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#: ../../source/compression/overview.rst:2
msgid "Overview of NNI Model Compression"
msgstr ""

#: ../../source/compression/overview.rst:4
msgid ""
"Deep neural networks (DNNs) have achieved great success in many tasks "
"like computer vision, nature launguage processing, speech processing. "
"However, typical neural networks are both computationally expensive and "
"energy-intensive, which can be difficult to be deployed on devices with "
"low computation resources or with strict latency requirements. Therefore,"
" a natural thought is to perform model compression to reduce model size "
"and accelerate model training/inference without losing performance "
"significantly. Model compression techniques can be divided into two "
"categories: pruning and quantization. The pruning methods explore the "
"redundancy in the model weights and try to remove/prune the redundant and"
" uncritical weights. Quantization refers to compress models by reducing "
"the number of bits required to represent weights or activations. We "
"further elaborate on the two methods, pruning and quantization, in the "
"following chapters. Besides, the figure below visualizes the difference "
"between these two methods."
msgstr ""

#: ../../source/compression/overview.rst:19
msgid ""
"NNI provides an easy-to-use toolkit to help users design and use model "
"pruning and quantization algorithms. For users to compress their models, "
"they only need to add several lines in their code. There are some popular"
" model compression algorithms built-in in NNI. On the other hand, users "
"could easily customize their new compression algorithms using NNI’s "
"interface."
msgstr ""

#: ../../source/compression/overview.rst:24
msgid "There are several core features supported by NNI model compression:"
msgstr ""

#: ../../source/compression/overview.rst:26
msgid "Support many popular pruning and quantization algorithms."
msgstr ""

#: ../../source/compression/overview.rst:27
msgid ""
"Automate model pruning and quantization process with state-of-the-art "
"strategies and NNI's auto tuning power."
msgstr ""

#: ../../source/compression/overview.rst:28
msgid ""
"Speedup a compressed model to make it have lower inference latency and "
"also make it smaller."
msgstr ""

#: ../../source/compression/overview.rst:29
msgid ""
"Provide friendly and easy-to-use compression utilities for users to dive "
"into the compression process and results."
msgstr ""

#: ../../source/compression/overview.rst:30
msgid "Concise interface for users to customize their own compression algorithms."
msgstr ""

#: ../../source/compression/overview.rst:34
msgid "Compression Pipeline"
msgstr ""

#: ../../source/compression/overview.rst:42
msgid ""
"The overall compression pipeline in NNI is shown above. For compressing a"
" pretrained model, pruning and quantization can be used alone or in "
"combination. If users want to apply both, a sequential mode is "
"recommended as common practise."
msgstr ""

#: ../../source/compression/overview.rst:46
msgid ""
"Note that NNI pruners or quantizers are not meant to physically compact "
"the model but for simulating the compression effect. Whereas NNI speedup "
"tool can truly compress model by changing the network architecture and "
"therefore reduce latency. To obtain a truly compact model, users should "
"conduct :doc:`pruning speedup <../tutorials/pruning_speedup>` or "
":doc:`quantizaiton speedup <../tutorials/quantization_speedup>`. The "
"interface and APIs are unified for both PyTorch and TensorFlow. Currently"
" only PyTorch version has been supported, and TensorFlow version will be "
"supported in future."
msgstr ""

#: ../../source/compression/overview.rst:52
msgid "Model Speedup"
msgstr ""

#: ../../source/compression/overview.rst:54
msgid ""
"The final goal of model compression is to reduce inference latency and "
"model size. However, existing model compression algorithms mainly use "
"simulation to check the performance (e.g., accuracy) of compressed model."
" For example, using masks for pruning algorithms, and storing quantized "
"values still in float32 for quantization algorithms. Given the output "
"masks and quantization bits produced by those algorithms, NNI can really "
"speedup the model."
msgstr ""

#: ../../source/compression/overview.rst:59
msgid "The following figure shows how NNI prunes and speeds up your models."
msgstr ""

#: ../../source/compression/overview.rst:67
msgid ""
"The detailed tutorial of Speedup Model with Mask can be found :doc:`here "
"<../tutorials/pruning_speedup>`. The detailed tutorial of Speedup Model "
"with Calibration Config can be found :doc:`here "
"<../tutorials/quantization_speedup>`."
msgstr ""

#: ../../source/compression/overview.rst:72
msgid ""
"NNI's model pruning framework has been upgraded to a more powerful "
"version (named pruning v2 before nni v2.6). The old version (`named "
"pruning before nni v2.6 "
"<https://nni.readthedocs.io/en/v2.6/Compression/pruning.html>`_) will be "
"out of maintenance. If for some reason you have to use the old pruning, "
"v2.6 is the last nni version to support old pruning version."
msgstr ""

