paddle.activation.Softmax())
-cost = paddle.layer.classification_cost(input=prediction, label=label)
-optimizer = paddle.optimizer.SGD(cost, learning_rate=0.01)
-session = paddle.session.NewRemote(num_trainer=3, num_ps=2, GPU_per_trainer=1)
-for i in range(1000):
-    _, cost_val = session.eval(targets=[cost, optimizer])
-    print cost_val
+
          The major components are: Python API, Distribute Transpiler and Remote Executor.
          
+
          
+
          Python API
          
+
          Python API is the Python library that user’s Python code invokes, to read the data, build the neural network topology, and start training, etc.
          
+
          images = fluid.layers.data(name='pixel', shape=[1, 28, 28], dtype='float32')
+label = fluid.layers.data(name='label', shape=[1], dtype='int64')
+...
+predict = fluid.layers.fc(input=conv_pool_2, size=10, act="softmax")
+cost = fluid.layers.cross_entropy(input=predict, label=label)
+avg_cost = fluid.layers.mean(x=cost)
+optimizer = fluid.optimizer.Adam(learning_rate=0.01)
+optimizer.minimize(avg_cost)
+
+train_reader = paddle.batch(
+    paddle.reader.shuffle(
+        paddle.dataset.mnist.train(), buf_size=500),
+    batch_size=BATCH_SIZE)
+
+place = fluid.CPUPlace()
+exe = fluid.Executor(place)
+
+for pass_id in range(10):
+    for data in train_reader():
+        loss, acc = exe.run(trainer_prog,
+                            feed=feeder.feed(data),
+                            fetch_list=[avg_cost])
 
          
 
          
-
          The above code is what a typical Python trainer code is, the neural network topology is built using the helper functions such as paddle.layer.fc. Training is done by calling session.eval iteratively.
          
-
          
-
          session.eval
          
-
          As shown in the graph, session.eval sends the IR and the evaluation inputs or targets to the PaddlePaddle cluster for evaluation.
-The targets can be any variable in the computation graph. When the target is say, the optimizer variable, the neural network will be optimized once. When the target is the cost variable, session.eval returns the cost value. Based on what the target is, an appropriate action is taken.
          
-
          The Python session is a wrapper of the C++ Session class. For more information about Session, refer to this document - Design Doc: Session.
          
+
          The code above is a typical local training program, the “Training Program” is built using helper functions such as
+fluid.layer.fc. The training is done by calling Executor.run
+iteratively.
          
+
          For more details, the implementation of IR is Program, and ProgramDesc is the protobuf type.
          
+
          Executor simply runs the ProgramDesc. For local training you generally use
+Executor to run the program locally. For any kind of distributed training, you can use
+RemoteExecutor to specify desired distributed training method with some optional arguments.
          
 
          
-
          
-
          
-
          PaddlePaddle Converter
          
-
          The PaddlePaddle converter automatically converts the IR in the request (IR and evaluation inputs/targets) from PaddlePaddle Python to partitioned IRs and dispatches the new IRs and evaluation inputs/targets to different PaddlePaddle runtimes. Below are the steps that are followed :
          
+
          
+
          Distributed Transpiler
          
+
          The Distributed Transpiler automatically converts the IR (in protobuf format) to partitioned IRs. Then
+the Remote Executor dispatches the new IRs to Remote Executors across the cluster.
+Below are the steps that are followed :
          
 
            
-
          1. Add a feed OP that feeds the eval inputs, and a fetch OP that fetches the eval targets to the IR.
            2. 
-
          2. Extract a new computation (sub)graph with the feed and fetch OPs as the boundary. The runtime does not need to run the OP that is not dependent on the fetch OP.
            3. 
-
          3. Optimize the computation graph.
            4. 
-
          4. Place the OPs in the graph onto different devices on different PaddlePaddle runtime according to a placement algorithm and the device constraints specified by the user.
            5. 
-
          5. Partition the graph according to runtime boundaries and add send / recv OP pair on the runtime boundaries.
            6. 
-
          6. Dispatch the partitioned graph to different PaddlePaddle runtimes.
            7. 
-
          7. PaddlePaddle runtimes with the fetch OP reports evaluation results back to the converter, the converter reports the evaluation results back to the PaddlePaddle Python.
            8. 
+
          8. User only need to change Executor to RemoteExecutor to change local program to distributed program.
            9. 
+
          9. RemoteExecutor calls Distributed Transpiler to “transpile” user’s program to several IRs representing a
+distributed training program:
              
+
            1. Parse configurations from RemoteExecutor.
              2. 
+
            2. Determine the type of distributed program, can be DataParallelism, ModelParallelism or Streaming.
              3. 
+
            3. Partition the ProgramDesc according to type and add send / recv OP pair on the boundaries. Take
+DataParallelism type for example, it removes the optimization operators and add a send OP to the
+“trainer” role, then add the optimization operators to the parameter server role within the recv OP.
              4. 
+
            
+
            10. 
+
          10. Dispatch the partitioned graph to different RemoteExecutor in the cluster.
            11. 
+
          11. RemoteExecutor on each node run the received ProgramDesc utill the end.
            12. 
 
