TensorFlow
SOL’s TensorFlow integration supports to translate tf.Function, tf.Module, Keras and tf.saved_model models into SOL models. If your tf.saved_model has multiple signatures, you need to select the preferred one using sol.optimize(my_saved_model.signatures['my_signature']). By default SOL uses the tf.saved_model.__call__ function.
import tensorflow as tf
import sol
import tensorflow.keras as keras
def AlexNet(input_shape=(224, 224, 3), format="channels_last"):
inputs = keras.Input(shape=(input_shape))
x = inputs
x = keras.layers.Conv2D (input_shape=input_shape, filters=64, kernel_size=(11,11), strides=(4,4), padding='same', activation='relu', data_format=format)(x)
x = keras.layers.MaxPooling2D (pool_size=3, strides=2, padding='valid', data_format=format)(x)
x = keras.layers.Conv2D (filters=192, kernel_size=5, strides=1, padding='same', activation='relu', data_format=format)(x)
x = keras.layers.MaxPooling2D (pool_size=3, strides=2, padding="valid", data_format=format)(x)
x = keras.layers.Conv2D (filters=384, kernel_size=3, strides=1, padding="same", activation='relu', data_format=format)(x)
x = keras.layers.Conv2D (filters=256, kernel_size=3, strides=1, padding="same", activation='relu', data_format=format)(x)
x = keras.layers.Conv2D (filters=256, kernel_size=3, strides=1, padding="same", activation='relu', data_format=format)(x)
x = keras.layers.MaxPooling2D (pool_size=3, strides=2, padding="valid", data_format=format)(x)
x = keras.layers.Flatten (data_format=format)(x)
x = keras.layers.Dropout (rate=0.5)(x)
x = keras.layers.Dense (4096, input_shape=(256*6*6,), activation='relu')(x)
x = keras.layers.Dropout (rate=0.5)(x)
x = keras.layers.Dense (4096, activation="relu")(x)
x = keras.layers.Dense (1000)(x)
return keras.models.Model (inputs=inputs, outputs=x)
@tf.function(input_signature=[tf.TensorSpec([None, 224, 224, 3], dtype=tf.float32)])
def tf_function(input):
return ...
class TFModule(tf.Module):
def init(self):
super().__init__()
self.var = tf.Variable(...)
@tf.function(input_signature=[tf.TensorSpec([None, 224, 224, 3], dtype=tf.float32)])
def __call__(self, input):
return ...
with tf.device('/CPU:0'):
sol_model = sol.optimize(AlexNet(), batch_size=1)
# or
sol_model = sol.optimize(tf_function)
# or
sol_model = sol.optimize(TFModule())
# or
sol_model = sol.optimize(tf.saved_model.load("/path/to/saved/model"))
# Inference
output = sol_model(inputs)
# Training for Keras Models
sol_model.compile(...)
sol_model.fit(inputs, targets)
# Training for tf.Function and tf.Module
# TODO:
F.A.Q.
| What are the best configurations for using SOL with TensorFlow? |
|---|
If you are using SOL with TensorFlow on X86 you should set the following env vars:
OMP_NUM_THREADS=$(lscpu -b -p=Core,Socket | grep -v '^#' | sort -u | wc -l)
OMP_PROC_BIND=TRUE
TF_NUM_INTEROP_THREADS=1
|
| How can I define that the model input shall return a gradient? |
|---|
By default all inputs get no gradients assigned. If you want to override this behavior, use
sol_model = sol.optimize(model, requires_grad={"input_1", "whatever"})
|
| How can I override the model input shapes? |
|---|
By default all inputs use the input shapes defined in the model.
If you want to override this behavior, use
sol_model = sol.optimize(model, shapes={"input_1": [1, 2, 3], "whatever": [77, 3, 5]})
|
| How can I update/downgrade to another TensorFlow version? |
|---|
Before switching version, please have a look at the compatibility list if your TensorFlow version is
supported by SOL. If yes, and if you are using SOL with the NEC SX-Aurora
TSUBASA, you can switch TensorFlow using pip3 install
veda-tensorflow~={VERSION}. If you are using SOL with any other device,
then you can just use pip3 install tensorflow~={VERSION}.
|
| How do I store/load a Tensorflow Keras model? |
|---|
|
SOL model's cannot be stored directly. For storing/loading a SOL Keras model, use
model.save_weights(...) and model.load_weights(...) methods.
# Storing
sol_model = sol.optimize(keras_model)
sol_model.save_weights(checkpoint_path)
# Loading
sol_model = sol.optimize(keras_model)
sol_model.load_weights(checkpoint_path)
More information on loading/storing the weights can be found
here
|
| Which activations/recurrent_activations are supported by RNN layers? |
|---|
SOL currently supports [None, 'linear', 'tanh', 'sigmoid', 'relu'].
If you need another RNN activation function, please get in contact with us.
|
Supported Layers
Please refer to https://www.tensorflow.org/api/stable for how these functions are used. This documentation only contains which layers, functions and tensor functionality is currently implemented within SOL.
Layers
Tested Models
- keras.applications
- convnext
- densenet
- efficientnet
- efficientnet_v2
- inception_resnet_v2
- inception_v3
- mobilenet
- mobilenet_v2
- mobilenet_v3
- nasnet
- regnet
- resnet
- vgg [16, 19]
- AlexNet
- SqueezeNet [1.0, 1.1]