devopsmarc/my-issues-dataset huggingface dataset:
Using a feature named "_type" fails with certain operations 
 A column named `_type` leads to a `TypeError: unhashable type: 'dict'` for certain operations:
```python
from datasets import Dataset, concatenate_datasets

ds = Dataset.from_dict({"_type": ["whatever"]}).map()
concatenate_datasets([ds])
# or simply
Dataset(ds._data)
```
Context: We are using datasets to persist data coming from elasticsearch to feed to our pipeline, and elasticsearch has a `_type` field, hence the strange name of the column.

Not sure if you wish to support this specific column name, but if you do i would be happy to try a fix and provide a PR. I already had a look into it and i think the culprit is the `datasets.features.generate_from_dict` function. It uses the hard coded `_type` string to figure out if it reached the end of the nested feature object from a serialized dict.

Best wishes and keep up the awesome work! 
 Thanks for reporting !

Indeed this is a keyword in the library that is used to encode/decode features to a python dictionary that we can save/load to json.
We can probably change `_type` to something that is less likely to collide with user feature names.
In this case we would want something backward compatible though.

Feel free to try a fix and open a PR, and to ping me if I can help :)
Add support to download kaggle datasets 
 We can use API key 
 Hey, I think this is great idea. Any plan to integrate kaggle private datasets loading to `datasets`?
Add support to download kaggle datasets 
 We can use API key 
 The workflow for downloading a Kaggle dataset and turning it into an HF dataset is pretty simple:
```python
!kaggle datasets download -p path
ds = load_dataset(path)
```

Native support would make our download logic even more complex, and I don't think this is a good idea considering this particular feature is not requested often. 

PS: Kaggle should integrate their API with `fsspec` to allow us to use a common interface if they are interested in tighter integrations
Not support links with 302 redirect  
 I have an issue adding this download link https://github.com/jitkapat/thailitcorpus/releases/download/v.2.0/tlc_v.2.0.tar.gz

it might be because it is not a direct link (it returns 302 and redirects to aws that returns 403 for head requests). 

```
r.head("https://github.com/jitkapat/thailitcorpus/releases/download/v.2.0/tlc_v.2.0.tar.gz", allow_redirects=True)                                      
# <Response [403]>
``` 
 > Hi !
> This kind of links is now supported by the library since #1316

I updated links in TLC datasets to be the github links in this pull request 
 https://github.com/huggingface/datasets/pull/1737

Everything works now. Thank you.
Dataset.map() turns tensors into lists? 
 I apply `Dataset.map()` to a function that returns a dict of torch tensors (like a tokenizer from the repo transformers). However, in the mapped dataset, these tensors have turned to lists!

```import datasets
import torch  
from datasets import load_dataset                                                                                                                 
print("version datasets", datasets.__version__)

dataset = load_dataset("snli", split='train[0:50]')  

def tokenizer_fn(example):
    # actually uses a tokenizer which does something like:
    return {'input_ids': torch.tensor([[0, 1, 2]])}

print("First item in dataset:\n", dataset[0])
tokenized = tokenizer_fn(dataset[0])
print("Tokenized hyp:\n", tokenized)
dataset_tok = dataset.map(tokenizer_fn, batched=False,
        remove_columns=['label', 'premise', 'hypothesis'])
print("Tokenized using map:\n", dataset_tok[0])
print(type(tokenized['input_ids']), type(dataset_tok[0]['input_ids']))
dataset_tok = dataset.map(tokenizer_fn, batched=False,
                          remove_columns=['label', 'premise', 'hypothesis'])
print("Tokenized using map:\n", dataset_tok[0])
print(type(tokenized['input_ids']), type(dataset_tok[0]['input_ids']))
```

The output is:

```
version datasets 1.1.3
Reusing dataset snli (/home/tom/.cache/huggingface/datasets/snli/plain_text/1.0.0/bb1102591c6230bd78813e229d5dd4c7fbf4fc478cec28f298761eb69e5b537c)
First item in dataset:
 {'premise': 'A person on a horse jumps over a broken down airplane.', 'hypothesis': 'A person is training his horse for a competition.', 'label': 1}
Tokenized hyp:
 {'input_ids': tensor([[0, 1, 2]])}
Loading cached processed dataset at /home/tom/.cache/huggingface/datasets/snli/plain_text/1.0.0/bb1102591c6230bd78813e229d5dd4c7fbf4fc478cec28f298761eb69e5b537c/cache-fe38f449fe9ac46f.arrow
Tokenized using map:
 {'input_ids': [[0, 1, 2]]}
<class 'torch.Tensor'> <class 'list'>
```

Or am I doing something wrong?
 
 A solution is to have the tokenizer return a list instead of a tensor, and then use `dataset_tok.set_format(type = 'torch')` to convert that list into a tensor. Still not sure if bug.
Dataset.map() turns tensors into lists? 
 I apply `Dataset.map()` to a function that returns a dict of torch tensors (like a tokenizer from the repo transformers). However, in the mapped dataset, these tensors have turned to lists!

```import datasets
import torch  
from datasets import load_dataset                                                                                                                 
print("version datasets", datasets.__version__)

dataset = load_dataset("snli", split='train[0:50]')  

def tokenizer_fn(example):
    # actually uses a tokenizer which does something like:
    return {'input_ids': torch.tensor([[0, 1, 2]])}

print("First item in dataset:\n", dataset[0])
tokenized = tokenizer_fn(dataset[0])
print("Tokenized hyp:\n", tokenized)
dataset_tok = dataset.map(tokenizer_fn, batched=False,
        remove_columns=['label', 'premise', 'hypothesis'])
print("Tokenized using map:\n", dataset_tok[0])
print(type(tokenized['input_ids']), type(dataset_tok[0]['input_ids']))
dataset_tok = dataset.map(tokenizer_fn, batched=False,
                          remove_columns=['label', 'premise', 'hypothesis'])
print("Tokenized using map:\n", dataset_tok[0])
print(type(tokenized['input_ids']), type(dataset_tok[0]['input_ids']))
```

