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The Tuning Project's History Discipline Core is a statement of the central habits of mind, skills, and understanding that students achieve when they major in history. The document reflects the iterative nature of the tuning process. The most recent version was published in November 2016.

Thank you for getting back. Actually i have lot of credits for the normal plan but I realize API credits is what I need because I am interested in fine tuning. Can I convert my existing credits into API credits?

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Right now the criteria is being a very high user of the fine tuning system, so someone who has trained hundreds of models with a lot of data and has a great deal of experience categorising, evaluating and creating feedback and reports on model performance against a set of high quality evaluations.

Fine-tuning gpt4 for gcode might make it possible to use gpt4 to create outputs that can safely be used to control things in the real world when integrated into a larger system with extremely robust safety protocols.

Because of the in-memory nature of most Spark computations, Spark programs can be bottleneckedby any resource in the cluster: CPU, network bandwidth, or memory.Most often, if the data fits in memory, the bottleneck is network bandwidth, but sometimes, youalso need to do some tuning, such asstoring RDDs in serialized form, todecrease memory usage.This guide will cover two main topics: data serialization, which is crucial for good networkperformance and can also reduce memory use, and memory tuning. We also sketch several smaller topics.

There are three considerations in tuning memory usage: the amount of memory used by your objects(you may want your entire dataset to fit in memory), the cost of accessing those objects, and theoverhead of garbage collection (if you have high turnover in terms of objects).

When your objects are still too large to efficiently store despite this tuning, a much simpler wayto reduce memory usage is to store them in serialized form, using the serialized StorageLevels inthe RDD persistence API, such as MEMORY_ONLY_SER.Spark will then store each RDD partition as one large byte array.The only downside of storing data in serialized form is slower access times, due to having todeserialize each object on the fly.We highly recommend using Kryo if you want to cache data in serialized form, asit leads to much smaller sizes than Java serialization (and certainly than raw Java objects).

The goal of GC tuning in Spark is to ensure that only long-lived RDDs are stored in the Old generation and thatthe Young generation is sufficiently sized to store short-lived objects. This will help avoid full GCs to collecttemporary objects created during task execution. Some steps which may be useful are:

Our experience suggests that the effect of GC tuning depends on your application and the amount of memory available.There are many more tuning options described online,but at a high level, managing how frequently full GC takes place can help in reducing the overhead.

For Spark SQL with file-based data sources, you can tune spark.sql.sources.parallelPartitionDiscovery.threshold andspark.sql.sources.parallelPartitionDiscovery.parallelism to improve listing parallelism. Pleaserefer to Spark SQL performance tuning guide for more details.

Skew tuning is performed in the case of more than one line. Every line in your circuit will have a certain delay. Differential pair traces carry the same signal but in opposite polarity and are synchronized with respect to the time.

Example of tuning approach for the ECHAM model (after Mauritsen et al. 2012). The figure illustrates the major uncertain climate-related cloud processes frequently used to tune the climate of the ECHAM model. Stratiform liquid and ice clouds and shallow and deep convective clouds are represented. The gray curve to the left represents tropospheric temperatures, and the dashed line is the top of the boundary layer. Parameters are (a) convective cloud mass flux above the level of nonbuoyancy, (b) shallow convective cloud lateral entrainment rate, (c) deep convective cloud lateral entrainment rate, (d) convective cloud water conversion rate to rain, (e) liquid cloud homogeneity, (f) liquid cloud water conversion rate to rain, (g) ice cloud homogeneity, and (h) ice particle fall velocity.

The process of parameter estimation targeting a chosen set of observations is an essential aspect of numerical modeling. This process is usually named tuning in the climate modeling community. In climate models, the variety and complexity of physical processes involved, and their interplay through a wide range of spatial and temporal scales, must be summarized in a series of approximate submodels. Most submodels depend on uncertain parameters. Tuning consists of adjusting the values of these parameters to bring the solution as a whole into line with aspects of the observed climate. Tuning is an essential aspect of climate modeling with its own scientific issues, which is probably not advertised enough outside the community of model developers. Optimization of climate models raises important questions about whether tuning methods a priori constrain the model results in unintended ways that would affect our confidence in climate projections. Here, we present the definition and rationale behind model tuning, review specific methodological aspects, and survey the diversity of tuning approaches used in current climate models. We also discuss the challenges and opportunities in applying so-called objective methods in climate model tuning. We discuss how tuning methodologies may affect fundamental results of climate models, such as climate sensitivity. The article concludes with a series of recommendations to make the process of climate model tuning more transparent.

The development of a climate model is a long-term project. When releasing a new model or new version of a model, a series of submodels, sometimes developed or improved over years in separate teams, are combined and optimized together to produce a climate that matches some key aspects of the observed climate. While the fundamental physics of climate is generally well established, submodels or parameterizations are approximate, either because of numerical cost issues (limitations in grid resolution, acceleration of radiative transfer computation) or, more fundamentally, because they try to summarize complex and multiscale processes through an idealized and approximate representation. Each parameterization relies on a set of internal equations and often depends on parameters, the values of which are often poorly constrained by observations. The process of estimating these uncertain parameters in order to reduce the mismatch between specific observations and model results is usually referred to as tuning in the climate modeling community.

Choices and compromises made during the tuning exercise may significantly affect model results and influence evaluations that measure a statistical distance between the simulated and observed climate. In theory, tuning should be taken into account in any evaluation, intercomparison, or interpretation of the model results. Although the need for parameter tuning was recognized in pioneering modeling work (e.g., Manabe and Wetherald 1975) and discussed as an important aspect in epistemological studies of climate modeling (Edwards 2001), the importance of tuning is probably not advertised as it should be. It is often ignored when discussing the performances of climate models in multimodel analyses. In fact, the tuning strategy was not even part of the required documentation of the CMIP phase 5 (CMIP5) simulations. In the best cases, the description of the tuning strategy was available in the reference publications of the modeling groups (Mauritsen et al. 2012; Golaz et al. 2013; Hourdin et al. 2013a,b; Schmidt et al. 2014). Why such a lack of transparency? This may be because tuning is often seen as an unavoidable but dirty part of climate modeling, more engineering than science, an act of tinkering that does not merit recording in the scientific literature. There may also be some concern that explaining that models are tuned may strengthen the arguments of those claiming to question the validity of climate change projections. Tuning may be seen indeed as an unspeakable way to compensate for model errors. e24fc04721