display: a browser-based graphics server

• Installation
• Quick Start
• Usage
• Development

A very lightweight display server for Torch. Best used as a remote desktop paired with a terminal of your choice.

Use a Torch REPL (e.g., trepl) via SSH to control Torch and tell it to display stuff (images, plots, audio) to the server. The server then forwards the display data to (one or more) web clients.

Installation

Install for Torch via:

luarocks install https://raw.githubusercontent.com/szym/display/master/display-scm-0.rockspec

Install for Python (numpy required) via:

python setup.py install [--user]

NOTE: The Python client is not yet fully developed.

Quick Start

Launch the server:

th -ldisplay.start [port [hostname]]

Note, there is no authentication so don't use "as is" for sensitive data. By default, the server listens on localhost. Pass 0.0.0.0 to allow external connections on any interface:

th -ldisplay.start 8000 0.0.0.0

Then open http://(hostname):(port)/ in your browser to load the remote desktop.

To actually display stuff on the server, use the display package in a Torch script or REPL:

-- Generic stuff you'll need to make images anyway.
torch = require 'torch'
image = require 'image'

-- Load the display package
display = require 'display'

-- Tell the library, if you used a custom port or a remote server (default is 127.0.0.1).
display.configure({hostname='myremoteserver.com', port=1234})

-- Display a torch tensor as an image. The image is automatically normalized to be renderable.
local lena = image.lena()
display.image(lena)

-- Plot some random data.
display.plot(torch.cat(torch.linspace(0, 10, 10), torch.randn(10), 2))

See example.lua or example.py for a bigger example.

Usage

Each command creates a new window (pane) on the desktop that can be independently positioned, resized, maximized. It also returns the id of the window which can be passed as the win option to reuse the window for a subsequent command. This can be used to show current progress of your script:

for i = 1, num_iterations do
   -- do some work
   ...
   -- update results
   local state_win = display.image(current_state, {win=state_win, title='state at iteration ' .. i})
end

Another common option is title. The title bar can be double-clicked to maximize the window. The x button closes the window. The o button "disconnects" the window so that it will not be overwritten when the script reuses the window id. This is useful if you want to make a quick "copy" of the window to compare progress between iterations.

Images

display.image(tensor, options)

Displays the tensor as an image. The tensor is normalized (by a scalar offset and scaling factor) to be displayable. The image can be panned around and zoomed (with the scroll wheel or equivalent). Double-click the image to restore original size or fill the window.

If the tensor has 4 dimensions, it is considered to be a list of images -- sliced by first dimension. Same thing if it has 3 dimensions but the first dimension has size more than 3 (so they cannot be considered the RGB channels). This is equivalent to passing a list (Lua table) of tensors or the explicit images command. This is convenient when visualizing the trained filters of convolutional layer. Each image is normalized independently. When displaying a list of images, the option labels can be used to put a small label on each sub-image:

display.images({a, b, c, d}, {labels={'a', 'b', 'c', 'd'}})

Finally, the option width can be used to specify the initial size of the window in pixels.

Plotting

display.plot(data, options)

Creates a Dygraph plot which is most useful for visualizing time series. The graph can be zoomed in by selecting a range of X values or zoomed-out by double-clicking it.

The data should either be a 2-dimensional tensor where the each row is a data point and each column is a series, or a Lua table of tables. The first column is always taken as the X dimension. The command supports all the Dygraph options. Most importantly labels is taken as a list (Lua table) of series labels. Again the first label is for the X axis. You can name the Y axis with ylabel.

local config = {
  title = "Global accuracy/recall over time",
  labels = {"epoch", "accuracy", "recall"},
  ylabel = "ratio",
}

for t = 1, noEpoch do
  -- update model, compute data
  local accuracy, recall = train()
  -- update plot data
  table.insert(data, {t, accuracy, recall})
  -- display
  config.win = display.plot(data, config)
end

Other

display.audio(tensor_with_audio, options)

Development

Supported commands

• pane: creates a new Pane of specified type; arguments are:
  • type: the registered type, e.g., image for ImagePane
  • win: identifier of the window to be reused (pick a random one if you want a new window)
  • title: title for the window title bar
  • content: passed to the Pane.setContent method

Built-in Pane types

image creates a zoomable element

• src: URL for the element
• width: initial width in pixels
• labels: array of 3-element arrays [ x, y, text ], where x, y are the coordinates (0, 0) is top-left, (1, 1) is bottom-right; text is the label content

plot creates a Dygraph, all Dygraph options are supported

• file: see Dygraph data formats for supported formats
• labels: list of strings, first element is the X label

text places raw text in

element

audio places raw audio content in an element

Technical overview

The server is a trivial message forwarder:

POST /events -> EventSource('/events')

The Lua client sends JSON commands directly to the server. The browser script interprets these commands, e.g.

{ command: 'pane', type: 'image', content: { src: 'data:image/png;base64,....' }, title: 'lena' }

History

Originally forked from gfx.js.

The initial goal was to remain compatible with the torch/python API of gfx.js, but remove the term.js/tty.js/pty.js stuff which is served just fine by ssh.

Compared to gfx.js:

• no terminal windows (no term.js)
• dygraphs instead of nvd3 (have built in zoom and are perfect for time-series plots)
• plots resize when windows are resized
• images support zoom and pan
• image lists are rendered as one image to speed up loading
• windows remember their positions
• implementation not relying on the filesystem, supports remote clients (sources)