In [1]:
#| default_exp feature_preprocessing.face_detection.target_encoding
%load_ext autoreload
%autoreload 2
In [2]:
# declare a list tasks whose products you want to use as inputs
upstream = ['parquet_conversion_face_detection']
In [3]:
# Parameters
upstream = {"parquet_conversion_face_detection": {"nb": "/home/ubuntu/vitmtsc_nbdev/output/101_data.face_detection.html", "FaceDetection_TRAIN_RAW": "/home/ubuntu/vitmtsc_nbdev/output/FaceDetection/raw/train", "FaceDetection_VALID_RAW": "/home/ubuntu/vitmtsc_nbdev/output/FaceDetection/raw/valid", "FaceDetection_TEST_RAW": "/home/ubuntu/vitmtsc_nbdev/output/FaceDetection/raw/test"}}
product = {"nb": "/home/ubuntu/vitmtsc_nbdev/output/201_feature_preprocessing.face_detection.target_encoding.html", "FaceDetection_TRAIN_TE": "/home/ubuntu/vitmtsc_nbdev/output/FaceDetection/target_encoding/train", "FaceDetection_VALID_TE": "/home/ubuntu/vitmtsc_nbdev/output/FaceDetection/target_encoding/valid", "FaceDetection_TEST_TE": "/home/ubuntu/vitmtsc_nbdev/output/FaceDetection/target_encoding/test", "FaceDetection_workflow_dir": "/home/ubuntu/vitmtsc_nbdev/output/FaceDetection/target_encoding/nvtabular_workflow"}
In [4]:
#| hide
from nbdev.showdoc import *
In [5]:
#| export
from vitmtsc import *
from vitmtsc.core import *
from vitmtsc.data.face_detection import *
import os
import nvtabular as nvt
import dask_cudf
from nvtabular import ops
In [6]:
#| export
upstream = {
    "parquet_conversion_face_detection": {
        "nb": "/home/ubuntu/vitmtsc_nbdev/output/101_data.face_detection.html",
        "FaceDetection_TRAIN_RAW": "/home/ubuntu/vitmtsc_nbdev/output/FaceDetection/raw/train",
        "FaceDetection_VALID_RAW": "/home/ubuntu/vitmtsc_nbdev/output/FaceDetection/raw/valid",
        "FaceDetection_TEST_RAW": "/home/ubuntu/vitmtsc_nbdev/output/FaceDetection/raw/test",
    }
}
product = {
    "nb": "/home/ubuntu/vitmtsc_nbdev/output/201_feature_preprocessing.face_detection.target_encoding.html",
    "FaceDetection_TRAIN_TE": "/home/ubuntu/vitmtsc_nbdev/output/FaceDetection/target_encoding/train",
    "FaceDetection_VALID_TE": "/home/ubuntu/vitmtsc_nbdev/output/FaceDetection/target_encoding/valid",
    "FaceDetection_TEST_TE": "/home/ubuntu/vitmtsc_nbdev/output/FaceDetection/target_encoding/test",
    "FaceDetection_workflow_dir": "/home/ubuntu/vitmtsc_nbdev/output/FaceDetection/target_encoding/nvtabular_workflow",
}
In [7]:
!conda list|grep -i nvtabular

Feature Preprocessing via NVTabular¶

Fill missing continuous features

Normalize continuous features

Categorify categorical features

Target Encoding of Categorical Variables

In [8]:
from dask.distributed import Client
from dask_cuda import LocalCUDACluster

cluster = LocalCUDACluster(memory_limit='auto', device_memory_limit=0.5, rmm_pool_size='20GB', rmm_managed_memory=True)
client = Client(cluster)
client
2022-09-23 18:55:34,626 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize
2022-09-23 18:55:34,626 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize
2022-09-23 18:55:34,628 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize
2022-09-23 18:55:34,628 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize
2022-09-23 18:55:34,649 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize
2022-09-23 18:55:34,649 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize
2022-09-23 18:55:34,659 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize
2022-09-23 18:55:34,659 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize
Out[8]:

Client

Client-4e902290-3b71-11ed-80b5-0a01290f6f4b

Connection method: Cluster object Cluster type: dask_cuda.LocalCUDACluster
Dashboard: http://127.0.0.1:8787/status

Cluster Info

LocalCUDACluster

8f9d8123

Dashboard: http://127.0.0.1:8787/status Workers: 4
Total threads: 4 Total memory: 150.00 GiB
Status: running Using processes: True

