DLRover helps model developers focus on model algorithm itself, without taking care of any engineering stuff, say, hardware acceleration, distribute running, etc. It provides static and dynamic workloads' configuration automatically,, before and during a model training job running on k8s or ray.
Metadata
Release files for dlrover 0.6.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| dlrover-0.6.1-py3-none-any.whl | Python 3 | none | any | Details |
Release files / dlrover-0.6.1-py3-none-any.whl
| Download URL | dlrover-0.6.1-py3-none-any.whl |
|---|---|
| Size | 750.7 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
f1416661ebdfda42410441796fec0f67a51558a4e002600446435e4a51bb6bd5
|
|
BLAKE2b-256 checksum How to use checksums |
fe106f4e123930a1a547e02b858b33d6c980ca52c3340122ce1bed905ef99dd4
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.8.14
|