The output is:

```
version datasets 1.1.3
Reusing dataset snli (/home/tom/.cache/huggingface/datasets/snli/plain_text/1.0.0/bb1102591c6230bd78813e229d5dd4c7fbf4fc478cec28f298761eb69e5b537c)
First item in dataset:
 {'premise': 'A person on a horse jumps over a broken down airplane.', 'hypothesis': 'A person is training his horse for a competition.', 'label': 1}
Tokenized hyp:
 {'input_ids': tensor([[0, 1, 2]])}
Loading cached processed dataset at /home/tom/.cache/huggingface/datasets/snli/plain_text/1.0.0/bb1102591c6230bd78813e229d5dd4c7fbf4fc478cec28f298761eb69e5b537c/cache-fe38f449fe9ac46f.arrow
Tokenized using map:
 {'input_ids': [[0, 1, 2]]}
<class 'torch.Tensor'> <class 'list'>
```

Or am I doing something wrong?
 
 It is expected behavior, you should set the format to `"torch"` as you mentioned to get pytorch tensors back.
By default datasets returns pure python objects.
how large datasets are handled under the hood  
 Hi
I want to use multiple large datasets with a mapping style dataloader, where they cannot fit into memory, could you tell me how you handled the datasets under the hood? is this you bring all in memory in case of mapping style ones? or is this some sharding under the hood and you bring in memory when necessary, thanks  
 This library uses Apache Arrow under the hood to store datasets on disk.
The advantage of Apache Arrow is that it allows to memory map the dataset. This allows to load datasets bigger than memory and with almost no RAM usage. It also offers excellent I/O speed.

For example when you access one element or one batch
```python
from datasets import load_dataset

squad = load_dataset("squad", split="train")
first_element = squad[0]
one_batch = squad[:8]
```

then only this element/batch is loaded in memory, while the rest of the dataset is memory mapped.
how large datasets are handled under the hood  
 Hi
I want to use multiple large datasets with a mapping style dataloader, where they cannot fit into memory, could you tell me how you handled the datasets under the hood? is this you bring all in memory in case of mapping style ones? or is this some sharding under the hood and you bring in memory when necessary, thanks  
 How can we change how much data is loaded to memory with Arrow? I think that I am having some performance issue with it. When Arrow loads the data from disk it does it in multiprocess? It's almost twice slower training with arrow than in memory.

EDIT:
My fault! I had not seen the `dataloader_num_workers` in `TrainingArguments` ! Now I can parallelize and go fast! Sorry, and thanks.
how large datasets are handled under the hood  
 Hi
I want to use multiple large datasets with a mapping style dataloader, where they cannot fit into memory, could you tell me how you handled the datasets under the hood? is this you bring all in memory in case of mapping style ones? or is this some sharding under the hood and you bring in memory when necessary, thanks  
 > How can we change how much data is loaded to memory with Arrow? I think that I am having some performance issue with it. When Arrow loads the data from disk it does it in multiprocess? It's almost twice slower training with arrow than in memory.

Loading arrow data from disk is done with memory-mapping. This allows to load huge datasets without filling your RAM.
Memory mapping is almost instantaneous and is done within one process.

Then, the speed of querying examples from the dataset is I/O bounded depending on your disk. If it's an SSD then fetching examples from the dataset will be very fast.
But since the I/O speed of an SSD is lower than the one of RAM it's expected to be slower to fetch data from disk than from memory.
Still, if you load the dataset in different processes then it can be faster but there will still be the I/O bottleneck of the disk.

> EDIT:
> My fault! I had not seen the `dataloader_num_workers` in `TrainingArguments` ! Now I can parallelize and go fast! Sorry, and thanks.

Ok let me know if that helps !
NotADirectoryError while loading the CNN/Dailymail dataset 
 
Downloading and preparing dataset cnn_dailymail/3.0.0 (download: 558.32 MiB, generated: 1.28 GiB, post-processed: Unknown size, total: 1.82 GiB) to /root/.cache/huggingface/datasets/cnn_dailymail/3.0.0/3.0.0/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602...

---------------------------------------------------------------------------

NotADirectoryError                        Traceback (most recent call last)

<ipython-input-9-cd4bf8bea840> in <module>()
     22 
     23 
---> 24 train = load_dataset('cnn_dailymail', '3.0.0', split='train')
     25 validation = load_dataset('cnn_dailymail', '3.0.0', split='validation')
     26 test = load_dataset('cnn_dailymail', '3.0.0', split='test')

5 frames

/root/.cache/huggingface/modules/datasets_modules/datasets/cnn_dailymail/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602/cnn_dailymail.py in _find_files(dl_paths, publisher, url_dict)
    132     else:
    133         logging.fatal("Unsupported publisher: %s", publisher)
--> 134     files = sorted(os.listdir(top_dir))
    135 
    136     ret_files = []

NotADirectoryError: [Errno 20] Not a directory: '/root/.cache/huggingface/datasets/downloads/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b/cnn/stories' 
 Looks like the google drive download failed.
I'm getting a `Google Drive - Quota exceeded` error while looking at the downloaded file.

