https://github.com/findmyway/visualdl.jl

A Julia wrapper for VisualDL aims for deep learning visualization
https://github.com/findmyway/visualdl.jl

datavisualization deep-learning julia

Last synced: 3 months ago
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A Julia wrapper for VisualDL aims for deep learning visualization

• Host: GitHub
• URL: https://github.com/findmyway/visualdl.jl
• Owner: findmyway
• License: apache-2.0
• Created: 2018-06-23T14:51:02.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2018-12-21T09:57:38.000Z (about 6 years ago)
• Last Synced: 2024-10-10T01:04:31.623Z (4 months ago)
• Topics: datavisualization, deep-learning, julia
• Language: Julia
• Size: 259 KB
• Stars: 8
• Watchers: 4
• Forks: 1
• Open Issues: 3
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.txt

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README

        
# VisualDL.jl

[![Build Status](https://travis-ci.org/findmyway/VisualDL.jl.svg?branch=master)](https://travis-ci.org/findmyway/VisualDL.jl)

[![](https://img.shields.io/badge/docs-latest-blue.svg)](https://findmyway.github.io/VisualDL.jl/latest)

This package provides a julia wrapper for [VisualDL](https://github.com/PaddlePaddle/VisualDL), which is a deep learning visualization tool that can help design deep learning jobs.

Currently, the wrapper is written on top of the Python SDK of VisualDL by [PyCall](https://github.com/JuliaPy/PyCall.jl). I have tried to write the wrapper on top of the C++ SDK by leveraging [CxxWrap.jl](https://github.com/JuliaInterop/CxxWrap.jl). But unluckily a strange error encountered. Hopefully I'll figured it out later and swap the backend into C++.

## Install

- First, install the Python client of VisualDL. Checkout [here](https://github.com/PaddlePaddle/VisualDL#install-with-virtualenv) for a detailed guide. 

- Then add this package as a dependent(only tested on Julia v0.7).

    `(v0.7) pkg> add VisualDL`

## Usage

First, initial the logger.

```julia

using VisualDL

train_logger = VisualDLLogger("tmp", 1, "train")

test_logger = as_mode(train_logger, "test")

```

### Scalar

```julia

for i in 1:100

    with_logger(train_logger) do

        @log_scalar s0=(i,rand()) s1=(i, rand())

    end

    with_logger(test_logger) do

        @log_scalar s0=(i,rand()) s1=(i, rand())

    end

end

```

![](docs/src/images/scalar_example.png)

### Histogram

```julia

for i in 1:100

    with_logger(train_logger) do

       @log_histogram h0=(i, randn(100))

    end

end

```

![](docs/src/images/histogram_example.png)

### Text

```julia

for i in 1:100

    with_logger(train_logger) do

       @log_text t0=(i, "This is test " * string(i))

    end

end

```

![](docs/src/images/text_example.png)

### Image

```julia

for i in 1:100

    with_logger(train_logger) do

       @log_image i0=([3,3,3], rand(27) * 255)

    end

end

for i in 1:100

    with_logger(test_logger) do

        @log_image image0=rand(10, 10, 3) * 255

    end

end

# force save and sync

save(train_logger)

save(test_logger)

```

![](docs/src/images/image_example.png)

Finally, run `visualDL --logdir ./tmp` in current dir. Then launch the visualdl service and watch the above pictures in browser. The default url is `http://localhost:8040`:

## TODO

- [x] More documentation

- [x] ~~Add `LogReader`~~ and tests

- [x] Precompile

- [x] Travis

- [x] Make Release

- [ ] Move out the `start_sampling` and `finish_sampling` from `@log_image` and `@log_audio`