Deep Learning Inferencing on IBM Cloud with NVIDIA TensorRT

Transcription

1 Deep Learning Inferencing on IBM Cloud with NVIDIA TensorRT Khoa Huynh Senior Technical Staff Member (STSM), IBM Larry Brown Senior Software Engineer, IBM

2 Agenda Introduction Inferencing with PyCaffe TensorRT Overview TensorRT Implementation Performance Results Conclusions Q & A 2

3 Introduction AI especially deep learning has seen rapid advancement in recent years Initial focus on image processing, expanding to natural language, different neural network models, recurrent networks, and DL frameworks Much attention to developing networks and training models Very compute intensive, GPUs nearly a necessity As DL becomes mainstream focus is shifting to inferencing (use of the trained network) Inferencing cloud service could handle requests from multiple users One request at a time or collect a batch of requests to inference at once But only wait a short time to fill a batch or latency is affected Or one user might submit a larger number of images to classify at once Inferencing cloud service needs quick response - latency seen by the user to handle large volume overall throughput 3

4 IBM Cloud GPU Offerings Bare Metal Servers Nvidia M60 GPUs (monthly & hourly) Nvidia K80 GPU PCIe cards (monthly & hourly) Nvidia P100 GPUs (monthly) Nvidia V100 GPUs (monthly) Virtual Servers Nvidia P100 GPUs (monthly & hourly) Nvidia V100 (coming soon monthly & hourly) Deep Learning as a Service (DLaaS) Part of Watson Machine Learning (WML) Focused on deep learning training Allows user to run training jobs on a cluster of GPU-enabled machines using various frameworks PowerAI Available in 2Q2018 with PowerAI R5 Delivered through IBM Cloud Catalog & supported by IBM Trusted Partner Nimbix On-demand cloud provisioning Containerized Native Distributed Deep Learning (DDL) and Large Model Support (LMS) 4

5 Inferencing with PyCaffe Given a trained model want to use it to classify images. Study performance of various GPUs, FPGAs, etc. Can use C++, but more familiar with Python so that was the language of choice. Unlike training, a single GPU is used. Use multiple threads, processes, or services if more volume needs to take advantage of more GPUs. root@v100:~/infer_caffe# python infer_caffe.py -h usage: infer_caffe.py [-h] -m MODEL -w WEIGHTS -l LMDB [-b BATCH] [-i ITERATIONS] [-c CAFFEROOT] [--blobname BLOBNAME] [--labels LABELS] [--meanimage MEANIMAGE] [--debug] [--gpu] [--csvfile CSVFILE] [--quiet] Use a trained Caffe model to classify images from a LMDB database. 5

6 infer_caffe Sample Output python infer_caffe.py -m ~/model_zoo/caffe/vgg16/pretrained/vgg_ilsvrc_16_layers_deploy.prototxt -w ~/model_zoo/caffe/vgg16/pretrained/vgg_ilsvrc_16_layers.caffemodel -l /datasets/x86_lmdb/lmdb/ilsvrc12_val_lmdb/ -c /opt/nvidia/caffe-0.16/caffe -b 1 -i 5 --gpu --csvfile./pycaffe.csv --quiet Final Stats (times in seconds) Date: 03/08/2018 Time: 09:05:54 Host: V100 Iterations: 5 Batch size: 1 Data type: NA Total run time: Stats for all iterations Total predictions: 5 Correct top 1 predictions: 5 Correct top 5 predictions: 5 Top 1 accuracy: % Top 5 accuracy: % Inference time -- Total: Mean: Min: Max: Range: STD: Median: Inference time/prediction: Images/sec:

7 Program Flow Parse command line # Create the neural network. net = caffe.net(model_def, # defines the structure of the model model_weights, # contains the trained weights caffe.test) # use test mode (e.g., don't perform dropout) for each iteration read a batch of images from LMDB # Call the network. out = net.forward() # Time only this step. output statistics 7

