In essence, the following function, called by the user of the django application that I am developing, uses the Scapy library to process 80-odd fairly large pcaps in order to initially parse their destination IP addresses.
I was wondering whether it would be possible to process several pcaps simultaneously, as the CPU is not being utilised to it's full capacity, ideally using multi-threading
def analyseall(request):
allpcaps = Pcaps.objects.all()
for individualpcap in allpcaps:
strfilename = str(individualpcap.filename)
print(strfilename)
pcapuuid = individualpcap.uuid
print(pcapuuid)
packets = rdpcap(strfilename)
print("hokay")
for packet in packets:
if packet.haslayer(IP):
# print(packet[IP].src)
# print(packet[IP].dst)
dstofpacket = packet[IP].dst
PcapsIps.objects.update_or_create(ip=dstofpacket, uuid=individualpcap)
return render(request, 'about.html', {"list": list})
You can use above answer (multiprocessing), and also improve scapy’s reading speed, by using the PcapReader generator rather than rdpcap
with PcapReader(filename) as fdesc:
for pkt in fdesc:
[actions on the pkt]
I consider mixing multiprocessing and Django tricky. I was working on such solution once and finally I decided to use Celery and RabbitMQ.
Using Celery you can easily define task of processing single pcap. Then you can start a few independent workers for processing files in the background. Such solution will result in a little more complicated architecture (you need to provide message queue e. g. RabbitMQ and the Celery workers), however you can gain a much simpler code.
http://docs.celeryproject.org/en/latest/django/first-steps-with-django.html
In my case Celery saved a lot of time.
You can also check this question and answers:
How to use python multiprocessing module in django view
Related
We have a Vertex AI model that takes a relatively long time to return a prediction.
When hitting the model endpoint with one instance, things work fine. But batch jobs of size say 1000 instances end up with around 150 504 errors (upstream request timeout). (We actually need to send batches of 65K but I'm troubleshooting with 1000).
I tried increasing the number of replicas assuming that the # of instances handed to the model would be (1000/# of replicas) but that doesn't seem to be the case.
I then read that the default batch size is 64 and so tried decreasing the batch size to 4 like this from the python code that creates the batch job:
model_parameters = dict(batch_size=4)
def run_batch_prediction_job(vertex_config):
aiplatform.init(
project=vertex_config.vertex_project, location=vertex_config.location
)
model = aiplatform.Model(vertex_config.model_resource_name)
model_params = dict(batch_size=4)
batch_params = dict(
job_display_name=vertex_config.job_display_name,
gcs_source=vertex_config.gcs_source,
gcs_destination_prefix=vertex_config.gcs_destination,
machine_type=vertex_config.machine_type,
accelerator_count=vertex_config.accelerator_count,
accelerator_type=vertex_config.accelerator_type,
starting_replica_count=replica_count,
max_replica_count=replica_count,
sync=vertex_config.sync,
model_parameters=model_params
)
batch_prediction_job = model.batch_predict(**batch_params)
batch_prediction_job.wait()
return batch_prediction_job
I've also tried increasing the machine type to n1-high-cpu-16 and that helped somewhat but I'm not sure I understand how batches are sent to replicas?
Is there another way to decrease the number of instances sent to the model?
Or is there a way to increase the timeout?
Is there log output I can use to help figure this out?
Thanks
Answering your follow up question above.
Is that timeout for a single instance request or a batch request. Also, is it in seconds?
This is a timeout for the batch job creation request.
The timeout is in seconds, according to create_batch_prediction_job() timeout refers to rpc timeout. If we trace the code we will end up here and eventually to gapic where timeout is properly described.
timeout (float): The amount of time in seconds to wait for the RPC
to complete. Note that if ``retry`` is used, this timeout
applies to each individual attempt and the overall time it
takes for this method to complete may be longer. If
unspecified, the the default timeout in the client
configuration is used. If ``None``, then the RPC method will
not time out.
What I could suggest is to stick with whatever is working for your prediction model. If ever adding the timeout will improve your model might as well build on it along with your initial solution where you used a machine with a higher spec. You can also try using a machine with higher memory like the n1-highmem-* family.
