未验证 提交 c6bc5905 编写于 作者: H Hao Wang 提交者: GitHub

visualdl en +cn official readme (#598)

上级 1264f9ec
......@@ -13,25 +13,28 @@ VisualDL是一个面向深度学习任务设计的可视化工具,包含了sca
实现原生的性能和定制效果。
## 组件
VisualDL 目前支持4种组件:
VisualDL 目前支持以下组件:
- graph
- scalar
- image
- histogram
- image
- audio
- graph
- high dimensional
### Graph
兼容 ONNX(Open Neural Network Exchange)[https://github.com/onnx/onnx], 通过与 python SDK的结合,VisualDL可以兼容包括 PaddlePaddle, pytorch, mxnet在内的大部分主流DNN平台。
### Scalar
可以用于展示训练测试的误差趋势
<p align="center">
<img src="https://raw.githubusercontent.com/daming-lu/large_files/master/graph_demo.gif" width="60%" />
<img src="https://raw.githubusercontent.com/daming-lu/large_files/master/loss_scalar.gif" width="60%"/>
</p>
### Scalar
可以用于展示训练测试的误差趋势
### Histogram
用于可视化任何tensor中元素分布的变化趋势
<p align="center">
<img src="https://raw.githubusercontent.com/daming-lu/large_files/master/loss_scalar.gif" width="60%"/>
<img src="https://raw.githubusercontent.com/daming-lu/large_files/master/histogram.gif" width="60%"/>
</p>
### Image
......@@ -41,12 +44,21 @@ VisualDL 目前支持4种组件:
<img src="https://raw.githubusercontent.com/daming-lu/large_files/master/loss_image.gif" width="60%"/>
</p>
### Histogram
### Audio
可用于播放输入或生成的音频样本
用于可视化任何tensor中元素分布的变化趋势
### Graph
兼容 ONNX(Open Neural Network Exchange)[https://github.com/onnx/onnx], 通过与 python SDK的结合,VisualDL可以兼容包括 PaddlePaddle, pytorch, mxnet在内的大部分主流DNN平台。
<p align="center">
<img src="https://raw.githubusercontent.com/daming-lu/large_files/master/histogram.gif" width="60%"/>
<img src="https://raw.githubusercontent.com/daming-lu/large_files/master/graph_demo.gif" width="60%" />
</p>
### High Dimensional
用高维度数据映射在2D/3D来可视化嵌入
<p align="center">
<img src="https://raw.githubusercontent.com/PaddlePaddle/VisualDL/develop/docs/getting_started/high_dimensional_3d.png" width="60%"/>
</p>
## 快速尝试
......@@ -58,12 +70,14 @@ pip install --upgrade visualdl
# 运行一个例子,vdl_create_scratch_log 将创建测试日志
vdl_create_scratch_log
visualDL --logdir=scratch_log --port=8080
visualdl --logdir=scratch_log --port=8080
# 访问 http://127.0.0.1:8080
```
如果以上步骤出现问题,很可能是因为python或pip不同版本或不同位置所致,以下安装方法能解决。
如果出现`TypeError: __init__() got an unexpected keyword argument 'file'`, 是因为protobuf不是3.5以上,运行`pip install --upgrade protobuf`就能解决。
如果以上步骤还有出现其他问题,很可能是因为python或pip不同版本或不同位置所致,以下安装方法能解决。
## 使用 virtualenv 安装
......@@ -100,13 +114,11 @@ pip install --upgrade visualdl
# 运行一个例子,vdl_create_scratch_log 将创建测试日志
vdl_create_scratch_log
visualDL --logdir=scratch_log --port=8080
visualdl --logdir=scratch_log --port=8080
# 访问 http://127.0.0.1:8080
```
如果出现`TypeError: __init__() got an unexpected keyword argument 'file'`, 是因为protobuf不是3.5以上,运行`pip install --upgrade protobuf`就能解决。
如果在虚拟环境下仍然遇到安装问题,请尝试以下方法。
......@@ -134,7 +146,7 @@ pip install --upgrade visualdl
# 运行一个例子,vdl_create_scratch_log 将创建测试日志
vdl_create_scratch_log
visualDL --logdir=scratch_log --port=8080
visualdl --logdir=scratch_log --port=8080
# 访问 http://127.0.0.1:8080
```
......@@ -151,7 +163,7 @@ python setup.py bdist_wheel
pip install --upgrade dist/visualdl-*.whl
```
如果打包和安装遇到其他问题,不安装只想运行Visual DL可以看[这里](https://github.com/PaddlePaddle/VisualDL/blob/develop/docs/develop/how_to_dev_frontend_cn.md)
如果打包和安装遇到其他问题,不安装只想运行Visual DL可以看[这里](https://github.com/PaddlePaddle/VisualDL/blob/develop/docs/how_to_dev_frontend_en.md)
## SDK
......@@ -210,11 +222,16 @@ int main() {
当训练过程中已经产生了日志数据,就可以启动board进行实时预览可视化信息
