Add a minimize_with_timeout wrapper for scipy.optimize.minimize #1403
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@@ Coverage Diff @@
## main #1403 +/- ##
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Coverage 100.00% 100.00%
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Files 153 154 +1
Lines 13686 13730 +44
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+ Hits 13686 13730 +44
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…rch#1403) Summary: Pull Request resolved: pytorch#1403 Unfortunately, scipy does not allow timing out the optimization based on wall time. This diff implements a lightweight wrapper around `scipy.optimize.minimize` to achieve this. The new `minimize_with_timeout` method calls `scipy.optimize.minimize` with all arguments forwarded verbatim. The only difference is that if provided a `timeout_sec` setting in the `options` dict, it automatically stops the optimization after the timeout is reached. Internally, this is achieved by automatically constructing a callback method that is injected to the `scipy.optimize.minimize` call that keeps track of the runtime and is used to extract the value of the optimization variables at the current iteration. Differential Revision: D39529835 fbshipit-source-id: 6caced5e689a79231699025a50f76fb69cfecb20
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…rch#1403) Summary: Pull Request resolved: pytorch#1403 Unfortunately, scipy does not allow timing out the optimization based on wall time. This diff implements a lightweight wrapper around `scipy.optimize.minimize` to achieve this. The new `minimize_with_timeout` method calls `scipy.optimize.minimize` with all arguments forwarded verbatim. The only difference is that if provided a `timeout_sec` setting in the `options` dict, it automatically stops the optimization after the timeout is reached. Internally, this is achieved by automatically constructing a callback method that is injected to the `scipy.optimize.minimize` call that keeps track of the runtime and is used to extract the value of the optimization variables at the current iteration. Differential Revision: D39529835 fbshipit-source-id: 71fa5eabbb3f073e32796d9d3d451b6786ef3a2d
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…rch#1403) Summary: Pull Request resolved: pytorch#1403 Unfortunately, scipy does not allow timing out the optimization based on wall time. This diff implements a lightweight wrapper around `scipy.optimize.minimize` to achieve this. The new `minimize_with_timeout` method calls `scipy.optimize.minimize` with all arguments forwarded verbatim. The only difference is that if provided a `timeout_sec` kwarg, it automatically stops the optimization after the timeout is reached. Internally, this is achieved by automatically constructing a callback method that is injected to the `scipy.optimize.minimize` call that keeps track of the runtime and is used to extract the value of the optimization variables at the current iteration. Differential Revision: D39529835 fbshipit-source-id: 27232f43dae509e5415ad412f6c21af16be49ff4
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…rch#1403) Summary: Pull Request resolved: pytorch#1403 Unfortunately, scipy does not allow timing out the optimization based on wall time. This diff implements a lightweight wrapper around `scipy.optimize.minimize` to achieve this. The new `minimize_with_timeout` method calls `scipy.optimize.minimize` with all arguments forwarded verbatim. The only difference is that if provided a `timeout_sec` kwarg, it automatically stops the optimization after the timeout is reached. Internally, this is achieved by automatically constructing a callback method that is injected to the `scipy.optimize.minimize` call that keeps track of the runtime and is used to extract the value of the optimization variables at the current iteration. Differential Revision: D39529835 fbshipit-source-id: 5921cd81f00033d47df39f3ab2c47d9448952356
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…rch#1403) Summary: Pull Request resolved: pytorch#1403 Unfortunately, scipy does not allow timing out the optimization based on wall time. This diff implements a lightweight wrapper around `scipy.optimize.minimize` to achieve this. The new `minimize_with_timeout` method calls `scipy.optimize.minimize` with all arguments forwarded verbatim. The only difference is that if provided a `timeout_sec` kwarg, it automatically stops the optimization after the timeout is reached. Internally, this is achieved by automatically constructing a callback method that is injected to the `scipy.optimize.minimize` call that keeps track of the runtime and is used to extract the value of the optimization variables at the current iteration. Differential Revision: D39529835 fbshipit-source-id: db1464963e6d04b6b1f68835099b44f9fc442d6b
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…rch#1403) Summary: Pull Request resolved: pytorch#1403 Unfortunately, scipy does not allow timing out the optimization based on wall time. This diff implements a lightweight wrapper around `scipy.optimize.minimize` to achieve this. The new `minimize_with_timeout` method calls `scipy.optimize.minimize` with all arguments forwarded verbatim. The only difference is that if provided a `timeout_sec` kwarg, it automatically stops the optimization after the timeout is reached. Internally, this is achieved by automatically constructing a callback method that is injected to the `scipy.optimize.minimize` call that keeps track of the runtime and is used to extract the value of the optimization variables at the current iteration. Differential Revision: D39529835 fbshipit-source-id: ce5bde193cc63ca4ed9a0a8f2b8cc3110a116640
