I'm working on a robot that uses a CNN that needs much more memory than my embedded computer (Jetson TX1) can handle. I was wondering if it would be possible (with an extremely low latency connection) to outsource the heavy computations to EC2 and send the results back to the be used in a Python script. If this is possible, how would I go about it and what would the latency look like (not computations, just sending to and from).
I think it's certainly possible. You would need some scripts or a web server to transfer data to and from. Here is how I think you might achieve it:
Send all your training data to an EC2 instance
Train your CNN
Save the weights and/or any other generated parameters you may need
Construct the CNN on your embedded system and input the weights
from the EC2 instance. Since you won't be needing to do any training
here and won't need to load in the training set, the memory usage
will be minimal.
Use your embedded device to predict whatever you may need
It's hard to give you an exact answer on latency because you haven't given enough information. The exact latency is highly dependent on your hardware, internet connection, amount of data you'd be transferring, software, etc. If you're only training once on an initial training set, you only need to transfer your weights once and thus latency will be negligible. If you're constantly sending data and training, or doing predictions on the remote server, latency will be higher.
Possible: of course it is.
You can use any kind of RPC to implement this. HTTPS requests, xml-rpc, raw UDP packets, and many more. If you're more interested in latency and small amounts of data, then something UDP based could be better than TCP, but you'd need to build extra logic for ordering the messages and retrying the lost ones. Alternatively something like Zeromq could help.
As for the latency: only you can answer that, because it depends on where you're connecting from. Start up an instance in the region closest to you and run ping, or mtr against it to find out what's the roundtrip time. That's the absolute minimum you can achieve. Your processing time goes on top of that.
I am a former employee of CENAPAD-UFC (National Centre of HPC, Federal University of Ceará), so I have something to say about outsourcing computer power.
CENAPAD has a big cluster, and it provides computational power for academic research. There, professors and students send their computation and their data, defined the output and go drink a coffee or two, while the cluster go on with the hard work. After lots of flops, the operation ended and they retrieve it via ssh and go back to their laptops.
For big chunks of computation, you wish to minimize any work that is not a useful computation. One such thing is commumication over detached computers. If you need to know when the computation has ended, let the HPC machine tell you that.
To compute stuff effectively, you may want to go deeper in the machine and performe some kind of distribution. I use OpenMP to distribute computation inside the same machine/thread distribution. To distribute between physically separated computers, but next (latency speaking), I use MPI. I have installed also another cluster in UFC for another department. There, the researchers used only MPI.
Maybe some read about distributed/grid/clusterized computing helps you:
https://en.m.wikipedia.org/wiki/SETI#home ; the first example of massive distributed computing that I ever heard
https://en.m.wikipedia.org/wiki/Distributed_computing
https://en.m.wikipedia.org/wiki/Grid_computing
https://en.m.wikipedia.org/wiki/Supercomputer ; this is CENAPAD like stuff
In my opinion, you wish to use a grid-like computation, with your personal PC working as a master node that may call the EC2 slaves; in this scenario, just use communication from master to slave to send program (if really needed) and data, in such a way that the master will have another thing to do not related with the sent data; also, let the slave tells your master when the computation reached it's end.
Related
I implemented a distributed monitoring solution which should be analyzed. Therefore it would be good to scale number of monitoring agents and to evaluate the resulting overhead (CPU, Memory, Disk, Network) on each host (or network). Unfortunately, I have not enough physical nodes. Therefore I made primarily attempts with mininet, however I ran into negativ timing and scheduling effects. In addition, CPU or memory usage on a host is difficult to investigate during emulation on only one physical host.
Hence, I tried simpy. But I'm not able to monitor CPU, memory, disk and network usage of processes. Is there any possibility to do so? It would be good to have a soultion for evaluation which does not depend on host resources and correct timing, like a simulation does. But I'm not sure if simpy is a good decision or how this tool could provide this.
