Comments (3)
@aireenmei to give context.
Mostly runtime performance is that both are fast enough to not be your bottleneck but Grain gives determinism guarantees!
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Hi @leandrolcampos,
The biggest benefit of grain is determinism. No matter how your run gets interrupted, you can always recover the same data order from your checkpoint, so your loss curve is reproducible, debuggable.
Other benefits includes:
- minimal dependency, especially if you don't want to depends on TensorFlow
- Close to a global shuffle, while the shuffle in tf.data is limited by memory
- For some transformations, it may be easier to implement in numpy
And like I mentioned with you in another thread under grain repo, I just put up some grain docs: https://github.com/google/grain/tree/main/docs
from maxtext.
Hi @rwitten and @aireenmei,
Thanks for your answers. They helped me to better understand the PyGrain value proposition. I'm looking forward to use it in my next research projects.
All the best,
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Related Issues (20)
- `attend_dtype` not used HOT 1
- Create a user friendly inference demo
- Converting checkpoints HOT 25
- Support for T5 HOT 6
- Supported features HOT 18
- Issues running test_llama2_7b.sh on TPU VM v3-8 HOT 1
- Gemma instructions were deleted in commit HOT 2
- Support Qwen1.5 HOT 1
- Support beam search
- Cannot do inference in float32 HOT 2
- Support for RecurrentGemma
- Clarification: how does Llama-2-7b fit on a v4-8 when using Adam? HOT 3
- Question: Gradient Accumulation HOT 4
- Support LoRA training HOT 1
- Consolidate inference related logic under jetstream-maxtext HOT 1
- DEFAULT_MASK_VALUE causes gradient explosion and nan loss on deep models HOT 1
- Asignación HOT 1
- Reproducing pure computation TFLOPs HOT 4
- How to convert a model to parameter only checkpoints (unscanned) on a CPU VM HOT 2
- Update Inference Microbenchmark scripts
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