8 TensorRT Overview Speeds up inferencing by Merging layers and tensors to reduce size of network and execute in a single kernel. Selects the best specialized kernel for the target hardware based on layer parameters and measured performance. Stages Build: optimize the network (layers, weights, labels) to produce a runtime plan or engine. Optimization can take some time so the resulting engine can be serialized to a file. Deploy: run the engine with given input data to get the resulting predictions. Supports Python and C++. TensorRT Lite is a simplified interface for Python (not used here). Can create the TRT network yourself or use TRT utility to import and convert framework model into TRT form. Caffe and UFF (Universal Framework Format) compatible frameworks such as TensorFlow. 8

9 Reduced Precision Inferencing Model trained in FLOAT (FP32). TensorRT inferencing can use FLOAT, HALF, or INT8 (as supported by the GPU). Increase speed with no or little loss of accuracy. HALF could be some reduction of accuracy, not noticeable in general. INT8 a small reduction of accuracy. INT8 requires calibration files. Uses sample runs of data through the net to determine the range of FLOAT values encountered. Maps that range to INT8 s smaller range. Caffe patch available to easily generate these calibration files during a short training run when environment variable TENSORRT_INT8_BATCH_DIRECTORY is set. NVIDIA suggests For ImageNet networks, around 500 calibration images is adequate. 9

10 TensorRT Implementation Re-implementation of caffe_infer.py using TRT instead of pycaffe. Shares code for getting images, collecting stats, overall flow. python infer_caffe_trt.py -h usage: infer_caffe_trt.py [-h] -m MODEL -w WEIGHTS -l LMDB [-b BATCH] [-i ITERATIONS] [-c CAFFEROOT] [--imageshape IMAGESHAPE] [--max_batch MAX_BATCH] [--outputlayer OUTPUTLAYER] [--outputsize OUTPUTSIZE] [--dtype {FLOAT,HALF,INT8}] [--labels LABELS] [--meanimage MEANIMAGE] [--csvfile CSVFILE] [--calbatchdir CALBATCHDIR] [--firstcalbatch FIRSTCALBATCH] [--numcalbatches NUMCALBATCHES] [--debug] [--quiet] Uses NVidia TensorRT to optimize and run inference on a trained Caffe model performing an image recognition task and prints performance and accuracy results. 10

11 TRT Program Flow # Create the engine using the TRT utilities for Caffe. # Use the caffe model converter utility in tensorrt.utils. # We provide it a logger, a path to the model prototxt, the model file, the max batch size, # the max workspace size, the output layer(s) and the data type of the weights. engine_dtype = trt.infer.datatype[dtype] calibrator = None if engine_dtype == trt.infer.datatype.int8: calibrator = infer_utils.calibrator.calibrator(cal_batch_dir, first_cal_batch, num_cal_batches, debug) engine = trt.utils.caffe_to_trt_engine(trt_logger, model, weights, max_batch, 1 << 25, [output_layer], engine_dtype, calibrator=calibrator) # Allocate memory on the GPU with PyCUDA and register it with the engine. # The size of the allocations is the size of the input and expected output * the batch size d_input = cuda.mem_alloc(batch_size * image_shape[0] * image_shape[1] * 3 * np.dtype(np.float32).itemsize) d_output = cuda.mem_alloc(batch_size * output.size * output.dtype.itemsize) # The engine needs bindings provided as pointers to the GPU memory. # PyCUDA lets us do this for memory allocations by casting those allocations to ints bindings = [int(d_input), int(d_output)] # Create a cuda stream to run inference in. stream = cuda.stream() 11

12 TRT Program Flow # Time moving the data to the GPU, running the network, and getting the results back to the host as part of the # inference operation for this iteration. stats.begin_iteration() cuda.memcpy_htod_async(d_input, batchin, stream) # execute model context.enqueue(batch_size, bindings, stream.handle, None) # transfer predictions back cuda.memcpy_dtoh_async(output, d_output, stream) # syncronize threads stream.synchronize() 12

13 0.5 V100 Caffe VGGNet16 TensorRT 3.01 Inference Latency PyCaffe FLOAT HALF INT Average Latency/Batch (sec) Batch Size 13

