#| default_exp feature_preprocessing.pen_digits.tabular_to_timeseries
%load_ext autoreload
%autoreload 2
# declare a list tasks whose products you want to use as inputs
upstream = ['feature_preprocessing_pen_digits']
# Parameters
upstream = {"feature_preprocessing_pen_digits": {"nb": "/home/ubuntu/vitmtsc_nbdev/output/203_feature_preprocessing.pen_digits.target_encoding.html", "PenDigits_TRAIN_TE": "/home/ubuntu/vitmtsc_nbdev/output/PenDigits/target_encoding/train", "PenDigits_VALID_TE": "/home/ubuntu/vitmtsc_nbdev/output/PenDigits/target_encoding/valid", "PenDigits_TEST_TE": "/home/ubuntu/vitmtsc_nbdev/output/PenDigits/target_encoding/test", "PenDigits_workflow_dir": "/home/ubuntu/vitmtsc_nbdev/output/PenDigits/target_encoding/nvtabular_workflow"}}
product = {"nb": "/home/ubuntu/vitmtsc_nbdev/output/303_feature_preprocessing.pen_digits.tabular_to_timeseries.html", "PenDigits_TRAIN_MODEL_INPUT": "/home/ubuntu/vitmtsc_nbdev/output/PenDigits/target_encoding-nn/train", "PenDigits_VALID_MODEL_INPUT": "/home/ubuntu/vitmtsc_nbdev/output/PenDigits/target_encoding-nn/valid", "PenDigits_TEST_MODEL_INPUT": "/home/ubuntu/vitmtsc_nbdev/output/PenDigits/target_encoding-nn/test"}
#| hide
from nbdev.showdoc import *
#| export
from vitmtsc import *
from vitmtsc.core import *
from vitmtsc.data.pen_digits import *
from vitmtsc.feature_preprocessing.pen_digits.target_encoding import *
import os
import glob
#| export
upstream = {
"feature_preprocessing_pen_digits": {
"nb": "/home/ubuntu/vitmtsc_nbdev/output/203_feature_preprocessing.pen_digits.target_encoding.html",
"PenDigits_TRAIN_TE": "/home/ubuntu/vitmtsc_nbdev/output/PenDigits/target_encoding/train",
"PenDigits_VALID_TE": "/home/ubuntu/vitmtsc_nbdev/output/PenDigits/target_encoding/valid",
"PenDigits_TEST_TE": "/home/ubuntu/vitmtsc_nbdev/output/PenDigits/target_encoding/test",
"PenDigits_workflow_dir": "/home/ubuntu/vitmtsc_nbdev/output/PenDigits/target_encoding/nvtabular_workflow",
}
}
product = {
"nb": "/home/ubuntu/vitmtsc_nbdev/output/303_feature_preprocessing.pen_digits.tabular_to_timeseries.html",
"PenDigits_TRAIN_MODEL_INPUT": "/home/ubuntu/vitmtsc_nbdev/output/PenDigits/target_encoding-nn/train",
"PenDigits_VALID_MODEL_INPUT": "/home/ubuntu/vitmtsc_nbdev/output/PenDigits/target_encoding-nn/valid",
"PenDigits_TEST_MODEL_INPUT": "/home/ubuntu/vitmtsc_nbdev/output/PenDigits/target_encoding-nn/test",
}
Convert Category Encoding data from tabular to time-series format
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 19:02:17,166 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize 2022-09-23 19:02:17,166 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize 2022-09-23 19:02:17,216 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize 2022-09-23 19:02:17,216 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize 2022-09-23 19:02:17,230 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize 2022-09-23 19:02:17,230 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize 2022-09-23 19:02:17,244 - distributed.preloading - INFO - Creating preload: dask_cuda.initialize 2022-09-23 19:02:17,244 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize
