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deepchecks
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deepchecks - npm Package Compare versions
| Metadata-Version: 2.1 | ||
| Name: deepchecks | ||
| Version: 0.17.2 | ||
| Version: 0.17.3 | ||
| Summary: Package for validating your machine learning model and data | ||
@@ -9,3 +9,3 @@ Home-page: https://github.com/deepchecks/deepchecks | ||
| License: UNKNOWN | ||
| Download-URL: https://github.com/deepchecks/deepchecks/releases/download/0.17.2/deepchecks-0.17.2.tar.gz | ||
| Download-URL: https://github.com/deepchecks/deepchecks/releases/download/0.17.3/deepchecks-0.17.3.tar.gz | ||
| Project-URL: Documentation, https://docs.deepchecks.com | ||
@@ -12,0 +12,0 @@ Project-URL: Bug Reports, https://github.com/deepchecks/deepchecks |
@@ -20,3 +20,3 @@ # ---------------------------------------------------------------------------- | ||
| from deepchecks.core import CheckResult, DatasetKind | ||
| from deepchecks.core.errors import NotEnoughSamplesError | ||
| from deepchecks.core.errors import DeepchecksValueError, NotEnoughSamplesError | ||
| from deepchecks.nlp import Context, SingleDatasetCheck | ||
@@ -50,3 +50,3 @@ from deepchecks.nlp.utils.nlp_plot import get_text_outliers_graph | ||
| min_samples : int , default : 10 | ||
| Minimum number of samples required to calculate IQR. If there are not enough non-null samples a specific | ||
| Minimum number of samples required to calculate IQR. If there are not enough non-null samples for a specific | ||
| property, the check will skip it. If all properties are skipped, the check will raise a NotEnoughSamplesError. | ||
@@ -78,61 +78,67 @@ """ | ||
| if all(len(np.hstack(v).squeeze()) < self.min_samples for v in properties.values()): | ||
| raise NotEnoughSamplesError(f'Need at least {self.min_samples} non-null samples to calculate outliers.') | ||
| # The values are in the same order as the batch order, so always keeps the same order in order to access | ||
| # the original sample at this index location | ||
| for name, values in properties.items(): | ||
| # If the property is single value per sample, then wrap the values in list in order to work on fixed | ||
| # structure | ||
| if not isinstance(values[0], list): | ||
| values = [[x] for x in values] | ||
| is_numeric = name not in cat_properties | ||
| if is_numeric: | ||
| values_arr = np.hstack(values).astype(float).squeeze() | ||
| values_arr = np.array([x for x in values_arr if pd.notnull(x)]) | ||
| else: | ||
| values_arr = np.hstack(values).astype(str).squeeze() | ||
| try: | ||
| if not isinstance(values[0], list): | ||
| if is_numeric: | ||
| # Check for non numeric data in the column | ||
| curr_nan_count = pd.isnull(values).sum() | ||
| values = pd.to_numeric(values, errors='coerce') | ||
| updated_nan_count = pd.isnull(values).sum() | ||
| if updated_nan_count > curr_nan_count: | ||
| raise DeepchecksValueError('Numeric property contains non-numeric values.') | ||
| # If the property is single value per sample, then wrap the values in list in order to | ||
| # work on fixed structure | ||
| values = [[x] for x in values] | ||
| if len(values_arr) < self.min_samples: | ||
| result[name] = 'Not enough non-null samples to calculate outliers.' | ||
| continue | ||
| if is_numeric: | ||
| values_arr = np.hstack(values).astype(float).squeeze() | ||
| values_arr = np.array([x for x in values_arr if pd.notnull(x)]) | ||
| else: | ||
| values_arr = np.hstack(values).astype(str).squeeze() | ||
| if is_numeric: | ||
| lower_limit, upper_limit = iqr_outliers_range(values_arr, self.iqr_percentiles, | ||
| self.iqr_scale, self.sharp_drop_ratio) | ||
| else: | ||
| # Counting the frequency of each category. Normalizing because distribution graph shows the percentage. | ||
| counts_map = pd.Series(values_arr.astype(str)).value_counts(normalize=True).to_dict() | ||
| lower_limit = sharp_drop_outliers_range(sorted(list(counts_map.values()), reverse=True), | ||
