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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "TsHV-7cpVkyK"
},
"source": [
"##### Copyright 2019 The TensorFlow Authors."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "atWM-s8yVnfX"
},
"outputs": [],
"source": [
"#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"# you may not use this file except in compliance with the License.\n",
"# You may obtain a copy of the License at\n",
"#\n",
"# https://www.apache.org/licenses/LICENSE-2.0\n",
"#\n",
"# Unless required by applicable law or agreed to in writing, software\n",
"# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"# See the License for the specific language governing permissions and\n",
"# limitations under the License."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "TB0wBWfcVqHz"
},
"source": [
"# Using TensorBoard in Notebooks\n",
"\n",
"
"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "elH58gbhWAmn"
},
"source": [
"TensorBoard can be used directly within notebook experiences such as [Colab](https://colab.research.google.com/) and [Jupyter](https://jupyter.org/). This can be helpful for sharing results, integrating TensorBoard into existing workflows, and using TensorBoard without installing anything locally."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "VszJNloY3ZU3"
},
"source": [
"## Setup"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "E6QhA_dp3eRq"
},
"source": [
"Start by installing TF 2.0 and loading the TensorBoard notebook extension:\n",
"\n",
"**For Jupyter users:** If you’ve installed Jupyter and TensorBoard into\n",
"the same virtualenv, then you should be good to go. If you’re using a\n",
"more complicated setup, like a global Jupyter installation and kernels\n",
"for different Conda/virtualenv environments, then you must ensure that\n",
"the `tensorboard` binary is on your `PATH` inside the Jupyter notebook\n",
"context. One way to do this is to modify the `kernel_spec` to prepend\n",
"the environment’s `bin` directory to `PATH`, [as described here][1].\n",
"\n",
"[1]: https://github.com/ipython/ipykernel/issues/395#issuecomment-479787997\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "9w7Baxc8aCtJ"
},
"source": [
"**For Docker users:** In case you are running a [Docker](https://docs.docker.com/install/) image of [Jupyter Notebook server using TensorFlow's nightly](https://www.tensorflow.org/install/docker#examples_using_cpu-only_images), it is necessary to expose not only the notebook's port, but the TensorBoard's port. Thus, run the container with the following command:\n",
"\n",
"```\n",
"docker run -it -p 8888:8888 -p 6006:6006 \\\n",
"tensorflow/tensorflow:nightly-py3-jupyter \n",
"```\n",
"\n",
"where the `-p 6006` is the default port of TensorBoard. This will allocate a port for you to run one TensorBoard instance. To have concurrent instances, it is necessary to allocate more ports. Also, pass `--bind_all` to `%tensorboard` to expose the port outside the container."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"id": "8p3Tbx8cWEFA"
},
"outputs": [],
"source": [
"# Load the TensorBoard notebook extension\n",
"%load_ext tensorboard"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "9GtR_cTTkf9G"
},
"source": [
"Import TensorFlow, datetime, and os:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "mVtYvbbIWRkV"
},
"outputs": [],
"source": [
"import tensorflow as tf\n",
"import datetime, os"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Cu1fbH-S3oAX"
},
"source": [
"## TensorBoard in notebooks"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "XfCa27_8kov6"
},
"source": [
"Download the [FashionMNIST](https://github.com/zalandoresearch/fashion-mnist) dataset and scale it:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"id": "z8b82G7YksOS"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
"32768/29515 [=================================] - 0s 0us/step\n",
"Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
"26427392/26421880 [==============================] - 0s 0us/step\n",
"Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
"8192/5148 [===============================================] - 0s 0us/step\n",
"Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
"4423680/4422102 [==============================] - 0s 0us/step\n"
]
}
],
"source": [
"fashion_mnist = tf.keras.datasets.fashion_mnist\n",
"\n",
"(x_train, y_train),(x_test, y_test) = fashion_mnist.load_data()\n",
"x_train, x_test = x_train / 255.0, x_test / 255.0"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "lBk1BqAZKEKd"
},
"source": [
"Create a very simple model:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "OS7qGYiMKGQl"
},
"outputs": [],
"source": [
"def create_model():\n",
" return tf.keras.models.Sequential([\n",
" tf.keras.layers.Flatten(input_shape=(28, 28), name='layers_flatten'),\n",
" tf.keras.layers.Dense(512, activation='relu', name='layers_dense'),\n",
" tf.keras.layers.Dropout(0.2, name='layers_dropout'),\n",
" tf.keras.layers.Dense(10, activation='softmax', name='layers_dense_2')\n",
" ])"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "RNaPPs5ZKNOV"
},
"source": [
"Train the model using Keras and the TensorBoard callback:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"id": "lpUO9HqUKP6z"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 60000 samples, validate on 10000 samples\n",
