/streamlit-tensorboard

Streamlit component for TensorBoard, TensorFlow's visualization toolkit

Primary LanguagePythonMIT LicenseMIT

streamlit-tensorboard

Streamlit App

This is a work-in-progress, providing a function to embed TensorBoard, TensorFlow's visualization toolkit, in Streamlit apps.

Installation 🎈

pip install --upgrade streamlit-tensorboard

Example Usage 💻

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

Contributing 🛠️

Please file a new GitHub issue (if one doesn't already exist) for bugs, feature requests, suggestions for improvements, etc. If you have solutions to any open issues, feel free to open a Pull Request!

Supported Platforms

  1. Ubuntu
  2. Debian GNU/Linux
  3. macOS (⚠️ unverified)

This component will not work on Streamlit Cloud. Due to security reasons, Streamlit Cloud does not allow users expose ports (as required by streamlit-tensorboard).