https://github.com/snehankekre/streamlit-tensorboard

Streamlit component for TensorBoard, TensorFlow's visualization toolkit
https://github.com/snehankekre/streamlit-tensorboard

streamlit tensorboard tensorflow

Last synced: 4 months ago
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Streamlit component for TensorBoard, TensorFlow's visualization toolkit

• Host: GitHub
• URL: https://github.com/snehankekre/streamlit-tensorboard
• Owner: snehankekre
• License: mit
• Created: 2021-06-13T12:39:14.000Z (over 3 years ago)
• Default Branch: master
• Last Pushed: 2021-12-08T11:38:25.000Z (about 3 years ago)
• Last Synced: 2024-09-30T09:02:48.591Z (4 months ago)
• Topics: streamlit, tensorboard, tensorflow
• Language: Python
• Homepage: https://pypi.org/project/streamlit-tensorboard/
• Size: 170 KB
• Stars: 40
• Watchers: 5
• Forks: 7
• Open Issues: 4
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# streamlit-tensorboard

[![Streamlit App](https://static.streamlit.io/badges/streamlit_badge_black_white.svg)](http://snehankekre.com:8501/)

This is a work-in-progress, providing a function to embed [TensorBoard](https://www.tensorflow.org/tensorboard), TensorFlow's visualization toolkit, in Streamlit apps.

## Installation :balloon:

`pip install --upgrade streamlit-tensorboard`

## Example Usage :computer:

```python

import streamlit as st

from streamlit_tensorboard import st_tensorboard

import tensorflow as tf

import datetime

import random

mnist = tf.keras.datasets.mnist

(x_train, y_train), (x_test, y_test) = mnist.load_data()

x_train, x_test = x_train / 255.0, x_test / 255.0

def create_model():

    return tf.keras.models.Sequential(

        [

            tf.keras.layers.Flatten(input_shape=(28, 28)),

            tf.keras.layers.Dense(512, activation="relu"),

            tf.keras.layers.Dropout(0.2),

            tf.keras.layers.Dense(10, activation="softmax"),

        ]

    )

model = create_model()

model.compile(

    optimizer="adam", loss="sparse_categorical_crossentropy", metrics=["accuracy"]

)

logdir = "logs/fit/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S")

tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=logdir, histogram_freq=1)

model.fit(

    x=x_train,

    y=y_train,

    epochs=5,

    validation_data=(x_test, y_test),

    callbacks=[tensorboard_callback],

)

# Start TensorBoard

st_tensorboard(logdir=logdir, port=6006, width=1080)

```

![st_tensorboard](https://github.com/snehankekre/streamlit-tensorboard/blob/master/_static/st-tensorboard-example.png)

## Contributing :hammer_and_wrench:

Please file a new [GitHub issue](https://github.com/snehankekre/streamlit-tensorboard/issues) (if one doesn't already exist) for bugs, feature requests, suggestions for improvements, etc. If you have solutions to any [open issues](https://github.com/snehankekre/streamlit-tensorboard/issues), feel free to open a [Pull Request](https://github.com/snehankekre/streamlit-tensorboard/pulls)!

## Supported Platforms

1. Ubuntu

2. Debian GNU/Linux

3. macOS (⚠️ unverified)

This component will not work on [Streamlit Cloud](https://share.streamlit.io/). Due to security reasons, Streamlit Cloud does not allow users expose ports (as required by streamlit-tensorboard).