Issue: Fix multi gpu map example
State: closed
Author: lhoestq

- use `orch.cuda.set_device` instead of `CUDA_VISIBLE_DEVICES `
- add `if __name__ == "__main__"`

fix https://github.com/huggingface/datasets/issues/6186

Comments:
<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) | 0.004537 / 0.011353 (-0.006816) | 0.002844 / 0.011008 (-0.008164) | 0.062506 / 0.038508 (0.023998) | 0.029675 / 0.023109 (0.006566) | 0.238080 / 0.275898 (-0.037818) | 0.259858 / 0.323480 (-0.063622) | 0.004015 / 0.007986 (-0.003970) | 0.002432 / 0.004328 (-0.001897) | 0.049477 / 0.004250 (0.045227) | 0.045383 / 0.037052 (0.008331) | 0.241934 / 0.258489 (-0.016555) | 0.270759 / 0.293841 (-0.023082) | 0.023207 / 0.128546 (-0.105339) | 0.007107 / 0.075646 (-0.068539) | 0.207626 / 0.419271 (-0.211645) | 0.056706 / 0.043533 (0.013173) | 0.239713 / 0.255139 (-0.015426) | 0.256639 / 0.283200 (-0.026560) | 0.017514 / 0.141683 (-0.124169) | 1.105201 / 1.452155 (-0.346953) | 1.173087 / 1.492716 (-0.319629) |

### 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) | 0.093391 / 0.018006 (0.075384) | 0.302673 / 0.000490 (0.302184) | 0.000218 / 0.000200 (0.000018) | 0.000043 / 0.000054 (-0.000011) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.019447 / 0.037411 (-0.017965) | 0.063349 / 0.014526 (0.048823) | 0.075600 / 0.176557 (-0.100957) | 0.121098 / 0.737135 (-0.616037) | 0.075028 / 0.296338 (-0.221311) |

### 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) | 0.291479 / 0.215209 (0.076270) | 2.787231 / 2.077655 (0.709576) | 1.480205 / 1.504120 (-0.023915) | 1.417656 / 1.541195 (-0.123538) | 1.394529 / 1.468490 (-0.073962) | 0.408843 / 4.584777 (-4.175934) | 2.398691 / 3.745712 (-1.347021) | 2.635457 / 5.269862 (-2.634404) | 1.591722 / 4.565676 (-2.973955) | 0.048445 / 0.424275 (-0.375830) | 0.004864 / 0.007607 (-0.002743) | 0.349014 / 0.226044 (0.122969) | 3.436962 / 2.268929 (1.168033) | 1.839266 / 55.444624 (-53.605359) | 1.535252 / 6.876477 (-5.341225) | 1.581048 / 2.142072 (-0.561025) | 0.491150 / 4.805227 (-4.314078) | 0.101279 / 6.500664 (-6.399385) | 0.041938 / 0.075469 (-0.033532) |

### 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) | 0.946986 / 1.841788 (-0.894801) | 11.766196 / 8.074308 (3.691888) | 10.425615 / 10.191392 (0.234223) | 0.129957 / 0.680424 (-0.550467) | 0.014859 / 0.534201 (-0.519342) | 0.268046 / 0.579283 (-0.311237) | 0.263724 / 0.434364 (-0.170640) | 0.311028 / 0.540337 (-0.229309) | 0.434715 / 1.386936 (-0.952221) |

</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) | 0.004874 / 0.011353 (-0.006479) | 0.002942 / 0.011008 (-0.008067) | 0.048250 / 0.038508 (0.009742) | 0.053726 / 0.023109 (0.030617) | 0.268870 / 0.275898 (-0.007028) | 0.289152 / 0.323480 (-0.034328) | 0.003982 / 0.007986 (-0.004004) | 0.002488 / 0.004328 (-0.001840) | 0.047902 / 0.004250 (0.043652) | 0.038732 / 0.037052 (0.001680) | 0.271021 / 0.258489 (0.012532) | 0.299967 / 0.293841 (0.006126) | 0.024672 / 0.128546 (-0.103874) | 0.007311 / 0.075646 (-0.068336) | 0.053721 / 0.419271 (-0.365550) | 0.032407 / 0.043533 (-0.011126) | 0.266604 / 0.255139 (0.011465) | 0.286816 / 0.283200 (0.003617) | 0.018973 / 0.141683 (-0.122710) | 1.122460 / 1.452155 (-0.329695) | 1.177720 / 1.492716 (-0.314997) |

