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License: Apache License 2.0
Adlik: Toolkit for Accelerating Deep Learning Inference
License: Apache License 2.0
ServingLite runtime framework should support Deep Learning model running in FPGA.
Support Benchmark test :
Different runtimes have their own definition for model config
Monitor the server's status which includes power on status and running status
Currently, there are some bad designs in the model compiler. We should come up with a new design.
Related: #152.
When i compile the onnx model to OpenVINO or TensorRT model , the error is as follows:
TypeError: expected str, bytes or os.PathLike object, not NoneType {'status': 'failure', 'error_msg': 'expected str, bytes or os.PathLike object, not NoneType'}
But there is an onnx model in the directory of input_model.
Adlik should support the framework to optimize deep-learned model, such as tensorflow checkpoint.
The optimizing techniques include:
When I use the docker images which built by ci/docker/build.sh file to compile the openvino model, the error message is as follows:
{'status': 'failure', 'error_msg': 'mo.py does not exist, path: /opt/intel/openvino_2019.3.344/deployment_tools/model_optimizer/mo.py'}
Considering two scenario:
When i use the tensorrt 7 to compile the onnx model, raise parser error.
Model compiler should support compiling framework for the Deep Learning model running in FPGA.
Support query model information include version, path, whether activated and so on
Run tensorRT model failed because some codes about cuda not be compiled into binary.
Add markdownlint to CI for checking Markdown files.
"" Build serving with OpenVINO runtime
Install intel-openvino-ie-rt-core package from OpenVINO.""
Is a installation process or a building process?
When the model compiler creates config.pbtxt, if the Number dimension of the input and output shapes is not None but a specific value, the created config.pbtxt cannot be inferred.
There is a bug in line 326 of model_loader.py in model_compiler
if len(self.input_formats) < len(self.input_names):
self.input_formats.extend([None for _ in range(len(self.input_formats), len(self.input_formats))])
User usually get model meta which described in "model.pbtxt" to construct input before do prediction. But prediction will fail if information in "model.pbtxt" and model representation not consistent.
After installing all of Adlik's packages and the build is complete, there should be a sample test to see if Adlik was successfully installed, similar to the "Hello world!" test of other software.
The second line should start with “Model Compiler”.
Adlik does compression optimization for the model, but ONNX Runtime, Baidu's PaddleSlim and others all have similar functions, so what's the difference between them?
The introduction part of adlik adds a little more description of application scenarios and advantages over other platforms
You can build Adlik inside the Docker image.
Image is not runtime, so you can't compile Adlik inside image. There are two ways to use the image:
What is the meaning of “Adlik”? How to pronounce it? Add these information to README.md
file.
1.'graph conversoin' should be 'graph conversion'.
2.'OP combinaton' should be 'OP combination'.
To complete model compiler scenario.
ML runtime support http req.
The function “exit” does not belong to the list of async-signal-safe functions.
I guess that a different program design will be needed for your function “handler”.
TensorFlow runtime doesn't enable any batch scheduler and can't batch multiple requests into one.
For a model which has 3 versions: 1, 2 and 3, when adlik_serving starts it doesn't load version 3 but load version 2.
Support redirect log to file
The "ModelOperateImpl::activateModel" function is too long and into chaos
Add benchmark test result with tf lite runtime.
Model is Resnet50.
Add the following CI checks:
Support update model (but not activate) via grpc/http interface
When I compile the model to OpenVINO type, the error is as follows:
AttributeError: module 'tensorflow' has no attribute 'NodeDef'
Support activate a specific model ( or version) via grpc/http interface
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