def test_log_param_writes_params_to_redis(self):
self._set_job_id('my_id')
log_param('this_param', 'cool_value')
log_param('that_param', 42)
expected_loaded_parameters = {
'this_param': 'cool_value',
'that_param': 42
}
self._assert_flattened_parameter_keys_in_project_job_parameter_names_set(
'default', expected_loaded_parameters)
self._assert_flattened_parameter_values_for_job_in_job_parameters(
'my_id', expected_loaded_parameters)
self._assert_flattened_parameter_keys_in_project_input_parameter_names_set(
'default', expected_loaded_parameters)
self._assert_flattened_parameter_names_for_job_in_job_input_parameters(
'my_id', expected_loaded_parameters)
"""
This sample main.py shows basic Atlas functionality.
In this script, we will log some arbitrary values & artifacts that can be viewed in the Atlas GUI
"""
import foundations
depth = 3
epochs = 5
batch_size = 256
lrate = 1e-3
# Log some hyper-parameters
foundations.log_param('depth', depth)
foundations.log_params({'epochs': epochs,
'batch_size': batch_size,
'learning_rate': lrate})
# Log some metrics
accuracy = 0.9
loss = 0.1
foundations.log_metric('accuracy', accuracy)
foundations.log_metric('loss', loss)
# Log an artifact that is already saved to disk
foundations.save_artifact('README.txt', 'Project_README')
import foundations
foundations.log_metric('key', 'value')
foundations.set_tag('key', value='value')
foundations.log_param('param', 'param_value')
print('Hello World!')
import foundations
from foundations_contrib.global_state import current_foundations_context, redis_connection
foundations.log_metric('ugh', 10)
with open('thomas_text.txt', 'w') as f:
f.write('ugh_square')
foundations.save_artifact('thomas_text.txt', 'just_some_artifact')
foundations.log_param('blah', 20)
redis_connection.set('foundations_testing_job_id', current_foundations_context().pipeline_context().job_id)
import numpy as np
import foundations
model_params = {
'num_freq_bin': 240,
'num_conv_blocks': 8,
'num_conv_filters': 32,
'spatial_dropout_fraction': 0.05,
'num_dense_layers': 1,
'num_dense_neurons': 50,
'dense_dropout': 0,
'learning_rate': 0.0001,
'epochs': 100,
'batch_size': 156,
'residual_con': 2,
'use_default': True,
'model_save_dir': 'fitted_objects'
}
for k, v in model_params.items():
foundations.log_param(k, v)
train_accuracy = np.random.rand()
foundations.log_metric("train_accuracy", train_accuracy)
foundations.log_metric("val_accuracy", train_accuracy * 0.85)
# foundations.save_artifact('visualize_inference_spectrogram.png', key='spectrogram')
import foundations
from time import sleep
foundations.log_param("param_float", 1.)
foundations.log_param("param_large_float", 999999999.8888888888888888)
foundations.log_param("param_list_of_floats", [1., 2.])
foundations.log_param("param_long_list_of_floats", [
1.,
2.,
1.,
2.,
1.,
2.,
1.,
2.,
1.,
2.,
1.,
2.,
1.,
2.,
1.,
2.,
1.,
2.,
1.,
2.,
])
foundations.log_param("param_long_list_of_long_floats", [
999999999.8888888888888888, 999999999.8888888888888888,
999999999.8888888888888888, 999999999.8888888888888888,
import foundations
from time import sleep
foundations.log_param("param_int", 1)
foundations.log_param("param_large_int", 8888888888888888888888888)
foundations.log_param("param_list_of_ints", [1, 2])
foundations.log_param("param_long_list_of_ints", [
1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1,
2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2
])
foundations.log_param("param_long_list_of_long_ints", [
8888888888888888888888888, 8888888888888888888888888,
8888888888888888888888888, 8888888888888888888888888,
8888888888888888888888888, 8888888888888888888888888,
8888888888888888888888888, 8888888888888888888888888,
8888888888888888888888888, 8888888888888888888888888,
8888888888888888888888888, 8888888888888888888888888,
8888888888888888888888888, 8888888888888888888888888,
8888888888888888888888888, 8888888888888888888888888,
8888888888888888888888888, 8888888888888888888888888,
8888888888888888888888888, 8888888888888888888888888,
8888888888888888888888888, 8888888888888888888888888,
8888888888888888888888888
])
foundations.log_param("param_mixed_type", 1)
for i in range(20):
foundations.log_param("param_repeat", i)
sleep(.1)
import foundations
from time import sleep
foundations.log_param("param_str", str(1.))
foundations.log_param(
"param_long_str",
"asdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdfasdf"
)
foundations.log_param("param_long_list_of_str", [
"qwe",
"qwe",
"qwe",
"qwe",
"qwe",
"qwe",
"qwe",
"qwe",
"qwe",
"qwe",
"qwe",
"qwe",
"qwe",
"qwe",
"qwe",
"qwe",
"qwe",
"qwe",
"qwe",
"qwe",
"qwe",
"qwe",