Client-3e7f3432-3b72-11ed-810b-02b68d644837
Connection method: Cluster object | Cluster type: dask_cuda.LocalCUDACluster |
Dashboard: http://127.0.0.1:8787/status |
96dcea03
Dashboard: http://127.0.0.1:8787/status | Workers: 4 |
Total threads: 4 | Total memory: 150.00 GiB |
Status: running | Using processes: True |
Scheduler-3ba0e312-3744-4205-8094-7ea1bcb024f9
Comm: tcp://127.0.0.1:46179 | Workers: 4 |
Dashboard: http://127.0.0.1:8787/status | Total threads: 4 |
Started: Just now | Total memory: 150.00 GiB |
Comm: tcp://127.0.0.1:38033 | Total threads: 1 |
Dashboard: http://127.0.0.1:40941/status | Memory: 37.50 GiB |
Nanny: tcp://127.0.0.1:35823 | |
Local directory: /tmp/dask-worker-space/worker-jeabj3t4 | |
GPU: Tesla T4 | GPU memory: 14.76 GiB |
Comm: tcp://127.0.0.1:42223 | Total threads: 1 |
Dashboard: http://127.0.0.1:35907/status | Memory: 37.50 GiB |
Nanny: tcp://127.0.0.1:39823 | |
Local directory: /tmp/dask-worker-space/worker-wc5xorgt | |
GPU: Tesla T4 | GPU memory: 14.76 GiB |
Comm: tcp://127.0.0.1:34225 | Total threads: 1 |
Dashboard: http://127.0.0.1:46691/status | Memory: 37.50 GiB |
Nanny: tcp://127.0.0.1:37621 | |
Local directory: /tmp/dask-worker-space/worker-2f8g2n5b | |
GPU: Tesla T4 | GPU memory: 14.76 GiB |
Comm: tcp://127.0.0.1:38599 | Total threads: 1 |
Dashboard: http://127.0.0.1:40853/status | Memory: 37.50 GiB |
Nanny: tcp://127.0.0.1:44981 | |
Local directory: /tmp/dask-worker-space/worker-timui3r3 | |
GPU: Tesla T4 | GPU memory: 14.76 GiB |
#| export
DATASET_NAME = 'PenDigits'
SEQUENCE_LENGTH = 8
NUMBER_OF_FEATURES = 2
NUM_TARGET = 10
Convert from Tabular to Time-Series Format
#| export
MTSC_COLUMN_NAMES = [
'dim_0',
'dim_1']
#| export
ALL_COLUMNS = ['case_id', 'case_id_seq', 'reading_id'] + MTSC_COLUMN_NAMES + ['class_vals']
Input Data Location
target_encoded_train_dir = os.path.join("./", upstream['feature_preprocessing_pen_digits']['PenDigits_TRAIN_TE'])
target_encoded_valid_dir = os.path.join("./", upstream['feature_preprocessing_pen_digits']['PenDigits_TRAIN_TE'])
target_encoded_test_dir = os.path.join("./", upstream['feature_preprocessing_pen_digits']['PenDigits_TEST_TE'])
Output Data Location
output_train_dir = os.path.join("./", product['PenDigits_TRAIN_MODEL_INPUT'])
output_valid_dir = os.path.join("./", product['PenDigits_VALID_MODEL_INPUT'])
output_test_dir = os.path.join("./", product['PenDigits_TEST_MODEL_INPUT'])
!mkdir -p $output_train_dir
!mkdir -p $output_valid_dir
!mkdir -p $output_test_dir
Tabular to Time-Series format conversion
%%time
convert_from_tabular_to_timeseries_format(input_dir = target_encoded_train_dir,