Scheduler Info

Scheduler

Scheduler-2e956b6c-5bc3-4804-9ede-2b76fd230a1a

Comm: tcp://127.0.0.1:43109 Workers: 4
Dashboard: http://127.0.0.1:8787/status Total threads: 4
Started: Just now Total memory: 150.00 GiB

Workers

Worker: 0

Comm: tcp://127.0.0.1:33639 Total threads: 1
Dashboard: http://127.0.0.1:36545/status Memory: 37.50 GiB
Nanny: tcp://127.0.0.1:42687
Local directory: /tmp/dask-worker-space/worker-2nuwguef
GPU: Tesla T4 GPU memory: 14.76 GiB

Worker: 1

Comm: tcp://127.0.0.1:42189 Total threads: 1
Dashboard: http://127.0.0.1:46569/status Memory: 37.50 GiB
Nanny: tcp://127.0.0.1:40397
Local directory: /tmp/dask-worker-space/worker-grlkfydj
GPU: Tesla T4 GPU memory: 14.76 GiB

Worker: 2

Comm: tcp://127.0.0.1:40765 Total threads: 1
Dashboard: http://127.0.0.1:39589/status Memory: 37.50 GiB
Nanny: tcp://127.0.0.1:46299
Local directory: /tmp/dask-worker-space/worker-bebdotnw
GPU: Tesla T4 GPU memory: 14.76 GiB

Worker: 3

Comm: tcp://127.0.0.1:40843 Total threads: 1
Dashboard: http://127.0.0.1:35191/status Memory: 37.50 GiB
Nanny: tcp://127.0.0.1:36379
Local directory: /tmp/dask-worker-space/worker-5x1utzur
GPU: Tesla T4 GPU memory: 14.76 GiB

COLUMNS: CATEGORICAL, CONTINUOUS and TARGET

In [9]:
#| export
import numpy as np
CATEGORICAL_COLUMNS_DONOT_NEED_ENCODING = ['case_id', 'case_id_seq', 'reading_id']
In [10]:
#| export
CATEGORICAL_COLUMNS_NEED_ENCODING = [

]
In [11]:
#| export
CONTINUOUS_COLUMNS = [
'dim_0',
'dim_1',
'dim_2',
'dim_3',
'dim_4',
'dim_5',
'dim_6',
'dim_7',
'dim_8',
'dim_9',
'dim_10',
'dim_11',
'dim_12',
'dim_13',
'dim_14',
'dim_15',
'dim_16',
'dim_17',
'dim_18',
'dim_19',
'dim_20',
'dim_21',
'dim_22',
'dim_23',
'dim_24',
'dim_25',
'dim_26',
'dim_27',
'dim_28',
'dim_29',
'dim_30',
'dim_31',
'dim_32',
'dim_33',
'dim_34',
'dim_35',
'dim_36',
'dim_37',
'dim_38',
'dim_39',
'dim_40',
'dim_41',
'dim_42',
'dim_43',
'dim_44',
'dim_45',
'dim_46',
'dim_47',
'dim_48',
'dim_49',
'dim_50',
'dim_51',
'dim_52',
'dim_53',
'dim_54',
'dim_55',
'dim_56',
'dim_57',
'dim_58',
'dim_59',
'dim_60',
'dim_61',
'dim_62',
'dim_63',
'dim_64',
'dim_65',
'dim_66',
'dim_67',
'dim_68',
'dim_69',
'dim_70',
'dim_71',
'dim_72',
'dim_73',
'dim_74',
'dim_75',
'dim_76',
'dim_77',
'dim_78',
'dim_79',
'dim_80',
'dim_81',
'dim_82',
'dim_83',
'dim_84',
'dim_85',
'dim_86',
'dim_87',
'dim_88',
'dim_89',
'dim_90',
'dim_91',
'dim_92',
'dim_93',
'dim_94',
'dim_95',
'dim_96',
'dim_97',
'dim_98',
'dim_99',
'dim_100',
'dim_101',
'dim_102',
'dim_103',
'dim_104',
'dim_105',
'dim_106',
'dim_107',
'dim_108',
'dim_109',
'dim_110',
'dim_111',
'dim_112',
'dim_113',
'dim_114',
'dim_115',
'dim_116',
'dim_117',
'dim_118',
'dim_119',
'dim_120',
'dim_121',
'dim_122',
'dim_123',
'dim_124',
'dim_125',
'dim_126',
'dim_127',
'dim_128',
'dim_129',
'dim_130',
'dim_131',
'dim_132',
'dim_133',
'dim_134',
'dim_135',
'dim_136',
'dim_137',
'dim_138',
'dim_139',
'dim_140',
'dim_141',
'dim_142',
'dim_143'
]
In [12]:
#| export
LABEL_COLUMNS = ['class_vals']

