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TensorFlow's Visualization Toolkit

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Quick Overview

TensorBoard is a visualization toolkit for TensorFlow, designed to help machine learning practitioners understand, debug, and optimize their models. It provides a suite of web-based tools for tracking and visualizing various aspects of the machine learning workflow, including model architecture, performance metrics, and data distributions.

Pros

  • Comprehensive visualization tools for TensorFlow models
  • Easy integration with TensorFlow projects
  • Supports custom plugins for extended functionality
  • Provides real-time monitoring of training progress

Cons

  • Primarily focused on TensorFlow, limiting its use with other frameworks
  • Can be resource-intensive for large-scale projects
  • Learning curve for advanced features and customizations
  • Occasional compatibility issues with different TensorFlow versions

Code Examples

  1. Logging scalar values:
import tensorflow as tf

writer = tf.summary.create_file_writer("logs/scalars")
with writer.as_default():
    for step in range(100):
        tf.summary.scalar("loss", 1.0 - 0.01 * step, step=step)
  1. Visualizing model architecture:
import tensorflow as tf

model = tf.keras.Sequential([
    tf.keras.layers.Dense(64, activation='relu'),
    tf.keras.layers.Dense(10, activation='softmax')
])

tf.keras.utils.plot_model(model, to_file='model.png', show_shapes=True)
  1. Logging images:
import tensorflow as tf
import numpy as np

writer = tf.summary.create_file_writer("logs/images")
with writer.as_default():
    image = np.random.rand(100, 100, 3)
    tf.summary.image("random_image", [image], step=0)

Getting Started

To use TensorBoard with your TensorFlow project:

  1. Install TensorBoard:
pip install tensorboard
  1. Add TensorBoard callback to your model:
import tensorflow as tf

tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir="./logs")
model.fit(x_train, y_train, epochs=5, callbacks=[tensorboard_callback])
  1. Launch TensorBoard:
tensorboard --logdir=./logs
  1. Open the provided URL in your web browser to view the TensorBoard dashboard.

Competitor Comparisons

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Pros of Wandb

  • More flexible and platform-agnostic, supporting various ML frameworks
  • Advanced experiment tracking features like artifact versioning and dataset lineage
  • Collaborative features for team projects and easy sharing of results

Cons of Wandb

  • Requires an account and potentially paid plans for larger projects
  • Steeper learning curve for beginners compared to TensorBoard

Code Comparison

TensorBoard:

from tensorflow import summary

writer = summary.create_file_writer("logs/")
with writer.as_default():
    summary.scalar("loss", loss_value, step=step)

Wandb:

import wandb

wandb.init(project="my_project")
wandb.log({"loss": loss_value})

Both tools offer logging capabilities, but Wandb's API is generally simpler and more intuitive. TensorBoard is tightly integrated with TensorFlow, while Wandb works across various frameworks and offers more advanced features for experiment tracking and collaboration. TensorBoard is free and open-source, making it a good choice for smaller projects or those exclusively using TensorFlow. Wandb, on the other hand, provides a more comprehensive solution for larger teams and complex ML workflows, but may require paid plans for advanced features.

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Cons of TensorWatch

  • Less mature and established compared to TensorBoard
  • Smaller community and fewer resources available
  • May require more setup and configuration

Code Comparison

TensorBoard:

from tensorflow import summary

writer = summary.create_file_writer("logs/")
with writer.as_default():
    summary.scalar("loss", loss_value, step=step)

TensorWatch:

import tensorwatch as tw

stream = tw.StreamingDataFrame()
w = tw.Watcher(stream)
w.observe(loss_value, "loss")

Both tools offer ways to log and visualize metrics during model training, but TensorWatch provides more flexibility in terms of data sources and visualization types. TensorBoard is more tightly integrated with TensorFlow, while TensorWatch can work with various ML frameworks. TensorBoard has a larger user base and more extensive documentation, making it easier for beginners to get started. However, TensorWatch's customizability and real-time monitoring capabilities make it an attractive option for more advanced users who need greater control over their visualizations.

