. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 3.3. 函数 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 276 13. 函数式语言特性：迭代器与闭包 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 278 13.1. 闭包：可以捕获其环境的匿名函数 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 488 20.4. 高级函数与闭包 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

n=500 USA adults,. Figures estimated based on overall AI tool usage adjusted for an average 72% usage rate of ChatGPT amongst respondents who use any AI tools. Actual ChatGPT penetration may vary by cohort Retention (ChatGPT as Proxy) = 80% vs. 58% Over Twenty-Seven Months, per YipitData Note: Retention Rate = Percentage of users from the immediately preceding week that were users again in the current week in 92 days to 200k GPUs. This is just the beginning… …We doubled our compute at an unprecedented rate, with a roadmap to 1M GPUs. Progress in AI is driven by compute and no one has come close to building

whether the number of waitersCHAPTER 33. PROFILING 438 Figure 33.1: CPU Profile is excessive given the rate at which work items are being produced in the channel. If we run this, we obtain the following PProf overhead, so a sample_rate argument can be passed to speed it up by making it skip some allocations. Passing sample_rate=1.0 will make it record everything (which is slow); sample_rate=0.1 will record only We collect the profile (specifying a sample rate), then we visualize it. using Profile, PProf Profile.Allocs.clear() Profile.Allocs.@profile sample_rate=0.0001 my_function() PProf.Allocs.pprof() Here

whether the number of waitersCHAPTER 33. PROFILING 439 Figure 33.1: CPU Profile is excessive given the rate at which work items are being produced in the channel. If we run this, we obtain the following PProf overhead, so a sample_rate argument can be passed to speed it up by making it skip some allocations. Passing sample_rate=1.0 will make it record everything (which is slow); sample_rate=0.1 will record only We collect the profile (specifying a sample rate), then we visualize it. using Profile, PProf Profile.Allocs.clear() Profile.Allocs.@profile sample_rate=0.0001 my_function() PProf.Allocs.pprof() Here

whether the number of waitersCHAPTER 33. PROFILING 438 Figure 33.1: CPU Profile is excessive given the rate at which work items are being produced in the channel. If we run this, we obtain the following PProf overhead, so a sample_rate argument can be passed to speed it up by making it skip some allocations. Passing sample_rate=1.0 will make it record everything (which is slow); sample_rate=0.1 will record only We collect the profile (specifying a sample rate), then we visualize it. using Profile, PProf Profile.Allocs.clear() Profile.Allocs.@profile sample_rate=0.0001 my_function() PProf.Allocs.pprof() Here

whether the number of waitersCHAPTER 33. PROFILING 438 Figure 33.1: CPU Profile is excessive given the rate at which work items are being produced in the channel. If we run this, we obtain the following PProf overhead, so a sample_rate argument can be passed to speed it up by making it skip some allocations. Passing sample_rate=1.0 will make it record everything (which is slow); sample_rate=0.1 will record only We collect the profile (specifying a sample rate), then we visualize it. using Profile, PProf Profile.Allocs.clear() Profile.Allocs.@profile sample_rate=0.0001 my_function() PProf.Allocs.pprof() Here

whether the number of waitersCHAPTER 33. PROFILING 438 Figure 33.1: CPU Profile is excessive given the rate at which work items are being produced in the channel. If we run this, we obtain the following PProf overhead, so a sample_rate argument can be passed to speed it up by making it skip some allocations. Passing sample_rate=1.0 will make it record everything (which is slow); sample_rate=0.1 will record only We collect the profile (specifying a sample rate), then we visualize it. using Profile, PProf Profile.Allocs.clear() Profile.Allocs.@profile sample_rate=0.0001 my_function() PProf.Allocs.pprof() Here

ceiling  Does not require a deliverable for payment  Profit is built into the hourly labor rate so it does not require extensive upfront fee negotiation  Unpopular contract type across the create the spending plan during the acquisition phase. This spending plan outlines how and at what rate the program will expend its funding over time. Because a reasonable and supportable budget is essential

overhead, so a sample_rate argument can be passed to speed it up by making it skip some allocations. Passing sample_rate=1.0 will make it record everything (which is slow); sample_rate=0.1 will record only We collect the profile (specifying a sample rate), then we visualize it. using Profile, PProf Profile.Allocs.clear() Profile.Allocs.@profile sample_rate=0.0001 my_function() PProf.Allocs.pprof() Here Here is a more in-depth example, showing how we can tune the sample rate. A good number of samples to aim for is around 1 - 10 thousand. Too many, and the profile visualizer can get overwhelmed, and profiling

overhead, so a sample_rate argument can be passed to speed it up by making it skip some allocations. Passing sample_rate=1.0 will make it record everything (which is slow); sample_rate=0.1 will record only We collect the profile (specifying a sample rate), then we visualize it. using Profile, PProf Profile.Allocs.clear() Profile.Allocs.@profile sample_rate=0.0001 my_function() PProf.Allocs.pprof() Here Here is a more in-depth example, showing how we can tune the sample rate. A good number of samples to aim for is around 1 - 10 thousand. Too many, and the profile visualizer can get overwhelmed, and profiling