### 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) | 0.093968 / 0.018006 (0.075962) | 0.304010 / 0.000490 (0.303521) | 0.000228 / 0.000200 (0.000028) | 0.000056 / 0.000054 (0.000002) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.021203 / 0.037411 (-0.016208) | 0.070318 / 0.014526 (0.055793) | 0.081688 / 0.176557 (-0.094869) | 0.120916 / 0.737135 (-0.616219) | 0.083452 / 0.296338 (-0.212886) |

### 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) | 0.293961 / 0.215209 (0.078752) | 2.858514 / 2.077655 (0.780860) | 1.556169 / 1.504120 (0.052049) | 1.431523 / 1.541195 (-0.109671) | 1.478145 / 1.468490 (0.009654) | 0.408927 / 4.584777 (-4.175850) | 2.440630 / 3.745712 (-1.305082) | 2.586327 / 5.269862 (-2.683534) | 1.529495 / 4.565676 (-3.036182) | 0.047387 / 0.424275 (-0.376888) | 0.004817 / 0.007607 (-0.002790) | 0.345009 / 0.226044 (0.118965) | 3.386313 / 2.268929 (1.117384) | 1.922361 / 55.444624 (-53.522264) | 1.640814 / 6.876477 (-5.235663) | 1.657005 / 2.142072 (-0.485068) | 0.483844 / 4.805227 (-4.321383) | 0.099470 / 6.500664 (-6.401194) | 0.040735 / 0.075469 (-0.034734) |

### 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) | 0.986311 / 1.841788 (-0.855476) | 12.327425 / 8.074308 (4.253117) | 10.995135 / 10.191392 (0.803743) | 0.146814 / 0.680424 (-0.533610) | 0.015820 / 0.534201 (-0.518381) | 0.272319 / 0.579283 (-0.306964) | 0.274858 / 0.434364 (-0.159506) | 0.305728 / 0.540337 (-0.234609) | 0.421400 / 1.386936 (-0.965536) |

</details>
</details>

![](https://cml.dev/watermark.png#611a03d70378d6e48a19fac89e7616cf556b920a "CML watermark")


_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) | 0.007995 / 0.011353 (-0.003358) | 0.004596 / 0.011008 (-0.006412) | 0.099818 / 0.038508 (0.061310) | 0.053539 / 0.023109 (0.030429) | 0.367757 / 0.275898 (0.091859) | 0.409351 / 0.323480 (0.085871) | 0.007423 / 0.007986 (-0.000563) | 0.003770 / 0.004328 (-0.000558) | 0.075635 / 0.004250 (0.071385) | 0.078844 / 0.037052 (0.041791) | 0.374523 / 0.258489 (0.116034) | 0.423378 / 0.293841 (0.129537) | 0.038901 / 0.128546 (-0.089645) | 0.009985 / 0.075646 (-0.065661) | 0.342793 / 0.419271 (-0.076479) | 0.098045 / 0.043533 (0.054512) | 0.368077 / 0.255139 (0.112938) | 0.394251 / 0.283200 (0.111051) | 0.030624 / 0.141683 (-0.111059) | 1.782728 / 1.452155 (0.330574) | 1.867571 / 1.492716 (0.374855) |

### 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) | 0.265550 / 0.018006 (0.247544) | 0.504045 / 0.000490 (0.503555) | 0.016523 / 0.000200 (0.016323) | 0.000757 / 0.000054 (0.000702) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.034239 / 0.037411 (-0.003172) | 0.099953 / 0.014526 (0.085427) | 0.113728 / 0.176557 (-0.062829) | 0.180113 / 0.737135 (-0.557023) | 0.114506 / 0.296338 (-0.181833) |

### 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) | 0.507186 / 0.215209 (0.291977) | 5.033590 / 2.077655 (2.955935) | 2.480111 / 1.504120 (0.975991) | 2.258966 / 1.541195 (0.717771) | 2.316045 / 1.468490 (0.847555) | 0.622482 / 4.584777 (-3.962295) | 4.400909 / 3.745712 (0.655197) | 4.012443 / 5.269862 (-1.257419) | 2.408294 / 4.565676 (-2.157383) | 0.067608 / 0.424275 (-0.356668) | 0.008638 / 0.007607 (0.001031) | 0.546558 / 0.226044 (0.320513) | 5.472973 / 2.268929 (3.204044) | 2.795147 / 55.444624 (-52.649477) | 2.371153 / 6.876477 (-4.505324) | 2.440883 / 2.142072 (0.298811) | 0.682380 / 4.805227 (-4.122847) | 0.156819 / 6.500664 (-6.343845) | 0.071969 / 0.075469 (-0.003500) |

