def setUp(self):
self.maxDiff = None
conn = engine.connect()
text = (
f"INSERT INTO tasks (uuid, name, description, image, commands, arguments, tags, experiment_notebook_path, deployment_notebook_path, is_default, created_at, updated_at) "
f"VALUES ('{TASK_ID}', '{NAME}', '{DESCRIPTION}', '{IMAGE}', '{COMMANDS_JSON}', '{ARGUMENTS_JSON}', '{TAGS_JSON}', '{EXPERIMENT_NOTEBOOK_PATH}', '{DEPLOYMENT_NOTEBOOK_PATH}', 0, '{CREATED_AT}', '{UPDATED_AT}')"
)
conn.execute(text)
conn.close()
try:
MINIO_CLIENT.make_bucket(BUCKET_NAME)
except BucketAlreadyOwnedByYou:
pass
file = BytesIO(
b'{"cells":[{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.6.9"}},"nbformat":4,"nbformat_minor":4}'
)
MINIO_CLIENT.put_object(
bucket_name=BUCKET_NAME,
object_name=EXPERIMENT_NOTEBOOK_PATH[len(f"minio://{BUCKET_NAME}/"
):],
data=file,
length=file.getbuffer().nbytes,
)
def creates_eucalyptus_metadata(dataset_object_name):
"""Create eucalyptus metadata into which the model will import.
Args:
dataset_object_name (str): name of the dataset that the Model will search in Minio.
"""
try:
MINIO_CLIENT.make_bucket(BUCKET_NAME)
except BucketAlreadyOwnedByYou:
pass
metadata = {
"columns": [
"Abbrev", "Rep", "Locality", "Map_Ref", "Latitude", "Altitude",
"Rainfall", "Frosts", "Year", "Sp", "PMCno", "DBH", "Ht", "Surv",
"Vig", "Ins_res", "Stem_Fm", "Crown_Fm", "Brnch_Fm", "Utility"
],
"featuretypes": [
"Categorical", "Numerical", "Categorical", "Categorical",
"Categorical", "Numerical", "Numerical", "Numerical", "Numerical",
"Categorical", "Numerical", "Numerical", "Numerical", "Numerical",
"Numerical", "Numerical", "Numerical", "Numerical", "Numerical",
"Categorical"
],
"filename":
"eucalyptus_mock.metadata"
}
buffer = BytesIO(dumps(metadata).encode())
MINIO_CLIENT.put_object(
bucket_name=BUCKET_NAME,
object_name=
f"datasets/{dataset_object_name}/{dataset_object_name}.metadata",
data=buffer,
length=buffer.getbuffer().nbytes,
)
def creates_boston_metadata(dataset_object_name):
"""Create boston metadata into which the model will import.
Args:
dataset_object_name (str): name of the dataset that the Model will search in Minio.
"""
try:
MINIO_CLIENT.make_bucket(BUCKET_NAME)
except BucketAlreadyOwnedByYou:
pass
metadata = {
"columns": [
"crim", "zn", "indus", "chas", "nox", "rm", "age", "dis", "rad",
"tax", "ptratio", "black", "lstat", "medv"
],
"featuretypes": [
"Numerical", "Numerical", "Numerical", "Numerical", "Numerical",
"Numerical", "Numerical", "Numerical", "Numerical", "Numerical",
"Numerical", "Numerical", "Numerical", "Numerical"
],
"filename":
"boston.metadata"
}
buffer = BytesIO(dumps(metadata).encode())
MINIO_CLIENT.put_object(
bucket_name=BUCKET_NAME,
object_name=
f"datasets/{dataset_object_name}/{dataset_object_name}.metadata",
data=buffer,
length=buffer.getbuffer().nbytes,
)
def creates_iris_metadata(dataset_object_name):
"""Create iris metadata into which the model will import.
Args:
dataset_object_name (str): name of the dataset that the Model will search in Minio.
