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has-medium-font-size\">Q15\uff1a\u5728\u7814\u7a76\u8fc7\u7a0b\u5f53\u4e2d\u60a8\u6709\u6ca1\u6709\u9047\u5230\u4e00\u4e9b\u7279\u522b\u7684\u6311\u6218\uff1f<\/h2>\n\n\n\n<p><em><strong>\u9648\u6559\u6388\uff1a<\/strong><\/em>\u6311\u6218\u4e00\u76f4\u5b58\u5728\uff0c\u79d1\u7814\u6ca1\u6709\u6807\u51c6\u7b54\u6848\u3002\u5c1d\u8bd5\u4e5f\u597d\uff0c\u5931\u8d25\u4e5f\u597d\uff0c\u5de5\u4e1a\u754c\u4e5f\u597d\uff0c\u5b66\u672f\u754c\u4e5f\u597d\uff0c\u90fd\u4f1a\u6709\u5404\u79cd\u5404\u6837\u5f80\u524d\u7684\u76ee\u6807\uff0c\u90fd\u6bd4\u8f83\u5377\u5427\uff08\u7b11\uff09\uff01\u4f46\u6211\u4eec\u5f88\u4eab\u53d7\u80fd\u591f\u53c2\u4e0e\u63a8\u52a8\u8fd9\u4e2a\u9886\u57df\u7684\u8fc7\u7a0b\u3002<\/p>\n\n\n\n<h2 class=\"wp-block-heading has-medium-font-size\">Q16\uff1a\u60a8\u600e\u4e48\u53bb\u5224\u65ad\u60a8\u7684\u7814\u7a76\u65b9\u5411\u662f\u4e00\u6761\u76f8\u5bf9\u6b63\u786e\u7684\u9053\u8def\uff1f<\/h2>\n\n\n\n<p><em><strong>\u9648\u6559\u6388\uff1a<\/strong><\/em>\u5224\u65ad\u4e0d\u4e86\uff0c\u53ea\u6709\u76f8\u4fe1\u3002\u4e54\u5e03\u65af\u6709\u53e5\u8bdd\uff1aThe journey is the reward. \u5f88\u591a\u65f6\u5019\u76ee\u6807\u4e0d\u662f\u6700\u7ec8\u7684\u5956\u676f\uff0c\u800c\u662f\u8d70\u7684\u8fc7\u7a0b\u3002<mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-white-color\">  CXO UNION-CXO\u8054\u76df\uff08cxounion.cn\uff09<\/mark><\/p>\n\n\n\n<p>\u81f3\u4e8e\u8bf4\u4e00\u4ef6\u4e8b\u60c5\u6b63\u786e\u4e0e\u5426\uff0c\u8b6c\u5982 12 \u5e74\u4e4b\u524d\u6211\u5f00\u59cb\u505a\u6df1\u5ea6\u5b66\u4e60\uff0c\u5c5e\u4e8e\u4e00\u4e2a\u975e\u5e38\u6b63\u786e\u53c8\u9519\u8bef\u7684\u51b3\u5b9a\uff0c\u5f53\u65f6\u7684\u76ee\u6807\u662f\u5229\u7528\u6df1\u5ea6\u5b66\u4e60\u7b97\u6cd5\u89e3\u51b3 ImageNet \u7684\u95ee\u9898\uff0c\u7ed3\u679c\u4e24\u5e74\u534a\u4ee5\u540e\uff0c\u6ca1\u6709\u4efb\u4f55\u7ed3\u679c\uff0c\u4f46\u662f\u7ecf\u9a8c\u79ef\u7d2f\u4e0b\u6765\uff0c\u53ef\u4ee5\u6cbf\u7528\u5230\u672a\u6765\u8981\u505a\u7684\u5176\u4ed6\u5185\u5bb9\u3002\u5173\u952e\u8fd8\u662f&#8221;\u505a\u89c9\u5f97\u6709\u8da3\u7684\u4e8b&#8221;\u3002<\/p>\n\n\n\n<h2 class=\"wp-block-heading has-medium-font-size\">Q17\uff1a\u60a8\u89c9\u5f97\u60a8\u81ea\u5df1\u662f\u4e00\u4e2a\u5f88\u5377\u7684\u4eba\u5417\uff1f<\/h2>\n\n\n\n<p><em><strong>\u9648\u6559\u6388\uff1a<\/strong><\/em>\u505a\u6709\u8da3\u7684\u4e8b\u60c5\u5c31\u4e0d\u5b58\u5728\u5377\uff0c\u4e3b\u8981\u662f\u4eab\u53d7\u505a\u7684\u8fc7\u7a0b\uff08\u7b11\uff09\u3002<\/p>\n\n\n\n<h2 class=\"wp-block-heading has-text-align-center\">\u53c2\u8d5e\u751f\u547d\u529b<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">\u4f60\u89c9\u5f97\u4ec0\u4e48\u662f\u79d1\u6280\u751f\u547d\u529b\uff1f&nbsp;<\/h3>\n\n\n\n<p>\u5728\u4e0d\u786e\u5b9a\u6027\u4e2d\u7684\u63a2\u7d22\uff0c\u5728\u524d\u8fdb\u7684\u8fc7\u7a0b\u4e2d\uff0c\u4f1a\u53d1\u73b0\u5f88\u591a\u6709\u8da3\u7684\u60ca\u559c\uff0c\u8fd9\u662f\u79d1\u6280\u5177\u6709\u751f\u547d\u529b\u5370\u8bc1<\/p>\n\n\n\n<p class=\"has-text-align-right\">\u2014\u2014<em>\u00a0\u9648\u5929\u5947\u6559\u6388\u673a\u5668\u5b66\u4e60\u7cfb\u548c\u8ba1\u7b97\u673a\u7cfb\u5361\u5185\u57fa\u6885\u9686\u5927\u5b66<\/em><\/p>\n\n\n\n<figure class=\"wp-block-image aligncenter size-large\"><img decoding=\"async\" src=\"http:\/\/cxounion.cn\/wp-content\/uploads\/2023\/06\/CXOUNION\u65b0\u4e8c\u7ef4\u7801.png\" alt=\"\"\/><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">\u7ffb\u8bd1\uff1a<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">Talk to Professor Chen Tianqi: From 0 to 1, believe the interesting things<\/h3>\n\n\n\n<p>TVM, MXNet, XGBoost author Tianqi Chen, assistant professor in CMU&#8217;s Department of Machine Learning and Computing, his work makes it possible to natively deploy any large language model on all kinds of hardware, computing power, or the problem? Enjoy<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Q1: Please talk about your recent research direction, or the work you are interested in.<\/h4>\n\n\n\n<p>Professor Chen: My research style is problem-oriented. The problem we&#8217;ve been working on for the past five years is how to make machine learning accessible to more people and run on more devices. Our recent research focuses on machine learning systems, not only to solve the algorithm, but also to make the system engineering itself more iterative; The second is to do more open source software, so that we can work in the open source community, let people directly try our research results, and through this way get feedback from industry and other fields.<mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-white-color\">  CXO UNION-CXO\u8054\u76df\uff08cxounion.cn\uff09<\/mark><\/p>\n\n\n\n<p>With the emergence of Generative AI and large models, we also hope to explore some new directions by combining large models and past accumulation.<\/p>\n\n\n\n<p>Large model deployments have also made a lot of progress recently. Machine learning compilation has also received little attention from the previous PyTorch, and major manufacturers have begun to gradually try this direction, and the entire field is in an unpredictable state. Just like when deep learning had just risen, the wave of big data seemed to be about to pass, and I did not know how to be in a good state. These are uncertain times again, but that is a good thing.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Q2: After the big language model came out, what is your latest work?<\/h4>\n\n\n\n<p>Professor Chen: In the past five years there has been a focus on machine learning compilation. Machine learning engineering is going to be a bigger and bigger problem. To run on more efficient devices, we need to build reusable architectures without reengineering systems on every hardware platform.<\/p>\n\n\n\n<p>In view of the large language model memory consumption and other characteristics, the core of our recent work is how to use machine learning compilation technology, so that machine learning in the deployment, training and support can be faster. Based on this work, we have a series of projects on the MAC that allow us to deploy language models on the phone, on the mobile side, or in the browser through WebAssembly and WebGPU technologies, as well as on all kinds of graphics cards, including Nvidia, AMD and Apple.<mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-white-color\">  CXO UNION-CXO\u8054\u76df\uff08cxounion.cn\uff09<\/mark><\/p>\n\n\n\n<p>When language models can be deployed on multiple devices, more open solutions can be built on this technology, and the cost of deploying open models can be reduced.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Q3: You&#8217;ve done a lot of interesting open source work, from the early Apache MXNet, to XGBoost, TVM. How do you connect the dots? How do you feel about the development of machine learning in more than ten years, from the algorithm and system level?<\/h4>\n\n\n\n<p>Prof Chen: Machine learning has changed dramatically in the last few years. Based on the original algorithm modeling, support vector machine and linear model are derived. After big data is applied to advertising and recommendation, we start to think about large-scale machine learning. This means that a well-built machine learning system is essential. This is why I began to turn to the field of machine learning system research, the goal is also the comprehensive consideration of algorithms, computing power and data, in the process to find the motivation to solve problems, and truly promote the advancement of machine learning field.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Q4: Nvidia has benefited greatly from the development of large language models, and your work is bound to diversify the hardware competition. How do you see the relationship between two paths like Nvidia and open source solutions?<\/h4>\n\n\n\n<p>Professor Chen: I don&#8217;t think the two are competitive, and we also do a lot of Nvidia graphics card optimization work. Our solution can take advantage of vendor native libraries in many cases. Nvidia is really leading in many ways right now, and we don&#8217;t intend to necessarily surpass Nvidia, and Nvidia isn&#8217;t perfect in all scenarios. We are interested in how to move the whole field forward faster, regardless of who is pushing it.<\/p>\n\n\n\n<p>Therefore, this is not A relationship that must be compared between A and B, in the stage where computing power is still relatively scarce, there are more possibilities for everyone to move forward together, which is what we want to see.<mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-white-color\">  CXO UNION-CXO\u8054\u76df\uff08cxounion.cn\uff09<\/mark><\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Q5: Can you talk about what areas NVIDIA is relatively leading in?<\/h4>\n\n\n\n<p>Prof. Chen: It&#8217;s the hardware and the programming model in general. The traditional Silicon approach has become difficult to solve without changing the programming model. As new cards are released, the programming model cannot be directly migrated.