https://github.com/iterait/scheduler
https://github.com/iterait/scheduler
Last synced: about 2 months ago
JSON representation
- Host: GitHub
- URL: https://github.com/iterait/scheduler
- Owner: iterait
- License: mit
- Created: 2018-11-09T21:20:57.000Z (over 7 years ago)
- Default Branch: dev
- Last Pushed: 2018-11-09T21:22:28.000Z (over 7 years ago)
- Last Synced: 2025-11-19T23:22:23.807Z (8 months ago)
- Language: Python
- Size: 21.5 KB
- Stars: 0
- Watchers: 9
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# **TensorFlow training scheduler**
TT scheduler is a simple tool facilitating effective GPU utilization for TensorFlow training(s) in local multi-GPU environment.
- auto `CUDA_VISIBLE_DEVICES` masking
- schedule arbitrary number of trainings in advance
- simple command line usage
## Quick start
```bash
alias sch=/path/to/scheduler.py
sch --init [NUM_GPUS]
sch task.py --your_arg value
sch -n 2 training.py # training on two GPUs
sch -f "[1,3]" training.py # force to use GPUs 1 and 3
```
Run `sch --help` for help.
If no GPUs are available, the task will be executed as soon as possible.
## Requirements
## Usage
```
usage: scheduler.py [-h] [-i INIT] [-n NUM] [-p PREFER] [-f FORCE] [-s]
[-r RELEASE [RELEASE ...]] [--cx]
[task [task ...]]
positional arguments:
task The task to run as soon as the required GPUs are
available.
optional arguments:
-h, --help show this help message and exit
-i INIT, --init INIT The number of available GPUs.
-n NUM, --num NUM The number of required GPUs.
-p PREFER, --prefer PREFER
Instruct the scheduler to prefer the specified GPU(s).
-f FORCE, --force FORCE
Force the scheduler to use the specified GPU(s).
-s, --status Show GPU usage status (user/GPU/taskPID/start)
-r RELEASE [RELEASE ...], --release RELEASE [RELEASE ...]
Releases the specified GPU(s).
--cx Append model.n_gpus=[NUM] to the task args.
```
## Contributing
You are welcome to participate in development of this project.
## License
Scheduler is distributed under the MIT License.