fossasia/visdom

Creating, organizing & sharing visualizations of live, rich data. Supports Python.

Jump To: Setup, Usage, API, Customizing, Contributing, License

Visdom aims to facilitate visualization of (remote) data with an emphasis on supporting scientific experimentation.
Broadcast visualizations of plots, images, and text for yourself and your collaborators. Organize your visualization space programmatically or through the UI to create dashboards for live data, inspect results of experiments, or debug experimental code.

Concepts

Visdom has a simple set of features that can be composed for various use-cases.

Windows

The UI begins as a blank slate – you can populate it with plots, images, and text. These appear in windows that you can drag, drop, resize, and destroy. The windows live in envs and the state of envs is stored across sessions. You can download the content of windows – including your plots in svg.

Tip: You can use the zoom of your browser to adjust the scale of the UI.

Callbacks

The python Visdom implementation supports callbacks on a window. The demo shows an example of this in the form of an editable text pad. The functionality of these callbacks allows the Visdom object to receive and react to events that happen in the frontend.

You can subscribe a window to events by adding a function to the event handlers dict for the window id you want to subscribe by calling viz.register_event_handler(handler, win_id, env=None) with your handler, the window id, and an optional environment name. Specifying the environment name prevents event handlers from firing across different environments with the same window id. Multiple handlers can be registered to the same window. You can remove event handlers from a window using viz.clear_event_handlers(win_id, env=None). When an event occurs to that window, your callbacks will be called on a dict containing:

• event_type: one of the below event types
• pane_data: all of the stored contents for that window including layout and content.
• eid: the current environment id
• target: the window id the event is called on

Additional parameters are defined below.

Right now the following callback events are supported:

1. Close - Triggers when a window is closed. Returns a dict with only the aforementioned fields.
2. KeyPress - Triggers when a key is pressed. Contains additional parameters:
   • key - A string representation of the key pressed (applying state modifiers such as SHIFT)
   • key_code - The javascript event keycode for the pressed key (no modifiers)
3. PropertyUpdate - Triggers when a property is updated in Property pane
   • propertyId - Position in properties list
   • value - New property value
4. Click - Triggers when Image pane is clicked on, has a parameter:
   • image_coord - dictionary with the fields x and y for the click coordinates in the coordinate frame of the possibly zoomed/panned image (not the enclosing pane).

Editable Plot Parameters

Use the top-right *edit*-Button to inspect all parameters used for plot in the respective window. The visdom client supports dynamic change of plot parameters as well. Just change one of the listed parameters, the plot will be altered on-the-fly. Click the button again to close the property list.

Environments

You can partition your visualization space with envs. By default, every user will have an env called main. New envs can be created in the UI or programmatically. The state of envs is persistently saved. Environments are able to keep entirely different pools of plots.

You can access a specific env via url: http://localhost:8097/env/main. If your server is hosted, you can share this url so others can see your visualizations too.

Environments are automatically hierarchically organized by the first _. Note that / characters in environment names are escaped to _, so both _ and / can affect how environments appear hierarchically in the UI.

Selecting Environments

From the main page it is possible to toggle between different environments using the environment selector. Selecting a new environment will query the server for the plots that exist in that environment. The environment selector allows for searching and filtering for the new environment.

Comparing Environments

From the main page it is possible to compare different environments using the environment selector. Selecting multiple environments in the check box will query the server for the plots with the same titles in all environments and plot them in a single plot. An additional compare legend pane is created with a number corresponding to each selected environment. Individual plots are updated with legends corresponding to "x_name" where x is a number corresponding with the compare legend pane and name is the original name in the legend.

Note: The compare envs view is not robust to high throughput data, as the server is responsible for generating the compared content. Do not compare an environment that is receiving a high quantity of updates on any plot, as every update will request regenerating the comparison. If you need to compare two plots that are receiving high quantities of data, have them share the same window on a singular env.

Clearing Environments

You can use the eraser button to remove all of the current contents of an environment. This closes the plot windows for that environment but keeps the empty environment for new plots.

Managing Environments

Pressing the folder icon opens a dialog that allows you to fork or force save the current environment, or delete any of your existing environments. Use of this feature is fully described in the State section.

Env Files: Your envs are loaded upon request by the user, by default from $HOME/.visdom/. Custom paths can be passed as a cmd-line argument. Envs are removed by using the delete button or by deleting the corresponding .json file from the env dir. In case you want the server to pre-load all files into cache, use the flag -eager_data_loading.

State

Once you've created a few visualizations, state is maintained. The server automatically caches your visualizations -- if you reload the page, your visualizations reappear.

• Save: You can manually do so with the save button. This will serialize the env's state (to disk, in JSON), including window positions. You can save an env programmatically.
  This is helpful for more sophisticated visualizations in which configuration is meaningful, e.g. a data-rich demo, a model training dashboard, or systematic experimentation. This also makes them easy to share and reuse.

• Fork: If you enter a new env name, saving will create a new env -- effectively forking the previous env.

Tip: Fork an environment before you begin to make edits to ensure that your changes are saved separately.

Filter

You can use the filter to dynamically sift through windows present in an env -- just provide a regular expression with which to match titles of window you want to show. This can be helpful in use cases involving an env with many windows e.g. when systematically checking experimental results.

Note: If you have saved your current view, the view will be restored after clearing the filter.

Views

It is possible to manage the views simply by dragging the tops of windows around, however additional features exist to keep views organized and save common views. View management can be useful for saving and switching between multiple common organizations of your windows.

Saving/Deleting Views

Using the folder icon, a dialog window opens where views can be forked in the same way that envs can be. Saving a view will retain the position and sizes of all of the windows in a given environment. Views are saved in $HOME/.visdom/view/layouts.json in the visdom filepath.

Note: Saved views are static, and editing a saved view copies that view over to the current view where editing can occur.

Re-Packing

Using the repack icon (9 boxes), visdom will attempt to pack your windows in a way that they best fit while retaining row/column ordering.

