From 3a1e2a329878a13855a22fe7a428f53349d44b3d Mon Sep 17 00:00:00 2001 From: Mark Kurtz Date: Fri, 26 Feb 2021 13:25:53 -0500 Subject: [PATCH] Update for docs 0.1.1 release - latest from sparsezoo, deepsparse, sparseml, sparsify - rebuild docs in repo - adjust makefile to not delete rst files in docs source --- .buildinfo | 4 + Makefile | 1 - _images/why-nm.jpg | Bin 0 -> 154636 bytes _sources/faqs.md.txt | 173 ++++++++++ _sources/getstarted.md.txt | 75 ++++ _sources/glossary.md.txt | 144 ++++++++ _sources/index.rst.txt | 38 +- deepsparse/_modules/deepsparse/cpu.html | 12 +- deepsparse/_modules/deepsparse/engine.html | 53 ++- deepsparse/_sources/index.rst.txt | 4 +- deepsparse/api/deepsparse.html | 20 +- deepsparse/index.html | 2 +- deepsparse/searchindex.js | 2 +- faqs.html | 355 +++++++++++++++++++ genindex.html | 19 +- getstarted.html | 325 ++++++++++++++++++ glossary.html | 382 +++++++++++++++++++++ index.html | 111 +++--- objects.inv | Bin 254 -> 299 bytes search.html | 19 +- searchindex.js | 2 +- sparseml/_sources/index.rst.txt | 2 +- sparseml/index.html | 2 +- sparseml/searchindex.js | 2 +- sparsezoo/_sources/index.rst.txt | 2 +- sparsezoo/index.html | 2 +- sparsezoo/searchindex.js | 2 +- sparsify/_sources/index.rst.txt | 2 +- sparsify/images/image_0.jpg | Bin 0 -> 15154 bytes sparsify/images/image_1.jpg | Bin 0 -> 130217 bytes sparsify/images/image_10.jpg | Bin 0 -> 69567 bytes sparsify/images/image_11.jpg | Bin 0 -> 68064 bytes sparsify/images/image_12.jpg | Bin 0 -> 51699 bytes sparsify/images/image_13.jpg | Bin 0 -> 67310 bytes sparsify/images/image_14.jpg | Bin 0 -> 160536 bytes sparsify/images/image_15.jpg | Bin 0 -> 61132 bytes sparsify/images/image_15a.jpg | Bin 0 -> 25966 bytes sparsify/images/image_15b.jpg | Bin 0 -> 34172 bytes sparsify/images/image_16.jpg | Bin 0 -> 66895 bytes sparsify/images/image_17.jpg | Bin 0 -> 90990 bytes sparsify/images/image_18.jpg | Bin 0 -> 1493 bytes sparsify/images/image_19.jpg | Bin 0 -> 43353 bytes 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sparsify/images/image_46.jpg | Bin 0 -> 75310 bytes sparsify/images/image_47.jpg | Bin 0 -> 101173 bytes sparsify/images/image_48.jpg | Bin 0 -> 104805 bytes sparsify/images/image_49.jpg | Bin 0 -> 92328 bytes sparsify/images/image_5.jpg | Bin 0 -> 2135 bytes sparsify/images/image_50.jpg | Bin 0 -> 95841 bytes sparsify/images/image_51.jpg | Bin 0 -> 93726 bytes sparsify/images/image_52.jpg | Bin 0 -> 97442 bytes sparsify/images/image_53.jpg | Bin 0 -> 65919 bytes sparsify/images/image_54.jpg | Bin 0 -> 30615 bytes sparsify/images/image_55.jpg | Bin 0 -> 27339 bytes sparsify/images/image_56.jpg | Bin 0 -> 30821 bytes sparsify/images/image_57.jpg | Bin 0 -> 671 bytes sparsify/images/image_58.jpg | Bin 0 -> 671 bytes sparsify/images/image_59.jpg | Bin 0 -> 67899 bytes sparsify/images/image_6.jpg | Bin 0 -> 28282 bytes sparsify/images/image_60.jpg | Bin 0 -> 63421 bytes sparsify/images/image_61.jpg | Bin 0 -> 24838 bytes sparsify/images/image_62.jpg | Bin 0 -> 38297 bytes sparsify/images/image_63.jpg | Bin 0 -> 49624 bytes sparsify/images/image_64.jpg | Bin 0 -> 35623 bytes sparsify/images/image_65.jpg | Bin 0 -> 14244 bytes sparsify/images/image_7.jpg | Bin 0 -> 51301 bytes sparsify/images/image_8.jpg | Bin 0 -> 54335 bytes sparsify/images/image_9.jpg | Bin 0 -> 40248 bytes sparsify/index.html | 2 +- sparsify/searchindex.js | 2 +- 107 files changed, 1663 insertions(+), 96 deletions(-) create mode 100644 .buildinfo create mode 100644 _images/why-nm.jpg create mode 100644 _sources/faqs.md.txt create mode 100644 _sources/getstarted.md.txt create mode 100644 _sources/glossary.md.txt create mode 100644 faqs.html create mode 100644 getstarted.html create mode 100644 glossary.html create mode 100644 sparsify/images/image_0.jpg create mode 100644 sparsify/images/image_1.jpg create mode 100644 sparsify/images/image_10.jpg create mode 100644 sparsify/images/image_11.jpg create mode 100644 sparsify/images/image_12.jpg create mode 100644 sparsify/images/image_13.jpg create mode 100644 sparsify/images/image_14.jpg create mode 100644 sparsify/images/image_15.jpg create mode 100644 sparsify/images/image_15a.jpg create mode 100644 sparsify/images/image_15b.jpg create mode 100644 sparsify/images/image_16.jpg create mode 100644 sparsify/images/image_17.jpg create mode 100644 sparsify/images/image_18.jpg create mode 100644 sparsify/images/image_19.jpg create mode 100644 sparsify/images/image_2.jpg create mode 100644 sparsify/images/image_20.jpg create mode 100644 sparsify/images/image_21.jpg create mode 100644 sparsify/images/image_21a.jpg create mode 100644 sparsify/images/image_21b.jpg create mode 100644 sparsify/images/image_22.jpg create mode 100644 sparsify/images/image_23.jpg create mode 100644 sparsify/images/image_24.jpg create mode 100644 sparsify/images/image_25.jpg create mode 100644 sparsify/images/image_26.jpg create mode 100644 sparsify/images/image_27.jpg create mode 100644 sparsify/images/image_28.jpg create mode 100644 sparsify/images/image_29.jpg create mode 100644 sparsify/images/image_29a.jpg create mode 100644 sparsify/images/image_29b.jpg create mode 100644 sparsify/images/image_29c.jpg create mode 100644 sparsify/images/image_29d.jpg create mode 100644 sparsify/images/image_29e.jpg create mode 100644 sparsify/images/image_3.jpg create mode 100644 sparsify/images/image_30.jpg create mode 100644 sparsify/images/image_31.jpg create mode 100644 sparsify/images/image_31a.jpg create mode 100644 sparsify/images/image_32.jpg create mode 100644 sparsify/images/image_33.jpg create mode 100644 sparsify/images/image_34.jpg create mode 100644 sparsify/images/image_35.jpg create mode 100644 sparsify/images/image_36.jpg create mode 100644 sparsify/images/image_36a.jpg create mode 100644 sparsify/images/image_37.jpg create mode 100644 sparsify/images/image_38.jpg create mode 100644 sparsify/images/image_39.jpg create mode 100644 sparsify/images/image_4.jpg create mode 100644 sparsify/images/image_40.jpg create mode 100644 sparsify/images/image_41.jpg create mode 100644 sparsify/images/image_42.jpg create mode 100644 sparsify/images/image_43.jpg create mode 100644 sparsify/images/image_44.jpg create mode 100644 sparsify/images/image_45.jpg create mode 100644 sparsify/images/image_46.jpg create mode 100644 sparsify/images/image_47.jpg create mode 100644 sparsify/images/image_48.jpg create mode 100644 sparsify/images/image_49.jpg create mode 100644 sparsify/images/image_5.jpg create mode 100644 sparsify/images/image_50.jpg create mode 100644 sparsify/images/image_51.jpg create mode 100644 sparsify/images/image_52.jpg create mode 100644 sparsify/images/image_53.jpg create mode 100644 sparsify/images/image_54.jpg create mode 100644 sparsify/images/image_55.jpg create mode 100644 sparsify/images/image_56.jpg create mode 100644 sparsify/images/image_57.jpg create mode 100644 sparsify/images/image_58.jpg create mode 100644 sparsify/images/image_59.jpg create mode 100644 sparsify/images/image_6.jpg create mode 100644 sparsify/images/image_60.jpg create mode 100644 sparsify/images/image_61.jpg create mode 100644 sparsify/images/image_62.jpg create mode 100644 sparsify/images/image_63.jpg create mode 100644 sparsify/images/image_64.jpg create mode 100644 sparsify/images/image_65.jpg create mode 100644 sparsify/images/image_7.jpg create mode 100644 sparsify/images/image_8.jpg create mode 100644 sparsify/images/image_9.jpg diff --git a/.buildinfo b/.buildinfo new file mode 100644 index 00000000000..f65390e42af --- /dev/null +++ b/.buildinfo @@ -0,0 +1,4 @@ +# Sphinx build info version 1 +# This file hashes the configuration used when building these files. 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z2byP6ny_-v+u*qUt|w+db|#w1*KXziv|Oo z9OBDkhiIys)CvGtP$b*41f?7FvL=IUvI_XXz<_Bv zVvBZz%qKR+14`dBPa&Tz!%Z$sCgq&oM+*LWRA~ydBquo&;F3F=f zg93vLMfV0z+W6INciunJuZyLM$-ykVHjF3vM2XXBhY@&81TfL8DQ_sH38o~bDsR>{ ztE`CBpOHTA8+0sSAa`Sf(sab@0x|$AWveb@t-`4$R98YQyw>4!u}=xPx`g!RVQoCi zWS=Ux#D+!8)`gHF5ZQbjbtDHo4GlfGY5n;%8a_Gy1J+8KOp!3@jPW*R@I#*RHcZ5h)uAtJIjQ;H?9v zZ7x1YiBGSFS@;AR1%Qh@UPo}H1;2g7jy-`>^~9s*;3Y+Dc?JP!TTAl|E6{abFYwFp zXPv>|ii~Hho+!JOICI7g@vK-YJbe_tT(0(ob0XD(^S3MzgRjd*#x6!A8+8q`RTnqg wu{dV-dCT$HS^{k{bh&}VErPFxHIqdd$k#;wS?Zb<8^<_O!pTX#^Y7IE00k|lI{*Lx literal 0 HcmV?d00001 diff --git a/_sources/faqs.md.txt b/_sources/faqs.md.txt new file mode 100644 index 00000000000..f3c89eab657 --- /dev/null +++ b/_sources/faqs.md.txt @@ -0,0 +1,173 @@ + + +# FAQs + +## General Product FAQs + +**What is Neural Magic?** + +Founded by a team of award-winning MIT computer scientists and funded by Comcast Ventures, Andreessen Horowitz, NEA, Pillar VC, and Amdocs, Neural Magic is the creator and maintainer of the Deep Sparse Platform. It has several components, including the [DeepSparse Engine](https://docs.neuralmagic.com/deepsparse), a CPU runtime that runs sparse models at GPU speeds. To enable companies the ability to use ubiquitous and unconstrained CPU resources, Neural Magic includes [Sparsify](https://docs.neuralmagic.com/sparsify), [SparseML](https://docs.neuralmagic.com/sparseml), and the [SparseZoo](https://docs.neuralmagic.com/sparsezoo), open-sourced model optimization technologies that allow users to achieve performance breakthroughs, at scale, with all the flexibility of software. + +**What is the DeepSparse Engine?** + +The DeepSparse Engine, created by Neural Magic, is a general purpose engine for machine learning, enabling machine learning to be practically run in new places, on new kinds of workloads. It delivers state of art, GPU-class performance for the deep learning applications running on x86 CPUs. The DeepSparse Engine achieves its performance using breakthrough algorithms that reduce the computation needed for neural network execution and accelerate the resulting memory-bound computation. + +**Why Neural Magic?** + +Learn more about Neural Magic and the DeepSparse Engine (formerly known as the Neural Magic Inference Engine). + +[![Watch the Why Neural Magic video](why-nm.jpg)](https://youtu.be/zJy_8uPZd0o) + +**How does Neural Magic make it work?** + +This is an older webinar (50m) where we went through the process of optimizing and deploying a model; we’ve enhanced our software since the recording went out but this will give you some background: [https://youtu.be/UhmmHTsfrzI](https://youtu.be/UhmmHTsfrzI) + +**Does Neural Magic support training of learning models?** + +Neural Magic does not support training of deep learning models at this time. We do see value in providing a consistent CPU environment for our end users to train and infer on for their deep learning needs, and have added this to our engineering backlog. + +**Do you have version compatibility on TensorFlow?** + +Our inference engine supports all versions of TensorFlow 1.x.; support for TensorFlow 2.0 is coming soon. We have specific performance improvements for SSD models currently for TensorFlow 1.12. + +**Do you run on AMD hardware?** + +The DeepSparse Engine is validated to work on x86 Intel (Haswell generation and later) and AMD CPUs running Linux. It is highly recommended to run on a CPU with AVX-512 instructions available for optimal algorithms to be enabled. Specific support details for some algorithms over different microarchitectures [is available](https://docs.neuralmagic.com/deepsparse/hardware.html). + +We are open to opportunities to expand our support footprint for different CPU-based processor architectures, based on market adoption and deep learning use cases. + +**Do you run on ARM architecture?