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You can either instantiate an optimizer before passing it to model.compile() , as in the above example,or you can pass it by its string identifier. In the latter case, the default parameters for the optimizer will be used.
The optimizer is part of the r.js adapter for Node and Nashorn, and it is designed to be run as part of a build or packaging step after you are done with development and are ready to deploy the code for your users.
The optimizer will only combine modules that are specified in arrays of string literals that are passed to top-level require and define calls, or the require('name') string literal calls in a simplified CommonJS wrapping. So, it will not find modules that are loaded via a variable name:
This behavior allows dynamic loading of modules even after optimization. You can always explicitly add modules that are not found via the optimizer's static analysis by using the include option.
The optimizer can be run using Node, Java with Rhino or Nashorn, or in the browser. The requirements for each option:
- Node: (preferred) Node 0.4.0 or later.
- Java: Java 1.6 or later.
- Browser: as of 2.1.2, the optimizer can run in a web browser that has array extras. While the optimizer options are the same as shown below, it is called via JavaScript instead of command line options. It is also only good for generating optimized single files, not a directory optimization. See the browser example. This option is really only useful for providing web-based custom builds of your library.
With this local install, you can run the optimizer by running the r.js or r.js.cmd file found in the project's node_modules/.bin directory.With the local install, you can also use the optimizer via a function call inside a node program.
The examples in this page will assume you downloaded and saved r.js in a directory that is a sibling to your project directory. The optimizer that is part of r.js can live anywhere you want, but you will likely need to adjust the paths accordingly in these examples.
There is a limitation on the command line argument syntax. Dots are viewed as object property separators, to allow something like paths.jquery=lib/jquery to be transformed to the following in the optimizer:
For properties that are module IDs, they should be module IDs, and not file paths. Examples arename, include, exclude, excludeShallow, deps.Config settings in your main JS module that is loaded in the browser at runtime are not read by default by the optimizer
In version 1.0.5+ of the optimizer, the mainConfigFile option can be used to specify the location of the runtime config. If specified with the path to your main JS file, the first requirejs(), requirejs.config(), require(), or require.config() found in that file will be parsed out and used as part of the configuration options passed to the optimizer:
The optimizer cannot load network resources, so if you want it included in the build, be sure to create a paths config to map the file to a module name. Then, for running the optimizer, download the CDN script and pass a paths config to the optimizer that maps the module name to the local file path.
This build profile tells RequireJS to copy all of appdirectory to a sibling directory called appdirectory-build and apply all the optimizations in the appdirectory-build directory. It is strongly suggested you use a different output directory than the source directory -- otherwise bad things will likely happen as the optimizer overwrites your source.
The default for the optimizer is to do the safest, most robust set of actions that avoid surprises after a build. However, depending on your project setup, you may want to turn off some of these features to get faster builds:
As of version 2.1.2, there are some speed shortcuts the optimizer will take by default if optimize is set to "none". However, if you are using "none" for optimize and you are planning to minify the built files after the optimizer runs, then you should turn set normalizeDirDefines to "all" so that define() calls are normalized correctly to withstand minification. If you are doing minification via the optimize option, then you do not need to worry about setting this option.
The optimizer has supported sourceURL (by setting useSourceUrl to true), for debugging combined modules as individual files. However, that only works with non-minified code. Source maps translate a minified file to a non-minified version. It does not make sense to use useSourceUrl with generateSourceMaps since useSourceUrl needs the source values as strings, which prohibits the useful minification done in combination with generateSourceMaps.
The r.js optimizer is designed to offer some primitives that can be used for different deployment scenarios by adding other code on top of it. See the deployment techniques wiki page for ideas on how to use the optimizer in that fashion.
To construct an Optimizer you have to give it an iterable containing theparameters (all should be Variable s) to optimize. Then,you can specify optimizer-specific options such as the learning rate, weight decay, etc.
Optimizer s also support specifying per-parameter options. To do this, insteadof passing an iterable of Variable s, pass in an iterable ofdict s. Each of them will define a separate parameter group, and should containa params key, containing a list of parameters belonging to it. Other keysshould match the keyword arguments accepted by the optimizers, and will be usedas optimization options for this group.
We have 3 major categories of implementations: for-loop, foreach (multi-tensor), andfused. The most straightforward implementations are for-loops over the parameters withbig chunks of computation. For-looping is usually slower than our foreachimplementations, which combine parameters into a multi-tensor and run the big chunksof computation all at once, thereby saving many sequential kernel calls. A few of ouroptimizers have even faster fused implementations, which fuse the big chunks ofcomputation into one kernel. We can think of foreach implementations as fusinghorizontally and fused implementations as fusing vertically on top of that.
Here the model model can be an arbitrary torch.nn.Module object. averaged_modelwill keep track of the running averages of the parameters of the model. To update theseaverages, you should use the update_parameters() function after the optimizer.step():
A power optimizer is a DC to DC converter technology developed to maximize the energy harvest from solar photovoltaic or wind turbine systems. They do this by individually tuning the performance of the panel or wind turbine through maximum power point tracking, and optionally tuning the output to match the performance of the string inverter (DC to AC inverter). Power optimizers are especially useful when the performance of the power generating components in a distributed system will vary widely, such as due to differences in equipment, shading of light or wind, or being installed facing different directions or widely separated locations.
Power optimizers for solar applications can be similar to microinverters in that both systems attempt to isolate individual panels in order to improve overall system performance. A smart module is a power optimizer integrated into a solar module. A microinverter essentially combines a power optimizer with a small inverter in a single enclosure that is used on every panel, while the power optimizer leaves the inverter in a separate box and uses only one inverter for the entire array. The claimed advantage to this "hybrid" approach is lower overall system costs, avoiding the distribution of electronics.
This leads, naturally, to the power optimizer concept, where only the MPPT system is distributed to the panels. In this case the conversion from DC to AC takes place in a single inverter, one that lacks the MPPT hardware or has it disabled. Advanced solutions are able to work correctly with all solar inverters, to make possible optimisation of already installed plants. According to its supporters, this "hybrid" approach produces the lowest-cost solution overall, while still maintaining the advantages of the microinverter approach.
Power optimizers are essentially DC-DC converters, taking the DC power from a solar panel at whatever voltage and current is optimal (via MPPT), then converting that to a different voltage and current that best suits the central / string inverter.
Some power optimizers are designed to work in conjunction with a central inverter from the same manufacturer, which allows the inverter to communicate with the optimizers to ensure that the inverter always receives the same total voltage from the panel string.[10] In this situation, if there is a string of panels in series and a single panel's output drops due to shade, its voltage will drop so that it can deliver the same amount of current (amps). This would cause the string voltage to drop as well, except that the central inverter adjusts all the other optimizers so that their output voltage increases slightly, maintaining the fixed string voltage required at the inverter (just at reduced available amperage while the single panel is shaded). The down side of this type of optimizer is that it requires a central inverter from the same manufacturer as the optimizers, so it is not possible to gradually retrofit these in an existing installation unless the inverter is also replaced, as well as optimizers installed on all panels at the same time.
The optimizer that will run optimizations based on all vehicles and shipments within the workspace. Each optimization is triggered by a client call. When the optimizer runs an optimization, it will construct an OptimizeToursRequest behind the scenes with the most up-to-date entity information and other constraints, and generate a solution. The corresponding OptimizeToursRequest for each solution can be retrieved.
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