| self.sharp_drop_ratio) or 0 | ||
| upper_limit = len(values_arr) # No upper limit for categorical properties | ||
| values_arr = np.array([counts_map[x] for x in values_arr]) # replace the values with the counts | ||
| if len(values_arr) < self.min_samples: | ||
| raise NotEnoughSamplesError(f'Not enough non-null samples to calculate outliers' | ||
| f'(min_samples={self.min_samples}).') | ||
| # Get the indices of the outliers | ||
| top_outliers = np.argwhere(values_arr > upper_limit).squeeze(axis=1) | ||
| # Sort the indices of the outliers by the original values | ||
| top_outliers = top_outliers[ | ||
| np.apply_along_axis(lambda i, sort_arr=values_arr: sort_arr[i], axis=0, arr=top_outliers).argsort() | ||
| ] | ||
| if is_numeric: | ||
| lower_limit, upper_limit = iqr_outliers_range(values_arr, self.iqr_percentiles, | ||
| self.iqr_scale, self.sharp_drop_ratio) | ||
| else: | ||
| # Counting the frequency of each category. Normalizing because distribution graph shows percentage. | ||
| counts_map = pd.Series(values_arr.astype(str)).value_counts(normalize=True).to_dict() | ||
| lower_limit = sharp_drop_outliers_range(sorted(list(counts_map.values()), reverse=True), | ||
| self.sharp_drop_ratio) or 0 | ||
| upper_limit = len(values_arr) # No upper limit for categorical properties | ||
| values_arr = np.array([counts_map[x] for x in values_arr]) # replace the values with the counts | ||
| # Doing the same for bottom outliers | ||
| bottom_outliers = np.argwhere(values_arr < lower_limit).squeeze(axis=1) | ||
| # Sort the indices of the outliers by the original values | ||
| bottom_outliers = bottom_outliers[ | ||
| np.apply_along_axis(lambda i, sort_arr=values_arr: sort_arr[i], axis=0, arr=bottom_outliers).argsort() | ||
| ] | ||
| # Get the indices of the outliers | ||
| top_outliers = np.argwhere(values_arr > upper_limit).squeeze(axis=1) | ||
| # Sort the indices of the outliers by the original values | ||
| top_outliers = top_outliers[ | ||
| np.apply_along_axis(lambda i, sort_arr=values_arr: sort_arr[i], axis=0, arr=top_outliers).argsort() | ||
| ] | ||
| text_outliers = np.concatenate([bottom_outliers, top_outliers]) | ||
| # Doing the same for bottom outliers | ||
| bottom_outliers = np.argwhere(values_arr < lower_limit).squeeze(axis=1) | ||
| # Sort the indices of the outliers by the original values | ||
| bottom_outliers = bottom_outliers[ | ||
| np.apply_along_axis(lambda i, sort_arr=values_arr: sort_arr[i], | ||
| axis=0, arr=bottom_outliers).argsort() | ||
| ] | ||
| result[name] = { | ||
| 'indices': [dataset.get_original_text_indexes()[i] for i in text_outliers], | ||
| # For the upper and lower limits doesn't show values that are smaller/larger than the actual values | ||
| # we have in the data | ||
| 'lower_limit': max(lower_limit, min(values_arr)), | ||
| 'upper_limit': min(upper_limit, max(values_arr)) if is_numeric else None, | ||
| 'outlier_ratio': len(text_outliers) / len(values_arr) | ||
| } | ||
| text_outliers = np.concatenate([bottom_outliers, top_outliers]) | ||
| result[name] = { | ||
| 'indices': [dataset.get_original_text_indexes()[i] for i in text_outliers], | ||
| # For the upper and lower limits doesn't show values that are smaller/larger than the actual values | ||
| # we have in the data | ||
| 'lower_limit': max(lower_limit, min(values_arr)), | ||
| 'upper_limit': min(upper_limit, max(values_arr)) if is_numeric else None, | ||
| 'outlier_ratio': len(text_outliers) / len(values_arr), | ||
| } | ||
| except Exception as exp: # pylint: disable=broad-except | ||
| result[name] = f'{exp}' | ||
| # Create display | ||
@@ -143,5 +149,10 @@ if context.with_display: | ||
| sorted_result_items = sorted(result.items(), key=lambda x: len(x[1]['indices']), reverse=True) | ||
| # Sort the result map based on the length of indices and if there are any error message associated to | ||
| # any property, keep that property at the very end. | ||