** + +We currently do not support ARM and it’s on the Neural Magic roadmap; however, we’d still like to hear your use cases. [Contact us to continue the conversation](https://neuralmagic.com/contact/). + +**To what use cases is the Deep Sparse Platform best suited?** + +We focus on the models and use cases related to computer vision due to cost sensitivity and both real time and throughput constraints. The belief now is GPUs are required for deployment. + +**What types of models does Neural Magic support?** + +Today, we offer support for CNN-based computer vision models, specifically classification and object detection model types. We are continuously adding models to [our supported model list and SparseZoo](https://docs.neuralmagic.com/sparsezoo). Additionally, we are investigating model architectures beyond computer vision such as NLP models like BERT. + +**Is dynamic shape supported?** + +Dynamic shape is currently not supported; be sure to use models with fixed inputs and compile the model for a particular batch size. Dynamic shape and dynamic batch sizes are on the Neural Magic roadmap; [subscribe for updates](https://neuralmagic.com/subscribe/). + +**Can multiple model inferences be executed?** + +Model inferences are executed as a single stream; concurrent execution is unsupported at this time. + +___ + +## Benchmarking FAQs + +**What is the average estimated savings for users??** + +This will vary but, in some cases, we are seeing 3x-10x savings. Typically, we offer 5-6x more price performance than hardware accelerators. + +**Do you have benchmarks to compare and contrast?** + +Yes. Check out our [benchmark demo video](https://neuralmagic.com/blog/neural-magic-demo/) or [contact us](https://neuralmagic.com/contact/) to discuss your particular performance requirements. If you’d rather observe performance for yourself, [head over to the Neural Magic GitHub repo](https://github.com/neuralmagic) to check out our tools and generate your own benchmarks in your environment. + +**Do you publish ML Perf inference benchmarks?** + +We are in the process of building the test harness to support ML Perf tests. + +___ + +## Infrastructure FAQs + +**Which instruction sets are supported and do we have to enable certain settings?** + +AVX-512, AVX2, and VNNI. The DeepSparse Engine will automatically utilize the most effective available instruction set for the task. Generally, if AVX-512 is available then we have no reason to use AVX2 instruction set. AVX-512 VNNI only comes into use for quantized models i.e., INT8 or UINT8. + +**Are you suitable for edge deployments (i.e., in-store devices, cameras)?** + +Yes, absolutely. We can run anywhere you have a CPU with x86 instructions, including on bare metal, in the cloud, on-prem, or at the edge. Additionally, our model optimization tools are able to reduce the footprint of models across all architectures. We only guarantee performance in the DeepSparse Engine. + +We’d love to hear from users highly interested in ML performance. If you want to chat about your use cases or how others are leveraging the Deep Sparse Platform, [please reach out](feedback@neuralmagic.com). Or simply head over to the [Neural Magic GitHub repo](https://github.com/neuralmagic) and check out our tools. + +**Do you have available solutions or applications on the Microsoft/Azure platform?** + +We deploy extremely easily. We are completely infrastructure-agnostic. As long as it has the "right" CPUs (80% of the entire Intel offering today), we can run on any cloud platform. For a Microsoft/Azure platform, we would just have to run Neural Magic on a Microsoft instance with the appropriate CPUs. + +**Can the inference engine run on Kubernetes? How do you containerize and take advantage of underlying infrastructure?** + +The DeepSparse Engine becomes a component of your model serving solution. As a result, it can simply plug into an existing CI/CD deployment pipeline. How you deploy, where you deploy, and what you deploy on becomes abstracted to the DeepSparse Engine so you can tailor your experiences. For example, you can run the DeepSparse Engine on a CPU VM environment, deployed via a Docker file and managed through a Kubernetes environment. + +___ + +## Model Compression FAQs + +**Can you comment on how you do pruning and effects on accuracy?** + +Neural networks are extremely over-parameterized, allowing most weights to be iteratively removed from the network without effect on accuracy. Eventually, though, pruning will begin affecting the overall capacity of the network, the degree of which varies based on the use case. However, this is something entirely under the data scientist control to choose whether to recover fully or to prune more for even better performance. + +For example, Neural Magic has been successful in removing 90% of ResNet-50 weights with no loss in accuracy. For more background on techniques that have informed our methodologies, check out this paper co-written by Neural Magic, _[WoodFisher: Efficient Second-Order Approximation for Neural Network Compression](https://arxiv.org/abs/2004.14340)_. + +**When does sparsification actually happen?** + +In a scenario in which you want to sparsify and then run your own model in the DeepSparse Engine, you would first sparsify your model to achieve the desired level of performance and accuracy using Neural Magic’s Sparsify and SparseML tooling. + +**What does the sparsification process look like?** + +Neural Magic’s Sparsify and SparseML tooling, at its core, uses well-established state-of-the-art research principles such as [Gradual Magnitude Pruning](https://neuralmagic.com/blog/pruning-gmp/) (GMP) to sparsify models. This is an iterative process in which groups of important weights are pruned away and then the network is allowed to recover. To significantly simplify the process, we offer tools and guidance for you to achieve the best performance possible. To peruse research papers contributed by Neural Magic staff, [check them out](https://neuralmagic.com/resources/technical-papers/). Or head over to the [Neural Magic GitHub repo](https://github.com/neuralmagic) to get started! + +**How does sparsification work in relation to TensorFlow?** + +Today, we are able to sparsify models trained in popular deep learning libraries like TensorFlow. Our unique approach works with the output supplied by the model library and provides layer sparsification techniques that then can be compiled in the existing library framework, within the user environment. + +**When using your software to transfer learn, what about other hyperparameters? Are you just freezing other layers?** + +For transfer learning, our tooling allows you to save the sparse architecture learned from larger datasets. Other hyperparameters are fully under your control and allow you the flexibility to easily freeze layers as well. + +**Do you support INT8 and INT16 (quantized) operations?** + +Currently, the DeepSparse Engine runs at FP32 and has some support for INT8. With the release of the Intel Cascade Lake generation chips and later, Intel CPUs now include VNNI instructions and support both INT8 and INT16 operations. On machines with VNNI support, the engine has INT8 support for the ONNX operators QLinearConv, QuantizeLinear, DequantizeLinear, and QLinearMatMul with constant weights. The DeepSparse Engine also supports 8-bit QLinearAdd, an ONNX Runtime custom operator. + +**Do you support FP16 (half precision) operations?** + +Neural Magic is waiting on support from Intel; we will assess inclusion into our roadmap at the appropriate time. + +___ + +## Runtime FAQs + +**Do users have to do any model conversion before using the DeepSparse Engine?** + +DeepSparse Engine executes on an ONNX (Open Neural Network Exchange) representation of a deep learning model. Our software allows you to produce an ONNX representation. If working with PyTorch, we use the built-in ONNX export and for TensorFlow, we convert from a standard exported protobuf file to ONNX. Outside of those frameworks, you would need to convert your model to ONNX first before passing it to the DeepSparse Engine. + +**Why is ONNX the file format used by Neural Magic?** + +ONNX (Open Neural Network Exchange) is emerging as a standard, open-source format for model representation. Based on the breadth of vendors supporting ONNX as well as the health of open-source community contributions, we believe ONNX offers a compelling solution for the market. + +**Are your users using ONNX runtime already?** + +End users are using a wide variety of runtimes, both open source and proprietary. Neural Magic is focused on ensuring we are open and flexible, to allow our users to achieve deep learning performance regardless of how they choose to build, deploy, and run their models. + +**What is the accuracy loss, if any, on the numbers Neural Magic demonstrates?** + +Results will depend on your use case and specific requirements. We are capable of maintaining 100% baseline accuracy. In cases where accuracy is not as important as performance, you can use our model optimization tools to further speed up the model at the expense of accuracy and weigh the tradeoffs. + +If you need sparsification, we provide the tooling for tradeoffs between accuracy and performance based on your specific requirements. + +**For the runtime engine, is Neural Magic modifying the architecture in any way or just optimizing the instruction set at that level?** + +Specifically for sparsification, our software keeps the architecture intact and changes the weights. For running dense, we do not change anything about the model. + +**For a CPU are you using all the cores?** + +The DeepSparse Engine optimizes _how_ the model is run on the infrastructure resources applied to it. But, the Neural Magic does not optimize for the number of cores. You are in control to specify how much of the system Neural Magic will use and run on. Depending on your goals (latency, throughput, and cost constraints), you can optimize your pipeline for maximum efficiency. diff --git a/_sources/getstarted.md.txt b/_sources/getstarted.md.txt new file mode 100644 index 00000000000..c873206f24e --- /dev/null +++ b/_sources/getstarted.md.txt @@ -0,0 +1,75 @@ + + +# Get Started + +## Overview + +Neural Magic’s vision is no-hardware AI, shattering the hardware barriers that hold back the field of machine learning. With Neural Magic’s novel algorithms that enable convolutional neural networks to run on commodity CPUs – at GPU speeds and better – data scientists no longer have to compromise on model design and input size, or deal with scarce and costly GPU resources. Neural Magic is making the power of deep learning simple, accessible, and affordable for anyone. As a part of this next great unlock of machine learning, data scientists will ask bigger questions, challenge norms, and unleash a new class of AI applications that live at the edge of imagination. + +To deliver on this vision, there are several components to the Deep Sparse Platform: + +1. [Sparsify](https://docs.neuralmagic.com/sparsify): Easy-to-use UI for automatically sparsifying neural networks and creating sparsification recipes for better inference performance and a smaller footprint +2. [SparseML](https://docs.neuralmagic.com/sparseml): Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models +3. [SparseZoo](https://docs.neuralmagic.com/sparsezoo): Neural network model repository for highly sparse and sparse-quantized models with matching sparsification recipes +4. [DeepSparse Engine](https://docs.neuralmagic.com/deepsparse): Neural network inference engine that delivers GPU-class performance for sparsified models on CPUs + +Our inference engine and model optimization technologies enable companies to use ubiquitous and unconstrained CPU resources to achieve performance breakthroughs, at scale, with all the flexibility of software. + +## Supported Architectures & Frameworks + + + + + + + + + + + + + + + + + + +