"""
try:
MINIO_CLIENT.make_bucket(BUCKET_NAME)
except BucketAlreadyOwnedByYou:
pass
metadata = {
"columns": [
"SepalLengthCm", "SepalWidthCm", "PetalLengthCm", "PetalWidthCm",
"Species"
],
"featuretypes":
["Numerical", "Numerical", "Numerical", "Numerical", "Categorical"],
"filename":
"iris_mock.metadata"
}
buffer = BytesIO(dumps(metadata).encode())
MINIO_CLIENT.put_object(
bucket_name=BUCKET_NAME,
object_name=
f"datasets/{dataset_object_name}/{dataset_object_name}.metadata",
data=buffer,
length=buffer.getbuffer().nbytes,
)
def creates_titanic_metadata(dataset_object_name):
"""Create titanic metadata into which the model will import.
Args:
dataset_object_name (str): name of the dataset that the Model will search in Minio.
"""
try:
MINIO_CLIENT.make_bucket(BUCKET_NAME)
except BucketAlreadyOwnedByYou:
pass
metadata = {
"columns": [
"PassengerId", "Survived", "Pclass", "Name", "Sex", "Age", "SibSp",
"Parch", "Ticket", "Fare", "Cabin", "Embarked"
],
"featuretypes": [
"Numerical", "Numerical", "Numerical", "Categorical",
"Categorical", "Numerical", "Numerical", "Numerical",
"Categorical", "Numerical", "Categorical", "Categorical"
],
"filename":
"iris_mock.metadata"
}
buffer = BytesIO(dumps(metadata).encode())
MINIO_CLIENT.put_object(
bucket_name=BUCKET_NAME,
object_name=
f"datasets/{dataset_object_name}/{dataset_object_name}.metadata",
data=buffer,
length=buffer.getbuffer().nbytes,
)
def setUp(self):
self.maxDiff = None
try:
MINIO_CLIENT.make_bucket(BUCKET_NAME)
except BucketAlreadyOwnedByYou:
pass
buffer = BytesIO(dumps(METRICS).encode())
MINIO_CLIENT.put_object(
bucket_name=BUCKET_NAME,
object_name=METRICS_NAME,
data=buffer,
length=buffer.getbuffer().nbytes,
)
def setUp(self):
self.maxDiff = None
try:
MINIO_CLIENT.make_bucket(BUCKET_NAME)
except BucketAlreadyOwnedByYou:
pass
file = BytesIO(
b'\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x00\xc0\x00\x00\x00\xba\x08\x02\x00\x00\x00w\x07\xd5\xf7\x00\x00\x00\x01sRGB\x00\xae\xce\x1c\xe9\x00\x00\x00\x04gAMA\x00\x00\xb1\x8f\x0b\xfca\x05\x00\x00\x00\tpHYs\x00\x00\x0e\xc3\x00\x00\x0e\xc3\x01\xc7o\xa8d\x00\x00\x01\xbaIDATx^\xed\xd21\x01\x00\x00\x0c\xc3\xa0\xf97\xdd\x89\xc8\x0b\x1a\xb8A \x10\x89@$\x02\x91\x08D"\x10\x89@$\x02\x91\x08D"\x10\x89@$\x02\x91\x08D"\x10\x89@$\x02\x91\x08D"\x10\x89@$\x02\x91\x08D"\x10\x89@$\x02\x91\x08D"\x10\x89@$\x02\x91\x08D"\x10\x89@$\x02\x91\x08D"\x10\x89@$\x02\x91\x08D"\x10\x89@$\x02\x91\x08D"\x10\x89@$\x02\x91\x08D"\x10\x89@$\x02\x91\x08D"\x10\x89@$\x02\x91\x08D"\x10\x89@$\x02\x91\x08D"\x10\x89@$\x02\x91\x08D"\x10\x89@$\x02\x91\x08D"\x10\x89@$\x02\x91\x08D"\x10\x89@$\x02\x91\x08D"\x10\x89@$\x02\x91\x08D"\x10\x89@$\x02\x91\x08D"\x10\x89@$\x02\x91\x08D"\x10\x89@$\x02\x91\x08D"\x10\x89@$\x02\x91\x08D"\x10\x89@$\x02\x91\x08D"\x10\x89@$\x02\x91\x08D"\x10\x89@$\x02\x91\x08D"\x10\x89@$\x02\x91\x08D"\x10\x89@$\x02\x91\x08D"\x10\x89@$\x02\x91\x08D"\x10\x89@$\x02\x91\x08D"\x10\x89@$\x02\x91\x08D"\x10\x89@$\x02\x91\x08D"\x10\x89@$\x02\x91\x08D"\x10\x89@$\x02\x91\x08D"\x10\x89@$\x02\x91\x08D"\x10\x89@$\x02\x91\x08D"\x10\x89@$\x02\x91\x08D"\x10\x89@$\x02\x91\x08D"\x10\x89@$\x02\x91\x08D"\x10\x89@$\x02\x91\x08D"\x10\x89@$\x02\x91\x08D"\x10\x89@$\x02\x91\x08D"\x10\x89@$\x02\x91\x08D"\x10\x89@$\x02\x91\x08D"\x10\x89@$\x02\x91\x08D"\x10\x89@$\x02\x91\x08D"\x10\xc1\xf6\x1a:\xf5\xe1\x06\x89A\xdf\x00\x00\x00\x00IEND\xaeB`\x82'
)
MINIO_CLIENT.put_object(
bucket_name=BUCKET_NAME,
object_name=FIGURE_NAME,
data=file,
length=file.getbuffer().nbytes,
)
def creates_mock_dataset(object_name, object_content):
"""Create a mock dataset to be used in unit test.
