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"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006536 / 0.011353 (-0.004817) | 0.003890 / 0.011008 (-0.007118) | 0.084044 / 0.038508 (0.045536) | 0.071893 / 0.023109 (0.048784) | 0.346926 / 0.275898 (0.071028) | 0.397487 / 0.323480 (0.074007) | 0.004065 / 0.007986 (-0.003921) | 0.003218 / 0.004328 (-0.001111) | 0.064670 / 0.004250 (0.060420) | 0.052414 / 0.037052 (0.015362) | 0.355413 / 0.258489 (0.096924) | 0.398894 / 0.293841 (0.105053) | 0.030763 / 0.128546 (-0.097783) | 0.008590 / 0.075646 (-0.067056) | 0.286857 / 0.419271 (-0.132415) | 0.051126 / 0.043533 (0.007593) | 0.346125 / 0.255139 (0.090986) | 0.395673 / 0.283200 (0.112474) | 0.025766 / 0.141683 (-0.115917) | 1.466238 / 1.452155 (0.014084) | 1.543117 / 1.492716 (0.050400) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.213210 / 0.018006 (0.195204) | 0.451981 / 0.000490 (0.451491) | 0.003784 / 0.000200 (0.003585) | 0.000096 / 0.000054 (0.000041) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027756 / 0.037411 (-0.009655) | 0.082446 / 0.014526 (0.067920) | 0.095414 / 0.176557 (-0.081142) | 0.151812 / 0.737135 (-0.585323) | 0.096296 / 0.296338 (-0.200042) |\n\n### Benchmark: benchmark_iterating.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.383729 / 0.215209 (0.168520) | 3.835126 / 2.077655 (1.757471) | 1.891972 / 1.504120 (0.387852) | 1.719934 / 1.541195 (0.178739) | 1.899980 / 1.468490 (0.431490) | 0.488741 / 4.584777 (-4.096036) | 3.634120 / 3.745712 (-0.111592) | 3.243314 / 5.269862 (-2.026547) | 2.028382 / 4.565676 (-2.537294) | 0.057355 / 0.424275 (-0.366920) | 0.007717 / 0.007607 (0.000110) | 0.459835 / 0.226044 (0.233790) | 4.591793 / 2.268929 (2.322864) | 2.346861 / 55.444624 (-53.097764) | 2.067357 / 6.876477 (-4.809120) | 2.254954 / 2.142072 (0.112882) | 0.587016 / 4.805227 (-4.218211) | 0.133918 / 6.500664 (-6.366746) | 0.060311 / 0.075469 (-0.015158) |\n\n### Benchmark: benchmark_map_filter.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.250016 / 1.841788 (-0.591772) | 19.674333 / 8.074308 (11.600025) | 14.522764 / 10.191392 (4.331372) | 0.145741 / 0.680424 (-0.534683) | 0.018593 / 0.534201 (-0.515608) | 0.392833 / 0.579283 (-0.186450) | 0.408194 / 0.434364 (-0.026170) | 0.455164 / 0.540337 (-0.085174) | 0.622722 / 1.386936 (-0.764214) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006583 / 0.011353 (-0.004770) | 0.004008 / 0.011008 (-0.007000) | 0.064688 / 0.038508 (0.026180) | 0.074969 / 0.023109 (0.051860) | 0.360504 / 0.275898 (0.084606) | 0.396926 / 0.323480 (0.073446) | 0.005190 / 0.007986 (-0.002796) | 0.003363 / 0.004328 (-0.000966) | 0.064372 / 0.004250 (0.060122) | 0.054428 / 0.037052 (0.017376) | 0.361204 / 0.258489 (0.102715) | 0.400917 / 0.293841 (0.107077) | 0.031117 / 0.128546 (-0.097429) | 0.008406 / 0.075646 (-0.067241) | 0.069655 / 0.419271 (-0.349617) | 0.048582 / 0.043533 (0.005049) | 0.365396 / 0.255139 (0.110257) | 0.381344 / 0.283200 (0.098145) | 0.023809 / 0.141683 (-0.117874) | 1.472926 / 1.452155 (0.020772) | 1.547298 / 1.492716 (0.054582) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.276912 / 0.018006 (0.258906) | 0.449096 / 0.000490 (0.448607) | 0.018921 / 0.000200 (0.018721) | 0.000111 / 0.000054 (0.000056) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030237 / 0.037411 (-0.007174) | 0.088610 / 0.014526 (0.074084) | 0.101529 / 0.176557 (-0.075027) | 0.154070 / 0.737135 (-0.583065) | 0.103471 / 0.296338 (-0.192867) |\n\n### Benchmark: benchmark_iterating.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.416047 / 0.215209 (0.200838) | 4.152374 / 2.077655 (2.074719) | 2.111181 / 1.504120 (0.607061) | 1.943582 / 1.541195 (0.402387) | 2.031729 / 1.468490 (0.563239) | 0.486740 / 4.584777 (-4.098037) | 3.631547 / 3.745712 (-0.114165) | 3.251202 / 5.269862 (-2.018660) | 2.041272 / 4.565676 (-2.524405) | 0.057287 / 0.424275 (-0.366988) | 0.007303 / 0.007607 (-0.000304) | 0.491027 / 0.226044 (0.264982) | 4.906757 / 2.268929 (2.637829) | 2.581694 / 55.444624 (-52.862931) | 2.250996 / 6.876477 (-4.625481) | 2.441771 / 2.142072 (0.299698) | 0.600714 / 4.805227 (-4.204514) | 0.133233 / 6.500664 (-6.367431) | 0.060856 / 0.075469 (-0.014613) |\n\n### Benchmark: benchmark_map_filter.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.340062 / 1.841788 (-0.501725) | 19.973899 / 8.074308 (11.899591) | 14.347381 / 10.191392 (4.155989) | 0.166651 / 0.680424 (-0.513773) | 0.018691 / 0.534201 (-0.515510) | 0.393580 / 0.579283 (-0.185703) | 0.409425 / 0.434364 (-0.024939) | 0.474409 / 0.540337 (-0.065929) | 0.649423 / 1.386936 (-0.737514) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c5da68102297c3639207a7901952d2765a4cdb8b \"CML watermark\")\n",
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6138). All of your documentation changes will be reflected on that endpoint."
] | "2023-08-10T11:03:15" | "2023-08-10T11:10:42" | null | MEMBER | null | false | {
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} | This PR ignores the violation of the lint rule E721 in `Pickler.memoize`.
The lint rule violation was introduced in this PR:
- #3182
@lhoestq is there a reason you did not use `isinstance` instead?
As a hotfix, we just ignore the violation of the lint rule.
Fix #6136. | {
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https://api.github.com/repos/huggingface/datasets/issues/6137 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6137/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6137/comments | https://api.github.com/repos/huggingface/datasets/issues/6137/events | https://github.com/huggingface/datasets/issues/6137 | 1,844,952,312 | I_kwDODunzps5t97z4 | 6,137 | (`from_spark()`) Unable to connect HDFS in pyspark YARN setting | {
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} | [] | open | false | null | [] | null | [] | "2023-08-10T11:03:08" | "2023-08-10T11:03:08" | null | NONE | null | null | null | ### Describe the bug
related issue: https://github.com/apache/arrow/issues/37057#issue-1841013613
---
Hello. I'm trying to interact with HDFS storage from a driver and workers of pyspark YARN cluster. Precisely I'm using **huggingface's `datasets`** ([link](https://github.com/huggingface/datasets)) library that relies on pyarrow to communicate with HDFS. The `from_spark()` ([link](https://huggingface.co/docs/datasets/use_with_spark#load-from-spark)) is what I'm invoking in my script.
Below is the error I'm encountering. Note that I've masked sensitive paths. My code is sent to worker containers (docker) from driver container then executed. I confirmed that in both driver and worker images I can connect to HDFS using pyarrow since the envs and required jars are properly set, but strangely that becomes impossible when the same image runs as remote worker process.
These are some peculiarities in my environment that might caused this issue.
* **Cluster requires kerberos authentication**
* But I think the error message implies that's not the problem in this case
* **The user that runs the worker process is different from that built the docker image**
* To avoid permission-related issues I made all directories that are accessed from the script accessible to everyone
* **Pyspark-part of my code has no problem interacting with HDFS.**
* Even pyarrow doesn't experience problem when I run the code in interactive session of the same docker images (driver, worker)
* The problem occurs only when it runs as cluster's worker runtime
Hope I could get some help. Thanks.
```bash
2023-08-08 18:51:19,638 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
2023-08-08 18:51:20,280 WARN shortcircuit.DomainSocketFactory: The short-circuit local reads feature cannot be used because libhadoop cannot be loaded.
