Skip to content

  • 体验新版
    • 正在加载...
  • 登录
  • PaddlePaddle
  • Paddle
  • Issue
  • #18

P
Paddle
  • 项目概览

PaddlePaddle / Paddle
大约 2 年 前同步成功

通知 2325
Star 20933
Fork 5424
  • 代码
    • 文件
    • 提交
    • 分支
    • Tags
    • 贡献者
    • 分支图
    • Diff
  • Issue 1423
    • 列表
    • 看板
    • 标记
    • 里程碑
  • 合并请求 543
  • Wiki 0
    • Wiki
  • 分析
    • 仓库
    • DevOps
  • 项目成员
  • Pages
P
Paddle
  • 项目概览
    • 项目概览
    • 详情
    • 发布
  • 仓库
    • 仓库
    • 文件
    • 提交
    • 分支
    • 标签
    • 贡献者
    • 分支图
    • 比较
  • Issue 1,423
    • Issue 1,423
    • 列表
    • 看板
    • 标记
    • 里程碑
  • 合并请求 543
    • 合并请求 543
  • Pages
  • 分析
    • 分析
    • 仓库分析
    • DevOps
  • Wiki 0
    • Wiki
  • 成员
    • 成员
  • 收起侧边栏
  • 动态
  • 分支图
  • 创建新Issue
  • 提交
  • Issue看板
已关闭
开放中
Opened 9月 01, 2016 by saxon_zh@saxon_zhGuest

Compatiblity issue with CUDA 8.0

Created by: stoneyang

Hi, there,

I can successfully build Paddle on my machine installed Linux 14.04 LTS and CUDA 8.0 as the official guide. And for sure, the CPU version runs well except the speed....

When I ran the image classification demo with the script train.sh in GPU mode (see train.sh for more details), it unfortunately failed and threw out the following info:

