onnxruntime | ONNX Runtime : cross-platform , high performance ML | Machine Learning library
kandi X-RAY | onnxruntime Summary
kandi X-RAY | onnxruntime Summary
ONNX Runtime inference can enable faster customer experiences and lower costs, supporting models from deep learning frameworks such as PyTorch and TensorFlow/Keras as well as classical machine learning libraries such as scikit-learn, LightGBM, XGBoost, etc. ONNX Runtime is compatible with different hardware, drivers, and operating systems, and provides optimal performance by leveraging hardware accelerators where applicable alongside graph optimizations and transforms. Learn more →. ONNX Runtime training can accelerate the model training time on multi-node NVIDIA GPUs for transformer models with a one-line addition for existing PyTorch training scripts. Learn more →.
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 onnxruntime
onnxruntime Key Features
onnxruntime Examples and Code Snippets
Lines of Code : 15License : Strong Copyleft (CC BY-SA 4.0)
Copyprediction = pred_out(img, model_selector, plant_model_dictionary)[0]
print(prediction.argmax(axis=0)) # 6
[2.8063682e-14 3.1059124e-05 5.7825161e-11 8.3977110e-09 2.5989549e-13
1.2324781sess = ort.InferenceSession("onnx_model.onnx")
input_name = sess.get_inputs()[0].name
label_name = sess.get_outputs()[0].name
pred = sess.run(...)[0]
([label_name],import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import random
torch.manual_seed(1)
random.seed(1)
device = torch.device('cpu')
class RNN(nn.Module):
def __init__(self, input_size, hidden_sconfigs.output_dir = "albert-base-v2-MRPC"
configs.model_name_or_path = "albert-base-v2-MRPC"
from onnxruntime.transformers.onnx_model import OnnxModel
model=onnx.load(path)
onnx_model=OnnxModel(model)
count = len(model.graph.initializer)
same = [-1] * count
for i in range(count - 1):
if same[i] >= 0:
continue
for j in rpip install torch_optimizer
import torch_optimizer as optim
# model = ...
optimizer = optim.DiffGrad(model.parameters(), lr=0.001)
optimizer.step()
torch.save(model.state_dict(), PATH)
tokenizer = AlbertTokenizer.from_pretrained('albert-base-v2')
import json
json_filename = './MRPC/config.json'
with open(json_filename) as json_file:
json_decoded = json.load(json_file)
json_decoded['model_type'] = # !!
with open(json_filename, 'w') as json_file:
json.dump(json_decoded, j# Install or upgrade PyTorch 1.8.0 and OnnxRuntime 1.7.0 for CPU-only.
pip install torch==1.10.0 # latest
model_name = bert-base-uncased
tokenizer = AutoTokenizer.from_pretrained(model_name )
model = AutoModelForMaskedLM.from_pretrained(model_name)
sequence = "Distilled models are smaller than the models they mimic. Using them instead of Community Discussions
Trending Discussions on onnxruntime
QUESTION
I can't find anyone who explains to a layman how to load an onnx model into a python script, then use that model to make a prediction when fed an image. All I could find were these lines of code:
...ANSWER
Answered 2022-Mar-27 at 20:02Let's first start by going over the code you provided, to make everything clear.
QUESTION
I've trained a quantized model (with help of quantized-aware-training method in pytorch). I want to create the calibration cache to do inference in INT8 mode by TensorRT. When create calib cache, I get the following warning and the cache is not created:
...ANSWER
Answered 2022-Mar-14 at 21:20If the ONNX model has Q/DQ nodes in it, you may not need calibration cache because quantization parameters such as scale and zero point are included in the Q/DQ nodes. You can run the Q/DQ ONNX model directly in TensorRT execution provider in OnnxRuntime (>= v1.9.0).
QUESTION
I'm trying to train a quantize model in pytorch and convert it to ONNX. I employ the quantized-aware-training technique with help of pytorch_quantization package. I used the below code to convert my model to ONNX:
...ANSWER
Answered 2022-Mar-06 at 07:24After some tries, I found that there is a version conflict. I changed the versions accordingly:
QUESTION
I want to export roberta-base based language model to ONNX format. The model uses ROBERTA embeddings and performs text classification task.
ANSWER
Answered 2022-Mar-01 at 20:25Have you tried to export after defining the operator for onnx? Something along the lines of the following code by Huawei.
On another note, when loading a model, you can technically override anything you want. Putting a specific layer to equal your modified class that inherits the original, keeps the same behavior (input and output) but execution of it can be modified. You can try to use this to save the model with changed problematic operators, transform it in onnx, and fine tune in such form (or even in pytorch).
This generally seems best solved by the onnx team, so long term solution might be to post a request for that specific operator on the github issues page (but probably slow).
QUESTION
I am trying to convert a fairly complex model from pytorch into ONNX. The conversion succeeds without error, but I am encountering this error when loading the model:
...ANSWER
Answered 2022-Feb-08 at 11:38From checking online I found a similar issue on GitHub about conv (https://github.com/microsoft/onnxruntime/issues/3130), could be that the types of the parameters used in torch are not compatible with the implementation of RandomNormalLike available in ONNX.
