Row 3940

Row ID: 3940 | Dataset Entry | Axioma AXP Content Repository

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I'm planning to build low budget machine for training object detection networks, such as yolo, retinanet, etc.

It looks like a dual P100 machine, with legacy xeon cpu, motherboard and memory can be purchased at around 1000$ - But is it too good to be true?

P100 was released in 2016 and does not support bfloats - Will that limit the use of current pytorch version for training purposes? How future proof is it? The entire build is based on PCIe3, upgrading it in the future is probably not possible.

Will the two GPUs be able to share compute/memory while training? Or is that only possible with the NVLink variety of servers?

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FieldValue
text I'm planning to build low budget machine for training object detection networks, such as yolo, retinanet, etc. It looks like a dual P100 machine, with legacy xeon cpu, motherboard and memory can be purchased at around 1000$ - But is it too good to be true? P100 was released in 2016 and does not support bfloats - Will that limit the use of current pytorch version for training purposes? How future proof is it? The entire build is based on PCIe3, upgrading it in the future is probably not possibl…
label r/pytorch
dataType post
communityName r/pytorch
datetime 2024-03-30
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Raw Record

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  "text": "I'm planning to build low budget machine for training object detection networks, such as yolo, retinanet, etc.\n\nIt looks like a dual P100 machine, with legacy xeon cpu, motherboard and memory can be purchased at around 1000$ - But is it too good to be true?\n\nP100 was released in 2016 and does not support bfloats - Will that limit the use of current pytorch version for training purposes? How future proof is it? The entire build is based on PCIe3, upgrading it in the future is probably not possible.\n\nWill the two GPUs be able to share compute/memory while training? Or is that only possible with the NVLink variety of servers?\n\n​",
  "label": "r/pytorch",
  "dataType": "post",
  "communityName": "r/pytorch",
  "datetime": "2024-03-30",
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}
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