Args:
object_name (str): dataset name.
object_content (bytes): data of the dataset.
"""
try:
MINIO_CLIENT.make_bucket(BUCKET_NAME)
except BucketAlreadyOwnedByYou:
pass
file = BytesIO(object_content)
MINIO_CLIENT.put_object(
bucket_name=BUCKET_NAME,
object_name=f"datasets/{object_name}/{object_name}",
data=file,
length=file.getbuffer().nbytes,
)
def setUp(self):
self.maxDiff = None
conn = engine.connect()
text = (
f"INSERT INTO components (uuid, name, description, tags, training_notebook_path, inference_notebook_path, is_default, created_at, updated_at) "
f"VALUES ('{COMPONENT_ID}', '{NAME}', '{DESCRIPTION}', '{TAGS_JSON}', '{TRAINING_NOTEBOOK_PATH}', '{INFERENCE_NOTEBOOK_PATH}', 0, '{CREATED_AT}', '{UPDATED_AT}')"
)
conn.execute(text)
conn.close()
try:
MINIO_CLIENT.make_bucket(BUCKET_NAME)
except BucketAlreadyOwnedByYou:
pass
file = BytesIO(SAMPLE_NOTEBOOK.encode("utf-8"))
MINIO_CLIENT.put_object(
bucket_name=BUCKET_NAME,
object_name=TRAINING_NOTEBOOK_PATH[len(f"minio://{BUCKET_NAME}/"
):],
data=file,
length=file.getbuffer().nbytes,
)
file = BytesIO(
b'{"cells":[{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.6.9"}},"nbformat":4,"nbformat_minor":4}'
)
MINIO_CLIENT.put_object(
bucket_name=BUCKET_NAME,
object_name=INFERENCE_NOTEBOOK_PATH[len(f"minio://{BUCKET_NAME}/"
):],
data=file,
length=file.getbuffer().nbytes,
)
session = requests.Session()
session.cookies.update(COOKIES)
session.headers.update(HEADERS)
session.hooks = {
"response": lambda r, *args, **kwargs: r.raise_for_status(),
}
session.put(
url=f"{JUPYTER_ENDPOINT}/api/contents/components",
data=dumps({
"type": "directory",
"content": None
}),
)
session.put(
url=f"{JUPYTER_ENDPOINT}/api/contents/components/{COMPONENT_ID}",
data=dumps({
"type": "directory",
"content": None
}),
)
session.put(
url=
f"{JUPYTER_ENDPOINT}/api/contents/components/{COMPONENT_ID}/Inference.ipynb",
data=dumps({
"type": "notebook",
"content": loads(SAMPLE_NOTEBOOK)
}),
)
session.put(
url=
f"{JUPYTER_ENDPOINT}/api/contents/components/{COMPONENT_ID}/Training.ipynb",
data=dumps({
"type": "notebook",
"content": loads(SAMPLE_NOTEBOOK)
}),
)
def setUp(self):
self.maxDiff = None
conn = engine.connect()
text = (
f"INSERT INTO projects (uuid, name, created_at, updated_at) "
f"VALUES ('{PROJECT_ID}', '{NAME}', '{CREATED_AT}', '{UPDATED_AT}')"
)
conn.execute(text)
text = (
f"INSERT INTO experiments (uuid, name, project_id, position, is_active, created_at, updated_at) "
f"VALUES ('{EXPERIMENT_ID}', '{NAME}', '{PROJECT_ID}', '{POSITION}', 1, '{CREATED_AT}', '{UPDATED_AT}')"
)
conn.execute(text)
text = (
f"INSERT INTO components (uuid, name, description, commands, tags, experiment_notebook_path, deployment_notebook_path, created_at, updated_at) "
