When I used kaggle's kernel, it reported such an error. Who knows how this should be solved?
\n\n[NbConvertApp] ERROR | Kernel died while waiting for execute reply.\nTraceback (most recent call last): File \"/opt/conda/lib/python3.6/site-packages/nbconvert/preprocessors/execute.py\", line 478, in _poll_for_reply msg = self.kc.shell_channel.get_msg(timeout=timeout) File \"/opt/conda/lib/python3.6/site-packages/jupyter_client/blocking/channels.py\", line 57, in get_msg raise Empty queue.Empty During handling of the above exception, another exception occurred: Traceback (most recent call last): File \"/opt/conda/bin/jupyter-nbconvert\", line 11, in sys.exit(main()) File \"/opt/conda/lib/python3.6/site-packages/jupyter_core/application.py\", line 268, in launch_instance return super(JupyterApp, cls).launch_instance(argv=argv, **kwargs) File \"/opt/conda/lib/python3.6/site-packages/traitlets/config/application.py\", line 664, in launch_instance app.start() File \"/opt/conda/lib/python3.6/site-packages/nbconvert/nbconvertapp.py\", line 340, in start self.convert_notebooks() File \"/opt/conda/lib/python3.6/site-packages/nbconvert/nbconvertapp.py\", line 510, in convert_notebooks self.convert_single_notebook(notebook_filename) File \"/opt/conda/lib/python3.6/site-packages/nbconvert/nbconvertapp.py\", line 481, in convert_single_notebook output, resources = self.export_single_notebook(notebook_filename, resources, input_buffer=input_buffer) File \"/opt/conda/lib/python3.6/site-packages/nbconvert/nbconvertapp.py\", line 410, in export_single_notebook output, resources = self.exporter.from_filename(notebook_filename, resources=resources) File \"/opt/conda/lib/python3.6/site-packages/nbconvert/exporters/exporter.py\", line 179, in from_filename return self.from_file(f, resources=resources, **kw) File \"/opt/conda/lib/python3.6/site-packages/nbconvert/exporters/exporter.py\", line 197, in from_file return self.from_notebook_node(nbformat.read(file_stream, as_version=4), resources=resources, **kw) File \"/opt/conda/lib/python3.6/site-packages/nbconvert/exporters/notebook.py\", line 32, in from_notebook_node nb_copy, resources = super(NotebookExporter, self).from_notebook_node(nb, resources, **kw) File \"/opt/conda/lib/python3.6/site-packages/nbconvert/exporters/exporter.py\", line 139, in from_notebook_node nb_copy, resources = self._preprocess(nb_copy, resources) File \"/opt/conda/lib/python3.6/site-packages/nbconvert/exporters/exporter.py\", line 316, in _preprocess nbc, resc = preprocessor(nbc, resc) File \"/opt/conda/lib/python3.6/site-packages/nbconvert/preprocessors/base.py\", line 47, in call return self.preprocess(nb, resources) File \"/opt/conda/lib/python3.6/site-packages/nbconvert/preprocessors/execute.py\", line 405, in preprocess nb, resources = super(ExecutePreprocessor, self).preprocess(nb, resources) File \"/opt/conda/lib/python3.6/site-packages/nbconvert/preprocessors/base.py\", line 69, in preprocess nb.cells[index], resources = self.preprocess_cell(cell, resources, index) File \"/opt/conda/lib/python3.6/site-packages/nbconvert/preprocessors/execute.py\", line 438, in preprocess_cell reply, outputs = self.run_cell(cell, cell_index, store_history) File \"/opt/conda/lib/python3.6/site-packages/nbconvert/preprocessors/execute.py\", line 578, in run_cell exec_reply = self._poll_for_reply(parent_msg_id, cell, timeout) File \"/opt/conda/lib/python3.6/site-packages/nbconvert/preprocessors/execute.py\", line 483, in _poll_for_reply self._check_alive() File \"/opt/conda/lib/python3.6/site-packages/nbconvert/preprocessors/execute.py\", line 510, in _check_alive raise DeadKernelError(\"Kernel died\") nbconvert.preprocessors.execute.DeadKernelError: Kernel died
", "messages": [ { "id": "661344", "postDate": "10/30/2019 07:04:18", "content": "When I used kaggle's kernel, it reported such an error. Who knows how this should be solved?
