def my_job():
# load MLRUN runtime context (will be set by the runtime framework e.g. KubeFlow)
context = get_or_create_ctx('train')
# get parameters from the runtime context (or use defaults)
p1 = context.get_param('p1', 1)
p2 = context.get_param('p2', 'a-string')
# access input metadata, values, files, and secrets (passwords)
print(f'Run: {context.name} (uid={context.uid})')
print(f'Params: p1={p1}, p2={p2}')
print('accesskey = {}'.format(context.get_secret('ACCESS_KEY')))
print('file\n{}\n'.format(context.get_input('infile.txt', 'infile.txt').get()))
# Run some useful code e.g. ML training, data prep, etc.
# log scalar result values (job result metrics)
context.log_result('accuracy', p1 * 2)
context.log_result('loss', p1 * 3)
context.set_label('framework', 'sklearn')
# log various types of artifacts (file, web page, table), will be versioned and visible in the UI
context.log_artifact('model', body=b'abc is 123', target_path='model.txt', labels={'framework': 'xgboost'})
context.log_artifact('html_result', body=b'<b> Some HTML <b>', target_path='result.html', viewer='web-app')
context.log_artifact(TableArtifact('dataset', '1,2,3\n4,5,6\n',
viewer='table', header=['A', 'B', 'C']), target_path='dataset.csv')
# create a chart output (will show in the pipelines UI)
chart = ChartArtifact('chart.html')
chart.labels = {'type': 'roc'}
chart.header = ['Epoch', 'Accuracy', 'Loss']
for i in range(1, 8):
chart.add_row([i, i/20+0.75, 0.30-i/20])
context.log_artifact(chart)
def my_job(context, p1=1, p2='x'):
# load MLRUN runtime context (will be set by the runtime framework e.g. KubeFlow)
# get parameters from the runtime context (or use defaults)
# access input metadata, values, files, and secrets (passwords)
print(f'Run: {context.name} (uid={context.uid})')
print(f'Params: p1={p1}, p2={p2}')
print('accesskey = {}'.format(context.get_secret('ACCESS_KEY')))
print('file\n{}\n'.format(
context.get_input('infile.txt', 'infile.txt').get()))
# Run some useful code e.g. ML training, data prep, etc.
# log scalar result values (job result metrics)
context.log_result('accuracy', p1 * 2)
context.log_result('loss', p1 * 3)
context.set_label('framework', 'sklearn')
# log various types of artifacts (file, web page, table), will be versioned and visible in the UI
context.log_artifact('model',
body=b'abc is 123',
local_path='model.txt',
labels={'framework': 'xgboost'})
context.log_artifact('html_result',
body=b'<b> Some HTML <b>',
local_path='result.html')
context.log_artifact(TableArtifact('dataset',
'1,2,3\n4,5,6\n',
visible=True,
header=['A', 'B', 'C']),
local_path='dataset.csv')
# create a chart output (will show in the pipelines UI)
chart = ChartArtifact('chart')
chart.labels = {'type': 'roc'}
chart.header = ['Epoch', 'Accuracy', 'Loss']
for i in range(1, 8):
chart.add_row([i, i / 20 + 0.75, 0.30 - i / 20])
context.log_artifact(chart)
raw_data = {
'first_name': ['Jason', 'Molly', 'Tina', 'Jake', 'Amy'],
'last_name': ['Miller', 'Jacobson', 'Ali', 'Milner', 'Cooze'],
'age': [42, 52, 36, 24, 73],
'testScore': [25, 94, 57, 62, 70]
}
df = pd.DataFrame(raw_data,
columns=['first_name', 'last_name', 'age', 'testScore'])
context.log_dataset('mydf', df=df, stats=True)
#if __name__ == "__main__":
# context = get_or_create_ctx('train')
# p1 = context.get_param('p1', 1)
# p2 = context.get_param('p2', 'a-string')
# my_job(context, p1, p2)
def my_job(context, p1=1, p2="x"):
# load MLRUN runtime context (will be set by the runtime framework e.g. KubeFlow)
# get parameters from the runtime context (or use defaults)
# access input metadata, values, files, and secrets (passwords)
print(f"Run: {context.name} (uid={context.uid})")
print(f"Params: p1={p1}, p2={p2}")
access_key = context.get_secret("ACCESS_KEY")
print(f"Access key = {access_key}")
input_file = context.get_input("infile.txt", "infile.txt").get()
print(f"File\n{input_file}\n")
# Run some useful code e.g. ML training, data prep, etc.
# log scalar result values (job result metrics)
context.log_result("accuracy", p1 * 2)
context.log_result("loss", p1 * 3)
context.set_label("framework", "sklearn")
# log various types of artifacts (file, web page, table), will be versioned and visible in the UI
context.log_artifact(
"model",
body=b"abc is 123",
local_path="model.txt",
labels={"framework": "xgboost"},
)
context.log_artifact("html_result",
body=b"<b> Some HTML <b>",
local_path="result.html")
# create a chart output (will show in the pipelines UI)
chart = ChartArtifact("chart")
chart.labels = {"type": "roc"}
chart.header = ["Epoch", "Accuracy", "Loss"]
for i in range(1, 8):
chart.add_row([i, i / 20 + 0.75, 0.30 - i / 20])
context.log_artifact(chart)
raw_data = {
"first_name": ["Jason", "Molly", "Tina", "Jake", "Amy"],
"last_name": ["Miller", "Jacobson", "Ali", "Milner", "Cooze"],
"age": [42, 52, 36, 24, 73],
"testScore": [25, 94, 57, 62, 70],
}
df = pd.DataFrame(raw_data,
columns=["first_name", "last_name", "age", "testScore"])
context.log_dataset("mydf", df=df, stats=True)