TensorBoard is a suite of web applications for inspecting and understanding your TensorFlow runs and graphs. TensorBoard currently supports five visualizations: scalars, images, audio, histograms, and the graph.
You can play with an interactive demo TensorBoard at tensorflow.org/tensorboard/.
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: Visualizing Learning. For in-depth information on the Graph Visualizer, see this tutorial: TensorBoard: Graph Visualization.
Before running TensorBoard, make sure you have generated summary data in a log
directory by creating a SummaryWriter:
# sess.graph_def is the graph definition; that enables the Graph Visualizer.
summary_writer = tf.train.SummaryWriter('/path/to/logs', sess.graph_def)For more details, see this 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 tensorflow/tensorboard:tensorboard
./bazel-bin/tensorflow/tensorboard/tensorboard --logdir=path/to/logs
This should print that TensorBoard has started. Next, connect to http://localhost:6006.
Note that TensorBoard requires a logdir to read logs from. For info on
configuring TensorBoard, run tensorboard --help.
The first step in using TensorBoard is acquiring data from your TensorFlow run.
For this, you need summary
ops.
Summary ops are ops, like
tf.matmul
or
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
SummaryWriter. A full explanation, with examples, is in the
tutorial.
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.
SummaryWriters 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.
You may want to visually compare multiple executions of your model; for example,
suppose you've changed the hyperparameters and want to see if its 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
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.
The Histogram Dashboard is for visualizing how the statistical distribution of a
Tensor has varied over time. It visualizes data recorded via a
tf.histogram_summary. Right now, its name is a bit of a misnomer, as it doesn't
show histograms; instead, 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.
This histogram visualization is a bit weird, and cannot meaningfully represent multimodal distributions. We are currently working on a true-histogram replacement.
The Image Dashboard can display pngs that were saved via a tf.image_summary. 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.
The Audio Dashboard can embed playable audio widgets for audio saved via a tf.audio_summary. 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.
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 graph visualizer tutorial.
No. Currently, TensorBoard expects that only a single summary writer will be active at a time, and as consequence, only a single events file is ever written to at a time. Violating this assumption may cause TensorBoard to lose data or stop updating unexpectedly. If you are running a distributed training process, we encourage you to designate a single worker as the "chief" that is responsible for writing all summaries to disk.
The first thing to do is ensure that TensorBoard is properly loading data from
the correct directory. Launch tensorboard --logdir=DIRECTORY_PATH --debug and
look for output of the form
INFO:tensorflow:TensorBoard path_to_run is: {'DIRECTORY_PATH': None}
Verify that the DIRECTORY_PATH TensorBoard is looking at is the path you expect. (Note: There's a known issue where TensorBoard does not handle paths starting in ~ properly).
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. Try running the command:
find DIRECTORY_PATH | grep tfevents
If you have at least one result, then TensorBoard should be able to load data.
Finally, let's make sure that the event files actually have data. Run tensorboard in inspector mode to inspect the contents of your event files.
tensorboard --inspect --logdir=DIRECTORY_PATH
If after running this procedure, it's still not working, please file an issue on GitHub. It will be much easier for us to debug it if you provide an event file that isn't working.
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
SummaryWriters simultaneously writing to the same directory. If you have
multiple summary writers, each one should be writing to a separate directory.
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.
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.
-
You may have a have a bug in your code where the global_step variable (passed to
SummaryWriter.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.
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).
If you'd like to export data to visualize elsewhere (e.g. iPython Notebook),
that's possible too. You can directly depend on the underlying classes that
TensorBoard uses for loading data: python/summary/event_accumulator.py (for
loading data from a single run) or python/summary/event_multiplexer.py (for
loading data from multiple runs, and keeping it organized). These classes load
groups of event files, discard data that was "orphaned" by TensorFlow crashes,
and organize the data by tag.
As another option, there is a script
(tensorboard/scripts/serialize_tensorboard.py) which will load a logdir just
like TensorBoard does, but write all of the data out to disk as json instead of
starting a server. This script is setup to make "fake TensorBoard backends" for
testing, so it is a bit rough around the edges.
Right now, you can overlap plots only if they are from different runs, and both have the same tag name.
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 SummaryWriter. Then, your
custom plot will appear in the TensorBoard image tab.
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 in tensorboard/backend/server.py.
This is because by default, TensorBoard serves on host 0.0.0.0 which is
publicly accessible. You can stop the popups by specifying --host=localhost at
startup.
See tensorflow/tensorboard/DEVELOPMENT.md.
First, try searching our GitHub issues and Stack Overflow. It may be that someone else has already had the same issue or question.
If you have a bug, please file a GitHub issue. If the bug is related to your specific data (e.g. the events aren't loading properly), please do both of the following things to make it easier for us to debug and fix:
- Run tensorboard in --inspect mode and copy paste the debug output.
- Upload some events files that will reproduce the issue.
If you have a feature request, please file a GitHub issue.
General usage questions should go to Stack Overflow.