Note: Due to the reliance on row/column ordering and ReactGridLayout the final layout might be slightly different than what might be expected. We're working on improving that experience or providing alternatives that give more fine-tuned control.

Reloading Views

Using the view dropdown it is possible to select previously saved views, restoring the locations and sizes of all of the windows within the current environment to the places they were when that view was saved last.

Setup

Python and web clients come bundled with the python server.

Install from pip

> pip install visdom

Install from source

> pip install git+https://github.com/fossasia/visdom

Optional: To save Plotly figures to image files from code (e.g. PNG/SVG) without using the browser download button, install plotly and kaleido: pip install plotly kaleido. See vis.plotlyplot and vis.save_plotly_figure.

Usage

Start the server (probably in a screen or tmux) from the command line:

> visdom

Visdom now can be accessed by going to http://localhost:8097 in your browser, or your own host address if specified.

The visdom command is equivalent to running python -m visdom.server.

If the above does not work, try using an SSH tunnel to your server by adding the following line to your local ~/.ssh/config: LocalForward 127.0.0.1:8097 127.0.0.1:8097.

Command Line Options

The following options can be provided to the server:

1. -port : The port to run the server on.
2. -hostname : The hostname to run the server on.
3. -base_url : The base server url (default = /).
4. -env_path : The path to the serialized session to reload.
5. -logging_level : Logging level (default = INFO). Accepts both standard text and numeric logging values.
6. -readonly : Flag to start server in readonly mode.
7. -enable_login : Flag to setup authentication for the server, requiring a username and password to login.
8. -force_new_cookie : Flag to reset the secure cookie used by the server, invalidating current login cookies. Requires -enable_login.
9. -bind_local : Flag to make the server accessible only from localhost.
10. -eager_data_loading : By default visdom loads environments lazily upon user request. Setting this flag lets visdom pre-fetch all environments upon startup.

When -enable_login flag is provided, the server asks user to input credentials using terminal prompt. Alternatively, you can setup VISDOM_USE_ENV_CREDENTIALS env variable, and then provide your username and password via VISDOM_USERNAME and VISDOM_PASSWORD env variables without manually interacting with the terminal. This setup is useful in case if you would like to launch visdom server from bash script, or from Jupyter notebook.

VISDOM_USERNAME=username
VISDOM_PASSWORD=password
VISDOM_USE_ENV_CREDENTIALS=1 visdom -enable_login

You can also use VISDOM_COOKIE variable to provide cookies value if the cookie file wasn't generated, or the flag -force_new_cookie was set.

HTTPS Support

To run the visdom server over HTTPS,user need to provide an SSL certificate and key file:

# Generate a self-signed certificate (for development only)
openssl req -x509 -newkey rsa:4096 -keyout key.pem -out cert.pem -days 365 -nodes

# Start the server with HTTPS
python -m visdom.server -ssl_certfile cert.pem -ssl_keyfile key.pem

Access the server at https://localhost:8097.

Connect via the Python client:

# For Production - real CA-signed certificate (default)
vis = visdom.Visdom(server="https://myserver.com")

# For Development - self signed certificate
vis = visdom.Visdom(server="https://localhost", ssl_verify=False)

Note: ssl_verify=False disables certificate verification and should only be used in development with self-signed certificates. Do not use in production.

Python example

import visdom
import numpy as np
vis = visdom.Visdom()
vis.text('Hello, world!')
vis.image(np.ones((3, 10, 10)))

Demos

If you have cloned this repository, you can run our demo showcase.

python example/demo.py

API

For a quick introduction into the capabilities of visdom, have a look at the example directory, or read the details below.

Visdom Arguments (Python only)

The python visdom client takes a few options:

• server: the hostname of your visdom server (default: 'http://localhost')
• port: the port for your visdom server (default: 8097)
• base_url: the base visdom server url (default: /)
• env: Default environment to plot to when no env is provided (default: main)
• raise_exceptions: Raise exceptions upon failure rather than printing them (default: True (soon))
• log_to_filename: If not none, log all plotting and updating events to the given file (append mode) so that they can be replayed later using replay_log (default: None)
• use_incoming_socket: enable use of the socket for receiving events from the web client, allowing user to register callbacks (default: True)
• http_proxy_host: Deprecated. Use Proxies argument for complete proxy support.
• http_proxy_port: Deprecated. Use Proxies argument for complete proxy support.
• username: username to use for authentication, if server started with -enable_login (default: None)
• password: password to use for authentication, if server started with -enable_login (default: None)
• proxies: Dictionary mapping protocol to the URL of the proxy (e.g. {http: foo.bar:3128}) to be used on each Request. (default: None)
• offline: Flag to run visdom in offline mode, where all requests are logged to file rather than to the server. Requires log_to_filename is set. In offline mode, all visdom commands that don't create or update plots will simply return True. (default: False)

Other options are either currently unused (endpoint, ipv6) or used for internal functionality.

Basics

Visdom offers the following basic visualization functions:

• vis.image : image
• vis.image_heatmap : image with heatmap overlay
• vis.update_image_slider : set visible frame of an image_history pane
• vis.images : list of images
• vis.text : arbitrary HTML
• vis.properties : properties grid
• vis.audio : audio
• vis.video : videos
• vis.svg : SVG object
• vis.matplot : matplotlib plot
• vis.plotlyplot : arbitrary Plotly figure
• vis.embeddings : interactive embedding projection
• vis.save : serialize state server-side

Plotting

We have wrapped several common plot types to make creating basic visualizations easily. These visualizations are powered by Plotly.