23/08/08 18:51:22 WARN TaskSetManager: Lost task 0.0 in stage 142.0 (TID 9732) (ac3bax2062.bdp.bdata.ai executor 1): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
File "<MASKED>/application_1682476586273_25865777/container_e143_1682476586273_25865777_01_000003/pyspark.zip/pyspark/worker.py", line 830, in main
process()
File "<MASKED>/application_1682476586273_25865777/container_e143_1682476586273_25865777_01_000003/pyspark.zip/pyspark/worker.py", line 820, in process
out_iter = func(split_index, iterator)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/root/spark/python/pyspark/rdd.py", line 5405, in pipeline_func
File "/root/spark/python/pyspark/rdd.py", line 828, in func
File "/opt/conda/lib/python3.11/site-packages/datasets/packaged_modules/spark/spark.py", line 130, in create_cache_and_write_probe
open(probe_file, "a")
File "/opt/conda/lib/python3.11/site-packages/datasets/streaming.py", line 74, in wrapper
return function(*args, download_config=download_config, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/datasets/download/streaming_download_manager.py", line 496, in xopen
file_obj = fsspec.open(file, mode=mode, *args, **kwargs).open()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/core.py", line 439, in open
out = open_files(
^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/core.py", line 282, in open_files
fs, fs_token, paths = get_fs_token_paths(
^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/core.py", line 609, in get_fs_token_paths
fs = filesystem(protocol, **inkwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/registry.py", line 267, in filesystem
return cls(**storage_options)
^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/spec.py", line 79, in __call__
obj = super().__call__(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/implementations/arrow.py", line 278, in __init__
fs = HadoopFileSystem(
^^^^^^^^^^^^^^^^^
File "pyarrow/_hdfs.pyx", line 96, in pyarrow._hdfs.HadoopFileSystem.__init__
File "pyarrow/error.pxi", line 144, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 115, in pyarrow.lib.check_status
OSError: HDFS connection failed
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:561)
at org.apache.spark.api.python.PythonRunner$$anon$3.read(PythonRunner.scala:767)
at org.apache.spark.api.python.PythonRunner$$anon$3.read(PythonRunner.scala:749)
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:514)
at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
at scala.collection.Iterator.foreach(Iterator.scala:943)
at scala.collection.Iterator.foreach$(Iterator.scala:943)
at org.apache.spark.InterruptibleIterator.foreach(InterruptibleIterator.scala:28)
at scala.collection.generic.Growable.$plus$plus$eq(Growable.scala:62)
at scala.collection.generic.Growable.$plus$plus$eq$(Growable.scala:53)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:105)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:49)
at scala.collection.TraversableOnce.to(TraversableOnce.scala:366)
at scala.collection.TraversableOnce.to$(TraversableOnce.scala:364)
at org.apache.spark.InterruptibleIterator.to(InterruptibleIterator.scala:28)
at scala.collection.TraversableOnce.toBuffer(TraversableOnce.scala:358)
at scala.collection.TraversableOnce.toBuffer$(TraversableOnce.scala:358)
at org.apache.spark.InterruptibleIterator.toBuffer(InterruptibleIterator.scala:28)
at scala.collection.TraversableOnce.toArray(TraversableOnce.scala:345)
at scala.collection.TraversableOnce.toArray$(TraversableOnce.scala:339)
at org.apache.spark.InterruptibleIterator.toArray(InterruptibleIterator.scala:28)
at org.apache.spark.rdd.RDD.$anonfun$collect$2(RDD.scala:1019)
at org.apache.spark.SparkContext.$anonfun$runJob$5(SparkContext.scala:2303)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:92)
at org.apache.spark.TaskContext.runTaskWithListeners(TaskContext.scala:161)
at org.apache.spark.scheduler.Task.run(Task.scala:139)
at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:554)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1529)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:557)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
23/08/08 18:51:24 WARN TaskSetManager: Lost task 0.1 in stage 142.0 (TID 9733) (ac3iax2079.bdp.bdata.ai executor 2): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
File "<MASKED>/application_1682476586273_25865777/container_e143_1682476586273_25865777_01_000005/pyspark.zip/pyspark/worker.py", line 830, in main
process()
File "<MASKED>/application_1682476586273_25865777/container_e143_1682476586273_25865777_01_000005/pyspark.zip/pyspark/worker.py", line 820, in process
out_iter = func(split_index, iterator)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/root/spark/python/pyspark/rdd.py", line 5405, in pipeline_func
File "/root/spark/python/pyspark/rdd.py", line 828, in func
File "/opt/conda/lib/python3.11/site-packages/datasets/packaged_modules/spark/spark.py", line 130, in create_cache_and_write_probe
open(probe_file, "a")
File "/opt/conda/lib/python3.11/site-packages/datasets/streaming.py", line 74, in wrapper
return function(*args, download_config=download_config, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/datasets/download/streaming_download_manager.py", line 496, in xopen
file_obj = fsspec.open(file, mode=mode, *args, **kwargs).open()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/core.py", line 439, in open
out = open_files(
^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/core.py", line 282, in open_files
fs, fs_token, paths = get_fs_token_paths(
^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/core.py", line 609, in get_fs_token_paths
fs = filesystem(protocol, **inkwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/registry.py", line 267, in filesystem
return cls(**storage_options)
^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/spec.py", line 79, in __call__
obj = super().__call__(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/implementations/arrow.py", line 278, in __init__
fs = HadoopFileSystem(
^^^^^^^^^^^^^^^^^
File "pyarrow/_hdfs.pyx", line 96, in pyarrow._hdfs.HadoopFileSystem.__init__
File "pyarrow/error.pxi", line 144, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 115, in pyarrow.lib.check_status
OSError: HDFS connection failed
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:561)
at org.apache.spark.api.python.PythonRunner$$anon$3.read(PythonRunner.scala:767)
at org.apache.spark.api.python.PythonRunner$$anon$3.read(PythonRunner.scala:749)
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:514)
at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
at scala.collection.Iterator.foreach(Iterator.scala:943)
at scala.collection.Iterator.foreach$(Iterator.scala:943)
at org.apache.spark.InterruptibleIterator.foreach(InterruptibleIterator.scala:28)
at scala.collection.generic.Growable.$plus$plus$eq(Growable.scala:62)
at scala.collection.generic.Growable.$plus$plus$eq$(Growable.scala:53)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:105)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:49)
at scala.collection.TraversableOnce.to(TraversableOnce.scala:366)
at scala.collection.TraversableOnce.to$(TraversableOnce.scala:364)
at org.apache.spark.InterruptibleIterator.to(InterruptibleIterator.scala:28)
at scala.collection.TraversableOnce.toBuffer(TraversableOnce.scala:358)
at scala.collection.TraversableOnce.toBuffer$(TraversableOnce.scala:358)
at org.apache.spark.InterruptibleIterator.toBuffer(InterruptibleIterator.scala:28)
at scala.collection.TraversableOnce.toArray(TraversableOnce.scala:345)
at scala.collection.TraversableOnce.toArray$(TraversableOnce.scala:339)
at org.apache.spark.InterruptibleIterator.toArray(InterruptibleIterator.scala:28)
at org.apache.spark.rdd.RDD.$anonfun$collect$2(RDD.scala:1019)
at org.apache.spark.SparkContext.$anonfun$runJob$5(SparkContext.scala:2303)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:92)
at org.apache.spark.TaskContext.runTaskWithListeners(TaskContext.scala:161)
at org.apache.spark.scheduler.Task.run(Task.scala:139)
at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:554)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1529)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:557)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
23/08/08 18:51:38 WARN TaskSetManager: Lost task 0.2 in stage 142.0 (TID 9734) (<MASKED> executor 4): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
File "<MASKED>/application_1682476586273_25865777/container_e143_1682476586273_25865777_01_000008/pyspark.zip/pyspark/worker.py", line 830, in main
process()
File "<MASKED>/application_1682476586273_25865777/container_e143_1682476586273_25865777_01_000008/pyspark.zip/pyspark/worker.py", line 820, in process
out_iter = func(split_index, iterator)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/root/spark/python/pyspark/rdd.py", line 5405, in pipeline_func
File "/root/spark/python/pyspark/rdd.py", line 828, in func
File "/opt/conda/lib/python3.11/site-packages/datasets/packaged_modules/spark/spark.py", line 130, in create_cache_and_write_probe
open(probe_file, "a")
File "/opt/conda/lib/python3.11/site-packages/datasets/streaming.py", line 74, in wrapper
return function(*args, download_config=download_config, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/datasets/download/streaming_download_manager.py", line 496, in xopen
file_obj = fsspec.open(file, mode=mode, *args, **kwargs).open()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/core.py", line 439, in open
out = open_files(
^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/core.py", line 282, in open_files
fs, fs_token, paths = get_fs_token_paths(
^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/core.py", line 609, in get_fs_token_paths
fs = filesystem(protocol, **inkwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/registry.py", line 267, in filesystem
return cls(**storage_options)
^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/spec.py", line 79, in __call__
obj = super().__call__(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/fsspec/implementations/arrow.py", line 278, in __init__
fs = HadoopFileSystem(
^^^^^^^^^^^^^^^^^
File "pyarrow/_hdfs.pyx", line 96, in pyarrow._hdfs.HadoopFileSystem.__init__
File "pyarrow/error.pxi", line 144, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 115, in pyarrow.lib.check_status
OSError: HDFS connection failed
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:561)
at org.apache.spark.api.python.PythonRunner$$anon$3.read(PythonRunner.scala:767)
at org.apache.spark.api.python.PythonRunner$$anon$3.read(PythonRunner.scala:749)
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:514)
at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
at scala.collection.Iterator.foreach(Iterator.scala:943)
at scala.collection.Iterator.foreach$(Iterator.scala:943)
at org.apache.spark.InterruptibleIterator.foreach(InterruptibleIterator.scala:28)
at scala.collection.generic.Growable.$plus$plus$eq(Growable.scala:62)
at scala.collection.generic.Growable.$plus$plus$eq$(Growable.scala:53)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:105)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:49)
at scala.collection.TraversableOnce.to(TraversableOnce.scala:366)
at scala.collection.TraversableOnce.to$(TraversableOnce.scala:364)
at org.apache.spark.InterruptibleIterator.to(InterruptibleIterator.scala:28)
at scala.collection.TraversableOnce.toBuffer(TraversableOnce.scala:358)
at scala.collection.TraversableOnce.toBuffer$(TraversableOnce.scala:358)
at org.apache.spark.InterruptibleIterator.toBuffer(InterruptibleIterator.scala:28)
at scala.collection.TraversableOnce.toArray(TraversableOnce.scala:345)
at scala.collection.TraversableOnce.toArray$(TraversableOnce.scala:339)
at org.apache.spark.InterruptibleIterator.toArray(InterruptibleIterator.scala:28)
at org.apache.spark.rdd.RDD.$anonfun$collect$2(RDD.scala:1019)
at org.apache.spark.SparkContext.$anonfun$runJob$5(SparkContext.scala:2303)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:92)
at org.apache.spark.TaskContext.runTaskWithListeners(TaskContext.scala:161)
at org.apache.spark.scheduler.Task.run(Task.scala:139)
at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:554)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1529)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:557)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
```
### Steps to reproduce the bug
Use `from_spark()` function in pyspark YARN setting. I set `cache_dir` to HDFS path.
