From f24b5dffc42063ddfe28229fdb242c3df4ec1aa7 Mon Sep 17 00:00:00 2001 From: Helin Wang Date: Tue, 26 Sep 2017 18:03:37 -0700 Subject: [PATCH] Update Session design doc --- doc/design/refactor/session.md | 160 +++++++++++++++++++++++++++++++++ doc/design/session.md | 72 --------------- 2 files changed, 160 insertions(+), 72 deletions(-) create mode 100644 doc/design/refactor/session.md delete mode 100644 doc/design/session.md diff --git a/doc/design/refactor/session.md b/doc/design/refactor/session.md new file mode 100644 index 000000000..5f58148f0 --- /dev/null +++ b/doc/design/refactor/session.md @@ -0,0 +1,160 @@ +# Design Doc: Session + +## Abstract + +The *session* object encapsulates the environment in which the +computation graph is executed. + +We will have *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 envrionment. + +The local runtime envrionment contains: + +1. computation devices (i.e., CPU, GPU) handles, and +1. the [scope](../scope.md) which holds all variables. + +The remote runtime envrionment 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 on which +`paddle.eval()` is executed. The pain point is: + +Since the user is not able to explicitly switch between runtime +environments such as the scope and the device contexts, 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 computation graph is implicitly inferred from the targets. + +- *feed_dict*: a dictionary that contains the tensors which overrides + the edges of the computation graph. + +``` +close() +``` + +Closes the session. Calling this method releases the scope. + + +### Create a Local Session + +``` +session( + gpu_ids=None +) +``` + +Creates a new session. One session owns one scope, so creating +multiple sessions will create different scopes. + +- *gpu_ids*: a single `int` or a list of `int` of the GPU IDs to be + used as the computation devices. If not specified, all avaiable GPUs + will be used. + + +#### Example + +```Python +a = paddle.constant(1.0) +b = paddle.constant(2.0) +c = a + b +sess = paddle.session(gpu_ids=[0,1]) +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 + +```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/doc/design/session.md b/doc/design/session.md deleted file mode 100644 index dc034c390..000000000 --- a/doc/design/session.md +++ /dev/null @@ -1,72 +0,0 @@ -# Design Doc: Session - -## Abstract - -This design doc proposes to have an object called *Session* which -encapsulates the environment in which the computation graph is -executed. - -The session is able to distinguish running a graph locally or -remotely, using CPU only or using one or more GPUs. Different sessions -have different runtime environments such as [scopes](./scope.md) and -device contexts. - - -## Background - -A computation graph runs in an environment which contains states such -as the scope and device contexts. The current design has an implicit -global session on which `paddle.eval()` is executed. - -Since the user is not able to explicitly switch between runtime -environments such as the scope and the device contexts, 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. Also, we have no concept that -can encapsulate a remote environment that could execute a computation -graph. - -We need a session concept to address above issues. - -## Session - -A session is an object that owns all runtime states such as scope, -reader OP's file handles, connection to a remote PaddlePaddle cluster, -etc. - -The session has two methods: `eval` and `close`. `eval` executes the -target OP in a given graph, and `close` closes the session and -releases all related resources: - -```Python -a = paddle.constant(1.0) -b = paddle.constant(2.0) -c = a + b -sess = paddle.session() -sess.eval(c) -sess.close() -``` - -### Remote Session - -Paddle Cloud will support user creating a remote session pointing to -the Paddle Cloud cluster. The user can send the computation graph to -be executed on the Paddle Cloud. In this way, the user can control a -cluster from her local computer: - -```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) - -remote_config = ... # remote configuration such as endpoint, number of nodes and authentication. -sess = paddle.remoteSession(remote_config) -for i in range(1000): - sess.eval(opt) -sess.close() -``` -- GitLab