microcosm-sagemaker

Opinionated machine learning with SageMaker

Usage

For best practices, see cookiecutter-microcosm-sagemaker.

Profiling

Make sure pyinstrument is installed, either using pip install pyinstrument or by installing microcosm-sagemaker with profiling extra dependencies:

pip install -e '.[profiling]'

To enable profiling of the app, use the --profile flag with runserver:

runserver --profile

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.

Experiment Tracking

To use Weights and Biases, install microcosm-sagemaker with wandb extra depdency:

pip install -e '.[wandb]'

To enable experiment tracking in an ML repository:

Choose the experiment tracking stores for your ML model. It is recommended to store metrics in both wandb and Amazon cloudwatch. To do so, add wandb and cloudwatch to graph.use() in app_hooks/train/app.py and app_hooks/evaluate/app.py.
Add the API key for wandb to your ML model's config.json file:

{
    "wandb": {
        "api_key": "XXXXXX"
    }
}

To define hyperparameters for your model:

from microcosm.api import defaults, binding
from microcosm_sagemaker.bundle import Bundle
from microcosm_sagemaker.hyperparameters import hyperparameter

@binding("my_classifier")
@defaults(
    param = 10,
    hyperparam = hyperparameter(20),
)
class MyClassifier(Bundle):
    ...

That automatically adds the hyperparameters to your experiment, which simplifies hyperparameter optimization and tuning.

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.

Release files for microcosm-sagemaker 0.2095.dev2095

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for microcosm-sagemaker 0.2095.dev2095
File	Size	Uploaded
microcosm-sagemaker-0.2095.dev2095.tar.gz	26.2 kB	Apr 30, 2020	Details

Release files / microcosm-sagemaker-0.2095.dev2095.tar.gz

Download URL	microcosm-sagemaker-0.2095.dev2095.tar.gz
Size	26.2 kB
Tags	Source
SHA-256 checksum How to use checksums	`e32c795e2eacb36a0a3fc752b3feea53a4a82681e68403a32aff109bb40ba59d`
BLAKE2b-256 checksum How to use checksums	`09d3c4d0e3bba4a1517739ec8f85e22199b6958bdb03956e6d1bdb68b9e1f6af`
Upload date	Apr 30, 2020
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/3.1.1 pkginfo/1.5.0.1 requests/2.23.0 setuptools/46.1.3 requests-toolbelt/0.8.0 tqdm/4.45.0 CPython/3.7.7`