Opinionated machine learning organization and configuration
Opinionated machine learning with SageMaker
For best practices, see
pyinstrument is installed, either using
pip install pyinstrument or by installing
profiling extra dependencies:
pip install -e '.[profiling]'
To enable profiling of the app, use the
--profile flag with
The service will log that it is in profiling mode and announce the directory to which it is exporting. Each call to the endpoint will be profiled and its results with be stored in a time-tagged html file in the profiling directory.
Weights and Biases, install
wandb extra depdency:
pip install -e '.[wandb]'
To enable experiment tracking in an ML repository:
Choose the experiment tracking stores for your ML model. Currently, we only support
wandb. To do so, add
Add the API key for
wandbto the environment variables injected by Circle CI into the docker instance, by visiting
WANDB_API_KEYas an environment variable.
Microcosm-sagemakerautomatically adds the config for the active bundle and its dependents to the
wandb's run config.
To report a static metric:
class MyClassifier(Bundle): ... def fit(self, input_data): ... self.experiment_metrics.log_static(<metric_name>=<metric_value>)
- To report a time-series metric:
class MyClassifier(Bundle): ... def fit(self, input_data): ... self.experiment_metrics.log_timeseries( <metric_name>=<metric_value>, step=<step_number> )
Note that the
step keyword argument must be provided for logging time-series.
If you want to report your artifact tests to wandb, add the following line to the top of your
For more information on using plugins in pytest, see here.
pytest_plugins = 'pytest_sagemaker'
It should be generated by
globality-build, but in case it is not, also make sure to run artifact
--capture=tee-sys. This will allow to both capture and show stdout.
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