def parquet_to_dask(context: MLClientCtx,
parquet_url: Union[DataItem, str, Path, IO[AnyStr]],
inc_cols: Optional[List[str]] = None,
index_cols: Optional[List[str]] = None,
shards: int = 4,
threads_per: int = 4,
processes: bool = False,
memory_limit: str = '2GB',
persist: bool = True,
dask_key: str = 'my_dask_dataframe',
target_path: str = '') -> None:
"""Load parquet dataset into dask cluster
If no cluster is found loads a new one and persist the data to it. It
shouold not be necessary to create a new cluster when the function
is run as a 'dask' job.
:param context: the function context
:param parquet_url: url of the parquet file or partitioned dataset as either
artifact DataItem, string, or path object (see pandas read_csv)
:param inc_cols: include only these columns (very fast)
:param index_cols: list of index column names (can be a long-running process)
:param shards: number of workers to launch
:param threads_per: number of threads per worker
:param processes:
"""
if hasattr(context, 'dask_client'):
context.logger.info('found cluster...')
dask_client = context.dask_client
else:
context.logger.info('starting new cluster...')
cluster = LocalCluster(n_workers=shards,
threads_per_worker=threads_per,
processes=processes,
memory_limit=memory_limit)
dask_client = Client(cluster)
context.logger.info(dask_client)
df = dd.read_parquet(parquet_url)
if persist and context:
df = dask_client.persist(df)
dask_client.publish_dataset(dask_key=df)
context.dask_client = dask_client
# share the scheduler
filepath = os.path.join(target_path, 'scheduler.json')
dask_client.write_scheduler_file(filepath)
context.log_artifact('scheduler', target_path=filepath)
print(df.head())
import os
import numpy as np
import pandas as pd
import cudf
import dask_cudf
from dask.distributed import Client
np.random.seed(12)
df = cudf.DataFrame([('a', list(range(20))),
('b', list(reversed(range(20)))),
('c', list(range(20)))])
print(df)
ddf = dask_cudf.from_cudf(df, npartitions=2)
print(ddf.persist())
client = Client("127.0.0.1:8487")
client.publish_dataset(shared_dataset1=ddf)
print(client.list_datasets())
# df = dd.read_csv('s3://dask-data/nyc-taxi/2015/*.csv',
# parse_dates=['tpep_pickup_datetime', 'tpep_dropoff_datetime'],
# storage_options={'anon': True})
print('read file')
# https://www.gov.uk/guidance/about-the-price-paid-data#download-options
column_names = [
'id', 'price', 'transfer_date', 'postcode', 'property_type', 'old_new',
'duration', 'primary_address_obj', 'secondary_address_obj', 'street',
'locality', 'city_town', 'district', 'county', 'ppd_cat', 'record_stat'
]
### dont set an index for saving parquet format
# df = dd.read_csv('./propdata/pp-complete.csv', header=None, names=column_names).set_index('id')
df = dd.read_csv('./propdata/pp-complete.csv', header=None, names=column_names)
# print(df.head())
print('persist dataframe')
df = client.persist(df)
#
print('publish dataframe')
df = client.publish_dataset(prop_paid=df)
# print('run calc')
# x = client.submit(lambda a: a.shape, df).result()
#
# # print(df)
# print(x)
