Datasets:

---

shalabh05

/

Shalabh_Dataset

like 0

Error code:   DatasetGenerationCastError
Exception:    DatasetGenerationCastError
Message:      An error occurred while generating the dataset

All the data files must have the same columns, but at some point there are 1 new columns ({'23andMe could migrate its existing environment with virtually no changes, and over time started incorporating more AWS services into its solution. The company is looking for further ways to optimize costs using AWS, exploring services like AWS Graviton processor, which delivers excellent price performance for cloud workloads running in Amazon EC2. The company is finding opportunities to be cost optimal while retaining the resources it needs for on-demand computing. “We’re about 10 months past migration, and the eventual goal is to drive a faster process from idea to validation. Our researchers are faster and more efficient, and our hope is to see a big research breakthrough,” says de Leon.\xa0\nIncreased scalability, supporting a compute job running on more than 80,000 virtual CPUs\n About 23andMe\nEspañol\n\t{font-family:"Cambria Math";\n日本語\n\tmso-font-pitch:variable;\n\tfont-family:"Arial",sans-serif;\n한국어\n\t{font-family:Cambria;\n Amazon MAP\n \n\tmso-bidi-font-size:12.0pt;\n AWS Services Used\nArnold de Leon Sr. Program Manager,\xa023andMe\n\tmargin:0in;\n Optimizing Value Running HPC on AWS\n          \xa0 \n\tmso-pagination:widow-orphan;\nOptimized costs @font-face\n\t{page:WordSection1;}ol\n23andMe can scale on demand to match compute capacity for actual workloads and then scale back down. “To give a sense of scale, we had a peak compute job running with over 80,000 virtual CPUs operating at once,” says de Leon. In addition, using Amazon EC2 ins
...
n more\xa0»\n\tmso-font-signature:3 0 0 0 -2147483647 0;}@font-face\n\t{mso-style-name:Normal0;\n\tfont-size:11.0pt;\n             Amazon Simple Storage Service (Amazon S3) is an object storage service offering industry-leading scalability, data availability, security, and performance. \nΡусский\nRemoved compute resource contention among researchers\n\tmso-font-charset:77;\n中文 (简体)\n\t{margin-bottom:0in;}\n          23andMe initially used an on-premises facility, but as its data storage and compute needs grew, the company began looking to the cloud for greater scalability and flexibility. Additionally, the company sought to reduce human operating costs for facility maintenance and accelerate its ability to adopt new hardware and tech by transitioning to the cloud. In 2016, the company began using \n\tmso-style-parent:"";\n             AWS Batch enables developers, scientists, and engineers to easily and efficiently run hundreds of thousands of batch computing jobs on AWS. \n          As it started using cloud services, 23andMe tried a hybrid solution, running workloads in its data center and on AWS concurrently. This solution provided some scalability but came with associated costs of migrating data back and forth between the on-premises data center and the cloud. To achieve better cost optimization while also gaining more flexibility and scalability, 23andMe decided to migrate fully to AWS in 2021. \n Get Started\n\tmso-generic-font-family:roman;\n  Contact Sales'}) and 2 missing columns ({'Content', 'ID'}).

This happened while the csv dataset builder was generating data using

hf://datasets/shalabh05/Shalabh_Dataset/output_updated.csv (at revision ccdff331387befbe517669379feeed22ee461f93)

Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2011, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 585, in write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2302, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2256, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              23andMe could migrate its existing environment with virtually no changes, and over time started incorporating more AWS services into its solution. The company is looking for further ways to optimize costs using AWS, exploring services like AWS Graviton processor, which delivers excellent price performance for cloud workloads running in Amazon EC2. The company is finding opportunities to be cost optimal while retaining the resources it needs for on-demand computing. “We’re about 10 months past migration, and the eventual goal is to drive a faster process from idea to validation. Our researchers are faster and more efficient, and our hope is to see a big research breakthrough,” says de Leon. 
              Increased scalability, supporting a compute job running on more than 80,000 virtual CPUs
               About 23andMe
              Español
              	{font-family:"Cambria Math";
              日本語
              	mso-font-pitch:variable;
              	font-family:"Arial",sans-serif;
              한국어
              	{font-family:Cambria;
               Amazon MAP
               
              	mso-bidi-font-size:12.0pt;
               AWS Services Used
              Arnold de Leon Sr. Program Manager, 23andMe
              	margin:0in;
               Optimizing Value Running HPC on AWS
                          