We should consider finding a better host than google drive for this dataset imo
related : #873 #864
NotADirectoryError while loading the CNN/Dailymail dataset 
 
Downloading and preparing dataset cnn_dailymail/3.0.0 (download: 558.32 MiB, generated: 1.28 GiB, post-processed: Unknown size, total: 1.82 GiB) to /root/.cache/huggingface/datasets/cnn_dailymail/3.0.0/3.0.0/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602...

---------------------------------------------------------------------------

NotADirectoryError                        Traceback (most recent call last)

<ipython-input-9-cd4bf8bea840> in <module>()
     22 
     23 
---> 24 train = load_dataset('cnn_dailymail', '3.0.0', split='train')
     25 validation = load_dataset('cnn_dailymail', '3.0.0', split='validation')
     26 test = load_dataset('cnn_dailymail', '3.0.0', split='test')

5 frames

/root/.cache/huggingface/modules/datasets_modules/datasets/cnn_dailymail/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602/cnn_dailymail.py in _find_files(dl_paths, publisher, url_dict)
    132     else:
    133         logging.fatal("Unsupported publisher: %s", publisher)
--> 134     files = sorted(os.listdir(top_dir))
    135 
    136     ret_files = []

NotADirectoryError: [Errno 20] Not a directory: '/root/.cache/huggingface/datasets/downloads/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b/cnn/stories' 
 It is working now, thank you. 

Should I leave this issue open to address the Quota-exceeded error?
NotADirectoryError while loading the CNN/Dailymail dataset 
 
Downloading and preparing dataset cnn_dailymail/3.0.0 (download: 558.32 MiB, generated: 1.28 GiB, post-processed: Unknown size, total: 1.82 GiB) to /root/.cache/huggingface/datasets/cnn_dailymail/3.0.0/3.0.0/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602...

---------------------------------------------------------------------------

NotADirectoryError                        Traceback (most recent call last)

<ipython-input-9-cd4bf8bea840> in <module>()
     22 
     23 
---> 24 train = load_dataset('cnn_dailymail', '3.0.0', split='train')
     25 validation = load_dataset('cnn_dailymail', '3.0.0', split='validation')
     26 test = load_dataset('cnn_dailymail', '3.0.0', split='test')

5 frames

/root/.cache/huggingface/modules/datasets_modules/datasets/cnn_dailymail/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602/cnn_dailymail.py in _find_files(dl_paths, publisher, url_dict)
    132     else:
    133         logging.fatal("Unsupported publisher: %s", publisher)
--> 134     files = sorted(os.listdir(top_dir))
    135 
    136     ret_files = []

NotADirectoryError: [Errno 20] Not a directory: '/root/.cache/huggingface/datasets/downloads/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b/cnn/stories' 
 I've looked into it and couldn't find a solution. This looks like a Google Drive limitation..
Please try to use other hosts when possible
NotADirectoryError while loading the CNN/Dailymail dataset 
 
Downloading and preparing dataset cnn_dailymail/3.0.0 (download: 558.32 MiB, generated: 1.28 GiB, post-processed: Unknown size, total: 1.82 GiB) to /root/.cache/huggingface/datasets/cnn_dailymail/3.0.0/3.0.0/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602...

---------------------------------------------------------------------------

NotADirectoryError                        Traceback (most recent call last)

<ipython-input-9-cd4bf8bea840> in <module>()
     22 
     23 
---> 24 train = load_dataset('cnn_dailymail', '3.0.0', split='train')
     25 validation = load_dataset('cnn_dailymail', '3.0.0', split='validation')
     26 test = load_dataset('cnn_dailymail', '3.0.0', split='test')

5 frames

/root/.cache/huggingface/modules/datasets_modules/datasets/cnn_dailymail/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602/cnn_dailymail.py in _find_files(dl_paths, publisher, url_dict)
    132     else:
    133         logging.fatal("Unsupported publisher: %s", publisher)
--> 134     files = sorted(os.listdir(top_dir))
    135 
    136     ret_files = []

NotADirectoryError: [Errno 20] Not a directory: '/root/.cache/huggingface/datasets/downloads/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b/cnn/stories' 
 The original links are google drive links.  Would it be feasible for HF to maintain their own servers for this? Also, I think the same issue must also exist with TFDS.
NotADirectoryError while loading the CNN/Dailymail dataset 
 
Downloading and preparing dataset cnn_dailymail/3.0.0 (download: 558.32 MiB, generated: 1.28 GiB, post-processed: Unknown size, total: 1.82 GiB) to /root/.cache/huggingface/datasets/cnn_dailymail/3.0.0/3.0.0/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602...

---------------------------------------------------------------------------

NotADirectoryError                        Traceback (most recent call last)

<ipython-input-9-cd4bf8bea840> in <module>()
     22 
     23 
---> 24 train = load_dataset('cnn_dailymail', '3.0.0', split='train')
     25 validation = load_dataset('cnn_dailymail', '3.0.0', split='validation')
     26 test = load_dataset('cnn_dailymail', '3.0.0', split='test')

5 frames

/root/.cache/huggingface/modules/datasets_modules/datasets/cnn_dailymail/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602/cnn_dailymail.py in _find_files(dl_paths, publisher, url_dict)
    132     else:
    133         logging.fatal("Unsupported publisher: %s", publisher)
--> 134     files = sorted(os.listdir(top_dir))
    135 
    136     ret_files = []

NotADirectoryError: [Errno 20] Not a directory: '/root/.cache/huggingface/datasets/downloads/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b/cnn/stories' 
 It's possible to host data on our side but we should ask the authors. TFDS has the same issue and doesn't have a solution either afaik.
Otherwise you can use the google drive link, but it it's not that convenient because of this quota issue.
NotADirectoryError while loading the CNN/Dailymail dataset 
 
Downloading and preparing dataset cnn_dailymail/3.0.0 (download: 558.32 MiB, generated: 1.28 GiB, post-processed: Unknown size, total: 1.82 GiB) to /root/.cache/huggingface/datasets/cnn_dailymail/3.0.0/3.0.0/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602...