### Expected behavior
Work as described in document
### Environment info
- `datasets` version: 2.14.4
- Platform: Linux-4.18.0-425.19.2.el8_7.x86_64-x86_64-with-glibc2.17
- Python version: 3.11.4
- Huggingface_hub version: 0.16.4
- PyArrow version: 10.0.1
- Pandas version: 1.5.3 | {
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] | null | [] | "2023-08-10T10:19:50" | "2023-08-10T10:19:50" | null | MEMBER | null | null | null | After latest release of `ruff` (https://pypi.org/project/ruff/0.0.284/), we get the following CI error:
```
src/datasets/utils/py_utils.py:689:12: E721 Do not compare types, use `isinstance()`
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6135). All of your documentation changes will be reflected on that endpoint.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009055 / 0.011353 (-0.002298) | 0.008835 / 0.011008 (-0.002173) | 0.117048 / 0.038508 (0.078540) | 0.096268 / 0.023109 (0.073159) | 0.474678 / 0.275898 (0.198780) | 0.550509 / 0.323480 (0.227029) | 0.005552 / 0.007986 (-0.002434) | 0.004315 / 0.004328 (-0.000013) | 0.094336 / 0.004250 (0.090086) | 0.061945 / 0.037052 (0.024892) | 0.461422 / 0.258489 (0.202933) | 0.521271 / 0.293841 (0.227430) | 0.049116 / 0.128546 (-0.079430) | 0.015007 / 0.075646 (-0.060639) | 0.414351 / 0.419271 (-0.004920) | 0.137520 / 0.043533 (0.093987) | 0.465627 / 0.255139 (0.210488) | 0.537244 / 0.283200 (0.254044) | 0.068577 / 0.141683 (-0.073106) | 1.921373 / 1.452155 (0.469219) | 2.506653 / 1.492716 (1.013937) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.273970 / 0.018006 (0.255963) | 0.750295 / 0.000490 (0.749805) | 0.004241 / 0.000200 (0.004041) | 0.000128 / 0.000054 (0.000073) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033793 / 0.037411 (-0.003618) | 0.105562 / 0.014526 (0.091037) | 0.131771 / 0.176557 (-0.044786) | 0.196890 / 0.737135 (-0.540245) | 0.119842 / 0.296338 (-0.176496) |\n\n### Benchmark: benchmark_iterating.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.634881 / 0.215209 (0.419672) | 6.069221 / 2.077655 (3.991566) | 2.678765 / 1.504120 (1.174646) | 2.460309 / 1.541195 (0.919114) | 2.517579 / 1.468490 (1.049089) | 0.869558 / 4.584777 (-3.715219) | 5.407686 / 3.745712 (1.661974) | 4.920687 / 5.269862 (-0.349175) | 3.130066 / 4.565676 (-1.435611) | 0.100337 / 0.424275 (-0.323938) | 0.009615 / 0.007607 (0.002008) | 0.745275 / 0.226044 (0.519231) | 7.577890 / 2.268929 (5.308962) | 3.607887 / 55.444624 (-51.836738) | 2.922211 / 6.876477 (-3.954266) | 3.205592 / 2.142072 (1.063519) | 1.052298 / 4.805227 (-3.752929) | 0.218798 / 6.500664 (-6.281866) | 0.082137 / 0.075469 (0.006667) |\n\n### Benchmark: benchmark_map_filter.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.696551 / 1.841788 (-0.145237) | 24.946074 / 8.074308 (16.871766) | 23.114202 / 10.191392 (12.922810) | 0.220498 / 0.680424 (-0.459925) | 0.029388 / 0.534201 (-0.504813) | 0.494721 / 0.579283 (-0.084562) | 0.603085 / 0.434364 (0.168722) | 0.573093 / 0.540337 (0.032756) | 0.784937 / 1.386936 (-0.601999) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009642 / 0.011353 (-0.001711) | 0.007551 / 0.011008 (-0.003457) | 0.085224 / 0.038508 (0.046716) | 0.099493 / 0.023109 (0.076384) | 0.503824 / 0.275898 (0.227926) | 0.546583 / 0.323480 (0.223103) | 0.006385 / 0.007986 (-0.001601) | 0.004751 / 0.004328 (0.000423) | 0.084699 / 0.004250 (0.080449) | 0.067875 / 0.037052 (0.030823) | 0.485313 / 0.258489 (0.226824) | 0.535808 / 0.293841 (0.241967) | 0.049935 / 0.128546 (-0.078611) | 0.014427 / 0.075646 (-0.061219) | 0.095531 / 0.419271 (-0.323741) | 0.068487 / 0.043533 (0.024954) | 0.502204 / 0.255139 (0.247065) | 0.514393 / 0.283200 (0.231193) | 0.037350 / 0.141683 (-0.104333) | 1.849380 / 1.452155 (0.397226) | 1.920151 / 1.492716 (0.427434) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.298363 / 0.018006 (0.280357) | 0.651555 / 0.000490 (0.651065) | 0.005910 / 0.000200 (0.005710) | 0.000103 / 0.000054 (0.000048) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.039170 / 0.037411 (0.001758) | 0.106436 / 0.014526 (0.091910) | 0.129880 / 0.176557 (-0.046677) | 0.185401 / 0.737135 (-0.551734) | 0.125732 / 0.296338 (-0.170607) |\n\n### Benchmark: benchmark_iterating.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.643248 / 0.215209 (0.428039) | 6.374807 / 2.077655 (4.297152) | 3.057296 / 1.504120 (1.553176) | 2.779534 / 1.541195 (1.238340) | 2.790165 / 1.468490 (1.321675) | 0.841580 / 4.584777 (-3.743197) | 5.371478 / 3.745712 (1.625766) | 4.973251 / 5.269862 (-0.296610) | 3.235817 / 4.565676 (-1.329860) | 0.097276 / 0.424275 (-0.326999) | 0.008840 / 0.007607 (0.001233) | 0.728678 / 0.226044 (0.502634) | 7.526382 / 2.268929 (5.257454) | 3.792550 / 55.444624 (-51.652074) | 3.439134 / 6.876477 (-3.437342) | 3.466626 / 2.142072 (1.324553) | 1.035894 / 4.805227 (-3.769333) | 0.211670 / 6.500664 (-6.288994) | 0.087596 / 0.075469 (0.012127) |\n\n### Benchmark: benchmark_map_filter.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.782755 / 1.841788 (-0.059033) | 25.704407 / 8.074308 (17.630099) | 23.799672 / 10.191392 (13.608280) | 0.233952 / 0.680424 (-0.446472) | 0.030810 / 0.534201 (-0.503391) | 0.505857 / 0.579283 (-0.073426) | 0.629331 / 0.434364 (0.194967) | 0.608530 / 0.540337 (0.068192) | 0.813688 / 1.386936 (-0.573248) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ed4d6bb5f1331576c41b04acd9872a5349a0915c \"CML watermark\")\n"
] | "2023-08-10T10:09:54" | "2023-08-10T10:22:54" | null | MEMBER | null | false | {
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https://api.github.com/repos/huggingface/datasets/issues/6134 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6134/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6134/comments | https://api.github.com/repos/huggingface/datasets/issues/6134/events | https://github.com/huggingface/datasets/issues/6134 | 1,844,535,142 | I_kwDODunzps5t8V9m | 6,134 | `datasets` cannot be installed alongside `apache-beam` | {
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} | [] | open | false | null | [] | null | [] | "2023-08-10T06:54:32" | "2023-08-10T06:55:46" | null | NONE | null | null | null | ### Describe the bug
If one installs `apache-beam` alongside `datasets` (which is required for the [wikipedia](https://huggingface.co/datasets/wikipedia#dataset-summary) dataset) in certain environments (such as a Google Colab notebook), they appear to install successfully, however, actually trying to something such as importing the `load_dataset` method from `datasets` results in a crashing error.