+ Computer Vision Applications +
Sample Models + ResNets, MobileNets, EfficientNets, Single-Shot Detectors (SSDs) +
Use Cases (Domains) + Image Classification, Object Detection +
Frameworks + ONNX, Keras, PyTorch, TensorFlow +
+ +Today, we offer support for convolutional neural network-based computer vision models, specifically classification and object detection model types such as [the models in SparseZoo](https://docs.neuralmagic.com/sparsezoo/models.html). + +We are continuously exploring models to add to our supported [model list](https://docs.neuralmagic.com/sparsezoo/models.html) and SparseZoo including model architectures beyond computer vision. Popular NLP models such as BERT are on the Neural Magic roadmap; [subscribe for updates](http://neuralmagic.com/subscribe). + +### Notes + +#### PyTorch and ONNX + +Sparsify and the DeepSparse Engine inputs are standardized on the ONNX format. PyTorch has native ONNX export and requires fewer steps than other supported frameworks, such as [Keras or TensorFlow](https://docs.neuralmagic.com/sparseml/quicktour.html#exporting-to-onnx). If you have flexibility in frameworks, consider PyTorch to start. + +#### Model Considerations + +Dynamic shape is currently not supported; be sure to use models with fixed inputs and compile the model for a particular batch size. Dynamic shape and dynamic batch sizes are on the Neural Magic roadmap; [subscribe for updates](http://neuralmagic.com/subscribe). + +Model inferences are executed as a single stream; concurrent execution is unsupported at this time. diff --git a/_sources/glossary.md.txt b/_sources/glossary.md.txt new file mode 100644 index 00000000000..96ec65e640f --- /dev/null +++ b/_sources/glossary.md.txt @@ -0,0 +1,144 @@ + + +# Glossary + +The machine learning community includes a vast array of terminology that can have variations in meaning depending on context. This glossary is not intended as a comprehensive list, but rather a clarification of terms you may encounter with Neural Magic and machine learning. + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
AutoMLAutomated Machine Learning. Platform that aims to reduce or eliminate the need for skilled data scientists to build ML and deep learning models. Google AutoML, for example, is a suite of cloud-based ML products.
AVX2Advanced Vector Extensions 2. Instruction set used for applications on an Intel CPU.
AVX-512Advanced Vector Extensions. Instruction set on Intel CPUs that impacts compute, storage, and network instructions. AVX-512 yields higher performance for demanding computational tasks.
Cascade Lake ChipsIntel CPU chips up to 28 cores that are improved for machine learning and added VNNI instructions. Cascade Lake Chips support FP16 and INT8 floating point operations.
Convolutional Neural Network (CNN)Artificial neural network used in image recognition and object detection as well as processing that is specifically designed to process pixel data.
Deep Learning (DL)Subset of machine learning in which artificial neural networks (algorithms inspired by the human brain) learn from large amounts of data.
Deep Learning FrameworksInterface, library, or tool that allows one to build deep learning models more easily and quickly without getting into details of underlying algorithms.
DLRMOpen-source Deep Learning Recommendation Model from Facebook.
Fully Connected NetworkNetwork in which every node in a layer (except the input and output layer) is connected to every node in the previous layer and following layer.
Image ClassificationSupervised learning problem to define a set of target classes (objects to identify in images) and train a model to recognize them using labeled example photos.
Image SegmentationIn computer vision, the process of partitioning a digital image into multiple segments to simplify and/or change the representation of an image into something more meaningful and easier to analyze.
InferenceProcess of using a trained machine learning algorithm to make predictions (done by machine learning engineers).
MobileNetsA family of mobile-first computer vision models for TensorFlow, designed to effectively maximize accuracy while being mindful of the restricted resources for an on-device or embedded application.
Model pipelinesIn machine learning deployment, multiple models chained together to achieve business goals (such as a detection model to select regions from an image for a later visual search model).
Model servingIn machine learning deployment, makes serving of models less expensive and faster to run by better using resources on the machine.
Multilayer Perceptron (MLP)A feedforward artificial neural network (ANN) model, composed of more than one perceptron, that maps sets of input data onto a set of appropriate outputs.
Neural NetworkSystem of hardware and/or software patterned after the operation of neurons in the human brain.
Object DetectionCategorization of an image based on the number of objects in the image and/or the location of the objects.
ONNXOpen Neural Network Exchange. Open-source inference engine that is a performance-focused complete scoring engine for ONNX models.
QuantizationThe process of approximating a neural network that uses floating-point numbers by a neural network of low bit width numbers. Quantization dramatically reduces the memory requirement and computational cost of using neural networks.
RecommendationsCategorization of an image based on relevant suggestions. This class of machine learning algorithms finds similarity between different images.
ResNetImage classification model that is structurally dense.
SparsificationA model optimization technique used to improve performance by reducing the number of nonperformance critical elements, vectors, and matrices.
SSDSingle Shot Detector. Convolutional neural network (CNN) algorithm for object detection that provides better balance between swiftness and precision. SSD runs CNN on an input image only one time and computes a feature map.
Structured pruningA method for compressing a neural network. Structured pruning alternates between removing channel connections and fine-tuning to reduce overall width of the network. Structured pruning severely limits the maximum sparsity that can be imposed on a network when compared with unstructured pruning.
TensorThe input to a convolutional layer. Tensor is a 3 or 4 dimensional representation of a 2D image.
TrainingThe process of feeding an ML algorithm with data to help identify and learn good values for all attributes involved.
U-NetFully convolutional network that does image segmentation (originally designed for medical image segmentation). The U-Net goal is to predict each pixel class.
Unstructured pruningA method for compressing a neural network. Unstructured pruning removes individual weight connections from a trained network. Software like Neural Magic's DeepSparse Engine runs these pruned networks faster.
VNNIVector Neural Network Instructions. New versions of Intel's CPU chips are optimized with VNNI, making them faster and more efficient for certain types of machine learning applications.
YOLOYou Only Look Once. Open-source type of CNN method of object detection that can recognize objects in images and videos swiftly.
diff --git a/_sources/index.rst.txt b/_sources/index.rst.txt index 9abfbfd06a1..373104d5e89 100644 --- a/_sources/index.rst.txt +++ b/_sources/index.rst.txt @@ -44,20 +44,44 @@ To file a docs issue for a specific software component, use its repo issue track +-------------------------------------------------------------------+--------------------------------------------------------+----------------------------------------------------------------+------------------------------------------------------------------+---------------------+ | Docs | GitHub | Issue Tracker | Releases | License | +===================================================================+========================================================+================================================================+==================================================================+=====================+ -| `SparseML `_ | `Repo `_ | `Queue `_ | `Index `_ | Apache 2.0 | +| `SparseML `_ | `Repo `_ | `Queue `_ | `Index `_ | Apache 2.0 | +-------------------------------------------------------------------+--------------------------------------------------------+----------------------------------------------------------------+------------------------------------------------------------------+---------------------+ -| `Sparsify `_ | `Repo `_ | `Queue `_ | `Index `_ | Apache 2.0 | +| `Sparsify `_ | `Repo `_ | `Queue `_ | `Index `_ | Apache 2.0 | +-------------------------------------------------------------------+--------------------------------------------------------+----------------------------------------------------------------+------------------------------------------------------------------+---------------------+ -| `SparseZoo `_ | `Repo `_ | `Queue `_ | `Index `_ | Apache 2.0 | +| `SparseZoo `_ | `Repo `_ | `Queue `_ | `Index `_ | Apache 2.0 | +-------------------------------------------------------------------+--------------------------------------------------------+----------------------------------------------------------------+------------------------------------------------------------------+---------------------+ -| `DeepSparse Engine `_ | `Repo `_ | `Queue `_ | `Index `_ | Neural Magic Engine | +| `DeepSparse Engine `_ | `Repo `_ | `Queue `_ | `Index `_ | Neural Magic Engine | +-------------------------------------------------------------------+--------------------------------------------------------+----------------------------------------------------------------+------------------------------------------------------------------+---------------------+ To contribute to Neural Magic documentation, please: -- Open a `GitHub issue `_ and give it the `documentation` label. -- Submit a `pull request `_ with your proposed documentation changes. +- Open a `GitHub issue `_ and give it the `documentation` label. +- Submit a `pull request `_ with your proposed documentation changes. - Get started with `writing and formatting on GitHub `_ -To learn more about Neural Magic, `check our website `_. +To learn more about Neural Magic, `check our website `_. + +.. toctree:: + :maxdepth: 3 + :caption: Get Started + + getstarted + +.. toctree:: + :maxdepth: 3 + :caption: Details + + Technical Papers + faqs + glossary + + +.. toctree:: + :maxdepth: 3 + :caption: Help + + Bugs, Feature Requests + Support, General Q&A + Neural Magic Docs + \ No newline at end of file diff --git a/deepsparse/_modules/deepsparse/cpu.html b/deepsparse/_modules/deepsparse/cpu.html index 5b9c8b05eab..aee3734a80e 100644 --- a/deepsparse/_modules/deepsparse/cpu.html +++ b/deepsparse/_modules/deepsparse/cpu.html @@ -197,12 +197,11 @@

