def notebooks(): folder_notebooks = path_notebooks() # Path for the notebooks paths = { "embedding_trainer": os.path.join( folder_notebooks, "embeddings", "embedding_trainer.ipynb" ), "similarity_embeddings_baseline": os.path.join( folder_notebooks, "sentence_similarity", "baseline_deep_dive.ipynb" ), "bert_encoder": os.path.join(folder_notebooks, "sentence_similarity", "bert_encoder.ipynb"), "gensen_local": os.path.join(folder_notebooks, "sentence_similarity", "gensen_local.ipynb"), "gensen_azureml": os.path.join( folder_notebooks, "sentence_similarity", "gensen_aml_deep_dive.ipynb" ), "similarity_automl_local": os.path.join( folder_notebooks, "sentence_similarity", "automl_local_deployment_aci.ipynb" ), "automl_with_pipelines_deployment_aks": os.path.join( folder_notebooks, "sentence_similarity", "automl_with_pipelines_deployment_aks.ipynb" ), "bert_qa_trainer": os.path.join( folder_notebooks, "question_answering", "pretrained-BERT-SQuAD-deep-dive-aml.ipynb" ), "bidaf_deep_dive": os.path.join( folder_notebooks, "question_answering", "bidaf_aml_deep_dive.ipynb" ), "bidaf_quickstart": os.path.join( folder_notebooks, "question_answering", "question_answering_system_bidaf_quickstart.ipynb", ), "entailment_multinli_bert": os.path.join( folder_notebooks, "entailment", "entailment_multinli_bert.ipynb" ), "entailment_bert_azureml": os.path.join( folder_notebooks, "entailment", "entailment_xnli_bert_azureml.ipynb" ), "tc_bert_azureml": os.path.join( folder_notebooks, "text_classification", "tc_bert_azureml.ipynb" ), "bert_senteval": os.path.join( folder_notebooks, "sentence_similarity", "bert_senteval.ipynb" ), "tc_mnli_bert": os.path.join(folder_notebooks, "text_classification", "tc_mnli_bert.ipynb"), "tc_dac_bert_ar": os.path.join( folder_notebooks, "text_classification", "tc_dac_bert_ar.ipynb" ), "tc_bbc_bert_hi": os.path.join( folder_notebooks, "text_classification", "tc_bbc_bert_hi.ipynb" ), "ner_wikigold_bert": os.path.join( folder_notebooks, "named_entity_recognition", "ner_wikigold_bert.ipynb" ), "deep_and_unified_understanding": os.path.join( folder_notebooks, "model_explainability", "interpret_dnn_layers.ipynb" ), } return paths
def notebooks(): folder_notebooks = path_notebooks() # Path for the notebooks paths = { "template": os.path.join(folder_notebooks, "template.ipynb"), "sar_single_node": os.path.join(folder_notebooks, "00_quick_start", "sar_movielens.ipynb"), "ncf": os.path.join(folder_notebooks, "00_quick_start", "ncf_movielens.ipynb"), "als_pyspark": os.path.join(folder_notebooks, "00_quick_start", "als_movielens.ipynb"), "fastai": os.path.join(folder_notebooks, "00_quick_start", "fastai_movielens.ipynb"), "xdeepfm_quickstart": os.path.join(folder_notebooks, "00_quick_start", "xdeepfm_synthetic.ipynb"), "dkn_quickstart": os.path.join(folder_notebooks, "00_quick_start", "dkn_synthetic.ipynb"), "lightgbm_quickstart": os.path.join(folder_notebooks, "00_quick_start", "lightgbm_tinycriteo.ipynb"), "wide_deep": os.path.join(folder_notebooks, "00_quick_start", "wide_deep_movielens.ipynb"), "data_split": os.path.join(folder_notebooks, "01_prepare_data", "data_split.ipynb"), "als_deep_dive": os.path.join(folder_notebooks, "02_model", "als_deep_dive.ipynb"), "surprise_svd_deep_dive": os.path.join(folder_notebooks, "02_model", "surprise_svd_deep_dive.ipynb"), "baseline_deep_dive": os.path.join(folder_notebooks, "02_model", "baseline_deep_dive.ipynb"), "ncf_deep_dive": os.path.join(folder_notebooks, "02_model", "ncf_deep_dive.ipynb"), "sar_deep_dive": os.path.join(folder_notebooks, "02_model", "sar_deep_dive.ipynb"), "vowpal_wabbit_deep_dive": os.path.join(folder_notebooks, "02_model", "vowpal_wabbit_deep_dive.ipynb"), "evaluation": os.path.join(folder_notebooks, "03_evaluate", "evaluation.ipynb"), "spark_tuning": os.path.join(folder_notebooks, "04_model_select_and_optimize", "tuning_spark_als.ipynb"), } return paths
def scripts(): folder_notebooks = path_notebooks() paths = { "ddp_bertsumext": os.path.join( folder_notebooks, "text_summarization", "extractive_summarization_cnndm_distributed_train.py", ), "ddp_bertsumabs": os.path.join( folder_notebooks, "text_summarization", "abstractive_summarization_bertsum_cnndm_distributed_train.py", ), } return paths
def notebooks(): folder_notebooks = path_notebooks() # Path for the notebooks paths = { "template": os.path.join(folder_notebooks, "template.ipynb"), "sar_single_node": os.path.join(folder_notebooks, "00_quick_start", "sar_single_node_movielens.ipynb"), "als_pyspark": os.path.join(folder_notebooks, "00_quick_start", "als_pyspark_movielens.ipynb"), "data_split": os.path.join(folder_notebooks, "01_prepare_data", "data_split.ipynb"), "als_deep_dive": os.path.join(folder_notebooks, "02_model", "als_deep_dive.ipynb"), "surprise_svd_deep_dive": os.path.join(folder_notebooks, "02_model", "surprise_svd_deep_dive.ipynb"), "evaluation": os.path.join(folder_notebooks, "03_evaluate", "evaluation.ipynb"), } return paths
def notebooks(): folder_notebooks = path_notebooks() # Path for the notebooks paths = { "template": os.path.join( folder_notebooks, "template.ipynb" ), "sar_single_node": os.path.join( folder_notebooks, "00_quick_start", "sar_movielens.ipynb" ), "ncf": os.path.join( folder_notebooks, "00_quick_start", "ncf_movielens.ipynb" ), "als_pyspark": os.path.join( folder_notebooks, "00_quick_start", "als_movielens.ipynb" ), "fastai": os.path.join( folder_notebooks, "00_quick_start", "fastai_movielens.ipynb" ), "xdeepfm_quickstart": os.path.join( folder_notebooks, "00_quick_start", "xdeepfm_synthetic.ipynb" ), "dkn_quickstart": os.path.join( folder_notebooks, "00_quick_start", "dkn_synthetic.ipynb" ), "lightgbm_quickstart": os.path.join( folder_notebooks, "00_quick_start", "lightgbm_tinycriteo.ipynb" ), "wide_deep": os.path.join( folder_notebooks, "00_quick_start", "wide_deep_movielens.ipynb" ), "data_split": os.path.join( folder_notebooks, "01_prepare_data", "data_split.ipynb" ), "als_deep_dive": os.path.join( folder_notebooks, "02_model", "als_deep_dive.ipynb" ), "surprise_svd_deep_dive": os.path.join( folder_notebooks, "02_model", "surprise_svd_deep_dive.ipynb" ), "baseline_deep_dive": os.path.join( folder_notebooks, "02_model", "baseline_deep_dive.ipynb" ), "ncf_deep_dive": os.path.join( folder_notebooks, "02_model", "ncf_deep_dive.ipynb" ), "sar_deep_dive": os.path.join( folder_notebooks, "02_model", "sar_deep_dive.ipynb" ), "vowpal_wabbit_deep_dive": os.path.join( folder_notebooks, "02_model", "vowpal_wabbit_deep_dive.ipynb" ), "mmlspark_lightgbm_criteo": os.path.join( folder_notebooks, "02_model", "mmlspark_lightgbm_criteo.ipynb" ), "evaluation": os.path.join( folder_notebooks, "03_evaluate", "evaluation.ipynb" ), "spark_tuning": os.path.join( folder_notebooks, "04_model_select_and_optimize", "tuning_spark_als.ipynb" ), "nni_tuning_svd": os.path.join( folder_notebooks, "04_model_select_and_optimize", "nni_surprise_svd.ipynb" ) } return paths
