CachedMeta
CachedMeta Examples
Examples of using the CachedMeta class are listed at the bottom of this page Examples.
CachedMeta: A class that provides caching for the Meta() class
CachedMeta
Bases: CloudMeta
CachedMeta: Singleton class for caching metadata functionality.
Common Usage
from sageworks.cached.cached_meta import CachedMeta
meta = CachedMeta()
# Get the AWS Account Info
meta.account()
meta.config()
# These are 'list' methods
meta.etl_jobs()
meta.data_sources()
meta.feature_sets(details=True/False)
meta.models(details=True/False)
meta.endpoints()
meta.views()
# These are 'describe' methods
meta.data_source("abalone_data")
meta.feature_set("abalone_features")
meta.model("abalone-regression")
meta.endpoint("abalone-endpoint")
Source code in src/sageworks/cached/cached_meta.py
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|
__del__()
__init__()
CachedMeta Initialization
Source code in src/sageworks/cached/cached_meta.py
account()
Cloud Platform Account Info
Returns:
Name | Type | Description |
---|---|---|
dict |
dict
|
Cloud Platform Account Info |
check()
clear_meta_cache()
close()
Explicitly close the thread pool, if needed.
Source code in src/sageworks/cached/cached_meta.py
config()
Return the current SageWorks Configuration
Returns:
Name | Type | Description |
---|---|---|
dict |
dict
|
The current SageWorks Configuration |
data_source(data_source_name, database='sageworks')
Get the details of a specific Data Source
Parameters:
Name | Type | Description | Default |
---|---|---|---|
data_source_name
|
str
|
The name of the Data Source |
required |
database
|
str
|
The Glue database. Defaults to 'sageworks'. |
'sageworks'
|
Returns:
Name | Type | Description |
---|---|---|
dict |
Union[dict, None]
|
The details of the Data Source (None if not found) |
Source code in src/sageworks/cached/cached_meta.py
data_sources()
Get a summary of the Data Sources deployed in the Cloud Platform
Returns:
Type | Description |
---|---|
DataFrame
|
pd.DataFrame: A summary of the Data Sources deployed in the Cloud Platform |
Source code in src/sageworks/cached/cached_meta.py
endpoint(endpoint_name)
Get the details of a specific Endpoint
Parameters:
Name | Type | Description | Default |
---|---|---|---|
endpoint_name
|
str
|
The name of the Endpoint |
required |
Returns:
Name | Type | Description |
---|---|---|
dict |
Union[dict, None]
|
The details of the Endpoint (None if not found) |
Source code in src/sageworks/cached/cached_meta.py
endpoints()
Get a summary of the Endpoints deployed in the Cloud Platform
Returns:
Type | Description |
---|---|
DataFrame
|
pd.DataFrame: A summary of the Endpoints in the Cloud Platform |
Source code in src/sageworks/cached/cached_meta.py
etl_jobs()
Get summary data about Extract, Transform, Load (ETL) Jobs
Returns:
Type | Description |
---|---|
DataFrame
|
pd.DataFrame: A summary of the ETL Jobs deployed in the Cloud Platform |
Source code in src/sageworks/cached/cached_meta.py
feature_set(feature_set_name)
Get the details of a specific Feature Set
Parameters:
Name | Type | Description | Default |
---|---|---|---|
feature_set_name
|
str
|
The name of the Feature Set |
required |
Returns:
Name | Type | Description |
---|---|---|
dict |
Union[dict, None]
|
The details of the Feature Set (None if not found) |
Source code in src/sageworks/cached/cached_meta.py
feature_sets(details=False)
Get a summary of the Feature Sets deployed in the Cloud Platform
Parameters:
Name | Type | Description | Default |
---|---|---|---|
details
|
bool
|
Include detailed information. Defaults to False. |
False
|
Returns:
Type | Description |
---|---|
DataFrame
|
pd.DataFrame: A summary of the Feature Sets deployed in the Cloud Platform |
Source code in src/sageworks/cached/cached_meta.py
glue_job(job_name)
Get the details of a specific Glue Job
Parameters:
Name | Type | Description | Default |
---|---|---|---|
job_name
|
str
|
The name of the Glue Job |
required |
Returns:
Name | Type | Description |
---|---|---|
dict |
Union[dict, None]
|
The details of the Glue Job (None if not found) |
Source code in src/sageworks/cached/cached_meta.py
incoming_data()
Get summary data about data in the incoming raw data
Returns:
Type | Description |
---|---|
DataFrame
|
pd.DataFrame: A summary of the incoming raw data |
list_meta_cache()
model(model_name)
Get the details of a specific Model
Parameters:
Name | Type | Description | Default |
---|---|---|---|
model_name
|
str
|
The name of the Model |
required |
Returns:
Name | Type | Description |
---|---|---|
dict |
Union[dict, None]
|
The details of the Model (None if not found) |
Source code in src/sageworks/cached/cached_meta.py
models(details=False)
Get a summary of the Models deployed in the Cloud Platform
Parameters:
Name | Type | Description | Default |
---|---|---|---|
details
|
bool
|
Include detailed information. Defaults to False. |
False
|
Returns:
Type | Description |
---|---|
DataFrame
|
pd.DataFrame: A summary of the Models deployed in the Cloud Platform |
Source code in src/sageworks/cached/cached_meta.py
views(database='sageworks')
Get a summary of the all the Views, for the given database, in AWS
Parameters:
Name | Type | Description | Default |
---|---|---|---|
database
|
str
|
Glue database. Defaults to 'sageworks'. |
'sageworks'
|
Returns:
Type | Description |
---|---|
DataFrame
|
pd.DataFrame: A summary of all the Views, for the given database, in AWS |
Source code in src/sageworks/cached/cached_meta.py
cache_result(method)
Decorator to cache method results in meta_cache
Source code in src/sageworks/cached/cached_meta.py
Examples
These example show how to use the CachedMeta()
class to pull lists of artifacts from AWS. DataSources, FeatureSets, Models, Endpoints and more. If you're building a web interface plugin, the CachedMeta class is a great place to start.