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Cons of Sacred

  • Less comprehensive visualization capabilities
  • Smaller community and ecosystem compared to TensorBoard
  • Limited real-time monitoring features

Code Comparison

Sacred:

from sacred import Experiment

ex = Experiment('my_experiment')

@ex.config
def config():
    learning_rate = 0.001
    batch_size = 32

@ex.automain
def run(learning_rate, batch_size):
    # Your experiment code here

TensorBoard:

import tensorflow as tf

writer = tf.summary.create_file_writer("logs/")
with writer.as_default():
    for step in range(100):
        tf.summary.scalar("loss", 0.1 * step, step=step)

Sacred focuses on experiment configuration and logging, while TensorBoard emphasizes visualization and monitoring of TensorFlow-specific metrics. Sacred is more framework-agnostic, whereas TensorBoard is tightly integrated with TensorFlow and provides richer visualizations for deep learning experiments.

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Pros of MLflow

  • More comprehensive experiment tracking and model management
  • Language-agnostic, supporting multiple ML frameworks
  • Built-in model serving and deployment capabilities

Cons of MLflow

  • Steeper learning curve for beginners
  • Less tightly integrated with TensorFlow ecosystem
  • Requires more setup and configuration

Code Comparison

MLflow tracking example:

import mlflow

with mlflow.start_run():
    mlflow.log_param("param1", 5)
    mlflow.log_metric("accuracy", 0.85)
    mlflow.sklearn.log_model(model, "model")

TensorBoard logging example:

import tensorflow as tf

writer = tf.summary.create_file_writer("logs/")
with writer.as_default():
    tf.summary.scalar("accuracy", 0.85, step=1)
    writer.flush()

MLflow offers a more comprehensive API for tracking experiments, while TensorBoard focuses primarily on visualization of TensorFlow-specific metrics and model graphs. MLflow's code is more verbose but provides greater flexibility in logging various types of data and artifacts. TensorBoard's integration with TensorFlow is more seamless, requiring less setup for basic use cases within the TensorFlow ecosystem.

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README

TensorBoard GitHub Actions CI GitHub Actions Nightly CI PyPI

TensorBoard is a suite of web applications for inspecting and understanding your TensorFlow runs and graphs.

This README gives an overview of key concepts in TensorBoard, as well as how to interpret the visualizations TensorBoard provides. For an in-depth example of using TensorBoard, see the tutorial: TensorBoard: Getting Started. Documentation on how to use TensorBoard to work with images, graphs, hyper parameters, and more are linked from there, along with tutorial walk-throughs in Colab.

TensorBoard is designed to run entirely offline, without requiring any access to the Internet. For instance, this may be on your local machine, behind a corporate firewall, or in a datacenter.

Usage

Before running TensorBoard, make sure you have generated summary data in a log directory by creating a summary writer:

# sess.graph contains the graph definition; that enables the Graph Visualizer.

file_writer = tf.summary.FileWriter('/path/to/logs', sess.graph)

For more details, see the TensorBoard tutorial. Once you have event files, run TensorBoard and provide the log directory. If you're using a precompiled TensorFlow package (e.g. you installed via pip), run:

tensorboard --logdir path/to/logs

Or, if you are building from source:

bazel build tensorboard:tensorboard
./bazel-bin/tensorboard/tensorboard --logdir path/to/logs

# or even more succinctly
bazel run tensorboard -- --logdir path/to/logs

This should print that TensorBoard has started. Next, connect to http://localhost:6006.

TensorBoard requires a logdir to read logs from. For info on configuring TensorBoard, run tensorboard --help.

TensorBoard can be used in Google Chrome or Firefox. Other browsers might work, but there may be bugs or performance issues.

Key Concepts

Summary Ops: How TensorBoard gets data from TensorFlow

The first step in using TensorBoard is acquiring data from your TensorFlow run. For this, you need summary ops. Summary ops are ops, just like tf.matmul and tf.nn.relu, which means they take in tensors, produce tensors, and are evaluated from within a TensorFlow graph. However, summary ops have a twist: the Tensors they produce contain serialized protobufs, which are written to disk and sent to TensorBoard. To visualize the summary data in TensorBoard, you should evaluate the summary op, retrieve the result, and then write that result to disk using a summary.FileWriter. A full explanation, with examples, is in the tutorial.

The supported summary ops include:

Tags: Giving names to data

When you make a summary op, you will also give it a tag. The tag is basically a name for the data recorded by that op, and will be used to organize the data in the frontend. The scalar and histogram dashboards organize data by tag, and group the tags into folders according to a directory/like/hierarchy. If you have a lot of tags, we recommend grouping them with slashes.