Source code for deepsparse.cpu

 code related to detecting the details of the currently available cpu
 """
 
-import json
+from typing import Tuple
 import os
-import subprocess
 import sys
-from typing import Tuple
-
+import subprocess
+import json
 
 __all__ = ["cpu_details", "VALID_VECTOR_EXTENSIONS"]
 
@@ -314,9 +313,8 @@ 

Source code for deepsparse.cpu

         )
         if avx_type_override not in VALID_VECTOR_EXTENSIONS:
             raise OSError(
-                "neuralmagic: invalid avx instruction set '{}' must be one of {}.".format(
-                    avx_type_override, ",".join(VALID_VECTOR_EXTENSIONS)
-                )
+                "neuralmagic: invalid avx instruction set '{}' must be one of {}."
+                .format(avx_type_override, ",".join(VALID_VECTOR_EXTENSIONS))
             )
         arch.override_isa(avx_type_override)
 
diff --git a/deepsparse/_modules/deepsparse/engine.html b/deepsparse/_modules/deepsparse/engine.html
index 7066a3d2e13..efde5367144 100644
--- a/deepsparse/_modules/deepsparse/engine.html
+++ b/deepsparse/_modules/deepsparse/engine.html
@@ -220,7 +220,7 @@ 

Source code for deepsparse.engine

 
 try:
     # flake8: noqa
-    from deepsparse.cpu import cpu_details
+    from deepsparse.cpu import cpu_architecture
     from deepsparse.lib import init_deepsparse_lib
     from deepsparse.version import *
 except ImportError:
@@ -233,7 +233,11 @@ 

