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renwei2024

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nemo-github-issues

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[ "FYI @titu1994 : regarding \r\n\r\n> Moved the links to the very bottom (cannot hide them, its used as the ordering of the items on the left).\r\n\r\nYou can hide the links while preserving their ordering for the sidebar by including the `:hidden:` option like this:\r\n\r\n```rst\r\n.. toctree::\r\n :hidden:\r\n```\r\n\r\nSee [sphinx docs](https://www.sphinx-doc.org/en/master/usage/restructuredtext/directives.html#:~:text=You%20can%20also%20give%20a%20%E2%80%9Chidden%E2%80%9D%20option%20to%20the%20directive%2C%20like%20this%3A)." ]

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[ "jenkins" ]

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[ "PR with fix has been merged, so closing this. Thank you for the fix." ]

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[ "jenkins", "Just noticed the base branch needs to be changed to `main`", "opened #8594 instead", "closed in favor of #8594 " ]

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[ "This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.", "fixed in nemo 24.03 release / megatron core 0.6.0 release" ]

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[ "jenkins" ]

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[ "+@athitten", "This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.", "This issue was closed because it has been inactive for 7 days since being marked as stale." ]

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[ "Thank you very much for raising this! We have fixed it in this PR - https://github.com/NVIDIA/NeMo/pull/8587 \r\nIt occurred due to a large refactor and unification of metrics in ASR to make it simpler to extend in the long run. \r\n\r\nThe patch will be there in the next NeMo release, and we have added a release note in the 1.23 release page https://github.com/NVIDIA/NeMo/releases/tag/v1.23.0 so that users are aware and can utilize correct metrics during evaluation by using the speech to text eval script (or disabling fused batch explicitly)", "Fixed via #8587 " ]

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[ "This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.", "This PR was closed because it has been inactive for 7 days since being marked as stale.", "@nithinraok Hi, can you please approve new PR and re-open it.", "@fedorovgv thanks, is this a new PR? Did you close earlier one? ", "this is new one with the same code. yes, i did because of the problems with commits signs.", "@nithinraok Hi! All minor comments have been corrected. Could you view it please?", "@nithinraok Sorry for the ping, but how long does Jenkins take? is it finite?)", "jenkins", "I have initiated the jenkins run, it should be done in couple of hrs", "@nithinraok so, can we merge it? or do I need to update the branch before your merge? ", "> @nithinraok so, can we merge it? or do I need to update the branch before your merge? \n\nThanks a lot for your contribution!", "> > @nithinraok so, can we merge it? or do I need to update the branch before your merge?\r\n> \r\n> Thanks a lot for your contribution!\r\n\r\nThanks a lot for great framework!" ]

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[ "jenkins", "jenkins", "jenkins", "jenkins", "jenkins" ]

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[ "This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.", "This issue was closed because it has been inactive for 7 days since being marked as stale." ]

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[ "jenkins", "I'm not convinced that users will be able to jump to this config as easily as Piotr believes, but I won't block it since it's optional and doesn't hurt to have as an option ", "> I'm not convinced that users will be able to jump to this config as easily as Piotr believes, but I won't block it since it's optional and doesn't hurt to have as an option\r\n\r\nIt's definitely an advanced-level user feature for those who need a fine-grained control in complex multidataset/multimodal setups. Hopefully with time we'll be able to release more models and materials demonstrating the usage.", "jenkins", "jenkins", "jenkins" ]

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[ "This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.", "This issue was closed because it has been inactive for 7 days since being marked as stale." ]

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[ "This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.", "This PR was closed because it has been inactive for 7 days since being marked as stale." ]

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[ "@athitten, can you take a look at this when you have time?", "@ksivaman this issue should be fixed in this PR: [PR 8663](https://github.com/NVIDIA/NeMo/pull/8663). Can you please help review it ? Thank you." ]

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[ "This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.", "This PR was closed because it has been inactive for 7 days since being marked as stale." ]

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[ "This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.", "This PR was closed because it has been inactive for 7 days since being marked as stale." ]

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[ "Yes, only one cycle. It is preferred to use max steps since it makes it easier to configure the scheduler but you can provide other args and it will work fine ", "Thank you for the clarification!\r\n\r\nRegarding model averaging, the paper mentions taking average of the last 5 checkpoint. In your implementation, do you take average of the last 5 checkpoints or simply take average of the 5 best checkpoints?", "Last 5 checkpoints is more stable and gets adequate improvements. Best 5 can be unstable based on how \"stale\" the checkpoints are - potentially an epoch 50 xheckpoint can somehow be merged with epoch 500 and that usually causes degradation in performance ", "I see. And the val_check_interval is 1.0 so the last 5 checkpoints are the model at the end of the last 5 epochs, right?", "Usually, but not always. It's usually within the bracket of last 5-15 Epochs ", "I see. Thank you very much!" ]

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[ "These collections are under active development, we hope to stabilize them soon ", "This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.", "This issue was closed because it has been inactive for 7 days since being marked as stale." ]

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[ "Jenkins" ]

