Title: Call fs.makedirs in save_to_disk
Author: lhoestq (MEMBER)
State: closed
Created: 2023-04-21T15:04:28Z

We need to call `fs.makedirs` when saving a dataset using `save_to_disk`, because some fs implementations have actual directories (S3 and others don't)

Close https://github.com/huggingface/datasets/issues/5775

Comments:
_The documentation is not available anymore as the PR was closed or merged._

<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) | 0.007490 / 0.011353 (-0.003862) | 0.004957 / 0.011008 (-0.006051) | 0.096952 / 0.038508 (0.058444) | 0.034125 / 0.023109 (0.011016) | 0.301926 / 0.275898 (0.026028) | 0.330538 / 0.323480 (0.007058) | 0.005999 / 0.007986 (-0.001987) | 0.003948 / 0.004328 (-0.000380) | 0.073024 / 0.004250 (0.068773) | 0.050020 / 0.037052 (0.012967) | 0.299987 / 0.258489 (0.041498) | 0.336077 / 0.293841 (0.042237) | 0.035781 / 0.128546 (-0.092765) | 0.012159 / 0.075646 (-0.063487) | 0.333311 / 0.419271 (-0.085960) | 0.059925 / 0.043533 (0.016392) | 0.297772 / 0.255139 (0.042633) | 0.313447 / 0.283200 (0.030247) | 0.100991 / 0.141683 (-0.040692) | 1.472182 / 1.452155 (0.020027) | 1.553010 / 1.492716 (0.060294) |

### 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) | 0.214222 / 0.018006 (0.196216) | 0.441579 / 0.000490 (0.441090) | 0.001030 / 0.000200 (0.000830) | 0.000194 / 0.000054 (0.000140) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.026149 / 0.037411 (-0.011262) | 0.107324 / 0.014526 (0.092798) | 0.113390 / 0.176557 (-0.063167) | 0.170282 / 0.737135 (-0.566854) | 0.120601 / 0.296338 (-0.175737) |

### 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) | 0.411795 / 0.215209 (0.196585) | 4.091412 / 2.077655 (2.013757) | 1.819597 / 1.504120 (0.315477) | 1.623413 / 1.541195 (0.082218) | 1.658959 / 1.468490 (0.190469) | 0.697671 / 4.584777 (-3.887106) | 3.868855 / 3.745712 (0.123143) | 3.220448 / 5.269862 (-2.049414) | 1.796472 / 4.565676 (-2.769204) | 0.085817 / 0.424275 (-0.338458) | 0.012422 / 0.007607 (0.004815) | 0.520302 / 0.226044 (0.294258) | 5.062477 / 2.268929 (2.793548) | 2.275065 / 55.444624 (-53.169560) | 1.936717 / 6.876477 (-4.939759) | 2.069924 / 2.142072 (-0.072148) | 0.838964 / 4.805227 (-3.966264) | 0.170632 / 6.500664 (-6.330032) | 0.066011 / 0.075469 (-0.009458) |

### 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) | 1.190673 / 1.841788 (-0.651114) | 14.679478 / 8.074308 (6.605169) | 14.099743 / 10.191392 (3.908351) | 0.142556 / 0.680424 (-0.537868) | 0.017601 / 0.534201 (-0.516600) | 0.421301 / 0.579283 (-0.157982) | 0.418035 / 0.434364 (-0.016329) | 0.503799 / 0.540337 (-0.036539) | 0.588809 / 1.386936 (-0.798127) |

</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) | 0.007556 / 0.011353 (-0.003797) | 0.005283 / 0.011008 (-0.005725) | 0.075616 / 0.038508 (0.037107) | 0.034127 / 0.023109 (0.011018) | 0.345145 / 0.275898 (0.069247) | 0.377490 / 0.323480 (0.054010) | 0.006532 / 0.007986 (-0.001454) | 0.004145 / 0.004328 (-0.000183) | 0.074724 / 0.004250 (0.070473) | 0.048658 / 0.037052 (0.011605) | 0.339989 / 0.258489 (0.081500) | 0.398240 / 0.293841 (0.104399) | 0.037433 / 0.128546 (-0.091114) | 0.012410 / 0.075646 (-0.063237) | 0.088110 / 0.419271 (-0.331162) | 0.050635 / 0.043533 (0.007103) | 0.351878 / 0.255139 (0.096739) | 0.365707 / 0.283200 (0.082508) | 0.104342 / 0.141683 (-0.037341) | 1.438009 / 1.452155 (-0.014145) | 1.533616 / 1.492716 (0.040900) |

