thepycoder

clearml-serving --id <your_service_ID> model add --engine triton \                           
    --endpoint "transformer_model" \
    --model-id <your_model_ID> \
    --preprocess examples/huggingface/preprocessing.py \
    --input-size "[-1]" "[-1]" "[-1]" \
    --input-type int32 int32 int32 \
    --input-name "input_ids" "token_type_ids" "attention_mask" \
    --output-size "[2]" \
    --output-type float32 \
    --output-name "output" \

thepycoder

clearml-serving --id <your_service_ID> model upload --name "Transformer ONNX" --project "Hugginface Serving" --path triton_models/transformer_tensorrt_model/1/model.bin

thepycoder

# Convert a Huggingface model to ONNX
docker run -it --rm --gpus all \
  -v $PWD:/project ghcr.io/els-rd/transformer-deploy:0.5.1 \
  bash -c "cd /project && \
    convert_model -m \"philschmid/MiniLM-L6-H384-uncased-sst2\" \
    --backend tensorrt onnx \
    --seq-len 16 128 128"

# This will have outputted a triton_models/ folder,
# which we can now serve using Triton

thepycoder

import torch
from fastapi import FastAPI
from transformers import AutoTokenizer, BatchEncoding, TensorType, AutoModelForSequenceClassification


application = FastAPI()
tokenizer = AutoTokenizer.from_pretrained("philschmid/MiniLM-L6-H384-uncased-sst2")
model = AutoModelForSequenceClassification.from_pretrained("philschmid/MiniLM-L6-H384-uncased-sst2").to('cuda:0')

thepycoder

# ...
# Loggers
data_dict = None
if RANK in {-1, 0}:
    loggers = Loggers(save_dir, weights, opt, hyp, LOGGER)  # loggers instance
    if loggers.clearml:
        loggers.clearml.task.execute_remotely(queue='my_queue')  # <------ ADD THIS LINE
        # Data_dict is either None is user did not choose for ClearML dataset or is filled in by ClearML
        data_dict = loggers.clearml.data_dict
# ...

thepycoder

clearml-agent daemon --queue <queues_to_listen_to> [--docker]

thepycoder

python train.py --img 640 --batch 16 --epochs 3 --data clearml://<your_dataset_id> --weights yolov5s.pt --cache

thepycoder

# Optionally add --parent <parent_dataset_id> if you want to base
# this version on another dataset version, so no duplicate files are uploaded!
clearml-data create --name coco128 --project YOLOv5
clearml-data add --files .
clearml-data close

thepycoder

cd coco128
clearml-data sync --project YOLOv5 --name coco128 --folder .

thepycoder

..
|_ yolov5
|_ datasets
    |_ coco128
        |_ images
        |_ labels
        |_ coco128.yaml  # <---- HERE!
        |_ LICENSE
        |_ README.txt