<\/p>\n\n\n\n<p>What we need now is a set of solutions that can quickly iterate as new hardware environments and new models emerge. Why has deep learning advanced so fast in the past decade? Because the threshold for deep learning modeling itself has been lowered very low. In the next five to 10 years, machine learning engineering will become important. For each possible combination of hardware model data, a specially engineered solution is required. The goal we are interested in is how to make engineering iterations faster. In the iteration of hardware, Nvidia is still no one can do it right, so how to better support the new hardware in the future is a very interesting topic.<\/p>\n\n\n\n<p>But now there is a difference, before the other manufacturers are not good, equal to 0. Now through our solution, AMD can run from not running to running, running well, from other manufacturers, it is also a progress. The process of going from 0 to 1 is valuable.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Q6: Since the threshold for deep learning has become very low, will the threshold for machine learning engineering for large language models also be very low in the foreseeable future?<\/h4>\n\n\n\n<p>Professor Chen: Just like before I did XGBoost, data science was very complicated, and now the basic adjustment effect is good. We want to lower the bar for machine learning engineering, and we&#8217;ve had some success. But how low it can be lowered, and when it can be lowered, is inseparable from the direction of research, the investment in research, especially the joint efforts of everyone in the open source community.<mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-white-color\">  CXO UNION-CXO\u8054\u76df\uff08cxounion.cn\uff09<\/mark><\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Q7: In this direction, there may be greater incentives for the open source community to work together, while the closed source leaders prefer to &#8220;hide&#8221; as expertise.<\/h4>\n\n\n\n<p>Professor Chen: Not to mention models, but from the perspective of machine learning engineering infrastructure, open source closed source will have a catalytic effect. If academia is to keep up with The Times, move on. Even for modeling, there will be completely closed source, and some will be at least open to LLaMa. I personally believe in open source, and the open source community will iterate faster.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Q8: What are the challenges of running models on mobile phones?<\/h4>\n\n\n\n<p>Professor Chen: Our solution can run the 7B model directly on the mobile phone, 7B to 3B is actually no pressure, the solution will definitely mature. And see if you&#8217;re going to run, or how big of a model you&#8217;re going to run, how big of a model it&#8217;s going to be useful, that&#8217;s another question.<\/p>\n\n\n\n<p>3B Our current solution can directly run to MAC, we have an APP Store APP download can play. 7B May be hot phone, energy consumption may be a little problem, high-end machine can run. The next question is how to integrate with vertical apps, and whether it is necessary to run on the mobile side, because tablets and laptops can be used. It&#8217;s on the phone because people care about keeping their data private.<\/p>\n\n\n\n<p>Our current MAC solution allows you to run on your mobile phone, you can run on the tablet, you can run on the Apple laptop, or run in the browser, of course, you can also run on the server, relatively speaking, the flexibility will be relatively large.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Q9: Since your solution allows people to run models on their mobile phones, what is the difference between this path and Nvidia and other big manufacturers?<\/h4>\n\n\n\n<p>Professor Chen: Why do you run on your phone? Why are you running on the end side? Because I think there are different applications. If you play a game, if you have to pay a dime for every word you say to the NPC, although it is not impossible, but if you can directly complete a good task in some scenes directly on the machine, I think it should be happy to see.<\/p>\n\n\n\n<p>For example, if you want to have a good personal assistant, you must disclose personal information to the maximum extent, are you willing to send these contents to a third party? Is it more secure locally? And personalization, how do you make the language model understand you better? These directions are applications that can evolve into different forms.