| sorted_result_items = sorted(result.items(), | ||
| key=lambda x: len(x[1].get('indices', [])) if isinstance(x[1], dict) else 0, | ||
| reverse=True) | ||
| for property_name, info in sorted_result_items: | ||
| # If info is string it means there was error | ||
@@ -154,7 +165,10 @@ if isinstance(info, str): | ||
| if len(display) < self.n_show_top: | ||
| dist = df_properties[property_name] | ||
| if len(dist[~pd.isnull(dist)]) >= self.min_samples: | ||
| lower_limit = info['lower_limit'] | ||
| upper_limit = info['upper_limit'] | ||
| if property_name not in cat_properties: | ||
| dist = df_properties[property_name].astype(float) | ||
| else: | ||
| dist = df_properties[property_name] | ||
| lower_limit = info['lower_limit'] | ||
| upper_limit = info['upper_limit'] | ||
| try: | ||
| fig = get_text_outliers_graph( | ||
@@ -170,9 +184,5 @@ dist=dist, | ||
| display.append(fig) | ||
| else: | ||
| no_outliers = pd.concat( | ||
| [no_outliers, pd.Series(property_name, index=[ | ||
| f'Not enough non-null samples to compute' | ||
| f' properties (min_samples={self.min_samples}).' | ||
| ])] | ||
| ) | ||
| except Exception as exp: # pylint: disable=broad-except | ||
| result[property_name] = f'{exp}' | ||
| no_outliers = pd.concat([no_outliers, pd.Series(property_name, index=[exp])]) | ||
| else: | ||
@@ -179,0 +189,0 @@ no_outliers = pd.concat([no_outliers, pd.Series(property_name, index=[ |
@@ -82,5 +82,5 @@ # ---------------------------------------------------------------------------- | ||
| """ | ||
| import math | ||
| import time | ||
| import typing as t | ||
| from typing import Tuple | ||
@@ -92,8 +92,6 @@ import numpy as np | ||
| __all__ = ['load_data', 'load_pre_calculated_prediction', 'load_pre_calculated_feature_importance'] | ||
| __all__ = ['load_data_and_predictions', 'load_pre_calculated_feature_importance'] | ||
| from numpy import ndarray | ||
| _TRAIN_DATA_URL = 'https://drive.google.com/uc?export=download&id=1UWkr1BQlyyUkbsW5hHIFTr-x0evZE3Ie' | ||
| _TEST_DATA_URL = 'https://drive.google.com/uc?export=download&id=1v_0ZyyycoFfltJ6wj_riGZoXhtPzrnqR' | ||
| _TEST_DATA_URL = 'https://drive.google.com/uc?export=download&id=1lfpWVtDktrnsLUzCN1tkRc1jRbguEz3a' | ||
| _target = 'price' | ||
@@ -108,4 +106,5 @@ _predictions = 'predictions' | ||
| def load_data(data_format: str = 'Dataset', as_train_test: bool = True, modify_timestamps: bool = True, | ||
| data_size: t.Optional[int] = 15000) -> t.Union[t.Tuple, t.Union[Dataset, pd.DataFrame]]: | ||
| def load_data_and_predictions(data_format: str = 'Dataset', load_train: bool = True, modify_timestamps: bool = True, | ||
| data_size: t.Optional[int] = 15000, random_state: int = 42) \ | ||
| -> t.Tuple[t.Union[Dataset, pd.DataFrame], np.ndarray]: | ||
| """Load and returns the Airbnb NYC 2019 dataset (regression). | ||
@@ -119,7 +118,4 @@ | ||
| 'Dataframe' will return the data as a pandas Dataframe object | ||
| as_train_test : bool , default: True | ||
| If True, the returned data is split into train and test exactly like the toy model | ||
| was trained. The first return value is the train data and the second is the test data. | ||
| In order to get this model, call the load_fitted_model() function. | ||
| Otherwise, returns a single object. | ||
| load_train : bool , default: True | ||
| If True, the returned data is the train data. otherwise the test dataset. | ||
| modify_timestamps : bool , default: True | ||
@@ -130,64 +126,39 @@ If True, the returned data timestamp column will be for the last 30 days. | ||
| The number of samples to return. If None, returns all the data. | ||
| random_state : int , default 42 | ||
| The random state to use for sampling. | ||
| Returns | ||
| ------- | ||
| dataset : Union[deepchecks.Dataset, pd.DataFrame] | ||
| the data object, corresponding to the data_format attribute. | ||
| train_data, test_data : Tuple[Union[deepchecks.Dataset, pd.DataFrame],Union[deepchecks.Dataset, pd.DataFrame] | ||
