Comments (2)
Hello @lbhli !
Your question is related also to the question, how do you use Triton in the multi-GPU setup? By default, if you have multi-GPU system, Triton runs one model instance per the GPU. Please refer to the instance groups tutorial to know more about how Triton works with multi-GPU system.
Going back on topic, by default Triton will run one DALI model per GPU in your system. If you like to control this behaviour, you can do it fully with instance_group
setting in DALI Model Configuration. In case of Triton, it doesn't matter what you pass as device_id
to the DALI pipeline definition, since Triton is going to manage it anyhow.
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Hello @lbhli !
Your question is related also to the question, how do you use Triton in the multi-GPU setup? By default, if you have multi-GPU system, Triton runs one model instance per the GPU. Please refer to the instance groups tutorial to know more about how Triton works with multi-GPU system.
Going back on topic, by default Triton will run one DALI model per GPU in your system. If you like to control this behaviour, you can do it fully with
instance_group
setting in DALI Model Configuration. In case of Triton, it doesn't matter what you pass asdevice_id
to the DALI pipeline definition, since Triton is going to manage it anyhow.
That's cool, make sense, thanks.
Close this issue.
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Related Issues (20)
- Memory increase when decoding exception occurs HOT 2
- dali backend device parameter setting question HOT 1
- 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
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