### 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) | 0.225570 / 0.018006 (0.207563) | 0.442482 / 0.000490 (0.441992) | 0.000402 / 0.000200 (0.000202) | 0.000063 / 0.000054 (0.000009) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.030348 / 0.037411 (-0.007063) | 0.111402 / 0.014526 (0.096877) | 0.123365 / 0.176557 (-0.053192) | 0.175604 / 0.737135 (-0.561531) | 0.128458 / 0.296338 (-0.167881) |

### 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) | 0.426054 / 0.215209 (0.210845) | 4.255050 / 2.077655 (2.177395) | 2.039568 / 1.504120 (0.535448) | 1.856842 / 1.541195 (0.315647) | 1.923792 / 1.468490 (0.455301) | 0.701023 / 4.584777 (-3.883754) | 3.746632 / 3.745712 (0.000920) | 2.055563 / 5.269862 (-3.214298) | 1.308068 / 4.565676 (-3.257608) | 0.085524 / 0.424275 (-0.338751) | 0.012103 / 0.007607 (0.004496) | 0.522929 / 0.226044 (0.296885) | 5.258133 / 2.268929 (2.989205) | 2.458440 / 55.444624 (-52.986185) | 2.141681 / 6.876477 (-4.734796) | 2.258667 / 2.142072 (0.116595) | 0.842533 / 4.805227 (-3.962694) | 0.168089 / 6.500664 (-6.332575) | 0.063707 / 0.075469 (-0.011762) |

### 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) | 1.312252 / 1.841788 (-0.529536) | 14.939185 / 8.074308 (6.864877) | 14.479845 / 10.191392 (4.288453) | 0.162557 / 0.680424 (-0.517867) | 0.017660 / 0.534201 (-0.516541) | 0.423261 / 0.579283 (-0.156023) | 0.417693 / 0.434364 (-0.016671) | 0.495440 / 0.540337 (-0.044897) | 0.589932 / 1.386936 (-0.797004) |

</details>
</details>

![](https://cml.dev/watermark.png#4e3c86574155961097b367d5cddda5bd13c42b09 "CML watermark")


<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) | 0.008796 / 0.011353 (-0.002557) | 0.005828 / 0.011008 (-0.005180) | 0.118629 / 0.038508 (0.080121) | 0.042435 / 0.023109 (0.019326) | 0.383780 / 0.275898 (0.107882) | 0.420344 / 0.323480 (0.096864) | 0.006855 / 0.007986 (-0.001130) | 0.006290 / 0.004328 (0.001962) | 0.087160 / 0.004250 (0.082910) | 0.057568 / 0.037052 (0.020516) | 0.378761 / 0.258489 (0.120272) | 0.426496 / 0.293841 (0.132655) | 0.041772 / 0.128546 (-0.086774) | 0.014226 / 0.075646 (-0.061420) | 0.400097 / 0.419271 (-0.019174) | 0.060402 / 0.043533 (0.016870) | 0.381955 / 0.255139 (0.126816) | 0.399110 / 0.283200 (0.115911) | 0.124608 / 0.141683 (-0.017075) | 1.737856 / 1.452155 (0.285702) | 1.829034 / 1.492716 (0.336318) |

### 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) | 0.219941 / 0.018006 (0.201934) | 0.497156 / 0.000490 (0.496666) | 0.005094 / 0.000200 (0.004894) | 0.000097 / 0.000054 (0.000043) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.032144 / 0.037411 (-0.005268) | 0.131782 / 0.014526 (0.117256) | 0.141543 / 0.176557 (-0.035014) | 0.211419 / 0.737135 (-0.525716) | 0.147338 / 0.296338 (-0.149001) |