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…rch#1403) Summary: Pull Request resolved: pytorch#1403 Unfortunately, scipy does not allow timing out the optimization based on wall time. This diff implements a lightweight wrapper around `scipy.optimize.minimize` to achieve this. The new `minimize_with_timeout` method calls `scipy.optimize.minimize` with all arguments forwarded verbatim. The only difference is that if provided a `timeout_sec` kwarg, it automatically stops the optimization after the timeout is reached. Internally, this is achieved by automatically constructing a callback method that is injected to the `scipy.optimize.minimize` call that keeps track of the runtime and is used to extract the value of the optimization variables at the current iteration. Differential Revision: D39529835 fbshipit-source-id: 4f54079fc395d9b1ec7e88edbdb2962ce36231f3
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…rch#1403) Summary: Pull Request resolved: pytorch#1403 Unfortunately, scipy does not allow timing out the optimization based on wall time. This diff implements a lightweight wrapper around `scipy.optimize.minimize` to achieve this. The new `minimize_with_timeout` method calls `scipy.optimize.minimize` with all arguments forwarded verbatim. The only difference is that if provided a `timeout_sec` kwarg, it automatically stops the optimization after the timeout is reached. Internally, this is achieved by automatically constructing a callback method that is injected to the `scipy.optimize.minimize` call that keeps track of the runtime and is used to extract the value of the optimization variables at the current iteration. Differential Revision: D39529835 fbshipit-source-id: adc79a40f1cfeebed4a5706ec4eea71691ea4619
Balandat
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Dec 28, 2022
…rch#1403) Summary: Pull Request resolved: pytorch#1403 Unfortunately, scipy does not allow timing out the optimization based on wall time. This diff implements a lightweight wrapper around `scipy.optimize.minimize` to achieve this. The new `minimize_with_timeout` method calls `scipy.optimize.minimize` with all arguments forwarded verbatim. The only difference is that if provided a `timeout_sec` kwarg, it automatically stops the optimization after the timeout is reached. Internally, this is achieved by automatically constructing a callback method that is injected to the `scipy.optimize.minimize` call that keeps track of the runtime and is used to extract the value of the optimization variables at the current iteration. Differential Revision: D39529835 fbshipit-source-id: 985ad2c06abf4c7326aeed6b29aa196b5d4b2a97
Balandat
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Dec 28, 2022
…rch#1403) Summary: Pull Request resolved: pytorch#1403 Unfortunately, scipy does not allow timing out the optimization based on wall time. This diff implements a lightweight wrapper around `scipy.optimize.minimize` to achieve this. The new `minimize_with_timeout` method calls `scipy.optimize.minimize` with all arguments forwarded verbatim. The only difference is that if provided a `timeout_sec` kwarg, it automatically stops the optimization after the timeout is reached. Internally, this is achieved by automatically constructing a callback method that is injected to the `scipy.optimize.minimize` call that keeps track of the runtime and is used to extract the value of the optimization variables at the current iteration. Differential Revision: D39529835 fbshipit-source-id: 50f773bfa2fb5b50ab9c6d724d6fc4a67e4f6a6e
Balandat
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…rch#1403) Summary: Pull Request resolved: pytorch#1403 Unfortunately, scipy does not allow timing out the optimization based on wall time. This diff implements a lightweight wrapper around `scipy.optimize.minimize` to achieve this. The new `minimize_with_timeout` method calls `scipy.optimize.minimize` with all arguments forwarded verbatim. The only difference is that if provided a `timeout_sec` kwarg, it automatically stops the optimization after the timeout is reached. Internally, this is achieved by automatically constructing a callback method that is injected to the `scipy.optimize.minimize` call that keeps track of the runtime and is used to extract the value of the optimization variables at the current iteration. Differential Revision: D39529835 fbshipit-source-id: f3f6c9654f0ea1a348610428ca5b01e8ef3d78ef
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Summary:
Unfortunately, scipy does not allow timing out the optimization based on wall time. This diff implements a lightweight wrapper around
scipy.optimize.minimizeto achieve this.The new
minimize_with_timeoutmethod callsscipy.optimize.minimizewith all arguments forwarded verbatim. The only difference is that if provided atimeout_secsetting in theoptionsdict, it automatically stops the optimization after the timeout is reached.Internally, this is achieved by automatically constructing a callback method that is injected to the
scipy.optimize.minimizecall that keeps track of the runtime and is used to extract the value of the optimization variables atthe current iteration.
Differential Revision: D39529835