I have a pipe coming from my Web server to my primary development desktop in order to have a slot open for heavy CPU processes without paying for the premium from Amazon or another cloud platform. I do however still use this machine for other personal things such as video encoding or gaming.
Is there a way to combine both a NICE value and a cpulimit value in order to slow down the maximum percentage of the CPU being used but it has the highest priority so it will absolutely be done when requested. Say for example I wanted 25% of my CPU available on demand to the process no matter what I was doing on the machine currently.
Ideally I would like it to be able to allow a higher percentage during times that I am not using the machine but setting a minimum that is always available.
Is there a clean way to do this? The only way that I found so far is by sticking the process in a separate virtual machine but it feels like I'm making things a whole lot more complicated than they need to be in order to make it run smoothly. On top of that, the ability to allow a higher percentage from a limited virtual machine currently doesn't exist as far as I know.
As a side note, I'm doing all this on a Mac so this solution will have to be Unix based. And the server I'm using is python's CherryPy for easy expansions on new developments.
Thank you in advance.
I am currently working on some simulation code written in C, which runs on different remote machines. While the C part is finished I want to simplify my work by extending it with a python simulation api and some kind of a job-queue system, which should do the following:
1.specifiy a set of parameters on which simulations should be performed and put them into a queue on a host computer
2.perform simulation on remote machines by workers
3.return results to host computer
I had a look at different frameworks for accomplishing this task and my first choice goes down to IPython.parallel. I had a look at the documentation and from what I tested out it seems pretty easy to use. My approach would be to use a load balanced view like explained at
http://ipython.org/ipython-doc/dev/parallel/parallel_task.html#creating-a-loadbalancedview-instance
But what I dont see is:
what happens i.e. if the ipcontroller crashes, is my job queue gone?
what happens if a remote machine crashes? is there some kind of error handling?
Since I run relatively long simulations (1-2 weeks) I don't want my simulations to fail if some part of the system crashes. So is there maybe some way to handle this in IPython.parallel?
My Second approach would be to use pyzmq and implement the jobsystem from scratch.
In this case what would be the best zmq-pattern for this situation?
And last but not least, is there maybe a better framework for this scenario?
What lies behind the curtain is a bit more complex view on how to arrange the work-package flow alongside the ( parallelised ) number-crunching pipeline(s).
Being the work-package of a many CPU-core-week(s),
or
being the lumpsum volume of the job above a few hundred-of-thousands of CPU-core-hours, the principles are similar and follow a common sense.
Key Features
scaleability of the computing performance of all resources involved ( ideally a linear one )
ease of task submission role
fault-resilience of submitted task(s) ( ideally with an automated self-healing )
feasible TCO cost of access to / use of a sufficient pool of resources ( upfront co$ts, recurring co$ts, adaptation$ co$ts, co$ts of $peed )
Approaches to Solution
home-brew architecture for a distributed massively parallel scheduler based self-healing computation engine
re-use of available grid-based computing resources
Based on own experience to solve a need for repetitive runs of numerical intensive optimisation problem over a vast parameterSetVectorSPACE ( which could not be de-composed into any trivialised GPU parallelisation scheme ), selection of the second approach has been validated to be more productive rather than an attempt to burn dozens of man*years in just-another-trial to re-invent a wheel.
Being in academia environment, one may get a way easier to an acceptable access to resources-pool(s) for processing the work-packages, while commercial entities may acquire the same, based on their acceptable budgeting tresholds.
My gut instinct is to suggest rolling your own solution for this, because like you said otherwise you're depending on IPython not crashing.
I would run a simple python service on each node which listens for run commands. When it receives one it launches your C program. However, I suggest you ensure the C program is a true Unix daemon, so when it runs it completely disconnects itself from python. That way if your node python instance crashes you can still get data if the C program executes successfully. Have the C program write the output data to a file or database, and when the task is finished write "finished" to a "status" or something similar. The python service should monitor that file and when finished is indicated it should retrieve the data and send it back to the server.