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[ "After some fiddling, I was able to get the nemo2riva part of the deployment working using a riva docker image. It can create an environment that allows the conversion of .nemo to .riva files for both pretrained and finetuned (regular and adapter variants) of FastPitch and HifiGan.\r\n\r\nThe step to create a .rmir file is also *successful* in that the riva-build command completes without error. **However when this model is being deployed by bash riva_start.sh, we get the new error:** \r\n`failed to load 'riva-onnx-fastpitch_encoder-Jaz_v1' version 1: Internal: onnx runtime error 1: Load model from /data/models/riva-onnx-fastpitch_encoder-Jaz_v1/1/model.onnx failed:/workspace/onnxruntime/onnxruntime/core/graph/model.cc:146 onnxruntime::Model::Model(onnx::ModelProto&&, const PathString&, const IOnnxRuntimeOpSchemaRegistryList*, const onnxruntime::logging::Logger&, const onnxruntime::ModelOptions&) Unsupported model IR version: 9, max supported IR version: 8`\r\n(The full docker log is listed at the bottom)\r\n\r\n\r\nDOCKERFILE\r\n```\r\nFROM nvcr.io/nvidia/riva/riva-speech:2.14.0-servicemaker\r\n\r\n#make sure pip ist installed\r\nRUN apt update && apt install python3-pip -y\r\n\r\n#get a simple fastpitch model to convert\r\nRUN wget --content-disposition \\\r\n'https://api.ngc.nvidia.com/v2/models/org/nvidia/team/nemo/tts_en_fastpitch/IPA_1.13.0/files?redirect=true&path=tts_en_fastpitch_align_ipa.nemo' \\\r\n-O tts_en_fastpitch_align_ipa.nemo\r\n\r\n#install NeMo dependencies\r\nRUN apt-get update && apt-get install -y libsndfile1 ffmpeg\r\nRUN pip install Cython\r\n\r\n#install nemo2riva dependencies\r\nRUN pip install nvidia-pyindex\r\n\r\n#install NeMo\r\nRUN git clone https://github.com/NVIDIA/NeMo\r\nWORKDIR NeMo\r\nRUN git switch 'r1.23.0'\r\nRUN pip install -e .\r\nWORKDIR ..\r\n\r\n#install some other required packages\r\nRUN pip install matplotlib\r\nRUN pip install einops\r\nRUN pip install transformers\r\nRUN pip install pandas\r\nRUN pip install inflect\r\nRUN pip install typing_extensions==4.7.1\r\nRUN pip install wandb\r\nRUN pip install youtokentome\r\nRUN pip install editdistance\r\nRUN pip install nemo_text_processing\r\nRUN pip install lhotse\r\nRUN pip install pyannote.audio\r\nRUN pip install webdataset\r\nRUN pip install datasets\r\nRUN pip install jiwer\r\n\r\n#install nemo2riva\r\nRUN pip install nemo2riva==2.14.0\r\n\r\n#fix the errors arising due to nemo requiring python 3.10\r\nRUN sed -i '1i from __future__ import annotations' /usr/local/lib/python3.8/dist-packages/nemo/collections/common/tokenizers/canary_tokenizer.py\r\nRUN sed -i '1i from __future__ import annotations' /usr/local/lib/python3.8/dist-packages/nemo/collections/asr/data/audio_to_text_lhotse.py\r\nRUN sed -i '1i from __future__ import annotations' /usr/local/lib/python3.8/dist-packages/nemo/collections/asr/data/audio_to_text_lhotse_prompted.py\r\nRUN sed -i '1i from __future__ import annotations' /usr/local/lib/python3.8/dist-packages/nemo/collections/common/data/lhotse/nemo_adapters.py\r\nRUN sed -i '1i from __future__ import annotations' /usr/local/lib/python3.8/dist-packages/nemo/collections/common/data/lhotse/dataloader.py\r\nRUN sed -i '1i from __future__ import annotations' /usr/local/lib/python3.8/dist-packages/hydra/_internal/utils.py\r\nRUN sed -i '1i from __future__ import annotations' /usr/local/lib/python3.8/dist-packages/nemo/collections/asr/models/aed_multitask_models.py\r\nRUN sed -i '1i from __future__ import annotations' /usr/local/lib/python3.8/dist-packages/hydra/_internal/utils.py\r\nRUN sed -i '1i from __future__ import annotations' /usr/local/lib/python3.8/dist-packages/nemo/collections/asr/data/huggingface/hf_audio_to_text.py\r\nRUN sed -i '1i from __future__ import annotations' /usr/local/lib/python3.8/dist-packages/nemo/collections/asr/parts/submodules/rnnt_greedy_decoding.py\r\n```\r\n\r\n\r\n<details>\r\n <summary>\r\n below is the full docker log. click to expand:\r\n </summary>\r\n ```\r\n==========================\r\n=== Riva Speech Skills ===\r\n==========================\r\n\r\nNVIDIA Release 23.12 (build 77214108)\r\n\r\nCopyright (c) 2018-2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.\r\n\r\nVarious files include modifications (c) NVIDIA CORPORATION & AFFILIATES. All rights reserved.\r\n\r\nThis container image and its contents are governed by the NVIDIA Deep Learning Container License.\r\nBy pulling and using the container, you accept the terms and conditions of this license:\r\nhttps://developer.nvidia.com/ngc/nvidia-deep-learning-container-license\r\n\r\n > Riva waiting for Triton server to load all models...retrying in 1 second\r\nI0304 06:27:43.963750 102 pinned_memory_manager.cc:240] Pinned memory pool is created at '0x7f0fc4000000' with size 268435456\r\nI0304 06:27:43.966148 102 cuda_memory_manager.cc:105] CUDA memory pool is created on device 0 with size 1000000000\r\nI0304 06:27:43.971749 102 model_lifecycle.cc:459] loading: riva-onnx-fastpitch_encoder-Jaz_v1:1\r\nI0304 06:27:43.971796 102 model_lifecycle.cc:459] loading: riva-trt-hifigan-Jaz_v1:1\r\nI0304 06:27:43.971839 102 model_lifecycle.cc:459] loading: spectrogram_chunker-Jaz_v1:1\r\nI0304 06:27:43.971880 102 model_lifecycle.cc:459] loading: tts_postprocessor-Jaz_v1:1\r\nI0304 06:27:43.971934 102 model_lifecycle.cc:459] loading: tts_preprocessor-Jaz_v1:1\r\nI0304 06:27:43.973206 102 onnxruntime.cc:2459] TRITONBACKEND_Initialize: onnxruntime\r\nI0304 06:27:43.973231 102 onnxruntime.cc:2469] Triton TRITONBACKEND API version: 1.10\r\nI0304 06:27:43.973236 102 onnxruntime.cc:2475] 'onnxruntime' TRITONBACKEND API version: 1.10\r\nI0304 06:27:43.973241 102 onnxruntime.cc:2505] backend configuration:\r\n{\"cmdline\":{\"auto-complete-config\":\"false\",\"min-compute-capability\":\"6.000000\",\"backend-directory\":\"/opt/tritonserver/backends\",\"default-max-batch-size\":\"4\"}}\r\nI0304 06:27:44.037319 102 tensorrt.cc:5444] TRITONBACKEND_Initialize: tensorrt\r\nI0304 06:27:44.037343 102 tensorrt.cc:5454] Triton TRITONBACKEND API version: 1.10\r\nI0304 06:27:44.037351 102 tensorrt.cc:5460] 'tensorrt' TRITONBACKEND API version: 1.10\r\nI0304 06:27:44.037356 102 tensorrt.cc:5488] backend configuration:\r\n{\"cmdline\":{\"auto-complete-config\":\"false\",\"min-compute-capability\":\"6.000000\",\"backend-directory\":\"/opt/tritonserver/backends\",\"default-max-batch-size\":\"4\"}}\r\nI0304 06:27:44.037540 102 onnxruntime.cc:2563] TRITONBACKEND_ModelInitialize: riva-onnx-fastpitch_encoder-Jaz_v1 (version 1)\r\nI0304 06:27:44.466490 102 onnxruntime.cc:2606] TRITONBACKEND_ModelInstanceInitialize: riva-onnx-fastpitch_encoder-Jaz_v1_0 (GPU device 0)\r\nI0304 06:27:44.583420 102 onnxruntime.cc:2640] TRITONBACKEND_ModelInstanceFinalize: delete instance state\r\nI0304 06:27:44.583435 102 tensorrt.cc:5578] TRITONBACKEND_ModelInitialize: riva-trt-hifigan-Jaz_v1 (version 1)\r\nI0304 06:27:44.583459 102 onnxruntime.cc:2586] TRITONBACKEND_ModelFinalize: delete model state\r\nE0304 06:27:44.583484 102 model_lifecycle.cc:596] failed to load 'riva-onnx-fastpitch_encoder-Jaz_v1' version 1: Internal: onnx runtime error 1: Load model from /data/models/riva-onnx-fastpitch_encoder-Jaz_v1/1/model.onnx failed:/workspace/onnxruntime/onnxruntime/core/graph/model.cc:146 onnxruntime::Model::Model(onnx::ModelProto&&, const PathString&, const IOnnxRuntimeOpSchemaRegistryList*, const onnxruntime::logging::Logger&, const onnxruntime::ModelOptions&) Unsupported model IR version: 9, max supported IR version: 8\r\n\r\nI0304 06:27:44.583984 102 backend_model.cc:188] Overriding execution policy to \"TRITONBACKEND_EXECUTION_BLOCKING\" for sequence model \"riva-trt-hifigan-Jaz_v1\"\r\nI0304 06:27:44.584806 102 spectrogram-chunker.cc:270] TRITONBACKEND_ModelInitialize: spectrogram_chunker-Jaz_v1 (version 1)\r\nI0304 06:27:44.585550 102 backend_model.cc:303] model configuration:\r\n{\r\n \"name\": \"spectrogram_chunker-Jaz_v1\",\r\n \"platform\": \"\",\r\n \"backend\": \"riva_tts_chunker\",\r\n \"version_policy\": {\r\n \"latest\": {\r\n \"num_versions\": 1\r\n }\r\n },\r\n \"max_batch_size\": 8,\r\n \"input\": [\r\n {\r\n \"name\": \"SPECTROGRAM\",\r\n \"data_type\": \"TYPE_FP32\",\r\n \"format\": \"FORMAT_NONE\",\r\n \"dims\": [\r\n 80,\r\n -1\r\n ],\r\n \"is_shape_tensor\": false,\r\n \"allow_ragged_batch\": false,\r\n \"optional\": false\r\n },\r\n {\r\n \"name\": \"IS_LAST_SENTENCE\",\r\n \"data_type\": \"TYPE_INT32\",\r\n \"format\": \"FORMAT_NONE\",\r\n \"dims\": [\r\n 1\r\n ],\r\n \"is_shape_tensor\": false,\r\n \"allow_ragged_batch\": false,\r\n \"optional\": false\r\n },\r\n {\r\n \"name\": \"NUM_VALID_FRAMES_IN\",\r\n \"data_type\": \"TYPE_INT64\",\r\n \"format\": \"FORMAT_NONE\",\r\n \"dims\": [\r\n 1\r\n ],\r\n \"is_shape_tensor\": false,\r\n \"allow_ragged_batch\": false,\r\n \"optional\": false\r\n },\r\n {\r\n \"name\": \"SENTENCE_NUM\",\r\n \"data_type\": \"TYPE_INT32\",\r\n \"format\": \"FORMAT_NONE\",\r\n \"dims\": [\r\n 1\r\n ],\r\n \"is_shape_tensor\": false,\r\n \"allow_ragged_batch\": false,\r\n \"optional\": false\r\n },\r\n {\r\n \"name\": \"DURATIONS\",\r\n \"data_type\": \"TYPE_FP32\",\r\n \"format\": \"FORMAT_NONE\",\r\n \"dims\": [\r\n -1\r\n ],\r\n \"is_shape_tensor\": false,\r\n \"allow_ragged_batch\": false,\r\n \"optional\": false\r\n },\r\n {\r\n \"name\": \"PROCESSED_TEXT\",\r\n \"data_type\": \"TYPE_STRING\",\r\n \"format\": \"FORMAT_NONE\",\r\n \"dims\": [\r\n 1\r\n ],\r\n \"is_shape_tensor\": false,\r\n \"allow_ragged_batch\": false,\r\n \"optional\": false\r\n },\r\n {\r\n \"name\": \"VOLUME\",\r\n \"data_type\": \"TYPE_FP32\",\r\n \"format\": \"FORMAT_NONE\",\r\n \"dims\": [\r\n -1\r\n ],\r\n \"is_shape_tensor\": false,\r\n \"allow_ragged_batch\": false,\r\n \"optional\": false\r\n }\r\n ],\r\n \"output\": [\r\n {\r\n \"name\": \"SPECTROGRAM_CHUNK\",\r\n \"data_type\": \"TYPE_FP32\",\r\n \"dims\": [\r\n 80,\r\n -1\r\n ],\r\n \"label_filename\": \"\",\r\n \"is_shape_tensor\": false\r\n },\r\n {\r\n \"name\": \"END_FLAG\",\r\n \"data_type\": \"TYPE_INT32\",\r\n \"dims\": [\r\n 1\r\n ],\r\n \"label_filename\": \"\",\r\n \"is_shape_tensor\": false\r\n },\r\n {\r\n \"name\": \"NUM_VALID_SAMPLES_OUT\",\r\n \"data_type\": \"TYPE_INT32\",\r\n \"dims\": [\r\n 1\r\n ],\r\n \"label_filename\": \"\",\r\n \"is_shape_tensor\": false\r\n },\r\n {\r\n \"name\": \"SENTENCE_NUM\",\r\n \"data_type\": \"TYPE_INT32\",\r\n \"dims\": [\r\n 1\r\n ],\r\n \"label_filename\": \"\",\r\n \"is_shape_tensor\": false\r\n },\r\n {\r\n \"name\": \"DURATIONS\",\r\n \"data_type\": \"TYPE_FP32\",\r\n \"dims\": [\r\n -1\r\n ],\r\n \"label_filename\": \"\",\r\n \"is_shape_tensor\": false\r\n },\r\n {\r\n \"name\": \"PROCESSED_TEXT\",\r\n \"data_type\": \"TYPE_STRING\",\r\n \"dims\": [\r\n 1\r\n ],\r\n \"label_filename\": \"\",\r\n \"is_shape_tensor\": false\r\n },\r\n {\r\n \"name\": \"VOLUME\",\r\n \"data_type\": \"TYPE_FP32\",\r\n \"dims\": [\r\n -1\r\n ],\r\n \"label_filename\": \"\",\r\n \"is_shape_tensor\": false\r\n }\r\n ],\r\n \"batch_input\": [],\r\n \"batch_output\": [],\r\n \"optimization\": {\r\n \"priority\": \"PRIORITY_DEFAULT\",\r\n \"input_pinned_memory\": {\r\n \"enable\": true\r\n },\r\n \"output_pinned_memory\": {\r\n \"enable\": true\r\n },\r\n \"gather_kernel_buffer_threshold\": 0,\r\n \"eager_batching\": false\r\n },\r\n \"sequence_batching\": {\r\n \"oldest\": {\r\n \"max_candidate_sequences\": 8,\r\n \"preferred_batch_size\": [\r\n 8\r\n ],\r\n \"max_queue_delay_microseconds\": 1000\r\n },\r\n \"max_sequence_idle_microseconds\": 60000000,\r\n \"control_input\": [\r\n {\r\n \"name\": \"START\",\r\n \"control\": [\r\n {\r\n \"kind\": \"CONTROL_SEQUENCE_START\",\r\n \"int32_false_true\": [\r\n 0,\r\n 1\r\n ],\r\n \"fp32_false_true\": [],\r\n \"bool_false_true\": [],\r\n \"data_type\": \"TYPE_INVALID\"\r\n }\r\n ]\r\n },\r\n {\r\n \"name\": \"READY\",\r\n \"control\": [\r\n {\r\n \"kind\": \"CONTROL_SEQUENCE_READY\",\r\n \"int32_false_true\": [\r\n 0,\r\n 1\r\n ],\r\n \"fp32_false_true\": [],\r\n \"bool_false_true\": [],\r\n \"data_type\": \"TYPE_INVALID\"\r\n }\r\n ]\r\n },\r\n {\r\n \"name\": \"END\",\r\n \"control\": [\r\n {\r\n \"kind\": \"CONTROL_SEQUENCE_END\",\r\n \"int32_false_true\": [\r\n 0,\r\n 1\r\n ],\r\n \"fp32_false_true\": [],\r\n \"bool_false_true\": [],\r\n \"data_type\": \"TYPE_INVALID\"\r\n }\r\n ]\r\n },\r\n {\r\n \"name\": \"CORRID\",\r\n \"control\": [\r\n {\r\n \"kind\": \"CONTROL_SEQUENCE_CORRID\",\r\n \"int32_false_true\": [],\r\n \"fp32_false_true\": [],\r\n \"bool_false_true\": [],\r\n \"data_type\": \"TYPE_UINT64\"\r\n }\r\n ]\r\n }\r\n ],\r\n \"state\": []\r\n },\r\n \"instance_group\": [\r\n {\r\n \"name\": \"spectrogram_chunker-Jaz_v1_0\",\r\n \"kind\": \"KIND_GPU\",\r\n \"count\": 1,\r\n \"gpus\": [\r\n 0\r\n ],\r\n \"secondary_devices\": [],\r\n \"profile\": [],\r\n \"passive\": false,\r\n \"host_policy\": \"\"\r\n }\r\n ],\r\n \"default_model_filename\": \"\",\r\n \"cc_model_filenames\": {},\r\n \"metric_tags\": {},\r\n \"parameters\": {\r\n \"num_mels\": {\r\n \"string_value\": \"80\"\r\n },\r\n \"num_samples_per_frame\": {\r\n \"string_value\": \"512\"\r\n },\r\n \"supports_volume\": {\r\n \"string_value\": \"True\"\r\n },\r\n \"chunk_length\": {\r\n \"string_value\": \"80\"\r\n },\r\n \"max_execution_batch_size\": {\r\n \"string_value\": \"8\"\r\n }\r\n },\r\n \"model_warmup\": [],\r\n \"model_transaction_policy\": {\r\n \"decoupled\": true\r\n }\r\n}\r\nI0304 06:27:44.585599 102 tensorrt.cc:5627] TRITONBACKEND_ModelInstanceInitialize: riva-trt-hifigan-Jaz_v1_0 (GPU device 0)\r\nI0304 06:27:44.640208 102 logging.cc:49] Loaded engine size: 28 MiB\r\n > Riva waiting for Triton server to load all models...retrying in 1 second\r\nI0304 06:27:44.787852 102 logging.cc:49] [MemUsageChange] TensorRT-managed allocation in engine deserialization: CPU +0, GPU +31, now: CPU 0, GPU 31 (MiB)\r\nI0304 06:27:44.796150 102 logging.cc:49] [MemUsageChange] TensorRT-managed allocation in IExecutionContext creation: CPU +0, GPU +186, now: CPU 0, GPU 217 (MiB)\r\nI0304 06:27:44.796536 102 tensorrt.cc:1547] Created instance riva-trt-hifigan-Jaz_v1_0 on GPU 0 with stream priority 0 and optimization profile default[0];\r\nI0304 06:27:44.796915 102 model_lifecycle.cc:693] successfully loaded 'riva-trt-hifigan-Jaz_v1' version 1\r\nI0304 06:27:44.802776 102 spectrogram-chunker.cc:272] TRITONBACKEND_ModelInstanceInitialize: spectrogram_chunker-Jaz_v1_0 (device 0)\r\nI0304 06:27:44.802834 102 tts-postprocessor.cc:305] TRITONBACKEND_ModelInitialize: tts_postprocessor-Jaz_v1 (version 1)\r\nI0304 06:27:44.803194 102 model_lifecycle.cc:693] successfully loaded 'spectrogram_chunker-Jaz_v1' version 1\r\nI0304 06:27:44.803487 102 backend_model.cc:303] model configuration:\r\n{\r\n \"name\": \"tts_postprocessor-Jaz_v1\",\r\n \"platform\": \"\",\r\n \"backend\": \"riva_tts_postprocessor\",\r\n \"version_policy\": {\r\n \"latest\": {\r\n \"num_versions\": 1\r\n }\r\n },\r\n \"max_batch_size\": 8,\r\n \"input\": [\r\n {\r\n \"name\": \"INPUT\",\r\n \"data_type\": \"TYPE_FP32\",\r\n \"format\": \"FORMAT_NONE\",\r\n \"dims\": [\r\n 1,\r\n -1\r\n ],\r\n \"is_shape_tensor\": false,\r\n \"allow_ragged_batch\": false,\r\n \"optional\": false\r\n },\r\n {\r\n \"name\": \"NUM_VALID_SAMPLES\",\r\n \"data_type\": \"TYPE_INT32\",\r\n \"format\": \"FORMAT_NONE\",\r\n \"dims\": [\r\n 1\r\n ],\r\n \"is_shape_tensor\": false,\r\n \"allow_ragged_batch\": false,\r\n \"optional\": false\r\n },\r\n {\r\n \"name\": \"Prosody_volume\",\r\n \"data_type\": \"TYPE_FP32\",\r\n \"format\": \"FORMAT_NONE\",\r\n \"dims\": [\r\n -1\r\n ],\r\n \"is_shape_tensor\": false,\r\n \"allow_ragged_batch\": false,\r\n \"optional\": false\r\n }\r\n ],\r\n \"output\": [\r\n {\r\n \"name\": \"OUTPUT\",\r\n \"data_type\": \"TYPE_FP32\",\r\n \"dims\": [\r\n -1\r\n ],\r\n \"label_filename\": \"\",\r\n \"is_shape_tensor\": false\r\n }\r\n ],\r\n \"batch_input\": [],\r\n \"batch_output\": [],\r\n \"optimization\": {\r\n \"priority\": \"PRIORITY_DEFAULT\",\r\n \"input_pinned_memory\": {\r\n \"enable\": true\r\n },\r\n \"output_pinned_memory\": {\r\n \"enable\": true\r\n },\r\n \"gather_kernel_buffer_threshold\": 0,\r\n \"eager_batching\": false\r\n },\r\n \"sequence_batching\": {\r\n \"oldest\": {\r\n \"max_candidate_sequences\": 8,\r\n \"preferred_batch_size\": [\r\n 8\r\n ],\r\n \"max_queue_delay_microseconds\": 100\r\n },\r\n \"max_sequence_idle_microseconds\": 60000000,\r\n \"control_input\": [\r\n {\r\n \"name\": \"START\",\r\n \"control\": [\r\n {\r\n \"kind\": \"CONTROL_SEQUENCE_START\",\r\n \"int32_false_true\": [\r\n 0,\r\n 1\r\n ],\r\n \"fp32_false_true\": [],\r\n \"bool_false_true\": [],\r\n \"data_type\": \"TYPE_INVALID\"\r\n }\r\n ]\r\n },\r\n {\r\n \"name\": \"READY\",\r\n \"control\": [\r\n {\r\n \"kind\": \"CONTROL_SEQUENCE_READY\",\r\n \"int32_false_true\": [\r\n 0,\r\n 1\r\n ],\r\n \"fp32_false_true\": [],\r\n \"bool_false_true\": [],\r\n \"data_type\": \"TYPE_INVALID\"\r\n }\r\n ]\r\n },\r\n {\r\n \"name\": \"END\",\r\n \"control\": [\r\n {\r\n \"kind\": \"CONTROL_SEQUENCE_END\",\r\n \"int32_false_true\": [\r\n 0,\r\n 1\r\n ],\r\n \"fp32_false_true\": [],\r\n \"bool_false_true\": [],\r\n \"data_type\": \"TYPE_INVALID\"\r\n }\r\n ]\r\n },\r\n {\r\n \"name\": \"CORRID\",\r\n \"control\": [\r\n {\r\n \"kind\": \"CONTROL_SEQUENCE_CORRID\",\r\n \"int32_false_true\": [],\r\n \"fp32_false_true\": [],\r\n \"bool_false_true\": [],\r\n \"data_type\": \"TYPE_UINT64\"\r\n }\r\n ]\r\n }\r\n ],\r\n \"state\": []\r\n },\r\n \"instance_group\": [\r\n {\r\n \"name\": \"tts_postprocessor-Jaz_v1_0\",\r\n \"kind\": \"KIND_GPU\",\r\n \"count\": 1,\r\n \"gpus\": [\r\n 0\r\n ],\r\n \"secondary_devices\": [],\r\n \"profile\": [],\r\n \"passive\": false,\r\n \"host_policy\": \"\"\r\n }\r\n ],\r\n \"default_model_filename\": \"\",\r\n \"cc_model_filenames\": {},\r\n \"metric_tags\": {},\r\n \"parameters\": {\r\n \"use_denoiser\": {\r\n \"string_value\": \"False\"\r\n },\r\n \"supports_volume\": {\r\n \"string_value\": \"True\"\r\n },\r\n \"filter_length\": {\r\n \"string_value\": \"1024\"\r\n },\r\n \"fade_length\": {\r\n \"string_value\": \"256\"\r\n },\r\n \"num_samples_per_frame\": {\r\n \"string_value\": \"512\"\r\n },\r\n \"chunk_num_samples\": {\r\n \"string_value\": \"40960\"\r\n },\r\n \"max_execution_batch_size\": {\r\n \"string_value\": \"8\"\r\n },\r\n \"max_chunk_size\": {\r\n \"string_value\": \"131072\"\r\n },\r\n \"hop_length\": {\r\n \"string_value\": \"256\"\r\n }\r\n },\r\n \"model_warmup\": [],\r\n \"model_transaction_policy\": {\r\n \"decoupled\": false\r\n }\r\n}\r\nI0304 06:27:44.803568 102 tts-postprocessor.cc:307] TRITONBACKEND_ModelInstanceInitialize: tts_postprocessor-Jaz_v1_0 (device 0)\r\nI0304 06:27:44.824235 102 tts-preprocessor.cc:337] TRITONBACKEND_ModelInitialize: tts_preprocessor-Jaz_v1 (version 1)\r\nI0304 06:27:44.824489 102 model_lifecycle.cc:693] successfully loaded 'tts_postprocessor-Jaz_v1' version 1\r\nW0304 06:27:44.824928 102 tts-preprocessor.cc:284] Parameter abbreviation_path is deprecated\r\nWARNING: Logging before InitGoogleLogging() is written to STDERR\r\nI0304 06:27:44.824993 112 preprocessor.cc:231] TTS character mapping loaded from /data/models/tts_preprocessor-Jaz_v1/1/mapping.txt\r\nI0304 06:27:44.921231 112 preprocessor.cc:269] TTS phonetic mapping loaded from /data/models/tts_preprocessor-Jaz_v1/1/ipa_cmudict-0.7b_nv22.08.txt\r\nI0304 06:27:44.921326 112 preprocessor.cc:282] Abbreviation mapping loaded from /data/models/tts_preprocessor-Jaz_v1/1/abbr.txt\r\nI0304 06:27:44.921344 112 normalize.cc:52] Speech Class far file missing:/data/models/tts_preprocessor-Jaz_v1/1/speech_class.far\r\nI0304 06:27:45.010165 112 