Can I run pre-trained-model on a worse GPU? - deep-learning

My apologies since my question may sound stupid question. But I am quite new in deep learning.
So basically I used a computer with TITAN X GPU, I created my data set, then I trained it on the TITAN X.
My question is, if I would save this trained model, then load the model on my second computer (which has worse GPU than my TITAN X), then test the model in the new computer - will it go successfully? or the fact that I'm using worse GPU may affect it?

First of all it will run slower.
Your model also needs to fit completely into the memory of the older GPU, otherwise you will have a problem running it.
If it runs, however, it should be executed correctly if you are using a modern framework.

Related

How to improve model latency for deployment

Question:
How to improve model latency for web deployment without retraining the models? What is the checklist that I should mark to improve the model speed?
Context:
I have multiple models that process a video sequentially on one machine with one K80 GPU; each model takes around 5 mins to process a video that is 1 min long. What ideas and suggestions should I try to improve each model latency without changing the model architecture? How should I structure my thinking about this problem?
Sampling frames is the easiest technique if it fits your usecase. Picking every 5th frame for inference will cut your inference time by ~5x(theoretically). Caveat is if you are working on tasks like object tracking you will have reduced accuracy.
fp32 to fp16 might increase your inference speed.
Batch Inference always lowers inference time by a decent bit. Ref: https://github.com/ultralytics/yolov5/issues/1806#issuecomment-752834571
Multiprocess Concurrent Inference is basically spinning up more than 1 instances of the same model on seperate processes and infer parallely. torch has a multiprocessing module torch.multiprocessing. I havent ever used this but i assume the setup would be somewhat significant and complex.
Nvidia Tesla K80 is quite an old GPU (2014), so that's probably the reason why the processing time is so long. If your machine has a modern Intel CPU and/or iGPU you could try OpenVINO. It's a heavily optimized toolkit for inference. Here are some performance benchmarks.
You can find a full tutorial on how to convert the PyTorch model here.
Some snippets below.
Install OpenVINO
The easiest way to do it is using PIP. Alternatively, you can use this tool to find the best way in your case.
pip install openvino-dev[pytorch,onnx]
Save your model to ONNX
OpenVINO cannot convert PyTorch model directly for now but it can do it with ONNX model. This sample code assumes the model is for computer vision.
dummy_input = torch.randn(1, 3, IMAGE_HEIGHT, IMAGE_WIDTH)
torch.onnx.export(model, dummy_input, "model.onnx", opset_version=11)
Use Model Optimizer to convert ONNX model
The Model Optimizer is a command line tool which comes from OpenVINO Development Package so be sure you have installed it. It converts the ONNX model to IR, which is a default format for OpenVINO. It also changes the precision to FP16 for even better performance (there shouldn't be much accuracy drop). Run in command line:
mo --input_model "model.onnx" --input_shape "[1,3, 224, 224]" --mean_values="[123.675, 116.28 , 103.53]" --scale_values="[58.395, 57.12 , 57.375]" --data_type FP16 --output_dir "model_ir"
Run the inference on the CPU
The converted model can be loaded by the runtime and compiled for a specific device e.g. CPU. Use AUTO if you want to deploy on the best device you have.
# Load the network
ie = Core()
model_ir = ie.read_model(model="model_ir/model.xml")
compiled_model_ir = ie.compile_model(model=model_ir, device_name="AUTO")
# Get output layer
output_layer_ir = compiled_model_ir.output(0)
# Run inference on the input image
result = compiled_model_ir([input_image])[output_layer_ir]
Disclaimer: I work on OpenVINO.

CUDA profiling information on part of a code [duplicate]