---------------------------------------------------------------------------

NotADirectoryError                        Traceback (most recent call last)

<ipython-input-9-cd4bf8bea840> in <module>()
     22 
     23 
---> 24 train = load_dataset('cnn_dailymail', '3.0.0', split='train')
     25 validation = load_dataset('cnn_dailymail', '3.0.0', split='validation')
     26 test = load_dataset('cnn_dailymail', '3.0.0', split='test')

5 frames

/root/.cache/huggingface/modules/datasets_modules/datasets/cnn_dailymail/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602/cnn_dailymail.py in _find_files(dl_paths, publisher, url_dict)
    132     else:
    133         logging.fatal("Unsupported publisher: %s", publisher)
--> 134     files = sorted(os.listdir(top_dir))
    135 
    136     ret_files = []

NotADirectoryError: [Errno 20] Not a directory: '/root/.cache/huggingface/datasets/downloads/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b/cnn/stories' 
 Okay. I imagine asking every author who shares their dataset on Google Drive will also be cumbersome.
NotADirectoryError while loading the CNN/Dailymail dataset 
 
Downloading and preparing dataset cnn_dailymail/3.0.0 (download: 558.32 MiB, generated: 1.28 GiB, post-processed: Unknown size, total: 1.82 GiB) to /root/.cache/huggingface/datasets/cnn_dailymail/3.0.0/3.0.0/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602...

---------------------------------------------------------------------------

NotADirectoryError                        Traceback (most recent call last)

<ipython-input-9-cd4bf8bea840> in <module>()
     22 
     23 
---> 24 train = load_dataset('cnn_dailymail', '3.0.0', split='train')
     25 validation = load_dataset('cnn_dailymail', '3.0.0', split='validation')
     26 test = load_dataset('cnn_dailymail', '3.0.0', split='test')

5 frames

/root/.cache/huggingface/modules/datasets_modules/datasets/cnn_dailymail/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602/cnn_dailymail.py in _find_files(dl_paths, publisher, url_dict)
    132     else:
    133         logging.fatal("Unsupported publisher: %s", publisher)
--> 134     files = sorted(os.listdir(top_dir))
    135 
    136     ret_files = []

NotADirectoryError: [Errno 20] Not a directory: '/root/.cache/huggingface/datasets/downloads/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b/cnn/stories' 
 Not as long as the data is stored on GG drive unfortunately.
Maybe we can ask if there's a mirror ?

Hi @JafferWilson is there a download link to get cnn dailymail from another host than GG drive ?

To give you some context, this library provides tools to download and process datasets. For CNN DailyMail the data are downloaded from the link you provide on your github repository. Unfortunately because of GG drive quotas, many users are not able to load this dataset.
NotADirectoryError while loading the CNN/Dailymail dataset 
 
Downloading and preparing dataset cnn_dailymail/3.0.0 (download: 558.32 MiB, generated: 1.28 GiB, post-processed: Unknown size, total: 1.82 GiB) to /root/.cache/huggingface/datasets/cnn_dailymail/3.0.0/3.0.0/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602...

---------------------------------------------------------------------------

NotADirectoryError                        Traceback (most recent call last)

<ipython-input-9-cd4bf8bea840> in <module>()
     22 
     23 
---> 24 train = load_dataset('cnn_dailymail', '3.0.0', split='train')
     25 validation = load_dataset('cnn_dailymail', '3.0.0', split='validation')
     26 test = load_dataset('cnn_dailymail', '3.0.0', split='test')

5 frames

/root/.cache/huggingface/modules/datasets_modules/datasets/cnn_dailymail/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602/cnn_dailymail.py in _find_files(dl_paths, publisher, url_dict)
    132     else:
    133         logging.fatal("Unsupported publisher: %s", publisher)
--> 134     files = sorted(os.listdir(top_dir))
    135 
    136     ret_files = []

NotADirectoryError: [Errno 20] Not a directory: '/root/.cache/huggingface/datasets/downloads/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b/cnn/stories' 
 Thanks for the link @mrazizi !

Apparently the original authors don't host the dataset themselves ("for legal reasons", source [here](https://github.com/abisee/cnn-dailymail/issues/9)).
Problem downloading amazon_reviews_multi 
 Thanks for adding the dataset. 
After trying to load the dataset, I am getting the following error: 
`ConnectionError: Couldn't reach https://amazon-reviews-ml.s3-us-west-2.amazonaws.com/json/train/dataset_fr_train.json
`
I used the following code to load the dataset: 
`load_dataset(
            dataset_name,
            "all_languages",
            cache_dir=".data"
        )`