| tuple if as_train_test = True. Tuple of two objects represents the dataset split to train and test sets. | ||
| dataset, predictions : Tuple[Union[deepchecks.Dataset, pd.DataFrame], np.ndarray] | ||
| Tuple of the deepchecks dataset or dataframe and the predictions. | ||
| """ | ||
| train = pd.read_csv(_TRAIN_DATA_URL, index_col=0).drop(_predictions, axis=1) | ||
| test = pd.read_csv(_TEST_DATA_URL, index_col=0).drop(_predictions, axis=1) | ||
| if load_train: | ||
| dataset = pd.read_csv(_TRAIN_DATA_URL) | ||
| else: | ||
| dataset = pd.read_csv(_TEST_DATA_URL) | ||
| if data_size is not None: | ||
| if data_size < len(train): | ||
| train = train.sample(data_size, random_state=42) | ||
| if data_size < len(test): | ||
| test = test.sample(data_size, random_state=42) | ||
| if data_size < len(dataset): | ||
| dataset = dataset.sample(data_size, random_state=random_state) | ||
| elif data_size > len(dataset): | ||
| dataset = pd.concat([dataset] * math.ceil(data_size / len(dataset)), axis=0, ignore_index=True) | ||
| dataset = dataset.sample(data_size, random_state=random_state) | ||
| if not load_train: | ||
| dataset = dataset.sort_values(_datetime) | ||
| if modify_timestamps: | ||
| if modify_timestamps and not load_train: | ||
| current_time = int(time.time()) | ||
| time_test_start = current_time - 86400 * 30 # Span data for 30 days | ||
| test[_datetime] = np.sort((np.random.rand(len(test)) * (current_time - time_test_start)) + time_test_start) | ||
| test[_datetime] = test[_datetime].apply(lambda x: pd.Timestamp(x, unit='s')) | ||
| dataset[_datetime] = np.sort( | ||
| (np.random.rand(len(dataset)) * (current_time - time_test_start)) + time_test_start | ||
| ) | ||
| dataset[_datetime] = dataset[_datetime].apply(lambda x: pd.Timestamp(x, unit='s')) | ||
| if not as_train_test: | ||
| dataset = pd.concat([train, test.drop(_datetime, axis=1)], axis=0, ignore_index=True) | ||
| if data_format == 'Dataset': | ||
| dataset = Dataset(dataset, label=_target, cat_features=_CAT_FEATURES, features=_FEATURES) | ||
| return dataset | ||
| else: | ||
| if data_format == 'Dataset': | ||
| train = Dataset(train, label=_target, cat_features=_CAT_FEATURES, | ||
| features=_FEATURES) | ||
| test = Dataset(test, label=_target, cat_features=_CAT_FEATURES, | ||
| datetime_name=_datetime, features=_FEATURES) | ||
| return train, test | ||
| predictions = np.asarray(dataset[_predictions]) | ||
| dataset.drop(_predictions, axis=1, inplace=True) | ||
| if data_format == 'Dataset': | ||
| dataset = Dataset(dataset, label=_target, cat_features=_CAT_FEATURES, | ||
| features=_FEATURES) | ||
| return dataset, predictions | ||
| def load_pre_calculated_prediction(data_size: t.Optional[int] = 15000) -> Tuple[ndarray, ndarray]: | ||
| """Load the pre-calculated prediction for the Airbnb NYC 2019 dataset. | ||
| Parameters | ||
| ---------- | ||
| data_size : t.Optional[int] , default: 15000 | ||
| The number of samples to return. If None, returns all the data. | ||
| Returns | ||
| ------- | ||
| predictions : Tuple(np.ndarray, np.ndarray) | ||
| The first element is the pre-calculated prediction for the train set. | ||
| The second element is the pre-calculated prediction for the test set. | ||
| """ | ||
| usable_columns = [_target, _predictions] | ||
| train = pd.read_csv(_TRAIN_DATA_URL, usecols=usable_columns) | ||
| test = pd.read_csv(_TEST_DATA_URL, usecols=usable_columns) | ||
| if data_size is not None: | ||
| if data_size < len(train): | ||
| train = train.sample(data_size, random_state=42) | ||
| if data_size < len(test): | ||
| test = test.sample(data_size, random_state=42) | ||
| return np.asarray(train[_predictions]), np.asarray(test[_predictions]) | ||
| def load_pre_calculated_feature_importance() -> pd.Series: | ||
@@ -194,0 +165,0 @@ """Load the pre-calculated feature importance for the Airbnb NYC 2019 dataset. |
+2
-2
| Metadata-Version: 2.1 | ||
| Name: deepchecks | ||
| Version: 0.17.2 | ||
