We lifted 14 functions out of this paper's own repositories and ran 11 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| jacopotagliabue/you-dont-need-a-bigger-boat | pwc_unofficial | 11 of 14 |
| Function | Status | Where it lives |
|---|---|---|
| enable_decorator | Ran | jacopotagliabue/you-dont-need-a-bigger-boat/local_flow/intent/src/custom_decorators.py code served (permissive licence) · get_code("11789a38a836540b") |
| get_filename | Ran | jacopotagliabue/you-dont-need-a-bigger-boat/local_flow/intent/src/utils.py code served (permissive licence) · get_code("5513291663ef8a28") |
| make_predictions | Ran | jacopotagliabue/you-dont-need-a-bigger-boat/remote_flow/metaflow/model.py code served (permissive licence) · get_code("9011d0930fb34a77") |
| pip | Ran | jacopotagliabue/you-dont-need-a-bigger-boat/local_flow/intent/src/custom_decorators.py code served (permissive licence) · get_code("a3ce5ce1f92ca929") |
| prepare_dataset | Ran | jacopotagliabue/you-dont-need-a-bigger-boat/local_flow/intent/src/prepare_dataset.py code served (permissive licence) · get_code("ec5dcfa1a6b0f7af") |
| read_sessions_from_training_file | Ran | jacopotagliabue/you-dont-need-a-bigger-boat/local_flow/intent/src/prepare_dataset.py code served (permissive licence) · get_code("07d49ac126d9b329") |
| return_json_file_content | Ran | jacopotagliabue/you-dont-need-a-bigger-boat/local_flow/intent/src/utils.py code served (permissive licence) · get_code("27a270038cf86be3") |
| session_indexed | Ran | jacopotagliabue/you-dont-need-a-bigger-boat/remote_flow/metaflow/model.py code served (permissive licence) · get_code("8f067378d424cbda") |
| session_indexed | Ran | jacopotagliabue/you-dont-need-a-bigger-boat/local_flow/intent/src/prepare_dataset.py code served (permissive licence) · get_code("3fe47de83b48302e") |
| tf_model_to_tar | Ran | jacopotagliabue/you-dont-need-a-bigger-boat/local_flow/intent/src/deploy_model.py code served (permissive licence) · get_code("b8bb11c469473bde") |
| train_lstm_model | Ran | jacopotagliabue/you-dont-need-a-bigger-boat/local_flow/intent/src/model.py code served (permissive licence) · get_code("2f93893e818afaa6") |
| process_raw_data | Not yet run | jacopotagliabue/you-dont-need-a-bigger-boat/local_flow/intent/src/process_raw_data.py code served (permissive licence) · get_code("1ba74f174fd45278") |
| read_from_parquet | Not yet run | jacopotagliabue/you-dont-need-a-bigger-boat/local_flow/intent/src/process_raw_data.py code served (permissive licence) · get_code("2c7fed7654dbad7e") |
| return_df | Not yet run | jacopotagliabue/you-dont-need-a-bigger-boat/local_flow/intent/src/process_raw_data.py code served (permissive licence) · get_code("17172882ad3ab9e8") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
We argue that immature data pipelines are preventing a large portion of industry practitioners from leveraging the latest research on recommender systems. We propose our template data stack for machine learning at "reasonable scale", and show how many challenges are solved by embracing a serverless paradigm. Leveraging our experience, we detail how modern open source can provide a pipeline processing terabytes of data with limited infrastructure work.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2107.07346")
get_code_for_paper("2107.07346")
have("2107.07346")
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