preprocessor.cc:292] TTS normalizer loaded from /data/models/tts_preprocessor-Jaz_v1/1/\r\nI0304 06:27:45.010266 102 backend_model.cc:303] model configuration:\r\n{\r\n \"name\": \"tts_preprocessor-Jaz_v1\",\r\n \"platform\": \"\",\r\n \"backend\": \"riva_tts_preprocessor\",\r\n \"version_policy\": {\r\n \"latest\": {\r\n \"num_versions\": 1\r\n }\r\n },\r\n \"max_batch_size\": 8,\r\n \"input\": [\r\n {\r\n \"name\": \"input_string\",\r\n \"data_type\": \"TYPE_STRING\",\r\n \"format\": \"FORMAT_NONE\",\r\n \"dims\": [\r\n 1\r\n ],\r\n \"is_shape_tensor\": false,\r\n \"allow_ragged_batch\": false,\r\n \"optional\": false\r\n }\r\n ],\r\n \"output\": [\r\n {\r\n \"name\": \"output\",\r\n \"data_type\": \"TYPE_INT64\",\r\n \"dims\": [\r\n -1\r\n ],\r\n \"label_filename\": \"\",\r\n \"is_shape_tensor\": false\r\n },\r\n {\r\n \"name\": \"output_mask\",\r\n \"data_type\": \"TYPE_FP32\",\r\n \"dims\": [\r\n 1,\r\n 400,\r\n 1\r\n ],\r\n \"label_filename\": \"\",\r\n \"is_shape_tensor\": false\r\n },\r\n {\r\n \"name\": \"output_length\",\r\n \"data_type\": \"TYPE_INT32\",\r\n \"dims\": [\r\n 1\r\n ],\r\n \"label_filename\": \"\",\r\n \"is_shape_tensor\": false\r\n },\r\n {\r\n \"name\": \"is_last_sentence\",\r\n \"data_type\": \"TYPE_INT32\",\r\n \"dims\": [\r\n 1\r\n ],\r\n \"label_filename\": \"\",\r\n \"is_shape_tensor\": false\r\n },\r\n {\r\n \"name\": \"output_string\",\r\n \"data_type\": \"TYPE_STRING\",\r\n \"dims\": [\r\n 1\r\n ],\r\n \"label_filename\": \"\",\r\n \"is_shape_tensor\": false\r\n },\r\n {\r\n \"name\": \"sentence_num\",\r\n \"data_type\": \"TYPE_INT32\",\r\n \"dims\": [\r\n 1\r\n ],\r\n \"label_filename\": \"\",\r\n \"is_shape_tensor\": false\r\n },\r\n {\r\n \"name\": \"pitch\",\r\n \"data_type\": \"TYPE_FP32\",\r\n \"dims\": [\r\n -1\r\n ],\r\n \"label_filename\": \"\",\r\n \"is_shape_tensor\": false\r\n },\r\n {\r\n \"name\": \"duration\",\r\n \"data_type\": \"TYPE_FP32\",\r\n \"dims\": [\r\n -1\r\n ],\r\n \"label_filename\": \"\",\r\n \"is_shape_tensor\": false\r\n },\r\n {\r\n \"name\": \"volume\",\r\n \"data_type\": \"TYPE_FP32\",\r\n \"dims\": [\r\n -1\r\n ],\r\n \"label_filename\": \"\",\r\n \"is_shape_tensor\": false\r\n }\r\n ],\r\n \"batch_input\": [],\r\n \"batch_output\": [],\r\n \"optimization\": {\r\n \"graph\": {\r\n \"level\": 0\r\n },\r\n \"priority\": \"PRIORITY_DEFAULT\",\r\n \"cuda\": {\r\n \"graphs\": false,\r\n \"busy_wait_events\": false,\r\n \"graph_spec\": [],\r\n \"output_copy_stream\": true\r\n },\r\n \"input_pinned_memory\": {\r\n \"enable\": true\r\n },\r\n \"output_pinned_memory\": {\r\n \"enable\": true\r\n },\r\n \"gather_kernel_buffer_threshold\": 0,\r\n \"eager_batching\": false\r\n },\r\n \"sequence_batching\": {\r\n \"oldest\": {\r\n \"max_candidate_sequences\": 8,\r\n \"preferred_batch_size\": [\r\n 8\r\n ],\r\n \"max_queue_delay_microseconds\": 100\r\n },\r\n \"max_sequence_idle_microseconds\": 60000000,\r\n \"control_input\": [\r\n {\r\n \"name\": \"START\",\r\n \"control\": [\r\n {\r\n \"kind\": \"CONTROL_SEQUENCE_START\",\r\n \"int32_false_true\": [\r\n 0,\r\n 1\r\n ],\r\n \"fp32_false_true\": [],\r\n \"bool_false_true\": [],\r\n \"data_type\": \"TYPE_INVALID\"\r\n }\r\n ]\r\n },\r\n {\r\n \"name\": \"READY\",\r\n \"control\": [\r\n {\r\n \"kind\": \"CONTROL_SEQUENCE_READY\",\r\n \"int32_false_true\": [\r\n 0,\r\n 1\r\n ],\r\n \"fp32_false_true\": [],\r\n \"bool_false_true\": [],\r\n \"data_type\": \"TYPE_INVALID\"\r\n }\r\n ]\r\n },\r\n {\r\n \"name\": \"END\",\r\n \"control\": [\r\n {\r\n \"kind\": \"CONTROL_SEQUENCE_END\",\r\n \"int32_false_true\": [\r\n 0,\r\n 1\r\n ],\r\n \"fp32_false_true\": [],\r\n \"bool_false_true\": [],\r\n \"data_type\": \"TYPE_INVALID\"\r\n }\r\n ]\r\n },\r\n {\r\n \"name\": \"CORRID\",\r\n \"control\": [\r\n {\r\n \"kind\": \"CONTROL_SEQUENCE_CORRID\",\r\n \"int32_false_true\": [],\r\n \"fp32_false_true\": [],\r\n \"bool_false_true\": [],\r\n \"data_type\": \"TYPE_UINT64\"\r\n }\r\n ]\r\n }\r\n ],\r\n \"state\": []\r\n },\r\n \"instance_group\": [\r\n {\r\n \"name\": \"tts_preprocessor-Jaz_v1_0\",\r\n \"kind\": \"KIND_GPU\",\r\n \"count\": 1,\r\n \"gpus\": [\r\n 0\r\n ],\r\n \"secondary_devices\": [],\r\n \"profile\": [],\r\n \"passive\": false,\r\n \"host_policy\": \"\"\r\n }\r\n ],\r\n \"default_model_filename\": \"\",\r\n \"cc_model_filenames\": {},\r\n \"metric_tags\": {},\r\n \"parameters\": {\r\n \"max_sequence_length\": {\r\n \"string_value\": \"400\"\r\n },\r\n \"supports_speaker_mixing\": {\r\n \"string_value\": \"False\"\r\n },\r\n \"upper_case_chars\": {\r\n \"string_value\": \"True\"\r\n },\r\n \"g2p_ignore_ambiguous\": {\r\n \"string_value\": \"False\"\r\n },\r\n \"phone_set\": {\r\n \"string_value\": \"ipa\"\r\n },\r\n \"dictionary_path\": {\r\n \"string_value\": \"/data/models/tts_preprocessor-Jaz_v1/1/ipa_cmudict-0.7b_nv22.08.txt\"\r\n },\r\n \"abbreviations_path\": {\r\n \"string_value\": \"/data/models/tts_preprocessor-Jaz_v1/1/abbr.txt\"\r\n },\r\n \"supports_ragged_batches\": {\r\n \"string_value\": \"True\"\r\n },\r\n \"norm_proto_path\": {\r\n \"string_value\": \"/data/models/tts_preprocessor-Jaz_v1/1/\"\r\n },\r\n \"mapping_path\": {\r\n \"string_value\": \"/data/models/tts_preprocessor-Jaz_v1/1/mapping.txt\"\r\n },\r\n \"normalize_pitch\": {\r\n \"string_value\": \"True\"\r\n },\r\n \"upper_case_g2p\": {\r\n \"string_value\": \"True\"\r\n },\r\n \"pitch_std\": {\r\n \"string_value\": \"50.46181106567383\"\r\n },\r\n \"max_input_length\": {\r\n \"string_value\": \"2000\"\r\n },\r\n \"language\": {\r\n \"string_value\": \"en-US\"\r\n },\r\n \"pad_with_space\": {\r\n \"string_value\": \"True\"\r\n },\r\n \"subvoices\": {\r\n \"string_value\": \"0:0\"\r\n }\r\n },\r\n \"model_warmup\": [],\r\n \"model_transaction_policy\": {\r\n \"decoupled\": true\r\n }\r\n}\r\nI0304 06:27:45.010360 102 tts-preprocessor.cc:339] TRITONBACKEND_ModelInstanceInitialize: tts_preprocessor-Jaz_v1_0 (device 0)\r\nI0304 06:27:45.010680 102 model_lifecycle.cc:693] successfully loaded 'tts_preprocessor-Jaz_v1' version 1\r\nE0304 06:27:45.010726 102 model_repository_manager.cc:481] Invalid argument: ensemble 'fastpitch_hifigan_ensemble-Jaz_v1' depends on 'riva-onnx-fastpitch_encoder-Jaz_v1' which has no loaded version\r\nI0304 06:27:45.010785 102 server.cc:563] \r\n+------------------+------+\r\n| Repository Agent | Path |\r\n+------------------+------+\r\n+------------------+------+\r\n\r\nI0304 06:27:45.010857 102 server.cc:590] \r\n+------------------------+---------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------+\r\n| Backend | Path | Config |\r\n+------------------------+---------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------+\r\n| onnxruntime | /opt/tritonserver/backends/onnxruntime/libtriton_onnxruntime.so | {\"cmdline\":{\"auto-complete-config\":\"false\",\"min-compute-capability\":\"6.000000\",\"backend-directory\":\"/opt/tritonserver/backends\",\"default-max-batch-size\":\"4\"}} |\r\n| riva_tts_preprocessor | /opt/tritonserver/backends/riva_tts_preprocessor/libtriton_riva_tts_preprocessor.so | {\"cmdline\":{\"auto-complete-config\":\"false\",\"min-compute-capability\":\"6.000000\",\"backend-directory\":\"/opt/tritonserver/backends\",\"default-max-batch-size\":\"4\"}} |\r\n| tensorrt | /opt/tritonserver/backends/tensorrt/libtriton_tensorrt.so | {\"cmdline\":{\"auto-complete-config\":\"false\",\"min-compute-capability\":\"6.000000\",\"backend-directory\":\"/opt/tritonserver/backends\",\"default-max-batch-size\":\"4\"}} |\r\n| riva_tts_chunker | /opt/tritonserver/backends/riva_tts_chunker/libtriton_riva_tts_chunker.so | {\"cmdline\":{\"auto-complete-config\":\"false\",\"min-compute-capability\":\"6.000000\",\"backend-directory\":\"/opt/tritonserver/backends\",\"default-max-batch-size\":\"4\"}} |\r\n| riva_tts_postprocessor | /opt/tritonserver/backends/riva_tts_postprocessor/libtriton_riva_tts_postprocessor.so | {\"cmdline\":{\"auto-complete-config\":\"false\",\"min-compute-capability\":\"6.000000\",\"backend-directory\":\"/opt/tritonserver/backends\",\"default-max-batch-size\":\"4\"}} |\r\n+------------------------+---------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------+\r\n\r\nI0304 06:27:45.010956 102 server.cc:633] \r\n+------------------------------------+---------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\r\n| Model | Version | Status |\r\n+------------------------------------+---------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\r\n| riva-onnx-fastpitch_encoder-Jaz_v1 | 1 | UNAVAILABLE: Internal: onnx runtime error 1: Load model from /data/models/riva-onnx-fastpitch_encoder-Jaz_v1/1/model.onnx failed:/workspace/onnxruntime/onnxruntime/core/graph/model.cc:146 onnxruntime::Model::Model(onnx::ModelProto&&, const PathString&, const IOnnxRuntimeOpSchemaRegistryList*, const onnxruntime::logging::Logger&, const onnxruntime::ModelOptions&) Unsupported model IR version: 9, max supported IR version: 8 |\r\n| riva-trt-hifigan-Jaz_v1 | 1 | READY |\r\n| spectrogram_chunker-Jaz_v1 | 1 | READY |\r\n| tts_postprocessor-Jaz_v1 | 1 | READY |\r\n| tts_preprocessor-Jaz_v1 | 1 | READY |\r\n+------------------------------------+---------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\r\n\r\nI0304 06:27:45.084929 102 metrics.cc:864] Collecting metrics for GPU 0: Tesla T4\r\nI0304 06:27:45.085172 102 metrics.cc:757] Collecting CPU metrics\r\nI0304 06:27:45.085354 102 tritonserver.cc:2264] \r\n+----------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\r\n| Option | Value |\r\n+----------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\r\n| server_id | triton |\r\n| server_version | 2.27.0 |\r\n| server_extensions | classification sequence model_repository model_repository(unload_dependents) schedule_policy model_configuration system_shared_memory cuda_shared_memory binary_tensor_data statistics trace logging |\r\n| model_repository_path[0] | /data/models |\r\n| model_control_mode | MODE_NONE |\r\n| strict_model_config | 1 |\r\n| rate_limit | OFF |\r\n| pinned_memory_pool_byte_size | 268435456 |\r\n| cuda_memory_pool_byte_size{0} | 1000000000 |\r\n| response_cache_byte_size | 0 |\r\n| min_supported_compute_capability | 6.0 |\r\n| strict_readiness | 1 |\r\n| exit_timeout | 30 |\r\n+----------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\r\n\r\nI0304 06:27:45.085368 102 server.cc:264] Waiting for in-flight requests to complete.