Comments (4)
from dali_backend.
Could you tell us more about what do you mean by:
Currently dali_backend just works with post and preprocessing for the functions which do not require initialization
In the case of DALI if you load the processing model all the necessary operators' initialization happens. In most cases, it is just the memory allocation. Do you have any particular operation in mind which may require extended initialization?
from dali_backend.
Hi @JanuszL ,
Thanks a lot for your answer.
The problem for my side is I'm wondering if I can use dali backend for transformer like models and import the tokenizer in the initialization part and afterward use the initialized tokenizer during the processing (as it takes time for initializing the tokenizers during each execution). Is it possible?
from dali_backend.
DALI is initialized once when the model is loaded. Then when the inference request is processed the pipeline is just run,
However I'm not sure if DALI is capable of tokenization. You may consider writing a custom plugin for DALI https://github.com/triton-inference-server/dali_backend/blob/main/docs/examples/dali_plugin/README.md.
from dali_backend.
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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from dali_backend.