I think the problem is that `apache-beam` version 2.49.0 requires `dill>=0.3.1.1,<0.3.2`, but the latest version of `multiprocess` (0.70.15) (on which `datasets` depends) requires `dill>=0.3.7,`, so this is causing the dependency resolver to use an older version of `multiprocess` which leads to the `datasets` crashing since it doesn't actually appear to be compatible with older versions.
### Steps to reproduce the bug
See this [Google Colab notebook](https://colab.research.google.com/drive/1PTeGlshamFcJZix_GiS3vMXX_YzAhGv0?usp=sharing) to easily reproduce the bug.
In some environments, I have been able to reproduce the bug by running the following in Bash:
```bash
$ pip install datasets apache-beam
```
then the following in a Python shell:
```python
from datasets import load_dataset
```
Here is my stacktrace from running on Google Colab:
<details>
<summary>stacktrace</summary>
```
[/usr/local/lib/python3.10/dist-packages/datasets/__init__.py](https://localhost:8080/#) in <module>
20 __version__ = "2.14.4"
21
---> 22 from .arrow_dataset import Dataset
23 from .arrow_reader import ReadInstruction
24 from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
[/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in <module>
64
65 from . import config
---> 66 from .arrow_reader import ArrowReader
67 from .arrow_writer import ArrowWriter, OptimizedTypedSequence
68 from .data_files import sanitize_patterns
[/usr/local/lib/python3.10/dist-packages/datasets/arrow_reader.py](https://localhost:8080/#) in <module>
28 import pyarrow.parquet as pq
29
---> 30 from .download.download_config import DownloadConfig
31 from .naming import _split_re, filenames_for_dataset_split
32 from .table import InMemoryTable, MemoryMappedTable, Table, concat_tables
[/usr/local/lib/python3.10/dist-packages/datasets/download/__init__.py](https://localhost:8080/#) in <module>
7
8 from .download_config import DownloadConfig
----> 9 from .download_manager import DownloadManager, DownloadMode
10 from .streaming_download_manager import StreamingDownloadManager
[/usr/local/lib/python3.10/dist-packages/datasets/download/download_manager.py](https://localhost:8080/#) in <module>
33 from ..utils.info_utils import get_size_checksum_dict
34 from ..utils.logging import get_logger, is_progress_bar_enabled, tqdm
---> 35 from ..utils.py_utils import NestedDataStructure, map_nested, size_str
36 from .download_config import DownloadConfig
37
[/usr/local/lib/python3.10/dist-packages/datasets/utils/py_utils.py](https://localhost:8080/#) in <module>
38 import dill
39 import multiprocess
---> 40 import multiprocess.pool
41 import numpy as np
42 from packaging import version
[/usr/local/lib/python3.10/dist-packages/multiprocess/pool.py](https://localhost:8080/#) in <module>
607 #
608
--> 609 class ThreadPool(Pool):
610
611 from .dummy import Process
[/usr/local/lib/python3.10/dist-packages/multiprocess/pool.py](https://localhost:8080/#) in ThreadPool()
609 class ThreadPool(Pool):
610
--> 611 from .dummy import Process
612
613 def __init__(self, processes=None, initializer=None, initargs=()):
[/usr/local/lib/python3.10/dist-packages/multiprocess/dummy/__init__.py](https://localhost:8080/#) in <module>
85 #
86
---> 87 class Condition(threading._Condition):
88 # XXX
89 if sys.version_info < (3, 0):
AttributeError: module 'threading' has no attribute '_Condition'
```
</details>
I've also found that attempting to install these `datasets` and `apache-beam` in certain environments (e.g. via pip inside a conda env) simply causes the installer to hang indefinitely.
### Expected behavior
I would expect to be able to import methods from `datasets` without crashing. I have tested that this is possible as long as I do not attempt to install `apache-beam`.
### Environment info
Google Colab | {
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} | [] | open | false | null | [] | null | [] | "2023-08-10T06:36:23" | "2023-08-10T06:36:23" | null | NONE | null | null | null | ### Describe the bug
Can anyone explain why looping over a dataset becomes slower after calling `to_iterable_dataset` to convert to `IterableDataset`
### Steps to reproduce the bug
Any dataset after converting to `IterableDataset`
### Expected behavior
Maybe it should be faster on big dataset? I only test on small dataset
### Environment info
- `datasets` version: 2.14.4
- Platform: Linux-5.15.0-76-generic-x86_64-with-glibc2.17
- Python version: 3.8.15
- Huggingface_hub version: 0.16.4
- PyArrow version: 12.0.1
- Pandas version: 1.5.3 | {
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} | [] | open | false | null | [] | null | [] | "2023-08-09T15:15:03" | "2023-08-09T15:15:03" | null | NONE | null | null | null | ### Describe the bug
to_iterable_dataset is missing in document
### Steps to reproduce the bug
to_iterable_dataset is missing in document
### Expected behavior
document enhancement
### Environment info
unrelated | {
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} | [] | open | false | null | [] | null | [] | "2023-08-09T14:53:31" | "2023-08-09T14:54:36" | null | CONTRIBUTOR | null | null | null | ### Describe the bug
Getting a tqdm issue when writing a Dask dataframe to the hub.
Similar to #6066. Using latest Datasets version doesn't seem to resolve it
### Steps to reproduce the bug
This is a minimal reproducer:
```
import dask.dataframe as dd
import pandas as pd
import random
import huggingface_hub
data = {"number": [random.randint(0,10) for _ in range(1000)]}
df = pd.DataFrame.from_dict(data)
dataframe = dd.from_pandas(df, npartitions=1)
dataframe = dataframe.repartition(npartitions=2)
repo_id = "nielsr/test-dask"
repo_path = f"hf://datasets/{repo_id}"
huggingface_hub.create_repo(repo_id=repo_id, repo_type="dataset", exist_ok=True)
dd.to_parquet(dataframe, path=f"{repo_path}/data")
```
Note: I'm intentionally repartioning the Dask dataframe to 2 partitions, as it does work when only having one partition.
### Expected behavior
Would expect to write to the hub without any problem.
### Environment info
Datasets version 2.14.4 | {
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https://api.github.com/repos/huggingface/datasets/issues/6130 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6130/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6130/comments | https://api.github.com/repos/huggingface/datasets/issues/6130/events | https://github.com/huggingface/datasets/issues/6130 | 1,843,158,846 | I_kwDODunzps5t3F8- | 6,130 | default config name doesn't work when config kwargs are specified. | {
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} | [] | open | false | null | [] | null | [] | "2023-08-09T12:43:15" | "2023-08-09T12:43:15" | null | NONE | null | null | null | ### Describe the bug
https://github.com/huggingface/datasets/blob/12cfc1196e62847e2e8239fbd727a02cbc86ddec/src/datasets/builder.py#L518-L522
If `config_name` is `None`, `DEFAULT_CONFIG_NAME` should be select. But once users pass `config_kwargs` to their customized `BuilderConfig`, the logic is ignored, and dataset cannot select the default config from multiple configs.
### Steps to reproduce the bug
```python
import datasets
datasets.load_dataset('/dataset/with/multiple/config'') # Ok
datasets.load_dataset('/dataset/with/multiple/config', some_field_in_config='some') # Err
```
### Expected behavior
Default config behavior should be consistent.