Event Files & LogDirs: How TensorBoard loads the data

summary.FileWriters take summary data from TensorFlow, and then write them to a specified directory, known as the logdir. Specifically, the data is written to an append-only record dump that will have "tfevents" in the filename. TensorBoard reads data from a full directory, and organizes it into the history of a single TensorFlow execution.

Why does it read the whole directory, rather than an individual file? You might have been using supervisor.py to run your model, in which case if TensorFlow crashes, the supervisor will restart it from a checkpoint. When it restarts, it will start writing to a new events file, and TensorBoard will stitch the various event files together to produce a consistent history of what happened.

Runs: Comparing different executions of your model

You may want to visually compare multiple executions of your model; for example, suppose you've changed the hyperparameters and want to see if it's converging faster. TensorBoard enables this through different "runs". When TensorBoard is passed a logdir at startup, it recursively walks the directory tree rooted at logdir looking for subdirectories that contain tfevents data. Every time it encounters such a subdirectory, it loads it as a new run, and the frontend will organize the data accordingly.

For example, here is a well-organized TensorBoard log directory, with two runs, "run1" and "run2".

/some/path/mnist_experiments/
/some/path/mnist_experiments/run1/
/some/path/mnist_experiments/run1/events.out.tfevents.1456525581.name
/some/path/mnist_experiments/run1/events.out.tfevents.1456525585.name
/some/path/mnist_experiments/run2/
/some/path/mnist_experiments/run2/events.out.tfevents.1456525385.name
/tensorboard --logdir /some/path/mnist_experiments

Logdir & Logdir_spec (Legacy Mode)

You may also pass a comma separated list of log directories, and TensorBoard will watch each directory. You can also assign names to individual log directories by putting a colon between the name and the path, as in

tensorboard --logdir_spec name1:/path/to/logs/1,name2:/path/to/logs/2

This flag (--logdir_spec) is discouraged and can usually be avoided. TensorBoard walks log directories recursively; for finer-grained control, prefer using a symlink tree. Some features may not work when using --logdir_spec instead of --logdir.

The Visualizations

Scalar Dashboard

TensorBoard's Scalar Dashboard visualizes scalar statistics that vary over time; for example, you might want to track the model's loss or learning rate. As described in Key Concepts, you can compare multiple runs, and the data is organized by tag. The line charts have the following interactions:

  • Clicking on the small blue icon in the lower-left corner of each chart will expand the chart

  • Dragging a rectangular region on the chart will zoom in

  • Double clicking on the chart will zoom out

  • Mousing over the chart will produce crosshairs, with data values recorded in the run-selector on the left.

Additionally, you can create new folders to organize tags by writing regular expressions in the box in the top-left of the dashboard.

Histogram Dashboard

The Histogram Dashboard displays how the statistical distribution of a Tensor has varied over time. It visualizes data recorded via tf.summary.histogram. Each chart shows temporal "slices" of data, where each slice is a histogram of the tensor at a given step. It's organized with the oldest timestep in the back, and the most recent timestep in front. By changing the Histogram Mode from "offset" to "overlay", the perspective will rotate so that every histogram slice is rendered as a line and overlaid with one another.

Distribution Dashboard

The Distribution Dashboard is another way of visualizing histogram data from tf.summary.histogram. It shows some high-level statistics on a distribution. Each line on the chart represents a percentile in the distribution over the data: for example, the bottom line shows how the minimum value has changed over time, and the line in the middle shows how the median has changed. Reading from top to bottom, the lines have the following meaning: [maximum, 93%, 84%, 69%, 50%, 31%, 16%, 7%, minimum]

These percentiles can also be viewed as standard deviation boundaries on a normal distribution: [maximum, μ+1.5σ, μ+σ, μ+0.5σ, μ, μ-0.5σ, μ-σ, μ-1.5σ, minimum] so that the colored regions, read from inside to outside, have widths [σ, 2σ, 3σ] respectively.

Image Dashboard

The Image Dashboard can display pngs that were saved via a tf.summary.image. The dashboard is set up so that each row corresponds to a different tag, and each column corresponds to a run. Since the image dashboard supports arbitrary pngs, you can use this to embed custom visualizations (e.g. matplotlib scatterplots) into TensorBoard. This dashboard always shows you the latest image for each tag.

Audio Dashboard

The Audio Dashboard can embed playable audio widgets for audio saved via a tf.summary.audio. The dashboard is set up so that each row corresponds to a different tag, and each column corresponds to a run. This dashboard always embeds the latest audio for each tag.