Workflow and Operations

In [13]:
import cudf
import numpy as np
cat_features_no_encoding = nvt.ColumnGroup(CATEGORICAL_COLUMNS_DONOT_NEED_ENCODING)
#te_features = CATEGORICAL_COLUMNS_NEED_ENCODING >> ops.TargetEncoding(LABEL_COLUMNS, kfold=5, fold_seed=42, p_smooth=20)
cont_features = CONTINUOUS_COLUMNS >> ops.FillMissing() >> ops.Normalize()
label_name = LABEL_COLUMNS

workflow = nvt.Workflow(
    #cat_features_no_encoding + te_features + cont_features + label_name
    #cat_features_no_encoding + te_features + label_name
    cat_features_no_encoding  + cont_features + label_name
)

Datasets

Input data

In [14]:
pre_processed_train_dir = os.path.join("./", upstream['parquet_conversion_face_detection']['FaceDetection_TRAIN_RAW'])
pre_processed_valid_dir = os.path.join("./", upstream['parquet_conversion_face_detection']['FaceDetection_VALID_RAW'])
pre_processed_test_dir = os.path.join("./", upstream['parquet_conversion_face_detection']['FaceDetection_TEST_RAW'])

Training, Validation and Test datasets

In [15]:
train_dataset = nvt.Dataset(pre_processed_train_dir, engine='parquet')
valid_dataset = nvt.Dataset(pre_processed_valid_dir, engine='parquet')
test_dataset = nvt.Dataset(pre_processed_test_dir, engine='parquet')

Output location

In [16]:
output_train_dir = os.path.join("./", product['FaceDetection_TRAIN_TE'])
output_valid_dir = os.path.join("./", product['FaceDetection_VALID_TE'])
output_test_dir = os.path.join("./", product['FaceDetection_TEST_TE'])
In [17]:
!mkdir -p $output_train_dir
!mkdir -p $output_valid_dir
!mkdir -p $output_test_dir

Path to save the workflow to

Fit: Train Dataset
¶

In [18]:
%%time
workflow.fit(train_dataset)
CPU times: user 418 ms, sys: 54 ms, total: 472 ms
Wall time: 2.95 s
Out[18]:
<nvtabular.workflow.workflow.Workflow at 0x7f015045d460>

Save workflow

In [19]:
%%time
workflow.save(product['FaceDetection_workflow_dir'])
CPU times: user 4.58 ms, sys: 667 µs, total: 5.25 ms
Wall time: 4.78 ms

Clear workflow

In [20]:
%%time
workflow = None
CPU times: user 1 µs, sys: 2 µs, total: 3 µs
Wall time: 5.72 µs

Load workflow

In [21]:
%%time
workflow = nvt.Workflow.load(product['FaceDetection_workflow_dir'], client=client)
CPU times: user 2.84 ms, sys: 23 µs, total: 2.86 ms
Wall time: 2.56 ms

Transform: Train Dataset
¶

In [22]:
%%time
# Write to new "shuffled" and "processed" dataset
workflow.transform(train_dataset).to_parquet(
    output_train_dir,
    out_files_per_proc=2,
    shuffle=nvt.io.Shuffle.PER_PARTITION,
)
/home/ubuntu/anaconda3/envs/rapids-22.08_ploomber/lib/python3.8/site-packages/merlin/io/dataset.py:862: UserWarning: Only created 4 files did not have enough partitions to create 8 files.
  warnings.warn(
CPU times: user 438 ms, sys: 46.7 ms, total: 485 ms
Wall time: 3.17 s

Transform: Valid Dataset
¶

In [23]:
%%time
# Write to new "shuffled" and "processed" dataset
workflow.transform(valid_dataset).to_parquet(
    output_valid_dir,
    out_files_per_proc=2,
    shuffle=nvt.io.Shuffle.PER_PARTITION,
)
CPU times: user 230 ms, sys: 25.9 ms, total: 256 ms
Wall time: 1.3 s

