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241 Table rendering in Chapter 7 is messed up #249

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Jun 4, 2024
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241 Table rendering in Chapter 7 is messed up #249

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26 changes: 13 additions & 13 deletions contents/training/training.qmd
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Expand Up @@ -1010,19 +1010,19 @@ However, as the model parts run on physically separate devices, they must commun

### Comparison

To summarize, @tbl-parallelism demonstrates some of the key characteristics for comparing data parallelism and model parallelism:

| Characteristic | Data Parallelism | Model Parallelism |
|-|-----------------|-------------------|
| Definition | Distribute data across devices with model replicas | Distribute model across devices |
| Objective | Accelerate training through compute scaling | Enable larger model training |
| Scaling Method | Scale devices/workers | Scale model size|
| Main Constraint | Model size per device | Device coordination overhead |
| Hardware Requirements | Multiple GPU/TPUs | Often specialized interconnect |
| Primary Challenge | Parameter synchronization | Complex partitioning + communication |
| Types | N/A | Layer-wise, filter-wise, spatial |
| Code Complexity | Minimal changes | More significant model surgery |
| Popular Libraries | Horovod, PyTorch Distributed | Mesh TensorFlow |
To summarize, `@tbl-parallelism` demonstrates some of the key characteristics for comparing data parallelism and model parallelism:

| Characteristic | Data Parallelism | Model Parallelism |
|----------------------|----------------------------------------------------|--------------------------------------|
| Definition | Distribute data across devices with model replicas | Distribute model across devices |
| Objective | Accelerate training through compute scaling | Enable larger model training |
| Scaling Method | Scale devices/workers | Scale model size |
| Main Constraint | Model size per device | Device coordination overhead |
| Hardware Requirements| Multiple GPU/TPUs | Often specialized interconnect |
| Primary Challenge | Parameter synchronization | Complex partitioning + communication |
| Types | N/A | Layer-wise, filter-wise, spatial |
| Code Complexity | Minimal changes | More significant model surgery |
| Popular Libraries | Horovod, PyTorch Distributed | Mesh TensorFlow |

: Comparing data parallelism and model parallelism. {#tbl-parallelism}

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