Baidu launched Visual DL for deep learning

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

Graph

Graph is compatible with ONNX(Open Neural Network Exchange)[github.com/onnx/onnx], Cooperated with Python SDK, VisualDL can be compatible with most major DNN frameworks, including PaddlePaddle, PyTorch and MXNet.

Scalar

Scalar can be used to show the trends of error during training.

Image

Image can be used to visualize any tensor or intermediate generated image.

Histogram

Histogram can be used to visualize parameter distribution and trends for any tensor.

SDK

VisualDL provides both Python SDK and C++ SDK in order to fit more use cases.

Python SDK

import random
from visualdl import LogWriter

logdir = "./tmp"
logger = LogWriter(dir, sync_cycle=10)

# 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())Copy the code

C++ SDK

Here is the C++ SDK identical to the Python SDK example above:

#include <cstdlib>
#include <string>
#include "visualdl/sdk.h"

namespace vs = visualdl;
namepsace cp = visualdl::components;

int main() {
  const std::string dir = "./tmp";
  vs::LogWriter logger(dir, 10);

  logger.SetMode("train");
  auto tablet = logger.NewTablet("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;
}Copy the code

Launch Board

visualDL --logdir <some log dir>
Copy the code

How to install from pypi

pip install --upgrade visualdl
Copy the code

How to build and install locally

git clone https://github.com/PaddlePaddle/VisualDL.git
cd VisualDL

python setup.py bdist_wheel
pip install --upgrade dist/visualdl-*.whl
Copy the code

Run a demo from scratch

vdl_scratch.py
visualDL --logdir=scratch_log --port=8080
Copy the code

That will start a server locally on Port 8080, then you can visit http://127.0.0.1:8080 the see the VisualDL board.

Contribute