Deprecate Beam API and download from HF GCS bucket
State: closed | Author: mariosasko (COLLABORATOR)

Deprecate the Beam API and download from the HF GCS bucked. 

TODO:
- [x] Convert the Beam-based [`wikipedia`](https://huggingface.co/datasets/wikipedia) to an Arrow-based dataset ([Hub PR](https://huggingface.co/datasets/wikipedia/discussions/19))
- [x] Make [`natural_questions`](https://huggingface.co/datasets/natural_questions) a no-code dataset ([Hub PR](https://huggingface.co/datasets/natural_questions/discussions/7))
- [x] Make [`wiki40b`](https://huggingface.co/datasets/wiki40b) a no-code dataset ([Hub PR](https://huggingface.co/datasets/wiki40b/discussions/5))
- [x] Make [`wiki_dpr`](https://huggingface.co/datasets/wiki_dpr) an Arrow-based dataset ([Hub PR](https://huggingface.co/datasets/wiki_dpr/discussions/14))

Comments: The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6474). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

<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) | 0.005280 / 0.011353 (-0.006073) | 0.003770 / 0.011008 (-0.007238) | 0.064320 / 0.038508 (0.025812) | 0.031250 / 0.023109 (0.008141) | 0.245113 / 0.275898 (-0.030785) | 0.268646 / 0.323480 (-0.054834) | 0.003147 / 0.007986 (-0.004839) | 0.002736 / 0.004328 (-0.001592) | 0.050802 / 0.004250 (0.046551) | 0.044539 / 0.037052 (0.007487) | 0.261702 / 0.258489 (0.003213) | 0.304696 / 0.293841 (0.010855) | 0.028601 / 0.128546 (-0.099945) | 0.010731 / 0.075646 (-0.064915) | 0.208183 / 0.419271 (-0.211089) | 0.036597 / 0.043533 (-0.006936) | 0.245107 / 0.255139 (-0.010032) | 0.265936 / 0.283200 (-0.017264) | 0.019430 / 0.141683 (-0.122253) | 1.184697 / 1.452155 (-0.267458) | 1.206073 / 1.492716 (-0.286643) |

### 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) | 0.110242 / 0.018006 (0.092236) | 0.304185 / 0.000490 (0.303695) | 0.000220 / 0.000200 (0.000020) | 0.000047 / 0.000054 (-0.000008) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.018839 / 0.037411 (-0.018573) | 0.062840 / 0.014526 (0.048314) | 0.075849 / 0.176557 (-0.100708) | 0.122983 / 0.737135 (-0.614153) | 0.075352 / 0.296338 (-0.220987) |

### 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) | 0.283617 / 0.215209 (0.068408) | 2.801903 / 2.077655 (0.724248) | 1.447678 / 1.504120 (-0.056442) | 1.327561 / 1.541195 (-0.213633) | 1.348714 / 1.468490 (-0.119776) | 0.574283 / 4.584777 (-4.010493) | 2.449396 / 3.745712 (-1.296316) | 2.908659 / 5.269862 (-2.361203) | 1.813935 / 4.565676 (-2.751742) | 0.063141 / 0.424275 (-0.361134) | 0.005024 / 0.007607 (-0.002583) | 0.338854 / 0.226044 (0.112810) | 3.346680 / 2.268929 (1.077752) | 1.833792 / 55.444624 (-53.610832) | 1.553680 / 6.876477 (-5.322796) | 1.590403 / 2.142072 (-0.551670) | 0.657442 / 4.805227 (-4.147785) | 0.119961 / 6.500664 (-6.380703) | 0.042602 / 0.075469 (-0.032867) |

### 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) | 0.977510 / 1.841788 (-0.864278) | 12.105448 / 8.074308 (4.031140) | 9.635282 / 10.191392 (-0.556110) | 0.132453 / 0.680424 (-0.547971) | 0.014225 / 0.534201 (-0.519976) | 0.292847 / 0.579283 (-0.286436) | 0.271498 / 0.434364 (-0.162866) | 0.329785 / 0.540337 (-0.210552) | 0.435058 / 1.386936 (-0.951879) |

</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) | 0.005656 / 0.011353 (-0.005697) | 0.003903 / 0.011008 (-0.007106) | 0.050670 / 0.038508 (0.012162) | 0.032405 / 0.023109 (0.009296) | 0.271443 / 0.275898 (-0.004455) | 0.298989 / 0.323480 (-0.024491) | 0.004397 / 0.007986 (-0.003589) | 0.003027 / 0.004328 (-0.001301) | 0.050227 / 0.004250 (0.045977) | 0.047798 / 0.037052 (0.010745) | 0.287610 / 0.258489 (0.029120) | 0.314084 / 0.293841 (0.020243) | 0.030406 / 0.128546 (-0.098140) | 0.011159 / 0.075646 (-0.064487) | 0.059510 / 0.419271 (-0.359762) | 0.056842 / 0.043533 (0.013309) | 0.274653 / 0.255139 (0.019514) | 0.290175 / 0.283200 (0.006976) | 0.019822 / 0.141683 (-0.121861) | 1.168894 / 1.452155 (-0.283261) | 1.205867 / 1.492716 (-0.286850) |

### 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) | 0.094907 / 0.018006 (0.076901) | 0.304920 / 0.000490 (0.304431) | 0.000218 / 0.000200 (0.000018) | 0.000048 / 0.000054 (-0.000006) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.023015 / 0.037411 (-0.014396) | 0.076868 / 0.014526 (0.062342) | 0.089099 / 0.176557 (-0.087458) | 0.128786 / 0.737135 (-0.608350) | 0.090836 / 0.296338 (-0.205503) |

### 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) | 0.284552 / 0.215209 (0.069343) | 2.800870 / 2.077655 (0.723215) | 1.577167 / 1.504120 (0.073047) | 1.448788 / 1.541195 (-0.092406) | 1.475493 / 1.468490 (0.007003) | 0.566639 / 4.584777 (-4.018138) | 2.503671 / 3.745712 (-1.242041) | 2.796769 / 5.269862 (-2.473092) | 1.829344 / 4.565676 (-2.736332) | 0.064200 / 0.424275 (-0.360075) | 0.005066 / 0.007607 (-0.002541) | 0.335390 / 0.226044 (0.109346) | 3.421265 / 2.268929 (1.152336) | 1.942245 / 55.444624 (-53.502379) | 1.641401 / 6.876477 (-5.235076) | 1.824574 / 2.142072 (-0.317499) | 0.661379 / 4.805227 (-4.143849) | 0.117207 / 6.500664 (-6.383457) | 0.041630 / 0.075469 (-0.033839) |

### 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) | 1.009636 / 1.841788 (-0.832152) | 12.998379 / 8.074308 (4.924071) | 10.609013 / 10.191392 (0.417621) | 0.142794 / 0.680424 (-0.537630) | 0.015648 / 0.534201 (-0.518553) | 0.292825 / 0.579283 (-0.286458) | 0.280790 / 0.434364 (-0.153574) | 0.329016 / 0.540337 (-0.211322) | 0.442440 / 1.386936 (-0.944496) |

</details>
</details>

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