Using AIminify

Using aiminify.minify in your Python code

To compress a model using a Python script it is possible to import aiminify and call the minify(...) function. As shown in the example below:
from aiminify import minify
from your_project import model
 
compressed_model, _ = minify(model)
Depending on the type of model you are compressing it can be stored using the appropriate methods. As aiminify supports PyTorch and Tensorflow as backends, the different storing methods are implemented in the save_model() function.
#
# Using PyTorch as a backend
#
from aiminify import minify
from aiminify import save_model
from your_project import model
 
# For Quantized Torch models, the input shape is needed to save the model
compressed_model, _ = minify(model)
save_model(compressed_model, "./compressed-pytorch-model.pt", input_shape)
 
# For non Quantized models, you can save without the input shape
compressed_model, _ = minify(model, quantization=false)
save_model(compressed_model, "./compressed-pytorch-model.pt")
#
# Using Tensorflow as a backend
#
from aiminify import minify
from aiminify import save_model
from your_project import model
 
compressed_model, _ = minify(model)
save_model(compressed_model, "./compressed-tensorflow-model.keras")

aiminify.minify arguments

Title
Title
Title
Name
Type and default value
model
Any, required
The model to compress
compression_strength
int(0, 5), default 3
Compression strength, setting the strength of the pruner
training_generator
Any, default None
Training set used for fine tuning
validation_generator
Any, default None
Validation set used for fine tuning
optimizer
Any, default None
Optimizer used for fine tuning
loss_function
Any, default None
Loss function used for fine tuning
verbose
int(0, 5), default 0
Verbosity level
quantization
bool, default False
Quantize model
fine_tune
bool, default False
Fine tune the model after compression
precision
str, default fp32
Precision for fine tuning. Other option is mixed
accumulation_steps
int, default 1
Number of training steps to accumulate gradients before a backward update
smart_pruning
bool, default True
Use a smart strategy for determining the amount of filters to prune instead of flat pruning x % of all filters.
debug_mode
bool, default False
Debug logging mode
The return value of minify is (compressed_model, feedback_dictionary). compressed_model is, as the name would suggest, your compressed model using the same backend as the input (Tensorflow, PyTorch). feedback_dictionary contains logs and miscellaneous messages from the compression algorithm.
training_generator, validation_generator, loss_function can be specific to the backend you’re using. For example when using pytorch the training_generator and validation_generator need to be a subclass of torch.utils.data.Dataset. For Tensorflow these can be a subclass of tensorflow.data.Dataset. Similar with loss_function for PyTorch this can be any member of torch.nn.modules.loss and for Tensorflow this can be any implementation of tf.keras.losses.Loss.