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Articles related to "tools"


Cuda on WSL2 for Deep Learning — First Impressions and Benchmarks

  • I know most deep learning libraries support Windows but the experience to get things working, especially open source A.I. software, was always a headache.
  • I decided to do some benchmarking to compare deep learning training performance of Ubuntu vs WSL2 Ubuntu vs Windows 10.
  • WSL2 with CUDA support takes 18% longer than native Ubuntu to train an MNIST model on my Nvidia RTX 2080 Ti. CUDA support with WSL2 is still in early preview mode and I’m hopeful that the engineers and researchers over and Microsoft and Nvidia will eventually reach a point where it gets close to Ubuntu Performance.
  • For some people, taking 18% longer to train models may be a non-starter, but for me, I can take the small performance hit if it means I can asynchronously work on training deep learning models as well as use my Windows compatible software tools to create content.

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Cuda on WSL2 for Deep Learning — First Impressions and Benchmarks

  • I know most deep learning libraries support Windows but the experience to get things working, especially open source A.I. software, was always a headache.
  • I decided to do some benchmarking to compare deep learning training performance of Ubuntu vs WSL2 Ubuntu vs Windows 10.
  • WSL2 with CUDA support takes 18% longer than native Ubuntu to train an MNIST model on my Nvidia RTX 2080 Ti. CUDA support with WSL2 is still in early preview mode and I’m hopeful that the engineers and researchers over and Microsoft and Nvidia will eventually reach a point where it gets close to Ubuntu Performance.
  • For some people, taking 18% longer to train models may be a non-starter, but for me, I can take the small performance hit if it means I can asynchronously work on training deep learning models as well as use my Windows compatible software tools to create content.

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Strap in -- a virtual Tour de France kicks off this weekend on the online racing platform Zwift

  • In fact, beginning this coming weekend, 23 top men’s teams and 17 women’s teams will participate in a virtual version of the event that’s being hosted by six-year-old Zwift, after it was chosen by the official race organizer of the real tour, Amaury Sport Organization (ASO), as its partner on the event.
  • Though Zwift has staged virtual races before —  including the Giro d’Italia, which is basically the Tour de France for Italy, and the Vuelta a Espana, an annual multi-stage race in Spain — it “doesn’t get any bigger than this,” said Min, who told us the idea was hatched six weeks ago with ASO and that Zwift has been working furiously to prepare for the race ever since.
  • It already has nearly two million accounts, and while subscribers ebb and flow, depending on the time of year, the virtual Tour is an opportunity for some of those riders to “reengage,” Min says, adding that Zwift has been growing 50 percent year over year, and has unsurprisingly seen pick-up accelerate throughout the pandemic.

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A Spark of Insight into Neanderthal Behavior

  • Martisius was studying a handful of small bone artifacts thought to be lissoirs, or bone tools used in working animal hides, that had come from two archaeological sites in southwest France.
  • Archaeologists can use ZooMS, or Zooarchaeology by Mass Spectrometry (a term coined by researcher Michael Buckley in 2009), to identify the animals to which archaeological bones once belonged.
  • In the conference session, Martisius listened to a research team from the University of York outline a new, nondestructive use for ZooMS: simply sampling a plastic box or bag in which a bone object had been kept.
  • In the archaeological layers where the bone tool pieces had been found, the majority of the animal bones were identified as belonging to reindeer.
  • To Martisius, this indicates that Neanderthals hunted plentiful reindeer for food but stuck with the larger animals when it came to choosing raw materials for tools.

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A History Of Artificial Intelligence — From the Beginning

  • Modern machines — like the above Boston Dynamics robot, Atlas — use hundreds of parts, including hydraulic joints, pistons, gears, valves, and so on to accomplish complex tasks, such as self-correcting stabilization, or even backflips.
  • Over the centuries, more and more complex contraptions were used to create automata, such as wind-powered moving machines.
  • As explored in the beginning, the rise of machines prompted Alan Turing to ask, in 1950, “can machines think?” Five years later, Dartmouth released a seminal paper on AI, and the field’s fundamental principles have remained similar since then.
  • A mechanical machine also has much stricter physical limitations, in terms of how many machine components (e.g. pulleys, levers, gears) can be fit in a contraption, while a modern digital machine’s CPU can fit billions of transistors.

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A History Of Artificial Intelligence — From the Beginning

  • Modern machines — like the above Boston Dynamics robot, Atlas — use hundreds of parts, including hydraulic joints, pistons, gears, valves, and so on to accomplish complex tasks, such as self-correcting stabilization, or even backflips.
  • Over the centuries, more and more complex contraptions were used to create automata, such as wind-powered moving machines.
  • As explored in the beginning, the rise of machines prompted Alan Turing to ask, in 1950, “can machines think?” Five years later, Dartmouth released a seminal paper on AI, and the field’s fundamental principles have remained similar since then.
  • A mechanical machine also has much stricter physical limitations, in terms of how many machine components (e.g. pulleys, levers, gears) can be fit in a contraption, while a modern digital machine’s CPU can fit billions of transistors.

save | comments | report | share on