I am using version 1.1.3 of `datasets`

Note that I can perform a successfull `wget https://amazon-reviews-ml.s3-us-west-2.amazonaws.com/json/train/dataset_fr_train.json` 
 Hi @hfawaz ! This is working fine for me. Is it a repeated occurence? Have you tried from the latest verion?
making sure datasets are not loaded in memory and distributed training of them 
 Hi
I am dealing with large-scale datasets which I need to train distributedly, I used the shard function to divide the dataset across the cores, without any sampler, this does not work for distributed training and does not become any faster than 1 TPU core. 1) how I can make sure data is not loaded in memory 2) in case of distributed training with iterative datasets which measures needs to be taken? Is this all sharding the data only. I was wondering if there can be possibility for me to discuss this with someone with distributed training with iterative datasets using dataset library. thanks  
 my implementation of sharding per TPU core: https://github.com/google-research/ruse/blob/d4dd58a2d8efe0ffb1a9e9e77e3228d6824d3c3c/seq2seq/trainers/t5_trainer.py#L316 
my implementation of dataloader for this case https://github.com/google-research/ruse/blob/d4dd58a2d8efe0ffb1a9e9e77e3228d6824d3c3c/seq2seq/tasks/tasks.py#L496
making sure datasets are not loaded in memory and distributed training of them 
 Hi
I am dealing with large-scale datasets which I need to train distributedly, I used the shard function to divide the dataset across the cores, without any sampler, this does not work for distributed training and does not become any faster than 1 TPU core. 1) how I can make sure data is not loaded in memory 2) in case of distributed training with iterative datasets which measures needs to be taken? Is this all sharding the data only. I was wondering if there can be possibility for me to discuss this with someone with distributed training with iterative datasets using dataset library. thanks  
 Hi!  You can use the `assert not bool(dataset.cache_files)` assertion to ensure your data is in memory. And I suggest using `accelerate` for distributed training.
sample multiple datasets  
 Hi
I am dealing with multiple datasets, I need to have a dataloader over them with a condition that in each batch data samples are coming from one of the datasets. My main question is: 
-  I need to have a way to sample the datasets first with some weights, lets say 2x dataset1 1x dataset2, could you point me how I can do it

sub-questions:
- I want to concat sampled datasets and define one dataloader on it, then I need a way to make sure batches come from 1 dataset in each iteration, could you assist me how I can do?
- I use iterative-type of datasets, but I need a method of shuffling still since it brings accuracy performance issues if not doing it, thanks for the help.  
 here I share my dataloader currently for multiple tasks: https://gist.github.com/rabeehkarimimahabadi/39f9444a4fb6f53dcc4fca5d73bf8195 

I need to train my model distributedly with this dataloader, "MultiTasksataloader", currently this does not work in distributed fasion,
to save on memory I tried to use iterative datasets, could you have a look in this dataloader and tell me if this is indeed the case? not sure how to make datasets being iterative to not load them in memory, then I remove the sampler for dataloader, and shard the data per core, could you tell me please how I should implement this case in datasets library? and how do you find my implementation in terms of correctness? thanks
sample multiple datasets  
 Hi
I am dealing with multiple datasets, I need to have a dataloader over them with a condition that in each batch data samples are coming from one of the datasets. My main question is: 
-  I need to have a way to sample the datasets first with some weights, lets say 2x dataset1 1x dataset2, could you point me how I can do it

sub-questions:
- I want to concat sampled datasets and define one dataloader on it, then I need a way to make sure batches come from 1 dataset in each iteration, could you assist me how I can do?
- I use iterative-type of datasets, but I need a method of shuffling still since it brings accuracy performance issues if not doing it, thanks for the help.  
 Thanks @rabeehk for sharing. 

The sampler basically returns a list of integers to sample from each task's dataset. I was wondering how to use it with two `torch.Dataset` of different tasks. Also, do I need to shard across processes while creating an Iterable Dataset?
sample multiple datasets  
 Hi
I am dealing with multiple datasets, I need to have a dataloader over them with a condition that in each batch data samples are coming from one of the datasets. My main question is: 
-  I need to have a way to sample the datasets first with some weights, lets say 2x dataset1 1x dataset2, could you point me how I can do it

sub-questions:
- I want to concat sampled datasets and define one dataloader on it, then I need a way to make sure batches come from 1 dataset in each iteration, could you assist me how I can do?
- I use iterative-type of datasets, but I need a method of shuffling still since it brings accuracy performance issues if not doing it, thanks for the help.  
 We now have `interleave_datasets` in the API that allows you to cycle/sample with probabilities (with various stopping strategies) through a list of datasets. However, more specific behavior should be implemented manually.
Local machine/cluster Beam Datasets example/tutorial 
 Hi,

I'm wondering if https://huggingface.co/docs/datasets/beam_dataset.html has an non-GCP or non-Dataflow version example/tutorial? I tried to migrate it to run on DirectRunner and SparkRunner, however, there were way too many runtime errors that I had to fix during the process, and even so I wasn't able to get either runner correctly producing the desired output.

Thanks!
Shang 
 I tried to make it run once on the SparkRunner but it seems that this runner has some issues when it is run locally.
From my experience the DirectRunner is fine though, even if it's clearly not memory efficient.

