Graphcore’s IPU could be cheaper and faster than NVIDIA’s A100, though need sharing on-board RAM.
Supports tensorflow, pytorch, paddlepaddle.
https://docs.graphcore.ai/en/latest/
pytorch: poptorch, pytorch-lightning(tpu and ipu)
tensorflow:
from tensorflow.python import ipu
Create an IPU distribution strategy
strategy = ipu.ipu_strategy.IPUStrategy()
with strategy.scope():
…
paddlepaddle:
https://github.com/graphcore/portfolio-examples/tree/master/paddlepaddle/bert-base
https://github.com/graphcore/Paddle.git
TensorFlow 1 & 2 support with full performant integration with TensorFlow XLA backend
PyTorch support for targeting IPU using the PyTorch ATEN backend
PopART™ (Poplar Advanced Runtime) for training & inference; supports Python/C++ model building plus ONNX model input
Full support for PaddlePaddle
Other frameworks support coming soon
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retrieval based attention net
regression test in stock market, which can also apply to bilibili
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