Use `HF_HUB_OFFLINE` instead of `HF_DATASETS_OFFLINE`
NONE
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6968). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.
2024-06-13T14:41:50Z
https://github.com/huggingface/datasets/pull/6968#issuecomment-2165878874
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CONTRIBUTOR
Oops, sorry for the style issue. Fixed in https://github.com/huggingface/datasets/pull/6968/commits/a4e2b28fa647b28190ae2615d7271e6ac63c8499.

Regarding docs, I can't find mentions of `HF_DATASETS_OFFLINE` anywhere else in `datasets`/`hub-docs`. Once this is merged and released, I'm planning to update some `transformers` docs that briefly mention it.
2024-06-13T14:57:15Z
https://github.com/huggingface/datasets/pull/6968#issuecomment-2165924696
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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) | 0.005173 / 0.011353 (-0.006180) | 0.003485 / 0.011008 (-0.007524) | 0.063867 / 0.038508 (0.025359) | 0.031338 / 0.023109 (0.008229) | 0.242093 / 0.275898 (-0.033805) | 0.266606 / 0.323480 (-0.056874) | 0.003069 / 0.007986 (-0.004916) | 0.003307 / 0.004328 (-0.001022) | 0.051059 / 0.004250 (0.046808) | 0.044396 / 0.037052 (0.007344) | 0.254896 / 0.258489 (-0.003593) | 0.282835 / 0.293841 (-0.011006) | 0.027548 / 0.128546 (-0.100998) | 0.010520 / 0.075646 (-0.065126) | 0.201701 / 0.419271 (-0.217570) | 0.035613 / 0.043533 (-0.007920) | 0.240955 / 0.255139 (-0.014184) | 0.271902 / 0.283200 (-0.011298) | 0.019826 / 0.141683 (-0.121857) | 1.116994 / 1.452155 (-0.335161) | 1.162886 / 1.492716 (-0.329831) |

### 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) | 0.093683 / 0.018006 (0.075677) | 0.297970 / 0.000490 (0.297480) | 0.000211 / 0.000200 (0.000011) | 0.000043 / 0.000054 (-0.000011) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.018952 / 0.037411 (-0.018459) | 0.062710 / 0.014526 (0.048184) | 0.073641 / 0.176557 (-0.102916) | 0.121200 / 0.737135 (-0.615935) | 0.075723 / 0.296338 (-0.220616) |

### 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) | 0.286056 / 0.215209 (0.070847) | 2.811424 / 2.077655 (0.733770) | 1.448045 / 1.504120 (-0.056075) | 1.338309 / 1.541195 (-0.202885) | 1.328371 / 1.468490 (-0.140119) | 0.557282 / 4.584777 (-4.027495) | 2.362235 / 3.745712 (-1.383477) | 2.732108 / 5.269862 (-2.537754) | 1.730911 / 4.565676 (-2.834765) | 0.061689 / 0.424275 (-0.362586) | 0.004947 / 0.007607 (-0.002660) | 0.346700 / 0.226044 (0.120656) | 3.355989 / 2.268929 (1.087060) | 1.828078 / 55.444624 (-53.616546) | 1.511531 / 6.876477 (-5.364946) | 1.535897 / 2.142072 (-0.606175) | 0.630276 / 4.805227 (-4.174951) | 0.115808 / 6.500664 (-6.384857) | 0.042199 / 0.075469 (-0.033270) |

### 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) | 0.969203 / 1.841788 (-0.872584) | 11.282997 / 8.074308 (3.208689) | 9.538914 / 10.191392 (-0.652478) | 0.140072 / 0.680424 (-0.540352) | 0.014021 / 0.534201 (-0.520180) | 0.283784 / 0.579283 (-0.295499) | 0.255973 / 0.434364 (-0.178391) | 0.320284 / 0.540337 (-0.220053) | 0.412689 / 1.386936 (-0.974247) |

</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) | 0.005201 / 0.011353 (-0.006152) | 0.003312 / 0.011008 (-0.007697) | 0.050044 / 0.038508 (0.011536) | 0.033610 / 0.023109 (0.010501) | 0.266429 / 0.275898 (-0.009469) | 0.287782 / 0.323480 (-0.035698) | 0.004316 / 0.007986 (-0.003670) | 0.002696 / 0.004328 (-0.001633) | 0.049667 / 0.004250 (0.045417) | 0.040244 / 0.037052 (0.003192) | 0.278870 / 0.258489 (0.020381) | 0.311415 / 0.293841 (0.017574) | 0.029150 / 0.128546 (-0.099396) | 0.010046 / 0.075646 (-0.065600) | 0.058527 / 0.419271 (-0.360744) | 0.032871 / 0.043533 (-0.010662) | 0.266582 / 0.255139 (0.011443) | 0.286157 / 0.283200 (0.002957) | 0.017197 / 0.141683 (-0.124486) | 1.120944 / 1.452155 (-0.331211) | 1.161111 / 1.492716 (-0.331606) |

### 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) | 0.092679 / 0.018006 (0.074672) | 0.299195 / 0.000490 (0.298705) | 0.000204 / 0.000200 (0.000004) | 0.000048 / 0.000054 (-0.000007) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.022212 / 0.037411 (-0.015199) | 0.076734 / 0.014526 (0.062208) | 0.088326 / 0.176557 (-0.088230) | 0.128209 / 0.737135 (-0.608926) | 0.088807 / 0.296338 (-0.207531) |

### 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) | 0.291782 / 0.215209 (0.076573) | 2.882990 / 2.077655 (0.805335) | 1.601638 / 1.504120 (0.097518) | 1.457560 / 1.541195 (-0.083635) | 1.470517 / 1.468490 (0.002027) | 0.565738 / 4.584777 (-4.019039) | 0.949235 / 3.745712 (-2.796478) | 2.661927 / 5.269862 (-2.607934) | 1.722178 / 4.565676 (-2.843498) | 0.063680 / 0.424275 (-0.360595) | 0.005339 / 0.007607 (-0.002268) | 0.344280 / 0.226044 (0.118235) | 3.432998 / 2.268929 (1.164070) | 1.985516 / 55.444624 (-53.459108) | 1.651826 / 6.876477 (-5.224651) | 1.764541 / 2.142072 (-0.377531) | 0.640219 / 4.805227 (-4.165008) | 0.116541 / 6.500664 (-6.384124) | 0.041237 / 0.075469 (-0.034232) |

### 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) | 1.013927 / 1.841788 (-0.827861) | 11.876661 / 8.074308 (3.802353) | 10.264144 / 10.191392 (0.072752) | 0.131151 / 0.680424 (-0.549273) | 0.015774 / 0.534201 (-0.518427) | 0.284948 / 0.579283 (-0.294335) | 0.125924 / 0.434364 (-0.308439) | 0.319845 / 0.540337 (-0.220493) | 0.431978 / 1.386936 (-0.954958) |

</details>
</details>

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2024-06-13T17:31:36Z
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To use `datasets` offline, one can use the `HF_DATASETS_OFFLINE` environment variable. This PR makes `HF_HUB_OFFLINE` the recommended environment variable for offline training. Goal is to be more consistent with the rest of HF ecosystem and have a single config value to set.

The changes are backward-compatible meaning that:
- `HF_DATASETS_OFFLINE` environment is still taken into account, though not documented
- `datasets.config.HF_DATASETS_OFFLINE` still exists, though it is not used anymore (in favor of `datasets.config.HF_HUB_OFFLINE`)


**Note:** it might break things in downstream libraries if they were monkeypatching `datasets.config.HF_DATASETS_OFFLINE` in their CI tests (for instance). Not much of a problem IMO.