Graph Explorer

The Graph Explorer can visualize a TensorBoard graph, enabling inspection of the TensorFlow model. To get best use of the graph visualizer, you should use name scopes to hierarchically group the ops in your graph - otherwise, the graph may be difficult to decipher. For more information, including examples, see the examining the TensorFlow graph tutorial.

Embedding Projector

The Embedding Projector allows you to visualize high-dimensional data; for example, you may view your input data after it has been embedded in a high- dimensional space by your model. The embedding projector reads data from your model checkpoint file, and may be configured with additional metadata, like a vocabulary file or sprite images. For more details, see the embedding projector tutorial.

Text Dashboard

The Text Dashboard displays text snippets saved via tf.summary.text. Markdown features including hyperlinks, lists, and tables are all supported.

Time Series Dashboard

The Time Series Dashboard shows a unified interface containing all your Scalars, Histograms, and Images saved via tf.summary.scalar, tf.summary.image, or tf.summary.histogram. It enables viewing your 'accuracy' line chart side by side with activation histograms and training example images, for example.

Features include:

  • Custom run colors: click on the colored circles in the run selector to change a run's color.

  • Pinned cards: click the 'pin' icon on any card to add it to the pinned section at the top for quick comparison.

  • Settings: the right pane offers settings for charts and other visualizations. Important settings will persist across TensorBoard sessions, when hosted at the same URL origin.

  • Autocomplete in tag filter: search for specific charts more easily.

Frequently Asked Questions

My TensorBoard isn't showing any data! What's wrong?

First, check that the directory passed to --logdir is correct. You can also verify this by navigating to the Scalars dashboard (under the "Inactive" menu) and looking for the log directory path at the bottom of the left sidebar.

If you're loading from the proper path, make sure that event files are present. TensorBoard will recursively walk its logdir, it's fine if the data is nested under a subdirectory. Ensure the following shows at least one result:

find DIRECTORY_PATH | grep tfevents

You can also check that the event files actually have data by running tensorboard in inspect mode to inspect the contents of your event files.

tensorboard --inspect --logdir DIRECTORY_PATH

The output for an event file corresponding to a blank TensorBoard may still sometimes show a few steps, representing a few initial events that aren't shown by TensorBoard (for example, when using the Keras TensorBoard callback):

tensor
   first_step           0
   last_step            2
   max_step             2
   min_step             0
   num_steps            2
   outoforder_steps     [(2, 0), (2, 0), (2, 0)]

In contrast, the output for an event file with more data might look like this:

tensor
   first_step           0
   last_step            55
   max_step             250
   min_step             0
   num_steps            60
   outoforder_steps     [(2, 0), (2, 0), (2, 0), (2, 0), (50, 9), (100, 19), (150, 29), (200, 39), (250, 49)]

TensorBoard is showing only some of my data, or isn't properly updating!

Update: After 2.3.0 release, TensorBoard no longer auto reloads every 30 seconds. To re-enable the behavior, please open the settings by clicking the gear icon in the top-right of the TensorBoard web interface, and enable "Reload data".

Update: the experimental --reload_multifile=true option can now be used to poll all "active" files in a directory for new data, rather than the most recent one as described below. A file is "active" as long as it received new data within --reload_multifile_inactive_secs seconds ago, defaulting to 86400.

This issue usually comes about because of how TensorBoard iterates through the tfevents files: it progresses through the events file in timestamp order, and only reads one file at a time. Let's suppose we have files with timestamps a and b, where a<b. Once TensorBoard has read all the events in a, it will never return to it, because it assumes any new events are being written in the more recent file. This could cause an issue if, for example, you have two FileWriters simultaneously writing to the same directory. If you have multiple summary writers, each one should be writing to a separate directory.

Does TensorBoard support multiple or distributed summary writers?

Update: the experimental --reload_multifile=true option can now be used to poll all "active" files in a directory for new data, defined as any file that received new data within --reload_multifile_inactive_secs seconds ago, defaulting to 86400.

No. TensorBoard expects that only one events file will be written to at a time, and multiple summary writers means multiple events files. If you are running a distributed TensorFlow instance, we encourage you to designate a single worker as the "chief" that is responsible for all summary processing. See supervisor.py for an example.

I'm seeing data overlapped on itself! What gives?

If you are seeing data that seems to travel backwards through time and overlap with itself, there are a few possible explanations.

