Load cached dataset as iterable 
@lhoestq Could you please look into that and review? _The documentation is not available anymore as the PR was closed or merged._ @lhoestq I refactored the code. Could you please check is it what you requested? @lhoestq  Thanks for a review. Excellent tips. All tips applied.  I think there is just PythonFormatter that needs to be imported in the test file and we should be good to merge @lhoestq that is weird. I have linter error when I do it. @lhoestq Now it should work properly. <details>
<summary>Show benchmarks</summary>

PyArrow==8.0.0

<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) | 0.006152 / 0.011353 (-0.005201) | 0.004169 / 0.011008 (-0.006839) | 0.097968 / 0.038508 (0.059460) | 0.028325 / 0.023109 (0.005216) | 0.308958 / 0.275898 (0.033060) | 0.341832 / 0.323480 (0.018352) | 0.005098 / 0.007986 (-0.002887) | 0.004721 / 0.004328 (0.000393) | 0.075067 / 0.004250 (0.070817) | 0.040514 / 0.037052 (0.003462) | 0.308355 / 0.258489 (0.049866) | 0.351063 / 0.293841 (0.057222) | 0.025261 / 0.128546 (-0.103285) | 0.008483 / 0.075646 (-0.067163) | 0.321219 / 0.419271 (-0.098052) | 0.058258 / 0.043533 (0.014725) | 0.312572 / 0.255139 (0.057433) | 0.330667 / 0.283200 (0.047467) | 0.091047 / 0.141683 (-0.050635) | 1.536541 / 1.452155 (0.084387) | 1.606566 / 1.492716 (0.113850) |

### 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) | 0.213234 / 0.018006 (0.195228) | 0.494801 / 0.000490 (0.494311) | 0.003764 / 0.000200 (0.003564) | 0.000074 / 0.000054 (0.000019) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.023653 / 0.037411 (-0.013758) | 0.097176 / 0.014526 (0.082650) | 0.102961 / 0.176557 (-0.073595) | 0.164285 / 0.737135 (-0.572851) | 0.107586 / 0.296338 (-0.188753) |

### 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) | 0.421402 / 0.215209 (0.206193) | 4.195828 / 2.077655 (2.118174) | 1.884664 / 1.504120 (0.380544) | 1.679750 / 1.541195 (0.138556) | 1.719725 / 1.468490 (0.251235) | 0.552290 / 4.584777 (-4.032486) | 3.386337 / 3.745712 (-0.359375) | 1.771527 / 5.269862 (-3.498334) | 1.133327 / 4.565676 (-3.432349) | 0.067911 / 0.424275 (-0.356364) | 0.012572 / 0.007607 (0.004965) | 0.518004 / 0.226044 (0.291960) | 5.192381 / 2.268929 (2.923453) | 2.316032 / 55.444624 (-53.128592) | 1.993264 / 6.876477 (-4.883212) | 2.071009 / 2.142072 (-0.071063) | 0.655062 / 4.805227 (-4.150165) | 0.135488 / 6.500664 (-6.365177) | 0.067273 / 0.075469 (-0.008196) |

### 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) | 1.217731 / 1.841788 (-0.624056) | 13.812927 / 8.074308 (5.738619) | 13.137886 / 10.191392 (2.946494) | 0.143102 / 0.680424 (-0.537322) | 0.016884 / 0.534201 (-0.517317) | 0.370106 / 0.579283 (-0.209178) | 0.392349 / 0.434364 (-0.042015) | 0.424501 / 0.540337 (-0.115837) | 0.509830 / 1.386936 (-0.877106) |

</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) | 0.006210 / 0.011353 (-0.005142) | 0.004215 / 0.011008 (-0.006793) | 0.076129 / 0.038508 (0.037621) | 0.027825 / 0.023109 (0.004716) | 0.403973 / 0.275898 (0.128075) | 0.441089 / 0.323480 (0.117609) | 0.005420 / 0.007986 (-0.002566) | 0.004870 / 0.004328 (0.000542) | 0.075558 / 0.004250 (0.071308) | 0.039464 / 0.037052 (0.002411) | 0.404329 / 0.258489 (0.145840) | 0.447213 / 0.293841 (0.153372) | 0.025877 / 0.128546 (-0.102669) | 0.008660 / 0.075646 (-0.066987) | 0.081849 / 0.419271 (-0.337422) | 0.044551 / 0.043533 (0.001018) | 0.379102 / 0.255139 (0.123963) | 0.403104 / 0.283200 (0.119905) | 0.094754 / 0.141683 (-0.046929) | 1.460772 / 1.452155 (0.008617) | 1.569531 / 1.492716 (0.076815) |

### 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) | 0.183923 / 0.018006 (0.165917) | 0.420708 / 0.000490 (0.420219) | 0.002091 / 0.000200 (0.001891) | 0.000080 / 0.000054 (0.000026) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.026180 / 0.037411 (-0.011231) | 0.101529 / 0.014526 (0.087003) | 0.108739 / 0.176557 (-0.067818) | 0.160702 / 0.737135 (-0.576433) | 0.111739 / 0.296338 (-0.184600) |

### 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) | 0.448671 / 0.215209 (0.233462) | 4.469287 / 2.077655 (2.391632) | 2.244335 / 1.504120 (0.740215) | 2.107495 / 1.541195 (0.566301) | 2.224763 / 1.468490 (0.756272) | 0.554006 / 4.584777 (-4.030771) | 3.390109 / 3.745712 (-0.355603) | 1.744189 / 5.269862 (-3.525673) | 1.008515 / 4.565676 (-3.557161) | 0.067904 / 0.424275 (-0.356371) | 0.012243 / 0.007607 (0.004636) | 0.557635 / 0.226044 (0.331590) | 5.610383 / 2.268929 (3.341454) | 2.687326 / 55.444624 (-52.757298) | 2.405262 / 6.876477 (-4.471214) | 2.527300 / 2.142072 (0.385227) | 0.662282 / 4.805227 (-4.142945) | 0.136225 / 6.500664 (-6.364439) | 0.068136 / 0.075469 (-0.007334) |

### 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) | 1.310791 / 1.841788 (-0.530997) | 14.370381 / 8.074308 (6.296072) | 14.122675 / 10.191392 (3.931283) | 0.152302 / 0.680424 (-0.528122) | 0.016624 / 0.534201 (-0.517577) | 0.359395 / 0.579283 (-0.219888) | 0.392131 / 0.434364 (-0.042233) | 0.423796 / 0.540337 (-0.116542) | 0.511387 / 1.386936 (-0.875549) |

</details>
</details>

![](https://cml.dev/watermark.png#d6a61a1af1502677a6f2333896a6ffeede9ca21b "CML watermark")

To be used to train models it allows to load an IterableDataset from the cached Arrow file.  
See https://github.com/huggingface/datasets/issues/5481