<\/p>\n\n\n\n<p>When computers were first invented, it was said that the world could meet its computing needs with only eight supercomputers in laboratories in a few countries, but then the personal computer came along. If the threshold of models can be further lowered, will there be an era of Personal AI? This is what we want to achieve.<mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-white-color\">  CXO UNION-CXO\u8054\u76df\uff08cxounion.cn\uff09<\/mark><\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Q10: If there are tens of thousands of A100s in training, from your point of view, what are the main problems that need to be solved by experienced infrastructure architects and systems people?<\/h4>\n\n\n\n<p>Professor Chen: Because I have no experience in large-scale training in school, my feeling is that first of all, the infrastructure of machine learning itself is in its infancy, and the infrastructure of the original data is not quite the same. Many Gpus work together for a long time, how to improve the utilization of different hardware and make good use of it, hardware itself optimization is also a cross-level process, involving hardware engineering and system level, and some linkage at the model level, which must be in demand. In the field of machine learning systems, we have also begun to pay more and more attention, so there will be more and more talents in this area. I also want to lower the barriers to entry in this area, such as reducing rework through machine learning compilation. The need for engineering deployment has always been there, but now there is a greater emphasis. Our core body sense is that there is a strong demand for talent and aspects in this field, which is naturally a good thing for practitioners in machine learning and engineering deployments. We have also been thinking about how to achieve four or two thousand pounds through some automation technology.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Q11: Nvidia, AMD, and Apple all have their own architectures. What&#8217;s the difference?<\/h4>\n\n\n\n<p>Professor Chen: In terms of the whole ecology, relatively Nvidia is the best at present. AMD&#8217;s main problem is that the software is not good, and different factories put completely different efforts into the software. The entire process from hardware to model to application is built, from model to foundation to memory optimization, and even the relevant infrastructure for modeling in the first place. After a few years of hard work, we can now build a more compatible infrastructure based on compilation. We have also tried AMD before, such as the flagship game card 7900 XT, which can probably run to 80% of the state of 4090. We hope to solve more software problems through automation, we can now run more cards, such as two game cards can run 70B, equivalent to the maximum can run to LLaMa, can play a variety of hardware, or quite exciting.<\/p>\n\n\n\n<p>Apple leads at the architectural level, especially the Ultra, which has a lot of memory. The machine directly runs LLaMA model, the easiest way is to buy a new Apple M2 notebook. Our solution is also available on Apple.<\/p>\n\n\n\n<p>Software problems require everyone&#8217;s effort, and they can be solved with infrastructure. Of course, it has to be engineered. Our solution is not limited to the reasoning side, it just needs to open up some ideas.<mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-white-color\">  CXO UNION-CXO\u8054\u76df\uff08cxounion.cn\uff09<\/mark><\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Q12: Some of the work you do, like XGBoost, is very influential. How do you choose research questions?<\/h4>\n\n\n\n<p>Professor Chen: We are more concerned about how to integrate all the work together. The challenge of machine learning engineering is not to solve a specific problem, but to have 10 solutions, one for morphing, another for Sparsity, another for batching, and possibly others\u2026 This is why deep learning is growing particularly fast because of the high degree of modularity in software engineering.<\/p>\n\n\n\n<p>Do not pay attention to what is the detection, because naturally the detection head and ResNet backbone can be connected to use. People who do optimization do not have to pay attention to what ResNet is, as long as the optimizer is written, ResNet can take over. Machine learning modeling is now modular and reusable, but machine learning engineering is not yet hot enough. So what we&#8217;ve been focusing on lately is designing an infrastructure to solve these kinds of problems.