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2024-06-24T14:47:49Z
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<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) | 0.005810 / 0.011353 (-0.005543) | 0.003984 / 0.011008 (-0.007024) | 0.064347 / 0.038508 (0.025839) | 0.031943 / 0.023109 (0.008834) | 0.252596 / 0.275898 (-0.023302) | 0.274032 / 0.323480 (-0.049448) | 0.003494 / 0.007986 (-0.004492) | 0.002817 / 0.004328 (-0.001511) | 0.050132 / 0.004250 (0.045881) | 0.048008 / 0.037052 (0.010955) | 0.249037 / 0.258489 (-0.009452) | 0.288526 / 0.293841 (-0.005315) | 0.031038 / 0.128546 (-0.097509) | 0.012542 / 0.075646 (-0.063104) | 0.205682 / 0.419271 (-0.213590) | 0.038022 / 0.043533 (-0.005511) | 0.259001 / 0.255139 (0.003862) | 0.267455 / 0.283200 (-0.015744) | 0.021980 / 0.141683 (-0.119703) | 1.123996 / 1.452155 (-0.328159) | 1.173801 / 1.492716 (-0.318915) |

### 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) | 0.102827 / 0.018006 (0.084821) | 0.317210 / 0.000490 (0.316720) | 0.000222 / 0.000200 (0.000022) | 0.000052 / 0.000054 (-0.000002) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.019483 / 0.037411 (-0.017928) | 0.064098 / 0.014526 (0.049572) | 0.076219 / 0.176557 (-0.100337) | 0.122898 / 0.737135 (-0.614237) | 0.080657 / 0.296338 (-0.215681) |

### 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) | 0.278378 / 0.215209 (0.063169) | 2.792314 / 2.077655 (0.714659) | 1.516439 / 1.504120 (0.012319) | 1.374052 / 1.541195 (-0.167143) | 1.370848 / 1.468490 (-0.097642) | 0.756002 / 4.584777 (-3.828775) | 2.349581 / 3.745712 (-1.396131) | 2.994094 / 5.269862 (-2.275768) | 1.904242 / 4.565676 (-2.661435) | 0.078769 / 0.424275 (-0.345506) | 0.005103 / 0.007607 (-0.002505) | 0.336331 / 0.226044 (0.110287) | 3.329502 / 2.268929 (1.060574) | 1.863545 / 55.444624 (-53.581079) | 1.554690 / 6.876477 (-5.321787) | 1.588448 / 2.142072 (-0.553624) | 0.787322 / 4.805227 (-4.017905) | 0.138345 / 6.500664 (-6.362320) | 0.042228 / 0.075469 (-0.033241) |

### 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) | 0.968607 / 1.841788 (-0.873181) | 11.972076 / 8.074308 (3.897768) | 9.927608 / 10.191392 (-0.263784) | 0.141666 / 0.680424 (-0.538758) | 0.014591 / 0.534201 (-0.519610) | 0.301995 / 0.579283 (-0.277288) | 0.274360 / 0.434364 (-0.160004) | 0.338396 / 0.540337 (-0.201941) | 0.431081 / 1.386936 (-0.955855) |

</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) | 0.006122 / 0.011353 (-0.005231) | 0.004201 / 0.011008 (-0.006807) | 0.050204 / 0.038508 (0.011695) | 0.033222 / 0.023109 (0.010113) | 0.274357 / 0.275898 (-0.001542) | 0.296238 / 0.323480 (-0.027242) | 0.004542 / 0.007986 (-0.003444) | 0.002880 / 0.004328 (-0.001449) | 0.049103 / 0.004250 (0.044852) | 0.042294 / 0.037052 (0.005242) | 0.286459 / 0.258489 (0.027970) | 0.324988 / 0.293841 (0.031147) | 0.032084 / 0.128546 (-0.096462) | 0.012329 / 0.075646 (-0.063318) | 0.060261 / 0.419271 (-0.359010) | 0.034130 / 0.043533 (-0.009403) | 0.271432 / 0.255139 (0.016293) | 0.306251 / 0.283200 (0.023051) | 0.019744 / 0.141683 (-0.121939) | 1.153483 / 1.452155 (-0.298672) | 1.209126 / 1.492716 (-0.283591) |

### 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) | 0.004737 / 0.018006 (-0.013270) | 0.313458 / 0.000490 (0.312968) | 0.000216 / 0.000200 (0.000017) | 0.000053 / 0.000054 (-0.000001) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.022472 / 0.037411 (-0.014939) | 0.076725 / 0.014526 (0.062199) | 0.091356 / 0.176557 (-0.085201) | 0.132427 / 0.737135 (-0.604708) | 0.091072 / 0.296338 (-0.205266) |

### 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) | 0.294414 / 0.215209 (0.079205) | 2.913695 / 2.077655 (0.836040) | 1.567309 / 1.504120 (0.063189) | 1.448664 / 1.541195 (-0.092531) | 1.466386 / 1.468490 (-0.002105) | 0.718605 / 4.584777 (-3.866172) | 0.951963 / 3.745712 (-2.793749) | 2.812565 / 5.269862 (-2.457297) | 1.886483 / 4.565676 (-2.679193) | 0.077912 / 0.424275 (-0.346363) | 0.005371 / 0.007607 (-0.002236) | 0.349528 / 0.226044 (0.123484) | 3.431049 / 2.268929 (1.162121) | 1.920210 / 55.444624 (-53.524414) | 1.637927 / 6.876477 (-5.238549) | 1.767502 / 2.142072 (-0.374570) | 0.808672 / 4.805227 (-3.996555) | 0.134261 / 6.500664 (-6.366403) | 0.041295 / 0.075469 (-0.034174) |

### 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) | 1.023454 / 1.841788 (-0.818334) | 12.433731 / 8.074308 (4.359423) | 10.413191 / 10.191392 (0.221799) | 0.156813 / 0.680424 (-0.523611) | 0.015446 / 0.534201 (-0.518755) | 0.301935 / 0.579283 (-0.277348) | 0.133655 / 0.434364 (-0.300709) | 0.340296 / 0.540337 (-0.200041) | 0.466314 / 1.386936 (-0.920622) |

</details>
</details>

![](https://cml.dev/watermark.png#6cf563fd57807e923a29ebbe327fecb4ef969877 "CML watermark")

2024-06-27T09:37:22Z
https://github.com/huggingface/datasets/pull/6993#issuecomment-2194230546
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Hi @lhoestq,

I was confused by `legacy` prefix added to the [image data loading](https://huggingface.co/docs/datasets/main/en/image_dataset#legacy-loading-script) script section. I have a custom image dataset and have looked through the documentation to find something similar but can't find a good alternative What is now the recommend way to create a custom image dataset then? I want the HF format but will not host it on the hub.

Apologies in advance if this is the wrong place to ask such questions...
2024-07-07T18:54:21Z
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We stopped making new features for datasets with scripts for obvious security reasons, that's why they are marked as "legacy". What is blocking you from hosting on HF ?
2024-07-08T10:09:50Z
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Hi, thanks for the prompt answer :) I am working on proprietary datasets for the company where I am employed. We want to keep the data in-house but would like to investigate the use of the HF ecosystem.
2024-07-08T12:04:16Z
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I see ! Note that it's possible to have private repos on HF (+ dataset viewer) and you can even choose the storage region, if it can help
2024-07-08T13:10:52Z
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+ mark as legacy in some parts of the docs since we'll not build new features for script datasets