Comments (7)
Arshi81099
commented on July 30, 2024
1
Hi @duncantech, I am already on it.
I will update it ASAP
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Arshi81099
commented on July 30, 2024
/assigntome
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duncantech
commented on July 30, 2024
@Arshi81099, haven't heard any updates from you yet and were wondering if you still had this ticket? We will be unassigning it if we don't hear back as a heads up!
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Arshi81099
commented on July 30, 2024
Changes and Improvements:
-
Device Selection: Added flexibility to choose between XLA (TPU) and CPU based on availability (
xm.is_xla_available()). -
Model Preparation: Ensured the model is properly moved to the selected device (
device), which is crucial for correct execution. -
Clarification: Provided additional comments and explanations to enhance understanding, particularly regarding device handling and model export.
-
Optional Dependencies: Clarified optional installation steps for TensorFlow Serving (
tf-nightly) if serving functionalities are required.
This version of the stablehlo.md file aims to provide comprehensive guidance while maintaining clarity and ease of use, suitable for both local setups (like Mac) and cloud environments (like Google Colab with CPU).
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Related Issues (20)
- Add example for training small LLM HOT 4
- Select a model to train and run on TPUs HOT 10
- How do I know which pytorch parameter corresponds to which parameter in hlo ir HOT 9
- Distributed spmd training with multiple compilations HOT 4
- RuntimeError: isDifferentiableType(variable.scalar_type()) INTERNAL ASSERT FAILED when using torch.repeat HOT 2
- In-place operations on an DLPack aliased XLA tensor does not propagate. HOT 8
- [RFC] PR Cherrypicking Process After a Release Branch Cut HOT 1
- Incomplete Checkpoints for Non-Sharded Parameters During SPMD Training in PyTorch XLA HOT 4
- Delete main branch HOT 4
- TPU Initialization Failed HOT 3
- How to convert hlo.pb to hlo text? HOT 3
- The combination of inplace ops and custom op resulted in incorrect results HOT 3
- 2.4 backport PR request list HOT 15
- [torchbench] `drq` training fails to run on non-dynamo. HOT 2
- [RFC] PyTorch/XLA eager mode as default HOT 3
- [RFC] torch_xla2 dynamo integration HOT 5
- Error: Check Failed: `it != inputs_.end()` When Using `torch` and `torch_xla` Nightly Version (Post-20240527) with SPMD HOT 4
- xla gpu train ResizeBicubic is not supported HOT 4
- SPMD on TPU Pod with Multiple Machines and Randomness/Seed HOT 6
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