Distributed support
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<summary>Show benchmarks</summary>

PyArrow==6.0.0

<details>
<summary>Show updated benchmarks!</summary>

### Benchmark: benchmark_array_xd.json

| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.015146 / 0.011353 (0.003793) | 0.006683 / 0.011008 (-0.004326) | 0.125994 / 0.038508 (0.087486) | 0.041345 / 0.023109 (0.018235) | 0.378609 / 0.275898 (0.102711) | 0.483139 / 0.323480 (0.159659) | 0.009669 / 0.007986 (0.001684) | 0.005143 / 0.004328 (0.000814) | 0.092015 / 0.004250 (0.087765) | 0.052728 / 0.037052 (0.015676) | 0.397166 / 0.258489 (0.138677) | 0.465820 / 0.293841 (0.171979) | 0.051025 / 0.128546 (-0.077521) | 0.018451 / 0.075646 (-0.057196) | 0.397311 / 0.419271 (-0.021960) | 0.054842 / 0.043533 (0.011309) | 0.391203 / 0.255139 (0.136064) | 0.412743 / 0.283200 (0.129543) | 0.111356 / 0.141683 (-0.030327) | 1.697526 / 1.452155 (0.245372) | 1.795017 / 1.492716 (0.302301) |

### Benchmark: benchmark_getitem\_100B.json

| metric | get_batch_of\_1024\_random_rows | get_batch_of\_1024\_rows | get_first_row | get_last_row |
|--------|---|---|---|---|
| new / old (diff) | 0.253737 / 0.018006 (0.235731) | 0.583071 / 0.000490 (0.582581) | 0.005958 / 0.000200 (0.005758) | 0.000110 / 0.000054 (0.000056) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.030397 / 0.037411 (-0.007014) | 0.112242 / 0.014526 (0.097716) | 0.138807 / 0.176557 (-0.037749) | 0.209820 / 0.737135 (-0.527316) | 0.139530 / 0.296338 (-0.156808) |

### Benchmark: benchmark_iterating.json

| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.574111 / 0.215209 (0.358902) | 5.623713 / 2.077655 (3.546058) | 2.416880 / 1.504120 (0.912760) | 1.951013 / 1.541195 (0.409819) | 2.124565 / 1.468490 (0.656075) | 1.268854 / 4.584777 (-3.315923) | 5.942368 / 3.745712 (2.196656) | 5.413814 / 5.269862 (0.143952) | 2.931638 / 4.565676 (-1.634038) | 0.135070 / 0.424275 (-0.289205) | 0.014290 / 0.007607 (0.006683) | 0.708384 / 0.226044 (0.482340) | 7.487994 / 2.268929 (5.219065) | 3.074210 / 55.444624 (-52.370414) | 2.380583 / 6.876477 (-4.495893) | 2.522298 / 2.142072 (0.380226) | 1.336741 / 4.805227 (-3.468486) | 0.236761 / 6.500664 (-6.263903) | 0.076592 / 0.075469 (0.001123) |

### Benchmark: benchmark_map_filter.json

| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |
|--------|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 1.629415 / 1.841788 (-0.212373) | 19.000640 / 8.074308 (10.926332) | 21.474058 / 10.191392 (11.282666) | 0.231227 / 0.680424 (-0.449197) | 0.046213 / 0.534201 (-0.487988) | 0.565703 / 0.579283 (-0.013580) | 0.662956 / 0.434364 (0.228592) | 0.656475 / 0.540337 (0.116137) | 0.762534 / 1.386936 (-0.624402) |

</details>
PyArrow==latest

<details>
<summary>Show updated benchmarks!</summary>

### Benchmark: benchmark_array_xd.json

| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.010952 / 0.011353 (-0.000400) | 0.006259 / 0.011008 (-0.004749) | 0.132430 / 0.038508 (0.093922) | 0.037920 / 0.023109 (0.014811) | 0.483565 / 0.275898 (0.207667) | 0.528190 / 0.323480 (0.204710) | 0.008116 / 0.007986 (0.000130) | 0.006768 / 0.004328 (0.002440) | 0.100520 / 0.004250 (0.096270) | 0.055208 / 0.037052 (0.018155) | 0.484672 / 0.258489 (0.226183) | 0.556937 / 0.293841 (0.263096) | 0.057938 / 0.128546 (-0.070609) | 0.020821 / 0.075646 (-0.054826) | 0.430735 / 0.419271 (0.011464) | 0.066317 / 0.043533 (0.022785) | 0.496652 / 0.255139 (0.241513) | 0.502004 / 0.283200 (0.218804) | 0.125403 / 0.141683 (-0.016280) | 1.833396 / 1.452155 (0.381241) | 1.974517 / 1.492716 (0.481800) |