SageWorks REPL
If you'd like to see exactly what data/details you get back from the CachedMeta()
class, you can spin up the SageWorks REPL, use the class and test out all the methods. Try it out! SageWorks REPL
CachedMeta = CachedMeta()
model_df = CachedMeta.models()
model_df
Model Group Health Owner ... Input Status Description
0 wine-classification healthy - ... wine_features Completed Wine Classification Model
1 abalone-regression-full healthy - ... abalone_features Completed Abalone Regression Model
2 abalone-regression healthy - ... abalone_features Completed Abalone Regression Model
[3 rows x 10 columns]
List the Models in AWS
from sageworks.cached.cached_meta import CachedMeta
# Create our CachedMeta Class and get a list of our Models
CachedMeta = CachedMeta()
model_df = CachedMeta.models()
print(f"Number of Models: {len(model_df)}")
print(model_df)
# Get more details data on the Models
model_names = model_df["Model Group"].tolist()
for name in model_names:
pprint(CachedMeta.model(name))
Output
Number of Models: 3
Model Group Health Owner ... Input Status Description
0 wine-classification healthy - ... wine_features Completed Wine Classification Model
1 abalone-regression-full healthy - ... abalone_features Completed Abalone Regression Model
2 abalone-regression healthy - ... abalone_features Completed Abalone Regression Model
[3 rows x 10 columns]
wine-classification
abalone-regression-full
abalone-regression
Getting Model Performance Metrics
from sageworks.cached.cached_meta import CachedMeta
# Create our CachedMeta Class and get a list of our Models
CachedMeta = CachedMeta()
model_df = CachedMeta.models()
print(f"Number of Models: {len(model_df)}")
print(model_df)
# Get more details data on the Models
model_names = model_df["Model Group"].tolist()
for name in model_names[:5]:
model_details = CachedMeta.model(name)
print(f"\n\nModel: {name}")
performance_metrics = model_details["sageworks_CachedMeta"]["sageworks_inference_metrics"]
print(f"\tPerformance Metrics: {performance_metrics}")
Output
wine-classification
ARN: arn:aws:sagemaker:us-west-2:507740646243:model-package-group/wine-classification
Description: Wine Classification Model
Tags: wine::classification
Performance Metrics:
[{'wine_class': 'TypeA', 'precision': 1.0, 'recall': 1.0, 'fscore': 1.0, 'roc_auc': 1.0, 'support': 12}, {'wine_class': 'TypeB', 'precision': 1.0, 'recall': 1.0, 'fscore': 1.0, 'roc_auc': 1.0, 'support': 14}, {'wine_class': 'TypeC', 'precision': 1.0, 'recall': 1.0, 'fscore': 1.0, 'roc_auc': 1.0, 'support': 9}]
abalone-regression
ARN: arn:aws:sagemaker:us-west-2:507740646243:model-package-group/abalone-regression
Description: Abalone Regression Model
Tags: abalone::regression
Performance Metrics:
[{'MAE': 1.64, 'RMSE': 2.246, 'R2': 0.502, 'MAPE': 16.393, 'MedAE': 1.209, 'NumRows': 834}]
List the Endpoints in AWS
from pprint import pprint
from sageworks.cached.cached_meta import CachedMeta
# Create our CachedMeta Class and get a list of our Endpoints
CachedMeta = CachedMeta()
endpoint_df = CachedMeta.endpoints()
print(f"Number of Endpoints: {len(endpoint_df)}")
print(endpoint_df)
# Get more details data on the Endpoints
endpoint_names = endpoint_df["Name"].tolist()
for name in endpoint_names:
pprint(CachedMeta.endpoint(name))
Output
Number of Endpoints: 2
Name Health Instance Created ... Status Variant Capture Samp(%)
0 wine-classification-end healthy Serverless (2GB/5) 2024-03-23 23:09 ... InService AllTraffic False -
1 abalone-regression-end healthy Serverless (2GB/5) 2024-03-23 21:11 ... InService AllTraffic False -
[2 rows x 10 columns]
wine-classification-end
<lots of details about endpoints>
Not Finding some particular AWS Data?
The SageWorks CachedMeta API Class also has (details=True)
arguments, so make sure to check those out.