```
visualDL --logdir <some log dir>
visualdl --logdir <some log dir>
```
board 还支持一下参数来实现远程的访问:
- `--host` 设定IP
- `--port` 设定端口
- `--model_pb` 指定 ONNX 格式的模型文件
- `-m / --model_pb` 指定 ONNX 格式的模型文件
### 贡献
VisualDL 是由 [PaddlePaddle](http://www.paddlepaddle.org/) 和
[ECharts](http://echarts.baidu.com/) 合作推出的开源项目。我们欢迎所有人使用,提意见以及贡献代码。
# Visual DL Toolset
<p align="center">
<img src="https://raw.githubusercontent.com/PaddlePaddle/VisualDL/develop/docs/images/vs-logo.png" width="60%" />
</p>
## Introduction
VisualDL is a deep learning visualization tool that can help design deep learning jobs.
It includes features such as scalar, parameter distribution, model structure and image visualization.
Currently it is being developed at a high pace.
New features will be continuously added.
At present, most DNN frameworks use Python as their primary language. VisualDL supports Python by nature.
Users can get plentiful visualization results by simply add a few lines of Python code into their model before training.
Besides Python SDK, VisualDL was writen in C++ on the low level. It also provides C++ SDK that
can be integrated into other platforms.
## Component
VisualDL provides following components:
- scalar
- histogram
- image
- audio
- graph
- high dimensional
### Scalar
Scalar can be used to show the trends of error during training.
<p align="center">
<img src="https://raw.githubusercontent.com/daming-lu/large_files/master/loss_scalar.gif" width="60%"/>
</p>
### Histogram
Histogram can be used to visualize parameter distribution and trends for any tensor.
<p align="center">
<img src="https://raw.githubusercontent.com/daming-lu/large_files/master/histogram.gif" width="60%"/>
</p>
### Image
Image can be used to visualize any tensor or intermediate generated image.
<p align="center">
<img src="https://raw.githubusercontent.com/daming-lu/large_files/master/loss_image.gif" width="60%"/>
</p>
### Audio
Audio can be used to play input audio samples or generated audio samples.
### Graph
Graph is compatible with ONNX ([Open Neural Network Exchange](https://github.com/onnx/onnx)),
Cooperated with Python SDK, VisualDL can be compatible with most major DNN frameworks, including
PaddlePaddle, PyTorch and MXNet.
<p align="center">
<img src="https://raw.githubusercontent.com/daming-lu/large_files/master/graph_demo.gif" width="60%" />
</p>
### High Dimensional
High Dimensional can be used to visualize data embeddings by projecting high-dimensional data into 2D / 3D.
<p align="center">
<img src="https://raw.githubusercontent.com/PaddlePaddle/VisualDL/develop/docs/getting_started/high_dimensional_3d.png" width="60%"/>
</p>
## Quick Start
To give the VisualDL a quick test, please use the following commands.
```
# Install the VisualDL. Preferably under a virtual environment or anaconda.
pip install --upgrade visualdl
# run a demo, vdl_create_scratch_log will create logs for testing.
vdl_create_scratch_log
visualdl --logdir=scratch_log --port=8080
# visit http://127.0.0.1:8080
```
If you encounter the error `TypeError: __init__() got an unexpected keyword argument 'file'`, that is due to protobuf version is not 3.5+,simply run `pip install --upgrade protobuf` will fix the issue.
If you run into any other issues in above steps, it could be error caused by environmental issues by different python or pip versions.
Following installation methods might fix the issues.