          
-
          The output IRs will be cached to optimize the conversion latency.
          
+
          
+
          
+
          RemoteExecutor
          
+
          As shown in the graph, RemoteExecutor.run sends the IR to the cluster for Execution.
+You can also use parameter fetch_list to interactively fetch variable back to local for
+log printing.
          
+
          The Python RemoteExecutor is derived from Executor class.
          
+
          exe = RemoteExecutor(
+    feed=feeder.feed(data),
+    fetch_list=[avg_cost],
+    job_desc=JobDesc(
+      jobname,
+      num_trainer,
+      num_pserver,
+      cpu_per_trainer,
+      gpu_per_trainer,
+      mem_per_trainer,
+      cpu_per_pserver,
+      mem_per_pserver
+    ))
+for data in train_reader():
+    loss, acc = exe.run(trainer_prog,
+                        feed=feeder.feed(data),
+                        fetch_list=[avg_cost])
+
          
+
          
+
          JobDesc object describe the distributed job resource specification to run on
+Cluster environment.
          
+
          
+
          RemoteExecutor.run sends the ProgramDesc and
+TrainingJob
+to a server in the cluster which executes RemoteExecutor.listen. This server is responsible
+to start the final Kubernetes Jobs to run the different role of ProgramDesc.
          
+
          
 
          
-
          Placement Algorithm
          
+
          Placement Algorithm
          
 
          Our first implementation will only support “trainer-parameter server” placement: the parameters, initializers, and optimizers are all placed on the PaddlePaddle runtimes with the parameter server role. Everything else will be placed on the PaddlePaddle runtimes with the trainer role. This has the same functionality as the “trainer-parameter server” architecture of PaddlePaddle v0.10.0, but is more generic and flexible.
          
 
          In the future, a more general placement algorithm should be implemented, which makes placements according to the input IR, and a model of device computation time and device communication time. Model parallelism requires the generic placement algorithm.
          
 
          
-
          
-
          
-
          PaddlePaddle Runtime
          
-
          The PaddlePaddle runtime owns multiple devices (e.g., CPUs, GPUs) and runs the IR. The runtime does not need to do OP placement since it is already done by the converter.
          
-
          
 
          
 
          Local Training Architecture
          
 
          The local training architecture will be the same as the distributed training architecture, the difference is that everything runs locally, and there is just one PaddlePaddle runtime:
          
@@ -327,8 +374,17 @@ The targets can be any variable in the computation graph. When the target is say
 
          
 
          
 
          Training Data
          
-
          In PaddlePaddle v0.10.0, training data is typically read with a data reader from Python. This approach is no longer efficient when training in a distributed fashion since the Python process no longer runs on the same node with the trainer processes. The Python reader will need to read from the distributed filesystem (assuming it has the required access) and send to the trainers, doubling the network traffic.
          
-
          When doing distributed training, the user can still use Python data reader: the training data are sent with session.eval. However this should be used for debugging purpose only. The users are encouraged to use the read data OPs.
          
+
          In PaddlePaddle v0.10.0, training data is typically read
+with data reader from Python. This approach is
+no longer efficient when training distributedly since the Python
+process no longer runs on the same node with the trainer processes,
+the Python reader will need to read from the distributed filesystem
+(assuming it has the access) and send to the trainers, doubling the
+network traffic.
          
+
          When doing distributed training, the user can still use Python data
+reader: the training data are sent with Executor.run. However, should
+be used for debugging purpose only. The users are encouraged to use
+the read data OPs.
          diff --git a/develop/doc_cn/design/refactor/multi_cpu.html b/develop/doc_cn/design/dist_refactor/multi_cpu.html similarity index 100% rename from develop/doc_cn/design/refactor/multi_cpu.html rename to develop/doc_cn/design/dist_refactor/multi_cpu.html diff --git a/develop/doc_cn/design/refactor/parameter_server.html b/develop/doc_cn/design/dist_refactor/parameter_server.html similarity index 91% rename from develop/doc_cn/design/refactor/parameter_server.html rename to develop/doc_cn/design/dist_refactor/parameter_server.html index fa7135e9bd6aa9091a86f8f4e682a3bd4c3b7899..794eaf59388dffb791d3e3237c0dd452b4e1658f 100644 --- a/develop/doc_cn/design/refactor/parameter_server.html +++ b/develop/doc_cn/design/dist_refactor/parameter_server.html @@ -8,7 +8,7 @@ - Design Doc: Operation Graph Based Parameter Server — PaddlePaddle 文档 + Design Doc: Parameter Server — PaddlePaddle 文档 @@ -212,7 +212,7 @@
            -
          • Design Doc: Operation Graph Based Parameter Server
            • +
          • Design Doc: Parameter Server
          @@ -221,8 +221,8 @@
          -
          -