### 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) | 1.500200 / 1.841788 (-0.341588) | 22.854103 / 8.074308 (14.779795) | 16.691945 / 10.191392 (6.500553) | 0.210945 / 0.680424 (-0.469479) | 0.023234 / 0.534201 (-0.510967) | 0.475641 / 0.579283 (-0.103642) | 0.491553 / 0.434364 (0.057189) | 0.549311 / 0.540337 (0.008974) | 0.858498 / 1.386936 (-0.528439) |

</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) | 0.009020 / 0.011353 (-0.002333) | 0.004768 / 0.011008 (-0.006240) | 0.082841 / 0.038508 (0.044333) | 0.095111 / 0.023109 (0.072002) | 0.486050 / 0.275898 (0.210151) | 0.527074 / 0.323480 (0.203594) | 0.006622 / 0.007986 (-0.001364) | 0.003961 / 0.004328 (-0.000367) | 0.083361 / 0.004250 (0.079111) | 0.068571 / 0.037052 (0.031518) | 0.494575 / 0.258489 (0.236086) | 0.545593 / 0.293841 (0.251752) | 0.047671 / 0.128546 (-0.080875) | 0.010715 / 0.075646 (-0.064932) | 0.096239 / 0.419271 (-0.323033) | 0.061556 / 0.043533 (0.018023) | 0.484301 / 0.255139 (0.229162) | 0.492189 / 0.283200 (0.208989) | 0.029374 / 0.141683 (-0.112309) | 1.911833 / 1.452155 (0.459678) | 2.005744 / 1.492716 (0.513028) |

### 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) | 0.265402 / 0.018006 (0.247396) | 0.501034 / 0.000490 (0.500545) | 0.004039 / 0.000200 (0.003839) | 0.000105 / 0.000054 (0.000051) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.041005 / 0.037411 (0.003594) | 0.119204 / 0.014526 (0.104678) | 0.134583 / 0.176557 (-0.041973) | 0.195995 / 0.737135 (-0.541140) | 0.133125 / 0.296338 (-0.163214) |

### 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) | 0.503012 / 0.215209 (0.287803) | 5.021972 / 2.077655 (2.944318) | 2.912987 / 1.504120 (1.408867) | 2.707637 / 1.541195 (1.166442) | 2.824065 / 1.468490 (1.355575) | 0.664285 / 4.584777 (-3.920492) | 4.341905 / 3.745712 (0.596193) | 4.152839 / 5.269862 (-1.117022) | 2.438138 / 4.565676 (-2.127539) | 0.076169 / 0.424275 (-0.348106) | 0.010471 / 0.007607 (0.002864) | 0.680918 / 0.226044 (0.454874) | 6.424209 / 2.268929 (4.155281) | 3.285353 / 55.444624 (-52.159271) | 2.865458 / 6.876477 (-4.011019) | 2.946246 / 2.142072 (0.804173) | 0.700051 / 4.805227 (-4.105176) | 0.155299 / 6.500664 (-6.345365) | 0.069372 / 0.075469 (-0.006097) |

### 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) | 1.749517 / 1.841788 (-0.092271) | 23.382582 / 8.074308 (15.308274) | 17.708718 / 10.191392 (7.517326) | 0.197042 / 0.680424 (-0.483382) | 0.023874 / 0.534201 (-0.510327) | 0.471631 / 0.579283 (-0.107652) | 0.512649 / 0.434364 (0.078285) | 0.614479 / 0.540337 (0.074142) | 0.771859 / 1.386936 (-0.615077) |

</details>
</details>

![](https://cml.dev/watermark.png#4f084b2d85f5004ed969d2387027093b2d765a4f "CML watermark")


Merging this one, but lmk if you have more comments for subsequent improvements @NielsRogge 

