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Description
Motivation
Currently, our Fluid implementation is incomplete and makes the AI application programming pretty nasty.
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Fluid was designed to make AI programming concise and easy, like examples in
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However, the currently runnable examples are as follows
In order to complete the design of Fluid, we create this issue as a table of context of the design of Fluid.
Questions to Answer
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How to train on one node
- Read data: Fluid data pipeline interface #10102
- Training data augmentation: augmentation programs pipes the output to PaddlePaddle programs
- The data format: RecordIO is good for training/testing data, but not good for model parameters. https://github.com/wangkuiyi/recordio
- Map records into Fluid variables.
- Save model parameters
- How to write the program Paddle API v4 Proposal #9912
- How to fetch and feed data from the front end language
- Should we allow the ability to fetch intermediate variables during execution?
- How to run the program
- How to debug
- Do we want to allow running arbitrary Python code during training? E.g., some custom Python logic to control save model or not during training.
- How to fetch data during training steps In discussion: Paddle API v4 proposal #10152
- How to initialize a Fluid program? In discussion: How to initialize a Fluid program? #10177
- Read data: Fluid data pipeline interface #10102
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How to do distributed training
- How to do distributed reading
- How to save huge models
- How to write program
- How to run the program
- SLURM + container
- MPI + container
- Kubernetes
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How to do inference
- Easy experiments
- Large-scale serving
- How to export to ONNX and pipe with TensorRT
Examples of an API proposal
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