Transform: Test Dataset
¶

In [24]:
%%time
# Write to new "shuffled" and "processed" dataset
workflow.transform(test_dataset).to_parquet(
    output_test_dir,
    out_files_per_proc=2,
    shuffle=nvt.io.Shuffle.PER_PARTITION,
)
CPU times: user 217 ms, sys: 21.7 ms, total: 238 ms
Wall time: 865 ms

Verify Data

In [25]:
train_gdf = dask_cudf.read_parquet(output_train_dir)
In [26]:
%%time
train_gdf.head()
CPU times: user 27.2 ms, sys: 0 ns, total: 27.2 ms
Wall time: 65.5 ms
Out[26]:
dim_0 dim_1 dim_2 dim_3 dim_4 dim_5 dim_6 dim_7 dim_8 dim_9 ... dim_138 dim_139 dim_140 dim_141 dim_142 dim_143 case_id case_id_seq reading_id class_vals
0 -0.121314 0.393771 0.079717 0.077422 -0.981932 0.357935 -0.030971 -0.694245 0.078503 0.073602 ... -1.123991 1.145524 0.441915 0.071174 -0.414679 0.074954 661 661 25 0
1 -0.841474 -0.797672 -0.263793 1.298514 -0.739589 -0.381691 0.588058 0.608006 -0.247028 0.193751 ... -0.440068 -1.319766 0.219988 0.081958 -1.039737 -0.218774 659 659 58 0
2 -0.234124 -0.014298 0.851526 -0.699747 -0.870767 0.805499 -0.786541 0.730033 -0.032784 -1.821225 ... -0.219486 -1.016099 1.122216 0.559855 -1.778112 0.560210 35 35 19 1
3 0.092242 -1.140328 -1.318047 0.468520 1.570720 -1.111088 0.701104 -0.652357 -0.760031 0.140818 ... -0.873041 -0.757116 0.953688 -0.137620 -0.252380 -0.262697 145 145 50 0
4 -1.052806 -0.138503 1.347148 -2.277875 0.361970 1.458319 0.831894 -1.265934 0.839550 -0.019017 ... -0.863022 1.170422 0.106141 0.650703 0.308283 0.802099 496 496 51 1

5 rows × 148 columns

In [27]:
%%time
train_gdf['case_id'].nunique().compute()
CPU times: user 118 ms, sys: 10.3 ms, total: 128 ms
Wall time: 159 ms
Out[27]:
4712
In [28]:
valid_gdf = dask_cudf.read_parquet(output_valid_dir)
In [29]:
%%time
valid_gdf.head()
CPU times: user 23.7 ms, sys: 3.67 ms, total: 27.3 ms
Wall time: 54.4 ms
Out[29]:
dim_0 dim_1 dim_2 dim_3 dim_4 dim_5 dim_6 dim_7 dim_8 dim_9 ... dim_138 dim_139 dim_140 dim_141 dim_142 dim_143 case_id case_id_seq reading_id class_vals
0 -1.171568 0.063259 1.015736 -0.603317 -0.068818 0.628579 -1.884459 -0.731820 0.468230 -0.309108 ... -0.057336 -0.179852 0.144208 -0.210516 0.664078 -0.668529 3425 3425 8 1
1 1.062399 0.965218 1.212485 0.076351 -0.475522 1.061865 -0.675021 -2.317005 0.837066 0.906042 ... -0.559026 0.347611 -0.171397 -0.918838 -0.932959 0.113725 3475 3475 41 1
2 0.116676 0.389882 0.880675 -1.038531 1.072086 0.739722 -0.344597 -0.158225 -0.273703 -1.396459 ... -0.687636 1.054171 -1.007294 -0.104828 0.093016 -0.221463 3652 3652 44 0
3 -1.258221 0.739932 0.593104 -0.613782 0.654134 0.956879 -2.337000 0.211169 0.336160 -0.995167 ... 0.388883 0.482879 0.002319 0.053174 0.364765 -1.163029 3262 3262 46 0
4 -1.168730 1.659656 0.043099 -1.012270 0.041338 0.602217 -1.092073 0.892507 0.115993 -1.454313 ... 0.397347 -1.141881 1.721545 0.485349 0.405445 1.025795 3365 3365 28 0

