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 and Tensorboard for MXNet. However, HyperBoard is independent from any specific Deep Learning platform and provides extra functions.

Watch a 3-minute demonstration on Youtube or Bilibili.

Screenshot:

Installation

# 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

cd ~
mkdir my_records

Launch the server inside the directory

cd my_records
hyperboard-run --user  --password 

You can use HyperBoard without security authentication by simply using

hyperboard-run

hyperboard-run -h

Start model training

For this part, you are recommended to run a simulation script

example/run_agent.py

Open dashboard on you local browser

If

hyperboard-run

--local

http://127.0.0.1:5000

http://: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:

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

username

password

address

port

Register a curve on Server

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

hyperparameters

metric

metric

overwrite = False

Send record during training iteration

agent.append(name, index, value)

Record Storage

Each curve is saved as a file with a suffix

.record

$ 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