### Benchmark: benchmark_getitem\_100B.json

| metric | get_batch_of\_1024\_random_rows | get_batch_of\_1024\_rows | get_first_row | get_last_row |
|--------|---|---|---|---|
| new / old (diff) | 0.269198 / 0.018006 (0.251191) | 0.620314 / 0.000490 (0.619824) | 0.000535 / 0.000200 (0.000335) | 0.000083 / 0.000054 (0.000029) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.032373 / 0.037411 (-0.005039) | 0.130043 / 0.014526 (0.115517) | 0.146217 / 0.176557 (-0.030339) | 0.200187 / 0.737135 (-0.536948) | 0.152839 / 0.296338 (-0.143499) |

### Benchmark: benchmark_iterating.json

| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.677478 / 0.215209 (0.462268) | 6.678856 / 2.077655 (4.601201) | 3.025870 / 1.504120 (1.521750) | 2.678196 / 1.541195 (1.137001) | 2.740640 / 1.468490 (1.272150) | 1.237163 / 4.584777 (-3.347614) | 5.752621 / 3.745712 (2.006908) | 3.170435 / 5.269862 (-2.099427) | 2.049174 / 4.565676 (-2.516502) | 0.147663 / 0.424275 (-0.276612) | 0.016107 / 0.007607 (0.008500) | 0.849666 / 0.226044 (0.623621) | 8.395212 / 2.268929 (6.126283) | 3.741120 / 55.444624 (-51.703505) | 3.102926 / 6.876477 (-3.773550) | 3.233655 / 2.142072 (1.091583) | 1.520349 / 4.805227 (-3.284878) | 0.267159 / 6.500664 (-6.233505) | 0.083646 / 0.075469 (0.008177) |

### Benchmark: benchmark_map_filter.json

| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |
|--------|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 1.640458 / 1.841788 (-0.201330) | 19.043169 / 8.074308 (10.968861) | 22.786126 / 10.191392 (12.594734) | 0.218040 / 0.680424 (-0.462384) | 0.032948 / 0.534201 (-0.501253) | 0.569574 / 0.579283 (-0.009710) | 0.658746 / 0.434364 (0.224382) | 0.650501 / 0.540337 (0.110164) | 0.730588 / 1.386936 (-0.656348) |

</details>
</details>

![](https://cml.dev/watermark.png "CML watermark")
 just added a note :)
To split your dataset across your training nodes, you can use the new [`datasets.distributed.split_dataset_by_node`]:

```python
import os
from datasets.distributed import split_dataset_by_node

ds = split_dataset_by_node(ds, rank=int(os.environ["RANK"]), world_size=int(os.environ["WORLD_SIZE"]))
```

This works for both map-style datasets and iterable datasets.
The dataset is split for the node at rank `rank` in a pool of nodes of size `world_size`.

For map-style datasets:

Each node is assigned a chunk of data, e.g. rank 0 is given the first chunk of the dataset.

For iterable datasets:

If the dataset has a number of shards that is a factor of `world_size` (i.e. if `dataset.n_shards % world_size == 0`),
then the shards are evenly assigned across the nodes, which is the most optimized.
Otherwise, each node keeps 1 example out of `world_size`, skipping the other examples.

This can also be combined with a `torch.utils.data.DataLoader` if you want each node to use multiple workers to load the data.

This also supports shuffling. At each epoch, the iterable dataset shards are reshuffled across all the nodes - you just have to call `iterable_ds.set_epoch(epoch_number)`.

TODO:
- [x] docs for usage in PyTorch
- [x] unit tests
- [x] integration tests with torch.distributed.launch

Related to https://github.com/huggingface/transformers/issues/20770
Close https://github.com/huggingface/datasets/issues/5360