I am somewhat familiar with the CUDA visual profiler and the occupancy spreadsheet, although I am probably not leveraging them as well as I could. Profiling & optimizing CUDA code is not like profiling & optimizing code that runs on a CPU. So I am hoping to learn from your experiences about how to get the most out of my code.
There was a post recently looking for the fastest possible code to identify self numbers, and I provided a CUDA implementation. I'm not satisfied that this code is as fast as it can be, but I'm at a loss as to figure out both what the right questions are and what tool I can get the answers from.
How do you identify ways to make your CUDA kernels perform faster?
If you're developing on Linux then the CUDA Visual Profiler gives you a whole load of information, knowing what to do with it can be a little tricky. On Windows you can also use the CUDA Visual Profiler, or (on Vista/7/2008) you can use Nexus which integrates nicely with Visual Studio and gives you combined host and GPU profile information.
Once you've got the data, you need to know how to interpret it. The Advanced CUDA C presentation from GTC has some useful tips. The main things to look out for are:
Optimal memory accesses: you need to know what you expect your code to do and then look for exceptions. So if you are always loading floats, and each thread loads a different float from an array, then you would expect to see only 64-byte loads (on current h/w). Any other loads are inefficient. The profiling information will probably improve in future h/w.
Minimise serialization: the "warp serialize" counter indicates that you have shared memory bank conflicts or constant serialization, the presentation goes into more detail and what to do about this as does the SDK (e.g. the reduction sample)
Overlap I/O and compute: this is where Nexus really shines (you can get the same info manually using cudaEvents), if you have a large amount of data transfer you want to overlap the compute and the I/O
Execution configuration: the occupancy calculator can help with this, but simple methods like commenting the compute to measure expected vs. measured bandwidth is really useful (and vice versa for compute throughput)
This is just a start, check out the GTC presentation and the other webinars on the NVIDIA website.
If you are using Windows... Check Nexus:
http://developer.nvidia.com/object/nexus.html
The CUDA profiler is rather crude and doesn't provide a lot of useful information. The only way to seriously micro-optimize your code (assuming you have already chosen the best possible algorithm) is to have a deep understanding of the GPU architecture, particularly with regard to using shared memory, external memory access patterns, register usage, thread occupancy, warps, etc.
Maybe you could post your kernel code here and get some feedback ?
The nVidia CUDA developer forum forum is also a good place to go for help with this kind of problem.
I hung back because I'm no CUDA expert, and the other answers are pretty good IF the code is already pretty near optimal. In my experience, that's a big IF, and there's no harm in verifying it.
To verify it, you need to find out if the code is for sure not doing anything it doesn't really have to do. Here are ways I can see to verify that:
Run the same code on the vanilla processor, and either take stackshots of it, or use a profiler such as Oprofile or RotateRight/Zoom that can give you equivalent information.
Running it on a CUDA processor, and doing the same thing, if possible.
What you're looking for are lines of code that have high occupancy on the call stack, as shown by the fraction of stack samples containing them. Those are your "bottlenecks". It does not take a very large number of samples to locate them.

Using CUDA GPUs at prediction time for high througput streams

We're trying to develop a Natural Language Processing application that has a user facing component. The user can call models through an API, and get the results back.
The models are pretrained using Keras with Theano. We use GPUs to speed up the training. However, prediction is still sped up significantly by using the GPU. Currently, we have a machine with two GPUs. However, at runtime (e.g. when running the user facing bits) there is a problem: multiple Python processes sharing the GPUs via CUDA does not seem to offer a parallelism speed up.
We're using nvidia-docker with libgpuarray (pygpu), Theano and Keras.
The GPUs are still mostly idle, but adding more Python workers does not speed up the process.
What is the preferred way of solving the problem of running GPU models behind an API? Ideally we'd utilize the existing GPUs more efficiently before buying new ones.
I can imagine that we want some sort of buffer before sending it off to the GPU, rather than requesting a lock for each HTTP call?
This is not an answer to your more general question, but rather an answer based on how I understand the scenario you described.
If someone has coded a system which uses a GPU for some computational task, they have (hopefully) taken the time to parallelize its execution so as to benefit from the full resources the GPU can offer, or something close to that.
That means that if you add a second similar task - even in parallel - the total amount of time to complete them should be similar to the amount of time to complete them serially, i.e. one after the other - since there are very little underutilized GPU resources for the second task to benefit from. In fact, it could even be the case that both tasks will be slower (if, say, they both somehow utilize the L2 cache a lot, and when running together they thrash it).
At any rate, when you want to improve performance, a good thing to do is profile your application - in this case, using the nvprof profiler or its nvvp frontend (the first link is the official documentation, the second link is a presentation).

Simple Compute-Intensive CUDA Program

I'm preparing an acceptance test for a new machine with Nvidia graphics cards and I'd like a simple CUDA program that will fully exercise the GPU for a full day. The intent is to generate large amounts of heat and ensure the new machine is stable under the load. I'd like the code to be very easy to compile and run (no dependencies, no large input data sets), and also very easy to verify (small amounts of output). Also, I'd like it to be command-line only, no GUI (the test will have to be automated).
I was originally thinking of repeatedly running Vector Dot Products of large vectors. However, that's mostly memory-intensive. So if the GPUs are constantly waiting on memory accesses, then they probably aren't generating as much heat as they could.
I'm running on a CentOS Linux machine.
Does anyone have any suggestions?
You didn't mention which OS you are on.
Ideally, you would want to stress the floating point units, the logic/integer units, the GPU memory, the GPU voltage regulators (VRMs) and the main PSU. I don't think there is any single utility out there that does that.
Memory:
http://sourceforge.net/projects/cudagpumemtest/
Integer (?):
http://sourceforge.net/projects/cudalucas/
PSU and VRMs (In the past, this program could cause GPUs to run out-of-spec, breaking the card. I don't think that's the case anymore):
http://www.ozone3d.net/benchmarks/fur/

raytracing with CUDA [closed]

