Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into fix-7195
yangyaming
8 years ago
commit
f05330b78b
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# Cluster Training Benchmark
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## Setup
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- Platform
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- Kubernetes: v1.6.2
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- Linux Kernel: v3.10.0
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- Resource
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- CPU: 10 Cores per Pod
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- Memory: 5GB per Pod
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- Docker Image
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We use different base Docker Image to run the benchmark on Kubernetes:
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- PaddlePaddle v2: paddlepaddle/paddle:0.11.0
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- PaddlePaddle Fluid: paddlepaddle/paddle:[commit-id]
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- TensorFlow: tensorflow/tensorflow:1.5.0-rc0
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- Model
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vgg16 is used in this benchmark.
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## Cases
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- Variable
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- Batch Size of training data.
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- PServer count of the training job.
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- The number of trainers.
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- Invariant
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- The resource of trainer/pserver Pod.
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### Measure the Performance for Different Batch Size
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- PServer Count: 40
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- Trainer Count: 100
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- Metrics: mini-batch / sec
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| Batch Size | 32 | 64 | 128 | 256 |
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| -- | -- | -- | -- | -- |
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| PaddlePaddle Fluid | - | - | - | - |
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| PaddlePaddle v2 | - | - | - | - |
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| TensorFlow | - | - | - | - |
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### Measure the Performance for Different PServer Count
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- Trainer Count: 100
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- Batch Size: 64
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- Metrics: mini-batch / sec
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| PServer Count | 10 | 20 | 40 | 60 |
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| -- | -- | -- | -- | -- |
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| PaddlePaddle Fluid | - | - | - | - |
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| PaddlePaddle v2 | - | - | - | - |
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| TensorFlow | - | - | - | - |
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### Measure Parallel Efficiency By Increasing Trainer Count
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- PServer Count: 20
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- Batch Size: 64
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- Metrics:
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$S = \div(T1, TN)$
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which S is the ratio of T1 over TN, training time of 1 and N trainers.
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The parallel efficiency is:
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$E = \div(S, N)$
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| Trainer Counter | 1 | 10 | 20 | 30 | 40 | 50 | 60 | 70 | 80 | 90 | 100 |
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| -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- |
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| PaddlePaddle Fluid | - | - | - | - | - | - | - | - | - | - | - |
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| PaddlePaddle v2 | - | - | - | - | - | - | - | - | - | - | - | - |
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| TensorFlow | - | - | - | - | - | - | - | - | - | - | - | - | - |
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## Reproduce the benchmark
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TODO
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@ -0,0 +1,10 @@
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===========
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IO
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===========
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is_parameter
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-----------
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.. autofunction:: paddle.v2.fluid.io.is_parameter
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:noindex:
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|
After Width: | Height: | Size: 361 KiB |
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After Width: | Height: | Size: 470 KiB |
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After Width: | Height: | Size: 448 KiB |
@ -0,0 +1,9 @@
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PaddlePaddle C-API
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==================
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.. toctree::
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:maxdepth: 1
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compile_paddle_lib_cn.md
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organization_of_the_inputs_cn.md
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workflow_of_capi_cn.md
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File diff suppressed because it is too large
Load Diff
@ -0,0 +1,8 @@
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from paddle.utils.merge_model import merge_v2_model
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from mnist_v2 import network
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net = network(is_infer=True)
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param_file = "models/params_pass_4.tar"
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output_file = "output.paddle.model"
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merge_v2_model(net, param_file, output_file)
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@ -0,0 +1,117 @@
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import os
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import sys
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import gzip
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import logging
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import argparse
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from PIL import Image
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import numpy as np
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import paddle.v2 as paddle
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from paddle.utils.dump_v2_config import dump_v2_config
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logger = logging.getLogger("paddle")
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logger.setLevel(logging.INFO)
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def multilayer_perceptron(img, layer_size, lbl_dim):
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for idx, size in enumerate(layer_size):
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hidden = paddle.layer.fc(input=(img if not idx else hidden),
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size=size,
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act=paddle.activation.Relu())
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return paddle.layer.fc(input=hidden,
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size=lbl_dim,
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act=paddle.activation.Softmax())
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def network(input_dim=784, lbl_dim=10, is_infer=False):
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images = paddle.layer.data(
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name='pixel', type=paddle.data_type.dense_vector(input_dim))
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predict = multilayer_perceptron(
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images, layer_size=[128, 64], lbl_dim=lbl_dim)
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if is_infer:
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return predict
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else:
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label = paddle.layer.data(
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name='label', type=paddle.data_type.integer_value(lbl_dim))
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return paddle.layer.classification_cost(input=predict, label=label)
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def main(task="train", use_gpu=False, trainer_count=1, save_dir="models"):
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if task == "train":
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if not os.path.exists(save_dir):
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os.mkdir(save_dir)
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paddle.init(use_gpu=use_gpu, trainer_count=trainer_count)
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cost = network()
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parameters = paddle.parameters.create(cost)
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optimizer = paddle.optimizer.Momentum(
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learning_rate=0.1 / 128.0,
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momentum=0.9,
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regularization=paddle.optimizer.L2Regularization(rate=0.0005 * 128))
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trainer = paddle.trainer.SGD(cost=cost,
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parameters=parameters,
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update_equation=optimizer)
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def event_handler(event):
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if isinstance(event, paddle.event.EndIteration):
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if event.batch_id % 100 == 0:
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logger.info("Pass %d, Batch %d, Cost %f, %s" %
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(event.pass_id, event.batch_id, event.cost,
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event.metrics))
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if isinstance(event, paddle.event.EndPass):
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with gzip.open(
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os.path.join(save_dir, "params_pass_%d.tar" %
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event.pass_id), "w") as f:
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trainer.save_parameter_to_tar(f)
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trainer.train(
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reader=paddle.batch(
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paddle.reader.shuffle(
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paddle.dataset.mnist.train(), buf_size=8192),
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batch_size=128),
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event_handler=event_handler,
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num_passes=5)
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elif task == "dump_config":
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predict = network(is_infer=True)
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dump_v2_config(predict, "trainer_config.bin", True)
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else:
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raise RuntimeError(("Error value for parameter task. "
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"Available options are: train and dump_config."))
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def parse_cmd():
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parser = argparse.ArgumentParser(
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description="PaddlePaddle MNIST demo for CAPI.")
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parser.add_argument(
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"--task",
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type=str,
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required=False,
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help=("A string indicating the taks type. "
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"Available options are: \"train\", \"dump_config\"."),
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default="train")
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parser.add_argument(
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"--use_gpu",
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type=bool,
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help=("A bool flag indicating whether to use GPU device or not."),
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default=False)
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parser.add_argument(
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"--trainer_count",
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type=int,
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help=("This parameter is only used in training task. It indicates "
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"how many computing threads are created in training."),
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default=1)
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parser.add_argument(
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"--save_dir",
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type=str,
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help=("This parameter is only used in training task. It indicates "
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"path of the directory to save the trained models."),
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default="models")
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return parser.parse_args()
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if __name__ == "__main__":
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args = parse_cmd()
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main(args.task, args.use_gpu, args.trainer_count, args.save_dir)
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