The following API is currently supported:

• vis.scatter : 2D or 3D scatter plots
• vis.sunburst : sunburst (hierarchy) charts
• vis.line : line plots
• vis.learning_curve : named training metric curves
• vis.stem : stem plots
• vis.heatmap : heatmap plots
• vis.confusion_matrix : confusion matrix plots
• vis.bar : bar graphs
• vis.histogram : histograms
• vis.histogram2d : 2D histograms (density maps)
• vis.boxplot : boxplots
• vis.violin : violin plots
• vis.pie : pie charts
• vis.surf : surface plots
• vis.contour : contour plots
• vis.roc_curve : ROC curves
• vis.pr_curve : precision-recall curves
• vis.quiver : quiver plots
• vis.mesh : mesh plots
• vis.sankey : sankey (flow) diagrams
• vis.dual_axis_lines : double y axis line plots
• vis.graph : network graphs

Generic Plots

Note that the server API adheres to the Plotly convention of data and layout objects, such that you can produce your own arbitrary Plotly visualizations:

import visdom
vis = visdom.Visdom()

trace = dict(x=[1, 2, 3], y=[4, 5, 6], mode="markers+lines", type='custom',
             marker={'color': 'red', 'symbol': 104, 'size': "10"},
             text=["one", "two", "three"], name='1st Trace')
layout = dict(title="First Plot", xaxis={'title': 'x1'}, yaxis={'title': 'x2'})

vis._send({'data': [trace], 'layout': layout, 'win': 'mywin'})

Others

• vis.close : close a window by id
• vis.delete_env : delete an environment by env_id
• vis.win_exists : check if a window already exists by id
• vis.get_env_list : get a list of all of the environments on your server
• vis.get_window_data: get current data for a window
• vis.save_plotly_figure: save a Plotly figure to an image file from code (no browser click)
• vis.check_connection: check if the server is connected
• vis.replay_log: replay the actions from the provided log file

Loggers

Framework-specific logging bridges that wrap the Visdom API so training loops stay focused on training. Each logger lives in its own submodule and handles window creation, step tracking, and throttling internally.

PyTorch

visdom.pytorch.VisdomLogger is a context manager for raw PyTorch training loops. Call tracker.log(name, value) for any scalar — no viz.line() arguments needed.

Epoch-level logging (recommended default — one call per epoch):

import visdom
from visdom.pytorch import VisdomLogger

viz = visdom.Visdom()

with VisdomLogger(viz, env="my_run") as tracker:
    for epoch in range(num_epochs):
        train_loss = run_train_epoch(model, loader)
        val_loss   = run_val_epoch(model, val_loader)

        tracker.log("Train Loss", train_loss)
        tracker.log("Val Loss",   val_loss)
        tracker.log("LR",         optimizer.param_groups[0]["lr"])

Per-batch logging with log_every — use when logging inside the batch loop on large datasets:

with VisdomLogger(viz, env="my_run", log_every=50) as tracker:
    for epoch in range(num_epochs):
        for inputs, targets in train_loader:
            loss = criterion(model(inputs), targets)
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()

            # sent to Visdom every 50 batches, not every batch
            tracker.log("Train Loss", loss.item(), xlabel="step")
            tracker.log("LR",         optimizer.param_groups[0]["lr"], xlabel="step")

Parameters:

• viz: a connected visdom.Visdom() instance
• env: environment name (default: auto-generated from timestamp)
• log_every: send every N calls per metric — use with per-batch logging on large datasets (default: 1)

Each unique name passed to tracker.log() gets its own window. The first call creates it; subsequent calls append. See example/train_example.py for a full working example.

scikit-learn

visdom.loggers.VisdomSklearnLogger patches all sklearn fit() calls so every estimator trained after autolog() logs to Visdom automatically — no per-estimator code needed.

Plain estimators (classifiers, regressors, clusterers) produce a text pane with the estimator name, dataset shape, training score, fit time, and all hyperparameters.

GridSearchCV / RandomizedSearchCV produce a bar chart of mean_test_score per parameter combination and a text pane with best_score_, best_params_, and fit time.

Iterative estimators additionally get a line chart of their per-iteration training history: MLPClassifier/MLPRegressor plot loss_curve_ (plus validation_scores_ when fit with early_stopping=True), and GradientBoostingClassifier/GradientBoostingRegressor plot train_score_.

Regressors get train_rmse and train_mae rows in the text pane alongside the R2 train_score (R2 alone can be misleading), plus a predicted-vs-residual scatter plot.

Note: train_score, train_rmse, train_mae and the residual scatter are all measured on the data passed to fit(). They describe fit quality on the training set and are not held-out estimates — score your own test set for that.

Note: panes are keyed on the estimator instance, so refitting the same estimator updates the panes it already owns instead of opening new ones. Two different estimator objects always get their own panes, even of the same class.

import visdom
from visdom.loggers import VisdomSklearnLogger
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import Ridge
from sklearn.model_selection import GridSearchCV

viz = visdom.Visdom()
VisdomSklearnLogger.autolog()

clf = RandomForestClassifier(n_estimators=100)
clf.fit(X_train, y_train)        # -> text pane

reg = Ridge(alpha=1.0)
reg.fit(X_train, y_train)        # -> text pane

gs = GridSearchCV(clf, param_grid, cv=3)
gs.fit(X_train, y_train)         # -> bar chart + text pane

If you have a custom Visdom connection (non-default port, remote server, auth), pass it explicitly:

import visdom

viz = visdom.Visdom(port=8098, server="http://myserver")
VisdomSklearnLogger.autolog(viz, env="sklearn_run")

Parameters:

• viz: a connected visdom.Visdom() instance (optional — created internally if not passed)
• env: environment name (default: viz.env if set, otherwise auto-generated from timestamp)

See example/train_sklearn_example.py for a full working example covering plain estimators and grid search.

XGBoost

visdom.loggers.VisdomXGBLogger implements XGBoost's TrainingCallback protocol, plotting train/eval metrics to Visdom after every boosting round.

