Certificate validation error between Fabric and Huggingface
Hi, I'm tyring to download a couple of transformer/sentence-transformer model weights into a notebook and I keep running into certificate mismatch issues between fabric and huggingface. I was wondering if anyone else has ran into this issue or if this is a known issue that Microsoft plans on handling in the very near future? Environment Microsoft Fabric Notebook (PySpark) Python 3.11 PyTorch 2.2.1 Canada tenant sentence-transformers==2.7.0 successfully installed What works Package installation succeeds %pip install sentence-transformers==2.7.0 Import succeeds from sentence_transformers import SentenceTransformer version check succeeds import torch import sentence_transformers print(torch.__version__) print(sentence_transformers.__version__) What fails from sentence_transformers import SentenceTransformer model = SentenceTransformer("all-MiniLM-L6-v2") Or Observations The notebook appears able to reach Hugging Face and download metadata files. For example, the following files are successfully cached: config.json config_sentence_transformers.json modules.json README.md sentence_bert_config.json However, the model weight file never downloads. The Hugging Face cache contains *.incomplete with a file size of 0 MB. Error The relevant error message appears to be "No CA certificates were loaded from the system" Is downloading Hugging Face model weights from Fabric notebooks currently supported? If so, is there a known issue with certificate validation? Is there a recommended workaround? If not. then is there a recommended approach for generating semantic text embeddings within Fabric notebooks utilizing some kind of ANN based algorithm instead of classic techniques such as TF-IDF or SVD followed by some type of clustering? The goal of this notebook is semantic clustering of approximately 500 short "type" descriptions for taxonomy development. Thanks!
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MLFlow - Not logging results of all epochs
I am trying to use MLFlow to log the results of my model training to do hyperparameter tuning however it is only logging the last val_loss & loss value in the experiment. Running in a Python 3.11 notebook with tensorflow: 2.18.0 mlflow: 3.8.1 pandas: 2.2.2 with mlflow.start_run() as run: mlflow.tensorflow.autolog() history, model = run_model(X_train, X_val) for epoch, (tr, vl) in enumerate( zip(history.history["loss"], history.history["val_loss"]) 😞 mlflow.log_metric("loss", tr, step=epoch) mlflow.log_metric("val_loss", vl, step=epoch) Then this is the only output in the experiment:
