This is a work-in-progress, providing a function to embed TensorBoard, TensorFlow's visualization toolkit, in Streamlit apps.
pip install --upgrade streamlit-tensorboard
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)
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!
- Ubuntu
- Debian GNU/Linux
- 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).
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