### Environment info
- `datasets` version: 2.14.3
- Platform: Linux-5.15.0-76-generic-x86_64-with-glibc2.17
- Python version: 3.8.15
- Huggingface_hub version: 0.16.4
- PyArrow version: 12.0.1
- Pandas version: 1.5.3 | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006053 / 0.011353 (-0.005299) | 0.003532 / 0.011008 (-0.007476) | 0.081930 / 0.038508 (0.043422) | 0.059043 / 0.023109 (0.035934) | 0.322785 / 0.275898 (0.046887) | 0.378158 / 0.323480 (0.054678) | 0.004709 / 0.007986 (-0.003277) | 0.002907 / 0.004328 (-0.001421) | 0.061516 / 0.004250 (0.057266) | 0.047209 / 0.037052 (0.010157) | 0.346885 / 0.258489 (0.088396) | 0.381011 / 0.293841 (0.087170) | 0.027491 / 0.128546 (-0.101055) | 0.008014 / 0.075646 (-0.067632) | 0.260663 / 0.419271 (-0.158608) | 0.045427 / 0.043533 (0.001894) | 0.315277 / 0.255139 (0.060138) | 0.377902 / 0.283200 (0.094703) | 0.021371 / 0.141683 (-0.120311) | 1.416350 / 1.452155 (-0.035804) | 1.483345 / 1.492716 (-0.009372) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.203660 / 0.018006 (0.185654) | 0.569081 / 0.000490 (0.568591) | 0.002742 / 0.000200 (0.002542) | 0.000074 / 0.000054 (0.000019) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023456 / 0.037411 (-0.013955) | 0.073954 / 0.014526 (0.059428) | 0.082991 / 0.176557 (-0.093566) | 0.144781 / 0.737135 (-0.592354) | 0.083346 / 0.296338 (-0.212992) |\n\n### Benchmark: benchmark_iterating.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.391542 / 0.215209 (0.176333) | 3.909505 / 2.077655 (1.831850) | 1.862234 / 1.504120 (0.358114) | 1.676076 / 1.541195 (0.134881) | 1.727595 / 1.468490 (0.259105) | 0.501769 / 4.584777 (-4.083008) | 3.083697 / 3.745712 (-0.662016) | 2.819751 / 5.269862 (-2.450111) | 1.867265 / 4.565676 (-2.698411) | 0.057575 / 0.424275 (-0.366700) | 0.006478 / 0.007607 (-0.001129) | 0.466684 / 0.226044 (0.240640) | 4.657982 / 2.268929 (2.389054) | 2.347052 / 55.444624 (-53.097573) | 1.964688 / 6.876477 (-4.911789) | 2.077821 / 2.142072 (-0.064252) | 0.590591 / 4.805227 (-4.214636) | 0.124585 / 6.500664 (-6.376079) | 0.059468 / 0.075469 (-0.016001) |\n\n### Benchmark: benchmark_map_filter.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.223484 / 1.841788 (-0.618304) | 18.104638 / 8.074308 (10.030330) | 13.755126 / 10.191392 (3.563734) | 0.143158 / 0.680424 (-0.537266) | 0.017147 / 0.534201 (-0.517054) | 0.337427 / 0.579283 (-0.241856) | 0.352270 / 0.434364 (-0.082094) | 0.383718 / 0.540337 (-0.156619) | 0.534973 / 1.386936 (-0.851963) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006039 / 0.011353 (-0.005314) | 0.003735 / 0.011008 (-0.007274) | 0.061954 / 0.038508 (0.023446) | 0.061786 / 0.023109 (0.038677) | 0.429420 / 0.275898 (0.153522) | 0.457629 / 0.323480 (0.134149) | 0.004748 / 0.007986 (-0.003237) | 0.002843 / 0.004328 (-0.001485) | 0.061811 / 0.004250 (0.057560) | 0.048740 / 0.037052 (0.011687) | 0.430066 / 0.258489 (0.171577) | 0.465971 / 0.293841 (0.172130) | 0.027577 / 0.128546 (-0.100969) | 0.007981 / 0.075646 (-0.067665) | 0.067580 / 0.419271 (-0.351692) | 0.042058 / 0.043533 (-0.001475) | 0.428412 / 0.255139 (0.173273) | 0.451054 / 0.283200 (0.167855) | 0.020850 / 0.141683 (-0.120833) | 1.453907 / 1.452155 (0.001752) | 1.509914 / 1.492716 (0.017197) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.237713 / 0.018006 (0.219707) | 0.418064 / 0.000490 (0.417575) | 0.006411 / 0.000200 (0.006211) | 0.000078 / 0.000054 (0.000024) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024950 / 0.037411 (-0.012462) | 0.076806 / 0.014526 (0.062281) | 0.085237 / 0.176557 (-0.091320) | 0.137940 / 0.737135 (-0.599196) | 0.086266 / 0.296338 (-0.210072) |\n\n### Benchmark: benchmark_iterating.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.418666 / 0.215209 (0.203457) | 4.160547 / 2.077655 (2.082893) | 2.135671 / 1.504120 (0.631551) | 1.964985 / 1.541195 (0.423790) | 2.009447 / 1.468490 (0.540957) | 0.501377 / 4.584777 (-4.083400) | 3.064293 / 3.745712 (-0.681419) | 2.827153 / 5.269862 (-2.442709) | 1.854698 / 4.565676 (-2.710978) | 0.057662 / 0.424275 (-0.366613) | 0.006829 / 0.007607 (-0.000778) | 0.496730 / 0.226044 (0.270686) | 4.964663 / 2.268929 (2.695735) | 2.583133 / 55.444624 (-52.861491) | 2.329700 / 6.876477 (-4.546776) | 2.415521 / 2.142072 (0.273449) | 0.591973 / 4.805227 (-4.213255) | 0.126801 / 6.500664 (-6.373863) | 0.062811 / 0.075469 (-0.012659) |\n\n### Benchmark: benchmark_map_filter.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.348575 / 1.841788 (-0.493212) | 18.282861 / 8.074308 (10.208553) | 13.734056 / 10.191392 (3.542664) | 0.154987 / 0.680424 (-0.525437) | 0.016996 / 0.534201 (-0.517205) | 0.335264 / 0.579283 (-0.244019) | 0.356907 / 0.434364 (-0.077456) | 0.399185 / 0.540337 (-0.141152) | 0.540209 / 1.386936 (-0.846727) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#887bef1217e0f4441d57bf0f4d1e806df12f2c50 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006768 / 0.011353 (-0.004585) | 0.004250 / 0.011008 (-0.006758) | 0.086780 / 0.038508 (0.048272) | 0.080872 / 0.023109 (0.057762) | 0.309281 / 0.275898 (0.033383) | 0.352293 / 0.323480 (0.028814) | 0.005604 / 0.007986 (-0.002382) | 0.003544 / 0.004328 (-0.000784) | 0.066910 / 0.004250 (0.062659) | 0.055568 / 0.037052 (0.018516) | 0.314931 / 0.258489 (0.056442) | 0.366026 / 0.293841 (0.072185) | 0.031247 / 0.128546 (-0.097300) | 0.008860 / 0.075646 (-0.066786) | 0.293210 / 0.419271 (-0.126061) | 0.052868 / 0.043533 (0.009335) | 0.316769 / 0.255139 (0.061630) | 0.352128 / 0.283200 (0.068929) | 0.025492 / 0.141683 (-0.116190) | 1.478379 / 1.452155 (0.026224) | 1.573652 / 1.492716 (0.080936) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.294975 / 0.018006 (0.276968) | 0.615093 / 0.000490 (0.614603) | 0.004279 / 0.000200 (0.004079) | 0.000102 / 0.000054 (0.000047) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031557 / 0.037411 (-0.005855) | 0.085026 / 0.014526 (0.070500) | 0.101221 / 0.176557 (-0.075336) | 0.157432 / 0.737135 (-0.579703) | 0.102350 / 0.296338 (-0.193988) |\n\n### Benchmark: benchmark_iterating.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.384158 / 0.215209 (0.168949) | 3.826656 / 2.077655 (1.749001) | 1.873510 / 1.504120 (0.369390) | 1.721913 / 1.541195 (0.180718) | 1.848779 / 1.468490 (0.380289) | 0.485128 / 4.584777 (-4.099649) | 3.656660 / 3.745712 (-0.089052) | 3.441964 / 5.269862 (-1.827898) | 2.150611 / 4.565676 (-2.415066) | 0.056869 / 0.424275 (-0.367406) | 0.007382 / 0.007607 (-0.000225) | 0.458751 / 0.226044 (0.232707) | 4.585028 / 2.268929 (2.316099) | 2.439538 / 55.444624 (-53.005086) | 2.116959 / 6.876477 (-4.759518) | 2.459220 / 2.142072 (0.317147) | 0.580907 / 4.805227 (-4.224321) | 0.134502 / 6.500664 (-6.366162) | 0.062528 / 0.075469 (-0.012941) |\n\n### Benchmark: benchmark_map_filter.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.251006 / 1.841788 (-0.590782) | 20.755849 / 8.074308 (12.681541) | 14.456950 / 10.191392 (4.265558) | 0.167074 / 0.680424 (-0.513350) | 0.018482 / 0.534201 (-0.515719) | 0.395867 / 0.579283 (-0.183416) | 0.415620 / 0.434364 (-0.018744) | 0.462247 / 0.540337 (-0.078090) | 0.645762 / 1.386936 (-0.741174) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007050 / 0.011353 (-0.004303) | 0.004421 / 0.011008 (-0.006587) | 0.065312 / 0.038508 (0.026804) | 0.089790 / 0.023109 (0.066681) | 0.366318 / 0.275898 (0.090420) | 0.403542 / 0.323480 (0.080062) | 0.005695 / 0.007986 (-0.002290) | 0.003642 / 0.004328 (-0.000687) | 0.064540 / 0.004250 (0.060289) | 0.060933 / 0.037052 (0.023881) | 0.369004 / 0.258489 (0.110515) | 0.408056 / 0.293841 (0.114215) | 0.032124 / 0.128546 (-0.096422) | 0.008960 / 0.075646 (-0.066686) | 0.071267 / 0.419271 (-0.348005) | 0.049745 / 0.043533 (0.006212) | 0.367203 / 0.255139 (0.112064) | 0.383009 / 0.283200 (0.099809) | 0.025330 / 0.141683 (-0.116353) | 1.518290 / 1.452155 (0.066135) | 1.581738 / 1.492716 (0.089022) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.338281 / 0.018006 (0.320275) | 0.538195 / 0.000490 (0.537706) | 0.008498 / 0.000200 (0.008298) | 0.000121 / 0.000054 (0.000067) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033279 / 0.037411 (-0.004133) | 0.093233 / 0.014526 (0.078707) | 0.106019 / 0.176557 (-0.070538) | 0.161262 / 0.737135 (-0.575874) | 0.109935 / 0.296338 (-0.186404) |\n\n### Benchmark: benchmark_iterating.