\r\nI0304 06:27:45.085383 102 server.cc:280] Timeout 30: Found 0 model versions that have in-flight inferences\r\nI0304 06:27:45.085634 102 server.cc:295] All models are stopped, unloading models\r\nI0304 06:27:45.085638 102 tts-postprocessor.cc:310] TRITONBACKEND_ModelInstanceFinalize: delete instance state\r\nI0304 06:27:45.085652 102 server.cc:302] Timeout 30: Found 4 live models and 0 in-flight non-inference requests\r\nI0304 06:27:45.085697 102 tensorrt.cc:5665] TRITONBACKEND_ModelInstanceFinalize: delete instance state\r\nI0304 06:27:45.085746 102 spectrogram-chunker.cc:275] TRITONBACKEND_ModelInstanceFinalize: delete instance state\r\nI0304 06:27:45.085788 102 tts-preprocessor.cc:342] TRITONBACKEND_ModelInstanceFinalize: delete instance state\r\nI0304 06:27:45.085799 102 spectrogram-chunker.cc:271] TRITONBACKEND_ModelFinalize: delete model state\r\nI0304 06:27:45.085826 102 tts-preprocessor.cc:338] TRITONBACKEND_ModelFinalize: delete model state\r\nI0304 06:27:45.085946 102 model_lifecycle.cc:578] successfully unloaded 'spectrogram_chunker-Jaz_v1' version 1\r\nI0304 06:27:45.092669 102 tts-postprocessor.cc:306] TRITONBACKEND_ModelFinalize: delete model state\r\nI0304 06:27:45.092836 102 model_lifecycle.cc:578] successfully unloaded 'tts_postprocessor-Jaz_v1' version 1\r\nI0304 06:27:45.102135 102 tensorrt.cc:5604] TRITONBACKEND_ModelFinalize: delete model state\r\nI0304 06:27:45.102301 102 model_lifecycle.cc:578] successfully unloaded 'riva-trt-hifigan-Jaz_v1' version 1\r\nI0304 06:27:45.106678 102 model_lifecycle.cc:578] successfully unloaded 'tts_preprocessor-Jaz_v1' version 1\r\n > Riva waiting for Triton server to load all models...retrying in 1 second\r\nI0304 06:27:46.085741 102 server.cc:302] Timeout 29: Found 0 live models and 0 in-flight non-inference requests\r\nerror: creating server: Internal - failed to load all models\r\n > Riva waiting for Triton server to load all models...retrying in 1 second\r\n > Riva waiting for Triton server to load all models...retrying in 1 second\r\n > Triton server died before reaching ready state. Terminating Riva startup.\r\nCheck Triton logs with: docker logs \r\n```\r\n</details>\r\n\r\n", "@LL-AI-dev the failure on deploy is very similar to what I saw deploying ASR models. It turned out to be caused by a discrepancy in the version of onnx that NeMo uses and the one that is used by Riva/Triton (older); see this issue https://github.com/nvidia-riva/nemo2riva/issues/36. For ASR the workaround was to either downgrade the onnx library in NeMo or avoid using the onnx runtime in Riva.", "@itzsimpl this is my script for testing different versions of onnx. before the script is run, i do a simple pip install onnx==1.13.0 as you mentioned in [https://github.com/nvidia-riva/nemo2riva/issues/36] but this has seemingly not solved the issue. I also tried with different versions of onnxruntime to no effect.\r\n\r\n```\r\nimport nemo\r\nfrom nemo.collections.tts.models import FastPitchModel, HifiGanModel\r\nimport onnx\r\n\r\nnemo_ver = nemo.__version__\r\nonnx_ver = onnx.__version__\r\nprint(\"onnxruntime: \",onnx_ver)\r\nprint(\"nemo: \",nemo_ver)\r\n\r\nfp_model_name = f\"FastPitch_nemo{nemo_ver}_onnx{onnx_ver}\"\r\nhg_model_name = f\"HifiGan_nemo{nemo_ver}_onnx{onnx_ver}\"\r\n\r\nmodel = FastPitchModel.from_pretrained(model_name=\"tts_en_fastpitch_ipa\")\r\nmodel.save_to(f'/models/{fp_model_name}.nemo')\r\n\r\nmodel = HifiGanModel.from_pretrained(model_name=\"tts_en_hifigan\")\r\nmodel.save_to(f'/models/{hg_model_name}.nemo')\r\n\r\nprint(\"models saved\")\r\nimport os\r\n\r\nos.system(f\"nemo2riva --out /models/riva/{fp_model_name}.riva --key tlt_encode /models/{fp_model_name}.nemo\")\r\nos.system(f\"nemo2riva --out /models/riva/{hg_model_name}.riva --key tlt_encode /models/{hg_model_name}.nemo\")\r\n\r\ncmd = f\"\"\"\r\nriva-build speech_synthesis --force \\\r\n/models/rmir/tts_pipeline_model__nemo{nemo_ver}_onnx{onnx_ver}.rmir:tlt_encode \\\r\n/models/riva/{fp_model_name}.riva:tlt_encode \\\r\n/models/riva/{hg_model_name}.riva:tlt_encode \\\r\n--language_code=en-US \\\r\n--num_speakers=1 \\\r\n--voice_name test_{onnx_ver} \\\r\n--sample_rate 22050 \\\r\n--abbreviations_file=/riva_aux/abbr.txt \\\r\n--wfst_tokenizer_model=/riva_tn/tokenize_and_classify.far \\\r\n--wfst_verbalizer_model=/riva_tn/verbalize.far \\\r\n--phone_set=ipa \\\r\n--phone_dictionary_file=/riva_aux/ipa_cmudict-0.7b_nv22.08.txt \\\r\n--upper_case_chars=True\r\n\"\"\"\r\nos.system(cmd)\r\n```\r\n\r\nMy next thought is to test using trt_32 or torchscript via the args in the riva-build command for the encoder (as thats where the error seems to be) or for both enc and dec if needed. [https://docs.nvidia.com/deeplearning/riva/user-guide/docs/tts/tts-custom.html#riva-build-optional-parameters]", "using the flag \"use_trt_fp32\" during riva-build results in a model that when deployed gets the same error as before about the IR version. \r\n`UNAVAILABLE: Internal: onnx runtime error 1: Load model from /data/models/riva-onnx-fastpitch_encoder-Jaz_v1/1/model.onnx failed:/workspace/onnxruntime/onnxruntime/core/graph/model.cc:146 onnxruntime::Model::Model(onnx::ModelProto&&, const PathString&, const IOnnxRuntimeOpSchemaRegistryList*, const onnxruntime::logging::Logger&, const onnxruntime::ModelOptions&) Unsupported model IR version: 9, max supported IR version: 8`\r\n\r\nWhen using the flag \"use_torchscript\" there is an issue during riva_init.sh due to a missing file. This is not resolved by using `--FORMAT TS` during the nemo2riva call. infact, setting the format to TS results in the main folder in the output location being empty.\r\n\r\nDespite the lack of success before I will go back to trying a combination of downgraded onnx and onnxruntime libraries. As I do not seem to be able to avoid the use of onnx as @itzsimpl suggested.\r\n", "all with nemo 1.23.0.\r\nversions of onnx/onnx runtime were set using pip install X commands in the docker file, before the script above is run to download models & save them (I believe saving them in this way is enough to update the model IR version used by onnxruntime. between onnx 1.13.0 and 1.14.0, and onnxruntime 1.16.3, 1.15.1, 1.14.0, 1.13.1 I was not able to find a combination that would allow the creation of a .rmir file that could be successfully deployed.\r\n\r\nTomorrow I will try the following approaches to resolve this:\r\n- [ ] use riva 2.13.1 as the base image. riva 2.14 has a known issue with the pytorch version in nemo:23.08 which is 2.1.0a0+29c30b1. Although the torch version in my environment (2.2.1+cu121) is more up-to-date than the one known not to work. reinstalling torch may help\r\n- [ ] try different onnx_opset settings with varying versions of onnxruntime as mention in https://github.com/nvidia-riva/nemo2riva/issues/36 and https://github.com/NVIDIA/NeMo/discussions/7278\r\n\r\n<details>\r\n <summary>\r\n Click this if you want to see the script I was using (still the same as the one 2 posts up)\r\n </summary>\r\n `import nemo\r\nfrom nemo.collections.tts.models import FastPitchModel, HifiGanModel\r\nimport onnx\r\nimport onnxruntime\r\nimport os\r\n\r\n#remove old runs\r\nos.system(\"rm -rf /models/models/*\")\r\nos.system(\"rm -rf /models/riva/*\")\r\nos.system(\"rm -rf /models/rmir/*\")\r\n\r\n#getting current versions\r\nnemo_ver = nemo.__version__\r\nonnx_ver = onnx.__version__\r\nonnx_rt_ver = onnxruntime.__version__\r\nprint(\"onnx: \",onnx_ver)\r\nprint(\"onnxruntime: \",onnx_rt_ver)\r\nprint(\"nemo: \",nemo_ver)\r\n\r\nfp_model_name = f\"FastPitch_nemo{nemo_ver}_onnx{onnx_ver}_onnxrt{onnx_rt_ver}\"\r\nhg_model_name = f\"HifiGan_nemo{nemo_ver}_onnx{onnx_ver}_onnxrt{onnx_rt_ver}\"\r\n\r\nmodel = FastPitchModel.from_pretrained(model_name=\"tts_en_fastpitch_ipa\")\r\nmodel.save_to(f'/models/{fp_model_name}.nemo')\r\n\r\nmodel = HifiGanModel.from_pretrained(model_name=\"tts_en_hifigan\")\r\nmodel.save_to(f'/models/{hg_model_name}.nemo')\r\n\r\nprint(\"models saved\")\r\n\r\nos.system(f\"nemo2riva --out /models/riva/{fp_model_name}.riva --key tlt_encode /models/{fp_model_name}.nemo\")\r\nos.system(f\"nemo2riva --out /models/riva/{hg_model_name}.riva --key tlt_encode /models/{hg_model_name}.nemo\")\r\n\r\ncmd = f\"\"\"\r\nriva-build speech_synthesis --force \\\r\n/models/rmir/tts_pipeline_model_nemo{nemo_ver}_onnx{onnx_ver}_onnxrt{onnx_rt_ver}.rmir:tlt_encode \\\r\n/models/riva/{fp_model_name}.riva:tlt_encode \\\r\n/models/riva/{hg_model_name}.riva:tlt_encode \\\r\n--language_code=en-US \\\r\n--num_speakers=1 \\\r\n--voice_name test__onnx{onnx_ver}_onnxrt{onnx_rt_ver} \\\r\n--sample_rate 22050 \\\r\n--abbreviations_file=/riva_aux/abbr.txt \\\r\n--wfst_tokenizer_model=/riva_tn/tokenize_and_classify.far \\\r\n--wfst_verbalizer_model=/riva_tn/verbalize.far \\\r\n--phone_set=ipa \\\r\n--phone_dictionary_file=/riva_aux/ipa_cmudict-0.7b_nv22.08.txt \\\r\n--upper_case_chars=True\r\n\"\"\"\r\nos.system(cmd)`\r\n</details>\r\n\r\n**onnx 1.14.0, onnxruntime 1.16.3 (defaults from dockerfile)**\r\n- Unsupported model IR version: 9, max supported IR version: 8\r\n\r\n**onnx 1.13.0, onnxruntime 1.16.3**\r\n- UNAVAILABLE: Internal: onnx runtime error 1: Load model from /data/models/riva-onnx-fastpitch_encoder-test__onnx1.13.0_onnxrt1.16.3/1/model.onnx failed:Invalid tensor data type 0.