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import os
import git
import warnings
import uvicorn
import logging
from fastapi import FastAPI
from prometheus_client import Gauge
from starlette_exporter import PrometheusMiddleware, handle_metrics
from fastapi.middleware.cors import CORSMiddleware
from .datatypes import Image
from .queue import QueueData
from .log_utils import Colors
from .models import Credentials, WaitFor
from .result_storage import ResultStorage
from .config_wrapper import ConfigWrapper
from .data_handlers import Encoder, Decoder
from .threaded_server import ThreadedServer
from .progress_tracker import fetch_progress_from_token
from .log_utils import log_levels, celery_log_levels, PREFIX
from .prometheus_utils import PrometheusMetrics
from .utils import stop_everything_gracefully, generate_random_string
from uvicorn.config import LOGGING_CONFIG
"""
Celery+redis is needed to be able to queue tasks
"""
from celery import Celery
from .celery_utils import run_celery_app
"""
tool to allocate gpus on queued tasks
"""
from .gpu_allocator import GPUAllocator
def host_block(
block,
port=8080,
host="0.0.0.0",
max_num_workers=4,
redis_port=6379,
redis_host="localhost",
requires_gpu=True,
log_level="warning",
logfile="logs.log",
exclude_gpu_ids: list = [],
remove_result_on_fetch = False
):
"""
Use this to host your eden.Block on a server. Supports multiple GPUs and queues tasks automatically with celery.
Args:
block (eden.block.Block): The eden block you'd want to host.
port (int, optional): Localhost port where the block would be hosted. Defaults to 8080.
host (str): specifies where the endpoint would be hosted. Defaults to '0.0.0.0'.
max_num_workers (int, optional): Maximum number of tasks to run in parallel. Defaults to 4.
redis_port (int, optional): Port number for celery's redis server. Defaults to 6379.
redis_host (str, optional): Place to host redis for `eden.queue.QueueData`. Defaults to localhost.
requires_gpu (bool, optional): Set this to False if your tasks dont necessarily need GPUs.
log_level (str, optional): Can be 'debug', 'info', or 'warning'. Defaults to 'warning'
logfile(str, optional): Name of the file where the logs would be stored. If set to None, it will show all logs on stdout. Defaults to 'logs.log'
exclude_gpu_ids (list, optional): List of gpu ids to not use for hosting. Example: [2,3]
"""
"""
Response templates:
/run:
{
'token': some_long_token,
}
/fetch:
if task is queued:
{
'status': {
'status': queued,
'queue_position': int
},
config: current_config
}
elif task is running:
{
'status': {
'status': 'running',
'progress': float between 0 and 1,
},
config: current_config,
'output': {} ## optionally the user should be able to write outputs here
}
elif task failed:
{
'status': {
'status': 'failed',
}
'config': current_config,
'output': {} ## will still include the outputs if any so that it gets returned even though the task failed
}
elif task succeeded:
{
'status': {
'status': 'complete'
},
'output': user_output,
'config': config
}
"""
"""
Initiating celery app
"""
celery_app = Celery(__name__, broker=f"redis://{redis_host}:{str(redis_port)}")
celery_app.conf.broker_url = os.environ.get(
"CELERY_BROKER_URL", f"redis://{redis_host}:{str(redis_port)}"
)
celery_app.conf.result_backend = os.environ.get(
"CELERY_RESULT_BACKEND", f"redis://{redis_host}:{str(redis_port)}"
)
celery_app.conf.task_track_started = os.environ.get(
"CELERY_TRACK_STARTED", default=True
)
celery_app.conf.worker_send_task_events = True
celery_app.conf.task_send_sent_event = True
"""
each block gets its wown queue
"""
celery_app.conf.task_default_queue = block.name
"""
set prefetch mult to 1 so that tasks dont get pre-fetched by workers
"""
celery_app.conf.worker_prefetch_multiplier = 1
"""
task messages will be acknowledged after the task has been executed
"""
celery_app.conf.task_acks_late = True
"""
Initiating GPUAllocator only if requires_gpu is True
"""
if requires_gpu == True:
gpu_allocator = GPUAllocator(exclude_gpu_ids=exclude_gpu_ids)
else:
print(PREFIX + " Initiating server with no GPUs since requires_gpu = False")
if requires_gpu == True:
if gpu_allocator.num_gpus < max_num_workers:
"""
if a task requires a gpu, and the number of workers is > the number of available gpus,
then max_num_workers is automatically set to the number of gpus available
this is because eden assumes that each task requires one gpu (all of it)
"""
warnings.warn(
"max_num_workers is greater than the number of GPUs found, overriding max_num_workers to be: "
+ str(gpu_allocator.num_gpus)
)