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.411563 / 0.215209 (0.196354) | 4.102149 / 2.077655 (2.024495) | 2.108513 / 1.504120 (0.604393) | 1.945344 / 1.541195 (0.404150) | 2.066964 / 1.468490 (0.598474) | 0.482771 / 4.584777 (-4.102006) | 3.659160 / 3.745712 (-0.086552) | 3.420833 / 5.269862 (-1.849029) | 2.147276 / 4.565676 (-2.418400) | 0.056957 / 0.424275 (-0.367318) | 0.007898 / 0.007607 (0.000290) | 0.482401 / 0.226044 (0.256357) | 4.821044 / 2.268929 (2.552115) | 2.567993 / 55.444624 (-52.876631) | 2.336165 / 6.876477 (-4.540312) | 2.545066 / 2.142072 (0.402994) | 0.580888 / 4.805227 (-4.224339) | 0.134092 / 6.500664 (-6.366572) | 0.062681 / 0.075469 (-0.012788) |\n\n### Benchmark: benchmark_map_filter.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.379124 / 1.841788 (-0.462664) | 21.627949 / 8.074308 (13.553641) | 15.064818 / 10.191392 (4.873426) | 0.169707 / 0.680424 (-0.510716) | 0.018671 / 0.534201 (-0.515530) | 0.400496 / 0.579283 (-0.178787) | 0.415542 / 0.434364 (-0.018822) | 0.484351 / 0.540337 (-0.055986) | 0.646046 / 1.386936 (-0.740890) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#53d55f33bfac9febb0c355e136f2847e5f3e3b53 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007113 / 0.011353 (-0.004240) | 0.004436 / 0.011008 (-0.006572) | 0.087422 / 0.038508 (0.048914) | 0.085996 / 0.023109 (0.062887) | 0.311772 / 0.275898 (0.035873) | 0.353281 / 0.323480 (0.029801) | 0.004562 / 0.007986 (-0.003423) | 0.003840 / 0.004328 (-0.000488) | 0.066500 / 0.004250 (0.062250) | 0.061293 / 0.037052 (0.024241) | 0.328840 / 0.258489 (0.070351) | 0.365587 / 0.293841 (0.071746) | 0.031802 / 0.128546 (-0.096744) | 0.008881 / 0.075646 (-0.066765) | 0.289671 / 0.419271 (-0.129601) | 0.053348 / 0.043533 (0.009816) | 0.307822 / 0.255139 (0.052683) | 0.342559 / 0.283200 (0.059360) | 0.025760 / 0.141683 (-0.115923) | 1.509944 / 1.452155 (0.057789) | 1.556634 / 1.492716 (0.063918) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.282036 / 0.018006 (0.264029) | 0.608350 / 0.000490 (0.607860) | 0.004843 / 0.000200 (0.004643) | 0.000108 / 0.000054 (0.000054) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029810 / 0.037411 (-0.007601) | 0.086215 / 0.014526 (0.071689) | 0.102200 / 0.176557 (-0.074356) | 0.158051 / 0.737135 (-0.579084) | 0.103083 / 0.296338 (-0.193255) |\n\n### Benchmark: benchmark_iterating.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.392119 / 0.215209 (0.176910) | 3.895796 / 2.077655 (1.818141) | 1.921118 / 1.504120 (0.416998) | 1.754271 / 1.541195 (0.213076) | 1.880991 / 1.468490 (0.412501) | 0.481158 / 4.584777 (-4.103618) | 3.609210 / 3.745712 (-0.136502) | 3.412018 / 5.269862 (-1.857843) | 2.131710 / 4.565676 (-2.433967) | 0.057122 / 0.424275 (-0.367153) | 0.007444 / 0.007607 (-0.000163) | 0.468880 / 0.226044 (0.242835) | 4.682441 / 2.268929 (2.413512) | 2.505613 / 55.444624 (-52.939012) | 2.149655 / 6.876477 (-4.726822) | 2.465904 / 2.142072 (0.323832) | 0.578877 / 4.805227 (-4.226350) | 0.133504 / 6.500664 (-6.367160) | 0.061422 / 0.075469 (-0.014047) |\n\n### Benchmark: benchmark_map_filter.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.269395 / 1.841788 (-0.572393) | 21.107558 / 8.074308 (13.033250) | 15.318502 / 10.191392 (5.127110) | 0.165273 / 0.680424 (-0.515151) | 0.018783 / 0.534201 (-0.515418) | 0.396259 / 0.579283 (-0.183024) | 0.412907 / 0.434364 (-0.021457) | 0.465723 / 0.540337 (-0.074615) | 0.638414 / 1.386936 (-0.748522) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007083 / 0.011353 (-0.004270) | 0.004216 / 0.011008 (-0.006793) | 0.065362 / 0.038508 (0.026854) | 0.095454 / 0.023109 (0.072345) | 0.364220 / 0.275898 (0.088322) | 0.417650 / 0.323480 (0.094170) | 0.006114 / 0.007986 (-0.001872) | 0.003577 / 0.004328 (-0.000751) | 0.064830 / 0.004250 (0.060579) | 0.062535 / 0.037052 (0.025483) | 0.381844 / 0.258489 (0.123355) | 0.418996 / 0.293841 (0.125155) | 0.031386 / 0.128546 (-0.097160) | 0.008913 / 0.075646 (-0.066733) | 0.070860 / 0.419271 (-0.348411) | 0.049132 / 0.043533 (0.005599) | 0.360406 / 0.255139 (0.105267) | 0.392407 / 0.283200 (0.109207) | 0.024611 / 0.141683 (-0.117072) | 1.509051 / 1.452155 (0.056896) | 1.570288 / 1.492716 (0.077572) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.368611 / 0.018006 (0.350605) | 0.537587 / 0.000490 (0.537098) | 0.028056 / 0.000200 (0.027856) | 0.000317 / 0.000054 (0.000262) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031570 / 0.037411 (-0.005841) | 0.088985 / 0.014526 (0.074460) | 0.105268 / 0.176557 (-0.071288) | 0.156724 / 0.737135 (-0.580412) | 0.105266 / 0.296338 (-0.191073) |\n\n### Benchmark: benchmark_iterating.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.413861 / 0.215209 (0.198652) | 4.127001 / 2.077655 (2.049347) | 2.112114 / 1.504120 (0.607994) | 1.945200 / 1.541195 (0.404005) | 2.083031 / 1.468490 (0.614540) | 0.488086 / 4.584777 (-4.096691) | 3.565584 / 3.745712 (-0.180128) | 3.380782 / 5.269862 (-1.889079) | 2.103481 / 4.565676 (-2.462195) | 0.058203 / 0.424275 (-0.366072) | 0.007996 / 0.007607 (0.000389) | 0.487986 / 0.226044 (0.261941) | 4.871023 / 2.268929 (2.602095) | 2.584632 / 55.444624 (-52.859992) | 2.240103 / 6.876477 (-4.636374) | 2.555165 / 2.142072 (0.413092) | 0.591950 / 4.805227 (-4.213278) | 0.134919 / 6.500664 (-6.365745) | 0.062868 / 0.075469 (-0.012601) |\n\n### Benchmark: benchmark_map_filter.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.369731 / 1.841788 (-0.472057) | 21.497888 / 8.074308 (13.423580) | 14.555054 / 10.191392 (4.363662) | 0.168768 / 0.680424 (-0.511656) | 0.018837 / 0.534201 (-0.515364) | 0.394512 / 0.579283 (-0.184771) | 0.405459 / 0.434364 (-0.028905) | 0.475479 / 0.540337 (-0.064858) | 0.631994 / 1.386936 (-0.754942) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#53d55f33bfac9febb0c355e136f2847e5f3e3b53 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009072 / 0.011353 (-0.002280) | 0.004894 / 0.011008 (-0.006114) | 0.108790 / 0.038508 (0.070282) | 0.081783 / 0.023109 (0.058674) | 0.381963 / 0.275898 (0.106064) | 0.450700 / 0.323480 (0.127220) | 0.006961 / 0.007986 (-0.001025) | 0.004035 / 0.004328 (-0.000293) | 0.081420 / 0.004250 (0.077169) | 0.058029 / 0.037052 (0.020976) | 0.437453 / 0.258489 (0.178964) | 0.472607 / 0.293841 (0.178766) | 0.048663 / 0.128546 (-0.079884) | 0.013512 / 0.075646 (-0.062134) | 0.406009 / 0.419271 (-0.013262) | 0.067616 / 0.043533 (0.024084) | 0.383641 / 0.255139 (0.128502) | 0.456734 / 0.283200 (0.173534) | 0.033391 / 0.141683 (-0.108292) | 1.753529 / 1.452155 (0.301375) | 1.859831 / 1.492716 (0.367115) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.215128 / 0.018006 (0.197122) | 0.538261 / 0.000490 (0.537771) | 0.005430 / 0.000200 (0.005230) | 0.000124 / 0.000054 (0.000069) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032664 / 0.037411 (-0.004748) | 0.093465 / 0.014526 (0.078939) | 0.106637 / 0.176557 (-0.069919) | 0.173642 / 0.737135 (-0.563494) | 0.113944 / 0.296338 (-0.182394) |\n\n### Benchmark: benchmark_iterating.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.629212 / 0.215209 (0.414003) | 6.116729 / 2.077655 (4.039075) | 2.818000 / 1.504120 (1.313880) | 2.515317 / 1.541195 (0.974122) | 2.466588 / 1.468490 (0.998098) | 0.850815 / 4.584777 (-3.733962) | 5.051292 / 3.745712 (1.305579) | 4.472138 / 5.269862 (-0.797724) | 2.968317 / 4.565676 (-1.597360) | 0.100173 / 0.424275 (-0.324102) | 0.008407 / 0.007607 (0.000800) | 0.743972 / 0.226044 (0.517928) | 7.397619 / 2.268929 (5.128690) | 3.596681 / 55.444624 (-51.847943) | 2.854674 / 6.876477 (-4.021803) | 3.114274 / 2.142072 (0.972201) | 1.064879 / 4.805227 (-3.740348) | 0.215981 / 6.500664 (-6.284683) | 0.078159 / 0.075469 (0.002690) |\n\n### Benchmark: benchmark_map_filter.