When working with TensorFlow, especially in a multi-GPU setup, it is often necessary to specify which devices or GPUs your computation should run on. This helps in efficiently utilizing system resources and achieving optimal performance. TensorFlow provides a way to programmatically select the devices on which operations are assigned and executed using the tf.config.experimental module.
Understanding Device Configuration in TensorFlow
TensorFlow automatically tries to utilize all available devices (GPUs) by default. However, there are situations where you might want to restrict TensorFlow to a specific GPU, either for debugging, optimization, or avoiding conflicts with other processes. For such cases, TensorFlow allows fine-grained control over device visibility.
Listing Available Devices
Before setting visible devices, it’s useful to list all available physical devices. You can do this using:
import tensorflow as tf
physical_devices = tf.config.list_physical_devices('GPU')
print("Available devices:", physical_devices)
This code snippet will output a list of all available GPUs, which can be used to verify how many devices are detectable by TensorFlow.
Setting Visible Devices
To set specific GPUs as visible, use the following approach:
import tensorflow as tf
# List all GPUs
physical_devices = tf.config.list_physical_devices('GPU')
try:
# Restrict TensorFlow to only the first GPU
tf.config.experimental.set_visible_devices(physical_devices[0], 'GPU')
print("Set GPU 0 as visible device.")
except RuntimeError as e:
# Visible devices must be set before GPUs have been initialized
print(e)
In this code, we are configuring TensorFlow to only see the first GPU in the list. It’s important to check for RuntimeError because visible devices can only be set before using the GPUs.
Setting Memory Growth
To prevent TensorFlow from pre-allocating the entire memory on a GPU, you can enable memory growth, which allows the usage of GPU memory to incrementally grow as needed by the editing.
You can do this with the following:
for device in physical_devices:
tf.config.experimental.set_memory_growth(device, True)
print(f"Enabled memory growth for {device}.")
This is particularly useful when the work requires only a fraction of the GPU memory or involve multiple integrations serving the GPU.
Verifying Configuration
After setting the visible devices, you can verify the current configuration using:
logical_devices = tf.config.experimental.list_logical_devices('GPU')
print("Configured devices:", logical_devices)
This will provide a listing of logical devices, showing only those GPUs that have been configured as visible.
Practical Considerations
There are several practical considerations to keep in mind:
- Always check the successful assignment of devices before any TensorFlow operations.
- If working in an environment with shared GPU resources (like a multi-user server), it's crucial to specify visible devices carefully to avoid performance conflicts.
- If TensorFlow is unable to access GPUs, it defaults to CPU execution, which is less efficient for deep learning tasks.
- The APIs discussed are marked as experimental; thus, they may change in future TensorFlow versions. Keep an eye on the official TensorFlow documentation for updates.
By setting the visible devices as needed, TensorFlow-based applications can be tuned for performance and compatibility with diverse computational environments. Whether for model training, inference, or shared environment uses, this configuration aids in effective use of available GPU resources.