I am having a issue with the celery , I will explain with the code
def samplefunction(request):
print("This is a samplefunction")
a=5,b=6
myceleryfunction.delay(a,b)
return Response({msg:" process execution started"}
#celery_app.task(name="sample celery", base=something)
def myceleryfunction(a,b):
c = a+b
my_obj = MyModel()
my_obj.value = c
my_obj.save()
In my case one person calling the celery it will work perfectly
If many peoples passing the request it will process one by one
So imagine that my celery function "myceleryfunction" take 3 Min to complete the background task .
So if 10 request are coming at the same time, last one take 30 Min delay to complete the output
How to solve this issue or any other alternative .
Thank you
I'm assuming you are running a single worker with default settings for the worker.
This will have the worker running with worker_pool=prefork and worker_concurrency=<nr of CPUs>
If the machine it runs on only has a single CPU, you won't get any parallel running tasks.
To get parallelisation you can:
set worker_concurrency to something > 1, this will use multiple processes in the same worker.
start additional workers
use celery multi to start multiple workers
when running the worker in a docker container, add replica's of the container
See Concurrency for more info.
===SIMPLE & SHORT===
Does anybody have working application that talks with Poloniex through WAMP in these days (January, 2018)?
===MORE SPECIFIC===
I used several info sources to make it work using combo: autobahn-cpp & C++. Windows 10 OS.
I was able to connect to wss://api.poloniex.com, realm1. Plus I was able to subscribe and get subscription ID. But I never got any events even when everything established.
===RESEARCH===
During research in the web I saw a lot of controversial information:
1. Claims, that wss://api2.poloniex.com should be used, and channels names are actually numbers - How to connect to poloniex.com websocket api using a python library
2. This answer gave me base code, but I am getting anything more than just connections, also by following this answer - wss://api.poloniex.com is correct address - Connecting to Poloniex Push-API
3. I saw post (sorry, lost the link), there were comments made that websockets implementation are basically broken on poloniex. They were posted 6 months ago.
===SPECS===
1. Windows 10
2. Autobahn-Cpp
3. wss://api.poloniex.com:443 ; realm1
4. Different subscriptions: ticker, BTC_ETH, 148, 1002, etc..
5. Source code I got from here
===WILL HELP AS WELL===
Is there any way to get all valid subscriptions or, probably, those, that have more than 0 subscribers? I mean, does WAMP have a way to do that?
Is there any known issues with Autobahn-Cpp and poloniex combo?
Is there any simpler way to test WAMP elsewhere to make sure Autobahn isn't a problem? Like any other well documented & supported online projects that accept WAMP websocket communication?
I can receive the correct tick order book data from wss://api2.poloniex.com use python3
but sometime The channel 1002 may stop sending the new tick info.
wss://api.poloniex.com:443 ; realm1
This may be the issue as I've been using api2 and here is the code that works, and has been working for the past 2 quarters non-stop. Its in python, but should be easy enough to port to C++.
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import websocket
import json
def on_error(ws, error):
print(error)
def on_close(ws):
print("### closed ###")
connection.close()
def on_open(ws):
print("ONOPEN")
ws.send(json.dumps({'command':'subscribe','channel':'BTC_ETH'}))
def on_message(ws, message):
message = json.loads(message)
print(message)
websocket.enableTrace(True)
ws = websocket.WebSocketApp("wss://api2.poloniex.com/",
on_message = on_message,
on_error = on_error,
on_close = on_close)
ws.on_open = on_open
ws.run_forever()
the code is pretty much self-explanatory (You can check all channels/pairs on Poloniex API website), just save it and run in terminal
python3 fileName.py
should provide You with BTCETH raw stream of orders and trades on console output.
Playing with the message/subscriptions You can then do as You please with it.
It seems that websockets in Poloniex are unstable. Therefore I can stop my attempts make Autobahn-Cpp work with it at least by now and move on.
Background
I want to predict pathology images using keras with Inception-Resnet_v2. I have trained the model already and got a .hdf5 file. Because the pathology image is very large (for example: 20,000 x 20,000 pixels), so I have to scan the image to get small patches for prediction.
