noiop.blogg.se - Lab master distilling

Lab master distilling update#
Lab master distilling full#
Lab master distilling code#

Clone DistillerĬlone the Distiller code repository from github: These instructions will help get Distiller up and running on your local machine. Export to ONNX (export of quantized models pending ONNX standardization).Logging to the console, text file and TensorBoard-formatted file.See the research papers discussions in our model-zoo. Sample implementations of published research papers, using library-provided building blocks.This notebook creates performance indicator graphs from model data.Take a look at this notebook, which compares visual aspects of dense and sparse Alexnet models.The graphs and visualizations you see on this page originate from the included Jupyter notebooks. A set of Jupyter notebooks to plan experiments and analyze compression results.Export statistics summaries using Pandas dataframes, which makes it easy to slice, query, display and graph the data.Training with knowledge distillation, in conjunction with the other available pruning / regularization / quantization methods.Support for quantization-aware training in the loop.Post-training quantization of trained full-precision models, dynamic and static (statistics-based).No need to re-write the model for different quantization methods. Automatic mechanism to transform existing models to quantized versions, with customizable bit-width configuration for different layers.Group Lasso an group variance regularization.Examine the data from some of the networks we analyzed, using this notebook.

Element-wise and filter-wise pruning sensitivity analysis (using L1-norm thresholding).

This dependency injection design decouples the Distiller scheduler and library from future extensions of algorithms.

The compression schedule is expressed in a YAML file so that a single file captures the details of experiments.

Automatic gradual schedule (AGP) for pruning individual connections and complete structures.

Lab master distilling full#

greedy layer by layer pruning to full model pruning).

Easily control what is performed each training step (e.g.One-shot and iterative pruning (and fine-tuning) are supported.Flexible scheduling of pruning, regularization, and learning rate decay (compression scheduling).Model thinning (AKA "network garbage removal") to permanently remove pruned neurons and connections.

Lab master distilling update#

Dual weight copies (compute loss on masked weights, but update unmasked weights).

Soft (mask on forward-pass only) and hard pruning (permanently disconnect neurons).

Structure-ranking with using weights or activations criteria (Lp-norm, APoZ, gradients, random, etc.).

Fully-connected: column-wise and row-wise structured pruning.

Convolution: 2D (kernel-wise), 3D (filter-wise), 4D (layer-wise), and channel-wise structured pruning.

Element-wise pruning using magnitude thresholding, sensitivity thresholding, target sparsity level, and activation statistics.

Distiller provides a PyTorch environment for prototyping and analyzing compression algorithms, such as sparsity-inducing methods and low-precision arithmetic. Network compression can reduce the memory footprint of a neural network, increase its inference speed and save energy. Distiller is an open-source Python package for neural network compression research.