[ML] take training_percent into account when estimating memory #1111
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
tveasey
reviewed
Apr 2, 2020
docs/CHANGELOG.asciidoc
Outdated
|
|
||
| === Enhancements | ||
|
|
||
| * Take `training_percent` into account when estimating memory usage. (See {ml-pull}1111[1111].) |
There was a problem hiding this comment.
Maybe worth mentioning analysis types it applies to
Suggested change
| * Take `training_percent` into account when estimating memory usage. (See {ml-pull}1111[1111].) | |
| * Take `training_percent` into account when estimating memory usage for classification and regression. (See {ml-pull}1111[#1111].) |
|
|
||
| std::string m_DependentVariableFieldName; | ||
| std::string m_PredictionFieldName; | ||
| double m_trainingPercent; |
There was a problem hiding this comment.
nit: consistency and naming conventions
Suggested change
| double m_trainingPercent; | |
| double m_TrainingPercent; |
|
|
||
| for (std::size_t test = 0; test < 3; ++test) { | ||
| TDoubleVecVec x(cols - 1); | ||
| std::size_t num_test_rows{((test + 1) * 100)}; |
There was a problem hiding this comment.
nit: can we stick with camel case
Suggested change
| std::size_t num_test_rows{((test + 1) * 100)}; | |
| std::size_t numTestRows{((test + 1) * 100)}; |
| LOG_DEBUG(<< "estimated memory usage = " << estimatedMemory); | ||
| LOG_DEBUG(<< "high water mark = " << instrumentation.maxMemoryUsage()); | ||
|
|
||
| BOOST_TEST_REQUIRE(instrumentation.maxMemoryUsage() < estimatedMemory); |
There was a problem hiding this comment.
👍 also let's assert that estimated memory decreases for each test since you're increasing test percentage.
tveasey
approved these changes
Apr 2, 2020
| std::size_t rows{1000}; | ||
| std::size_t cols{6}; | ||
| std::size_t capacity{600}; | ||
| std::int64_t previousEstimatedMemory{LLONG_MAX}; |
There was a problem hiding this comment.
nit: is the C++ way of doing this
Suggested change
| std::int64_t previousEstimatedMemory{LLONG_MAX}; | |
| std::int64_t previousEstimatedMemory{std::numeric_limits<std::int64_t>::max()}; |
…d-training-percent-param
benwtrent
added a commit
that referenced
this pull request
Apr 2, 2020
#1116) This change adjust the memory estimation by taking the parameter `training_percent` into account. It is an optional parameter that defaults to `100.0`. The Java side will write out the parameter as it is input by the user.
benwtrent
added a commit
to elastic/elasticsearch
that referenced
this pull request
Apr 2, 2020
This adds training_percent parameter to the analytics process for Classification and Regression. This parameter is then used to give more accurate memory estimations. See native side pr: elastic/ml-cpp#1111
benwtrent
added a commit
to benwtrent/elasticsearch
that referenced
this pull request
Apr 2, 2020
This adds training_percent parameter to the analytics process for Classification and Regression. This parameter is then used to give more accurate memory estimations. See native side pr: elastic/ml-cpp#1111
benwtrent
added a commit
to elastic/elasticsearch
that referenced
this pull request
Apr 2, 2020
This adds training_percent parameter to the analytics process for Classification and Regression. This parameter is then used to give more accurate memory estimations. See native side pr: elastic/ml-cpp#1111
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
This change adjust the memory estimation by taking the parameter
training_percentinto account.It is an optional parameter that defaults to
100.0. The Java side will write out the parameter as it is input by the user.