f"VALUES ('{COMPONENT_ID}', '{NAME}', '{DESCRIPTION}', '{COMMANDS_JSON}', '{TAGS_JSON}', '{EXPERIMENT_NOTEBOOK_PATH}', '{DEPLOYMENT_NOTEBOOK_PATH}', '{CREATED_AT}', '{UPDATED_AT}')"
)
conn.execute(text)
text = (
f"INSERT INTO operators (uuid, experiment_id, component_id, parameters, created_at, updated_at) "
f"VALUES ('{OPERATOR_ID}', '{EXPERIMENT_ID}', '{COMPONENT_ID}', '{PARAMETERS_JSON}', '{CREATED_AT}', '{UPDATED_AT}')"
)
conn.execute(text)
text = (
f"INSERT INTO operators (uuid, experiment_id, component_id, parameters, created_at, updated_at) "
f"VALUES ('{OPERATOR_ID2}', '{EXPERIMENT_ID}', '{COMPONENT_ID}', '{PARAMETERS_JSON}', '{CREATED_AT}', '{UPDATED_AT}')"
)
conn.execute(text)
conn.close()
# uploads mock dataset
try:
MINIO_CLIENT.make_bucket(BUCKET_NAME)
except BucketAlreadyOwnedByYou:
pass
file = BytesIO((
b'col0,col1,col2,col3,col4,col5\n'
b'01/01/2000,5.1,3.5,1.4,0.2,Iris-setosa\n'
b'01/01/2000,5.1,3.5,1.4,0.2,Iris-setosa\n'
b'01/01/2000,5.1,3.5,1.4,0.2,Iris-setosa\n'
))
MINIO_CLIENT.put_object(
bucket_name=BUCKET_NAME,
object_name=f"datasets/{DATASET}/{DATASET}",
data=file,
length=file.getbuffer().nbytes,
)
metadata = {
"columns": ["col0", "col1", "col2", "col3", "col4", "col5"],
"featuretypes": [DATETIME, NUMERICAL, NUMERICAL, NUMERICAL, NUMERICAL, CATEGORICAL],
"filename": DATASET,
"run_id": RUN_ID,
}
buffer = BytesIO(dumps(metadata).encode())
MINIO_CLIENT.put_object(
bucket_name=BUCKET_NAME,
object_name=f"datasets/{DATASET}/{DATASET}.metadata",
data=buffer,
length=buffer.getbuffer().nbytes,
)
MINIO_CLIENT.copy_object(
bucket_name=BUCKET_NAME,
object_name=f"datasets/{DATASET}/runs/{RUN_ID}/operators/{OPERATOR_ID}/{DATASET}/{DATASET}",
object_source=f"/{BUCKET_NAME}/datasets/{DATASET}/{DATASET}",
)
MINIO_CLIENT.copy_object(
bucket_name=BUCKET_NAME,
object_name=f"datasets/{DATASET}/runs/{RUN_ID}/operators/{OPERATOR_ID}/{DATASET}/{DATASET}.metadata",
object_source=f"/{BUCKET_NAME}/datasets/{DATASET}/{DATASET}.metadata",
)
def setUp(self):
self.maxDiff = None
conn = engine.connect()
text = (
f"INSERT INTO tasks (uuid, name, description, image, commands, arguments, tags, parameters, experiment_notebook_path, deployment_notebook_path, is_default, created_at, updated_at) "
f"VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)"
)
conn.execute(text, (TASK_ID, NAME, DESCRIPTION, IMAGE, COMMANDS_JSON, ARGUMENTS_JSON, TAGS_JSON,
dumps([]), EXPERIMENT_NOTEBOOK_PATH, DEPLOYMENT_NOTEBOOK_PATH, 0, CREATED_AT, UPDATED_AT,))
text = (
f"INSERT INTO tasks (uuid, name, description, image, commands, arguments, tags, parameters, experiment_notebook_path, deployment_notebook_path, is_default, created_at, updated_at) "
f"VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)"
)
