Comments (12)
We have a quick start guide in the Python backend repository. Have you tried that example?
https://github.com/triton-inference-server/python_backend/tree/r21.05#quick-start
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You can also check out the image_client.py
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In addition to the above you can use DALI along with Triton ensembling to perform pre/post processing. For example: https://github.com/triton-inference-server/dali_backend/tree/main/docs/examples/inception_ensemble
It is not clear from your question where you are trying to use Python and where you want to perform the pre/post processing.
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In addition to the above you can use DALI along with Triton ensembling to perform pre/post processing. For example: https://github.com/triton-inference-server/dali_backend/tree/main/docs/examples/inception_ensemble
It is not clear from your question where you are trying to use Python and where you want to perform the pre/post processing.
@deadeyegoodwin
So, how to run the examples?
https://github.com/triton-inference-server/dali_backend/tree/main/docs/examples/inception_ensemble
There is no clear guide to run the model, but only generate the model.dali file. Could you tell me how to get the ensemble model, and how to use grpc to inference it?
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The readme points to a script that generates the model repository for you. Did you follow the quickstart guide to learn how to run triton once you have a model repository? https://github.com/triton-inference-server/server/blob/main/docs/quickstart.md.
Here is another example that has some more details: https://github.com/triton-inference-server/dali_backend/tree/main/docs/examples/resnet50_trt
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hello @deadeyegoodwin can i use batching for python-backend. Can you provide any examples for batching in python_backend?
ex: batching for add_sub example? i only use "for loop" for processing batch in each request. :( i known it is bad idea
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@HoangTienDuc it would be better if you didn't ask unrelated questions in this issue. Your python model is receiving the batch of requests... how you can efficiently process those requests is going to depend on what you are trying to do.
from dali_backend.
In addition to the above you can use DALI along with Triton ensembling to perform pre/post processing. For example: https://github.com/triton-inference-server/dali_backend/tree/main/docs/examples/inception_ensemble
It is not clear from your question where you are trying to use Python and where you want to perform the pre/post processing.@deadeyegoodwin
So, how to run the examples?
https://github.com/triton-inference-server/dali_backend/tree/main/docs/examples/inception_ensemble
There is no clear guide to run the model, but only generate the model.dali file. Could you tell me how to get the ensemble model, and how to use grpc to inference it?
Can we use TEXT data in DALI for pre/post processing? It looks like it supports only image, audio and video type of data. I want to perform pre/post processing within the scope of TritonServer.
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@szalpal Is seems like the instructions for the DALI inception ensemble can be improved. I'm going to move this issue to the dali_backend repo issues.
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@HoangTienDuc , if you have more questions about Python Backend, please create an issue in python_backend repo
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as @deadeyegoodwin mentioned, good place to start would be to get accustomed to the quickstart guide. If you have any specific question, don't hesitate do create an issue in dali_backend repository
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Unfortunately, DALI is not the best tool for raw text processing. The meat and potatos of DALI is to transfer data processing operations to the GPU. That's why we focus on image, audio and video: offloading these kinds of data bring the most benefit in terms of processing time.
I believe, that the best shot for you would be to use Python Backend for Triton. This way you still preprocess within the scope of TritonServer, while coding in python.
Should you have any more questions for DALI, don't hesitate to open an issue here, or in DALI repository
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Related Issues (20)
- layout parameter to external_source causes assert error HOT 2
- DALI backend not releasing device memory HOT 8
- How to provide mean & stddev to dali.fn.normalize HOT 3
- Error when executing Mixed operator decoders__Image when sending image binary to dali in triton HOT 9
- 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
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