Day: April 23, 2020

If you’d like $1,000 in DoorDash credit, we’ve got a little proposition for you.

TLDR: Enter to win the $1,000 DoorDash Giveaway and you’ll have a grand’s worth of amazing meal options delivered right to your home for free.

What was once a passion for the well-off and millennials has now reached the mainstream, courtesy of stay-at-home social distancing. Meal delivery services are very, very, VERY in right now.

By now, you’re probably getting very familiar with quick app orders, followed 20 to 30 minutes later by a ring of the doorbell and an opening of the door to discover a porch filled with a delicious meal from a nearby restaurant favorite.

However, paying

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GPipe and PipeDream: Scaling AI training in every direction

Data science is hard work, not a magical incantation. Whether an AI model performs as advertised depends on how well it’s been trained, and there’s no “one size fits all” approach for training AI models.

The necessary evil of distributed AI training

Scaling is one of the trickiest considerations when training AI models. Training can be especially challenging when a model grows too resource hungry to be processed in its entirety on any single computing platform. A model may have grown so large it exceeds the memory limit of a single processing platform, or an accelerator has required developing special

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Go language use still anchored in technology companies

While the Google-developed Go (golang) language has branched out into industries such as finance and media, much of its usage remains concentrated in the technology industry itself, according to the Go Developer 2019 Survey.

A report featuring results of the survey was published on April 20. Forty-three percent of respondents reported working in the technology sector while 12 percent were in financial services, 9 percent in media/gaming, and 7 percent in retail/consumer packaged goods.

A large majority of respondents said Go was working well for their teams (86 percent) and that they would prefer to use Go for their

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Innovating distributed AI training in every direction

Data science is hard work, not a magical incantation. Whether an AI model performs as advertised depends on how well it’s been trained, and there’s no “one size fits all” approach for training AI models.

The necessary evil of distributed AI training

Scaling is one of the trickiest considerations when training AI models. Training can be especially challenging when a model grows too resource hungry to be processed in its entirety on any single computing platform. A model may have grown so large it exceeds the memory limit of a single processing platform, or an accelerator has required developing special

Read More