DeepLearningExamples | State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproduci | Machine Learning library
kandi X-RAY | DeepLearningExamples Summary
kandi X-RAY | DeepLearningExamples Summary
This repository provides State-of-the-Art Deep Learning examples that are easy to train and deploy, achieving the best reproducible accuracy and performance with NVIDIA CUDA-X software stack running on NVIDIA Volta, Turing and Ampere GPUs.
Support
Quality
Security
License
Reuse
Top functions reviewed by kandi - BETA
Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of DeepLearningExamples
DeepLearningExamples Key Features
DeepLearningExamples Examples and Code Snippets
Lines of Code : 18
License : No License
Copystd::cout << "Optimizing TorchScript model with TRTorch \n\n";
auto in_tensor = torch::randn({1, 3, input_image_size_, input_image_size_});
auto in_tensor_sizes = std::vector({in_tensor.sizes()});
trtorch::ExtraInfo compile_spec_info(in_tensor # ./main --input-dir ../../input_image/ --model-file ../../model/ssd300_trtorch-fp16_B1-C3-H300-W300_torch1-5-1_cuda10-2_gpu.pt
# ./main ../../input_image/ ../../model/ssd300_trtorch-fp16_B1-C3-H300-W300_torch1-5-1_cuda10-2_gpu.pt
usage:
main [OP $ git clone https://github.com/hotsuyuki/SSD300_PyTorch_cpp_TRTorch.git
$ cd SSD300_PyTorch_cpp_TRTorch/
$ docker container run --gpus all --rm -it -v $PWD:/workspace/SSD300_PyTorch_cpp_TRTorch hotsuyuki/ubuntu18_04-cuda10_2-cudnn7_6_5-tensorrt7_0_0- Community Discussions
Trending Discussions on DeepLearningExamples
QUESTION
I've checked some related post Tensorflow vs Numpy math functions, but still have some confusion: when shall we use tensorflow library function, and when shall we use python library function? Both are often mixed in the code as shown below.
For example, why shall we use tf.math.pow instead of python library math.pow? Both variables warmup_percent_done and self.power are scalars instead of tensorflow tensors.
ANSWER
Answered 2022-Mar-15 at 20:13TensorFlow operations are on tensor objects, as numpy operations are on arrays.
Try to think about the difference between these implementations of the same function.
QUESTION
I am executing my test configuration and this is the error I am facing. I have a trained model of size 327mb and layers of 250mb required for the inference of my Text To Speech trained model. So the size of model and layers might be the reason?? Please help me clarify and provide a solution. I am importing the trained model from s3 bucket and then loading it for the further processing. HERE IS THE CODE AND ERROR.
...ANSWER
Answered 2021-Jun-30 at 13:56AWS Lambdas local storage in /tmp is only 512MB. You are apparently exceeding this limit.
There are five solutions I can think of:
- Mount a EFS volume (which already contains your trained model) to the Lambda.
- Reduce the size of your model.
- Stream the model in chunks to your Lambda (might be hard).
- Not use Lambda (maybe just a plain EC2 or EKS).
- Use a Docker container that already contains your model as Lambda.
It is hard to tell what the best solution for you is, since so much information is missing. But those solutions should give you a good starting point.
QUESTION
NVIDIA DeepLearningExamples provide inference benchmark tests for Resnet50v1.5 in float32 and float16 precision but not in int8, so is there anyone that has tried running these tests in int8 before? Any tips and suggestions for how to do it would be really appreciated.
Update: I am trying to test with int8, but is currently stuck at tf.layers.conv2d complaining about type mismatch since it does not accept integer types.
ANSWER
Answered 2020-Sep-17 at 07:06For anyone who is interested, there would be some layers that required to be updated in order to accept integer types. There are some options available:
- Write your own custom layers (e.g. tf.layers.conv2d) to accept integer inputs.
- Use
tf.castto cast inputs and outputs between float and integer types. - Export the model to some formats (e.g. SavedModel) and use some conversion tool (e.g. TensorRT, TensorFlow Lite) to convert the model to int8.
Since I need the layers themselves to be optimized for int8 but do not want to spend time implementing them by myself, I have chosen the last option.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install DeepLearningExamples
Support
Reuse Trending Solutions
Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items
Find more librariesStay Updated
Subscribe to our newsletter for trending solutions and developer bootcamps
Share this Page