output_dir = output_train_dir,
all_columns = ALL_COLUMNS,
mtsc_column_names = MTSC_COLUMN_NAMES,
chunk_size_processing = 50000,
number_of_features = NUMBER_OF_FEATURES,
seq_len = SEQUENCE_LENGTH,
chunk_size_file = 10000)
case_id_seq_min: 1 case_id_seq_max: 7493 Total number of chunks to be processed: 1 Started processing chunk: 0 with case_id_seq from : 0 to 7493 Before CumCount Min: 0 CumCount Max: 7 After CumCount Min: 0 CumCount Max: 7 sorted flattened_gdf.shape: (5995, 18) Total number of files to be created: 1 Writing to output file: /home/ubuntu/vitmtsc_nbdev/output/PenDigits/target_encoding-nn/train/chunk_0_part_0.parquet with records from iloc: 0 to 5995 Finished processing chunk: 0 with case_id_seq from : 0 to 7493 CPU times: user 2.5 s, sys: 581 ms, total: 3.08 s Wall time: 4.8 s
Tabular to Time-Series format conversion
%%time
convert_from_tabular_to_timeseries_format(input_dir = target_encoded_valid_dir,
output_dir = output_valid_dir,
all_columns = ALL_COLUMNS,
mtsc_column_names = MTSC_COLUMN_NAMES,
chunk_size_processing = 50000,
number_of_features = NUMBER_OF_FEATURES,
seq_len = SEQUENCE_LENGTH,
chunk_size_file = 10000)
case_id_seq_min: 1 case_id_seq_max: 7493 Total number of chunks to be processed: 1 Started processing chunk: 0 with case_id_seq from : 0 to 7493 Before CumCount Min: 0 CumCount Max: 7 After CumCount Min: 0 CumCount Max: 7 sorted flattened_gdf.shape: (5995, 18) Total number of files to be created: 1 Writing to output file: /home/ubuntu/vitmtsc_nbdev/output/PenDigits/target_encoding-nn/valid/chunk_0_part_0.parquet with records from iloc: 0 to 5995 Finished processing chunk: 0 with case_id_seq from : 0 to 7493 CPU times: user 117 ms, sys: 20.3 ms, total: 137 ms Wall time: 179 ms
Tabular to Time-Series format conversion
%%time
convert_from_tabular_to_timeseries_format(input_dir = target_encoded_test_dir,
output_dir = output_test_dir,
all_columns = ALL_COLUMNS,
mtsc_column_names = MTSC_COLUMN_NAMES,
chunk_size_processing = 50000,
number_of_features = NUMBER_OF_FEATURES,
seq_len = SEQUENCE_LENGTH,
chunk_size_file = 10000)
case_id_seq_min: 0 case_id_seq_max: 3497 Total number of chunks to be processed: 1 Started processing chunk: 0 with case_id_seq from : 0 to 3497 Before CumCount Min: 0 CumCount Max: 7 After CumCount Min: 0 CumCount Max: 7 sorted flattened_gdf.shape: (3498, 18) Total number of files to be created: 1 Writing to output file: /home/ubuntu/vitmtsc_nbdev/output/PenDigits/target_encoding-nn/test/chunk_0_part_0.parquet with records from iloc: 0 to 3498 Finished processing chunk: 0 with case_id_seq from : 0 to 3497 CPU times: user 121 ms, sys: 8.57 ms, total: 130 ms Wall time: 172 ms
%%time
import dask_cudf
train_gdf = dask_cudf.read_parquet(output_train_dir)
train_gdf.head()
CPU times: user 30.5 ms, sys: 2.36 ms, total: 32.9 ms Wall time: 69.4 ms
