You need create your container first.
$ docker exec -t <container_id> python weather_pipeline.py >> /path/to/log_file/weather.log 2>&1
Ctrl + Z, to stop the job,
and, bg to continue the job
finally, disown %1 to disown the job. You can close terminal now and go home :)
| self.pattern_happy_emoji = re.compile(r'([:;=] ?-?(\)|D+|d+|P))') | |
| self.pattern_unhappy_emoji = re.compile(r'(: ?-?(\(|/|([Ss]))+)') | |
| def _extract_feature(self, sentence): | |
| emoji_flag = np.array([0, 0]) | |
| # search for happy emoji | |
| emoji_flag[0] = len(self.pattern_happy_emoji.findall(sentence)) | |
| # search for unhappy emoji |
| import os | |
| import pickle | |
| import itertools | |
| from sklearn.model_selection import cross_val_score | |
| from sklearn.svm import LinearSVC | |
| from sklearn.ensemble import RandomForestClassifier | |
| def train_model(data, word2vec, **params): |
| """ | |
| https://discuss.pytorch.org/t/solved-torch-grid-sample/51662 | |
| I have used this code. | |
| """ | |
| from torchvision import transforms | |
| from PIL import Image | |
| from PIL import ImageFile | |
| ImageFile.LOAD_TRUNCATED_IMAGES = True |