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