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I'm currently implementing a raytracer. Since raytracing is extremely computation heavy and since I am going to be looking into CUDA programming anyway, I was wondering if anyone has any experience with combining the two. I can't really tell if the computational models match and I would like to know what to expect. I get the impression that it's not exactly a match made in heaven, but a decent speed increasy would be better than nothing.
One thing to be very wary of in CUDA is that divergent control flow in your kernel code absolutely KILLS performance, due to the structure of the underlying GPU hardware. GPUs typically have massively data-parallel workloads with highly-coherent control flow (i.e. you have a couple million pixels, each of which (or at least large swaths of which) will be operated on by the exact same shader program, even taking the same direction through all the branches. This enables them to make some hardware optimizations, like only having a single instruction cache, fetch unit, and decode logic for each group of 32 threads. In the ideal case, which is common in graphics, they can broadcast the same instruction to all 32 sets of execution units in the same cycle (this is known as SIMD, or Single-Instruction Multiple-Data). They can emulate MIMD (Multiple-Instruction) and SPMD (Single-Program), but when threads within a Streaming Multiprocessor (SM) diverge (take different code paths out of a branch), the issue logic actually switches between each code path on a cycle-by-cycle basis. You can imagine that, in the worst case, where all threads are on separate paths, your hardware utilization just went down by a factor of 32, effectively killing any benefit you would've had by running on a GPU over a CPU, particularly considering the overhead associated with marshalling the dataset from the CPU, over PCIe, to the GPU.
That said, ray-tracing, while data-parallel in some sense, has widely-diverging control flow for even modestly-complex scenes. Even if you manage to map a bunch of tightly-spaced rays that you cast out right next to each other onto the same SM, the data and instruction locality you have for the initial bounce won't hold for very long. For instance, imagine all 32 highly-coherent rays bouncing off a sphere. They will all go in fairly different directions after this bounce, and will probably hit objects made out of different materials, with different lighting conditions, and so forth. Every material and set of lighting, occlusion, etc. conditions has its own instruction stream associated with it (to compute refraction, reflection, absorption, etc.), and so it becomes quite difficult to run the same instruction stream on even a significant fraction of the threads in an SM. This problem, with the current state of the art in ray-tracing code, reduces your GPU utilization by a factor of 16-32, which may make performance unacceptable for your application, especially if it's real-time (e.g. a game). It still might be superior to a CPU for e.g. a render farm.
There is an emerging class of MIMD or SPMD accelerators being looked at now in the research community. I would look at these as logical platforms for software, real-time raytracing.
If you're interested in the algorithms involved and mapping them to code, check out POVRay. Also look into photon mapping, it's an interesting technique that even goes one step closer to representing physical reality than raytracing.
It can certainly be done, has been done, and is a hot topic currently among the raytracing and Cuda gurus. I'd start by perusing http://www.nvidia.com/object/cuda_home.html
But it's basically a research problem. People who are doing it well are getting peer-reviewed research papers out of it. But well at this point still means that the best GPU/Cuda results are approximately competitive with best-of-class solutions on CPU/multi-core/SSE. So I think that it's a little early to assume that using Cuda is going to accelerate a ray tracer. The problem is that although ray tracing is "embarrassingly parallel" (as they say), it is not the kind of "fixed input and output size" problem that maps straightforwardly to GPUs -- you want trees, stacks, dynamic data structures, etc. It can be done with Cuda/GPU, but it's tricky.
Your question wasn't clear about your experience level or the goals of your project. If this is your first ray tracer and you're just trying to learn, I'd avoid Cuda -- it'll take you 10x longer to develop and you probably won't get good speed. If you're a moderately experienced Cuda programmer and are looking for a challenging project and ray tracing is just a fun thing to learn, by all means, try to do it in Cuda. If you're making a commercial app and you're looking to get a competitive speed edge -- well, it's probably a crap shoot at this point... you might get a performance edge, but at the expense of more difficult development and dependence on particular hardware.
Check back in a year, the answer may be different after another generation or two of GPU speed, Cuda compiler development, and research community experience.