def get_job_name():
"""
Get the current job name.
:return The job name:
"""
if env.get_env(_JOB_NAME_ENV_VAR) is not None:
return env.get_env(_JOB_NAME_ENV_VAR)
def get_worker_index():
"""
Get the current worker index.
:return: The worker index:
"""
# Get TensorFlow worker index
if env.get_env(_TF_CONFIG) is not None:
tf_config = json.loads(os.environ.get(_TF_CONFIG))
task_config = tf_config.get(_TASK)
task_type = task_config.get(_JOB_NAME)
task_index = task_config.get(_TASK_INDEX)
worker_index = task_type + '-' + str(task_index)
elif env.get_env(_CLUSTER_SPEC) is not None:
cluster_spec = json.loads(os.environ.get(_CLUSTER_SPEC))
task_config = cluster_spec.get(_TASK)
task_type = task_config.get(_JOB_NAME)
task_index = task_config.get(_TASK_INDEX)
worker_index = task_type + '-' + str(task_index)
# Get PyTorch worker index
elif env.get_env(_RANK) is not None:
rank = env.get_env(_RANK)
if rank == "0":
worker_index = "master-0"
else:
worker_index = "worker-" + rank
# Set worker index to "worker-0" When running local training
else:
worker_index = "worker-0"
return worker_index
def get_job_id():
"""
Get the current experiment id.
:return The experiment id:
"""
# Get yarn application or K8s experiment ID when running distributed training
if env.get_env(_JOB_ID_ENV_VAR) is not None:
return env.get_env(_JOB_ID_ENV_VAR)
else: # set Random ID when running local training
job_id = uuid.uuid4().hex
os.environ[_JOB_ID_ENV_VAR] = job_id
return job_id
def get_db_uri() -> str:
"""
Get the current DB URI.
:return: The DB URI.
"""
global _db_uri
if _db_uri is not None:
return _db_uri
elif env.get_env(_DB_URI_ENV_VAR) is not None:
return env.get_env(_DB_URI_ENV_VAR)
else:
return DEFAULT_SUBMARINE_JDBC_URL
def get_tracking_uri():
"""
Get the current tracking URI. This may not correspond to the tracking URI of
the currently active run, since the tracking URI can be updated via ``set_tracking_uri``.
:return: The tracking URI.
"""
global _tracking_uri
if _tracking_uri is not None:
return _tracking_uri
elif env.get_env(_TRACKING_URI_ENV_VAR) is not None:
return env.get_env(_TRACKING_URI_ENV_VAR)
else:
return DEFAULT_SUBMARINE_JDBC_URL
def is_tracking_uri_set():
"""Returns True if the tracking URI has been set, False otherwise."""
if _tracking_uri or env.get_env(_TRACKING_URI_ENV_VAR):
return True
return False
def test_get_env():
environ["test"] = "hello"
assert get_env("test") == "hello"
def is_db_uri_set() -> bool:
"""Returns True if the DB URI has been set, False otherwise."""
if _db_uri or env.get_env(_DB_URI_ENV_VAR):
return True
return False
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from comet_ml import Experiment
import submarine
from submarine.utils import env
#experiment = Experiment(api_key="ej6XeyCVjqHM8uLDNj5VGrzjP",
# project_name="testing", workspace="pingsutw")
if __name__ == "__main__":
submarine.set_tracking_uri(
"mysql+pymysql://submarine:password@submarine-database/submarine")
print("TF_CONFIG", env.get_env("TF_CONFIG"))
print("JOB_NAME: ", env.get_env("JOB_NAME"))
print("TYPE: ", env.get_env("TPYE"))
print("TASK_INDEX: ", env.get_env("TASK_INDEX"))
print("CLUSTER_SPEC: ", env.get_env("CLUSTER_SPEC"))
print("RANK: ", env.get_env("RANK"))
submarine.log_param("max_iter", 100)
submarine.log_param("learning_rate", 0.0001)
submarine.log_param("alpha", 20)
submarine.log_param("batch_size", 256)
submarine.log_metric("score", 2)
submarine.log_metric("score", 5)
submarine.log_metric("score", 8)
submarine.log_metric("score", 5)
