.vscode assets notebooks trainer 01_how_to_train.ipynb 02_how_to_generate.ipynb 03_reformer.ipynb 05_encoder_decoder.ipynb 08_warm_starting_encoder_decoder.ipynb 101_train-decision-transformers.ipynb 10_tf_serving.ipynb 111_fine_tune_whisper.ipynb 111_tf_serving_vision.ipynb 112...
#4.run %%ai in *.ipynb file on ec2 instead of SageMaker notebook instance / SageMaker Studio [also can run in VSCODE] after making sure your Amazon SageMaker endpoint is health %load_ext jupyter_ai %%ai sagemaker-endpoint:jumpstart-dft-meta-textgeneration-llama-2-7b --region-name=us-east...
File: validate_expectations.ipynb In this notebook we show how to validate if a new dataset meets the predefined expectations. We will pass the new data through the data pipeline (pipeline.py) and validate the data output against the expectation suite that we have created earlier. ...
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.vscode assets notebooks trainer 01_how_to_train.ipynb 02_how_to_generate.ipynb 03_reformer.ipynb 05_encoder_decoder.ipynb 08_warm_starting_encoder_decoder.ipynb 101_train-decision-transformers.ipynb 10_tf_serving.ipynb 111_fine_tune_whisper.ipynb 111_tf_serving_vision.ipynb 112...
.vscode assets notebooks trainer 01_how_to_train.ipynb 02_how_to_generate.ipynb 03_reformer.ipynb 05_encoder_decoder.ipynb 08_warm_starting_encoder_decoder.ipynb 101_train-decision-transformers.ipynb 10_tf_serving.ipynb 111_fine_tune_whisper.ipynb 111_tf_serving_vision.ipynb 112_...
.vscode assets notebooks trainer 01_how_to_train.ipynb 02_how_to_generate.ipynb 03_reformer.ipynb 05_encoder_decoder.ipynb 08_warm_starting_encoder_decoder.ipynb 101_train-decision-transformers.ipynb 10_tf_serving.ipynb 111_fine_tune_whisper.ipynb 111_tf_serving_vision.ipynb 112_v...