<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) | 0.004874 / 0.011353 (-0.006479) | 0.002866 / 0.011008 (-0.008142) | 0.061761 / 0.038508 (0.023253) | 0.052185 / 0.023109 (0.029076) | 0.242264 / 0.275898 (-0.033634) | 0.267816 / 0.323480 (-0.055664) | 0.002844 / 0.007986 (-0.005142) | 0.002349 / 0.004328 (-0.001979) | 0.048393 / 0.004250 (0.044142) | 0.038590 / 0.037052 (0.001538) | 0.257483 / 0.258489 (-0.001006) | 0.279704 / 0.293841 (-0.014137) | 0.023125 / 0.128546 (-0.105421) | 0.007044 / 0.075646 (-0.068602) | 0.203606 / 0.419271 (-0.215665) | 0.035489 / 0.043533 (-0.008044) | 0.248419 / 0.255139 (-0.006719) | 0.266357 / 0.283200 (-0.016843) | 0.020178 / 0.141683 (-0.121505) | 1.163674 / 1.452155 (-0.288481) | 1.191340 / 1.492716 (-0.301376) |

### 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) | 0.092972 / 0.018006 (0.074966) | 0.295260 / 0.000490 (0.294770) | 0.000214 / 0.000200 (0.000014) | 0.000050 / 0.000054 (-0.000004) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.018109 / 0.037411 (-0.019302) | 0.061743 / 0.014526 (0.047217) | 0.073965 / 0.176557 (-0.102592) | 0.119493 / 0.737135 (-0.617642) | 0.075646 / 0.296338 (-0.220692) |

### 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) | 0.275700 / 0.215209 (0.060491) | 2.666846 / 2.077655 (0.589191) | 1.401452 / 1.504120 (-0.102668) | 1.276009 / 1.541195 (-0.265186) | 1.309914 / 1.468490 (-0.158576) | 0.396411 / 4.584777 (-4.188365) | 2.347193 / 3.745712 (-1.398519) | 2.568006 / 5.269862 (-2.701856) | 1.564572 / 4.565676 (-3.001105) | 0.045450 / 0.424275 (-0.378825) | 0.004827 / 0.007607 (-0.002780) | 0.333092 / 0.226044 (0.107048) | 3.284295 / 2.268929 (1.015367) | 1.809928 / 55.444624 (-53.634696) | 1.486041 / 6.876477 (-5.390436) | 1.528198 / 2.142072 (-0.613875) | 0.470053 / 4.805227 (-4.335174) | 0.098559 / 6.500664 (-6.402105) | 0.041637 / 0.075469 (-0.033832) |

### 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) | 0.948915 / 1.841788 (-0.892873) | 11.513211 / 8.074308 (3.438903) | 10.386419 / 10.191392 (0.195027) | 0.129513 / 0.680424 (-0.550910) | 0.021772 / 0.534201 (-0.512429) | 0.295627 / 0.579283 (-0.283656) | 0.261008 / 0.434364 (-0.173355) | 0.305869 / 0.540337 (-0.234469) | 0.399676 / 1.386936 (-0.987260) |

</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) | 0.004799 / 0.011353 (-0.006553) | 0.002764 / 0.011008 (-0.008244) | 0.048469 / 0.038508 (0.009961) | 0.051346 / 0.023109 (0.028236) | 0.274853 / 0.275898 (-0.001045) | 0.300770 / 0.323480 (-0.022710) | 0.003986 / 0.007986 (-0.003999) | 0.002376 / 0.004328 (-0.001952) | 0.048545 / 0.004250 (0.044294) | 0.039854 / 0.037052 (0.002801) | 0.280053 / 0.258489 (0.021564) | 0.312797 / 0.293841 (0.018957) | 0.024513 / 0.128546 (-0.104033) | 0.006971 / 0.075646 (-0.068675) | 0.053030 / 0.419271 (-0.366241) | 0.035580 / 0.043533 (-0.007953) | 0.276078 / 0.255139 (0.020939) | 0.299345 / 0.283200 (0.016145) | 0.020423 / 0.141683 (-0.121260) | 1.103053 / 1.452155 (-0.349102) | 1.179747 / 1.492716 (-0.312969) |

### 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) | 0.093042 / 0.018006 (0.075036) | 0.299421 / 0.000490 (0.298932) | 0.000232 / 0.000200 (0.000033) | 0.000052 / 0.000054 (-0.000002) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.021966 / 0.037411 (-0.015445) | 0.070978 / 0.014526 (0.056452) | 0.083841 / 0.176557 (-0.092715) | 0.121223 / 0.737135 (-0.615912) | 0.082829 / 0.296338 (-0.213510) |

