mirror of
https://github.com/clearml/clearml
synced 2025-01-31 09:07:00 +00:00
48 lines
2.1 KiB
Python
48 lines
2.1 KiB
Python
# ClearML - Example of manual model configuration
|
|
import json
|
|
import yaml
|
|
from clearml import Task, OutputModel
|
|
|
|
|
|
# Connecting ClearML with the current process,
|
|
task = Task.init(project_name='examples', task_name='Model configuration example')
|
|
|
|
# Connect a local configuration file in json format
|
|
config_file_json = 'data_samples/sample.json'
|
|
# In the web UI, this file will appear in the CONFIGURATION OBJECTS tab,
|
|
# under the "json file" subsection because of the `name` parameter entered here
|
|
task.connect_configuration(name="json file", configuration=config_file_json)
|
|
|
|
# Read configuration as usual, the backend will contain a copy of it.
|
|
# When executing remotely, the returned `config_file_json` will be a temporary file
|
|
# that contains a new copy of the configuration retrieved form the backend
|
|
model_config_dictionary_json = json.load(open(config_file_json, 'rt'))
|
|
|
|
|
|
# Connecting a local configuration file in yaml format
|
|
config_file_yaml = 'data_samples/config_yaml.yaml'
|
|
task.connect_configuration(configuration=config_file_yaml, name="yaml file")
|
|
# Read configuration as usual
|
|
model_config_dictionary_yaml = yaml.load(open(config_file_yaml), Loader=yaml.FullLoader)
|
|
|
|
# Connecting a dictionary of definitions for a specific network design
|
|
model_config_dict = {
|
|
'CHANGE ME': 13.37,
|
|
'dict': {'sub_value': 'string', 'sub_integer': 11},
|
|
'list_of_ints': [1, 2, 3, 4],
|
|
}
|
|
model_config_dict = task.connect_configuration(name='dictionary', configuration=model_config_dict)
|
|
|
|
# Update the dictionary after connecting it, and the changes will be tracked as well.
|
|
model_config_dict['new value'] = 10
|
|
model_config_dict['CHANGE ME'] *= model_config_dict['new value']
|
|
|
|
# Connecting label enumeration
|
|
labels = {'background': 0, 'cat': 1, 'dog': 2}
|
|
task.connect_label_enumeration(labels)
|
|
|
|
# Manually log a local model file, which will have the labels connected above
|
|
OutputModel().update_weights('my_best_model.bin')
|
|
|
|
# Any saved model (keras / pytorch / tensorflow / etc.) will have the task network configuration and label enumeration
|
|
print('Any model stored from this point onwards, will contain both model_config and label_enumeration') |