I want to speed up the prediction procedure using multiprocessing lib with python2.7. The main idea is using different subprocesses to scan different lines and then sending patches to model.
I saw somebody suggests importing keras and loading model in subprocesses. But I don't think it is suitable for my task. Loading model usingkeras.models.load_model() one time will take about 47s, which is very time-consuming. So I can't reload the model every time when I start a new subprocess.
Question
My question is can I load the model in my main process and pass it as a parameter to subprocesses?
I have tried two methods but both of them didn't work.
Method 1. Using multiprocessing.Pool
The code is :
import keras
from keras.models import load_model
import multiprocessing
def predict(num,model):
print dir(model)
print num
model.predict("image data, type:list")
if __name__ == '__main__':
model = load_model("path of hdf5 file")
list = [(1,model),(2,model),(3,model),(4,model),(5,model),(6,model)]
pool = multiprocessing.Pool(4)
pool.map(predict,list)
pool.close()
pool.join()
The output is
cPickle.PicklingError: Can't pickle <type 'module'>: attribute lookup __builtin__.module failed
I searched the error and found Pool can't map unpickelable parameters, so I try method 2.
Method 2. Using multiprocessing.Process
The code is
import keras
from keras.models import load_model
import multiprocessing
def predict(num,model):
print num
print dir(model)
model.predict("image data, type:list")
if __name__ == '__main__':
model = load_model("path of hdf5 file")
list = [(1,model),(2,model),(3,model),(4,model),(5,model),(6,model)]
proc = []
for i in range(4):
proc.append(multiprocessing.Process(predict, list[i]))
proc[i].start()
for i in range(4):
proc[i].join()
In Method 2, I can print dir(model). I think it means the model is passed to subprocesses successfully. But I got this error
E tensorflow/stream_executor/cuda/cuda_driver.cc:1296] failed to enqueue async memcpy from host to device: CUDA_ERROR_NOT_INITIALIZED; GPU dst: 0x13350b2200; host src: 0x2049e2400; size: 4=0x4
The environment which I use:
Ubuntu 16.04, python 2.7
keras 2.0.8 (tensorflow backend)
one Titan X, Driver version 384.98, CUDA 8.0
Looking forward to reply! Thanks!
Maybe you can use apply_async() instead of Pool()
and you can find more details here:
Python multiprocessing pickling error
Multi-processing works on CPU, while model prediction happened in GPU, which there is only one. I cannot see how multi-processing can help you on prediction.
Instead, I think you can use multi-processing to scan different patches, which you seems to have already managed to achieve. Then stack these patches into a batch or batches to predict in parallel in GPU.
As noted by Statham multiprocess requires all args to be compatible with pickle. This blog post describes how to save a keras model as a pickle: [http://zachmoshe.com/2017/04/03/pickling-keras-models.html][1]
It may be a sufficient workaround to get your keras model passed as an arg to multiprocess, but I have not tested the idea myself.
I will also add that I had better luck running two keras processes on a single gpu using windows rather than linux. On linux I was getting out of memory errors on the 2nd process, but the same memory allocation (45% of total GPU ram for each) worked on windows. In my case they were fits - for running predictions only, maybe the memory requirements are less.
I have a genetic algorithm which I would like to speed up. I'm thinking the easiest way to achieve this is by pythons multiprocessing module. After running cProfile on my GA, I found out that most of the computational time takes place in the evaluation function.
def evaluation():
scores = []
for chromosome in population:
scores.append(costly_function(chromosome))
How would I go about to parallelize this method? It is important that all the scores append in the same order as they would if the program would run sequentially.
I'm using python 2.7
Use pool (I show both imap and map because of some results on google say map may not be OK for ordering though I have yet to see proof):
from multiprocessing import Pool
def evaluation(population):
return list(Pool(processes=nprocs).imap(costly_function,population))
or (what I use):
return Pool(processes=nprocs).map(costly_function,population)
Define nprocs to the number of parallel process you want.
From:
https://docs.python.org/dev/library/multiprocessing.html#multiprocessing.pool.Pool