| Version: 0.17.3 | ||
| Summary: Package for validating your machine learning model and data | ||
@@ -9,3 +9,3 @@ Home-page: https://github.com/deepchecks/deepchecks | ||
| License: UNKNOWN | ||
| Download-URL: https://github.com/deepchecks/deepchecks/releases/download/0.17.2/deepchecks-0.17.2.tar.gz | ||
| Download-URL: https://github.com/deepchecks/deepchecks/releases/download/0.17.3/deepchecks-0.17.3.tar.gz | ||
| Project-URL: Documentation, https://docs.deepchecks.com | ||
@@ -12,0 +12,0 @@ Project-URL: Bug Reports, https://github.com/deepchecks/deepchecks |
+44
-36
@@ -38,7 +38,7 @@ <!-- | ||
| <a target="_blank" href="https://deepchecks.com/?utm_source=github.com&utm_medium=referral&utm_campaign=readme&utm_content=logo"> | ||
| <a target="_blank" href="https://docs.deepchecks.com/?utm_source=github.com&utm_medium=referral&utm_campaign=readme&utm_content=logo"> | ||
| <picture> | ||
| <source media="(prefers-color-scheme: dark)" srcset="docs/source/_static/images/readme/cont_validation_dark.png"> | ||
| <source media="(prefers-color-scheme: light)" srcset="docs/source/_static/images/readme/cont_validation_light.png"> | ||
| <img alt="Deepchecks continuous validation parts." src="docs/source/_static/images//readme/cont_validation_light.png"> | ||
| <source media="(prefers-color-scheme: dark)" srcset="docs/source/_static/images/readme/deepchecks_continuous_validation_dark.png"> | ||
| <source media="(prefers-color-scheme: light)" srcset="docs/source/_static/images/readme/deepchecks_continuous_validation_light.png"> | ||
| <img alt="Deepchecks continuous validation parts." src="docs/source/_static/images//readme/deepchecks_continuous_validation_light.png"> | ||
| </picture> | ||
@@ -60,25 +60,2 @@ </a> | ||
| ## 🧮 How does it work? | ||
| At its core, deepchecks includes a wide variety of built-in Checks, | ||
| for testing all types of data and model related issues. | ||
| These checks are implemented for various models and data types (Tabular, NLP, Vision), | ||
| and can easily be customized and expanded. | ||
| The check results can be used to automatically make informed decisions | ||
| about your model's production-readiness, and for monitoring it over time in production. | ||
| The check results can be examined with visual reports (by saving them to an HTML file, or seeing them in Jupyter), | ||
| processed with code (using their pythonic / json output), and inspected and collaborated on with Deepchecks' dynamic UI | ||
| (for examining test results and for production monitoring). | ||
| <!--- | ||
| At its core, Deepchecks has a wide variety of built-in Checks and Suites (lists of checks) | ||
| for all data types (Tabular, NLP, Vision), | ||
| These includes checks for validating your model's performance (e.g. identify weak segments), the data's | ||
| distribution (e.g. detect drifts or leakages), data integrity (e.g. find conflicting labels) and more. | ||
| These checks results can be run manually (e.g. during research) or trigerred automatically (e.g. during CI | ||
| and production monitoring) and enable automatically making informed decisions regarding your model pipelines' | ||
| production-readiness, and behavior over time. | ||
| ---> | ||
| ## 🧩 Components | ||
@@ -131,14 +108,19 @@ | ||
| To use deepchecks for production monitoring, you can either use our SaaS service, or deploy a local instance in one line on Linux/MacOS (Windows is WIP!) with Docker: | ||
| To use deepchecks for production monitoring, you can either use our SaaS service, or deploy a local instance in one line on Linux/MacOS (Windows is WIP!) with Docker. | ||
| Create a new directory for the installation files, open a terminal within that directory and run the following: | ||
| ``` | ||
| /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/deepchecks/monitoring/main/deploy/deploy-oss.sh)" | ||
| pip install deepchecks-installer | ||
| deepchecks-installer monitoring-install | ||
| ``` | ||
