| import wandb | |
| import pandas as pd | |
| from fastcore.all import Path | |
| def val_pred_table(run_id, entity='wandb_course', proj='lemon-project'): | |
| "Get prediction table on the validation set for the lemon project." | |
| api = wandb.Api() | |
| path = api.artifact(f'{entity}/{proj}/run-{run_id}-predictions_table:v0').download() | |
| preds = (Path(path)/'predictions_table.table.json').read_json() | |
| return pd.DataFrame([{'pred':p, 'label':t} for _,p,_,t in preds['data']]) |
talk_title: "Literate Programming With Quarto: A new way to write, distribute, test and document your software that will blow your mind." talk_title_short: Literate Programming With Quarto talk_materials_url: https://github.com/fastai/nbdev-demo speakers:
| from metaflow import FlowSpec, step | |
| url = "https://raw.githubusercontent.com/Netflix/metaflow/master/metaflow/tutorials/02-statistics/movies.csv" | |
| local_path = "./movies.csv" | |
| class CSVFlow(FlowSpec): | |
| @step | |
| def start(self): | |
| self.next(self.get_csv_from_web) | |
| @step |
| -- Access this query at https://console.cloud.google.com/bigquery?sq=235037502967:a71a4b32d74442558a2739b581064e5f | |
| SELECT url, title, body | |
| FROM( | |
| SELECT url, title, body | |
| , RANK() OVER (PARTITION BY SUBSTR(body, 75, 125) ORDER BY url) as count_body_beg | |
| FROM( | |
| SELECT url, title, body | |
| , RANK() OVER (PARTITION BY SUBSTR(body, 50, 100) ORDER BY url) as count_body_beg | |
| FROM( |
| <DocSection type="class" name="ExampleError" module="test_lib.example" link="test_lib/example.py#L155"> | |
| <SigArgSection> | |
| <SigArg name="msg" type="str" default="foo"/> | |
| <SigArg name="code" type="str" default="foo"/> | |
| </SigArgSection> | |
| <Description summary="Exceptions are documented in the same way as classes." extended_summary="The __init__ method may be documented in either the class level\ndocstring, or as a docstring on the __init__ method itself.\n\nEither form is acceptable, but the two should not be mixed. Choose one\nconvention to document the __init__ method and be consistent with it." /> | |
| <ParamSection name="Parameters"> | |
| <Parameter name="msg" type="str" desc="Human readable string describing the exception." /> | |
| <Parameter name="code" type=":obj:`int`, optional" desc="Numeric error code." /> | |
| </ParamSection> |
| #From nbdev.export2html | |
| class ExecuteShowDocPreprocessor(ExecutePreprocessor): | |
| "An `ExecutePreprocessor` that only executes `show_doc` and `import` cells" | |
| def preprocess_cell(self, cell, resources, index): | |
| if not check_re(cell, _re_notebook2script): | |
| if check_re(cell, _re_show_doc): | |
| return super().preprocess_cell(cell, resources, index) | |
| elif check_re_multi(cell, [_re_import, _re_lib_import.re]): | |
| if check_re_multi(cell, [_re_export, 'show_doc', '^\s*#\s*import']): | |
| # r = list(filter(_non_comment_code, cell['source'].split('\n'))) |
| @patch | |
| def predict_batch(self:Learner, item, rm_type_tfms=None, with_input=False): | |
| dl = self.dls.test_dl(item, rm_type_tfms=rm_type_tfms, num_workers=0) | |
| inp,preds,_,dec_preds = self.get_preds(dl=dl, with_input=True, with_decoded=True) | |
| i = getattr(self.dls, 'n_inp', -1) | |
| inp = (inp,) if i==1 else tuplify(inp) | |
| dec_inp, nm = zip(*self.dls.decode_batch(inp + tuplify(dec_preds))) | |
| res = preds,nm,dec_preds | |
| if with_input: res = (dec_inp,) + res | |
| return res |