\n\n[NbConvertApp] ERROR | Kernel died while waiting for execute reply.\nTraceback (most recent call last): File \"/opt/conda/lib/python3.6/site-packages/nbconvert/preprocessors/execute.py\", line 478, in _poll_for_reply msg = self.kc.shell_channel.get_msg(timeout=timeout) File \"/opt/conda/lib/python3.6/site-packages/jupyter_client/blocking/channels.py\", line 57, in get_msg raise Empty queue.Empty During handling of the above exception, another exception occurred: Traceback (most recent call last): File \"/opt/conda/bin/jupyter-nbconvert\", line 11, in sys.exit(main()) File \"/opt/conda/lib/python3.6/site-packages/jupyter_core/application.py\", line 268, in launch_instance return super(JupyterApp, cls).launch_instance(argv=argv, **kwargs) File \"/opt/conda/lib/python3.6/site-packages/traitlets/config/application.py\", line 664, in launch_instance app.start() File \"/opt/conda/lib/python3.6/site-packages/nbconvert/nbconvertapp.py\", line 340, in start self.convert_notebooks() File \"/opt/conda/lib/python3.6/site-packages/nbconvert/nbconvertapp.py\", line 510, in convert_notebooks self.convert_single_notebook(notebook_filename) File \"/opt/conda/lib/python3.6/site-packages/nbconvert/nbconvertapp.py\", line 481, in convert_single_notebook output, resources = self.export_single_notebook(notebook_filename, resources, input_buffer=input_buffer) File \"/opt/conda/lib/python3.6/site-packages/nbconvert/nbconvertapp.py\", line 410, in export_single_notebook output, resources = self.exporter.from_filename(notebook_filename, resources=resources) File \"/opt/conda/lib/python3.6/site-packages/nbconvert/exporters/exporter.py\", line 179, in from_filename return self.from_file(f, resources=resources, **kw) File \"/opt/conda/lib/python3.6/site-packages/nbconvert/exporters/exporter.py\", line 197, in from_file return self.from_notebook_node(nbformat.read(file_stream, as_version=4), resources=resources, **kw) File \"/opt/conda/lib/python3.6/site-packages/nbconvert/exporters/notebook.py\", line 32, in from_notebook_node nb_copy, resources = super(NotebookExporter, self).from_notebook_node(nb, resources, **kw) File \"/opt/conda/lib/python3.6/site-packages/nbconvert/exporters/exporter.py\", line 139, in from_notebook_node nb_copy, resources = self._preprocess(nb_copy, resources) File \"/opt/conda/lib/python3.6/site-packages/nbconvert/exporters/exporter.py\", line 316, in _preprocess nbc, resc = preprocessor(nbc, resc) File \"/opt/conda/lib/python3.6/site-packages/nbconvert/preprocessors/base.py\", line 47, in call return self.preprocess(nb, resources) File \"/opt/conda/lib/python3.6/site-packages/nbconvert/preprocessors/execute.py\", line 405, in preprocess nb, resources = super(ExecutePreprocessor, self).preprocess(nb, resources) File \"/opt/conda/lib/python3.6/site-packages/nbconvert/preprocessors/base.py\", line 69, in preprocess nb.cells[index], resources = self.preprocess_cell(cell, resources, index) File \"/opt/conda/lib/python3.6/site-packages/nbconvert/preprocessors/execute.py\", line 438, in preprocess_cell reply, outputs = self.run_cell(cell, cell_index, store_history) File \"/opt/conda/lib/python3.6/site-packages/nbconvert/preprocessors/execute.py\", line 578, in run_cell exec_reply = self._poll_for_reply(parent_msg_id, cell, timeout) File \"/opt/conda/lib/python3.6/site-packages/nbconvert/preprocessors/execute.py\", line 483, in _poll_for_reply self._check_alive() File \"/opt/conda/lib/python3.6/site-packages/nbconvert/preprocessors/execute.py\", line 510, in _check_alive raise DeadKernelError(\"Kernel died\") nbconvert.preprocessors.execute.DeadKernelError: Kernel died
", "rawMarkdown": "When I used kaggle's kernel, it reported such an error. Who knows how this should be solved?\n\n[NbConvertApp] ERROR | Kernel died while waiting for execute reply.\nTraceback (most recent call last): File \"/opt/conda/lib/python3.6/site-packages/nbconvert/preprocessors/execute.py\", line 478, in _poll_for_reply msg = self.kc.shell_channel.get_msg(timeout=timeout) File \"/opt/conda/lib/python3.6/site-packages/jupyter_client/blocking/channels.py\", line 57, in get_msg raise Empty queue.Empty During handling of the above exception, another exception occurred: Traceback (most recent call last): File \"/opt/conda/bin/jupyter-nbconvert\", line 11, in", "votes": null }, { "id": "662310", "postDate": "10/31/2019 12:11:33", "content": "The reason is you are running out of memory (exceeding 16B) while training your model or predicting . Some of the solutions are\n1. Prepare/preprocess your data in one kernel . After merging the weather and building data with your test and train dataframes delete all original dataframes. Reduce the size of the merged train and test data by using of some memory reduction techniques like this kernel or the modified reduce_memory_uasge as discussed here and save the train and test as pickle files.\n2. Build your models in a second kernel with the data from the first kernel and save your models again as pickle files .\n3. Download the models in a third kernel and make your predictions and submission here. take care as you are predicting over 41 million rows of data.\ni hope this helps.