dim_0_0 | dim_0_1 | dim_0_2 | dim_0_3 | dim_0_4 | dim_0_5 | dim_0_6 | dim_0_7 | dim_1_0 | dim_1_1 | dim_1_2 | dim_1_3 | dim_1_4 | dim_1_5 | dim_1_6 | dim_1_7 | class_vals | case_id | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | -0.856053 | 1.240864 | 1.477136 | 0.620649 | -0.117702 | -0.856053 | -1.476268 | -1.358132 | 0.954561 | 1.380989 | 1.352560 | 0.783990 | 0.215419 | -0.353152 | -0.921722 | -1.461864 | 1.0 | 5596.0 |
1 | -1.476268 | -0.176770 | 1.477136 | 1.299932 | 0.532047 | 0.177638 | 0.059502 | 0.118570 | 1.295703 | 1.352560 | 1.380989 | 1.324132 | 0.670276 | -0.040438 | -0.751151 | -1.461864 | 7.0 | 4695.0 |
2 | 0.029968 | 0.886455 | 1.477136 | -0.708383 | 0.709251 | 1.270398 | 0.975057 | -1.476268 | 1.210418 | 0.215419 | 1.380989 | 1.039846 | 0.442847 | 0.329133 | -1.206007 | -1.461864 | 9.0 | 2777.0 |
3 | 1.063660 | -0.531179 | -1.446734 | -1.476268 | 0.295775 | 1.477136 | -0.560713 | -1.269530 | 1.380989 | 1.153561 | 0.101705 | -1.007008 | -1.461864 | -0.580580 | -0.495294 | -1.433436 | 6.0 | 6982.0 |
4 | -1.476268 | -0.147236 | 0.709251 | 0.207172 | -0.442576 | -0.737917 | 0.089036 | 1.477136 | 1.181989 | 1.380989 | 0.670276 | -0.410009 | -1.461864 | -0.779580 | -0.296295 | -0.410009 | 7.0 | 242.0 |
train_gdf.tail()
dim_0_0 | dim_0_1 | dim_0_2 | dim_0_3 | dim_0_4 | dim_0_5 | dim_0_6 | dim_0_7 | dim_1_0 | dim_1_1 | dim_1_2 | dim_1_3 | dim_1_4 | dim_1_5 | dim_1_6 | dim_1_7 | class_vals | case_id | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
5990 | -1.121859 | -1.476268 | 0.207172 | 1.477136 | 1.299932 | 0.738785 | 0.207172 | -0.294906 | 1.181989 | 1.295703 | 1.380989 | 1.039846 | 0.414419 | -0.239437 | -0.836437 | -1.461864 | 7.0 | 4219.0 |
5991 | 1.477136 | 0.354843 | -1.476268 | -1.092325 | 0.768319 | 1.299932 | 0.650183 | -0.117702 | 1.181989 | 0.727133 | 0.272276 | 1.039846 | 1.380989 | 0.442847 | -0.523723 | -1.461864 | 9.0 | 1776.0 |
5992 | 0.738785 | -0.590247 | -1.476268 | 0.354843 | 1.477136 | 0.591115 | 0.148104 | -0.353974 | 1.380989 | 0.613418 | -0.211009 | -0.211009 | -0.012009 | 0.755561 | -0.381580 | -1.461864 | 4.0 | 1015.0 |
5993 | -1.476268 | -1.180927 | 0.177638 | 1.477136 | 0.561581 | -0.294906 | -0.974189 | -1.121859 | 1.380989 | 1.096703 | 1.125132 | 1.153561 | 0.556561 | -0.097295 | -0.864865 | -1.461864 | 7.0 | 7094.0 |
5994 | -1.476268 | -0.590247 | -0.058634 | 0.768319 | 0.856921 | 0.768319 | 0.768319 | 1.477136 | -0.381580 | 0.300705 | 1.011418 | 1.380989 | 0.641847 | -0.068866 | -0.779580 | -1.461864 | 1.0 | 3940.0 |
%%time
train_gdf['case_id'].nunique().compute(), train_gdf['class_vals'].nunique().compute()
CPU times: user 236 ms, sys: 2.43 ms, total: 238 ms Wall time: 273 ms
(5995, 10)
%%time
import dask_cudf
valid_gdf = dask_cudf.read_parquet(output_valid_dir)
valid_gdf.head()
CPU times: user 22.5 ms, sys: 2.28 ms, total: 24.7 ms Wall time: 38 ms