  • You may have multiple execution of TensorFlow that all wrote to the same log directory. Please have each TensorFlow run write to its own logdir.

    Update: the experimental --reload_multifile=true option can now be used to poll all "active" files in a directory for new data, defined as any file that received new data within --reload_multifile_inactive_secs seconds ago, defaulting to 86400.

  • You may have a bug in your code where the global_step variable (passed to FileWriter.add_summary) is being maintained incorrectly.

  • It may be that your TensorFlow job crashed, and was restarted from an earlier checkpoint. See How to handle TensorFlow restarts, below.

As a workaround, try changing the x-axis display in TensorBoard from steps to wall_time. This will frequently clear up the issue.

How should I handle TensorFlow restarts?

TensorFlow is designed with a mechanism for graceful recovery if a job crashes or is killed: TensorFlow can periodically write model checkpoint files, which enable you to restart TensorFlow without losing all your training progress.

However, this can complicate things for TensorBoard; imagine that TensorFlow wrote a checkpoint at step a, and then continued running until step b, and then crashed and restarted at timestamp a. All of the events written between a and b were "orphaned" by the restart event and should be removed.

To facilitate this, we have a SessionLog message in tensorflow/core/util/event.proto which can record SessionStatus.START as an event; like all events, it may have a step associated with it. If TensorBoard detects a SessionStatus.START event with step a, it will assume that every event with a step greater than a was orphaned, and it will discard those events. This behavior may be disabled with the flag --purge_orphaned_data false (in versions after 0.7).

How can I export data from TensorBoard?

The Scalar Dashboard supports exporting data; you can click the "enable download links" option in the left-hand bar. Then, each plot will provide download links for the data it contains.

If you need access to the full dataset, you can read the event files that TensorBoard consumes by using the summary_iterator method.

Can I make my own plugin?

Yes! You can clone and tinker with one of the examples and make your own, amazing visualizations. More documentation on the plugin system is described in the ADDING_A_PLUGIN guide. Feel free to file feature requests or questions about plugin functionality.

Once satisfied with your own groundbreaking new plugin, see the distribution section on how to publish to PyPI and share it with the community.

Can I customize which lines appear in a plot?

Using the custom scalars plugin, you can create scalar plots with lines for custom run-tag pairs. However, within the original scalars dashboard, each scalar plot corresponds to data for a specific tag and contains lines for each run that includes that tag.

Can I visualize margins above and below lines?

Margin plots (that visualize lower and upper bounds) may be created with the custom scalars plugin. The original scalars plugin does not support visualizing margins.

Can I create scatterplots (or other custom plots)?

This isn't yet possible. As a workaround, you could create your custom plot in your own code (e.g. matplotlib) and then write it into an SummaryProto (core/framework/summary.proto) and add it to your FileWriter. Then, your custom plot will appear in the TensorBoard image tab.

Is my data being downsampled? Am I really seeing all the data?

TensorBoard uses reservoir sampling to downsample your data so that it can be loaded into RAM. You can modify the number of elements it will keep per tag by using the --samples_per_plugin command line argument (ex: --samples_per_plugin=scalars=500,images=20). See this Stack Overflow question for some more information.

I get a network security popup every time I run TensorBoard on a mac!

Versions of TensorBoard prior to TensorBoard 2.0 would by default serve on host 0.0.0.0, which is publicly accessible. For those versions of TensorBoard, you can stop the popups by specifying --host localhost at startup.

In TensorBoard 2.0 and up, --host localhost is the default. Use --bind_all to restore the old behavior of serving to the public network on both IPv4 and IPv6.

Can I run tensorboard without a TensorFlow installation?

TensorBoard 1.14+ can be run with a reduced feature set if you do not have TensorFlow installed. The primary limitation is that as of 1.14, only the following plugins are supported: scalars, custom scalars, image, audio, graph, projector (partial), distributions, histograms, text, PR curves, mesh. In addition, there is no support for log directories on Google Cloud Storage.

How can I contribute to TensorBoard development?

See DEVELOPMENT.md.

I have a different issue that wasn't addressed here!

First, try searching our GitHub issues and Stack Overflow. It may be that someone else has already had the same issue or question.

General usage questions (or problems that may be specific to your local setup) should go to Stack Overflow.

If you have found a bug in TensorBoard, please file a GitHub issue with as much supporting information as you can provide (e.g. attaching events files, including the output of tensorboard --inspect, etc.).