This release is a pre-release and may not be stable for production use.
This library powers Amazon SageMaker Debugger, and helps you develop better, faster and cheaper models by catching common errors quickly. It allows you to save tensors from training jobs and makes these tensors available for analysis, all through a flexible and powerful API. It supports TensorFlow, PyTorch, MXNet, and XGBoost on Python 3.6+.
- Zero Script Change experience on SageMaker when using supported versions of SageMaker Framework containers or AWS Deep Learning containers
- Full visibility into any tensor which is part of the training process
- Real-time training job monitoring through Rules
- Automated anomaly detection and state assertions
- Interactive exploration of saved tensors
- Distributed training support
- TensorBoard support
1 maintainer
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distributions
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file smdebug-0.9.0b20200630-py2.py3-none-any.whl.
File metadata
- Download URL: smdebug-0.9.0b20200630-py2.py3-none-any.whl
- Upload date:
- Size: 167.0 kB
- Tags: Python 2, Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via:
twine/3.2.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.0.1 requests-toolbelt/0.9.1 tqdm/4.47.0 CPython/3.6.6
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
4f61d79c021c4a10a9304ef668ae26fa0cd821a35892672d77673defe3daa694
|
|
| MD5 |
e0190a720f02e6b3a6adcaa6a02dd2a8
|
|
| BLAKE2b-256 |
ff1500597d3e13a9eb18d6999f5447003359ea414d81abf6d4d27d1387ed4941
|