Source code for deepsparse.engine

 __all__ = ["Engine", "compile_model", "benchmark_model", "analyze_model"]
 
 
-CORES_PER_SOCKET, AVX_TYPE, VNNI = cpu_details()
+ARCH = cpu_architecture()
+CORES_PER_SOCKET = ARCH.available_cores_per_socket
+NUM_SOCKETS = ARCH.available_sockets
+AVX_TYPE = ARCH.isa
+VNNI = ARCH.vnni
 
 LIB = init_deepsparse_lib()
 
@@ -281,6 +285,16 @@ 

Source code for deepsparse.engine

     return num_cores
 
 
+def _validate_num_sockets(num_sockets: Union[None, int]) -> int:
+    if not num_sockets:
+        num_sockets = NUM_SOCKETS
+
+    if num_sockets < 1:
+        raise ValueError("num_sockets must be greater than 0")
+
+    return num_sockets
+
+
 
[docs]class Engine(object): """ Create a new DeepSparse Engine that compiles the given onnx file @@ -303,13 +317,22 @@

Source code for deepsparse.engine

     :param num_cores: The number of physical cores to run the model on.
         Pass None or 0 to run on the max number of cores
         in one socket for the current machine, default None
+    :param num_sockets: The number of physical sockets to run the model on.
+        Pass None or 0 to run on the max number of sockets for the
+        current machine, default None
     """
 
-    def __init__(self, model: Union[str, Model, File], batch_size: int, num_cores: int):
+    def __init__(
+        self,
+        model: Union[str, Model, File],
+        batch_size: int,
+        num_cores: int,
+        num_sockets: int = None,
+    ):
         self._model_path = _model_to_path(model)
         self._batch_size = _validate_batch_size(batch_size)
         self._num_cores = _validate_num_cores(num_cores)
-        self._num_sockets = 1  # only single socket is supported currently
+        self._num_sockets = _validate_num_sockets(num_sockets)
         self._cpu_avx_type = AVX_TYPE
         self._cpu_vnni = VNNI
         self._eng_net = LIB.deepsparse_engine(
@@ -651,7 +674,10 @@ 

Source code for deepsparse.engine

 
 
 
[docs]def compile_model( - model: Union[str, Model, File], batch_size: int = 1, num_cores: int = None + model: Union[str, Model, File], + batch_size: int = 1, + num_cores: int = None, + num_sockets: int = None, ) -> Engine: """ Convenience function to compile a model in the DeepSparse Engine @@ -666,9 +692,12 @@

Source code for deepsparse.engine

     :param num_cores: The number of physical cores to run the model on.
         Pass None or 0 to run on the max number of cores
         in one socket for the current machine, default None
+    :param num_sockets: The number of physical sockets to run the model on.
+        Pass None or 0 to run on the max number of sockets for the
+        current machine, default None
     :return: The created Engine after compiling the model
     """
-    return Engine(model, batch_size, num_cores)
+ return Engine(model, batch_size, num_cores, num_sockets)
[docs]def benchmark_model( @@ -681,6 +710,7 @@

Source code for deepsparse.engine

     include_inputs: bool = False,
     include_outputs: bool = False,
     show_progress: bool = False,
+    num_sockets: int = None,
 ) -> BenchmarkResults:
     """
     Convenience function to benchmark a model in the DeepSparse Engine
@@ -708,9 +738,12 @@ 

Source code for deepsparse.engine

     :param include_outputs: If True, outputs from forward passes during benchmarking
         will be added to the results. Default is False
     :param show_progress: If True, will display a progress bar. Default is False
+    :param num_sockets: The number of physical sockets to run the model on.
+        Pass None or 0 to run on the max number of sockets for the
+        current machine, default None
     :return: the results of benchmarking
     """
-    model = compile_model(model, batch_size, num_cores)
+    model = compile_model(model, batch_size, num_cores, num_sockets)
 
     return model.benchmark(
         inp,
@@ -732,6 +765,7 @@ 

Source code for deepsparse.engine

     optimization_level: int = 1,
     imposed_as: Optional[float] = None,
     imposed_ks: Optional[float] = None,
+    num_sockets: int = None,
 ) -> dict:
     """
     Function to analyze a model's performance in the DeepSparse Engine.
@@ -762,12 +796,15 @@ 

Source code for deepsparse.engine

         Will force all prunable layers in the graph to have weights with
         this desired sparsity level (percentage of 0's in the tensor).
         Beneficial for seeing how pruning affects the performance of the model.
+    :param num_sockets: The number of physical sockets to run the model on.
+        Pass None or 0 to run on the max number of sockets for the
+        current machine, default None
     :return: the analysis structure containing the performance details of each layer
     """
     model = _model_to_path(model)
     num_cores = _validate_num_cores(num_cores)
     batch_size = _validate_batch_size(batch_size)
-    num_sockets = 1
+    num_sockets = _validate_num_sockets(num_sockets)
     eng_net = LIB.deepsparse_engine(model, batch_size, num_cores, num_sockets)
 
     return eng_net.benchmark(
diff --git a/deepsparse/_sources/index.rst.txt b/deepsparse/_sources/index.rst.txt
index 0a2344b29b2..ef78b48a2a4 100644
--- a/deepsparse/_sources/index.rst.txt
+++ b/deepsparse/_sources/index.rst.txt
@@ -62,7 +62,7 @@ Sparsification
 Sparsification is the process of taking a trained deep learning model and removing redundant information from the overprecise and over-parameterized network resulting in a faster and smaller model.
 Techniques for sparsification are all encompassing including everything from inducing sparsity using `pruning `_ and `quantization `_ to enabling naturally occurring sparsity using `activation sparsity `_ or `winograd/FFT `_.
 When implemented correctly, these techniques result in significantly more performant and smaller models with limited to no effect on the baseline metrics.
-For example, pruning plus quantization can give over `7x improvements in performance `_ while recovering to nearly the same baseline accuracy.
+For example, pruning plus quantization can give noticeable improvements in performance while recovering to nearly the same baseline accuracy.
 
 The Deep Sparse product suite builds on top of sparsification enabling you to easily apply the techniques to your datasets and models using recipe-driven approaches.
 Recipes encode the directions for how to sparsify a model into a simple, easily editable format.
@@ -131,4 +131,4 @@ Additionally, more information can be found via
 
     Bugs, Feature Requests 
     Support, General Q&A 
-    Neural Magic Docs 
\ No newline at end of file
+    Neural Magic Docs 
diff --git a/deepsparse/api/deepsparse.html b/deepsparse/api/deepsparse.html
index 7f2be874cd4..6f0d7303210 100644
--- a/deepsparse/api/deepsparse.html
+++ b/deepsparse/api/deepsparse.html
@@ -575,7 +575,7 @@ 

Submodules
-class deepsparse.engine.Engine(model: Union[str, sparsezoo.objects.model.Model, sparsezoo.objects.file.File], batch_size: int, num_cores: int)[source]
+class deepsparse.engine.Engine(model: Union[str, sparsezoo.objects.model.Model, sparsezoo.objects.file.File], batch_size: int, num_cores: int, num_sockets: Optional[int] = None)[source]

Bases: object

Create a new DeepSparse Engine that compiles the given onnx file for GPU class performance on commodity CPUs.

@@ -600,6 +600,9 @@

Submodules
-deepsparse.engine.analyze_model(model: Union[str, sparsezoo.objects.model.Model, sparsezoo.objects.file.File], inp: List[numpy.ndarray], batch_size: int = 1, num_cores: Optional[int] = None, num_iterations: int = 20, num_warmup_iterations: int = 5, optimization_level: int = 1, imposed_as: Optional[float] = None, imposed_ks: Optional[float] = None) → dict[source]
+deepsparse.engine.analyze_model(model: Union[str, sparsezoo.objects.model.Model, sparsezoo.objects.file.File], inp: List[numpy.ndarray], batch_size: int = 1, num_cores: Optional[int] = None, num_iterations: int = 20, num_warmup_iterations: int = 5, optimization_level: int = 1, imposed_as: Optional[float] = None, imposed_ks: Optional[float] = None, num_sockets: Optional[int] = None) → dict[source]

Function to analyze a model’s performance in the DeepSparse Engine. The model must be defined in an ONNX graph and stored in a local file. Gives defaults of batch_size == 1 and num_cores == None @@ -875,6 +878,9 @@

