Hyperdash is a machine learning monitoring library capable of running alongside Tensorflow, Scikit-Learn, and other modeling libraries. It was developed with a focus on enabling fast knowledge gain.
Use Hyperdash if you’re looking for cloud-based model monitoring that:
Is fast and easy-to-use with scripts and Jupyter.
Tracks your hyperparameters across different model experiments.
Graphs performance metrics (loss, reward, etc.) in real-time.
Can be viewed remotely on the Web, iOS, and Android without self-hosting (e.g. Tensorboard).
Saves your experiment’s print output (standard out / error) as a local log file.
Notifies you when a long-running experiment has finished.
Hyperdash is compatible with: Python 2.7 - 3.6
1 maintainer
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
File details
Details for the file hyperdash-0.15.3.tar.gz.
File metadata
- Download URL: hyperdash-0.15.3.tar.gz
- Upload date:
- Size: 24.9 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
03a56ac855debf83944968dd2ed988879a94cc2e2a38db2029777f11d985e342
|
|
| MD5 |
f056430f11658302de8711c43be101e0
|
|
| BLAKE2b-256 |
fba12606aa8a8c3bf083cb305dba3164cb00285f97db34080d63c22b6c413175
|