| This will automatically download the necessary dependencies and start the application locally. | ||
| This will automatically download the necessary dependencies, run the installation prcoess | ||
| and then start the application locally. | ||
| The installation will take a few minutes. Then you can open the deployment url (default is http://localhost), | ||
| and start the system onboarding. Check out the full monitoring [open source installation & quickstart](https://docs.deepchecks.com/monitoring/stable/getting-started/deploy_self_host_open_source.html). | ||
| Note that the open source product is built such that each deployment supports monitoring of | ||
| a single model. | ||
| Check out the full installation instructions for deepchecks monitoring [here](https://docs.deepchecks.com/monitoring/stable/installation/index.html). | ||
| </details> | ||
@@ -179,3 +161,3 @@ | ||
| <p align="center"> | ||
| <img src="docs/source/_static/images/general/model_evaluation_suite.gif" width="800"> | ||
| <img src="docs/source/_static/images/readme/model-evaluation-suite.gif" width="600"> | ||
| </p> | ||
@@ -193,3 +175,3 @@ | ||
| Jump right into the | ||
| [monitoring quickstart docs](https://docs.deepchecks.com/monitoring/stable/user-guide/tabular/auto_quickstarts/plot_quickstart.html) | ||
| [open source monitoring quickstart docs](https://docs.deepchecks.com/monitoring/stable/getting-started/deploy_self_host_open_source.html) | ||
| to have it up and running on your data. | ||
@@ -200,3 +182,3 @@ You'll then be able to see the checks results over time, set alerts, and interact | ||
| <p align="center"> | ||
| <img src="docs/source/_static/images/general/monitoring-app-ui.gif" width="800"> | ||
| <img src="docs/source/_static/images/general/monitoring-app-ui.gif" width="600"> | ||
| </p> | ||
@@ -217,3 +199,3 @@ | ||
| <p align="center"> | ||
| <img src="docs/source/_static/images/general/deepchecks-ci-checks.png" width="800"> | ||
| <img src="docs/source/_static/images/general/deepchecks-ci-checks.png" width="600"> | ||
| </p> | ||
@@ -226,2 +208,27 @@ | ||
| ## 🧮 How does it work? | ||
| At its core, deepchecks includes a wide variety of built-in Checks, | ||
| for testing all types of data and model related issues. | ||
| These checks are implemented for various models and data types (Tabular, NLP, Vision), | ||
| and can easily be customized and expanded. | ||
| The check results can be used to automatically make informed decisions | ||
| about your model's production-readiness, and for monitoring it over time in production. | ||
| The check results can be examined with visual reports (by saving them to an HTML file, or seeing them in Jupyter), | ||
| processed with code (using their pythonic / json output), and inspected and collaborated on with Deepchecks' dynamic UI | ||
| (for examining test results and for production monitoring). | ||
| <!--- | ||
| At its core, Deepchecks has a wide variety of built-in Checks and Suites (lists of checks) | ||
| for all data types (Tabular, NLP, Vision), | ||
| These includes checks for validating your model's performance (e.g. identify weak segments), the data's | ||
| distribution (e.g. detect drifts or leakages), data integrity (e.g. find conflicting labels) and more. | ||
| These checks results can be run manually (e.g. during research) or trigerred automatically (e.g. during CI | ||
| and production monitoring) and enable automatically making informed decisions regarding your model pipelines' | ||
| production-readiness, and behavior over time. | ||
| ---> | ||
| <details open> | ||
@@ -261,2 +268,3 @@ <summary> | ||
| ## 📜 Open Source vs Paid | ||
@@ -263,0 +271,0 @@ |
@@ -44,3 +44,2 @@ twine | ||
| wandb>=0.12.15,<0.13.0 | ||
| protobuf>=3.12.0,<4.0dev # wandb does not work if version is higer that 4.0 | ||
| beautifulsoup4>=4.11.1 | ||
@@ -47,0 +46,0 @@ |
+1
-1
@@ -1,1 +0,1 @@ | ||
| 0.17.2 | ||
| 0.17.3 |
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