It would be awesome though to make it work locally on a SparkRunner !
Did you manage to make your processing work ?
wrong length with datasets  
 Hi
I have a MRPC dataset which I convert it to seq2seq format, then this is of this format:

`Dataset(features: {'src_texts': Value(dtype='string', id=None), 'tgt_texts': Value(dtype='string', id=None)}, num_rows: 10)
`

I feed it to a dataloader:
```
dataloader = DataLoader(
            train_dataset,
            batch_size=self.args.train_batch_size,
            sampler=train_sampler,
            collate_fn=self.data_collator,
            drop_last=self.args.dataloader_drop_last,
            num_workers=self.args.dataloader_num_workers,
        )
```

now if I type len(dataloader) this is 1, which is wrong, and this needs to be 10. could you assist me please? thanks 
 
 Also, I cannot first convert it to torch format, since huggingface seq2seq_trainer codes process the datasets afterwards during datacollector function to make it optimize for TPUs.
Shall we change the hashing to encoding to reduce potential replicated cache files? 
 Hi there. For now, we are using `xxhash` to hash the transformations to fingerprint and we will save a copy of the processed dataset to disk if there is a new hash value. However, there are some transformations that are idempotent or commutative to each other. I think that encoding the transformation chain as the fingerprint may help in those cases, for example, use `base64.urlsafe_b64encode`. In this way, before we want to save a new copy, we can decode the transformation chain and normalize it to prevent omit potential reuse. As the main targets of this project are the really large datasets that cannot be loaded entirely in memory, I believe it would save a lot of time if we can avoid some write.

If you have interest in this, I'd love to help :). 
 This is an interesting idea !
Do you have ideas about how to approach the decoding and the normalization ?
Shall we change the hashing to encoding to reduce potential replicated cache files? 
 Hi there. For now, we are using `xxhash` to hash the transformations to fingerprint and we will save a copy of the processed dataset to disk if there is a new hash value. However, there are some transformations that are idempotent or commutative to each other. I think that encoding the transformation chain as the fingerprint may help in those cases, for example, use `base64.urlsafe_b64encode`. In this way, before we want to save a new copy, we can decode the transformation chain and normalize it to prevent omit potential reuse. As the main targets of this project are the really large datasets that cannot be loaded entirely in memory, I believe it would save a lot of time if we can avoid some write.

If you have interest in this, I'd love to help :). 
 @lhoestq
I think we first need to save the transformation chain to a list in `self._fingerprint`. Then we can
- decode all the current saved datasets to see if there is already one that is equivalent to the transformation we need now.
- or, calculate all the possible hash value of the current chain for comparison so that we could continue to use hashing.
If we find one, we can adjust the list in `self._fingerprint` to it.

As for the transformation reordering rules, we can just start with some manual rules, like two sort on the same column should merge to one, filter and select can change orders.

And for encoding and decoding, we can just manually specify `sort` is 0, `shuffling` is 2 and create a base-n number or use some general algorithm like `base64.urlsafe_b64encode`.

Because we are not doing lazy evaluation now, we may not be able to normalize the transformation to its minimal form. If we want to support that, we can provde a `Sequential` api and let user input a list or transformation, so that user would not use the intermediate datasets. This would look like tf.data.Dataset.
Dataset viewer issues 
 I was looking through the dataset viewer and I like it a lot. Version numbers, citation information, everything's there! I've spotted a few issues/bugs though:

- the URL is still under `nlp`, perhaps an alias for `datasets` can be made
- when I remove a **feature** (and the feature list is empty), I get an error. This is probably expected, but perhaps a better error message can be shown to the user

```bash
IndexError: list index out of range
Traceback:
File "/home/sasha/streamlit/lib/streamlit/ScriptRunner.py", line 322, in _run_script
    exec(code, module.__dict__)
File "/home/sasha/nlp-viewer/run.py", line 316, in <module>
    st.table(style)
File "/home/sasha/streamlit/lib/streamlit/DeltaGenerator.py", line 122, in wrapped_method
    return dg._enqueue_new_element_delta(marshall_element, delta_type, last_index)
File "/home/sasha/streamlit/lib/streamlit/DeltaGenerator.py", line 367, in _enqueue_new_element_delta
    rv = marshall_element(msg.delta.new_element)
File "/home/sasha/streamlit/lib/streamlit/DeltaGenerator.py", line 120, in marshall_element
    return method(dg, element, *args, **kwargs)
File "/home/sasha/streamlit/lib/streamlit/DeltaGenerator.py", line 2944, in table
    data_frame_proto.marshall_data_frame(data, element.table)
File "/home/sasha/streamlit/lib/streamlit/elements/data_frame_proto.py", line 54, in marshall_data_frame
    _marshall_styles(proto_df.style, df, styler)
File "/home/sasha/streamlit/lib/streamlit/elements/data_frame_proto.py", line 73, in _marshall_styles
    translated_style = styler._translate()
File "/home/sasha/.local/share/virtualenvs/lib-ogGKnCK_/lib/python3.7/site-packages/pandas/io/formats/style.py", line 351, in _translate
    * (len(clabels[0]) - len(hidden_columns))
```

- there seems to be **an encoding issue** in the default view, the dataset examples are shown as raw monospace text, without a decent encoding. That makes it hard to read for languages that use a lot of special characters. Take for instance the [cs-en WMT19 set](https://huggingface.co/nlp/viewer/?dataset=wmt19&config=cs-en). This problem goes away when you enable "List view", because then some syntax highlighteris used, and the special characters are coded correctly.
 