There was no way to visualize XGBoost training runs in Visdom without manually attaching a TrainingCallback and wiring up viz.line() calls yourself. This adds opt-in auto-logging behind a single autolog() call, with no changes required to model, train(), or fit() code.

import xgboost as xgb
import visdom
from visdom.loggers import VisdomXGBLogger

viz = visdom.Visdom()
VisdomXGBLogger.autolog(viz, env="xgb_run")

booster = xgb.train(params, dtrain, evals=[(dtrain, "train"), (dval, "eval")])  # logged automatically
clf = xgb.XGBClassifier().fit(X_train, y_train, eval_set=[(X_val, y_val)])      # logged automatically
xgb.cv(params, dtrain, nfold=3)                                                 # logged automatically

Or attach a logger to a single run without patching anything:

callback = VisdomXGBLogger(viz, env="xgb_run")
booster = xgb.train(params, dtrain, evals=[(dtrain, "train"), (dval, "eval")], callbacks=[callback])

Each eval metric gets its own window with one trace per data name (train/eval), and best_iteration/best_score are logged as a text pane once training finishes. See example/train_xgboost_example.py for a full working example.

Note: one logger is active at a time. Calling autolog() again for a different env moves logging there and warns; the previous env keeps the windows it already has. Import cross_validate after autolog() — importing it first binds the original function, and every fold then opens its own window instead of sharing one per metric.

Details

Basics

vis.image

This function draws an img. It takes as input an CxHxW tensor img that contains the image. Most Python image libraries (e.g. OpenCV, PIL, matplotlib) return images in HxWxC format. Passing images in that format will raise errors or lead to incorrect rendering.

For example:

# Convert HxWxC → CxHxW before passing to vis.image
img = img.transpose(2, 0, 1)   # NumPy
img = img.permute(2, 0, 1)     # PyTorch

The following opts are supported:

• jpgquality: JPG quality (number 0-100). If defined image will be saved as JPG to reduce file size. If not defined image will be saved as PNG.
• caption: Caption for the image
• store_history: Keep all images stored to the same window and attach a slider to the bottom that will let you select the image to view. You must always provide this opt when sending new images to an image with history.

vis.update_image_slider

Programmatically set the visible frame of an image_history pane from Python:

win = vis.image(img, opts=dict(store_history=True))
vis.image(img2, win=win, opts=dict(store_history=True))
vis.update_image_slider(win, index=1)  # show second frame

The index is 0-based and is clamped to the valid range by the server. NumPy integer scalars (e.g. np.int64) are accepted and coerced automatically. Passing a non-integer or a non-image_history window raises an error.

Note You can use alt on an image pane to view the x/y coordinates of the cursor. You can also ctrl-scroll to zoom, alt scroll to pan vertically, and alt-shift scroll to pan horizontally. Double click inside the pane to restore the image to default.

vis.image_heatmap

This function overlays a saliency or attention heatmap on top of an image. It takes a CxHxW or HxW array img (uint8 or float) and an HxW float array heatmap with values in [0, 1]. The blending is per-pixel — pixels where the heatmap is near zero stay close to the original image, so a zero-gradient background does not get tinted by the colormap.

import numpy as np
from visdom import Visdom

viz = Visdom()

# img: CxHxW uint8 or float in [0, 1]
# heatmap: HxW float in [0, 1] — e.g. from a saliency method or attention map
viz.image_heatmap(img, heatmap, opts=dict(title="Saliency", alpha=0.6, colormap="jet"))

Any attribution method that produces an HxW numpy array works — gradient saliency, GradCAM, SHAP, or a hand-computed attention map.

The following opts are supported:

• alpha: blend strength (float in [0, 1]; default = 0.5). Higher values make the heatmap more visible.
• colormap: matplotlib colormap name (string; default = 'jet'). Falls back to a blue-red gradient if matplotlib is not installed.
• caption: caption for the image pane
• jpgquality: JPG quality (number 0-100). If set, the result is encoded as JPEG. Otherwise PNG.
• normalize: normalize the image to [0, 1] before blending (boolean; default = False)

Note heatmap accepts any finite float range. Values outside [0, 1] are rescaled automatically via min-max normalization, so methods like SHAP or Integrated Gradients that return signed or unnormalized values work without any pre-processing. NaN maps to 0; infinite values are clamped to the [0, 1] boundary.

vis.images

This function draws a list of images. It takes an input B x C x H x W tensor or a list of images all of the same size. It makes a grid of images of size (B / nrow, nrow).

The following arguments and opts are supported:

• nrow: Number of images in a row
• padding: Padding around the image, equal padding around all 4 sides
• opts.jpgquality: JPG quality (number 0-100). If defined image will be saved as JPG to reduce file size. If not defined image will be saved as PNG.
• opts.caption: Caption for the image

vis.text

This function prints text in a box. You can use this to embed arbitrary HTML. It takes as input a text string. No specific opts are currently supported.

vis.properties

This function shows editable properties in a pane. Properties are expected to be a List of Dicts e.g.:

    properties = [
        {'type': 'text', 'name': 'Text input', 'value': 'initial'},
        {'type': 'number', 'name': 'Number input', 'value': '12'},
        {'type': 'button', 'name': 'Button', 'value': 'Start'},
        {'type': 'checkbox', 'name': 'Checkbox', 'value': True},
        {'type': 'select', 'name': 'Select', 'value': 1, 'values': ['Red', 'Green', 'Blue']},
    ]

Supported types:

• text: string
• number: decimal number
• button: button labeled with "value"
• checkbox: boolean value rendered as a checkbox
• select: multiple values select box
  • value: id of selected value (zero based)
  • values: list of possible values

Callback are called on property value update:

• event_type: "PropertyUpdate"
• propertyId: position in the properties list
• value: new value

No specific opts are currently supported.

vis.audio

This function plays audio. It takes as input the filename of the audio file or an N tensor containing the waveform (use an Nx2 matrix for stereo audio). The function does not support any plot-specific opts.

The following opts are supported:

• opts.sample_frequency: sample frequency (integer > 0; default = 44100)

Known issue: Visdom uses scipy to convert tensor inputs to wave files. Some versions of Chrome are known not to play these wave files (Firefox and Safari work fine).

vis.video

This function plays a video. It takes as input the filename of the video videofile or a LxHxWxC-sized tensor containing all the frames of the video as input. The function does not support any plot-specific opts.