14 2500 V100 Caffe VGGNet16 TensorRT 3.01 Inference Throughput PyCaffe FLOAT HALF INT Images/Second Batch Size 14

15 100 V100 Caffe VGGNet16 TensorRT Inference Accuracy PyCaffe FLOAT HALF INT Accuracy Per Cent Top 1 Top 5 15

16 90 V100 Caffe VGGNet16 TensortRT INT8 Inference Accuracy Top 1 Top Accuracy Per Cent Number of Calibration Images 16

17 Deep-Learning Model Inferencing Image Classification with VGG-16 on Caffe (Single Precision) Number of Images Processed Per Second (Inferening) Higher is better x Nvidia K80 GPU 1 x Nvidia P4 GPU 1 x Nvidia P100 GPU 1 x Nvidia V100 GPU 1 x Intel DLIA 1 x Nvidia K80 GPU SL Bare-Metal (Dual Xeon E5-2690v4) PyCaffe TensorRT TensorRT (HALF2) TensorRT (INT8) Intel Bare-Metal (Dual Xeon E5-2650v3) SL Bare-Metal (Dual Xeon E5-2690v4) SL Bare-Metal (Dual Xeon E5-2690v4) Intel Bare-Metal (Dual Xeon E5-2690v4) AWS p2.16xlarge (Dual Xeon E5-2686v4) Notes: Nvidia TensorRT with half-precision support improves DL inferencing performance by 5X on a V100 GPU The Nvidia P4 GPU is comparable to many FPGAs in terms of power consumption (30-70W) 17

18 Deep-Learning Model Inferencing Latency Per Iteration (ms) Deep Learning Model Inferencing - Image Classification VGG-16 Neural Net on Caffe Framework with TensorRT Lower is better (Single Precision Except Where Noted Otherwise) Batch Size K80 GPU P100 GPU P100 GPU w/ HALF2 V100 GPU V100 GPU w/ HALF2 Notes: The P100 and V100 GPUs deliver much better latencies and handles much larger batch sizes than the K80 GPU The Nvidia P4 GPU is comparable to many FPGAs in terms of power consumption (30-70W) Nvidia P100 GPU delivers 5X inferencing performance, and could handle much larger batch sizes, than previous-generation K80 GPU Nvidia P4 GPU could deliver up to 50% inferencing performance at less than 25% power consumption of a P100 GPU making the P4 a very cost-effective DL inferencing engine for a cloud platform Nvidia P4 GPU Intel DLIA Deep-Learning Model Inferencing (Image Classification) Nvidia P4 GPU vs. FPGA for DL VGG-16 Neural Net on Caffe Framework (Single Precision) Higher is better Classification Power Efficiency (Images/Second/Watt)

19 TensorRT Impressions Python interface was very important Utility to convert Caffe model to TRT network saved much work Building the network with native TRT calls is much more advanced Allows flexibility and customization for those who need it INT8 Calibration was challenging Doc was not complete Found a good blog that referenced classes not in the doc Need to write a Calibrator implementation by extending a class class Calibrator(trt.infer.Int8EntropyCalibrator): # from the doc did not work class Calibrator(trt.infer.EntropyCalibrator): # from the blog did work Overall for Caffe our experience was good. Other frameworks might require more use of lower TRT calls. 19

20 Conclusions V100 GPU is faster than anything else Nvidia TensorRT with half-precision support improves DL inferencing performance by 5X on a V100 GPU TensorRT is much faster than pycaffe HALF and INT8 are significantly faster than FLOAT with no or little loss of accuracy INT8 slightly better than HALF for batch size 1 Latency 1.8 vs 2.0 msec Throughput 558 vs 505 imagers/sec HALF has better throughput and latency at larger batch sizes Surprising that INT8 accuracy when calibration used only 1 image performed so well Suggest to follow guidance of using more images as calibration is not that slow and need only be done once Experiment with your own network and data as your results could vary 20

21 Thank You Khoa Huynh Senior Technical Staff Member (STSM), IBM Larry Brown Senior Software Engineer, IBM 21