SubmodulesReturns @@ -885,7 +891,7 @@

Submodules
-deepsparse.engine.benchmark_model(model: Union[str, sparsezoo.objects.model.Model, sparsezoo.objects.file.File], inp: List[numpy.ndarray], batch_size: int = 1, num_cores: Optional[int] = None, num_iterations: int = 20, num_warmup_iterations: int = 5, include_inputs: bool = False, include_outputs: bool = False, show_progress: bool = False)deepsparse.benchmark.BenchmarkResults[source]
+deepsparse.engine.benchmark_model(model: Union[str, sparsezoo.objects.model.Model, sparsezoo.objects.file.File], inp: List[numpy.ndarray], batch_size: int = 1, num_cores: Optional[int] = None, num_iterations: int = 20, num_warmup_iterations: int = 5, include_inputs: bool = False, include_outputs: bool = False, show_progress: bool = False, num_sockets: Optional[int] = None)deepsparse.benchmark.BenchmarkResults[source]

Convenience function to benchmark a model in the DeepSparse Engine from an ONNX file for inference. Gives defaults of batch_size == 1 and num_cores == None @@ -913,6 +919,9 @@

SubmodulesReturns @@ -923,7 +932,7 @@

Submodules
-deepsparse.engine.compile_model(model: Union[str, sparsezoo.objects.model.Model, sparsezoo.objects.file.File], batch_size: int = 1, num_cores: Optional[int] = None)deepsparse.engine.Engine[source]
+deepsparse.engine.compile_model(model: Union[str, sparsezoo.objects.model.Model, sparsezoo.objects.file.File], batch_size: int = 1, num_cores: Optional[int] = None, num_sockets: Optional[int] = None)deepsparse.engine.Engine[source]

Convenience function to compile a model in the DeepSparse Engine from an ONNX file for inference. Gives defaults of batch_size == 1 and num_cores == None @@ -938,6 +947,9 @@

SubmodulesReturns diff --git a/deepsparse/index.html b/deepsparse/index.html index 6ae56065d9e..2cbe46adb43 100644 --- a/deepsparse/index.html +++ b/deepsparse/index.html @@ -220,7 +220,7 @@

Sparsificationpruning and quantization to enabling naturally occurring sparsity using activation sparsity or winograd/FFT. When implemented correctly, these techniques result in significantly more performant and smaller models with limited to no effect on the baseline metrics. -For example, pruning plus quantization can give over 7x improvements in performance while recovering to nearly the same baseline accuracy.

+For example, pruning plus quantization can give noticeable improvements in performance while recovering to nearly the same baseline accuracy.

The Deep Sparse product suite builds on top of sparsification enabling you to easily apply the techniques to your datasets and models using recipe-driven approaches. Recipes encode the directions for how to sparsify a model into a simple, easily editable format. - Download a sparsification recipe and sparsified model from the SparseZoo. diff --git a/deepsparse/searchindex.js b/deepsparse/searchindex.js index 86fe5c57b7f..96127011450 100644 --- a/deepsparse/searchindex.js +++ b/deepsparse/searchindex.js @@ -1 +1 @@ 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FAQs

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General Product FAQs

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What is Neural Magic?

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Founded by a team of award-winning MIT computer scientists and funded by Comcast Ventures, Andreessen Horowitz, NEA, Pillar VC, and Amdocs, Neural Magic is the creator and maintainer of the Deep Sparse Platform. It has several components, including the DeepSparse Engine, a CPU runtime that runs sparse models at GPU speeds. To enable companies the ability to use ubiquitous and unconstrained CPU resources, Neural Magic includes Sparsify, SparseML, and the SparseZoo, open-sourced model optimization technologies that allow users to achieve performance breakthroughs, at scale, with all the flexibility of software.

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What is the DeepSparse Engine?

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The DeepSparse Engine, created by Neural Magic, is a general purpose engine for machine learning, enabling machine learning to be practically run in new places, on new kinds of workloads. It delivers state of art, GPU-class performance for the deep learning applications running on x86 CPUs. The DeepSparse Engine achieves its performance using breakthrough algorithms that reduce the computation needed for neural network execution and accelerate the resulting memory-bound computation.

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Why Neural Magic?

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Learn more about Neural Magic and the DeepSparse Engine (formerly known as the Neural Magic Inference Engine).

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Watch the Why Neural Magic video

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How does Neural Magic make it work?

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This is an older webinar (50m) where we went through the process of optimizing and deploying a model; we’ve enhanced our software since the recording went out but this will give you some background: https://youtu.be/UhmmHTsfrzI

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Does Neural Magic support training of learning models?

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Neural Magic does not support training of deep learning models at this time. We do see value in providing a consistent CPU environment for our end users to train and infer on for their deep learning needs, and have added this to our engineering backlog.

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Do you have version compatibility on TensorFlow?

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Our inference engine supports all versions of TensorFlow 1.x.; support for TensorFlow 2.0 is coming soon. We have specific performance improvements for SSD models currently for TensorFlow 1.12.

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Do you run on AMD hardware?

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The DeepSparse Engine is validated to work on x86 Intel (Haswell generation and later) and AMD CPUs running Linux. It is highly recommended to run on a CPU with AVX-512 instructions available for optimal algorithms to be enabled. Specific support details for some algorithms over different microarchitectures is available.

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We are open to opportunities to expand our support footprint for different CPU-based processor architectures, based on market adoption and deep learning use cases.

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Do you run on ARM architecture?

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We currently do not support ARM and it’s on the Neural Magic roadmap; however, we’d still like to hear your use cases. Contact us to continue the conversation.

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To what use cases is the Deep Sparse Platform best suited?

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We focus on the models and use cases related to computer vision due to cost sensitivity and both real time and throughput constraints. The belief now is GPUs are required for deployment.

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What types of models does Neural Magic support?

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Today, we offer support for CNN-based computer vision models, specifically classification and object detection model types. We are continuously adding models to our supported model list and SparseZoo. Additionally, we are investigating model architectures beyond computer vision such as NLP models like BERT.

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Is dynamic shape supported?

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Dynamic shape is currently not supported; be sure to use models with fixed inputs and compile the model for a particular batch size. Dynamic shape and dynamic batch sizes are on the Neural Magic roadmap; subscribe for updates.

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Can multiple model inferences be executed?

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Model inferences are executed as a single stream; concurrent execution is unsupported at this time.

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Benchmarking FAQs

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What is the average estimated savings for users??

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This will vary but, in some cases, we are seeing 3x-10x savings. Typically, we offer 5-6x more price performance than hardware accelerators.

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Do you have benchmarks to compare and contrast?

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Yes. Check out our benchmark demo video or contact us to discuss your particular performance requirements. If you’d rather observe performance for yourself, head over to the Neural Magic GitHub repo to check out our tools and generate your own benchmarks in your environment.

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Do you publish ML Perf inference benchmarks?

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We are in the process of building the test harness to support ML Perf tests.

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Infrastructure FAQs

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Which instruction sets are supported and do we have to enable certain settings?

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AVX-512, AVX2, and VNNI. The DeepSparse Engine will automatically utilize the most effective available instruction set for the task. Generally, if AVX-512 is available then we have no reason to use AVX2 instruction set. AVX-512 VNNI only comes into use for quantized models i.e., INT8 or UINT8.

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Are you suitable for edge deployments (i.e., in-store devices, cameras)?

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Yes, absolutely. We can run anywhere you have a CPU with x86 instructions, including on bare metal, in the cloud, on-prem, or at the edge. Additionally, our model optimization tools are able to reduce the footprint of models across all architectures. We only guarantee performance in the DeepSparse Engine.

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We’d love to hear from users highly interested in ML performance. If you want to chat about your use cases or how others are leveraging the Deep Sparse Platform, please reach out. Or simply head over to the Neural Magic GitHub repo and check out our tools.

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Do you have available solutions or applications on the Microsoft/Azure platform?

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We deploy extremely easily. We are completely infrastructure-agnostic. As long as it has the “right” CPUs (80% of the entire Intel offering today), we can run on any cloud platform. For a Microsoft/Azure platform, we would just have to run Neural Magic on a Microsoft instance with the appropriate CPUs.

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Can the inference engine run on Kubernetes? How do you containerize and take advantage of underlying infrastructure?

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The DeepSparse Engine becomes a component of your model serving solution. As a result, it can simply plug into an existing CI/CD deployment pipeline. How you deploy, where you deploy, and what you deploy on becomes abstracted to the DeepSparse Engine so you can tailor your experiences. For example, you can run the DeepSparse Engine on a CPU VM environment, deployed via a Docker file and managed through a Kubernetes environment.

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Model Compression FAQs

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Can you comment on how you do pruning and effects on accuracy?

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Neural networks are extremely over-parameterized, allowing most weights to be iteratively removed from the network without effect on accuracy. Eventually, though, pruning will begin affecting the overall capacity of the network, the degree of which varies based on the use case. However, this is something entirely under the data scientist control to choose whether to recover fully or to prune more for even better performance.