\r\n\r\n**onnx 1.13.0, onnxruntime 1.15.1**\r\n-UNAVAILABLE: Internal: onnx runtime error 1: Load model from /data/models/riva-onnx-fastpitch_encoder-test__onnx1.13.0_onnxrt1.15.1/1/model.onnx failed:Invalid tensor data type 0.\r\n(accidentally ran twice, same result)\r\n\r\n**onnx 1.14.0, onnxruntime 1.15.1**\r\n-UNAVAILABLE: Internal: onnx runtime error 1: Load model from /data/models/riva-onnx-fastpitch_encoder-test__onnx1.14.0_onnxrt1.15.1/1/model.onnx failed:/workspace/onnxruntime/onnxruntime/core/graph/model.cc:146 onnxruntime::Model::Model(onnx::ModelProto&&, const PathString&, const IOnnxRuntimeOpSchemaRegistryList*, const onnxruntime::logging::Logger&, const onnxruntime::ModelOptions&) Unsupported model IR version: 9, max supported IR version: 8\r\n\r\n**onnx 1.14.0, onnxruntime 1.14.0**\r\n-UNAVAILABLE: Internal: onnx runtime error 1: Load model from /data/models/riva-onnx-fastpitch_encoder-test__onnx1.14.0_onnxrt1.14.0/1/model.onnx failed:/workspace/onnxruntime/onnxruntime/core/graph/model.cc:146 onnxruntime::Model::Model(onnx::ModelProto&&, const PathString&, const IOnnxRuntimeOpSchemaRegistryList*, const onnxruntime::logging::Logger&, const onnxruntime::ModelOptions&) Unsupported model IR version: 9, max supported IR version: 8\r\n\r\n**onnx 1.14.0, onnxruntime 1.13.1**\r\n-UNAVAILABLE: Internal: onnx runtime error 1: Load model from /data/models/riva-onnx-fastpitch_encoder-test__onnx1.14.0_onnxrt1.13.1/1/model.onnx failed:/workspace/onnxruntime/onnxruntime/core/graph/model.cc:146 onnxruntime::Model::Model(onnx::ModelProto&&, const PathString&, const IOnnxRuntimeOpSchemaRegistryList*, const onnxruntime::logging::Logger&, const onnxruntime::ModelOptions&) Unsupported model IR version: 9, max supported IR version: 8 \r\n", "Using pre-trained models DIRECTLY from ngc is ok:\r\nonnx 1.13.0\r\nonnxruntime 1.15.1\r\nnemo_toolkit 1.20.0 (pip install nemo_toolkit[all]==1.20.0)\r\n```\r\nwget --content-disposition 'https://api.ngc.nvidia.com/v2/models/org/nvidia/team/nemo/tts_hifigan/1.0.0rc1/files?redirect=true&path=tts_hifigan.nemo' -O tts_hifigan.nemo\r\nwget --content-disposition 'https://api.ngc.nvidia.com/v2/models/org/nvidia/team/nemo/tts_en_fastpitch/IPA_1.13.0/files?redirect=true&path=tts_en_fastpitch_align_ipa.nemo' -O tts_en_fastpitch_align_ipa.nemo\r\n\r\nnemo2riva --out ../nemo_models/riva/tts_en_fastpitch_align_ipa.riva --key tlt_encode tts_en_fastpitch_align_ipa.nemo\r\nnemo2riva --out ../nemo_models/riva/tts_hifigan.riva --key tlt_encode tts_hifigan.nemo\r\n\r\n#below is run within the docker container after it is launched\r\nriva-build speech_synthesis --force \\\r\n/models/rmir/tts_pipeline_simple.rmir:tlt_encode \\\r\n/models/riva/tts_en_fastpitch_align_ipa.riva:tlt_encode \\\r\n/models/riva/tts_hifigan.riva:tlt_encode \\\r\n--language_code=en-US \\\r\n--num_speakers=1 \\\r\n--voice_name simple \\\r\n--sample_rate 22050 \\\r\n--abbreviations_file=/riva_aux/abbr.txt \\\r\n--wfst_tokenizer_model=/riva_tn/tokenize_and_classify.far \\\r\n--wfst_verbalizer_model=/riva_tn/verbalize.far \\\r\n--phone_set=ipa \\\r\n--phone_dictionary_file=/riva_aux/ipa_cmudict-0.7b_nv22.08.txt \\\r\n--upper_case_chars=True\r\n```\r\nWhen I ran this combo previously with a higher nemo version, 1.23 (installed via git clone + pip install -e) and saved the models through nemo before doing the conversion, I was getting some error related to the tensor data type. This implies the data type error is resulting from the interaction with nemo.\r\n\r\nRerunning the above code:\r\n\r\n- updrade onnx to 1.15\r\n - script failed\r\n- downgrade onnx to 1.13 \r\n - successful deploy\r\n- running similar script, but save the models through nemo instead of wget\r\n - WORKS\r\n\r\nWill be doing some testing but it seems that a working combination to deploy nemo models requires:\r\n- python 3.8\r\n- nemo_toolkit 1.20.0\r\n- nemo2riva 2.11.0\r\n- onnx 1.13.0\r\n- onnxruntime 1.15.0\r\n\r\nwhen i have narrowed down the specific requirements I will post a dockerfile that can be used for training and converting. \r\n\r\nOverall, it would be great if the nemo, nemo2riva, and riva packages can be made more compatible\r\n", "After testing many combinations of packages, I have discovered the current compatibility for training and converting .nemo files is limited to:\r\n \r\n**Training Environment:**\r\n- Simplest solution is to use a Nemo image.\r\n- Doesn't matter what Nemo, ONNX, or ONNXRuntime version is installed while saving the model to a `.nemo` file.\r\n\r\n**Converting Environment:**\r\n- Use a Riva-service maker image to simplify things & because Python 3.8 is the max version we can use anyway.\r\n- Package version for ONNX: 1.13.0 is required\r\n - Any higher and the deployment fails with the “Unsupported model IR version: 9, max supported IR version: 8” error.\r\n - Lower versions also seem to cause issues but this has not been thoroughly investigated.\r\n- Package version for nemo2riva: 2.13.0 is required. \r\n - Any higher fails with the error related to “Invalid tensor data type 0”. Even 2.13.1 fails. \r\n - 2.11.0 or 2.12.0 work if required.\r\n- Package version for Nemo: 1.20.0 is required. \r\n - Later versions cannot be used due to various package incompatibilities with Python 3.8. \r\n - Nvidia-eff, a critical nemo2riva dependency, requires Python 3.8, meaning that currently, no converter environment can use a Python version greater than 3.8, which negates the use of Nemo versions later than 1.20. \r\n - Earlier versions of Nemo most likely work, but no investigation has been done.\r\n\r\n-----------------------------\r\nUntil the conflicts are resolved, it will not possible for models or functions introduced in nemo after version 1.20 to be converted to a .riva file", "@LL-AI-dev could you please try \"pip install nvidia-eff>=0.6.4 nvidia-eff-tao-encryption>=0.1.8\" in your python3.10 environment and see whether that helps with this error? Thanks.", "@rmittal-github thanks for the suggestion. that does allow the nemo2riva (version 2.14) conversion to run, but when deploying the downstream .rmir file, it gets the error:\r\n`UNAVAILABLE: Internal: onnx runtime error 1: Load model from /data/models/riva-onnx-fastpitch_encoder-test_py310_onnx13/1/model.onnx failed:Invalid tensor data type 0. `\r\n\r\nI suspect this might be related to a torch version issue (from riva release logs, 2.14 known issues)\r\n\r\n>When generating .riva models from .nemo using nemo2riva, the nemo:23.08 image is not compatible with Riva due to updated torch version. To avoid any Riva deployment issues, the recommendation is to continue using the last working nemo image\r\n\r\nthese are the pytorch versions in question:\r\n - environment I used to convert the files, torch version 2.2.0a0+81ea7a4\r\n - nemo:23.08 image, torch version 2.1.0a0+29c30b1\r\n \r\nDo you have any suggestions to resolve this?", "I tried downgrading nemo2riva to version 2.13 as that had resolved the data type error previously\r\n> - Package version for nemo2riva: 2.13.0 is required.\r\n - Any higher fails with the error related to “Invalid tensor data type 0”. Even 2.13.1 fails.\r\n\r\ndowngrading results in the error:\r\n\r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"/usr/local/bin/nemo2riva\", line 8, in <module>\r\n sys.exit(nemo2riva())\r\n File \"/usr/local/lib/python3.10/dist-packages/nemo2riva/cli/nemo2riva.py\", line 49, in nemo2riva\r\n Nemo2Riva(args)\r\n File \"/usr/local/lib/python3.10/dist-packages/nemo2riva/convert.py\", line 42, in Nemo2Riva\r\n trainer = Trainer(cfg_trainer)\r\n File \"/usr/local/lib/python3.10/dist-packages/pytorch_lightning/utilities/argparse.py\", line 70, in insert_env_defaults\r\n return fn(self, **kwargs)\r\n File \"/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/trainer.py\", line 399, in __init__\r\n self._accelerator_connector = _AcceleratorConnector(\r\n File \"/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/connectors/accelerator_connector.py\", line 140, in __init__\r\n self._check_config_and_set_final_flags(\r\n File \"/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/connectors/accelerator_connector.py\", line 222, in _check_config_and_set_final_flags\r\n raise ValueError(\r\nValueError: You selected an invalid accelerator name: `accelerator=TrainerConfig(logger=False, callbacks=None, default_root_dir=None, gradient_clip_val=0, num_nodes=1, enable_progress_bar=True, overfit_batches=0.0, check_val_every_n_epoch=1, fast_dev_run=False, accumulate_grad_batches=1, max_epochs=1000, min_epochs=1, max_steps=-1, min_steps=None, limit_train_batches=1.0, limit_val_batches=1.0, limit_test_batches=1.0, val_check_interval=1.0, log_every_n_steps=50, accelerator='cpu', sync_batchnorm=False, precision=32, num_sanity_val_steps=2, profiler=None, benchmark=False, deterministic=False, use_distributed_sampler=True, detect_anomaly=False, plugins=None, limit_predict_batches=1.0, gradient_clip_algorithm='norm', max_time=None, reload_dataloaders_every_n_epochs=0, devices='auto', strategy='auto', enable_checkpointing=False, enable_model_summary=True, inference_mode=True, barebones=False)`. Available names are: auto, cpu, cuda, hpu, ipu, mps, tpu.\r\n```\r\ni attempt to change the line where the accelerator is set in `/usr/local/lib/python3.10/dist-packages/nemo2riva/convert.py` but regardless of which value I tried, the conversion failed\r\n", "@LL-AI-dev I think downgrading to 2.13 is not required. Could you please try with nemo:23.06 image and 2.14.0, along with the pip install packages I mentioned in my last comment, and see if that resolves the issue?\r\n\r\nAs per the 2.14.0 release notes known issues, nemo:23.08 is not compatible and need to try previous nemo image (23.06)", "This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.", "This issue was closed because it has been inactive for 7 days since being marked as stale." ]