conn.execute(text, (TASK_ID_2, 'foo 2', DESCRIPTION, IMAGE, COMMANDS_JSON, ARGUMENTS_JSON, TAGS_JSON,
dumps([]), EXPERIMENT_NOTEBOOK_PATH_2, DEPLOYMENT_NOTEBOOK_PATH_2, 0, CREATED_AT, UPDATED_AT,))
conn.close()
try:
MINIO_CLIENT.make_bucket(BUCKET_NAME)
except BucketAlreadyOwnedByYou:
pass
file = BytesIO(SAMPLE_NOTEBOOK.encode("utf-8"))
MINIO_CLIENT.put_object(
bucket_name=BUCKET_NAME,
object_name=EXPERIMENT_NOTEBOOK_PATH[len(f"minio://{BUCKET_NAME}/"):],
data=file,
length=file.getbuffer().nbytes,
)
file = BytesIO(b'{"cells":[{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.6.9"}},"nbformat":4,"nbformat_minor":4}')
MINIO_CLIENT.put_object(
bucket_name=BUCKET_NAME,
object_name=DEPLOYMENT_NOTEBOOK_PATH[len(f"minio://{BUCKET_NAME}/"):],
data=file,
length=file.getbuffer().nbytes,
)
session = requests.Session()
session.cookies.update(COOKIES)
session.headers.update(HEADERS)
session.hooks = {
"response": lambda r, *args, **kwargs: r.raise_for_status(),
}
session.put(
url=f"{JUPYTER_ENDPOINT}/api/contents/tasks",
data=dumps({"type": "directory", "content": None}),
)
session.put(
url=f"{JUPYTER_ENDPOINT}/api/contents/tasks/{NAME}",
data=dumps({"type": "directory", "content": None}),
)
session.put(
url=f"{JUPYTER_ENDPOINT}/api/contents/tasks/{NAME}/Deployment.ipynb",
data=dumps({"type": "notebook", "content": loads(SAMPLE_NOTEBOOK)}),
)
session.put(
url=f"{JUPYTER_ENDPOINT}/api/contents/tasks/{NAME}/Experiment.ipynb",
data=dumps({"type": "notebook", "content": loads(SAMPLE_NOTEBOOK)}),
)
def setUp(self):
self.maxDiff = None
conn = engine.connect()
text = (f"INSERT INTO projects (uuid, name, created_at, updated_at) "
f"VALUES (%s, %s, %s, %s)")
conn.execute(text, (
PROJECT_ID,
NAME,
CREATED_AT,
UPDATED_AT,
))
text = (
f"INSERT INTO experiments (uuid, name, project_id, position, is_active, created_at, updated_at) "
f"VALUES (%s, %s, %s, %s, %s, %s, %s)")
conn.execute(text, (
EXPERIMENT_ID,
NAME,
PROJECT_ID,
POSITION,
1,
CREATED_AT,
UPDATED_AT,
))
text = (
f"INSERT INTO experiments (uuid, name, project_id, position, is_active, created_at, updated_at) "
f"VALUES (%s, %s, %s, %s, %s, %s, %s)")
conn.execute(text, (
EXPERIMENT_ID_2,
NAME,
PROJECT_ID,
POSITION,
1,
CREATED_AT,
UPDATED_AT,
))
text = (
f"INSERT INTO experiments (uuid, name, project_id, position, is_active, created_at, updated_at) "
f"VALUES (%s, %s, %s, %s, %s, %s, %s)")
conn.execute(text, (
EXPERIMENT_ID_3,
NAME,
PROJECT_ID,
POSITION,
1,
CREATED_AT,
UPDATED_AT,
))
text = (
f"INSERT INTO tasks (uuid, name, description, image, commands, arguments, tags, parameters, experiment_notebook_path, deployment_notebook_path, is_default, created_at, updated_at) "
f"VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)")
conn.execute(text, (
TASK_ID,
NAME,
DESCRIPTION,
IMAGE,
COMMANDS_JSON,
