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@@ -146,6 +146,9 @@ NVIDIA DGX A100 (1x A100 80GB): bash ./triton/runner/start_NVIDIA-DGX-A100-\(1x-
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NVIDIA T4: bash ./triton/runner/start_NVIDIA-T4.sh
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```
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+If one encounters an error like `the provided PTX was compiled with an unsupported toolchain`, follow the steps in
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+[Step by step deployment process](#step-by-step-deployment-process).
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+
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## Performance
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The performance measurements in this document were conducted at the time of publication and may not reflect
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the performance achieved from NVIDIA’s latest software release. For the most up-to-date performance measurements, go to
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@@ -2077,7 +2080,7 @@ Please use the data download from the [Main QSG](https://github.com/NVIDIA/DeepL
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#### Prepare Checkpoint
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Please place a `checkpoint.pt` from TFT trained on electricity in `runner_workspace/checkpoints/electricity_bin/`. Note that the `electricity_bin`
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subdirectory may not be created yet. In addition one can download a zip archive of a trained checkpoint
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-[here](https://api.ngc.nvidia.com/v2/models/nvidia/tft_pyt_ckpt_base_eletricity_amp/versions/21.06.0/zip)
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+[here](https://api.ngc.nvidia.com/v2/models/nvidia/dle/tft_base_pyt_ckpt_ds-electricity/versions/22.11.0_amp/zip)
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#### Setup Container
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Build and run a container that extends the NGC PyTorch container with the Triton Inference Server client libraries and dependencies.
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@@ -2242,7 +2245,7 @@ mkdir -p ${SHARED_DIR}/input_data
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python triton/prepare_input_data.py \
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--input-data-dir ${SHARED_DIR}/input_data/ \
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--dataset ${DATASETS_DIR}/${DATASET} \
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- --checkpoint ${CHECKPOINT_DIR}/ \
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+ --checkpoint ${CHECKPOINT_DIR}/
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```
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</details>
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Izzy Putterman