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[ "This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.", "This issue was closed because it has been inactive for 7 days since being marked as stale.", "Hi, I'm facing the same problem in NeMo 2.0. It took me a long time to see that the '-sub1', 'sub2'.... don't work. This kind a huge issue since with audio lasting up to 1 hour, you don't want to divide them into every segment you have.", "I believe you'll be able to avoid these issues by switching to lhotse dataloaders. For ASR recipes, just add `train_ds.use_lhotse: True`.", "> I believe you'll be able to avoid these issues by switching to lhotse dataloaders. For ASR recipes, just add `train_ds.use_lhotse: True`.\n\nHi, thanks for the tip but when I try to use lhotse dataloaders I have the following error:\n\n```\n[NeMo I 2024-11-27 10:24:19 nemo_logging:381] Creating a Lhotse DynamicCutSampler (bucketing is disabled, (max_batch_duration=None max_batch_size=1)\nError executing job with overrides: []\nTraceback (most recent call last):\n File \"NeMo/nemo/collections/asr/models/ctc_models.py\", line 385, in setup_training_data\n * ceil((len(self._train_dl.dataset) / self.world_size) / train_data_config['batch_size'])\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\nTypeError: object of type 'IterableDatasetWrapper' has no len()\n```\n\nI have switched to using sharded manifests as it seems to be required for that type of dataloader. I don't know if this is normal and I need to change something else? but what?\n\nI have the following config for my train_ds:\n```\ntrain_ds:\n manifest_filepath:\n - - bucket11/sharded_manifests/manifest__OP_0..23_CL_.json\n - - bucket12/sharded_manifests/manifest__OP_0..23_CL_.json\n tarred_audio_filepaths:\n - - bucket11/audio__OP_0..23_CL_.tar\n - - bucket12/audio__OP_0..23_CL_.tar\n sample_rate: ${model.sample_rate}\n batch_size: 1 # must be 1 if using bucketing_batch_size\n shuffle: true\n num_workers: 8\n use_lhotse: true\n pin_memory: true\n max_duration: 30.1\n min_duration: 0.1\n shuffle_n: 2048\n # tarred datasets\n is_tarred: true\n bucketing_strategy: \"synced_randomized\" # \"fixed_order\" \"synced_randomized\" \"fully_randomized\"\n bucketing_weights: [1, 1]\n bucketing_batch_size: 8\n shard_strategy: scatter\n```\n@pzelasko " ]