5 rows × 148 columns

In [30]:
%%time
valid_gdf['case_id'].nunique().compute()
CPU times: user 24.6 ms, sys: 0 ns, total: 24.6 ms
Wall time: 55.1 ms
Out[30]:
1178
In [31]:
test_gdf = dask_cudf.read_parquet(output_test_dir)
In [32]:
%%time
test_gdf.head()
CPU times: user 22.7 ms, sys: 2.56 ms, total: 25.2 ms
Wall time: 61.1 ms
Out[32]:
dim_0 dim_1 dim_2 dim_3 dim_4 dim_5 dim_6 dim_7 dim_8 dim_9 ... dim_138 dim_139 dim_140 dim_141 dim_142 dim_143 case_id case_id_seq reading_id class_vals
0 -0.260010 -0.489144 0.656876 0.815443 -1.202993 0.525935 -0.255497 -1.572738 0.652261 -0.035183 ... -0.353590 -0.996325 -1.411985 -1.146100 -0.456622 -0.571731 73 73 44 1
1 -0.809563 2.231060 -0.720697 -0.630477 1.837997 -0.958108 -1.111681 0.387655 -1.175156 -1.467910 ... -0.291857 -1.778868 0.294018 -1.296351 -1.230731 0.473268 235 235 8 0
2 1.141494 -0.968512 0.786951 0.102245 -0.277603 0.819223 -0.489941 0.603016 0.746006 -0.624365 ... 0.615759 -0.401089 -0.092868 -0.397284 0.553770 -1.266353 193 193 42 0
3 1.004016 -1.891377 0.658439 0.303093 -0.724539 0.268960 1.223566 -1.376977 0.542260 -0.992891 ... -0.173092 0.488560 -1.316807 -1.073103 -0.584971 -0.645980 350 350 25 0
4 -0.686177 -0.800100 0.220677 -0.225374 0.300138 0.571094 -2.010938 -1.444248 0.146692 0.097607 ... -0.182355 -0.060018 0.325988 0.027354 0.819301 -1.251991 261 261 5 1

5 rows × 148 columns

In [33]:
%%time
test_gdf['case_id'].nunique().compute()
CPU times: user 24.5 ms, sys: 3.56 ms, total: 28.1 ms
Wall time: 60.1 ms
Out[33]:
3524
In [34]:
test_gdf.columns
Out[34]:
Index(['dim_0', 'dim_1', 'dim_2', 'dim_3', 'dim_4', 'dim_5', 'dim_6', 'dim_7',
       'dim_8', 'dim_9',
       ...
       'dim_138', 'dim_139', 'dim_140', 'dim_141', 'dim_142', 'dim_143',
       'case_id', 'case_id_seq', 'reading_id', 'class_vals'],
      dtype='object', length=148)
In [35]:
!ls -lrt --block-size=M $output_train_dir
total 323M
-rw-r--r-- 1 root root  1M Sep 23 18:55 schema.pbtxt
-rw-r--r-- 1 root root 41M Sep 23 18:55 part_7.parquet
-rw-r--r-- 1 root root 41M Sep 23 18:55 part_6.parquet
-rw-r--r-- 1 root root 41M Sep 23 18:55 part_5.parquet
-rw-r--r-- 1 root root 41M Sep 23 18:55 part_4.parquet
-rw-r--r-- 1 root root 41M Sep 23 18:55 part_3.parquet
-rw-r--r-- 1 root root 41M Sep 23 18:55 part_2.parquet
-rw-r--r-- 1 root root 41M Sep 23 18:55 part_1.parquet
-rw-r--r-- 1 root root 41M Sep 23 18:55 part_0.parquet
-rw-r--r-- 1 root root  1M Sep 23 18:55 _metadata.json
-rw-r--r-- 1 root root  1M Sep 23 18:55 _file_list.txt
-rw-r--r-- 1 root root  1M Sep 23 18:55 _metadata
In [36]:
!ls -lrt --block-size=M $output_valid_dir
total 82M
-rw-r--r-- 1 root root  1M Sep 23 18:55 schema.pbtxt
-rw-r--r-- 1 root root 11M Sep 23 18:55 part_7.parquet
-rw-r--r-- 1 root root 11M Sep 23 18:55 part_6.parquet
-rw-r--r-- 1 root root 11M Sep 23 18:55 part_3.parquet
-rw-r--r-- 1 root root 11M Sep 23 18:55 part_2.parquet
-rw-r--r-- 1 root root 11M Sep 23 18:55 part_5.parquet
-rw-r--r-- 1 root root 11M Sep 23 18:55 part_4.parquet
-rw-r--r-- 1 root root 11M Sep 23 18:55 part_1.parquet
-rw-r--r-- 1 root root 11M Sep 23 18:55 part_0.parquet
-rw-r--r-- 1 root root  1M Sep 23 18:55 _metadata.json
-rw-r--r-- 1 root root  1M Sep 23 18:55 _file_list.txt
-rw-r--r-- 1 root root  1M Sep 23 18:55 _metadata
In [37]:
!ls -lrt --block-size=M $output_test_dir
total 242M
-rw-r--r-- 1 root root  1M Sep 23 18:55 schema.pbtxt
-rw-r--r-- 1 root root 31M Sep 23 18:55 part_5.parquet
-rw-r--r-- 1 root root 31M Sep 23 18:55 part_4.parquet
-rw-r--r-- 1 root root 31M Sep 23 18:55 part_7.parquet
-rw-r--r-- 1 root root 31M Sep 23 18:55 part_6.parquet
-rw-r--r-- 1 root root 31M Sep 23 18:55 part_3.parquet
-rw-r--r-- 1 root root 30M Sep 23 18:55 part_2.parquet
-rw-r--r-- 1 root root 31M Sep 23 18:55 part_1.parquet
-rw-r--r-- 1 root root 31M Sep 23 18:55 part_0.parquet
-rw-r--r-- 1 root root  1M Sep 23 18:55 _metadata.json
-rw-r--r-- 1 root root  1M Sep 23 18:55 _file_list.txt
-rw-r--r-- 1 root root  1M Sep 23 18:55 _metadata