I0901 19:18:02.916951 31272 Util.cpp:144] commandline: ../../build/paddle/trainer/paddle_trainer --config=vgg_16_cifar.py --dot_period=10 --log_period=100 --test_all_data_in_one_period=1 --use_gpu=1 --gpu_id=0 --trainer_count=1 --num_passes=200 --save_dir=./cifar_vgg_model
I0901 19:18:09.213749 31272 Util.cpp:113] Calling runInitFunctions
I0901 19:18:09.428228 31272 Util.cpp:126] Call runInitFunctions done.
[INFO 2016-09-01 19:18:09,580 layers.py:1430] channels=3 size=3072
[INFO 2016-09-01 19:18:09,580 layers.py:1430] output size for __conv_0__ is 32
[INFO 2016-09-01 19:18:09,583 layers.py:1430] channels=64 size=65536
[INFO 2016-09-01 19:18:09,583 layers.py:1430] output size for __conv_1__ is 32
[INFO 2016-09-01 19:18:09,586 layers.py:1490] output size for __pool_0__ is 16*16
[INFO 2016-09-01 19:18:09,587 layers.py:1430] channels=64 size=16384
[INFO 2016-09-01 19:18:09,587 layers.py:1430] output size for __conv_2__ is 16
[INFO 2016-09-01 19:18:09,590 layers.py:1430] channels=128 size=32768
[INFO 2016-09-01 19:18:09,590 layers.py:1430] output size for __conv_3__ is 16
[INFO 2016-09-01 19:18:09,592 layers.py:1490] output size for __pool_1__ is 8*8
[INFO 2016-09-01 19:18:09,593 layers.py:1430] channels=128 size=8192
[INFO 2016-09-01 19:18:09,594 layers.py:1430] output size for __conv_4__ is 8
[INFO 2016-09-01 19:18:09,596 layers.py:1430] channels=256 size=16384
[INFO 2016-09-01 19:18:09,597 layers.py:1430] output size for __conv_5__ is 8
[INFO 2016-09-01 19:18:09,599 layers.py:1430] channels=256 size=16384
[INFO 2016-09-01 19:18:09,599 layers.py:1430] output size for __conv_6__ is 8
[INFO 2016-09-01 19:18:09,601 layers.py:1490] output size for __pool_2__ is 4*4
[INFO 2016-09-01 19:18:09,602 layers.py:1430] channels=256 size=4096
[INFO 2016-09-01 19:18:09,603 layers.py:1430] output size for __conv_7__ is 4
[INFO 2016-09-01 19:18:09,605 layers.py:1430] channels=512 size=8192
[INFO 2016-09-01 19:18:09,605 layers.py:1430] output size for __conv_8__ is 4
[INFO 2016-09-01 19:18:09,608 layers.py:1430] channels=512 size=8192
[INFO 2016-09-01 19:18:09,608 layers.py:1430] output size for __conv_9__ is 4
[INFO 2016-09-01 19:18:09,610 layers.py:1490] output size for __pool_3__ is 2*2
[INFO 2016-09-01 19:18:09,611 layers.py:1490] output size for __pool_4__ is 1*1
[INFO 2016-09-01 19:18:09,615 networks.py:960] The input order is [image, label]
[INFO 2016-09-01 19:18:09,615 networks.py:963] The output order is [__cost_0__]
I0901 19:18:09.653937 31272 Trainer.cpp:169] trainer mode: Normal
F0901 19:18:09.658243 31272 hl_gpu_matrix_kernel.cuh:181] Check failed: cudaSuccess == err (0 vs. 8) [hl_gpu_apply_unary_op failed] CUDA error: invalid device function
*** Check failure stack trace: ***
    @     0x7efd1b172daa  (unknown)
    @     0x7efd1b172ce4  (unknown)
    @     0x7efd1b1726e6  (unknown)
    @     0x7efd1b175687  (unknown)
    @           0x78b159  hl_gpu_apply_unary_op<>()
    @           0x753edf  paddle::BaseMatrixT<>::applyUnary<>()
    @           0x753ac9  paddle::BaseMatrixT<>::applyUnary<>()
    @           0x73e04f  paddle::BaseMatrixT<>::zero()
    @           0x62af8e  paddle::Parameter::enableType()
    @           0x6272ec  paddle::parameterInitNN()
    @           0x62975b  paddle::NeuralNetwork::init()
    @           0x62eda3  paddle::GradientMachine::create()
    @           0x6a84e5  paddle::TrainerInternal::init()
    @           0x6a4907  paddle::Trainer::init()
    @           0x543935  main
    @     0x7efd1a37ef45  (unknown)
    @           0x54efd5  (unknown)
    @              (nil)  (unknown)
Aborted (core dumped)
No data to plot. Exiting!

It seems that Paddle still does not support the latest version of CUDA....

Appended my train.sh as a clue:

(omitted the original copyright info)
#!/bin/bash

set -e
config=vgg_16_cifar.py
output=./cifar_vgg_model
log=train.log

../../build/paddle/trainer/paddle_trainer \
--config=$config \
--dot_period=10 \
--log_period=100 \
--test_all_data_in_one_period=1 \
--use_gpu=1 \
--gpu_id=0 \
--trainer_count=1 \
--num_passes=200 \
--save_dir=$output \
2>&1 | tee $log

python -m paddle.utils.plotcurve -i $log > plot.png
指派人
分配到
无
里程碑
无
分配里程碑
工时统计
无
截止日期
无
标识: paddlepaddle/Paddle#18
渝ICP备2023009037号

京公网安备11010502055752号

网络110报警服务 Powered by GitLab CE v13.7
开源知识
Git 入门 Pro Git 电子书 在线学 Git
Markdown 基础入门 IT 技术知识开源图谱
帮助
使用手册 反馈建议 博客
《GitCode 隐私声明》 《GitCode 服务条款》 关于GitCode
Powered by GitLab CE v13.7