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[ "jenkins", "jenkins", "jenkins", "jenkins", "jenkins", "jenkins", "This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.", "jenkins", "jenkins", "jenkins", "jenkins", "jenkins", "jenkins" ]

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[ "the latest megatron version for python 3.8 is 0.4.0. you will need to upgrage to python 3.10 for any nemo version after 1.20.0 (from memory, that is when the megatron version requirement was upgraded from 0.4.0 which is still python 3.8 compatible)\r\n\r\nI believe megatron core is just used for NLP so if you install all the extras but that you might be able to stick to python 3.8", "3.10 python is a requirement for both NeMo ASR and NLP as of 1.23. Python 3.8 will be EOL this year, so it would be good to update to a newer version", "This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.", "This issue was closed because it has been inactive for 7 days since being marked as stale." ]

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[ "Not to be merged to main since the fix is already there.", "This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.", "This PR was closed because it has been inactive for 7 days since being marked as stale." ]

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[ "jenkins", "jenkins", "jenkins", "jenkins", "jenkins", "jenkins", "jenkins" ]

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[ "Let me know if you want to fast merge after additional approvals ", "jenkins", "@titu1994 could you please fast merge this? ", "jenkins", "jenkins", "Docs changes does not need CI to pass, I can merge it if it's ready ", "Merged " ]

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[ "This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.", "This issue was closed because it has been inactive for 7 days since being marked as stale." ]

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[ "jenkins", "jenkins", "jenkins", "jenkins", "jenkins", "jenkins", "jenkins", "jenkins", "jenkins", "jenkins", "jenkins", "jenkins", "jenkins", "jenkins" ]

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[ "This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.", "This PR was closed because it has been inactive for 7 days since being marked as stale." ]

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[ "jenkins" ]

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[ "LGTM. Thanks.", "jenkins", "jenkins" ]

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[ "jenkins", "This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.", "This PR was closed because it has been inactive for 7 days since being marked as stale." ]

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[ "@cuichenx for review", "This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.", "This PR was closed because it has been inactive for 7 days since being marked as stale." ]

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[ "@ericharper ", "@aschilling-nv All the doc strings errors with this PR should be resolved" ]

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[ "This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.", "This issue was closed because it has been inactive for 7 days since being marked as stale." ]

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[ "jenkins", "jenkins", "@michal2409 as far as I know we install `triton` in [here](https://github.com/NVIDIA/NeMo/blob/main/requirements/requirements.txt#L13) instead of `pytorch-triton`. Looks like you install `pytorch-triton` in your container? ", "> @michal2409 as far as I know we install `triton` in [here](https://github.com/NVIDIA/NeMo/blob/main/requirements/requirements.txt#L13) instead of `pytorch-triton`. Looks like you install `pytorch-triton` in your container?\r\n\r\nYou're right. I checked the DLFW containers and they do have `pytorch-triton` and it's different from `triton` that gets installed by NeMo. We'll revert this change.\r\n\r\nThe other outstanding issue is - what is the expected behavior if `triton` is not installed? Right now it raises an error that is not being handled, but I would imagine that we should catch it and set `flash_attn_func_triton = None` and continue the execution. Is that expectation correct?", "If triton is not installed, we'll get a `ModuleNotFoundError` when we try to do `from flash_attn.flash_attn_triton import flash_attn_func as flash_attn_func_triton` in [here](https://github.com/Dao-AILab/flash-attention/blob/main/flash_attn/flash_attn_triton.py#L45). Then, we'll have `flash_attn_func_triton = None`", "I don't think this is true. I'm getting:\r\n```\r\n>>> a= pkg_resources.get_distribution(\"triton\")\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/usr/local/lib/python3.10/dist-packages/pkg_resources/__init__.py\", line 528, in get_distribution\r\n dist = get_provider(dist)\r\n File \"/usr/local/lib/python3.10/dist-packages/pkg_resources/__init__.py\", line 400, in get_provider\r\n return working_set.find(moduleOrReq) or require(str(moduleOrReq))[0]\r\n File \"/usr/local/lib/python3.10/dist-packages/pkg_resources/__init__.py\", line 968, in require\r\n needed = self.resolve(parse_requirements(requirements))\r\n File \"/usr/local/lib/python3.10/dist-packages/pkg_resources/__init__.py\", line 829, in resolve\r\n dist = self._resolve_dist(\r\n File \"/usr/local/lib/python3.10/dist-packages/pkg_resources/__init__.py\", line 870, in _resolve_dist\r\n raise DistributionNotFound(req, requirers)\r\npkg_resources.DistributionNotFound: The 'triton' distribution was not found and is required by the application\r\n```\r\n\r\nwhich is not being handled. Could you please double check @hsiehjackson ? Maybe I'm doing something wrong\r\n\r\nEDIT:\r\nEssentially what we need is to swap L76 from:\r\n`except (ImportError, ModuleNotFoundError, AssertionError):`\r\nto\r\n`except (ImportError, ModuleNotFoundError, AssertionError, pkg_resources.DistributionNotFound):`\r\n(the second line that is changed in this PR)", "When my container install `pytorch-triton` instead of `triton`, I'll get the following error. How do you pass `from flash_attn.flash_attn_triton import flash_attn_func as flash_attn_func_triton` when you just install `pytorch-triton`?\r\n```\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/usr/local/lib/python3.10/dist-packages/flash_attn/flash_attn_triton.py\", line 45, in <module>\r\n import triton\r\n File \"/usr/local/lib/python3.10/dist-packages/triton/__init__.py\", line 1, in <module>\r\n raise RuntimeError(\"Should never be installed\")\r\nRuntimeError: Should never be installed\r\n```", "I don't know, it works for me. But sure, if you need `triton` instead of `pytorch-triton` then we'll revert the change, no problem - this is actually not a blocker for us. The blocker is that you are not handling the `pkg_resources.DistributionNotFound` exception and my code crashes. Can we handle that exception as well?", "Sure! @michal2409 Can you help me revert the first line `assert pkg_resources.get_distribution(\"pytorch-triton\").version == '2.0.0.dev20221202'`? I think we only need the second line as @mmarcinkiewicz suggested.", "@hsiehjackson done", "jenkins", "jenkins", "jenkins", "jenkins", "jenkins" ]

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[ "jenkins", "jenkins" ]

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[ "I also meet the problem can't pull nvcr.io/ea-bignlp/ga-participants/nemofw-training too.", "@thangdt277 Can you start to train with other images?\r\n", "This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days." ]

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[ "jenkins" ]

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[ "Yes its deprecated anyway in favor of @erastorgueva-nv notebook" ]

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[ "jenkins" ]

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[ "jenkins", "This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.", "This PR was closed because it has been inactive for 7 days since being marked as stale." ]

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[ "jenkins", "jenkins", "jenkins", "jenkins" ]

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[ "This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.", "This PR was closed because it has been inactive for 7 days since being marked as stale." ]

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[ "jenkins", "jenkins" ]