          Design Doc: Operation Graph Based Parameter Server

          +
          +

          Design Doc: Parameter Server

          Abstract

          We propose an approach to implement the parameter server. In this @@ -233,7 +233,7 @@ different purposes.

          Background

          The previous implementations of the parameter server does not run a -subgraph. parameter initialization, optimizer computation, network +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 @@ -245,10 +245,10 @@ server becomes a natural extension.

          Design

          -
          -

          Graph Converter

          -

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

          +

          Distributed Transpiler

          +

          The Distributed Transpiler converts the user-defined fluid program +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 @@ -265,8 +265,8 @@ subgraphs for the trainer and the parameter server:

            After converting:

              -
            1. The parameter variable W and it’s optimizer subgraph are placed on the parameter server.
              2. -
            2. Operators are added to the subgraphs.
                +
              • The parameter variable W and it’s optimizer program are placed on the parameter server.
                • +
              • 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 @@ -284,11 +284,10 @@ tensors.

                  • Benefits

                  • Model parallelism become easier to implement: it’s an extension to -the trainer - parameter server approach. we already have the -communication OPs, but need to extend the graph converter’s -placement functionality.
                    • +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 -a subgraph.
                    • +a sub-program.
                  • No more duplication logic inside the trainer and the parameter server mentioned in the background section.
                  @@ -296,24 +295,21 @@ server mentioned in the background section.

                  Challenges

                    -
                  • It might be hard for the graph converter to cut a general graph -(without any hint for which subgraph is the optimizer). We may need -to label which subgraph inside the OP graph is the optimizer.
                  • It’s important to balance the parameter shards of on multiple parameter server. If a single parameter is very big (some word-embedding, fully connected, softmax layer), we need to automatically partition the single parameter onto different parameter servers when possible (only element-wise optimizer depends on the parameter variable).
                    • +
                  • In the “Aync SGD” figure, the “W” variable on the parameter server +could be read and wrote concurrently. See +here for more +details about concurrent program in fluid.

                  Discussion

                    -
                  • In the “Aync SGD” figure, the “W” variable on the parameter server -could be read and wrote concurrently, what is our locking strategy? -E.g., each variable have a lock cpp method to be invoked by every -OP, or, have a lock OP.
                  • Can the Enqueue OP be implemented under our current tensor design (puts the input tensor into the queue tensor)?
                  • Dequeue OP will have variable numbers of output (depends on the diff --git a/develop/doc_cn/design/refactor/session.html b/develop/doc_cn/design/refactor/session.html deleted file mode 100644 index 3a90302a1a4ffc589f4f972dcf6b0cdafb3a0f64..0000000000000000000000000000000000000000 --- a/develop/doc_cn/design/refactor/session.html +++ /dev/null @@ -1,446 +0,0 @@ - - - - - - - - - - - Design Doc: Session — PaddlePaddle 文档 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
                    - -
                    -
                    - Fork me on Github -
                    - - English - - - - -
                    - -
                    -
                    - - - - -
                    -
                    - - - -
                    -
                    -
                    -
                    -
                    - -
                    - - - - -
                    - - - - - - - - - - - -
                    -
                      - -
                    • Design Doc: Session
                      • -
                    -
                    - -
                    -
                    -
                    -
                    - -
                    -

                    Design Doc: Session

                    -
                    -

                    Abstract

                    -

                    The session object encapsulates the environment in which the -computation graph is executed.

                    -

                    We will have the local session and remote session, they offer the -same interface. The local session encapsulates the local -runtime environment and the remote session encapsulates the cluster -runtime environment.

                    -

                    The local runtime environment contains:

                    -
                      -
                    1. computation devices (i.e., CPU, GPU) handles, and
                      2. -
                    2. the scope which holds all variables.
                      3. -
                    -

                    The remote runtime environment contains:

                    -
                      -
                    1. computation devices (i.e., CPU and GPU on node 0, 1) in a cluster, -and
                      2. -
                    2. the distributed scope in a cluster which holds all -variables.
                      3. -
                    -

                    The user can create a remote session on Paddle Cloud and evaluate the -computation graph with it. In this way, the user can control the -remote computation resource in a cluster from his local computer.