### 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) | 0.289436 / 0.215209 (0.074227) | 2.838074 / 2.077655 (0.760419) | 1.597013 / 1.504120 (0.092893) | 1.476888 / 1.541195 (-0.064307) | 1.504582 / 1.468490 (0.036092) | 0.398050 / 4.584777 (-4.186727) | 2.434446 / 3.745712 (-1.311266) | 2.493545 / 5.269862 (-2.776316) | 1.584159 / 4.565676 (-2.981517) | 0.046461 / 0.424275 (-0.377814) | 0.004876 / 0.007607 (-0.002731) | 0.344166 / 0.226044 (0.118122) | 3.388530 / 2.268929 (1.119602) | 1.939585 / 55.444624 (-53.505039) | 1.672495 / 6.876477 (-5.203982) | 1.811825 / 2.142072 (-0.330247) | 0.470798 / 4.805227 (-4.334429) | 0.097522 / 6.500664 (-6.403142) | 0.040887 / 0.075469 (-0.034582) |

### 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) | 0.990081 / 1.841788 (-0.851707) | 12.619827 / 8.074308 (4.545519) | 10.748062 / 10.191392 (0.556670) | 0.130409 / 0.680424 (-0.550015) | 0.016624 / 0.534201 (-0.517577) | 0.272381 / 0.579283 (-0.306902) | 0.270597 / 0.434364 (-0.163767) | 0.306458 / 0.540337 (-0.233879) | 0.408700 / 1.386936 (-0.978236) |

</details>
</details>

![](https://cml.dev/watermark.png#bc44d2188a1baac50d28a6c8110d6e5497f409de "CML watermark")


This is a little hard to follow — where is the documentation currently? I am trying to follow from snippets, here is what I have based on your convo in this thread:

```>>> import os
>>>
>>> for i in range(torch.cuda.device_count()):  # send model to every GPU
...     model.to(f"cuda:{i}")
>>>
>>> def gpu_computation(example, rank):
...     torch.cuda.set_device(f"cuda:{rank}")  # use one GPU
...     inputs = tokenizer(texts, truncation=True, return_tensors="pt").to(f"cuda:{rank}")
...     outputs = model(**inputs)
...      .... 
```

but I'm getting device errors (data is on device 3, but it thinks model is on device 0, despite setting `torch.cuda.set_device`

Is this correct? What version of Torch are you using for this? 

Anyway, this didn't work for me:

`torch.cuda.set_device(f"cuda:{rank}")  # use one GPU`

but substituting it for:

`model.to(f"cuda:{rank}")`

(`.to` doesn't make a million copies of the model on the device, which I was worried it would do... so you can use it in an inner process)

(btw, versions: `torch==2.1.1`, `cuda=12.2`)

Yeah for me this issue isn't resolved yet, we need a better code example

Hi @alex2awesome, could you open a PR with your suggestion to improve this code snippet ?

i'm happy to when i get it fully working, but i feel like there are some fundamentals that I'm not fully understanding...

I've set it up twice now, for 2 GPU-processing pipelines. 

In one pipelines, my memory usage is fine, it delivers me a huge speedup, and everything is great. In the second pipeline, I keep getting OOM errors when `num_proc > 1` that I don't get when `num_proc=1`. 

There is a discussion here: https://github.com/pytorch/pytorch/issues/44156 about CUDA memory leaks in multiprocessing setups, and I haven't had the time to fully read the source code to `datasets.map` to understand whether the situations are parallel. Also, if they are, then I don't know what the solution is, not really knowing how it is implemented under the hood. In that discussion, one guy offers a work-around, but it doesn't look great.

So, I haven't fully tested out enough to see what the issue. If I feel comfortable over the next several days to generate a slimmed-down example that will generalize to real-world cases such as those I'm working with now, then I will contribute it.



@lhoestq do you know how `datasets` does multiprocessing? Do we use:
https://pytorch.org/docs/stable/multiprocessing.html#module-torch.multiprocessing?

If so, there are lots of points around memory usage, here:
https://pytorch.org/docs/stable/notes/multiprocessing.html

EDIT: ahh I see it is using python's native multiprocessing library: https://github.com/huggingface/datasets/blob/2.15.0/src/datasets/arrow_dataset.py#L3172-L3189

After some more research and playing around, I can't pinpoint the source of my CUDA memory leak nor can I determine with confidence what works and what doesn't in this setup.

