radT
radT (Resource Aware Data science Tracker) is an extension to MLFlow that simplifies the collection and exploration of hardware metrics of machine learning and deep learning applications. Usually, collecting and processing all the required metrics for these workloads is a hassle. In contrast, RADT is easy to deploy and use, with minimal impact on both performance and time investment. The codebase of RADT is documented and easily expandable.
This work has been published at the SIGMOD workshop DEEM 2023: Data Management and Visualization for Benchmarking Deep Learning Training Systems
pip install radt
1 maintainer
Release files for radt 0.2.29
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| radt-0.2.29.tar.gz | 19.4 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| radt-0.2.29-py2.py3-none-any.whl | Python 2, Python 3 | none | any | Details |
Total release size: 44.7 kB
Release files / radt-0.2.29.tar.gz
| Download URL | radt-0.2.29.tar.gz |
|---|---|
| Size | 19.4 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
12519dd303c8f3fefc6cbb2c1df2fa9971bc326f7fa4d052c14bb7f4809718c4
|
|
BLAKE2b-256 checksum How to use checksums |
b8c460ac7e2eea62caac83d6bfc34ef99f3a9e4cd7e351d69397ed8422ab6173
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
python-requests/2.32.5
|
Release files / radt-0.2.29-py2.py3-none-any.whl
| Download URL | radt-0.2.29-py2.py3-none-any.whl |
|---|---|
| Size | 25.4 kB |
| Tags | Python 2 Python 3 |
|
SHA-256 checksum How to use checksums |
a2d3be4d2d48bb897014cabd908a03d8c2b6762e8e777f3b33cf0ad305171226
|
|
BLAKE2b-256 checksum How to use checksums |
04ff3caa41774dac59a794e5575df4c7ee63165aa606fe2b9a1f021a4d62be77
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
python-requests/2.32.5
|