 Thanks for reporting !
cc @srush for the empty feature list issue and the encoding issue
cc @julien-c maybe we can update the url and just have a redirection from the old url to the new one ?
Dataset viewer issues 
 I was looking through the dataset viewer and I like it a lot. Version numbers, citation information, everything's there! I've spotted a few issues/bugs though:

- the URL is still under `nlp`, perhaps an alias for `datasets` can be made
- when I remove a **feature** (and the feature list is empty), I get an error. This is probably expected, but perhaps a better error message can be shown to the user

```bash
IndexError: list index out of range
Traceback:
File "/home/sasha/streamlit/lib/streamlit/ScriptRunner.py", line 322, in _run_script
    exec(code, module.__dict__)
File "/home/sasha/nlp-viewer/run.py", line 316, in <module>
    st.table(style)
File "/home/sasha/streamlit/lib/streamlit/DeltaGenerator.py", line 122, in wrapped_method
    return dg._enqueue_new_element_delta(marshall_element, delta_type, last_index)
File "/home/sasha/streamlit/lib/streamlit/DeltaGenerator.py", line 367, in _enqueue_new_element_delta
    rv = marshall_element(msg.delta.new_element)
File "/home/sasha/streamlit/lib/streamlit/DeltaGenerator.py", line 120, in marshall_element
    return method(dg, element, *args, **kwargs)
File "/home/sasha/streamlit/lib/streamlit/DeltaGenerator.py", line 2944, in table
    data_frame_proto.marshall_data_frame(data, element.table)
File "/home/sasha/streamlit/lib/streamlit/elements/data_frame_proto.py", line 54, in marshall_data_frame
    _marshall_styles(proto_df.style, df, styler)
File "/home/sasha/streamlit/lib/streamlit/elements/data_frame_proto.py", line 73, in _marshall_styles
    translated_style = styler._translate()
File "/home/sasha/.local/share/virtualenvs/lib-ogGKnCK_/lib/python3.7/site-packages/pandas/io/formats/style.py", line 351, in _translate
    * (len(clabels[0]) - len(hidden_columns))
```

- there seems to be **an encoding issue** in the default view, the dataset examples are shown as raw monospace text, without a decent encoding. That makes it hard to read for languages that use a lot of special characters. Take for instance the [cs-en WMT19 set](https://huggingface.co/nlp/viewer/?dataset=wmt19&config=cs-en). This problem goes away when you enable "List view", because then some syntax highlighteris used, and the special characters are coded correctly.
 
 Ok, I redirected on our side to a new url. ⚠️ @srush: if you update the Streamlit config too to `/datasets/viewer`, let me know because I'll need to change our nginx config at the same time
Nested lists are zipped unexpectedly 
 I might misunderstand something, but I expect that if I define:
```python
"top": datasets.features.Sequence({
  "middle": datasets.features.Sequence({
    "bottom": datasets.Value("int32")
  })
})
```

And I then create an example:
```python
yield 1, {
  "top": [{
    "middle": [
      {"bottom": 1},
      {"bottom": 2}
    ]
  }]
}
```

I then load my dataset:
```python
train = load_dataset("my dataset")["train"]
```

and expect to be able to access `data[0]["top"][0]["middle"][0]`.

That is not the case. Here is `data[0]` as JSON:

```json
{"top": {"middle": [{"bottom": [1, 2]}]}}
```

Clearly different than the thing I inputted.
```json
{"top": [{"middle": [{"bottom": 1},{"bottom": 2}]}]}
``` 
 Yes following the Tensorflow Datasets convention, objects with type `Sequence of a Dict` are actually stored as a `dictionary of lists`.
See the [documentation](https://huggingface.co/docs/datasets/features.html?highlight=features) for more details
Nested lists are zipped unexpectedly 
 I might misunderstand something, but I expect that if I define:
```python
"top": datasets.features.Sequence({
  "middle": datasets.features.Sequence({
    "bottom": datasets.Value("int32")
  })
})
```

And I then create an example:
```python
yield 1, {
  "top": [{
    "middle": [
      {"bottom": 1},
      {"bottom": 2}
    ]
  }]
}
```

I then load my dataset:
```python
train = load_dataset("my dataset")["train"]
```

and expect to be able to access `data[0]["top"][0]["middle"][0]`.

That is not the case. Here is `data[0]` as JSON:

```json
{"top": {"middle": [{"bottom": [1, 2]}]}}
```

Clearly different than the thing I inputted.
```json
{"top": [{"middle": [{"bottom": 1},{"bottom": 2}]}]}
``` 
 Thanks.
This is a bit (very) confusing, but I guess if its intended, I'll just work with it as if its how my data was originally structured :)
pyarrow.lib.ArrowNotImplementedError: MakeBuilder: cannot construct builder for type extension<arrow.py_extension_type> 
 I set up a new dataset, with a sequence of arrays (really, I want to have an array of (None, 137, 2), and the first dimension is dynamic) 

```python
    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            # This defines the different columns of the dataset and their types
            features=datasets.Features(
                {
                    "pose": datasets.features.Sequence(datasets.features.Array2D(shape=(137, 2), dtype="float32"))
                }
            ),
            homepage=_HOMEPAGE,
            citation=_CITATION,
        )
    def _generate_examples(self):
        """ Yields examples. """

        yield 1, {
            "pose": [np.zeros(shape=(137, 2), dtype=np.float32)]
        }
```

But this doesn't work -
> pyarrow.lib.ArrowNotImplementedError: MakeBuilder: cannot construct builder for type extension<arrow.py_extension_type> 
 Yes right now `ArrayXD` can only be used as a column feature type, not a subtype.
With the current Arrow limitations I don't think we'll be able to make it work as a subtype, however it should be possible to allow dimensions of dynamic sizes (`Array3D(shape=(None, 137, 2), dtype="float32")` for example since the [underlying arrow  type](https://github.com/huggingface/datasets/blob/master/src/datasets/features.py#L236) allows dynamic sizes.