", "rawMarkdown": "The reason is you are running out of memory (exceeding 16B) while training your model or predicting . Some of the solutions are\n1. Prepare/preprocess your data in one kernel . After merging the weather and building data with your test and train dataframes delete all original dataframes. Reduce the size of the merged train and test data by using of some memory reduction techniques like [this kernel](https://www.kaggle.com/alexeykupershtokh/safe-memory-reduction) or the modified reduce_memory_uasge as discussed [here](https://www.kaggle.com/c/champs-scalar-coupling/discussion/96655#latest-620729) and save the train and test as pickle files.\n2. Build your models in a second kernel with the data from the first kernel and save your models again as pickle files .\n3. Download the models in a third kernel and make your predictions and submission here. take care as you are predicting over 41 million rows of data.\ni hope this helps.", "votes": null }, { "id": "663262", "postDate": "11/01/2019 17:52:09", "content": "My solution of that problem in ASHRAE-competition: Very significant safe memory + LightGBM
", "rawMarkdown": "My solution of that problem in ASHRAE-competition: [Very significant safe memory + LightGBM](https://www.kaggle.com/vbmokin/very-significant-safe-memory-lightgbm)", "votes": null }, { "id": "664614", "postDate": "11/04/2019 01:19:48", "content": "cool ;-)
", "rawMarkdown": "cool ;-)", "votes": null } ], "comments": [ { "id": 662310, "author_name": "rajeshcv", "author_url": "", "post_date": "10/31/2019 12:11:33", "content": "The reason is you are running out of memory (exceeding 16B) while training your model or predicting . Some of the solutions are\n1. Prepare/preprocess your data in one kernel . After merging the weather and building data with your test and train dataframes delete all original dataframes. Reduce the size of the merged train and test data by using of some memory reduction techniques like this kernel or the modified reduce_memory_uasge as discussed here and save the train and test as pickle files.\n2. Build your models in a second kernel with the data from the first kernel and save your models again as pickle files .\n3. Download the models in a third kernel and make your predictions and submission here. take care as you are predicting over 41 million rows of data.\ni hope this helps.
", "votes": null, "replies": [ { "id": 664614, "author_name": "hanjoonchoe", "author_url": "", "post_date": "11/04/2019 01:19:48", "content": "cool ;-)
", "votes": null, "replies": [] } ] }, { "id": 663262, "author_name": "vbmokin", "author_url": "", "post_date": "11/01/2019 17:52:09", "content": "My solution of that problem in ASHRAE-competition: Very significant safe memory + LightGBM
", "votes": null, "replies": [] } ], "raw_markdown_by_id": { "661344": "When I used kaggle's kernel, it reported such an error. Who knows how this should be solved?\n\n[NbConvertApp] ERROR | Kernel died while waiting for execute reply.\nTraceback (most recent call last): File \"/opt/conda/lib/python3.6/site-packages/nbconvert/preprocessors/execute.py\", line 478, in _poll_for_reply msg = self.kc.shell_channel.get_msg(timeout=timeout) File \"/opt/conda/lib/python3.6/site-packages/jupyter_client/blocking/channels.py\", line 57, in get_msg raise Empty queue.Empty During handling of the above exception, another exception occurred: Traceback (most recent call last): File \"/opt/conda/bin/jupyter-nbconvert\", line 11, in", "662310": "The reason is you are running out of memory (exceeding 16B) while training your model or predicting . Some of the solutions are\n1. Prepare/preprocess your data in one kernel . After merging the weather and building data with your test and train dataframes delete all original dataframes. Reduce the size of the merged train and test data by using of some memory reduction techniques like [this kernel](https://www.kaggle.com/alexeykupershtokh/safe-memory-reduction) or the modified reduce_memory_uasge as discussed [here](https://www.kaggle.com/c/champs-scalar-coupling/discussion/96655#latest-620729) and save the train and test as pickle files.\n2. Build your models in a second kernel with the data from the first kernel and save your models again as pickle files .\n3. Download the models in a third kernel and make your predictions and submission here. take care as you are predicting over 41 million rows of data.\ni hope this helps.", "663262": "My solution of that problem in ASHRAE-competition: [Very significant safe memory + LightGBM](https://www.kaggle.com/vbmokin/very-significant-safe-memory-lightgbm)", "664614": "cool ;-)" }, "source": "meta" }