def notebooks(): folder_notebooks = path_notebooks() # Path for the notebooks paths = { "template": os.path.join(folder_notebooks, "template.ipynb"), "sar_single_node": os.path.join( folder_notebooks, "00_quick_start", "sar_movielens.ipynb" ), "ncf": os.path.join(folder_notebooks, "00_quick_start", "ncf_movielens.ipynb"), "als_pyspark": os.path.join( folder_notebooks, "00_quick_start", "als_movielens.ipynb" ), "fastai": os.path.join( folder_notebooks, "00_quick_start", "fastai_movielens.ipynb" ), "xdeepfm_quickstart": os.path.join( folder_notebooks, "00_quick_start", "xdeepfm_criteo.ipynb" ), "dkn_quickstart": os.path.join( folder_notebooks, "00_quick_start", "dkn_synthetic.ipynb" ), "lightgbm_quickstart": os.path.join( folder_notebooks, "00_quick_start", "lightgbm_tinycriteo.ipynb" ), "wide_deep": os.path.join( folder_notebooks, "00_quick_start", "wide_deep_movielens.ipynb" ), "slirec_quickstart": os.path.join( folder_notebooks, "00_quick_start", "sequential_recsys_amazondataset.ipynb" ), "nrms_quickstart": os.path.join( folder_notebooks, "00_quick_start", "nrms_synthetic.ipynb" ), "naml_quickstart": os.path.join( folder_notebooks, "00_quick_start", "naml_synthetic.ipynb" ), "lstur_quickstart": os.path.join( folder_notebooks, "00_quick_start", "lstur_synthetic.ipynb" ), "npa_quickstart": os.path.join( folder_notebooks, "00_quick_start", "npa_synthetic.ipynb" ), "rlrmc_quickstart": os.path.join( folder_notebooks, "00_quick_start", "rlrmc_movielens.ipynb" ), "data_split": os.path.join( folder_notebooks, "01_prepare_data", "data_split.ipynb" ), "wikidata_knowledge_graph": os.path.join( folder_notebooks, "01_prepare_data", "wikidata_knowledge_graph.ipynb" ), "als_deep_dive": os.path.join( folder_notebooks, "02_model", "als_deep_dive.ipynb" ), "surprise_svd_deep_dive": os.path.join( folder_notebooks, "02_model", "surprise_svd_deep_dive.ipynb" ), "baseline_deep_dive": os.path.join( folder_notebooks, "02_model", "baseline_deep_dive.ipynb" ), "ncf_deep_dive": os.path.join( folder_notebooks, "02_model", "ncf_deep_dive.ipynb" ), "sar_deep_dive": os.path.join( folder_notebooks, "02_model", "sar_deep_dive.ipynb" ), "vowpal_wabbit_deep_dive": os.path.join( folder_notebooks, "02_model", "vowpal_wabbit_deep_dive.ipynb" ), "mmlspark_lightgbm_criteo": os.path.join( folder_notebooks, "02_model", "mmlspark_lightgbm_criteo.ipynb" ), "cornac_bpr_deep_dive": os.path.join( folder_notebooks, "02_model", "cornac_bpr_deep_dive.ipynb" ), "xlearn_fm_deep_dive": os.path.join( folder_notebooks, "02_model", "fm_deep_dive.ipynb" ), "evaluation": os.path.join(folder_notebooks, "03_evaluate", "evaluation.ipynb"), "spark_tuning": os.path.join( folder_notebooks, "04_model_select_and_optimize", "tuning_spark_als.ipynb" ), "nni_tuning_svd": os.path.join( folder_notebooks, "04_model_select_and_optimize", "nni_surprise_svd.ipynb" ), } return paths