dim_0_0 | dim_0_1 | dim_0_2 | dim_0_3 | dim_0_4 | dim_0_5 | dim_0_6 | dim_0_7 | dim_1_0 | dim_1_1 | dim_1_2 | dim_1_3 | dim_1_4 | dim_1_5 | dim_1_6 | dim_1_7 | class_vals | case_id | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 0.000434 | -1.121859 | 0.148104 | -0.974189 | -1.476268 | -1.446734 | -0.442576 | 1.477136 | 1.324132 | 0.272276 | -0.410009 | -1.461864 | -0.751151 | 0.613418 | 1.380989 | 1.324132 | 5.0 | 6196.0 |
1 | -1.269530 | 0.118570 | 0.561581 | -0.294906 | 1.211330 | 1.477136 | 0.059502 | -1.476268 | 1.068275 | 1.380989 | 0.698704 | 0.016419 | -0.182580 | -0.921722 | -1.291293 | -1.461864 | 3.0 | 3507.0 |
2 | -0.501645 | 0.975057 | 1.477136 | -0.088168 | -1.476268 | -0.826519 | 0.089036 | -1.446734 | 0.783990 | 1.352560 | 1.380989 | 0.926132 | 0.300705 | -0.381580 | -1.063865 | -1.461864 | 5.0 | 6895.0 |
3 | 1.477136 | -0.058634 | -0.974189 | -1.269530 | -0.235838 | 1.359000 | 0.295775 | -1.476268 | 1.380989 | 0.869275 | 0.101705 | -0.779580 | -1.461864 | -1.120722 | -0.523723 | -0.665865 | 6.0 | 6740.0 |
4 | 0.738785 | -0.442576 | -1.417200 | -1.476268 | -0.176770 | 1.477136 | 0.532047 | -1.003723 | 1.380989 | 0.869275 | 0.044848 | -0.864865 | -1.461864 | -1.234436 | -0.580580 | -0.864865 | 6.0 | 1977.0 |
valid_gdf.tail()
dim_0_0 | dim_0_1 | dim_0_2 | dim_0_3 | dim_0_4 | dim_0_5 | dim_0_6 | dim_0_7 | dim_1_0 | dim_1_1 | dim_1_2 | dim_1_3 | dim_1_4 | dim_1_5 | dim_1_6 | dim_1_7 | class_vals | case_id | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
5990 | -0.353974 | -1.476268 | -0.885587 | 1.447602 | 1.477136 | 0.738785 | 0.236706 | -0.206304 | 1.380989 | 0.641847 | -0.097295 | -0.182580 | 0.385990 | 0.329133 | -0.580580 | -1.461864 | 4.0 | 3784.0 |
5991 | 0.118570 | 0.827387 | 0.591115 | -0.324440 | -1.476268 | -1.003723 | 0.236706 | 1.477136 | 0.499704 | 1.380989 | 0.101705 | -0.950151 | -1.461864 | -0.808008 | -0.950151 | -1.177579 | 2.0 | 2039.0 |
5992 | 1.093194 | 0.029968 | -1.476268 | 0.709251 | 1.477136 | 1.152262 | 0.827387 | 0.295775 | 1.380989 | 0.840847 | -0.182580 | -0.154152 | 0.414419 | 1.096703 | -0.211009 | -1.461864 | 4.0 | 893.0 |
5993 | -1.417200 | -1.476268 | -0.206304 | 1.063660 | 1.477136 | 0.797853 | 0.177638 | 0.000434 | -0.211009 | 0.186990 | 0.783990 | 1.380989 | 1.181989 | 0.329133 | -0.552151 | -1.461864 | 1.0 | 1220.0 |
5994 | 1.211330 | 0.177638 | -0.915121 | -1.476268 | -0.826519 | 1.477136 | 1.447602 | -0.915121 | 1.380989 | 1.125132 | 0.300705 | -0.609008 | -1.461864 | -1.319722 | -0.523723 | -0.211009 | 6.0 | 7406.0 |
%%time
valid_gdf['case_id'].nunique().compute(), valid_gdf['class_vals'].nunique().compute()
CPU times: user 35.5 ms, sys: 170 µs, total: 35.7 ms Wall time: 78.5 ms
(5995, 10)
%%time
import dask_cudf
test_gdf = dask_cudf.read_parquet(output_test_dir)
test_gdf.head()
CPU times: user 21.6 ms, sys: 1.42 ms, total: 23 ms Wall time: 32.8 ms
