diff --git a/develop/doc/_sources/design/refactor/session.md.txt b/develop/doc/_sources/design/refactor/session.md.txt new file mode 100644 index 0000000000000000000000000000000000000000..1d9a26683c14f54e3b5fe41675cd03b5620646b8 --- /dev/null +++ b/develop/doc/_sources/design/refactor/session.md.txt @@ -0,0 +1,180 @@ +# 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/refactor/distributed_architecture.html index 975a8602fcd7cd6ff022fa608b3da5ac0c043b11..b068135cb1e14eaff0b8a51c48f25bc680b6caa9 100644 --- a/develop/doc/design/refactor/distributed_architecture.html +++ b/develop/doc/design/refactor/distributed_architecture.html @@ -295,7 +295,7 @@ once. When the target is the co the cost value.

The Python session is a wrapper of the C++ Session class. For more information about Session, please -see Design Doc: Session.

+see Design Doc: Session.

diff --git a/develop/doc/design/refactor/session.html b/develop/doc/design/refactor/session.html new file mode 100644 index 0000000000000000000000000000000000000000..9e74ea7e7df4e341f6717762970aecad528b8e3a --- /dev/null +++ b/develop/doc/design/refactor/session.html @@ -0,0 +1,401 @@ + + + + + + + + + + + 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 22f4b89502b283377178bff3180002fa3470d15d..6946c2d4098ae0d54cb3ad2212416a368f3e3054 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 986e0bf9681e1626a03fa344545c1a3890b424c1..142969da337e6f731fc668e4bdd53cdc3fba253f 100644 --- a/develop/doc/searchindex.js +++ b/develop/doc/searchindex.js @@ -1 +1 @@ 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\ No newline at end of file diff --git a/develop/doc_cn/_sources/design/refactor/session.md.txt b/develop/doc_cn/_sources/design/refactor/session.md.txt new file mode 100644 index 0000000000000000000000000000000000000000..1d9a26683c14f54e3b5fe41675cd03b5620646b8 --- /dev/null +++ b/develop/doc_cn/_sources/design/refactor/session.md.txt @@ -0,0 +1,180 @@ +# 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/refactor/distributed_architecture.html index f2835550e0a308f0169d4bea1d8a0105c185f1e9..99c663797a0a096178337abfa32f3f96f6656922 100644 --- a/develop/doc_cn/design/refactor/distributed_architecture.html +++ b/develop/doc_cn/design/refactor/distributed_architecture.html @@ -309,7 +309,7 @@ once. When the target is the co the cost value.

The Python session is a wrapper of the C++ Session class. For more information about Session, please -see Design Doc: Session.

+see Design Doc: Session.

diff --git a/develop/doc_cn/design/refactor/session.html b/develop/doc_cn/design/refactor/session.html new file mode 100644 index 0000000000000000000000000000000000000000..03cee4c88573a2985330d42a5e75ae267b2ba9b6 --- /dev/null +++ b/develop/doc_cn/design/refactor/session.html @@ -0,0 +1,416 @@ + + + + + + + + + + + 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 eb0102c05a65389bb93f92771675f4da1ac33d4f..1d63476b3521ab1afbe3fc8a5aa4791c3860b508 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 458f4f11c6de51b850fdc0d59240db3873c076d6..f8681aa10bee38adc2c57e3d142c7585ba0d1b96 100644 --- a/develop/doc_cn/searchindex.js +++ b/develop/doc_cn/searchindex.js @@ -1 +1 @@ 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