### 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) | 0.478345 / 0.215209 (0.263136) | 4.749506 / 2.077655 (2.671851) | 2.195794 / 1.504120 (0.691674) | 1.978126 / 1.541195 (0.436932) | 2.059941 / 1.468490 (0.591451) | 0.821959 / 4.584777 (-3.762818) | 5.737479 / 3.745712 (1.991767) | 2.507125 / 5.269862 (-2.762737) | 2.051772 / 4.565676 (-2.513905) | 0.100619 / 0.424275 (-0.323656) | 0.014437 / 0.007607 (0.006830) | 0.599484 / 0.226044 (0.373440) | 5.977579 / 2.268929 (3.708651) | 2.708143 / 55.444624 (-52.736482) | 2.320279 / 6.876477 (-4.556198) | 2.510172 / 2.142072 (0.368100) | 1.006279 / 4.805227 (-3.798948) | 0.199812 / 6.500664 (-6.300853) | 0.077967 / 0.075469 (0.002498) |

### 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) | 1.510171 / 1.841788 (-0.331616) | 21.099446 / 8.074308 (13.025138) | 17.634225 / 10.191392 (7.442833) | 0.223506 / 0.680424 (-0.456918) | 0.023845 / 0.534201 (-0.510356) | 0.613489 / 0.579283 (0.034206) | 0.685735 / 0.434364 (0.251371) | 0.652485 / 0.540337 (0.112148) | 0.734756 / 1.386936 (-0.652180) |

</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) | 0.008444 / 0.011353 (-0.002909) | 0.005789 / 0.011008 (-0.005220) | 0.088297 / 0.038508 (0.049789) | 0.040847 / 0.023109 (0.017737) | 0.411748 / 0.275898 (0.135850) | 0.452320 / 0.323480 (0.128841) | 0.006689 / 0.007986 (-0.001296) | 0.006029 / 0.004328 (0.001701) | 0.086080 / 0.004250 (0.081830) | 0.053310 / 0.037052 (0.016257) | 0.402568 / 0.258489 (0.144079) | 0.459047 / 0.293841 (0.165206) | 0.041203 / 0.128546 (-0.087343) | 0.014216 / 0.075646 (-0.061431) | 0.102729 / 0.419271 (-0.316543) | 0.057170 / 0.043533 (0.013637) | 0.407137 / 0.255139 (0.151998) | 0.429703 / 0.283200 (0.146503) | 0.123528 / 0.141683 (-0.018155) | 1.690026 / 1.452155 (0.237872) | 1.797793 / 1.492716 (0.305077) |

### 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) | 0.264581 / 0.018006 (0.246575) | 0.498981 / 0.000490 (0.498492) | 0.000462 / 0.000200 (0.000262) | 0.000096 / 0.000054 (0.000041) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.034613 / 0.037411 (-0.002798) | 0.136596 / 0.014526 (0.122070) | 0.142183 / 0.176557 (-0.034374) | 0.201816 / 0.737135 (-0.535320) | 0.148843 / 0.296338 (-0.147496) |

### 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) | 0.506708 / 0.215209 (0.291499) | 5.042829 / 2.077655 (2.965175) | 2.448414 / 1.504120 (0.944295) | 2.213251 / 1.541195 (0.672056) | 2.255805 / 1.468490 (0.787315) | 0.829929 / 4.584777 (-3.754848) | 5.145717 / 3.745712 (1.400004) | 2.493947 / 5.269862 (-2.775915) | 1.676171 / 4.565676 (-2.889506) | 0.102097 / 0.424275 (-0.322178) | 0.014545 / 0.007607 (0.006938) | 0.635473 / 0.226044 (0.409429) | 6.306767 / 2.268929 (4.037839) | 3.050284 / 55.444624 (-52.394341) | 2.653175 / 6.876477 (-4.223302) | 2.850569 / 2.142072 (0.708496) | 1.355280 / 4.805227 (-3.449947) | 0.248112 / 6.500664 (-6.252552) | 0.091993 / 0.075469 (0.016524) |

### 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) | 1.837509 / 1.841788 (-0.004279) | 21.268838 / 8.074308 (13.194530) | 17.338053 / 10.191392 (7.146660) | 0.232263 / 0.680424 (-0.448161) | 0.029093 / 0.534201 (-0.505108) | 0.651056 / 0.579283 (0.071773) | 0.617623 / 0.434364 (0.183259) | 0.773921 / 0.540337 (0.233584) | 0.705118 / 1.386936 (-0.681818) |

</details>
</details>

![](https://cml.dev/watermark.png#35846fd54fa16aa72ff344d15c98b5e08c5effe0 "CML watermark")