The following opts are supported:

• opts.fps: FPS for the video (integer > 0; default = 25)

Note: Using tensor input requires that ffmpeg is installed and working. Your ability to play video may depend on the browser you use: your browser has to support the Theano codec in an OGG container (Chrome supports this).

vis.svg

This function draws an SVG object. It takes as input a SVG string svgstr or the name of an SVG file svgfile. The function does not support any specific opts.

vis.matplot

This function draws a Matplotlib plot. The function supports one plot-specific option: resizable.

Note When set to True the plot is resized with the pane. You need beautifulsoup4 and lxml packages installed to use this option.

Note: matplot is not rendered using the same backend as plotly plots, and is somewhat less efficient. Using too many matplot windows may degrade visdom performance.

vis.plotlyplot

This function draws a Plotly Figure object. It does not explicitly take options as it assumes you have already explicitly configured the figure's layout.

Note You must have the plotly Python package installed to use this function. It can typically be installed by running pip install plotly.

Saving plots as images from code (without using the browser download button): Pass save_path to save the figure to a file when plotting, e.g. vis.plotlyplot(fig, save_path="plot.png"). You can also save a figure without displaying it using vis.save_plotly_figure(fig, "plot.png"). Both require the optional kaleido package: pip install kaleido.

import plotly.graph_objects as go
from visdom import Visdom

viz = Visdom()
fig = go.Figure(go.Scatter(x=[1, 2, 3], y=[4, 5, 6], mode="lines+markers"))

viz.plotlyplot(fig, save_path="my_plot.png")
viz.save_plotly_figure(fig, "my_plot.png")

vis.embeddings

This function visualizes a collection of features using t-SNE dimensionality reduction (powered by openTSNE or bhtsne as a fallback). openTSNE is installed automatically as a dependency of visdom, so this feature works out of the box.

The function accepts the following arguments:

• features: a list of tensors
• labels: a list of corresponding labels for the tensors provided for features
• data_getter=fn: (optional) a function that takes as a parameter an index into the features array and returns a summary representation of the tensor. If this is set, data_type must also be set.
• data_type=str: (optional) currently the only acceptable value here is "html"
• opts.register_embedding_events: (optional) set to False to skip registering the default Python client event handler for hover previews and lasso drilldown. This leaves embeddings interaction events for external server or frontend code to handle.

We currently assume that there are no more than 10 unique labels, in the future we hope to provide a colormap in opts for other cases.

From the UI you can also draw a lasso around a subset of features. This will rerun the t-SNE visualization on the selected subset.

vis.save

This function saves the envs that are alive on the visdom server. It takes input a list of env ids to be saved.

Plotting

Further details on the wrapped plotting functions are given below.

The exact inputs into the plotting functions vary, although most of them take as input a tensor X that contains the data and an (optional) tensor Y that contains optional data variables (such as labels or timestamps). All plotting functions take as input an optional win that can be used to plot into a specific window; each plotting function also returns the win of the window it plotted in. One can also specify the env to which the visualization should be added.

vis.scatter

This function draws a 2D or 3D scatter plot. It takes as input an Nx2 or Nx3 tensor X that specifies the locations of the N points in the scatter plot. An optional N tensor Y containing discrete labels that range between 1 and K can be specified as well -- the labels will be reflected in the colors of the markers.

update can be used to efficiently update the data of an existing plot. Use 'append' to append data, 'replace' to use new data, or 'remove' to remove the trace specified by name. Using update='append' will create a plot if it doesn't exist and append to the existing plot otherwise. If updating a single trace, use name to specify the name of the trace to be updated. Update data that is all NaN is ignored (can be used for masking update).

The following opts are supported:

• opts.markersymbol : marker symbol (string; default = 'dot')
• opts.markersize : marker size (number; default = '10')
• opts.markercolor : color per marker. (torch.*Tensor; default = nil)
• opts.markerborderwidth: marker border line width (float; default = 0.5)
• opts.legend : table containing legend names
• opts.textlabels : text label for each point (list: default = None)
• opts.layoutopts : dict of any additional options that the graph backend accepts for a layout. For example layoutopts = {'plotly': {'legend': {'x':0, 'y':0}}}.
• opts.traceopts : dict mapping trace names or indices to dicts of additional options that the graph backend accepts. For example traceopts = {'plotly': {'myTrace': {'mode': 'markers'}}}.
• opts.webgl : use WebGL for plotting (boolean; default = false). It is faster if a plot contains too many points. Use sparingly as browsers won't allow more than a couple of WebGL contexts on a single page.

opts.markercolor is a Tensor with Integer values. The tensor can be of size N or N x 3 or K or K x 3.

• Tensor of size N: Single intensity value per data point. 0 = black, 255 = red
• Tensor of size N x 3: Red, Green and Blue intensities per data point. 0,0,0 = black, 255,255,255 = white
• Tensor of size K and K x 3: Instead of having a unique color per data point, the same color is shared for all points of a particular label.

vis.sunburst

This function draws a sunburst chart. It takes two input arrays: parents and labels. Values from the parents array define the hierarchical structure, indicating which parent sector a sector belongs to. Values from the labels array define the sector's label or name. Keep in mind that the parents and labels arrays must be of equal length. There is an optional third array called values, which is used to display a numerical value when hovering over a sector. If provided, the values array must be the same length as parents and labels.

Examples: vis.sunburst(parents, labels, opts) or vis.sunburst(parents, labels, values, opts)

Following opts are currently supported:

• opts.font_size : define font size of label (int)
• opts.font_color : define font color of label (string)
• opts.opacity : define opacity of chart (float)
• opts.line_width : define distance between two sectors and sector to its parents (int)

vis.line

This function draws a line plot. It takes as input an N or NxM tensor Y that specifies the values of the M lines (that connect N points) to plot. It also takes an optional X tensor that specifies the corresponding x-axis values; X can be an N tensor (in which case all lines will share the same x-axis values) or have the same size as Y.

update can be used to efficiently update the data of an existing plot. Use 'append' to append data, 'replace' to use new data, or 'remove' to remove the trace specified by name. If updating a single trace, use name to specify the name of the trace to be updated. Update data that is all NaN is ignored (can be used for masking update).

