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Drop saving binary format for memory snapshot. (#6513)

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This commit is contained in:
Jiaming Yuan
2020-12-17 00:14:57 +08:00
committed by GitHub
parent 0e97d97d50
commit c5876277a8
5 changed files with 23 additions and 103 deletions
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41
doc/tutorials/saving_model.rst
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@@ -9,10 +9,9 @@ open format that can be easily reused. The support for binary format will be co
the future until JSON format is no-longer experimental and has satisfying performance.
This tutorial aims to share some basic insights into the JSON serialisation method used in
XGBoost. Without explicitly mentioned, the following sections assume you are using the
experimental JSON format, which can be enabled by passing
``enable_experimental_json_serialization=True`` as training parameter, or provide the file
name with ``.json`` as file extension when saving/loading model:
``booster.save_model('model.json')``. More details below.
JSON format, which can be enabled by providing the file name with ``.json`` as file
extension when saving/loading model: ``booster.save_model('model.json')``. More details
below.
Before we get started, XGBoost is a gradient boosting library with focus on tree model,
which means inside XGBoost, there are 2 distinct parts:
@@ -66,26 +65,7 @@ a filename with ``.json`` as file extension:
xgb.save(bst, 'model_file_name.json')
To use JSON to store memory snapshots, add ``enable_experimental_json_serialization`` as a training
parameter. In Python this can be done by:
.. code-block:: python
bst = xgboost.train({'enable_experimental_json_serialization': True}, dtrain)
with open('filename', 'wb') as fd:
pickle.dump(bst, fd)
Notice the ``filename`` is for Python intrinsic function ``open``, not for XGBoost. Hence
parameter ``enable_experimental_json_serialization`` is required to enable JSON format.
Similarly, in the R package, add ``enable_experimental_json_serialization`` to the training
parameter:
.. code-block:: r
params <- list(enable_experimental_json_serialization = TRUE, ...)
bst <- xgboost.train(params, dtrain, nrounds = 10)
saveRDS(bst, 'filename.rds')
While for memory snapshot, JSON is the default starting with xgboost 1.3.
***************************************************************
A note on backward compatibility of models and memory snapshots
@@ -110,11 +90,11 @@ Custom objective and metric
***************************
XGBoost accepts user provided objective and metric functions as an extension. These
functions are not saved in model file as they are language dependent feature. With
functions are not saved in model file as they are language dependent features. With
Python, user can pickle the model to include these functions in saved binary. One
drawback is, the output from pickle is not a stable serialization format and doesn't work
on different Python version or XGBoost version, not to mention different language
environment. Another way to workaround this limitation is to provide these functions
on different Python version nor XGBoost version, not to mention different language
environments. Another way to workaround this limitation is to provide these functions
again after the model is loaded. If the customized function is useful, please consider
making a PR for implementing it inside XGBoost, this way we can have your functions
working with different language bindings.
@@ -128,9 +108,9 @@ models are valuable. One way to restore it in the future is to load it back wit
specific version of Python and XGBoost, export the model by calling `save_model`. To help
easing the mitigation, we created a simple script for converting pickled XGBoost 0.90
Scikit-Learn interface object to XGBoost 1.0.0 native model. Please note that the script
suits simple use cases, and it's advised not to use pickle when stability is needed.
It's located in ``xgboost/doc/python`` with the name ``convert_090to100.py``. See
comments in the script for more details.
suits simple use cases, and it's advised not to use pickle when stability is needed. It's
located in ``xgboost/doc/python`` with the name ``convert_090to100.py``. See comments in
the script for more details.
A similar procedure may be used to recover the model persisted in an old RDS file. In R, you are
able to install an older version of XGBoost using the ``remotes`` package:
@@ -172,7 +152,6 @@ Will print out something similiar to (not actual output as it's too long for dem
{
"Learner": {
"generic_parameter": {
"enable_experimental_json_serialization": "0",
"gpu_id": "0",
"gpu_page_size": "0",
"n_jobs": "0",