Comments (3)
Hi @wangshaobobetter.
I assume you're using Triton to perform inference. In that case to connect the DALI model with TensorRT network you should use medel ensembles.
You can see an example of resnet50 setup in our repository here.
The DALI preprocessing model is setup using the following config file:
name: "dali"
backend: "dali"
max_batch_size: 256
input [
{
name: "DALI_INPUT_0"
data_type: TYPE_UINT8
dims: [ -1 ]
}
]
output [
{
name: "DALI_OUTPUT_0"
data_type: TYPE_FP32
dims: [ 3, 224, 224 ]
}
]
The model.dali
file should be placed in the model directory inside a folder named with version name, e.g. 1
, so the directory structure would look like this:
model_repository/
dali/
config.pbtxt
1/
model.dali
To connect the this model together with the network you should add the ensemble model. It's configured like this:
name: "ensemble_dali_resnet50"
platform: "ensemble"
max_batch_size: 256
input [
{
name: "INPUT"
data_type: TYPE_UINT8
dims: [ -1 ]
}
]
output [
{
name: "OUTPUT"
data_type: TYPE_FP32
dims: [ 1000 ]
}
]
ensemble_scheduling {
step [
{
model_name: "dali"
model_version: -1
input_map {
key: "DALI_INPUT_0"
value: "INPUT"
}
output_map {
key: "DALI_OUTPUT_0"
value: "preprocessed_image"
}
},
{
model_name: "resnet50_trt"
model_version: -1
input_map {
key: "input"
value: "preprocessed_image"
}
output_map {
key: "output"
value: "OUTPUT"
}
}
]
}
from dali_backend.
Thank you for answering my question. As you said, I am using Triton to perform inference. I have learned the example of resnet50 setup in your repository, and I have followed the tutorial of README to build model.dali successfully. The directory structure is the same as you showed.
I want to use C++ to do the preprocessing part, can we make "Model. Dali" into something similar to a dynamic link library directly call?
from dali_backend.
Okay, so, if I understand correctly, you have some custom piece of C++ code and you want to use it instead of DALI for preprocessing?
In that case, Triton gives a way of doing that by providing a custom backend. You can read more about implementing a custom backend here: triton-inference-server/backend. An example of such backend implemented is here.
from dali_backend.
Related Issues (20)
- how to use the numpy data in the DALI HOT 3
- Batching does not improve performance with dali HOT 10
- Can dali backend support default values or optional input? HOT 2
- Unexpected large memory needed for gpu resize HOT 4
- Error in thread 31: nvJPEG error (5): The user-provided allocator functions, for either memory allocation or for releasing the memory, returned a non-zero code. HOT 6
- Cannot compile dali_backend with older version of triton HOT 2
- how to provide batch input data for dali pipeline whicn input shapes [-1] HOT 1
- if I want to crop from different start point, how can I build pipe to do this? HOT 2
- Test issue
- Connecting InputOperator with no explicit inputs to Triton HOT 12
- Could not serialize dali.fn.python_function HOT 1
- when using crop_mirror_normalize func, Output layout "CHW" is slower than "HWC" HOT 5
- dlopen libcuda.so failed!. Please install GPU dirverTraceback (most recent call last): HOT 4
- Prefeed multiple input batches to the inference pipeline HOT 7
- Unable to load numpy module in a DALI backend HOT 3
- DALI pipeline in Triton - formatting InferInput batch of images for UINT8 HOT 3
- 'NoneType' object has no attribute 'loader' when trying to load DALI model. HOT 11
- How to format client code for inception example HOT 14
- How to get list of image paths into dali pipeline? HOT 4
- How to use scalar inputs HOT 3
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from dali_backend.