Smoothing: Line plots can be smoothened using Savitzky-Golay filtering. This feature can be enabled by clicking the ~-symbol in the top right corner of a window that contains a line plot.

The following opts are supported:

• opts.fillarea : fill area below line (boolean)
• opts.markers : show markers (boolean; default = false)
• opts.markersymbol: marker symbol (string; default = 'dot')
• opts.markersize : marker size (number; default = '10')
• opts.linecolor : line colors (np.array; default = None)
• opts.dash : line dash type for each line (np.array; default = 'solid'), one of solid, dash, dashdot or dash, size should match number of lines being drawn
• opts.legend : table containing legend names
• opts.layoutopts : dict of any additional options that the graph backend accepts for a layout. For example layoutopts = {'plotly': {'legend': {'x':0, 'y':0}}}.
• opts.traceopts : dict mapping trace names or indices to dicts of additional options that plot.ly accepts for a trace.
• opts.webgl : use WebGL for plotting (boolean; default = false). It is faster if a plot contains too many points. Use sparingly as browsers won't allow more than a couple of WebGL contexts on a single page.

vis.roc_curve

This function draws a ROC curve for binary classification.

It accepts either:

• raw binary labels and scores via y_true and y_score, or
• precomputed curve points via fpr and tpr.

The following opts are supported:

• opts.title : plot title (string; default includes ROC-AUC)
• opts.legend : two legend labels for curve and baseline (list)
• opts.xlabel : x-axis label (string; default = False Positive Rate)
• opts.ylabel : y-axis label (string; default = True Positive Rate)
• opts.layoutopts : additional backend layout options (dict)

vis.pr_curve

This function draws a precision-recall curve for binary classification.

It accepts either:

• raw binary labels and scores via y_true and y_score, or
• precomputed curve points via precision and recall.

The following opts are supported:

• opts.title : plot title (string; default includes PR-AUC)
• opts.legend : two legend labels for curve and baseline (list)
• opts.xlabel : x-axis label (string; default = Recall)
• opts.ylabel : y-axis label (string; default = Precision)
• opts.layoutopts : additional backend layout options (dict)

vis.learning_curve

This function draws named machine-learning metrics as line plots. It accepts a mapping from metric names to scalar values or equal-length 1D series and forwards to vis.line.

For example:

win = vis.learning_curve(
    {"train_loss": [1.0, 0.8, 0.6], "val_loss": [1.1, 0.9, 0.7]},
    step=[1, 2, 3],
    env="training",
    opts={"title": "Loss", "ylabel": "loss"},
)

vis.learning_curve(
    {"train_loss": 0.55, "val_loss": 0.68},
    step=4,
    win=win,
    env="training",
    update="append",
)

vis.stem

This function draws a stem plot. It takes as input an N or NxM tensor X that specifies the values of the N points in the M time series. An optional N or NxM tensor Y containing timestamps can be specified as well; if Y is an N tensor then all M time series are assumed to have the same timestamps.

The following opts are supported:

• opts.colormap: colormap (string; default = 'Viridis')
• opts.legend : table containing legend names
• opts.layoutopts : dict of any additional options that the graph backend accepts for a layout. For example layoutopts = {'plotly': {'legend': {'x':0, 'y':0}}}.

vis.heatmap

This function draws a heatmap. It takes as input an NxM tensor X that specifies the value at each location in the heatmap.

update can be used to efficiently update the data of an existing plot. Use 'appendRow' to append data row-wise, 'appendColumn' to append data column-wise, 'prependRow' to prepend data row-wise, 'prependColumn' to prepend data column-wise, 'replace' to use new data, or 'remove' to remove the plot specified by win.

The following opts are supported:

• opts.colormap : colormap (string; default = 'Viridis')
• opts.xmin : clip minimum value (number; default = X:min())
• opts.xmax : clip maximum value (number; default = X:max())
• opts.columnnames: table containing x-axis labels
• opts.rownames : table containing y-axis labels
• opts.layoutopts : dict of any additional options that the graph backend accepts for a layout. For example layoutopts = {'plotly': {'legend': {'x':0, 'y':0}}}.
• opts.nancolor : color for plotting NaNs. If this is None, NaNs will be plotted as transparent. (string; default = None)

vis.confusion_matrix

This function draws a confusion matrix for classification evaluation.

It accepts either:

• raw label vectors via y_true and y_pred, or
• a precomputed confusion matrix via cm.

Optional normalization can be applied with the normalize parameter:

• 'true': normalize by row (actual class)
• 'pred': normalize by column (predicted class)
• 'all': normalize by total count

An existing confusion matrix window can be modified with the update parameter:

• 'replace': redraw the whole matrix in the window given by win
• 'remove': delete the window given by win

The following opts are supported:

• opts.title : plot title (string; default = Confusion Matrix)
• opts.xlabel : x-axis label (string; default = Predicted)
• opts.ylabel : y-axis label (string; default = Actual)
• opts.colormap : Plotly colorscale (string; default = Blues)
• opts.showCounts : show raw counts in cells (bool; default = True)
• opts.showPercent : show percentages in cells (bool; default = True when normalized, False otherwise)
• opts.layoutopts : dict of any additional options that the graph backend accepts for a layout.

vis.bar

This function draws a regular, stacked, or grouped bar plot. It takes as input an N or NxM tensor X that specifies the height of each of the bars. If X contains M columns, the values corresponding to each row are either stacked or grouped (depending on how opts.stacked is set). In addition to X, an (optional) N tensor Y can be specified that contains the corresponding x-axis values.