dim_0_0 | dim_0_1 | dim_0_2 | dim_0_3 | dim_0_4 | dim_0_5 | dim_0_6 | dim_0_7 | dim_1_0 | dim_1_1 | dim_1_2 | dim_1_3 | dim_1_4 | dim_1_5 | dim_1_6 | dim_1_7 | class_vals | case_id | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | -0.737917 | 0.532047 | 0.325309 | -1.358132 | -0.235838 | 1.477136 | 0.532047 | -1.476268 | 0.471276 | -0.239437 | -1.461864 | -1.206007 | -0.097295 | 0.869275 | 1.380989 | 0.641847 | 8.0 | 819.0 |
1 | 0.797853 | -0.058634 | 0.532047 | -0.353974 | -1.476268 | 0.000434 | 1.477136 | 0.413911 | 0.926132 | 0.584990 | -0.523723 | -1.461864 | -0.978579 | -0.097295 | 0.783990 | 1.380989 | 8.0 | 2500.0 |
2 | -0.531179 | 0.325309 | 1.477136 | 0.591115 | 1.122728 | 1.122728 | -0.206304 | -1.476268 | 0.897704 | 1.380989 | 1.181989 | 0.442847 | -0.466866 | -1.291293 | -1.461864 | -1.376579 | 3.0 | 2683.0 |
3 | -0.915121 | -0.649315 | -0.147236 | -0.442576 | -1.092325 | -1.476268 | -0.029100 | 1.477136 | 0.528133 | 1.380989 | 1.181989 | -0.068866 | -1.262865 | -1.461864 | -1.262865 | -1.092293 | 1.0 | 3266.0 |
4 | -0.147236 | 0.738785 | 1.477136 | 0.856921 | 0.856921 | 1.181796 | -0.029100 | -1.476268 | 0.926132 | 1.380989 | 0.783990 | 0.073276 | -0.324723 | -0.978579 | -1.461864 | -1.234436 | 3.0 | 2445.0 |
test_gdf.tail()
dim_0_0 | dim_0_1 | dim_0_2 | dim_0_3 | dim_0_4 | dim_0_5 | dim_0_6 | dim_0_7 | dim_1_0 | dim_1_1 | dim_1_2 | dim_1_3 | dim_1_4 | dim_1_5 | dim_1_6 | dim_1_7 | class_vals | case_id | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
3493 | -1.476268 | -0.885587 | 0.207172 | -0.147236 | -1.062791 | -1.239995 | 0.089036 | 1.477136 | 0.329133 | 1.380989 | 1.039846 | -0.125723 | -1.063865 | -1.461864 | -1.234436 | -1.376579 | 2.0 | 3181.0 |
3494 | -0.442576 | -1.476268 | -1.299064 | 0.207172 | 1.329466 | 1.477136 | 0.266240 | -0.590247 | 0.812418 | -0.154152 | -1.405007 | -1.461864 | -0.523723 | 0.783990 | 1.380989 | 0.414419 | 0.0 | 2392.0 |
3495 | 1.477136 | 0.886455 | -0.235838 | -1.121859 | -1.476268 | -0.029100 | 0.148104 | -1.476268 | 1.267275 | 1.380989 | 0.556561 | -0.324723 | -1.348150 | -1.461864 | -0.609008 | -0.921722 | 6.0 | 1736.0 |
3496 | 1.181796 | 0.148104 | -0.974189 | -1.476268 | -0.442576 | 1.477136 | 1.093194 | -0.826519 | 1.380989 | 0.954561 | 0.186990 | -0.722722 | -1.461864 | -1.319722 | -0.523723 | -0.438437 | 6.0 | 1641.0 |
3497 | -0.619781 | 0.059502 | 0.000434 | 0.059502 | 0.059502 | -1.476268 | -0.147236 | 1.477136 | 0.300705 | 1.380989 | 1.238846 | 0.073276 | -1.120722 | -1.461864 | -1.348150 | -1.319722 | 1.0 | 2782.0 |
%%time
test_gdf['case_id'].nunique().compute(), test_gdf['class_vals'].nunique().compute()
CPU times: user 35.8 ms, sys: 0 ns, total: 35.8 ms Wall time: 78 ms
(3498, 10)
We reset the kernel!!!
%%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 35 ms, sys: 6.82 ms, total: 41.8 ms Wall time: 608 ms
from nbdev import nbdev_export
nbdev_export()