danielsc

Package                      Version
---------------------------- ------------
adal                         1.2.7
aiohttp                      3.8.1
aiosignal                    1.2.0
alembic                      1.7.7
anyio                        3.5.0
argcomplete                  2.0.0
argon2-cffi                  21.3.0
argon2-cffi-bindings         21.2.0

danielsc

name: dask
channels:
  - defaults
  - conda-forge
dependencies:
  - python=3.8
  - dask==2022.2.1
  - distributed==2022.2.1
  - pip==21.3.1
  - jupyterlab

danielsc

from azureml.train.estimator import Estimator

est = Estimator(source_directory='.',
                compute_target=target, 
                entry_script='test.py', 
                script_params={'--data': ws.get_default_datastore()})

run = experiment.submit(est)

danielsc

from __future__ import print_function

import argparse
import os
import sys
import subprocess
import time


def run_test(path):

danielsc

if __name__ == '__main__':
  parser = argparse.ArgumentParser()
  parser.add_argument('--fake_data', nargs='?', const=True, type=bool,
                      default=False,
                      help='If true, uses fake data for unit testing.')
  parser.add_argument('--max_steps', type=int, default=1000,
                      help='Number of steps to run trainer.')
  parser.add_argument('--learning_rate', type=float, default=0.001,
                      help='Initial learning rate')
  parser.add_argument('--dropout', type=float, default=0.9,