The central idea of this design is to have as few possible points of failure as possible. As long as the C program doesn't crash, you can still get the data one way or another. As far as handling system crashes, network disconnects, etc, that's up to you.
I have a network of 100 machines, all running Ubuntu Linux.
On a continuous (streaming) basis, machine X is 'fed' with some real-time data. I need to write a python script that would get the data as input, load it in-memory, process it, and then save it to disk.
It's a lot of data, hence, I would ideally want to split the data in memory (using some logic) and just send pieces of it to each individual computer, in the fastest possible way. each individual computer will accept its piece of data, handle it and write it to its local disk.
Suppose I have a container of data in Python (be it a list, a dictionary etc), already processed and split to pieces. What is the fastest way to send each 'piece' of data to each individual machine?
You should take a look at pyzmq:
http://www.zeromq.org/bindings:python
and general guides to zeromq (0mq)
http://nichol.as/zeromq-an-introduction
http://www.zeromq.org/
You have two (classes of) choices:
You could build some distribution mechanism yourself.
You could use an existing tool to handle the distribution and storage.
In the simplest case, you write a program on each machine in your network that simply listens, processes and writes. You distribute from X to each machine in your pool round-robin. But, you might want to address higher-level concerns like handling node failures or dealing with requests that take longer to process than others, adding new nodes to the system, etc.
As you want more functionality, you'll probably want to find some existing tool to help you. It sounds like you might want to investigate some combinations of AMQP (for reliable messaging), Hadoop (for distributed data processing) or more complete NoSQL solutions like Cassandra or Riak. By leveraging these tools, your system will be significantly more robust than what you could probably build out yourself.
What you want is a message queue like RabbitMQ. It is easy to add consumers and producers to a queue. Consumer can either poll or get notified through a callback...
Upon hearing that the scientific computing project (happens to be the stochastic tractography method described here) I'm currently running for an investigator would take 4 months on our 50 node cluster, the investigator has asked me to examine other options. The project is currently using parallel python to farm out chunks of a 4d array to different cluster nodes, and put the processed chunks back together.
The jobs I'm currently working with are probably much too coarsely grained, (5 seconds to 10 minutes, I had to increase the timeout default in parallel python) and I estimate I could speed up the process by 2-4 times by rewriting it to make better use of resources (splitting up and putting back together the data is taking too long, that should be parallelized as well). Most of the work in done by numpy arrays.
Let's assume that 2-4 times isn't enough, and I decide to get the code off of our local hardware. For high throughput computing like this, what are my commercial options and how will I need to modify the code?
You might be interested in PiCloud. I have never used it, but their offer apparently includes the Enthought Python Distribution, which covers the standard scientific libraries.
It's tough to say if this will work for your specific case, but the Parallel Python interface is pretty generic. So hopefully not too many changes would be needed. Maybe you can even write a custom scheduler class (implementing the same interface as PP). Actually that might be useful for many people, so maybe you can drum up some support in the PP forum.
The most obvious commercial options which come to mind are Amazon EC2 and the Rackspace Cloud. I have played with both and found the Rackspace API a little easier to use.
The good news is that you can prototype and play with their compute instances (short- or long-lived virtual machines of the OS of your choice) for very little investment, typically $US 0.10 / hr or so. You create them on demand and then release them back to the cloud when you are done, and only pay for what you use. For example, I saw a demo on Django deployment using 6 Rackspace instances which took perhaps an hour and cost the speakers less than a dollar.
For your use case (not clear exactly what you meant by 'high throughput'), you will have to look at your budget and your computing needs, as well as your total network throughput (you pay for that, too). A few small-scale tests and a simple spreadsheet calculation should tell you if it's really practical or not.
There are Python APIs for both Rackspace Cloud and Amazon EC2. Whichever you use, I recommend python-based Fabric for automated deployment and configuration of your instances.