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hyperboard

(★ 337)

A web-based dashboard for Deep Learning

파일 탐색기

최종 버전 다운로드 (.zip)

- run_agent.py
- screenshot.jpg
- - curve.html
- __init__.py
- agent.py
- hyperboard_run.py
- recorder.py
- server.py
.gitignore
LICENSE
MANIFEST.in
README.md
README.rst
setup.py

# 설치 가이드

1. 코드 내려받기

git clone https://github.com/WarBean/hyperboard

깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.

cd hyperboard

방금 내려받은 프로젝트 폴더 안으로 이동합니다.

2. 공식 설치 스크립트

쉬움 추천

사전 준비물

Python 3 pip 명령어를 쓰려면 Python이 필요합니다.

pip install flask requests numpy

PyPI에 배포된 패키지를 바로 설치합니다. 소스 클론이 필요 없습니다.

pip install flask-httpauth # depend on flask

PyPI에 배포된 패키지를 바로 설치합니다. 소스 클론이 필요 없습니다.

✅ 설치 후 새 터미널을 열고, 프로그램의 버전 확인 명령(예: --version)으로 정상 설치됐는지 확인하세요.

이 레포의 README에 적힌 실제 명령어를 그대로 가져왔습니다.

3. Python

쉬움

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
Python 3 설치 시 'Add Python to PATH' 옵션을 꼭 체크하세요 (Windows).

pip install flask requests numpy

PyPI에 배포된 패키지를 바로 설치합니다. 소스 클론이 필요 없습니다.

pip install flask-httpauth # depend on flask

PyPI에 배포된 패키지를 바로 설치합니다. 소스 클론이 필요 없습니다.

✅ 에러 메시지 없이 실행되고 터미널에 안내 문구가 출력되면 정상입니다.

이 레포의 README에 적힌 실제 명령어를 그대로 가져왔습니다.

// repository documentation

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# HyperBoard: A web-based dashboard for Deep Learning

HyperBoard is a simple visualization tool to facilitate hyperparameter tuning for Deep Learning players. It helps you to

- train on a remote server and visualize training curves on the local browser
- update curves in real time
- interactively compare hundreds of training curves for hyperparameter tuning, with filtering and visibility control
- save hundreds of training records on disk and re-load them when needed

HyperBoard resembles [Tensorboard](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/tensorboard) and [Tensorboard for MXNet](https://github.com/dmlc/tensorboard). However, HyperBoard is independent from any specific Deep Learning platform and provides extra functions.

**Watch a 3-minute demonstration on [Youtube](https://youtu.be/sWmVZyRfejc) or [Bilibili](http://www.bilibili.com/video/av8215364/).**

Screenshot:

![alt text](https://github.com/WarBean/hyperboard/blob/master/example/screenshot.jpg)

## Installation

```shell
# install dependencies
pip install flask requests numpy
pip install flask-httpauth # depend on flask
git clone https://github.com/WarBean/hyperboard.git
cd hyperboard/
python setup.py install
```

## Usage

HyperBoard is easy to use. It is composed of three parts: **Server**, **Agent** and **Dashboard**. In general, when playing with deep neural networks on some remote server, you can

- launch **HyperBoard Server** on the same remote server
- set up your new hyperparameters, then call **HyperBoard Agent** to register some curves at the **HyperBoard Server** for this time
- start model training and for each K iteration, call **HyperBoard Agent** to send cross entropy, accuracy, BELU, etc to **HyperBoard Server**
- open **HyperBoard Dashboard** on your local browser, watch the curves growing

You can also run HyperBoard Server on your local PC. Currently, HyperBoard Server and Agent have been tested on Mac OS and Ubuntu. HyperBoard Dashboard has been tested on Firefox, Chrome and Safari.

Here are the details:

### Prepare a directory for storing records

```shell
cd ~
mkdir my_records
```

### Launch the server inside the directory

```shell
cd my_records
hyperboard-run --user <your_user_name> --password <your_password>
```

You can use HyperBoard without security authentication by simply using `hyperboard-run` without any arguments. Also use `hyperboard-run -h` for more help info.

### Start model training

For this part, you are recommended to run a simulation script [`example/run_agent.py`](https://github.com/WarBean/hyperboard/blob/master/example/run_agent.py) first. Detailed usage of HyperBoard Agent will be explained below.

### Open dashboard on you local browser

If `hyperboard-run` is run with argument `--local`, open `http://127.0.0.1:5000`, otherwise `http://<your_remote_server_address>:5000`.

The dashboard provides convenient interactive controls on visualization. Feel free to play with them - except the **Delete from Server** button :).

## HyperBoard Agent Usage

### Initialize an agent object:

```python
from hyperboard import Agent
agent = Agent(username = '', password = '', address = '127.0.0.1', port = 5000)
```

`username` and `password` can be omitted if you have launched HyperBoard Server without security authentication. `address` and `port` can be omitted to use default value.

### Register a curve on Server

```python
name = agent.register(hyperparameters, metric, overwrite = False)
```

`hyperparameters` should be a dictionary.

`metric` is a string label indicating the numerical range of the values you send next. Curves sharing the same `metric` will share one y-axis on the dashboard. It helps you properly visualize cross entropy and accuracy in the same graph.

`overwrite = False` lets Agent to ask you for confirmation before removing existing records with the same hyperparameter setup.

### Send record during training iteration

```python
agent.append(name, index, value)
```

## Record Storage

Each curve is saved as a file with a suffix `.record`, in the very directory where you launch HyperBoard Server. The content of record file is simple:

```shell
$ head my_records/fd8e3e27e4ef661488932e9a58197d90.record
{"batch size": 256, "corpus": "PennTreeBank", "criteria": "train accu", "learning rate": 0.001, "momentum": 0.9, "num hidden": 300, "optimizer": "Adam"}
accuracy
0 -0.22079159783278235
1 -0.15177436116678278
2 -0.0847468825330926
3 -0.009928149024110766
4 0.07511021349995883
5 0.16286792223048174
6 0.22981841687923243
7 0.25812625639630005
```

The first line is hyperparameters. The second line is metric. Each line below contains the iteration index and the criteria value.

The next time you launch HyperBoard Server, it will reload all `.record` files (except those you delete manually) into memory.