changed neptune send command in readme to run for single model

minerva-ml · Jan 9, 2018 · 04c3a13 · 04c3a13
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diff --git a/README.md b/README.md
@@ -34,15 +34,15 @@ Note that [neptune](https://neptune.ml/ 'machine learning lab') (experiment moni
 ### Run experiment
 To run an experiment in the Neptune cloud use this command
 ```bash
-$ neptune send experiment_manager.py --environment keras-2.0-gpu-py3 --worker gcp-gpu-medium --config neptune_config.yaml -- train-pipeline --pipeline_name ensemble_train_pipeline
+$ neptune send experiment_manager.py --environment keras-2.0-gpu-py3 --worker gcp-gpu-medium --config neptune_config.yaml -- train_evaluate_predict_pipeline --pipeline_name glove_lstm
 ```
 
 Check [Neptune documentation](https://docs.neptune.ml/cli/neptune_send/) for more options.
 
 
 # Solution visualization
 Below end-to-end pipeline is visualized. You can run exactly this one!
-![pipeline_001](https://github.com/neptune-ml/kaggle-toxic-starter/blob/master/pipelines_visualizations/pipeline_001.png 'our initial pipeline')
+![pipeline_001](https://github.com/neptune-ml/kaggle-toxic-starter/blob/master/imgs/log_reg_ensemble.png 'our initial pipeline')
 
 
 # Contributing

diff --git a/pipelines_visualizations/char_vdcnn.png → imgs/char_vdcnn.png b/pipelines_visualizations/char_vdcnn.png → imgs/char_vdcnn.png
diff --git a/pipelines_visualizations/glove_dpcnn.png → imgs/glove_dpcnn.png b/pipelines_visualizations/glove_dpcnn.png → imgs/glove_dpcnn.png
diff --git a/pipelines_visualizations/glove_lstm.png → imgs/glove_lstm.png b/pipelines_visualizations/glove_lstm.png → imgs/glove_lstm.png
diff --git a/pipelines_visualizations/glove_scnn.png → imgs/glove_scnn.png b/pipelines_visualizations/glove_scnn.png → imgs/glove_scnn.png
diff --git a/...lines_visualizations/log_reg_pipeline.png → imgs/log_reg_ensemble.png b/...lines_visualizations/log_reg_pipeline.png → imgs/log_reg_ensemble.png
diff --git a/pipelines_visualizations/word_lstm.png → imgs/word_lstm.png b/pipelines_visualizations/word_lstm.png → imgs/word_lstm.png
diff --git a/neptune_config.yaml b/neptune_config.yaml
@@ -1,7 +1,7 @@
 project-key: TOX
 
 name: toxic
-tags: [ensembling, log_reg]
+tags: [glove, lstm, adam]
 
 
 metric:
@@ -13,7 +13,7 @@ metric:
 
 exclude:
   - output
-  - pipelines_visualizations
+  - imgs
   - neptune.log
   - offline_job.log
   - .git
@@ -22,19 +22,18 @@ exclude:
 
 parameters:
   # Cloud Environment
-#  data_dir: /public/toxic_comments
-#  experiment_dir: /output/experiments/word_lstm
-#  embedding_filepath: /public/models/glove/glove.840B.300d.txt
-
+  data_dir: /public/toxic_comments
+  experiment_dir: /output/experiments/glove_lstm
+  embedding_filepath: /public/models/glove/glove.840B.300d.txt
   # Local Environment
-  data_dir: /mnt/ml-team/minerva/toxic/data
-  experiment_dir: /mnt/ml-team/minerva/toxic/starter_experiments/log_reg_ensemble
-  embedding_filepath: /mnt/ml-team/minerva/pretrained/glove.840B.300d.txt
+#  data_dir: /mnt/ml-team/minerva/toxic/data
+#  experiment_dir: /mnt/ml-team/minerva/toxic/starter_experiments/log_reg_ensemble
+#  embedding_filepath: /mnt/ml-team/minerva/pretrained/glove.840B.300d.txt
   overwrite: 0
   num_workers: -2
 
   # Preprocessing
-  max_features_char: 100000 #100 for vdcnnneural networks, for tfidf something like 20000 should be used
+  max_features_char: 100 #100 for vdcnn, for tfidf something like 20000 should be used
   max_features_word: 100000
   maxlen_char: 512
   maxlen_words: 100
@@ -63,27 +62,27 @@ parameters:
 #  global_pooling: 1
 
   # Char VDCNN Architecture
-  filter_nr: 32
-  kernel_size: 3
-  repeat_block: 2
-  global_pooling: 1
-  trainable_embedding: 1
-  dense_size: 256
-  repeat_dense: 2
-  char_embedding_size: 32
-  word_embedding_size: None
-
-
-  # Glove LSTM Architecture
-#  filter_nr: 64
-#  kernel_size: None
+#  filter_nr: 32
+#  kernel_size: 3
 #  repeat_block: 2
+#  global_pooling: 1
+#  trainable_embedding: 1
 #  dense_size: 256
 #  repeat_dense: 2
-#  global_pooling: 1
-#  trainable_embedding: 0
-#  word_embedding_size: 300
-#  char_embedding_size: None
+#  char_embedding_size: 32
+#  word_embedding_size: None
+
+
+  # Glove LSTM Architecture
+  filter_nr: 64
+  kernel_size: None
+  repeat_block: 2
+  dense_size: 256
+  repeat_dense: 2
+  global_pooling: 1
+  trainable_embedding: 0
+  word_embedding_size: 300
+  char_embedding_size: None
 
   # Word LSTM Architecture
 #  filter_nr: 64