[](https://pypi.org/project/sagemaker-experiments-logger/)
[](https://tsenst.github.io/lightning-experiments-logger/index.html)
# SagemakerExperimentsLogger
SagemakerExperimentsLogger provides a simple way to log experimental data such as hyperparameter settings and evaluation metrics via [AWS SageMaker Experiments API](https://aws.amazon.com/blogs/aws/amazon-sagemaker-experiments-organize-track-and-compare-your-machine-learning-trainings/). It can be easily integration into the concept of [Pytorch Lightning Trainer class](https://lightning.ai/docs/pytorch/stable/common/trainer.html).
For detailed documentation, including the API reference, see [Read the Docs](https://tsenst.github.io/lightning-experiments-logger/index.html)
## Installation
You can install the latest (nightly) version with pip using ssh with
```bash
pip install sagemaker-experiments-logger
```
## Quickstart
The SageMaker Experiments logger can be easily applied by setup an own run context:
```Python
from pytorch_lightning import Trainer
from sagemaker.experiments.run import Run
from experiments_addon.logger import SagemakerExperimentsLogger
with Run(experiment_name="testExperiment", run_name="testRun1"):
logger = SagemakerExperimentsLogger()
trainer = Trainer(
logger=logger,
...
)
...
```
or by using an existing run context. For example in a SageMaker Training Step
```Python
from pytorch_lightning import Trainer
from experiments_addon.logger import SagemakerExperimentsLogger
logger = SagemakerExperimentsLogger(experiment_name="TestExp", run_name="TestRun")
trainer = Trainer(
logger=logger,
...
)
...
```
## Usage
Try [Tutorial Notebook](https://github.com/tsenst/lightning-experiments-logger/blob/main/example/tutorial.ipynb) to learn more about the usage of the [SagemakerExperimentsLogger](https://tsenst.github.io/lightning-experiments-logger/api_logger.html) class.
It is also worth to read the blog post: [Experiment Tracking With AWS SageMaker and PyTorch Lightning](https://medium.com/idealo-tech-blog/experiment-tracking-with-aws-sagemaker-and-pytorch-lightning-68b22fd4deee)
## Contributing
I welcome all contributions!
To file a bug or request a feature, please file a GitHub issue. Pull requests are welcome.
## License
This library is licensed under the Apache 2.0 License.
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"description": "\n[](https://pypi.org/project/sagemaker-experiments-logger/)\n[](https://tsenst.github.io/lightning-experiments-logger/index.html)\n# SagemakerExperimentsLogger\nSagemakerExperimentsLogger provides a simple way to log experimental data such as hyperparameter settings and evaluation metrics via [AWS SageMaker Experiments API](https://aws.amazon.com/blogs/aws/amazon-sagemaker-experiments-organize-track-and-compare-your-machine-learning-trainings/). It can be easily integration into the concept of [Pytorch Lightning Trainer class](https://lightning.ai/docs/pytorch/stable/common/trainer.html).\n\nFor detailed documentation, including the API reference, see [Read the Docs](https://tsenst.github.io/lightning-experiments-logger/index.html)\n\n## Installation\nYou can install the latest (nightly) version with pip using ssh with\n\n```bash\npip install sagemaker-experiments-logger\n```\n\n## Quickstart\nThe SageMaker Experiments logger can be easily applied by setup an own run context:\n```Python\nfrom pytorch_lightning import Trainer\nfrom sagemaker.experiments.run import Run\nfrom experiments_addon.logger import SagemakerExperimentsLogger\n\nwith Run(experiment_name=\"testExperiment\", run_name=\"testRun1\"):\n logger = SagemakerExperimentsLogger()\n trainer = Trainer(\n logger=logger,\n ...\n )\n ...\n```\nor by using an existing run context. For example in a SageMaker Training Step\n```Python\nfrom pytorch_lightning import Trainer\nfrom experiments_addon.logger import SagemakerExperimentsLogger\n\nlogger = SagemakerExperimentsLogger(experiment_name=\"TestExp\", run_name=\"TestRun\")\ntrainer = Trainer(\nlogger=logger,\n ...\n)\n...\n```\n\n## Usage \n\nTry [Tutorial Notebook](https://github.com/tsenst/lightning-experiments-logger/blob/main/example/tutorial.ipynb) to learn more about the usage of the [SagemakerExperimentsLogger](https://tsenst.github.io/lightning-experiments-logger/api_logger.html) class. \n\nIt is also worth to read the blog post: [Experiment Tracking With AWS SageMaker and PyTorch Lightning](https://medium.com/idealo-tech-blog/experiment-tracking-with-aws-sagemaker-and-pytorch-lightning-68b22fd4deee) \n\n## Contributing\nI welcome all contributions!\n\nTo file a bug or request a feature, please file a GitHub issue. Pull requests are welcome.\n\n## License\nThis library is licensed under the Apache 2.0 License.\n",
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