def tb():
import os
ws = Workspace.from_config()
print(ws.name)
proj = Project.attach(ws, 'tbhistory', '/tmp/tb-test')
shutil.copy('tftb.py', os.path.join(proj.project_directory, 'tftb.py'))
from azureml.core.compute_target import RemoteTarget
rt = RemoteTarget(name='dsvm', address='hai2.eastus2.cloudapp.azure.com:5022', username='******', password='******')
proj.attach_legacy_compute_target(rt)
rc = RunConfiguration.load(proj, "dsvm")
rc.environment.python.user_managed_dependencies = True
rc.environment.python.interpreter_path = '/anaconda/envs/tf/bin/python'
print(rc.target)
run = Run.submit(proj, rc, 'tftb.py')
print(run.id)
#run.wait_for_completion(show_output=True)
from azureml.contrib.tensorboard import Tensorboard
tb = Tensorboard([run])
print('starting tensorboard...')
print(tb.start())
print('tensorboard started.')
run.wait_for_completion(show_output=True)
tb.stop()
def submit_job():
ws = Workspace.from_config()
proj = Project.attach(ws, 'util', '/tmp/random_proj')
rc = RunConfiguration(proj, "local")
shutil.copy('./train-sklearn-one-model.py', '/tmp/random_proj/train-sklearn-one-model.py')
#run = Run.submit(proj, rc, "train-sklearn-one-model.py", "--alpha 0.9")
run = Run.submit(proj, rc, "train-sklearn-one-model.py", arguments_list=["--alpha", "0.9"])
run.wait_for_completion(show_output=True)
def test():
ws = Workspace.from_config()
proj = Project.attach(ws, 'test_rh', '/tmp/randomproj1')
rc = RunConfiguration(proj, 'local')
rc.environment.python.interpreter_path = '/Users/haining/miniconda3/envs/comet/bin/python'
with open('/tmp/randomproj1/test.py', 'w') as file:
file.write('import sys; print(sys.version);import os;os.makedirs("./outputs", exist_ok=True);fs=open("./outputs/f.txt","w");fs.write("hello!");')
r = Run.submit(proj, rc, 'test.py')
print(helpers.get_run_history_url(r))
r.wait_for_completion(show_output=True)
cd = CondaDependencies()
cd.add_conda_package('numpy')
# overwrite the default conda_dependencies.yml file
cd.save_to_file(project_dir = project_folder, file_name='conda_dependencies.yml')
# auto-prepare the Docker image when used for execution (if it is not already prepared)
run_config.prepare_environment = True
print()
print('##################################################')
print('submitting {} for a Spark run on ACI...'.format(train_script))
print('##################################################')
print()
run = Run.submit(project_object = project,
run_config = run_config,
script_to_run = "train-spark.py")
print(helpers.get_run_history_url(run))
# Shows output of the run on stdout.
run.wait_for_completion(show_output = True)
print('attach a VM target:')
from azureml.exceptions.azureml_exception import UserErrorException
from azureml.core.compute_target import RemoteTarget
try:
# Attaches a remote docker on a remote vm as a compute target.
project.attach_legacy_compute_target(RemoteTarget(name = "cpu-dsvm",
cd.add_conda_package('joblib')
cd.add_pip_package('azureml-contrib-daskonbatch')
# overwrite the default conda_dependencies.yml file
cd.save_to_file(project_dir=project_folder, file_name='conda_dependencies.yml')
print()
print('##################################################')
print('submitting {} for a batch ai run...'.format(train_script))
print('##################################################')
print()
print("prepare run...")
prep = Run.prepare_compute_target(project_object=project, run_config=rc)
print(helpers.get_run_history_url(prep))
prep.wait_for_completion(show_output=True)
print('now run...')
run = Run.submit(project_object=project,
run_config=rc,
script_to_run=train_script)
print("run history URL is here:")
print(helpers.get_run_history_url(run))
run.wait_for_completion(show_output=True)
print(helpers.get_run_history_url(run))