## Install with Virtualenv
[Virtualenv](https://virtualenv.pypa.io/en/stable/) creates isolated Python environment that prevents interfering
by other Python programs on the same machine and make sure Python and pip are located properly.
On macOS, install pip and virtualenv by:
```
sudo easy_install pip
pip install --upgrade virtualenv
```
On Linux, install pip and virtualenv by:
```
sudo apt-get install python3-pip python3-dev python-virtualenv
```
Then create a Virtualenv environment by one of following command:
```
virtualenv ~/vdl # for Python2.7
virtualenv -p python3 ~/vdl for Python 3.x
```
```~/vdl``` will be your Virtualenv directory, you may choose to install anywhere.
Activate your Virtualenv environment by:
```
source ~/vdl/bin/activate
```
Now you should be able to install VisualDL and run our demo:
```
pip install --upgrade visualdl
# run a demo, vdl_create_scratch_log will create logs for testing.
vdl_create_scratch_log
visualdl --logdir=scratch_log --port=8080
# visit http://127.0.0.1:8080
```
If you still have issues installing VisualDL from Virtualenv, try following installation method.
## Install with Anaconda
Anaconda is a python distribution, with installation and package management tools. Also it is an environment manager,
which provides the facility to create different python environments, each with their own settings.
Follow the instructions on the [Anaconda download site](https://www.anaconda.com/download) to download and install Anaconda.
Download Python 3.6 version command-Line installer.
Create a conda environment named ```vdl``` or anything you want by:
```
conda create -n vdl pip python=2.7 # or python=3.3, etc.
```
Activate the conda environment by:
```
source activate vdl
```
Now you should be able to install VisualDL and run our demo:
```
pip install --upgrade visualdl
# run a demo, vdl_create_scratch_log will create logs for testing.
vdl_create_scratch_log
visualdl --logdir=scratch_log --port=8080
# visit http://127.0.0.1:8080
```
If you still have issues installing VisualDL, try installing from sources as in following section.
### Install from source
```
#Preferably under a virtualenv or anaconda.
git clone https://github.com/PaddlePaddle/VisualDL.git
cd VisualDL
python setup.py bdist_wheel
pip install --upgrade dist/visualdl-*.whl
```
If there are still issues regarding the ```pip install```, you can still start Visual DL by starting the dev server
[here](https://github.com/PaddlePaddle/VisualDL/blob/develop/docs/how_to_dev_frontend_en.md)
## SDK
VisualDL provides both Python SDK and C++ SDK in order to fit more use cases.
### Python SDK
VisualDL now supports both Python 2 and Python 3.
Below is an example of creating a simple Scalar component and inserting data from different timestamps:
```python
import random
from visualdl import LogWriter
logdir = "./tmp"
logger = LogWriter(logdir, sync_cycle=10000)
# mark the components with 'train' label.
with logger.mode("train"):
# create a scalar component called 'scalars/scalar0'
scalar0 = logger.scalar("scalars/scalar0")
# add some records during DL model running.
for step in range(100):
scalar0.add_record(step, random.random())
```
### C++ SDK
Here is the C++ SDK identical to the Python SDK example above:
```c++
#include <cstdlib>
#include <string>
#include "visualdl/logic/sdk.h"
namespace vs = visualdl;
namespace cp = visualdl::components;
int main() {
const std::string dir = "./tmp";
vs::LogWriter logger(dir, 10000);
logger.SetMode("train");
auto tablet = logger.AddTablet("scalars/scalar0");
cp::Scalar<float> scalar0(tablet);
for (int step = 0; step < 1000; step++) {
float v = (float)std::rand() / RAND_MAX;
scalar0.AddRecord(step, v);
}
return 0;
}
```
## Launch Visual DL
After some logs have been generated during training, users can launch Visual DL application to see real-time data visualization by:
```
visualdl --logdir <some log dir>
```
visualDL also supports following optional parameters:
- `--host` set IP
- `--port` set port
- `-m / --model_pb` specify ONNX format for model file to view graph
### Contribute
VisualDL is initially created by [PaddlePaddle](http://www.paddlepaddle.org/) and
[ECharts](http://echarts.baidu.com/).
We welcome everyone to use, comment and contribute to Visual DL :)
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