Test for sofia

_posts/2022-5-18-testing-for-push.md

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+---
+layout: blog_detail
+title: "Test"
+author: PyTorch
+featured-img: "/assets/images/METAPT-002-BarGraph-02-static.png"
+---
+
+Let's test using github.dev
+
+<p align="center">
+  <img src="/assets/images/intro-graphic-accelerated-pytorch-training-revised.png" width="100%">
+</p>
+
+## Metal Acceleration
+
+Lorem ipsum dolor sit amet, consectetur adipiscing elit. Praesent fringilla aliquet gravida. Curabitur mattis tempus odio sit amet maximus. Pellentesque aliquam leo at diam gravida porttitor. Nunc eu nibh ut elit vestibulum sodales. Nullam imperdiet vestibulum felis a commodo. Fusce sit amet dapibus lectus, quis tristique sem. Duis vel egestas nulla. Duis at vehicula libero.
+
+## Training Benefits on Apple Silicon
+
+Lorem ipsum dolor sit amet, consectetur adipiscing elit. Praesent fringilla aliquet gravida. Curabitur mattis tempus odio sit amet maximus. Pellentesque aliquam leo at diam gravida porttitor. Nunc eu nibh ut elit vestibulum sodales. Nullam imperdiet vestibulum felis a commodo. Fusce sit amet dapibus lectus, quis tristique sem. Duis vel egestas nulla. Duis at vehicula libero.
+
+## Metal Acceleration
+
+Lorem ipsum dolor sit amet, consectetur adipiscing elit. Praesent fringilla aliquet gravida. Curabitur mattis tempus odio sit amet maximus. Pellentesque aliquam leo at diam gravida porttitor. Nunc eu nibh ut elit vestibulum sodales. Nullam imperdiet vestibulum felis a commodo. Fusce sit amet dapibus lectus, quis tristique sem. Duis vel egestas nulla. Duis at vehicula libero.
+
+## Training Benefits on Apple Silicon
+
+Lorem ipsum dolor sit amet, consectetur adipiscing elit. Praesent fringilla aliquet gravida. Curabitur mattis tempus odio sit amet maximus. Pellentesque aliquam leo at diam gravida porttitor. Nunc eu nibh ut elit vestibulum sodales. Nullam imperdiet vestibulum felis a commodo. Fusce sit amet dapibus lectus, quis tristique sem. Duis vel egestas nulla. Duis at vehicula libero.
+
+<p align="center">
+  <img src="/assets/images/applause.gif" width="100%">
+</p>
+
+<p align="center">
+Figure 1: Correspondence of `backward`/`grad` arguments in the graphs.
+</p>
+
+<p align="center">
+The Encoder Structure of the Transformer <br> 
+Architecture Taken from "<a href="https://arxiv.org/abs/1706.03762" target="_blank">Attention Is All You Need</a>".
+</p>
+
+\-**Lorem ipsum**\- dolor sit amet, consectetur adipiscing elit. Praesent fringilla aliquet gravida. Curabitur mattis tempus odio sit amet maximus. Pellentesque aliquam leo at diam gravida porttitor. Nunc eu nibh ut elit vestibulum sodales. Nullam imperdiet vestibulum felis a commodo. Fusce sit amet dapibus lectus, quis tristique sem. Duis vel egestas nulla. Duis at vehicula libero.
+
+**Lorem ipsum** dolor sit amet, consectetur adipiscing elit. Praesent fringilla aliquet gravida. Curabitur mattis tempus odio sit amet maximus. Pellentesque aliquam leo at diam gravida porttitor. Nunc eu nibh ut elit vestibulum sodales. Nullam imperdiet vestibulum felis a commodo. Fusce sit amet dapibus lectus, quis tristique sem. Duis vel egestas nulla. Duis at vehicula libero.
+Lorem ipsum dolor sit amet, consectetur adipiscing elit. Praesent fringilla aliquet gravida. Curabitur mattis tempus odio sit amet maximus. Pellentesque aliquam leo at diam gravida porttitor. Nunc eu nibh ut elit vestibulum sodales. Nullam imperdiet vestibulum felis a commodo. Fusce sit amet dapibus lectus, quis tristique sem. Duis vel egestas nulla. Duis at vehicula libero.
+
+## References
+
+[1] Davison, Beth A., Stephen A. Harrison, Gad Cotter, Naim Alkhouri, Arun Sanyal, Christopher Edwards, Jerry R. Colca, Julie Iwashita, Gary G. Koch, and Howard C. Dittrich. 2020. “Suboptimal Reliability of Liver Biopsy Evaluation Has Implications for Randomized Clinical Trials.” Journal of Hepatology 73 (6): 1322–32.
+
+[2] Pathologists’ diagnosis of invasive melanoma and melanocytic proliferations: observer accuracy and reproducibility study, [https://www.bmj.com/content/357/bmj.j2813.full](https://www.bmj.com/content/357/bmj.j2813.full)
+
+[3] [https://openaccess.thecvf.com/content/CVPR2022W/CVMI/papers/Dwivedi_Multi_Stain_Graph_Fusion_for_Multimodal \_Integration_in_Pathology_CVPRW_2022_paper.pdf](https://openaccess.thecvf.com/content/CVPR2022W/CVMI/papers/Dwivedi_Multi_Stain_Graph_Fusion_for_Multimodal_Integration_in_Pathology_CVPRW_2022_paper.pdf)
+
+[4] Ciyue Shen, Collin Schlager, Deepta Rajan, Maryam Pouryahya, Mary Lin, Victoria Mountain, Ilan Wapinski, Amaro Taylor-Weiner, Benjamin Glass, Robert Egger, Andrew Beck. Application of an interpretable graph neural network to predict gene expression signatures associated with tertiary lymphoid structures in histopathological images [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research; 2022 Apr 8–13; New Orleans, LA. Philadelphia (PA): AACR; 2022. Abstract nr 1922.
+
+[5] [https://proceedings.mlr.press/v156/jaume21a/jaume21a.pdf](https://proceedings.mlr.press/v156/jaume21a/jaume21a.pdf)
+
+[6] [https://openaccess.thecvf.com/content_CVPRW_2020/papers/w16/Lu_Capturing_Cellular_Topology_in_Multi-Gigapixel_Pathology_Images_CVPRW_2020_paper.pdf](https://openaccess.thecvf.com/content_CVPRW_2020/papers/w16/Lu_Capturing_Cellular_Topology_in_Multi-Gigapixel_Pathology_Images_CVPRW_2020_paper.pdf)
+
+[7] Javed, S. A., Juyal, D., Padigela, H., Taylor-Weiner, A., Yu, L., & Prakash, A. (2022). Additive MIL: Intrinsic Interpretability for Pathology. arXiv preprint arXiv:2206.01794.