Could you check in netron what's inside the RandomNormalLike node/nodes to see if they comply with the spec: https://github.com/onnx/onnx/blob/main/docs/Operators.md#RandomNormal or https://github.com/onnx/onnx/blob/main/docs/Operators.md#RandomNormalLike
Cheers
EDIT: turns out the RandomNormal node has a type of 10 which corresponds to fp16
While the onnxruntime implementation only supports float and doubles see source code here: https://github.com/microsoft/onnxruntime/blob/24e35fba3217bf33b0e4064bc71d271a61938ba0/onnxruntime/core/providers/cpu/generator/random.cc#L354
Solution here is either to run the whole model in fp32 or ask explicitely RandomNormalLike to use floats or doubles hoping that torch allows mixed computation on fp16 and fp32/fp64 I guess
QUESTION
Goal: re-develop this BERT Notebook to use textattack/albert-base-v2-MRPC.
Kernel: conda_pytorch_p36. PyTorch 1.8.1+cpu.
I convert a PyTorch / HuggingFace Transformers model to ONNX and store it. DecodeError occurs on onnx.load().
Are my ONNX files corrupted? This seems to be a common solution; but I don't know how to check for this.
ALBert Notebook and model files on Google Colab.
I've also this Git Issue, detailing debugging.
Problem isn't...- Quantisation - any Quantisation code I try, throws the same error.
- Optimisation - error occurs with or without Optimisation.
Section 2.2 Quantize ONNX model:
...ANSWER
Answered 2022-Jan-31 at 18:53The problem was with updating the config variables for my new model.
Changes:
QUESTION
I'm trying to accelerate my model's performance by converting it to OnnxRuntime. However, I'm getting weird results, when trying to measure inference time.
While running only 1 iteration OnnxRuntime's CPUExecutionProvider greatly outperforms OpenVINOExecutionProvider:
- CPUExecutionProvider - 0.72 seconds
- OpenVINOExecutionProvider - 4.47 seconds
But if I run let's say 5 iterations the result is different:
- CPUExecutionProvider - 3.83 seconds
- OpenVINOExecutionProvider - 14.13 seconds
And if I run 100 iterations, the result is drastically different:
- CPUExecutionProvider - 74.19 seconds
- OpenVINOExecutionProvider - 46.96seconds
It seems to me, that the inference time of OpenVinoEP is not linear, but I don't understand why. So my questions are:
- Why does OpenVINOExecutionProvider behave this way?
- What ExecutionProvider should I use?
The code is very basic:
...ANSWER
Answered 2022-Jan-27 at 09:16The use of ONNX Runtime with OpenVINO Execution Provider enables the inferencing of ONNX models using ONNX Runtime API while the OpenVINO toolkit runs in the backend. This accelerates ONNX model's performance on the same hardware compared to generic acceleration on Intel® CPU, GPU, VPU and FPGA.
Generally, CPU Execution Provider works best with small iteration since its intention is to keep the binary size small. Meanwhile, the OpenVINO Execution Provider is intended for Deep Learning inference on Intel CPUs, Intel integrated GPUs, and Intel® MovidiusTM Vision Processing Units (VPUs).
This is why the OpenVINO Execution Provider outperforms the CPU Execution Provider during larger iterations.
You should choose Execution Provider that would suffice your own requirements. If you going to execute complex DL with large iteration, then go for OpenVINO Execution Provider. For a simpler use case, where you need the binary size to be smaller with smaller iterations, you can choose the CPU Execution Provider instead.
For more information, you may refer to this ONNX Runtime Performance Tuning
QUESTION
Goal: run Inference in parallel on multiple CPU cores
I'm experimenting with Inference using simple_onnxruntime_inference.ipynb.
Individually:
...ANSWER
Answered 2022-Jan-21 at 16:56def run_inference(i):
output_name = session.get_outputs()[0].name
return session.run([output_name], {input_name: inputs[i]})[0] # [0] bc array in list
outputs = pool.map(run_inference, [i for i in range(test_data_num)])
QUESTION
Goal: Use this Notebook to perform quantisation on albert-base-v2 model.
Kernel: conda_pytorch_p36.
Outputs in Sections 1.2 & 2.2 show that:
- converting vanilla BERT from PyTorch to ONNX stays the same size,
417.6 MB. - Quantization models are smaller than vanilla BERT, PyTorch
173.0 MBand ONNX104.8 MB.
However, when running ALBert:
- PyTorch and ONNX model sizes are different.
- Quantized model sizes are bigger than vanilla.
I think this is the reason for poorer model performance of both Quantization methods of ALBert, compared to vanilla ALBert.
PyTorch:
...ANSWER
Answered 2022-Jan-21 at 12:09ALBert model has shared weights among layers. torch.onnx.export outputs the weights to different tensors, which causes the model size to grow larger.
A number of Git Issues have been marked Solved regarding this phenomena.
The most common solution is to remove shared weights, that is to remove tensor arrays that contain the exact same values.
SolutionsSection "Removing shared weights" in onnx_remove_shared_weights.ipynb.
QUESTION
Goal: Amend this Notebook to work with albert-base-v2 model
Kernel: conda_pytorch_p36.
Section 2.1 exports the finalised model. It too uses a BERT specific function. However, I cannot find an equivalent for Albert.
I've successfully implemented alternatives for Albert up until this section.
Code:
...ANSWER
Answered 2022-Jan-18 at 15:35Optimise any PyTorch model, using torch_optimizer.
Installation:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install onnxruntime
ONNX Runtime Inferencing: microsoft/onnxruntime-inference-examples
ONNX Runtime Training: microsoft/onnxruntime-training-examples
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