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Q13: Some people say that you are a creator of a whole new discipline of machine learning compilation, what do you think about this?<\/h4>\n\n\n\n<p>Professor Chen: This is not a new discipline, we have a lot of accumulation and investment in the compilation itself. My personal research method is problem-driven, and the idea at that time was to explore what methods should be used to solve the engineering problems of machine learning, so many hardware back-end, how to leverage this field with the least capacity? We feel that automation is the way to go, and compilation engineering is one of those paths. The definition of compilation itself is evolving, and our latest solutions combine manual and automated solutions to accelerate engineering iterations.<mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-white-color\">  CXO UNION-CXO\u8054\u76df\uff08cxounion.cn\uff09<\/mark><\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Q14: What are the prospects for tree-based models?<\/h4>\n\n\n\n<p>Professor Chen: Tree-based models are still essential technology solutions in many industries, and XGBoost is still in the top five tools for data scientists. Future technologies in every direction will require tree models, which will be very important in the foreseeable future, especially in tabular data, finance and other fields, and will be used a lot.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Q15: Did you face any particular challenges during your research?<\/h4>\n\n\n\n<p>Professor Chen: There are always challenges, and there is no standard answer in scientific research. Try or fail, industry or academia, there will be a variety of forward goals, are more voluminous (laughter)! But we enjoy being part of the process of pushing the field forward.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Q16: How do you judge that your research direction is a relatively correct path?<\/h4>\n\n\n\n<p>Professor Chen: I can&#8217;t judge, I just believe. Steve Jobs has a saying: The journey is the reward. Many times the goal is not the final trophy, but the process of walking.<\/p>\n\n\n\n<p>As for whether one thing is right or not, for example, 12 years ago I began to do deep learning, which is a very right and wrong decision, the goal was to use deep learning algorithms to solve the problem of ImageNet, the result after two and a half years, there is no result, but the experience accumulated, can be used to do other content in the future. The key is to &#8220;do what&#8217;s fun&#8221;.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Q17: Do you consider yourself a very voluptuous person?<\/h4>\n\n\n\n<p>Professor Chen: There is no volume in doing interesting things, mainly enjoying the process of doing them (laughs).<mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-white-color\">  CXO UNION-CXO\u8054\u76df\uff08cxounion.cn\uff09<\/mark><\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Counsellor vitality<\/h4>\n\n\n\n<p>What do you think is technological vitality?<\/p>\n\n\n\n<p>In the exploration of uncertainty, in the process of progress, you will find a lot of interesting surprises, which is the vitality of science and technology<\/p>\n\n\n\n<p>Chen Tianqi, Professor, Department of Machine Learning and Department of Computer Science, Carnegie Mellon University<\/p>\n\n\n\n<p class=\"has-text-align-center has-cyan-bluish-gray-color has-text-color has-small-font-size\">\u7531<a href=\"http:\/\/cxounion.cn\">CXO UNION-CXO\u8054\u76df\uff08cxounion.cn\uff09<\/a>\u8f6c\u8f7d\u800c\u6210\uff0c\u6765\u6e90\u4e8e\u7eff\u6d32\u8d44\u672c Vitalbridge\uff1b\u7f16\u8f91\/\u7ffb\u8bd1\uff1aCXO UNIONCXO\u8054\u76df\u5c0fU\u3002<\/p>\n\n\n\n<p class=\"has-text-align-center has-medium-font-size\">\u5982\u9700\u52a0\u5165<a href=\"http:\/\/weixin.qq.com\/r\/fxJxaVHE1AyrrRUK90dt\">CXO UNION\uff08CXO\u8054\u76df\uff09<\/a>\u9ad8\u7ba1\u793e\u7fa4\uff0c\u8bf7\u8054\u7cfb\u793e\u7fa4\u5c0f\u4f19\u4f34\u54e6~<\/p>\n\n\n\n<figure class=\"wp-block-image aligncenter size-large\"><img decoding=\"async\" src=\"http:\/\/cxounion.cn\/wp-content\/uploads\/2023\/06\/CXOUNION\u65b0\u4e8c\u7ef4\u7801.png\" alt=\"\"\/><\/figure>\n\n\n\n<p class=\"has-cyan-bluish-gray-color has-text-color has-small-font-size\">\u514d\u8d23\u58f0\u660e: \u672c\u7f51\u7ad9(<a 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