Return a list of physical devices visible to the host runtime.
Physical devices are hardware devices present on the host machine. By default all discovered CPU and GPU devices are considered visible.
This API allows querying the physical hardware resources prior to runtime initialization. Thus, giving an opportunity to call any additional configuration APIs. This is in contrast to tf.config.list_logical_devices, which triggers runtime initialization in order to list the configured devices.
The following example lists the number of visible GPUs on the host.
However, the number of GPUs available to the runtime may change during runtime initialization due to marking certain devices as not visible or configuring multiple logical devices.
# tf.config.set_visible_devices
Set the list of visible devices.
Specifies which PhysicalDevice objects are visible to the runtime. TensorFlow will only allocate memory and place operations on visible physical devices, as otherwise no LogicalDevice will be created on them. By default all discovered devices are marked as visible.
The following example demonstrates disabling the first GPU on the machine
# Invalid device or cannot modify virtual devices once initialized.
pass
```
# tf.config.list_logical_devices
Return a list of logical devices created by runtime
Logical devices may correspond to physical devices or remote devices in the cluster. Operations and tensors may be placed on these devices by using the name of the tf.config.LogicalDevice.
Calling tf.config.list_logical_devices triggers the runtime to configure any tf.config.PhysicalDevice visible to the runtime, thereby preventing further configuration. To avoid runtime initialization, call tf.config.list_physical_devices instead.
In machine learning, to improve something you often need to be able to measure it. TensorBoard is a tool for providing the measurements and visualizations needed during the machine learning workflow. It enables tracking experiment metrics like loss and accuracy, visualizing the model graph, projecting embeddings to a lower dimensional space, and much more.
Using the MNIST dataset as the example, normalize the data and write a function that creates a simple Keras model for classifying the images into 10 classes.
When training with Keras's Model.fit(), adding the tf.keras.callbacks.TensorBoard callback ensures that logs are created and stored. Additionally, enable histogram computation every epoch with histogram_freq=1 (this is off by default)
Place the logs in a timestamped subdirectory to allow easy selection of different training runs.
A brief overview of the dashboards shown (tabs in top navigation bar):
- The **Scalars** dashboard shows how the loss and metrics change with every epoch. You can use it to also track training speed, learning rate, and other scalar values.
- The **Graphs** dashboard helps you visualize your model. In this case, the Keras graph of layers is shown which can help you ensure it is built correctly.
- The **Distributions** and **Histograms** dashboards show the distribution of a Tensor over time. This can be useful to visualize weights and biases and verify that they are changing in an expected way.
Additional TensorBoard plugins are automatically enabled when you log other types of data. For example, the Keras TensorBoard callback lets you log images and embeddings as well. You can see what other plugins are available in TensorBoard by clicking on the "inactive" dropdown towards the top right
Start training. Use tf.summary.scalar() to log metrics (loss and accuracy) during training/testing within the scope of the summary writers to write the summaries to disk. You have control over which metrics to log and how often to do it. Other tf.summary functions enable logging other types of data.
template='Epoch {}, Loss: {}, Accuracy: {}, Test Loss: {}, Test Accuracy: {}'
print(template.format(epoch+1,
train_loss.result(),
train_accuracy.result()*100,
test_loss.result(),
test_accuracy.result()*100))
#Resetmetricseveryepoch
train_loss.reset_states()
test_loss.reset_states()
train_accuracy.reset_states()
test_accuracy.reset_states()
```
TensorBoard.dev is a free public service that enables you to upload your TensorBoard logs and get a permalink that can be shared with everyone in academic papers, blog posts, social media, etc. This can enable better reproducibility and collaboration
To use TensorBoard.dev, run the following command:
```
!tensorboard dev upload \
--logdir logs/fit \
--name "(optional) My latest experiment" \
--description "(optional) Simple comparison of several hyperparameters" \
