2. Neural Networks / L4. GPU Workspaces Demo - GPU Workspaces for iterator wrapper

GPU Workspaces for iterator wrapper

 - GPU 사용 환경에서 지속적으로 세션을 유지할 경우 아래의 두 명령어를 이용하여 유지할 수 있음.

import signal

from contextlib import contextmanager

import requests


DELAY = INTERVAL = 4 * 60  # interval time in seconds
MIN_DELAY = MIN_INTERVAL = 2 * 60
KEEPALIVE_URL = "https://nebula.udacity.com/api/v1/remote/keep-alive"
TOKEN_URL = "http://metadata.google.internal/computeMetadata/v1/instance/attributes/keep_alive_token"
TOKEN_HEADERS = {"Metadata-Flavor":"Google"}


def _request_handler(headers):
    def _handler(signum, frame):
        requests.request("POST", KEEPALIVE_URL, headers=headers)
    return _handler


@contextmanager
def active_session(delay=DELAY, interval=INTERVAL):
    """
    Example:

    from workspace_utils import active session

    with active_session():
        # do long-running work here
    """
    token = requests.request("GET", TOKEN_URL, headers=TOKEN_HEADERS).text
    headers = {'Authorization': "STAR " + token}
    delay = max(delay, MIN_DELAY)
    interval = max(interval, MIN_INTERVAL)
    original_handler = signal.getsignal(signal.SIGALRM)
    try:
        signal.signal(signal.SIGALRM, _request_handler(headers))
        signal.setitimer(signal.ITIMER_REAL, delay, interval)
        yield
    finally:
        signal.signal(signal.SIGALRM, original_handler)
        signal.setitimer(signal.ITIMER_REAL, 0)


def keep_awake(iterable, delay=DELAY, interval=INTERVAL):
    """
    Example:

    from workspace_utils import keep_awake

    for i in keep_awake(range(5)):
        # do iteration with lots of work here
    """
    with active_session(delay, interval): yield from iterable

 - active_session 과 keep_awake 는 동일한 역할을 함 둘중에 더 편한걸 사용하면 되고,

 - 긴 연산이 필요한경우 해당 함수 내에 처리시켜주면 됨. (네트워크의 결과를 도출해내는경우 등..)