                    -
                    -
                    -

                    Background

                    -

                    The current design has an implicit global session in which -paddle.eval() is executed. The pain point is:

                    -

                    Since the user is not able to explicitly switch between runtime -environments, the user cannot run a topology in two independent -environments.

                    -

                    For example, in reinforcement learning, the user may want to have a -stale model for inference and a fresh model for training, and only -replace the stale model with the fresh model periodically.

                    -

                    Furthermore, we have no concept that encapsulates a remote environment -that executes a computation graph.

                    -

                    We need the session object to address above issues.

                    -
                    -
                    -

                    Session

                    -

                    A session is an object that owns the runtime environment. All -computations are executed through session.eval().

                    -
                    -

                    Interface

                    -
                    eval(
-    targets,
-    feed_dict=None,
-)
-
                    -
                    -

                    Evaluates the target Operations or Variables in targets.

                    -
                      -

                    • targets: the evaluation targets. Can be a single Operation or -Variable, or a list with the Operations or Variables as -elements. The value returned by eval() has the same shape as the -target argument.

                      -

                      The PaddlePaddle program is represented by -the ProgramDesc, eval() will infer the -ProgramDesc from the given targets and run the PaddlePaddle -program. Please -see -this graph for -the detailed illustration for the local session -and -this graph for -the detailed illustration for the remote session.

                      -
                      • -

                    • feed_dict: a dictionary that contains the tensors which override -the edges of the computation graph.

                      -

                      feed_dict not only can provide the input data, it can override any -OP’s input as well:

                      -
                      a = pd.constant(2.0, name="a")
-b = pd.variable(name="b")
-c = pd.mul(a,b)
-sess.eval(targets=c, feed_dict={"b":3.0}) # returns 6.0
-
                      -
                      -
                      • -
                    -
                    close()
-
                    -
                    -

                    Closes the session and releases the scope that the session owns.

                    -
                    -
                    -

                    Create a Local Session

                    -
                    session(
-    devices=None
-)
-
                    -
                    -

                    Creates a new session. One session owns one global scope, so creating -multiple sessions will create different scopes.

                    -
                      -
                    • devices: a single string or a list of string of device names, -the corresponding devices will be the computation devices for -eval(). If not specified, all available devices (e.g., all GPUs) -will be used. The user doesn’t need to specify the CPU device since -it will be always used. Multiple sessions can use the same device.
                      • -
                    -
                    -

                    Example

                    -
                    a = paddle.constant(1.0)
-b = paddle.constant(2.0)
-c = a + b
-sess = paddle.session(devices=["gpu:0", "gpu:1", "fpga:0"])
-sess.eval(c)
-sess.close()
-
                    -
                    -
                    -
                    -
                    -

                    Create a Remote Session

                    -
                    create_cloud_job(
-    name,
-    num_trainer,
-    mem_per_trainer,
-    gpu_per_trainer,
-    cpu_per_trainer,
-    num_ps,
-    mem_per_ps,
-    cpu_per_ps,
-)
-
                    -
                    -

                    Creates a Paddle Cloud job. Fails if the job name exists.

                    -
                    get_cloud_job(
-    name
-)
-
                    -
                    -

                    Gets a Paddle Cloud job.

                    -
                    remote_session(
-    job
-)
-
                    -
                    -
                      -
                    • job: the Paddle Cloud job.
                      • -
                    -
                    -

                    Example

                    -
                    reader = paddle.reader.recordio("/pfs/home/peter/mnist-train-*") # data stored on Paddle Cloud
-image = reader.column(0)
-label = reader.column(1)
-fc1 = paddle.op.fc(image, size=256, act="sigmoid")
-fc2 = paddle.op.fc(fc1, size=10, act="softmax")
-cost = paddle.op.cross_entropy(fc2, label)
-opt = paddle.optimizer.sgd(cost)
-
-job = paddle.create_cloud_job("test", 3, "1G", 1, 1, 2, "1G", 1)
-sess = paddle.remote_ession(job)
-for i in range(1000):
-    sess.eval(opt)
-sess.close()
-
                    -
                    -
                    -
                    -
                    -
                    - - -
                    -
                    -
                    - - -
                    - -
                    -

                    - © 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/objects.inv b/develop/doc_cn/objects.inv index 03461aab3601ecc6305d20603216fbb4fd7cf260..6ae2ae84fa06b64cde33fb04a043f540b92b295a 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 b/develop/doc_cn/searchindex.js index 67661d50c4feabe8ed2b4054dbe66a7dcc2d7d0f..8b5aa899bd910ff467382187840779688e1d6a1e 100644 --- a/develop/doc_cn/searchindex.js +++ b/develop/doc_cn/searchindex.js @@ -1 +1 @@ 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