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.543291 / 1.841788 (-0.298497) | 23.244641 / 8.074308 (15.170333) | 20.784610 / 10.191392 (10.593218) | 0.222002 / 0.680424 (-0.458422) | 0.028584 / 0.534201 (-0.505617) | 0.478563 / 0.579283 (-0.100720) | 0.556101 / 0.434364 (0.121737) | 0.547446 / 0.540337 (0.007109) | 0.764318 / 1.386936 (-0.622618) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008651 / 0.011353 (-0.002702) | 0.004925 / 0.011008 (-0.006083) | 0.078995 / 0.038508 (0.040487) | 0.092878 / 0.023109 (0.069769) | 0.485615 / 0.275898 (0.209717) | 0.532157 / 0.323480 (0.208677) | 0.008228 / 0.007986 (0.000243) | 0.004777 / 0.004328 (0.000449) | 0.076892 / 0.004250 (0.072642) | 0.066905 / 0.037052 (0.029853) | 0.465497 / 0.258489 (0.207008) | 0.520153 / 0.293841 (0.226312) | 0.047357 / 0.128546 (-0.081189) | 0.016870 / 0.075646 (-0.058776) | 0.090481 / 0.419271 (-0.328791) | 0.060774 / 0.043533 (0.017241) | 0.474368 / 0.255139 (0.219229) | 0.503981 / 0.283200 (0.220781) | 0.036025 / 0.141683 (-0.105658) | 1.769939 / 1.452155 (0.317784) | 1.851518 / 1.492716 (0.358802) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.265947 / 0.018006 (0.247941) | 0.532317 / 0.000490 (0.531828) | 0.004997 / 0.000200 (0.004797) | 0.000130 / 0.000054 (0.000076) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034112 / 0.037411 (-0.003299) | 0.102290 / 0.014526 (0.087764) | 0.109989 / 0.176557 (-0.066567) | 0.182813 / 0.737135 (-0.554323) | 0.111774 / 0.296338 (-0.184565) |\n\n### Benchmark: benchmark_iterating.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.584893 / 0.215209 (0.369684) | 6.138505 / 2.077655 (4.060850) | 2.925761 / 1.504120 (1.421641) | 2.607320 / 1.541195 (1.066125) | 2.655827 / 1.468490 (1.187337) | 0.871140 / 4.584777 (-3.713637) | 5.051171 / 3.745712 (1.305459) | 4.708008 / 5.269862 (-0.561854) | 3.027485 / 4.565676 (-1.538191) | 0.100970 / 0.424275 (-0.323305) | 0.009640 / 0.007607 (0.002033) | 0.747818 / 0.226044 (0.521774) | 7.539930 / 2.268929 (5.271001) | 3.611693 / 55.444624 (-51.832931) | 2.924087 / 6.876477 (-3.952390) | 3.141993 / 2.142072 (0.999920) | 1.062921 / 4.805227 (-3.742306) | 0.213185 / 6.500664 (-6.287479) | 0.077146 / 0.075469 (0.001677) |\n\n### Benchmark: benchmark_map_filter.json\n\n| 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 |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.669182 / 1.841788 (-0.172606) | 23.810242 / 8.074308 (15.735934) | 21.220649 / 10.191392 (11.029257) | 0.212639 / 0.680424 (-0.467785) | 0.026705 / 0.534201 (-0.507496) | 0.469231 / 0.579283 (-0.110053) | 0.551672 / 0.434364 (0.117308) | 0.575043 / 0.540337 (0.034706) | 0.767511 / 1.386936 (-0.619425) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#53d55f33bfac9febb0c355e136f2847e5f3e3b53 \"CML watermark\")\n"
] | "2023-08-08T15:43:56" | "2023-08-08T16:08:22" | "2023-08-08T15:49:06" | MEMBER | null | false | {
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"Hi @TomasAndersonFang,\r\n\r\nHave you tried instead to use `torch_compile` in `transformers.TrainingArguments`? https://huggingface.co/docs/transformers/v4.31.0/en/main_classes/trainer#transformers.TrainingArguments.torch_compile",
"> \r\n\r\nI tried this and got the following error:\r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py\", line 324, in _compile\r\n out_code = transform_code_object(code, transform)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/bytecode_transformation.py\", line 445, in transform_code_object\r\n transformations(instructions, code_options)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py\", line 311, in transform\r\n tracer.run()\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py\", line 1726, in run\r\n super().run()\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py\", line 576, in run\r\n and self.step()\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py\", line 540, in step\r\n getattr(self, inst.opname)(inst)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py\", line 1030, in LOAD_ATTR\r\n result = BuiltinVariable(getattr).call_function(\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/variables/builtin.py\", line 566, in call_function\r\n result = handler(tx, *args, **kwargs)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/variables/builtin.py\", line 931, in call_getattr\r\n return obj.var_getattr(tx, name).add_options(options)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/variables/nn_module.py\", line 124, in var_getattr\r\n subobj = inspect.getattr_static(base, name)\r\n File \"/apps/Arch/software/Python/3.10.8-GCCcore-12.2.0/lib/python3.10/inspect.py\", line 1777, in getattr_static\r\n raise AttributeError(attr)\r\nAttributeError: config\r\n\r\nfrom user code:\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/peft/peft_model.py\", line 909, in forward\r\n if self.base_model.config.model_type == \"mpt\":\r\n\r\nSet torch._dynamo.config.verbose=True for more information\r\n\r\n\r\nYou can suppress this exception and fall back to eager by setting:\r\n torch._dynamo.config.suppress_errors = True\r\n\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/llm-copt/fine-tune/falcon/falcon_sft.py\", line 228, in <module>\r\n main()\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/llm-copt/fine-tune/falcon/falcon_sft.py\", line 221, in main\r\n trainer.train()\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/transformers/trainer.py\", line 1539, in train\r\n return inner_training_loop(\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/transformers/trainer.py\", line 1809, in _inner_training_loop\r\n tr_loss_step = self.training_step(model, inputs)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/transformers/trainer.py\", line 2654, in training_step\r\n loss = self.compute_loss(model, inputs)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/transformers/trainer.py\", line 2679, in compute_loss\r\n outputs = model(**inputs)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1501, in _call_impl\r\n return forward_call(*args, **kwargs)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py\", line 82, in forward\r\n return self.dynamo_ctx(self._orig_mod.forward)(*args, **kwargs)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py\", line 209, in _fn\r\n return fn(*args, **kwargs)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/accelerate/utils/operations.py\", line 581, in forward\r\n return model_forward(*args, **kwargs)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/accelerate/utils/operations.py\", line 569, in __call__\r\n return convert_to_fp32(self.model_forward(*args, **kwargs))\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/amp/autocast_mode.py\", line 14, in decorate_autocast\r\n return func(*args, **kwargs)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py\", line 337, in catch_errors\r\n return callback(frame, cache_size, hooks)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py\", line 404, in _convert_frame\r\n result = inner_convert(frame, cache_size, hooks)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py\", line 104, in _fn\r\n return fn(*args, **kwargs)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py\", line 262, in _convert_frame_assert\r\n return _compile(\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/utils.py\", line 163, in time_wrapper\r\n r = func(*args, **kwargs)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py\", line 394, in _compile\r\n raise InternalTorchDynamoError() from e\r\ntorch._dynamo.exc.InternalTorchDynamoError\r\n```",
"Hi @TomasAndersonFang,\r\n\r\nI guess in this case it may be an issue with `transformers` (or `PyTorch`). I would recommend you open an issue on their repo."
] | "2023-08-08T15:32:08" | "2023-08-10T09:31:12" | null | NONE | null | null | null | ### Describe the bug
This bug generates when I use torch.compile(model) in my code, which seems to raise an error in datasets lib.