We reset the kernel!!!

In [38]:
%%time
client.shutdown()
client.close()
Traceback (most recent call last):
  File "/home/ubuntu/anaconda3/envs/rapids-22.08_ploomber/lib/python3.8/site-packages/distributed/utils.py", line 778, in wrapper
    return await func(*args, **kwargs)
  File "/home/ubuntu/anaconda3/envs/rapids-22.08_ploomber/lib/python3.8/site-packages/distributed/client.py", line 1211, in _reconnect
    await self._ensure_connected(timeout=timeout)
  File "/home/ubuntu/anaconda3/envs/rapids-22.08_ploomber/lib/python3.8/site-packages/distributed/client.py", line 1241, in _ensure_connected
    comm = await connect(
  File "/home/ubuntu/anaconda3/envs/rapids-22.08_ploomber/lib/python3.8/site-packages/distributed/comm/core.py", line 315, in connect
    await asyncio.sleep(backoff)
  File "/home/ubuntu/anaconda3/envs/rapids-22.08_ploomber/lib/python3.8/asyncio/tasks.py", line 659, in sleep
    return await future
asyncio.exceptions.CancelledError

Traceback (most recent call last):
  File "/home/ubuntu/anaconda3/envs/rapids-22.08_ploomber/lib/python3.8/site-packages/distributed/utils.py", line 778, in wrapper
    return await func(*args, **kwargs)
  File "/home/ubuntu/anaconda3/envs/rapids-22.08_ploomber/lib/python3.8/site-packages/distributed/client.py", line 1400, in _handle_report
    await self._reconnect()
  File "/home/ubuntu/anaconda3/envs/rapids-22.08_ploomber/lib/python3.8/site-packages/distributed/utils.py", line 778, in wrapper
    return await func(*args, **kwargs)
  File "/home/ubuntu/anaconda3/envs/rapids-22.08_ploomber/lib/python3.8/site-packages/distributed/client.py", line 1211, in _reconnect
    await self._ensure_connected(timeout=timeout)
  File "/home/ubuntu/anaconda3/envs/rapids-22.08_ploomber/lib/python3.8/site-packages/distributed/client.py", line 1241, in _ensure_connected
    comm = await connect(
  File "/home/ubuntu/anaconda3/envs/rapids-22.08_ploomber/lib/python3.8/site-packages/distributed/comm/core.py", line 315, in connect
    await asyncio.sleep(backoff)
  File "/home/ubuntu/anaconda3/envs/rapids-22.08_ploomber/lib/python3.8/asyncio/tasks.py", line 659, in sleep
    return await future
asyncio.exceptions.CancelledError
CPU times: user 26.4 ms, sys: 22.3 ms, total: 48.7 ms
Wall time: 669 ms
In [39]:
from nbdev import nbdev_export
nbdev_export()