For now I'd suggest the use of nested `Sequence` types. Once we have the dynamic sizes you can update the dataset.
What do you think ?
pyarrow.lib.ArrowNotImplementedError: MakeBuilder: cannot construct builder for type extension<arrow.py_extension_type> 
 I set up a new dataset, with a sequence of arrays (really, I want to have an array of (None, 137, 2), and the first dimension is dynamic) 

```python
    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            # This defines the different columns of the dataset and their types
            features=datasets.Features(
                {
                    "pose": datasets.features.Sequence(datasets.features.Array2D(shape=(137, 2), dtype="float32"))
                }
            ),
            homepage=_HOMEPAGE,
            citation=_CITATION,
        )
    def _generate_examples(self):
        """ Yields examples. """

        yield 1, {
            "pose": [np.zeros(shape=(137, 2), dtype=np.float32)]
        }
```

But this doesn't work -
> pyarrow.lib.ArrowNotImplementedError: MakeBuilder: cannot construct builder for type extension<arrow.py_extension_type> 
 > Yes right now ArrayXD can only be used as a column feature type, not a subtype. 

Meaning it can't be nested under `Sequence`?
If so, for now I'll just make it a python list and make it with the nested `Sequence` type you suggested.
pyarrow.lib.ArrowNotImplementedError: MakeBuilder: cannot construct builder for type extension<arrow.py_extension_type> 
 I set up a new dataset, with a sequence of arrays (really, I want to have an array of (None, 137, 2), and the first dimension is dynamic) 

```python
    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            # This defines the different columns of the dataset and their types
            features=datasets.Features(
                {
                    "pose": datasets.features.Sequence(datasets.features.Array2D(shape=(137, 2), dtype="float32"))
                }
            ),
            homepage=_HOMEPAGE,
            citation=_CITATION,
        )
    def _generate_examples(self):
        """ Yields examples. """

        yield 1, {
            "pose": [np.zeros(shape=(137, 2), dtype=np.float32)]
        }
```

But this doesn't work -
> pyarrow.lib.ArrowNotImplementedError: MakeBuilder: cannot construct builder for type extension<arrow.py_extension_type> 
 Yea unfortunately..
That's a current limitation with Arrow ExtensionTypes that can't be used in the default Arrow Array objects.
We already have an ExtensionArray that allows us to use them as column types but not for subtypes.
Maybe we can extend it, I haven't experimented with that yet
pyarrow.lib.ArrowNotImplementedError: MakeBuilder: cannot construct builder for type extension<arrow.py_extension_type> 
 I set up a new dataset, with a sequence of arrays (really, I want to have an array of (None, 137, 2), and the first dimension is dynamic) 

```python
    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            # This defines the different columns of the dataset and their types
            features=datasets.Features(
                {
                    "pose": datasets.features.Sequence(datasets.features.Array2D(shape=(137, 2), dtype="float32"))
                }
            ),
            homepage=_HOMEPAGE,
            citation=_CITATION,
        )
    def _generate_examples(self):
        """ Yields examples. """

        yield 1, {
            "pose": [np.zeros(shape=(137, 2), dtype=np.float32)]
        }
```

But this doesn't work -
> pyarrow.lib.ArrowNotImplementedError: MakeBuilder: cannot construct builder for type extension<arrow.py_extension_type> 
 Cool
So please consider this issue as a feature request for:
```
Array3D(shape=(None, 137, 2), dtype="float32")
```

its a way to represent videos, poses, and other cool sequences
pyarrow.lib.ArrowNotImplementedError: MakeBuilder: cannot construct builder for type extension<arrow.py_extension_type> 
 I set up a new dataset, with a sequence of arrays (really, I want to have an array of (None, 137, 2), and the first dimension is dynamic) 

```python
    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            # This defines the different columns of the dataset and their types
            features=datasets.Features(
                {
                    "pose": datasets.features.Sequence(datasets.features.Array2D(shape=(137, 2), dtype="float32"))
                }
            ),
            homepage=_HOMEPAGE,
            citation=_CITATION,
        )
    def _generate_examples(self):
        """ Yields examples. """

        yield 1, {
            "pose": [np.zeros(shape=(137, 2), dtype=np.float32)]
        }
```

But this doesn't work -
> pyarrow.lib.ArrowNotImplementedError: MakeBuilder: cannot construct builder for type extension<arrow.py_extension_type> 
 @lhoestq well, so sequence of sequences doesn't work either...

```
pyarrow.lib.ArrowCapacityError: List array cannot contain more than 2147483646 child elements, have 2147483648
```
pyarrow.lib.ArrowNotImplementedError: MakeBuilder: cannot construct builder for type extension<arrow.py_extension_type> 
 I set up a new dataset, with a sequence of arrays (really, I want to have an array of (None, 137, 2), and the first dimension is dynamic) 

```python
    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            # This defines the different columns of the dataset and their types
            features=datasets.Features(
                {
                    "pose": datasets.features.Sequence(datasets.features.Array2D(shape=(137, 2), dtype="float32"))
                }
            ),
            homepage=_HOMEPAGE,
            citation=_CITATION,
        )
    def _generate_examples(self):
        """ Yields examples. """

        yield 1, {
            "pose": [np.zeros(shape=(137, 2), dtype=np.float32)]
        }
```

But this doesn't work -
> pyarrow.lib.ArrowNotImplementedError: MakeBuilder: cannot construct builder for type extension<arrow.py_extension_type> 
 Working with Arrow can be quite fun sometimes.
You can fix this issue by trying to reduce the writer batch size (same trick than the one used to reduce the RAM usage in https://github.com/huggingface/datasets/issues/741).

Let me know if it works.
I haven't investigated yet on https://github.com/huggingface/datasets/issues/741 since I was preparing this week's sprint to add datasets but this is in my priority list for early next week.