gpu-watchmen 0.3.0

watchmen for GPU scheduling

watchmen

A simple and easy-to-use toolkit for GPU scheduling.

Dependencies

• Python >= 3.6
  • requests >= 2.24.0
  • pydantic >= 1.7.1
  • gpustat >= 0.6.0
  • flask >= 1.1.2
  • apscheduler >= 3.6.3

Installation

1. Install dependencies.

$ pip install -r requirements.txt

2. Install watchmen.

Install from source code:

$ pip install -e .

Or you can install the stable version package from pypi.

$ pip install gpu-watchmen -i https://pypi.org/simple

Quick Start

1. Start the server

The default port of the server is 62333

$ python -m watchmen.server

If you want the server to be running backend, try:

$ nohup python -m watchmen.server &

There are some configurations for the server

usage: server.py [-h] [--host HOST] [--port PORT]
                 [--queue_timeout QUEUE_TIMEOUT]
                 [--request_interval REQUEST_INTERVAL]
                 [--status_queue_keep_time STATUS_QUEUE_KEEP_TIME]

optional arguments:
  -h, --help            show this help message and exit
  --host HOST           host address for api server
  --port PORT           port for api server
  --queue_timeout QUEUE_TIMEOUT
                        timeout for queue waiting (seconds)
  --request_interval REQUEST_INTERVAL
                        interval for gpu status requesting (seconds)
  --status_queue_keep_time STATUS_QUEUE_KEEP_TIME
                        hours for keeping the client status

2. Modify the source code in your project:

from watchmen import Client

client = Client(id="short description of this running", gpus=[1],
                server_host="127.0.0.1", server_port=62333)
client.wait()

When the program goes on after client.wait(), you are in the queue. You can check examples in example/ for further reading.

$ cd example && python single_card_mnist.py --id="single" --cuda=0 --wait
# queue mode
$ cd example && python multi_card_mnist.py --id="multi" --cuda=2,3 --wait
# schedule mode
$ cd example && python multi_card_mnist.py --id='multi card scheduling wait' --cuda=1,0,3 --req_gpu_num=2 --wait=schedule

3. Check the queue in browser.

Open the following link to your browser: http://<server ip address>:<server port>, for example: http://192.168.126.143:62333.

And you can get a result like the demo below. Please be aware that the page is not going to change dynamically, so you can refresh the page manually to check the latest status.

New Demo (scheduling mode supported)

Old Demo (queue mode supported)

4. Reminder when program is finished.

watchmen also support email and other kinds of reminders for message informing. For example, you can send yourself an email when the program is finished.

from watchmen.reminder import send_email

... # your code here

send_email(
    host="smtp.163.com", # email host to login, like `smtp.163.com`
    port=25, # email port to login, like `25`
    user="***@163.com", # user email address for login, like `***@163.com`
    password="***", # password or auth code for login
    receiver="***@outlook.com", # receiver email address
    html_message="<h1>Your program is finished!</h1>", # content, html format supported
    subject="Proram Finished Notice" # email subject
)

To get more reminders, please check watchmen/reminder.py.

UPDATE

• v0.3.0: support gpu scheduling, fix blank input output, fix check_gpus_existence
• v0.2.2: fix html package data, add multi-card example

TODO

• gpu using stats for each user and process
• add schedule feature, so clients only have to request for a number and range of gpus, and the server will assign the gpu num to clients
• add reminders
• add webui html support
• add examples