tensorflow2-extra
- Version:
2.7.0, 2.5.2
- Category:
ai
- Cluster:
Loki
Description
TensorFlow 2.7.0 is a powerful open-source platform for machine learning and deep learning. This build includes GPU support with CUDA 11.2, and additional features typically included in the tensorflow[extra] install target — such as tools for distributed training, data pipelines, and experimental modules.
Key features:
Keras 2.x high-level APIs
TensorBoard integration
SavedModel export and serving
Accelerated training using cuDNN, cuBLAS, and Tensor Cores
AutoGraph and XLA optimizations
GPU execution via CUDA 11.2
Python 3.9 compatibility
Documentation
$ python -c "import tensorflow as tf; print(tf.__version__)"
Common APIs:
------------
tf.keras.Model → High-level model class
tf.data.Dataset → Input pipelines
tf.function → Compiled graph execution
tf.distribute → Distributed strategy APIs
tf.summary → Logging for TensorBoard
CLI:
$ tensorboard --logdir=logs/
Help:
>>> help(tf)
>>> tf.config.list_physical_devices('GPU')
Examples/Usage
Load the module:
$ module load tensorflow2-extra-py39-cuda11.2-gcc9/2.7.0
Run a quick test in Python:
import tensorflow as tf
print("TensorFlow version:", tf.__version__)
print("Available GPUs:", tf.config.list_physical_devices('GPU'))
Example: build and train a simple model:
import tensorflow as tf
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
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape=(28, 28)),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(10)
])
model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
model.fit(x_train, y_train, epochs=1)
model.evaluate(x_test, y_test)
Unload the module:
$ module unload tensorflow2-extra-py39-cuda11.2-gcc9/2.7.0
Installation
Source code is obtained from TensorFlow