              	mso-pagination:widow-orphan;
              Optimized costs @font-face
              	{page:WordSection1;}ol
              23andMe can scale on demand to match compute capacity for actual workloads and then scale back down. “To give a sense of scale, we had a peak compute job running with over 80,000 virtual CPUs operating at once,” says de Leon. In addition, using Amazon EC2 instances has removed resource contention f
              ...
              l0;
              	font-size:11.0pt;
                           Amazon Simple Storage Service (Amazon S3) is an object storage service offering industry-leading scalability, data availability, security, and performance. 
              Ρусский
              Removed compute resource contention among researchers
              	mso-font-charset:77;
              中文 (简体)
              	{margin-bottom:0in;}
                        23andMe initially used an on-premises facility, but as its data storage and compute needs grew, the company began looking to the cloud for greater scalability and flexibility. Additionally, the company sought to reduce human operating costs for facility maintenance and accelerate its ability to adopt new hardware and tech by transitioning to the cloud. In 2016, the company began using 
              	mso-style-parent:"";
                           AWS Batch enables developers, scientists, and engineers to easily and efficiently run hundreds of thousands of batch computing jobs on AWS. 
                        As it started using cloud services, 23andMe tried a hybrid solution, running workloads in its data center and on AWS concurrently. This solution provided some scalability but came with associated costs of migrating data back and forth between the on-premises data center and the cloud. To achieve better cost optimization while also gaining more flexibility and scalability, 23andMe decided to migrate fully to AWS in 2021. 
               Get Started
              	mso-generic-font-family:roman;
                Contact Sales: string
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 22231
              to
              {'ID': Value(dtype='string', id=None), 'Content': Value(dtype='string', id=None)}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1317, in compute_config_parquet_and_info_response
                  parquet_operations = convert_to_parquet(builder)
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 932, in convert_to_parquet
                  builder.download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1027, in download_and_prepare
                  self._download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1122, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1882, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2013, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
              datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
              
              All the data files must have the same columns, but at some point there are 1 new columns ({'23andMe could migrate its existing environment with virtually no changes, and over time started incorporating more AWS services into its solution. The company is looking for further ways to optimize costs using AWS, exploring services like AWS Graviton processor, which delivers excellent price performance for cloud workloads running in Amazon EC2. The company is finding opportunities to be cost optimal while retaining the resources it needs for on-demand computing. “We’re about 10 months past migration, and the eventual goal is to drive a faster process from idea to validation. Our researchers are faster and more efficient, and our hope is to see a big research breakthrough,” says de Leon.\xa0\nIncreased scalability, supporting a compute job running on more than 80,000 virtual CPUs\n About 23andMe\nEspañol\n\t{font-family:"Cambria Math";\n日本語\n\tmso-font-pitch:variable;\n\tfont-family:"Arial",sans-serif;\n한국어\n\t{font-family:Cambria;\n Amazon MAP\n \n\tmso-bidi-font-size:12.0pt;\n AWS Services Used\nArnold de Leon Sr. Program Manager,\xa023andMe\n\tmargin:0in;\n Optimizing Value Running HPC on AWS\n          \xa0 \n\tmso-pagination:widow-orphan;\nOptimized costs @font-face\n\t{page:WordSection1;}ol\n23andMe can scale on demand to match compute capacity for actual workloads and then scale back down. “To give a sense of scale, we had a peak compute job running with over 80,000 virtual CPUs operating at once,” says de Leon. In addition, using Amazon EC2 ins
              ...
              n more\xa0»\n\tmso-font-signature:3 0 0 0 -2147483647 0;}@font-face\n\t{mso-style-name:Normal0;\n\tfont-size:11.0pt;\n             Amazon Simple Storage Service (Amazon S3) is an object storage service offering industry-leading scalability, data availability, security, and performance. \nΡусский\nRemoved compute resource contention among researchers\n\tmso-font-charset:77;\n中文 (简体)\n\t{margin-bottom:0in;}\n          23andMe initially used an on-premises facility, but as its data storage and compute needs grew, the company began looking to the cloud for greater scalability and flexibility. Additionally, the company sought to reduce human operating costs for facility maintenance and accelerate its ability to adopt new hardware and tech by transitioning to the cloud. In 2016, the company began using \n\tmso-style-parent:"";\n             AWS Batch enables developers, scientists, and engineers to easily and efficiently run hundreds of thousands of batch computing jobs on AWS. \n          As it started using cloud services, 23andMe tried a hybrid solution, running workloads in its data center and on AWS concurrently. This solution provided some scalability but came with associated costs of migrating data back and forth between the on-premises data center and the cloud. To achieve better cost optimization while also gaining more flexibility and scalability, 23andMe decided to migrate fully to AWS in 2021. \n Get Started\n\tmso-generic-font-family:roman;\n  Contact Sales'}) and 2 missing columns ({'Content', 'ID'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/shalabh05/Shalabh_Dataset/output_updated.csv (at revision ccdff331387befbe517669379feeed22ee461f93)
              
              Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)