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2						PyTorch model to the portable ONNX format. The ONNX Runtime team maintains these
3						exporter APIs to ensure a high level of compatibility with PyTorch models.</p> <div class="card-actions"><a href="https://onnxruntime.ai/docs/api/" class="btn btn-primary rounded-sm"><span class="hidden md:grid">Get your PyTorch Models ready for</span> optimized deployment
4							→</a></div>`,l1,O,u1,Y,r1,C1,Q,n=`<div class="card-body col-span-2 order-last md:order-first"><h2 class="card-title">Production Ready</h2> <p>Train and deploy models reliably and at scale using a built-in PyTorch environment
5						within Azure Machine Learning to ensure that the latest PyTorch version is fully
6						supported through a lightweight, standalone environment that includes needed components
7						like ONNX Runtime for Training to effectively run optimized training for large models.</p> <div class="card-actions"><a href="https://techcommunity.microsoft.com/t5/ai-machine-learning-blog/azure-container-for-pytorch-is-now-generally-available-in-azure/ba-p/3774616" class="btn btn-primary rounded-sm">Learn about ACPT →</a></div></div> <figure class="md:col-span-1 col-span-2 order-first sm:order-last pr-5"><img src="${M2}" alt=""/></figure>`,V1,p1,c1,m1,g1,J1=`<h2 class="card-title">Lower latency, higher throughput</h2> <p>Better performance can help improve your user experience and lower your operating costs.
8						A wide range of models from computer vision (ResNet, MobileNet, Inception, YOLO, super
9						resolution, etc) to speech and NLP (BERT, RoBERTa, GPT-2, T5, etc) can benefit from ONNX
10						Runtime&#39;s optimized performance. The ONNX Runtime team regularly benchmarks and
11						optimizes top models for performance. ONNX Runtime also integrates with top hardware
12						accelerator libraries like TensorRT and OpenVINO so you can get the best performance on
13						the hardware available while using the same common APIs across all your target
14						platforms.</p> <div class="card-actions"><a class="btn btn-primary hidden md:grid rounded-sm" href="https://cloudblogs.microsoft.com/opensource/tag/onnx">Check the latest on performance enhancements →</a> <a class="btn btn-primary md:hidden rounded-sm" href="https://cloudblogs.microsoft.com/opensource/tag/onnx">Performance enhancements →</a></div>`,U1,H1,L1,q1,f1,w1,K1=`<h2 class="card-title">Get innovations into production faster</h2> <p>Development agility is a key factor in overall costs. ONNX Runtime was built on the
15						experience of taking PyTorch models to production in high scale services like Microsoft
16						Office, Bing, and Azure. It used to take weeks and months to take a model from R&amp;D to
17						production. With ONNX Runtime, models can be ready to be deployed at scale in hours or
18						days.</p> <div class="card-actions"><a href="./testimonials" class="btn btn-primary hidden md:grid rounded-sm">See what ONNX Runtime users are saying →</a> <a href="./testimonials" class="btn btn-primary md:hidden rounded-sm">Testimonials →</a></div>`,Y1,Z1,E1,P1;return v=new C2({props:{title:x2,description:G1,imgsrc:b2,imgalt:_2}}),y=new c2({}),k=new f2({}),S=new g2({}

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21
22torch.onnx.export(
23model,
24inputs,
25"model.onnx")`;let W1="https://i.ibb.co/0YBy62j/ORT-icon-for-light-bg.png",H2="https://i.ibb.co/0YBy62j/ORT-icon-for-light-bg.png",Z2="onnxruntime, onnx runtime pytorch, onnx runtime pytorch models, onnx runtime pytorch deployment, onnx runtime pytorch performance, onnx runtime pytorch time to market, onnx runtime pytorch deploy anywhere, onnx runtime pytorch boost performance, onnx runtime pytorch improve time to market, onnx runtime pytorch production ready, onnx runtime pytorch lower latency, onnx runtime pytorch higher throughput, onnx runtime pytorch get innovations into production faster, onnx runtime pytorch testimonials, onnx runtime pytorch performance enhancements, onnx runtime pytorch production ready, onnx runtime pytorch lower latency, onnx runtime pytorch higher throughput, onnx runtime pytorch get innovations into production faster, onnx runtime pytorch testimonials, onnx runtime pytorch performance enhancements";function $2(W){return[[""]]}class N2 extends T1{constructor(o){super(),I1(this,o,$2,y2,F1,{})}}export{N2 as component};