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For example, Neural Magic has been successful in removing 90% of ResNet-50 weights with no loss in accuracy. For more background on techniques that have informed our methodologies, check out this paper co-written by Neural Magic, WoodFisher: Efficient Second-Order Approximation for Neural Network Compression.

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When does sparsification actually happen?

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In a scenario in which you want to sparsify and then run your own model in the DeepSparse Engine, you would first sparsify your model to achieve the desired level of performance and accuracy using Neural Magic’s Sparsify and SparseML tooling.

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What does the sparsification process look like?

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Neural Magic’s Sparsify and SparseML tooling, at its core, uses well-established state-of-the-art research principles such as Gradual Magnitude Pruning (GMP) to sparsify models. This is an iterative process in which groups of important weights are pruned away and then the network is allowed to recover. To significantly simplify the process, we offer tools and guidance for you to achieve the best performance possible. To peruse research papers contributed by Neural Magic staff, check them out. Or head over to the Neural Magic GitHub repo to get started!

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How does sparsification work in relation to TensorFlow?

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Today, we are able to sparsify models trained in popular deep learning libraries like TensorFlow. Our unique approach works with the output supplied by the model library and provides layer sparsification techniques that then can be compiled in the existing library framework, within the user environment.

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When using your software to transfer learn, what about other hyperparameters? Are you just freezing other layers?

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For transfer learning, our tooling allows you to save the sparse architecture learned from larger datasets. Other hyperparameters are fully under your control and allow you the flexibility to easily freeze layers as well.

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Do you support INT8 and INT16 (quantized) operations?

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Currently, the DeepSparse Engine runs at FP32 and has some support for INT8. With the release of the Intel Cascade Lake generation chips and later, Intel CPUs now include VNNI instructions and support both INT8 and INT16 operations. On machines with VNNI support, the engine has INT8 support for the ONNX operators QLinearConv, QuantizeLinear, DequantizeLinear, and QLinearMatMul with constant weights. The DeepSparse Engine also supports 8-bit QLinearAdd, an ONNX Runtime custom operator.

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Do you support FP16 (half precision) operations?

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Neural Magic is waiting on support from Intel; we will assess inclusion into our roadmap at the appropriate time.

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Runtime FAQs

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Do users have to do any model conversion before using the DeepSparse Engine?

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DeepSparse Engine executes on an ONNX (Open Neural Network Exchange) representation of a deep learning model. Our software allows you to produce an ONNX representation. If working with PyTorch, we use the built-in ONNX export and for TensorFlow, we convert from a standard exported protobuf file to ONNX. Outside of those frameworks, you would need to convert your model to ONNX first before passing it to the DeepSparse Engine.

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Why is ONNX the file format used by Neural Magic?

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ONNX (Open Neural Network Exchange) is emerging as a standard, open-source format for model representation. Based on the breadth of vendors supporting ONNX as well as the health of open-source community contributions, we believe ONNX offers a compelling solution for the market.

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Are your users using ONNX runtime already?

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End users are using a wide variety of runtimes, both open source and proprietary. Neural Magic is focused on ensuring we are open and flexible, to allow our users to achieve deep learning performance regardless of how they choose to build, deploy, and run their models.

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What is the accuracy loss, if any, on the numbers Neural Magic demonstrates?

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Results will depend on your use case and specific requirements. We are capable of maintaining 100% baseline accuracy. In cases where accuracy is not as important as performance, you can use our model optimization tools to further speed up the model at the expense of accuracy and weigh the tradeoffs.

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If you need sparsification, we provide the tooling for tradeoffs between accuracy and performance based on your specific requirements.

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For the runtime engine, is Neural Magic modifying the architecture in any way or just optimizing the instruction set at that level?

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Specifically for sparsification, our software keeps the architecture intact and changes the weights. For running dense, we do not change anything about the model.

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For a CPU are you using all the cores?

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The DeepSparse Engine optimizes how the model is run on the infrastructure resources applied to it. But, the Neural Magic does not optimize for the number of cores. You are in control to specify how much of the system Neural Magic will use and run on. Depending on your goals (latency, throughput, and cost constraints), you can optimize your pipeline for maximum efficiency.

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+ © Copyright 2021 - present / Neuralmagic, Inc. All Rights Reserved. Refer to various software components for license attribution.. + +

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Get Started

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Details

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Help

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Overview

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Neural Magic’s vision is no-hardware AI, shattering the hardware barriers that hold back the field of machine learning. With Neural Magic’s novel algorithms that enable convolutional neural networks to run on commodity CPUs – at GPU speeds and better – data scientists no longer have to compromise on model design and input size, or deal with scarce and costly GPU resources. Neural Magic is making the power of deep learning simple, accessible, and affordable for anyone. As a part of this next great unlock of machine learning, data scientists will ask bigger questions, challenge norms, and unleash a new class of AI applications that live at the edge of imagination.

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To deliver on this vision, there are several components to the Deep Sparse Platform:

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  1. Sparsify: Easy-to-use UI for automatically sparsifying neural networks and creating sparsification recipes for better inference performance and a smaller footprint

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  3. SparseML: Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models

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  5. SparseZoo: Neural network model repository for highly sparse and sparse-quantized models with matching sparsification recipes

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  7. DeepSparse Engine: Neural network inference engine that delivers GPU-class performance for sparsified models on CPUs

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Our inference engine and model optimization technologies enable companies to use ubiquitous and unconstrained CPU resources to achieve performance breakthroughs, at scale, with all the flexibility of software.

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Supported Architectures & Frameworks

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+ Computer Vision Applications +
Sample Models + ResNets, MobileNets, EfficientNets, Single-Shot Detectors (SSDs) +
Use Cases (Domains) + Image Classification, Object Detection +
Frameworks + ONNX, Keras, PyTorch, TensorFlow +

Today, we offer support for convolutional neural network-based computer vision models, specifically classification and object detection model types such as the models in SparseZoo.

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We are continuously exploring models to add to our supported model list and SparseZoo including model architectures beyond computer vision. Popular NLP models such as BERT are on the Neural Magic roadmap; subscribe for updates.

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Notes

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PyTorch and ONNX

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Sparsify and the DeepSparse Engine inputs are standardized on the ONNX format. PyTorch has native ONNX export and requires fewer steps than other supported frameworks, such as Keras or TensorFlow. If you have flexibility in frameworks, consider PyTorch to start.

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Model Considerations

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Dynamic shape is currently not supported; be sure to use models with fixed inputs and compile the model for a particular batch size. Dynamic shape and dynamic batch sizes are on the Neural Magic roadmap; subscribe for updates.

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Model inferences are executed as a single stream; concurrent execution is unsupported at this time.

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+ © Copyright 2021 - present / Neuralmagic, Inc. All Rights Reserved. Refer to various software components for license attribution.. + +

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Glossary

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The machine learning community includes a vast array of terminology that can have variations in meaning depending on context. This glossary is not intended as a comprehensive list, but rather a clarification of terms you may encounter with Neural Magic and machine learning.