The following plot-specific opts are currently supported:

• opts.rownames: table containing x-axis labels
• opts.stacked : stack multiple columns in X
• opts.legend : table containing legend labels
• opts.layoutopts : dict of any additional options that the graph backend accepts for a layout. For example layoutopts = {'plotly': {'legend': {'x':0, 'y':0}}}.

vis.histogram

This function draws a histogram of the specified data. It takes as input an N tensor X that specifies the data of which to construct the histogram.

The following plot-specific opts are currently supported:

• opts.numbins: number of bins (number; default = 30)
• opts.layoutopts : dict of any additional options that the graph backend accepts for a layout. For example layoutopts = {'plotly': {'legend': {'x':0, 'y':0}}}.

vis.histogram2d

This function draws a 2D histogram (density map) of paired data. It takes as input two N tensors X and Y of equal length that hold the coordinates of N points; the points are binned into a 2D grid and each cell is colored by the number of points that fall in it.

The following plot-specific opts are currently supported:

• opts.xnumbins: number of bins along the x-axis (number; default lets Plotly choose)
• opts.ynumbins: number of bins along the y-axis (number; default lets Plotly choose)
• opts.colormap: colormap (string; default = 'Viridis')
• opts.histnorm: normalization of the bin counts, one of '', 'percent', 'probability', 'density', or 'probability density'

vis.boxplot

This function draws boxplots of the specified data. It takes as input an N or an NxM tensor X that specifies the N data values of which to construct the M boxplots.

The following plot-specific opts are currently supported:

• opts.legend: labels for each of the columns in X
• opts.layoutopts : dict of any additional options that the graph backend accepts for a layout. For example layoutopts = {'plotly': {'legend': {'x':0, 'y':0}}}.

vis.violin

This function draws violin plots of the specified data. It takes as input an N or an NxM tensor X that specifies the N data values of which to construct the M violin plots.

The following plot-specific opts are currently supported:

• opts.legend: labels for each of the columns in X
• opts.showbox: overlay a mini box plot inside the violin (bool; default = True)
• opts.showmeanline: overlay the mean line (bool; default = True)
• opts.points: which raw points to show alongside the violin, one of 'all', 'outliers', 'suspectedoutliers', or False (default = False)
• opts.jitter: amount of jitter applied to displayed points (float in [0, 1]; default = 0.3)
• opts.orientation: 'v' for vertical or 'h' for horizontal violins (default = 'v')
• opts.bandwidth: bandwidth of the kernel density estimate (None lets Plotly choose automatically)
• opts.side: which side of the centre line to draw, one of 'both', 'positive', or 'negative' (default = 'both')

vis.pie

This function draws a pie chart based on the N tensor X. The values in X must be non-negative and define the size of each slice.

The following plot-specific opts are currently supported:

• opts.legend: list containing legend names
• opts.layoutopts : dict of any additional options that the graph backend accepts for a layout. For example layoutopts = {'plotly': {'legend': {'x':0, 'y':0}}}.

vis.surf

This function draws a surface plot. It takes as input an NxM tensor X that specifies the value at each location in the surface plot.

The following opts are supported:

• opts.colormap: colormap (string; default = 'Viridis')
• opts.xmin : clip minimum value (number; default = X:min())
• opts.xmax : clip maximum value (number; default = X:max())
• opts.layoutopts : dict of any additional options that the graph backend accepts for a layout. For example layoutopts = {'plotly': {'legend': {'x':0, 'y':0}}}.

vis.contour

This function draws a contour plot. It takes as input an NxM tensor X that specifies the value at each location in the contour plot.

The following opts are supported:

• opts.colormap: colormap (string; default = 'Viridis')
• opts.xmin : clip minimum value (number; default = X:min())
• opts.xmax : clip maximum value (number; default = X:max())
• opts.layoutopts : dict of any additional options that the graph backend accepts for a layout. For example layoutopts = {'plotly': {'legend': {'x':0, 'y':0}}}.

vis.quiver

This function draws a quiver plot in which the direction and length of the arrows is determined by the NxM tensors X and Y. Two optional NxM tensors gridX and gridY can be provided that specify the offsets of the arrows; by default, the arrows will be done on a regular grid.

The following opts are supported:

• opts.normalize: length of longest arrows (number)
• opts.arrowheads: show arrow heads (boolean; default = true)
• opts.layoutopts : dict of any additional options that the graph backend accepts for a layout. For example layoutopts = {'plotly': {'legend': {'x':0, 'y':0}}}.

vis.mesh

This function draws a mesh plot from a set of vertices defined in an Nx2 or Nx3 matrix X, and polygons defined in an optional Mx2 or Mx3 matrix Y.

The following opts are supported:

• opts.color: color (string)
• opts.opacity: opacity of polygons (number between 0 and 1)
• opts.layoutopts : dict of any additional options that the graph backend accepts for a layout. For example layoutopts = {'plotly': {'legend': {'x':0, 'y':0}}}.

vis.sankey

This function draws a Sankey (flow) diagram. Flows are defined by three equal-length arrays:

• source: source node index of each link (N array of ints)
• target: target node index of each link (N array of ints)
• value : magnitude of each link (N array of non-negative numbers)

labels is an optional list of node names. If omitted, nodes are referenced by their index alone.

The following opts are supported:

• opts.labels : list of node labels (alternative to the labels arg)
• opts.pad : node padding in px (number; default = 15)
• opts.thickness : node thickness in px (number; default = 20)
• opts.orientation: 'h' (default) or 'v'
• opts.nodecolor : node color(s) (string or list of strings)
• opts.linkcolor : link color(s) (string or list of strings)
• opts.layoutopts : dict of any additional options that the graph backend accepts for a layout.

vis.dual_axis_lines

This function will create a line plot using plotly with different Y-Axis.

X = A numpy array of the range.

Y1 = A numpy array of the same count as X.

Y2 = A numpy array of the same count as X.