"Epoch 1/5\n",
"60000/60000 [==============================] - 11s 182us/sample - loss: 0.4976 - accuracy: 0.8204 - val_loss: 0.4143 - val_accuracy: 0.8538\n",
"Epoch 2/5\n",
"60000/60000 [==============================] - 10s 174us/sample - loss: 0.3845 - accuracy: 0.8588 - val_loss: 0.3855 - val_accuracy: 0.8626\n",
"Epoch 3/5\n",
"60000/60000 [==============================] - 10s 175us/sample - loss: 0.3513 - accuracy: 0.8705 - val_loss: 0.3740 - val_accuracy: 0.8607\n",
"Epoch 4/5\n",
"60000/60000 [==============================] - 11s 177us/sample - loss: 0.3287 - accuracy: 0.8793 - val_loss: 0.3596 - val_accuracy: 0.8719\n",
"Epoch 5/5\n",
"60000/60000 [==============================] - 11s 178us/sample - loss: 0.3153 - accuracy: 0.8825 - val_loss: 0.3360 - val_accuracy: 0.8782\n"
]
}
],
"source": [
"def train_model():\n",
" \n",
" model = create_model()\n",
" model.compile(optimizer='adam',\n",
" loss='sparse_categorical_crossentropy',\n",
" metrics=['accuracy'])\n",
"\n",
" logdir = os.path.join(\"logs\", datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\"))\n",
" tensorboard_callback = tf.keras.callbacks.TensorBoard(logdir, histogram_freq=1)\n",
"\n",
" model.fit(x=x_train, \n",
" y=y_train, \n",
" epochs=5, \n",
" validation_data=(x_test, y_test), \n",
" callbacks=[tensorboard_callback])\n",
"\n",
"train_model()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "SxvXc4hoKW7d"
},
"source": [
"Start TensorBoard within the notebook using [magics](https://ipython.readthedocs.io/en/stable/interactive/magics.html):"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "KBHp6M_zgjp4"
},
"outputs": [],
"source": [
"%tensorboard --logdir logs"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Po7rTfQswAMT"
},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "aQq3UHgmLBpC"
},
"source": [
"You can now view dashboards such as **Time Series**, **Graphs**, **Distributions**, and others. Some dashboards are not available yet in Colab (such as the profile plugin).\n",
"\n",
"The `%tensorboard` magic has exactly the same format as the TensorBoard command line invocation, but with a `%`-sign in front of it."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "NiIMwOG8MR_g"
},
"source": [
"You can also start TensorBoard before training to monitor it in progress:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "qyI5lrXoMw9K"
},
"outputs": [],
"source": [
"%tensorboard --logdir logs"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ALxC8BbWWV91"
},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "GUSM8yLrO2yZ"
},
"source": [
"The same TensorBoard backend is reused by issuing the same command. If a different logs directory was chosen, a new instance of TensorBoard would be opened. Ports are managed automatically. \n",
"\n",
"Start training a new model and watch TensorBoard update automatically every 30 seconds or refresh it with the button on the top right:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"id": "ixZlmtWhMyr4"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 60000 samples, validate on 10000 samples\n",
"Epoch 1/5\n",
"60000/60000 [==============================] - 11s 184us/sample - loss: 0.4968 - accuracy: 0.8223 - val_loss: 0.4216 - val_accuracy: 0.8481\n",
"Epoch 2/5\n",
"60000/60000 [==============================] - 11s 176us/sample - loss: 0.3847 - accuracy: 0.8587 - val_loss: 0.4056 - val_accuracy: 0.8545\n",
"Epoch 3/5\n",
"60000/60000 [==============================] - 11s 176us/sample - loss: 0.3495 - accuracy: 0.8727 - val_loss: 0.3600 - val_accuracy: 0.8700\n",
"Epoch 4/5\n",
"60000/60000 [==============================] - 11s 179us/sample - loss: 0.3282 - accuracy: 0.8795 - val_loss: 0.3636 - val_accuracy: 0.8694\n",
"Epoch 5/5\n",
"60000/60000 [==============================] - 11s 176us/sample - loss: 0.3115 - accuracy: 0.8839 - val_loss: 0.3438 - val_accuracy: 0.8764\n"
]
}
],
"source": [
"train_model()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "IlDz2oXBgnZ9"
},
"source": [
"You can use the `tensorboard.notebook` APIs for a bit more control:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"id": "ko9qeSQHLrEh"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Known TensorBoard instances:\n",
" - port 6006: logdir logs (started 0:00:54 ago; pid 265)\n"
]
}
],
"source": [
"from tensorboard import notebook\n",
"notebook.list() # View open TensorBoard instances"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "hzm9DNVILxJe"
},
"outputs": [],
"source": [
"# Control TensorBoard display. If no port is provided, \n",
"# the most recently launched TensorBoard is used\n",
"notebook.display(port=6006, height=1000) "
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "za2GqzKiWY-R"
},
"source": [
""
]
}
],
"metadata": {
"colab": {
"collapsed_sections": [],
"name": "tensorboard_in_notebooks.ipynb",
"toc_visible": true
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
![](\"https://www.tensorflow.org/images/tf_logo_32px.png\"){kind=logo}
View on TensorFlow.org\n",
" ![](\"https://www.tensorflow.org/images/colab_logo_32px.png\"){kind=logo}
Run in Google Colab\n",
" ![](\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\")
View source on GitHub\n",
" ![](\"https://www.tensorflow.org/images/download_logo_32px.png\"){kind=logo}
Download notebook\n",
"