oneflow-inc / serving Goto Github PK
View Code? Open in Web Editor NEWOneFlow Serving
License: Apache License 2.0
serving's People
Contributors
Stargazers
Watchers
Forkers
mosoutserving's Issues
error: #"sbp"<"parallel<[] -> [[#sbp.B]]>"> : 'none' attribute created with unregistered dialect. If this is intended, please call allowUnregisteredDialects() on the MLIRContext, or use -allow-unregistered-dialect with the MLIR opt tool used
按照官方教程示例将模型以图形式保存后，部署至Triton服务中时出现异常。
程序：
import oneflow as flow
import oneflow.nn as nn
from flowvision.models.alexnet import alexnet
class MyGraph(nn.Graph):
def __init__(self, model):
super().__init__()
self.model = model
def build(self, *input):
return self.model(*input)
if __name__ == "__main__":
fake_image = flow.ones((1, 3, 256, 256))
model = alexnet(pretrained=True, progress=True)
model.eval()
graph = MyGraph(model)
out = graph(fake_image)
MODEL_SAVE_DIR = "./alexnet"
import os
if not os.path.exists(MODEL_SAVE_DIR):
os.makedirs(MODEL_SAVE_DIR)
flow.save(graph, MODEL_SAVE_DIR)异常截图：
URL in README is not accessible
oneflow-backend is not accessible
How does the function ModelInstanceState::ProcessRequests do inference for each request?
@zzk0 @hjchen2 @mosout Hi, I'm confusing whether SetInputTensors and Execute functions serve for only one request or all the requests simultaneously. If they serve for all the requests, how is parallelism implemented？Could you please give some advice?
// collect input
std::vector<const char*> input_names;
std::vector<oneflow_api::Tensor> input_tensors;
std::vector<BackendMemory*> input_memories;
bool cuda_copy = false;
BackendInputCollector collector(
requests, request_count, &responses, model_state_->TritonMemoryManager(),
model_state_->EnablePinnedInput(), CudaStream());
SetInputTensors(
total_batch_size, requests, request_count, &responses, &collector,
&input_names, &input_tensors, &input_memories, &cuda_copy);
SynchronizeStream(CudaStream(), cuda_copy);
// execute
uint64_t compute_start_ns = 0;
SET_TIMESTAMP(compute_start_ns);
std::vector<oneflow_api::Tensor> output_tensors;
Execute(&responses, request_count, &input_tensors, &output_tensors);Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.