Comments (2)
You should be able to bring in all your dependencies in the same model artifact. The model_fn
is given model_dir
. You could access any file/files in that folder.
The entrypoint name can also be changed. Similar to PT inference toolkit, you would have to export "SAGEMAKER_PROGRAM=your_script.py
".. Only catch is that "your_script.py
" should be in "PYTHONPATH
" and by default the "code" folder inside the model artifact is a part of PYTHONPATH.
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perfect thanks @vdantu feel free to close
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Related Issues (20)
- Using custom inference script and models from Hub HOT 1
- get_pipeline function passes Path object rather than PretrainedTokenizer
- No support for multi-GPU HOT 2
- 🏷️ invalid
- Sagemaker endpoint inferencing error with HF model loading from s3bucket with new transformer update HOT 5
- Support multiple return sequences
- HF_TASK Enviournment Variable error HOT 1
- Endpoint creation completes before custom model_fn finishes loading resources
- ARCHITECTURES_2_TASK is limiting the tasks able to be deployed with HF DLC HOT 11
- Support passing model_kwargs to pipeline HOT 1
- InternalServerException at runtime HOT 3
- trust_remote_code=True in new Hugging Face LLM Inference Container for Amazon SageMaker HOT 2
- How to access CustomAttributes in async inferece request input_fn HOT 1
- [DOCS] List of available HF_TASK and default inference scripts HOT 4
- Dead Link for Available HF_Tasks HOT 1
- SageMaker deployment errors HOT 2
- Error on Sagemaker deployment for v1.0.1 HOT 1
- How can I delpoy a model with AWS S3 and without downloading model from hunggingface via TGI image on Sagemaker? HOT 2
- How to enable Batch inference on AWS deployed Serverless model from Hub? HOT 1
- Where is the logic for detecting custom inference.py? HOT 6
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