ARGUMENTS_JSON,
TAGS_JSON,
dumps([]),
EXPERIMENT_NOTEBOOK_PATH,
DEPLOYMENT_NOTEBOOK_PATH,
0,
CREATED_AT,
UPDATED_AT,
))
text = (
f"INSERT INTO operators (uuid, experiment_id, task_id, parameters, created_at, updated_at) "
f"VALUES (%s, %s, %s, %s, %s, %s)")
conn.execute(text, (
OPERATOR_ID,
EXPERIMENT_ID,
TASK_ID,
PARAMETERS_JSON,
CREATED_AT,
UPDATED_AT,
))
text = (
f"INSERT INTO operators (uuid, experiment_id, task_id, parameters, created_at, updated_at) "
f"VALUES (%s, %s, %s, %s, %s, %s)")
conn.execute(text, (
OPERATOR_ID_2,
EXPERIMENT_ID_2,
TASK_ID,
PARAMETERS_JSON_2,
CREATED_AT,
UPDATED_AT,
))
text = (
f"INSERT INTO operators (uuid, experiment_id, task_id, parameters, created_at, updated_at) "
f"VALUES (%s, %s, %s, %s, %s, %s)")
conn.execute(text, (
OPERATOR_ID_3,
EXPERIMENT_ID_3,
TASK_ID,
PARAMETERS_JSON_3,
CREATED_AT,
UPDATED_AT,
))
conn.close()
# uploads mock dataset
try:
MINIO_CLIENT.make_bucket(BUCKET_NAME)
except BucketAlreadyOwnedByYou:
pass
file = BytesIO((b'col0,col1,col2,col3,col4,col5\n'
b'01/01/2000,5.1,3.5,1.4,0.2,Iris-setosa\n'
b'01/01/2000,5.1,3.5,1.4,0.2,Iris-setosa\n'
b'01/01/2000,5.1,3.5,1.4,0.2,Iris-setosa\n'))
MINIO_CLIENT.put_object(
bucket_name=BUCKET_NAME,
object_name=f"datasets/{DATASET}/{DATASET}",
data=file,
length=file.getbuffer().nbytes,
)
metadata = {
"columns": ["col0", "col1", "col2", "col3", "col4", "col5"],
"featuretypes": [
DATETIME, NUMERICAL, NUMERICAL, NUMERICAL, NUMERICAL,
CATEGORICAL
],
"filename":
DATASET,
"run_id":
RUN_ID,
}
buffer = BytesIO(dumps(metadata).encode())
MINIO_CLIENT.put_object(
bucket_name=BUCKET_NAME,
object_name=f"datasets/{DATASET}/{DATASET}.metadata",
data=buffer,
length=buffer.getbuffer().nbytes,
)
file = BytesIO((b'foo,bar,baz,qux\n'
b'01/01/2000,foo,1.2,2.3\n'
b'01/01/2000,bar,2.3,3.4\n'
b'01/01/2000,baz,4.5,4.5\n'))
MINIO_CLIENT.put_object(
bucket_name=BUCKET_NAME,
object_name=f"datasets/{DATASET_2}/{DATASET_2}",
data=file,
length=file.getbuffer().nbytes,
)
metadata = {
"columns": ["foo", "bar", "baz", "qux"],
"featuretypes": [DATETIME, CATEGORICAL, NUMERICAL, NUMERICAL],
"filename": DATASET_2,
"runId": None
}
buffer = BytesIO(dumps(metadata).encode())
MINIO_CLIENT.put_object(
bucket_name=BUCKET_NAME,
object_name=f"datasets/{DATASET_2}/{DATASET_2}.metadata",
data=buffer,
length=buffer.getbuffer().nbytes,
)
MINIO_CLIENT.copy_object(
bucket_name=BUCKET_NAME,
object_name=
f"datasets/{DATASET}/runs/{RUN_ID}/operators/{OPERATOR_ID}/{DATASET}/{DATASET}",
object_source=f"/{BUCKET_NAME}/datasets/{DATASET}/{DATASET}",
)
MINIO_CLIENT.copy_object(
bucket_name=BUCKET_NAME,
object_name=
f"datasets/{DATASET}/runs/{RUN_ID}/operators/{OPERATOR_ID}/{DATASET}/{DATASET}.metadata",
object_source=
f"/{BUCKET_NAME}/datasets/{DATASET}/{DATASET}.metadata",
)