The following opts are supported:

• opts.height : Height of the plot
• opts.width : Width of the plot
• opts.name_y1 : Axis name for Y1 plot
• opts.name_y2 : Axis name for Y2 plot
• opts.title : Title of the plot
• opts.color_title_y1 : Color of the Y1 axis Title
• opts.color_tick_y1 : Color of the Y1 axis Ticks
• opts.color_title_y2 : Color of the Y2 axis Title
• opts.color_tick_y2 : Color of the Y2 axis Ticks
• opts.side : side on which the Y2 tick has to be placed. Has values 'right' or left.
• opts.showlegend : Display legends (boolean values)
• opts.top : Set the top margin of the plot
• opts.bottom : Set the bottom margin of the plot
• opts.right : Set the right margin of the plot
• opts.left : Set the left margin of the plot

This is the image of the output:

vis.graph

This function draws a graph, in which the nodes and edges are taken from a 2-D matrix of size [,2] where each row contains a source and destination node value. The numeric value used to define nodes should be strictly between (0 to n-1), where n is the number of nodes.

There are two optional arguments :

• edgeLabels : list of custom edge labels. If not provided each edge gets a label, "source-destination", eg "1-2", size should be equal to size of input "edges". Optional.
• nodeLabels : list of custom node labels. If not provided each node gets a label same as the numeric value defined in the "edges". size should be equal to number of nodes present. Optional.

The following opts are supported:

• opts.height : Height of the plot. Default : 500
• opts.width : Width of the plot. Default : 500
• opts.directed : whether the plot should have an arrow or not. Default : false
• opts.showVertexLabels : Whether to show vertex labels. Default : true
• opts.showEdgeLabels : Whether to show edge labels. Default : false
• opts.scheme : Whether all nodes should have "same" color or "different". Default : "same"

Customizing plots

The plotting functions take an optional opts table as input that can be used to change (generic or plot-specific) properties of the plots.

All input arguments are specified in a single table; the input arguments are matches based on the keys they have in the input table.

The following opts are generic in the sense that they are the same for all visualizations (except plot.image, plot.text, plot.video, and plot.audio):

• opts.title : figure title
• opts.width : figure width
• opts.height : figure height
• opts.showlegend : show legend (true or false)
• opts.xtype : type of x-axis ('linear' or 'log')
• opts.xlabel : label of x-axis
• opts.xtick : show ticks on x-axis (boolean)
• opts.xtickmin : first tick on x-axis (number)
• opts.xtickmax : last tick on x-axis (number)
• opts.xtickvals : locations of ticks on x-axis (table of numbers)
• opts.xticklabels : ticks labels on x-axis (table of strings)
• opts.xtickstep : distances between ticks on x-axis (number)
• opts.xtickfont : font for x-axis labels (dict of font information)
• opts.ytype : type of y-axis ('linear' or 'log')
• opts.ylabel : label of y-axis
• opts.ytick : show ticks on y-axis (boolean)
• opts.ytickmin : first tick on y-axis (number)
• opts.ytickmax : last tick on y-axis (number)
• opts.ytickvals : locations of ticks on y-axis (table of numbers)
• opts.yticklabels : ticks labels on y-axis (table of strings)
• opts.ytickstep : distances between ticks on y-axis (number)
• opts.ytickfont : font for y-axis labels (dict of font information)
• opts.marginleft : left margin (in pixels)
• opts.marginright : right margin (in pixels)
• opts.margintop : top margin (in pixels)
• opts.marginbottom: bottom margin (in pixels)
• opts.caption : caption displayed below the plot (string; optional)

opts are passed as dictionary in python scripts.You can pass opts like:

opts=dict(title="my title", xlabel="x axis",ylabel="y axis")

OR

opts={"title":"my title", "xlabel":"x axis","ylabel":"y axis"}

The other options are visualization-specific, and are described in the documentation of the functions.

Others

vis.close

This function closes a specific window. It takes input window id win and environment id eid. Use win as None to close all windows in an environment.

vis.delete_env

This function deletes a specified env entirely. It takes env id eid as input.

Note: delete_env deletes all data for an environment and is IRREVERSIBLE. Do not use unless you absolutely want to remove an environment.

vis.fork_env

This function forks an environment, similar to the UI feature.

Arguments:

• prev_eid: Environment ID that we want to fork.
• eid: New Environment ID that will be created with the fork.

Note: fork_env an exception will occur if an env that doesn't exist is forked.

vis.win_exists

This function returns a bool indicating whether or not a window win exists on the server already. Returns None if something went wrong.

Optional arguments:

• env: Environment to search for the window in. Default is None.

vis.get_env_list

This function returns a list of all of the environments on the server at the time of calling. It takes no arguments.

vis.get_window_data

This function returns the window data for the given window. Returns data for all windows in an env if win is None.

Arguments:

• env: Environment to search for the window in.
• win: Window to return data for. Set to None to retrieve all the windows in an environment.

vis.check_connection

This function returns a bool indicating whether or not the server is connected. It accepts an optional argument timeout_seconds for a number of seconds to wait for the server to come up.

vis.replay_log

This function takes the contents of a visdom log and replays them to the current server to restore a state or handle any missing entries.

Arguments:

• log_filename: log file to replay the contents of.

Customizing Visdom

The user config directory for visdom is

• ~/.config/visdom for Linux
• ~/Library/Preferences/visdom for OSX
• %APPDATA%/visdom for Windows

By placing a style.css in you user config directory, visdom will serve the customized css file along with the default style-file. In addition, it is also possible to create a project-specific file; just place the file style.css in your env_path.

License

visdom is Apache 2.0 licensed, as found in the LICENSE file.

Note on Lua Torch Support

Support for Lua Torch was deprecated following v0.1.8.4. If you'd like to use torch support, you'll need to download that release. You can follow the usage instructions there, but it is no longer officially supported.

Contributing

See guidelines for contributing and running E2E/visual tests (Cypress and Playwright) here.

Acknowledgments

Visdom was inspired by tools like display and relies on Plotly as a plotting front-end.