I'm not really an expert on multiprocessing in general, but my gut is that the current set-up isn't ideal for multiprocessing and I'm not sure I would recommend users to do this. 

Kinda unfortunate, because I don't see any great tools for distributed inference out there, and in theory, `datasets.map` could be the standard.

Are either of you more experienced in this?

Not sure about your GPU's OOM :/

Still, I opened a PR with your suggestion here: https://github.com/huggingface/datasets/pull/6550

I still get only 0 rank...

Here is my code: https://pastebin.com/c6du8jaM

from this ^ i just improt one function:


```
from test import map_train
from multiprocess import set_start_method


set_start_method("spawn")
map_train()
```

And here is the traceback:
https://pastebin.com/YijspwQK 

Also this code from your docs is not valid (source: https://huggingface.co/docs/datasets/main/en/process#multiprocessing):
```
for i in range(torch.cuda.device_count()):
    model.to(f"cuda:{i}")
```


This for me sends the model only to the second GPU
```
vae = AutoencoderKL.from_pretrained(
    pretrained_model_name_or_path, subfolder="vae"
)
vae.to("cuda:0")
vae.to("cuda:1")
```

Could you please provide a working example of multi-GPU mapping?

Not just an example in docs, but a real working example starting from all imports loading datasets and models.

@alex2awesome the same issue with CUDA OOM. It should not be happening, since it should 2 different GPUs be handling different loads. But in fact something wrong is happening.

I haven't experimented much with the multi-GPU code documentation.

Can you try using the code example at https://github.com/huggingface/datasets/pull/6550 instead ? That would be super helpful if you could confirm that it works on your side

Though if you have some fixes/improvements ideas feel free to open a PR !

@lhoestq the mapping does not start at all in this case:
<img width="855" alt="image" src="https://github.com/huggingface/datasets/assets/17604849/7f29a3c1-c6dc-4bab-9955-5311256aa217">

Here is the updated code: https://pastebin.com/Kn9aGfZr

@lhoestq with this code: https://pastebin.com/muDm78kp
i now getting this error:

```
Map (num_proc=2):   1%|       | 26288/3043663 [06:11<11:51:08, 70.72 examples/s]
Traceback (most recent call last):
  File "/workspace/compute.py", line 229, in <module>
    map_train()
  File "/workspace/compute.py", line 224, in map_train
    return train_dataset.map(compute_embeddings_fn, batched=True, batch_size=16, with_rank=True, num_proc=2, keep_in_memory=True)
  File "/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py", line 593, in wrapper
    out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py", line 558, in wrapper
    out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py", line 3193, in map
    for rank, done, content in iflatmap_unordered(
  File "/usr/local/lib/python3.10/dist-packages/datasets/utils/py_utils.py", line 658, in iflatmap_unordered
    raise RuntimeError(
RuntimeError: One of the subprocesses has abruptly died during map operation.To debug the error, disable multiprocessing.
```

Also when trying to download my dataset there were no issues from one machine, but from another:
```
SSLError: (MaxRetryError("HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /api/datasets/kopyl/3M_icons_monochrome_only_no_captioning/revision/753dca4be462dad7022f34cc273555ab6deb5832 (Caused by SSLError(SSLEOFError(8, '[SSL: UNEXPECTED_EOF_WHILE_READING] EOF occurred in violation of protocol (_ssl.c:1007)')))"), '(Request ID: 7d0881f3-1b93-4d73-bcb6-52e816d84529)')
```

Can't download my dataset at all...

Hmm this is not good, do you know a way to make it work ?

Basically `map` creates two subprocesses and runs the function in the subprocesses. Since each function has a parameter `rank` it should be possible to choose which GPU to use

I can confirm that PR #6550 works. All GPUs are at full throttle. You have to manually move the model to all GPUs. 

> I haven't experimented much with the multi-GPU code documentation.
> 
> Can you try using the code example at #6550 instead ? That would be super helpful if you could confirm that it works on your side
> 
> Though if you have some fixes/improvements ideas feel free to open a PR !



I wrote a [blog post](https://forrestbao.github.io/2024/01/30/datasets_map_with_rank_multiple_GPUs.html) with a complete example by compiling information from several PRs and issues here. Hope it can help. Let me know how it works. 

> Could you please provide a working example of multi-GPU mapping?
> 
> Not just an example in docs, but a real working example starting from all imports loading datasets and models.