Row 7520

Row ID: 7520 | Dataset Entry | Axioma AXP Content Repository

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I am looking into building a solution to make it easy to use Mac machines with large VRAM to run Pytorch projects for dev/test purposes. I understand they can't be used for running production inference or large-scale training. However, these machines are more readily available and cheaper than Nvidia GPU instances (A100/H100) and have large VRAM so they can run Pytorch experiments. I see challenges in terms of their current usability. They can't run Pytorch container environments (similar to running Pytorch containers on Linux instances running Nvidia GPUs), and they have no management tools similar to RAY, Kubernetes so a company can't build a dev/test rig with multiple machines for their data scientists team. With this solution, I envision companies using hosted Macs or Mac instances on AWS(with 32Gb or more VRAM) for their Data scientists to run Pytorch experiments. Am I thinking about it in the right way?

FieldValue
text I am looking into building a solution to make it easy to use Mac machines with large VRAM to run Pytorch projects for dev/test purposes. I understand they can't be used for running production inference or large-scale training. However, these machines are more readily available and cheaper than Nvidia GPU instances (A100/H100) and have large VRAM so they can run Pytorch experiments. I see challenges in terms of their current usability. They can't run Pytorch container environments (similar to run…
label r/machinelearning
dataType post
communityName r/MachineLearning
datetime 2024-05-16
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url_encoded Z0FBQUFBQm5Lak9IX2JrOU9IdWtWYWtFUnJvbFI3ZW9ZQXZOSENoTjR2c19mSFhIM2pyLS1TdU1CNlNNek1XSC05SXdjM0VtclBiLVRwUmM3S2FrYkQ2M1BHNGhzWUtsNEE0MXZ0VXVpTmREeUJMMHF2NU1VSVpzNTVVT3dqU1VJcUhhajRuZHdyZGRwUlZwa0xfMTNubGN3bDhnQmZSUjVXaHJSd3UzNUJ0dmhuZElpRlduYXkwS0hxaWNBMmRsSXFscW9FVV9KRmRJ

Raw Record

{
  "text": "I am looking into building a solution to make it easy to use Mac machines with large VRAM to run Pytorch projects for dev/test purposes. I understand they can't be used for running production inference or large-scale training. However, these machines are more readily available and cheaper than Nvidia GPU instances (A100/H100) and have large VRAM so they can run Pytorch experiments. I see challenges in terms of their current usability. They can't run Pytorch container environments (similar to running Pytorch containers on Linux instances running Nvidia GPUs), and they have no management tools similar to RAY, Kubernetes so a company can't build a dev/test rig with multiple machines for their data scientists team. With this solution, I envision companies using hosted Macs or Mac instances on AWS(with 32Gb or more VRAM) for their Data scientists to run Pytorch experiments. Am I thinking about it in the right way?",
  "label": "r/machinelearning",
  "dataType": "post",
  "communityName": "r/MachineLearning",
  "datetime": "2024-05-16",
  "username_encoded": "Z0FBQUFBQm5LakwzeW81TTZLOVc3ejQwQ3hJMWF3NlkxaEM4Zk1vVzJ3ZHhSVzEtLUtoX05aT2dYaERyZ1NRVk4wUXNpdVBrTnRyZjVyMmNoTDMwb2dEcmpjZlVYc3RWQ1dMS1o0bTZJdTZmbGFHczRRZjhPV2c9",
  "url_encoded": "Z0FBQUFBQm5Lak9IX2JrOU9IdWtWYWtFUnJvbFI3ZW9ZQXZOSENoTjR2c19mSFhIM2pyLS1TdU1CNlNNek1XSC05SXdjM0VtclBiLVRwUmM3S2FrYkQ2M1BHNGhzWUtsNEE0MXZ0VXVpTmREeUJMMHF2NU1VSVpzNTVVT3dqU1VJcUhhajRuZHdyZGRwUlZwa0xfMTNubGN3bDhnQmZSUjVXaHJSd3UzNUJ0dmhuZElpRlduYXkwS0hxaWNBMmRsSXFscW9FVV9KRmRJ"
}
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