API documentation for preview
package.
Packages
tuning
API documentation for tuning
package.
reasoning_engines
API documentation for reasoning_engines
package.
Modules
generative_models
Classes for working with the Gemini models.
language_models
Classes for working with language models.
vision_models
Classes for working with vision models.
Packages Functions
end_run
end_run
(
state
:
google
.
cloud
.
aiplatform_v1
.
types
.
execution
.
Execution
.
State
=
State
.
COMPLETE
,
)
Ends the the current experiment run.
aiplatform.start_run('my-run')
...
aiplatform.end_run()
get_experiment_df
get_experiment_df
(
experiment
:
typing
.
Optional
[
str
]
=
None
,
*
,
include_time_series
:
bool
=
True
)
-
> pd
.
DataFrame
Returns a Pandas DataFrame of the parameters and metrics associated with one experiment.
Example:
aiplatform.init(experiment='exp-1')
aiplatform.start_run(run='run-1')
aiplatform.log_params({'learning_rate': 0.1})
aiplatform.log_metrics({'accuracy': 0.9})
aiplatform.start_run(run='run-2')
aiplatform.log_params({'learning_rate': 0.2})
aiplatform.log_metrics({'accuracy': 0.95})
aiplatform.get_experiment_df()
Will result in the following DataFrame:
experiment_name | run_name | param.learning_rate | metric.accuracy
exp-1 | run-1 | 0.1 | 0.9
exp-1 | run-2 | 0.2 | 0.95
experiment
Name of the Experiment to filter results. If not set, return results of current active experiment.
include_time_series
Optional. Whether or not to include time series metrics in df. Default is True. Setting to False will largely improve execution time and reduce quota contributing calls. Recommended when time series metrics are not needed or number of runs in Experiment is large. For time series metrics consider querying a specific run using get_time_series_data_frame.
log_classification_metrics
log_classification_metrics
(
*
,
labels
:
typing
.
Optional
[
typing
.
List
[
str
]]
=
None
,
matrix
:
typing
.
Optional
[
typing
.
List
[
typing
.
List
[
int
]]]
=
None
,
fpr
:
typing
.
Optional
[
typing
.
List
[
float
]]
=
None
,
tpr
:
typing
.
Optional
[
typing
.
List
[
float
]]
=
None
,
threshold
:
typing
.
Optional
[
typing
.
List
[
float
]]
=
None
,
display_name
:
typing
.
Optional
[
str
]
=
None
)
-
> (
google
.
cloud
.
aiplatform
.
metadata
.
schema
.
google
.
artifact_schema
.
ClassificationMetrics
)
Create an artifact for classification metrics and log to ExperimentRun. Currently support confusion matrix and ROC curve.
my_run = aiplatform.ExperimentRun('my-run', experiment='my-experiment')
classification_metrics = my_run.log_classification_metrics(
display_name='my-classification-metrics',
labels=['cat', 'dog'],
matrix=[[9, 1], [1, 9]],
fpr=[0.1, 0.5, 0.9],
tpr=[0.1, 0.7, 0.9],
threshold=[0.9, 0.5, 0.1],
)
labels
Optional. List of label names for the confusion matrix. Must be set if 'matrix' is set.
matrix
Optional. Values for the confusion matrix. Must be set if 'labels' is set.
fpr
Optional. List of false positive rates for the ROC curve. Must be set if 'tpr' or 'thresholds' is set.
tpr
Optional. List of true positive rates for the ROC curve. Must be set if 'fpr' or 'thresholds' is set.
threshold
Optional. List of thresholds for the ROC curve. Must be set if 'fpr' or 'tpr' is set.
display_name
Optional. The user-defined name for the classification metric artifact.
log_metrics
log_metrics
(
metrics
:
typing
.
Dict
[
str
,
typing
.
Union
[
float
,
int
,
str
]])
Log single or multiple Metrics with specified key and value pairs.
Metrics with the same key will be overwritten.
aiplatform.start_run('my-run', experiment='my-experiment')
aiplatform.log_metrics({'accuracy': 0.9, 'recall': 0.8})
metrics
Required. Metrics key/value pairs.
log_params
log_params
(
params
:
typing
.
Dict
[
str
,
typing
.
Union
[
float
,
int
,
str
]])
Log single or multiple parameters with specified key and value pairs.
Parameters with the same key will be overwritten.
aiplatform.start_run('my-run')
aiplatform.log_params({'learning_rate': 0.1, 'dropout_rate': 0.2})
params
Required. Parameter key/value pairs.
log_time_series_metrics
log_time_series_metrics
(
metrics
:
typing
.
Dict
[
str
,
float
],
step
:
typing
.
Optional
[
int
]
=
None
,
wall_time
:
typing
.
Optional
[
google
.
protobuf
.
timestamp_pb2
.
Timestamp
]
=
None
,
)
Logs time series metrics to to this Experiment Run.
Requires the experiment or experiment run has a backing Vertex Tensorboard resource.
my_tensorboard = aiplatform.Tensorboard(...)
aiplatform.init(experiment='my-experiment', experiment_tensorboard=my_tensorboard)
aiplatform.start_run('my-run')
# increments steps as logged
for i in range(10):
aiplatform.log_time_series_metrics({'loss': loss})
# explicitly log steps
for i in range(10):
aiplatform.log_time_series_metrics({'loss': loss}, step=i)
metrics
Required. Dictionary of where keys are metric names and values are metric values.
step
Optional. Step index of this data point within the run. If not provided, the latest step amongst all time series metrics already logged will be used.
wall_time
Optional. Wall clock timestamp when this data point is generated by the end user. If not provided, this will be generated based on the value from time.time()
start_run
start_run
(
run
:
str
,
*
,
tensorboard
:
typing
.
Optional
[
typing
.
Union
[
google
.
cloud
.
aiplatform
.
tensorboard
.
tensorboard_resource
.
Tensorboard
,
str
]
]
=
None
,
resume
=
False
)
-
> google
.
cloud
.
aiplatform
.
metadata
.
experiment_run_resource
.
ExperimentRun
Start a run to current session.
aiplatform.init(experiment='my-experiment')
aiplatform.start_run('my-run')
aiplatform.log_params({'learning_rate':0.1})
Use as context manager. Run will be ended on context exit:
aiplatform.init(experiment='my-experiment')
with aiplatform.start_run('my-run') as my_run:
my_run.log_params({'learning_rate':0.1})
Resume a previously started run:
aiplatform.init(experiment='my-experiment')
with aiplatform.start_run('my-run', resume=True) as my_run:
my_run.log_params({'learning_rate':0.1})
run
Required. Name of the run to assign current session with.
resume
Whether to resume this run. If False a new run will be created.