max_num_workers = gpu_allocator.num_gpus
"""
Initiating queue data to keep track of the queue
"""
queue_data = QueueData(
redis_port=redis_port, redis_host=redis_host, queue_name=block.name
)
"""
Initiate encoder and decoder
"""
data_encoder = Encoder()
data_decoder = Decoder()
"""
Initiate fastAPI app
"""
app = FastAPI()
origins = ["*"]
app.add_middleware(
CORSMiddleware,
allow_origins=origins,
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
app.add_middleware(PrometheusMiddleware)
app.add_route("/metrics", handle_metrics)
"""
Initiate result storage on redis
"""
result_storage = ResultStorage(
redis_host=redis_host,
redis_port=redis_port,
)
## set up result storage and data encoder for block
block.result_storage = result_storage
block.data_encoder = data_encoder
"""
initiate a wrapper which handles 4 metrics for prometheus:
* number of queued jobs
* number of running jobs
* number of failed jobs
* number of succeeded jobs
"""
prometheus_metrics = PrometheusMetrics()
"""
define celery task
"""
@celery_app.task(name="run")
def run(args, token: str):
## job moves from queue to running
prometheus_metrics.queued.dec(1)
prometheus_metrics.running.inc(1)
args = data_decoder.decode(args)
"""
allocating a GPU ID to the tast based on usage
for now let's settle for max 1 GPU per task :(
"""
if requires_gpu == True:
# returns None if there are no gpus available
gpu_name = gpu_allocator.get_gpu()
else:
gpu_name = None ## default value either if there are no gpus available or requires_gpu = False
"""
If there are no GPUs available, then it returns a sad message.
But if there ARE GPUs available, then it starts run()
"""
if (
gpu_name == None and requires_gpu == True
): ## making sure there are no gpus available
status = {
"status": "No GPUs are available at the moment, please try again later",
}
else:
"""
refer:
https://github.com/abraham-ai/eden/issues/14
"""
args = ConfigWrapper(
data=args,
token=token,
result_storage=result_storage,
gpu=None, ## will be provided later on in the run
progress=None, ## will be provided later on in the run
)
if requires_gpu == True:
args.gpu = gpu_name
if block.progress == True:
"""
if progress was set to True on @eden.Block.run() decorator, then add a progress tracker into the config
"""
args.progress = block.get_progress_bar(
token=token, result_storage=result_storage
)
try:
output = block.__run__(args)
# job moves from running to succeeded
prometheus_metrics.running.dec(1)
prometheus_metrics.succeeded.inc(1)
# prevent further jobs from hitting a busy gpu after a caught exception
except Exception as e:
# job moves from running to failed
prometheus_metrics.running.dec(1)
prometheus_metrics.failed.inc(1)
if requires_gpu == True:
gpu_allocator.set_as_free(name=gpu_name)
raise Exception(str(e))
if requires_gpu == True:
gpu_allocator.set_as_free(name=gpu_name)
success = block.write_results(output=output, token=token)
return success ## return None because results go to result_storage instead
@app.post("/run")
def start_run(config: block.data_model):
## job moves into queue
prometheus_metrics.queued.inc(1)
"""
refer:
https://github.com/celery/celery/issues/1813#issuecomment-33142648
"""
token = generate_random_string(len=10)
kwargs = dict(args=dict(config), token=token)
res = run.apply_async(kwargs=kwargs, task_id=token, queue_name=block.name)
initial_dict = {"config": dict(config), "output": {}, "progress": "__none__"}
success = result_storage.add(token=token, encoded_results=initial_dict)
response = {"token": token}
return response
@app.post("/update")
def update(credentials: Credentials, config: block.data_model):
token = credentials.token
config = dict(config)
status = queue_data.get_status(token=token)
if status["status"] != "invalid token":
if (
status["status"] == "queued"
or status["status"] == "running"
or status["status"] == "starting"
):
output_from_storage = result_storage.get(token=token)
output_from_storage["config"] = config
success = result_storage.add(
encoded_results=output_from_storage, token=token
)
response = {
"status": {
"status": "successfully updated config",
}
}
return response
elif status["status"] == "failed":
return {
"status": {
"status": "could not update config because job failed",
}
}
elif status["status"] == "complete":
return {
"status": {
"status": "could not update config because job is already complete",
}
}
else:
response = {"status": {"status": "invalid token"}}
return response
@app.post("/fetch")
def fetch(credentials: Credentials):
"""
Returns either the status of the task or the result depending on whether it's queued, running, complete or failed.