### Steps to reproduce the bug
I use the following code to fine-tune Falcon on my private dataset.
```python
import transformers
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
AutoConfig,
DataCollatorForSeq2Seq,
Trainer,
Seq2SeqTrainer,
HfArgumentParser,
Seq2SeqTrainingArguments,
BitsAndBytesConfig,
)
from peft import (
LoraConfig,
get_peft_model,
get_peft_model_state_dict,
prepare_model_for_int8_training,
set_peft_model_state_dict,
)
import torch
import os
import evaluate
import functools
from datasets import load_dataset
import bitsandbytes as bnb
import logging
import json
import copy
from typing import Dict, Optional, Sequence
from dataclasses import dataclass, field
# Lora settings
LORA_R = 8
LORA_ALPHA = 16
LORA_DROPOUT= 0.05
LORA_TARGET_MODULES = ["query_key_value"]
@dataclass
class ModelArguments:
model_name_or_path: Optional[str] = field(default="Salesforce/codegen2-7B")
@dataclass
class DataArguments:
data_path: str = field(default=None, metadata={"help": "Path to the training data."})
train_file: str = field(default=None, metadata={"help": "Path to the evaluation data."})
eval_file: str = field(default=None, metadata={"help": "Path to the evaluation data."})
cache_path: str = field(default=None, metadata={"help": "Path to the cache directory."})
num_proc: int = field(default=4, metadata={"help": "Number of processes to use for data preprocessing."})
@dataclass
class TrainingArguments(transformers.TrainingArguments):
# cache_dir: Optional[str] = field(default=None)
optim: str = field(default="adamw_torch")
model_max_length: int = field(
default=512,
metadata={"help": "Maximum sequence length. Sequences will be right padded (and possibly truncated)."},
)
is_lora: bool = field(default=True, metadata={"help": "Whether to use LORA."})
def tokenize(text, tokenizer, max_seq_len=512, add_eos_token=True):
result = tokenizer(
text,
truncation=True,
max_length=max_seq_len,
padding=False,
return_tensors=None,
)
if (
result["input_ids"][-1] != tokenizer.eos_token_id
and len(result["input_ids"]) < max_seq_len
and add_eos_token
):
result["input_ids"].append(tokenizer.eos_token_id)
result["attention_mask"].append(1)
if add_eos_token and len(result["input_ids"]) >= max_seq_len:
result["input_ids"][max_seq_len - 1] = tokenizer.eos_token_id
result["attention_mask"][max_seq_len - 1] = 1
result["labels"] = result["input_ids"].copy()
return result
def main():
parser = HfArgumentParser((ModelArguments, DataArguments, TrainingArguments))
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
config = AutoConfig.from_pretrained(
model_args.model_name_or_path,
cache_dir=data_args.cache_path,
trust_remote_code=True,
)
if training_args.is_lora:
model = AutoModelForCausalLM.from_pretrained(
model_args.model_name_or_path,
cache_dir=data_args.cache_path,
torch_dtype=torch.float16,
trust_remote_code=True,
load_in_8bit=True,
quantization_config=BitsAndBytesConfig(
load_in_8bit=True,
llm_int8_threshold=6.0
),
)
model = prepare_model_for_int8_training(model)
config = LoraConfig(
r=LORA_R,
lora_alpha=LORA_ALPHA,
target_modules=LORA_TARGET_MODULES,
lora_dropout=LORA_DROPOUT,
bias="none",
task_type="CAUSAL_LM",
)
model = get_peft_model(model, config)
else:
model = AutoModelForCausalLM.from_pretrained(
model_args.model_name_or_path,
torch_dtype=torch.float16,
cache_dir=data_args.cache_path,
trust_remote_code=True,
)
model.config.use_cache = False
def print_trainable_parameters(model):
"""
Prints the number of trainable parameters in the model.
"""
trainable_params = 0
all_param = 0
for _, param in model.named_parameters():
all_param += param.numel()
if param.requires_grad:
trainable_params += param.numel()
print(
f"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param}"
)
print_trainable_parameters(model)
tokenizer = AutoTokenizer.from_pretrained(
model_args.model_name_or_path,
cache_dir=data_args.cache_path,
model_max_length=training_args.model_max_length,
padding_side="left",
use_fast=True,
trust_remote_code=True,
)
tokenizer.pad_token = tokenizer.eos_token
# Load dataset
def generate_and_tokenize_prompt(sample):
input_text = sample["input"]
target_text = sample["output"] + tokenizer.eos_token
full_text = input_text + target_text
tokenized_full_text = tokenize(full_text, tokenizer, max_seq_len=512)
tokenized_input_text = tokenize(input_text, tokenizer, max_seq_len=512)
input_len = len(tokenized_input_text["input_ids"]) - 1 # -1 for eos token
tokenized_full_text["labels"] = [-100] * input_len + tokenized_full_text["labels"][input_len:]
return tokenized_full_text
data_files = {}
if data_args.train_file is not None:
data_files["train"] = data_args.train_file
if data_args.eval_file is not None:
data_files["eval"] = data_args.eval_file
dataset = load_dataset(data_args.data_path, data_files=data_files)
train_dataset = dataset["train"]
eval_dataset = dataset["eval"]
train_dataset = train_dataset.map(generate_and_tokenize_prompt, num_proc=data_args.num_proc)
eval_dataset = eval_dataset.map(generate_and_tokenize_prompt, num_proc=data_args.num_proc)
data_collator = DataCollatorForSeq2Seq(tokenizer, pad_to_multiple_of=8, return_tensors="pt", padding=True)
# Evaluation metrics
def compute_metrics(eval_preds, tokenizer):
metric = evaluate.load('exact_match')
preds, labels = eval_preds
# In case the model returns more than the prediction logits
if isinstance(preds, tuple):
preds = preds[0]
decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True, clean_up_tokenization_spaces=False)
# Replace -100s in the labels as we can't decode them
labels[labels == -100] = tokenizer.pad_token_id
decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True, clean_up_tokenization_spaces=False)
# Some simple post-processing
decoded_preds = [pred.strip() for pred in decoded_preds]
decoded_labels = [label.strip() for label in decoded_labels]
result = metric.compute(predictions=decoded_preds, references=decoded_labels)
return {'exact_match': result['exact_match']}
compute_metrics_fn = functools.partial(compute_metrics, tokenizer=tokenizer)
model = torch.compile(model)
# Training
trainer = Trainer(
model=model,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
args=training_args,
data_collator=data_collator,
compute_metrics=compute_metrics_fn,
)
trainer.train()
trainer.save_state()
trainer.save_model(output_dir=training_args.output_dir)
tokenizer.save_pretrained(save_directory=training_args.output_dir)
if __name__ == "__main__":
main()
```
When I didn't use `torch.cpmpile(model)`, my code worked well. But when I added this line to my code, It produced the following error:
```
Traceback (most recent call last):
File "falcon_sft.py", line 230, in <module>
main()
File "falcon_sft.py", line 223, in main
trainer.train()
File "python3.10/site-packages/transformers/trainer.py", line 1539, in train
return inner_training_loop(
File "python3.10/site-packages/transformers/trainer.py", line 1787, in _inner_training_loop
for step, inputs in enumerate(epoch_iterator):
File "python3.10/site-packages/accelerate/data_loader.py", line 384, in __iter__
current_batch = next(dataloader_iter)
File "python3.10/site-packages/torch/utils/data/dataloader.py", line 633, in __next__
data = self._next_data()
File "python3.10/site-packages/torch/utils/data/dataloader.py", line 677, in _next_data
data = self._dataset_fetcher.fetch(index) # may raise StopIteration
File "python3.10/site-packages/torch/utils/data/_utils/fetch.py", line 49, in fetch
data = self.dataset.__getitems__(possibly_batched_index)
File "python3.10/site-packages/datasets/arrow_dataset.py", line 2807, in __getitems__
batch = self.__getitem__(keys)
File "python3.10/site-packages/datasets/arrow_dataset.py", line 2803, in __getitem__
return self._getitem(key)
File "python3.10/site-packages/datasets/arrow_dataset.py", line 2787, in _getitem
pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None)
File "python3.10/site-packages/datasets/formatting/formatting.py", line 583, in query_table
_check_valid_index_key(key, size)
File "python3.10/site-packages/datasets/formatting/formatting.py", line 536, in _check_valid_index_key
_check_valid_index_key(int(max(key)), size=size)
File "python3.10/site-packages/datasets/formatting/formatting.py", line 526, in _check_valid_index_key
raise IndexError(f"Invalid key: {key} is out of bounds for size {size}")
IndexError: Invalid key: 88 is out of bounds for size 0
```
So I'm confused about why this error was generated, and how to fix it. Is this error produced by datasets or `torch.compile`?
### Expected behavior
I want to use `torch.compile` in my code.
### Environment info
- `datasets` version: 2.14.3
- Platform: Linux-4.18.0-425.19.2.el8_7.x86_64-x86_64-with-glibc2.28
- Python version: 3.10.8
- Huggingface_hub version: 0.16.4
- PyArrow version: 12.0.1
- Pandas version: 2.0.3 | {
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