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AutoMLAutomated Machine Learning. Platform that aims to reduce or eliminate the need for skilled data scientists to build ML and deep learning models. Google AutoML, for example, is a suite of cloud-based ML products.
AVX2Advanced Vector Extensions 2. Instruction set used for applications on an Intel CPU.
AVX-512Advanced Vector Extensions. Instruction set on Intel CPUs that impacts compute, storage, and network instructions. AVX-512 yields higher performance for demanding computational tasks.
Cascade Lake ChipsIntel CPU chips up to 28 cores that are improved for machine learning and added VNNI instructions. Cascade Lake Chips support FP16 and INT8 floating point operations.
Convolutional Neural Network (CNN)Artificial neural network used in image recognition and object detection as well as processing that is specifically designed to process pixel data.
Deep Learning (DL)Subset of machine learning in which artificial neural networks (algorithms inspired by the human brain) learn from large amounts of data.
Deep Learning FrameworksInterface, library, or tool that allows one to build deep learning models more easily and quickly without getting into details of underlying algorithms.
DLRMOpen-source Deep Learning Recommendation Model from Facebook.
Fully Connected NetworkNetwork in which every node in a layer (except the input and output layer) is connected to every node in the previous layer and following layer.
Image ClassificationSupervised learning problem to define a set of target classes (objects to identify in images) and train a model to recognize them using labeled example photos.
Image SegmentationIn computer vision, the process of partitioning a digital image into multiple segments to simplify and/or change the representation of an image into something more meaningful and easier to analyze.
InferenceProcess of using a trained machine learning algorithm to make predictions (done by machine learning engineers).
MobileNetsA family of mobile-first computer vision models for TensorFlow, designed to effectively maximize accuracy while being mindful of the restricted resources for an on-device or embedded application.
Model pipelinesIn machine learning deployment, multiple models chained together to achieve business goals (such as a detection model to select regions from an image for a later visual search model).
Model servingIn machine learning deployment, makes serving of models less expensive and faster to run by better using resources on the machine.
Multilayer Perceptron (MLP)A feedforward artificial neural network (ANN) model, composed of more than one perceptron, that maps sets of input data onto a set of appropriate outputs.
Neural NetworkSystem of hardware and/or software patterned after the operation of neurons in the human brain.
Object DetectionCategorization of an image based on the number of objects in the image and/or the location of the objects.
ONNXOpen Neural Network Exchange. Open-source inference engine that is a performance-focused complete scoring engine for ONNX models.
QuantizationThe process of approximating a neural network that uses floating-point numbers by a neural network of low bit width numbers. Quantization dramatically reduces the memory requirement and computational cost of using neural networks.
RecommendationsCategorization of an image based on relevant suggestions. This class of machine learning algorithms finds similarity between different images.
ResNetImage classification model that is structurally dense.
SparsificationA model optimization technique used to improve performance by reducing the number of nonperformance critical elements, vectors, and matrices.
SSDSingle Shot Detector. Convolutional neural network (CNN) algorithm for object detection that provides better balance between swiftness and precision. SSD runs CNN on an input image only one time and computes a feature map.
Structured pruningA method for compressing a neural network. Structured pruning alternates between removing channel connections and fine-tuning to reduce overall width of the network. Structured pruning severely limits the maximum sparsity that can be imposed on a network when compared with unstructured pruning.
TensorThe input to a convolutional layer. Tensor is a 3 or 4 dimensional representation of a 2D image.
TrainingThe process of feeding an ML algorithm with data to help identify and learn good values for all attributes involved.
U-NetFully convolutional network that does image segmentation (originally designed for medical image segmentation). The U-Net goal is to predict each pixel class.
Unstructured pruningA method for compressing a neural network. Unstructured pruning removes individual weight connections from a trained network. Software like Neural Magic's DeepSparse Engine runs these pruned networks faster.
VNNIVector Neural Network Instructions. New versions of Intel's CPU chips are optimized with VNNI, making them faster and more efficient for certain types of machine learning applications.
YOLOYou Only Look Once. Open-source type of CNN method of object detection that can recognize objects in images and videos swiftly.
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+ © Copyright 2021 - present / Neuralmagic, Inc. All Rights Reserved. Refer to various software components for license attribution.. + +

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Started","Glossary","Neural Magic Documentation"],titleterms:{architectur:1,benchmark:0,compress:0,consider:1,document:3,faq:0,framework:1,gener:0,get:1,glossari:2,infrastructur:0,magic:3,model:[0,1],neural:3,note:1,onnx:1,overview:1,product:0,pytorch:1,runtim:0,start:1,support:1}}) \ No newline at end of file diff --git a/sparseml/_sources/index.rst.txt b/sparseml/_sources/index.rst.txt index 80e266e9277..d89f3a9f6b2 100644 --- a/sparseml/_sources/index.rst.txt +++ b/sparseml/_sources/index.rst.txt @@ -59,7 +59,7 @@ Sparsification Sparsification is the process of taking a trained deep learning model and removing redundant information from the overprecise and over-parameterized network resulting in a faster and smaller model. Techniques for sparsification are all encompassing including everything from inducing sparsity using `pruning `_ and `quantization `_ to enabling naturally occurring sparsity using `activation sparsity `_ or `winograd/FFT `_. When implemented correctly, these techniques result in significantly more performant and smaller models with limited to no effect on the baseline metrics. -For example, pruning plus quantization can give over `7x improvements in performance `_ while recovering to nearly the same baseline accuracy. +For example, pruning plus quantization can give noticeable improvements in performance while recovering to nearly the same baseline accuracy. The Deep Sparse product suite builds on top of sparsification enabling you to easily apply the techniques to your datasets and models using recipe-driven approaches. Recipes encode the directions for how to sparsify a model into a simple, easily editable format. diff --git a/sparseml/index.html b/sparseml/index.html index 239d653e555..4f0bf1b80bf 100644 --- a/sparseml/index.html +++ b/sparseml/index.html @@ -213,7 +213,7 @@

Sparsificationpruning and quantization to enabling naturally occurring sparsity using activation sparsity or winograd/FFT. When implemented correctly, these techniques result in significantly more performant and smaller models with limited to no effect on the baseline metrics. -For example, pruning plus quantization can give over 7x improvements in performance while recovering to nearly the same baseline accuracy.

+For example, pruning plus quantization can give noticeable improvements in performance while recovering to nearly the same baseline accuracy.

The Deep Sparse product suite builds on top of sparsification enabling you to easily apply the techniques to your datasets and models using recipe-driven approaches. Recipes encode the directions for how to sparsify a model into a simple, easily editable format. - Download a sparsification recipe and sparsified model from the SparseZoo. diff --git a/sparseml/searchindex.js b/sparseml/searchindex.js index b9fe31ac24d..bb6a2c19023 100644 --- a/sparseml/searchindex.js +++ b/sparseml/searchindex.js @@ -1 +1 @@ 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\ No newline at end of file diff --git a/sparsezoo/_sources/index.rst.txt b/sparsezoo/_sources/index.rst.txt index 1918a9a67fe..251a0767d65 100644 --- a/sparsezoo/_sources/index.rst.txt +++ b/sparsezoo/_sources/index.rst.txt @@ -62,7 +62,7 @@ Sparsification Sparsification is the process of taking a trained deep learning model and removing redundant information from the overprecise and over-parameterized network resulting in a faster and smaller model. Techniques for sparsification are all encompassing including everything from inducing sparsity using `pruning `_ and `quantization `_ to enabling naturally occurring sparsity using `activation sparsity `_ or `winograd/FFT `_. When implemented correctly, these techniques result in significantly more performant and smaller models with limited to no effect on the baseline metrics. -For example, pruning plus quantization can give over `7x improvements in performance `_ while recovering to nearly the same baseline accuracy. +For example, pruning plus quantization can give noticeable improvements in performance while recovering to nearly the same baseline accuracy. The Deep Sparse product suite builds on top of sparsification enabling you to easily apply the techniques to your datasets and models using recipe-driven approaches. Recipes encode the directions for how to sparsify a model into a simple, easily editable format. diff --git a/sparsezoo/index.html b/sparsezoo/index.html index ea0306f028c..0735c47a4de 100644 --- a/sparsezoo/index.html +++ b/sparsezoo/index.html @@ -216,7 +216,7 @@

Sparsificationpruning and quantization to enabling naturally occurring sparsity using activation sparsity or winograd/FFT. When implemented correctly, these techniques result in significantly more performant and smaller models with limited to no effect on the baseline metrics. -For example, pruning plus quantization can give over 7x improvements in performance while recovering to nearly the same baseline accuracy.

+For example, pruning plus quantization can give noticeable improvements in performance while recovering to nearly the same baseline accuracy.

The Deep Sparse product suite builds on top of sparsification enabling you to easily apply the techniques to your datasets and models using recipe-driven approaches. Recipes encode the directions for how to sparsify a model into a simple, easily editable format. - Download a sparsification recipe and sparsified model from the SparseZoo. diff --git a/sparsezoo/searchindex.js b/sparsezoo/searchindex.js index e2d64d101c9..3862f4673aa 100644 --- a/sparsezoo/searchindex.js +++ b/sparsezoo/searchindex.js @@ -1 +1 @@ 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of file diff --git a/sparsify/_sources/index.rst.txt b/sparsify/_sources/index.rst.txt index 80bca3602a8..9efef42872a 100644 --- a/sparsify/_sources/index.rst.txt +++ b/sparsify/_sources/index.rst.txt @@ -61,7 +61,7 @@ Sparsification Sparsification is the process of taking a trained deep learning model and removing redundant information from the overprecise and over-parameterized network resulting in a faster and smaller model. Techniques for sparsification are all encompassing including everything from inducing sparsity using `pruning `_ and `quantization `_ to enabling naturally occurring sparsity using `activation sparsity `_ or `winograd/FFT `_. When implemented correctly, these techniques result in significantly more performant and smaller models with limited to no effect on the baseline metrics. -For example, pruning plus quantization can give over `7x improvements in performance `_ while recovering to nearly the same baseline accuracy. +For example, pruning plus quantization can give noticeable improvements in performance while recovering to nearly the same baseline accuracy. The Deep Sparse product suite builds on top of sparsification enabling you to easily apply the techniques to your datasets and models using recipe-driven approaches. 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