Args:
credentials (Credentials): should contain a token that points to a task
"""
token = credentials.token
status = queue_data.get_status(token=token)
if status["status"] != "invalid token":
if status["status"] == "running":
results = result_storage.get(token=token)
response = {
"status": status,
"config": results["config"],
"output": results["output"],
}
if block.progress == True:
progress_value = fetch_progress_from_token(
result_storage=result_storage, token=token
)
response["status"]["progress"] = progress_value
elif status["status"] == "complete":
results = result_storage.get(token=token)
## if results are deleted, it still returns the same schema
if results == None and remove_result_on_fetch == True:
response = {
"status": {
"status": "removed"
},
}
else:
response = {
"status": status,
"config": results["config"],
"output": results["output"],
}
if remove_result_on_fetch == True:
result_storage.delete(token=token)
elif (
status["status"] == "queued"
or status["status"] == "starting"
or status["status"] == "failed"
or status["status"] == "revoked"
):
results = result_storage.get(token=token)
response = {"status": status, "config": results["config"]}
else:
response = {"status": status} ## invalid token
return response
@app.post("/stop")
async def stop(wait_for: WaitFor):
"""
Stops the eden block, and exits the script
Args:
config (dict, optional): Amount of time in seconds before the server shuts down. Defaults to {'time': 0}.
"""
logging.info(f"Stopping gracefully in {wait_for.seconds} seconds")
stop_everything_gracefully(t=wait_for.seconds)
@app.post("/get_identity")
def get_identity():
"""
Returns name and active commit hash of the generator
"""
try:
repo = git.Repo(search_parent_directories=True)
name = repo.remotes.origin.url.split('.git')[0].split('/')[-1]
sha = repo.head.object.hexsha
except git.exc.InvalidGitRepositoryError:
name = "repo-less-eden"
sha = "none"
response = {
"name": name,
"commit": sha
}
return response
## overriding the boring old [INFO] thingy
LOGGING_CONFIG["formatters"]["default"]["fmt"] = (
"[" + Colors.CYAN + "EDEN" + Colors.END + "] %(asctime)s %(message)s"
)
LOGGING_CONFIG["formatters"]["access"]["fmt"] = (
"["
+ Colors.CYAN
+ "EDEN"
+ Colors.END
+ "] %(levelprefix)s %(client_addr)s - '%(request_line)s' %(status_code)s"
)
config = uvicorn.config.Config(app=app, host=host, port=port, log_level=log_level)
server = ThreadedServer(config=config)
# context starts fastAPI stuff and run_celery_app starts celery
with server.run_in_thread():
message = (
PREFIX
+ " Initializing celery worker on: "
+ f"redis://localhost:{str(redis_port)}"
)
print(message)
## starts celery app
run_celery_app(
celery_app,
max_num_workers=max_num_workers,
loglevel=celery_log_levels[log_level],
logfile=logfile,
queue_name=block.name,
)
message = PREFIX + " Stopped"
print(message)