Ecu Tuning Software Free Download WORK

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.

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.

Ecu Tuning Software Free Download

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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.

Automatic tuning is a fully managed intelligent performance service that uses built-in intelligence to continuously monitor queries executed on a database and automatically improve their performance. This is achieved through dynamically adapting a database to changing workloads and applying tuning recommendations. Automatic tuning learns horizontally from all databases on Azure through AI, and dynamically improves its tuning actions. The longer a database runs with automatic tuning on, the better it performs.

Azure SQL automatic tuning shares its core logic with the SQL Server automatic tuning feature in the database engine. For additional technical information on the built-in intelligence mechanism, see SQL Server automatic tuning.

Tuning operations applied to databases are fully safe for performance of your most intense workloads. The system has been designed with care not to interfere with user workloads. Automated tuning recommendations are applied only at the times of a low utilization of CPU, Data IO, and Log IO. The system can also temporarily disable automatic tuning operations to protect workload performance. In such case, "Disabled by the system" message will be shown in Azure portal and in sys.database_automatic_tuning_options DMV. Automatic tuning is designed to give user workloads the highest resource priority.

Automatic tuning mechanisms are mature and have been perfected on several million databases running on Azure. Automated tuning operations applied are verified automatically to ensure there is a notable positive improvement to workload performance. If there is no improvement, or in the unlikely case performance regresses, changes made by automatic tuning are promptly reverted. Through the tuning history recorded, there exists a clear trace of tuning improvements made to each database in Azure SQL Database.

Automatic tuning for Azure SQL Database uses the CREATE INDEX, DROP INDEX, and FORCE_LAST_GOOD_PLAN database advisor recommendations to optimize your database performance. For more information, see Database advisor recommendations in the Azure portal, in PowerShell, and in the REST API.

You can either manually apply tuning recommendations using the Azure portal, or you can let automatic tuning autonomously apply tuning recommendations for you. The benefits of letting the system autonomously apply tuning recommendations for you is that it automatically validates there exists a positive gain to workload performance, and if there is no significant performance improvement detected or if performance regresses, the system automatically reverts the changes that were made. Depending on query execution frequency, the validation process can take from 30 minutes to 72 hours, taking longer for less frequently executing queries. If at any point during validation a regression is detected, changes are reverted immediately.

In case you are applying tuning recommendations through T-SQL, the automatic performance validation and reversal mechanisms are not available. Recommendations applied in such way will remain active and shown in the list of tuning recommendations for 24-48 hours before the system automatically withdraws them. If you would like to remove a recommendation sooner, you can discard it from Azure portal.

Automatic tuning options can be independently enabled or disabled for each database, or they can be configured at the server-level and applied on every database that inherits settings from the server. By default, new servers inherit Azure defaults for automatic tuning settings. Azure defaults are set to FORCE_LAST_GOOD_PLAN enabled, CREATE_INDEX disabled, and DROP_INDEX disabled.

Configuring automatic tuning options on a server and inheriting settings for databases belonging to the parent server is the recommended method for configuring automatic tuning. It simplifies management of automatic tuning options for a large number of databases.

Automatic tuning for SQL Managed Instance only supports FORCE LAST GOOD PLAN. For more information about configuring automatic tuning options through T-SQL, see Automatic tuning introduces automatic plan correction and Automatic plan correction.

For Azure SQL Database, the history of changes made by automatic tuning is retained for 21 days. It can be viewed in Azure portal on the Performance recommendations page for a database, or using PowerShell with the Get-AzSqlDatabaseRecommendedAction cmdlet. For longer retention, history data can also be streamed to several types of destinations by enabling the AutomaticTuning diagnostic setting.

Tuning a foundation model can improve its performance. Foundation models arepretrained using prompt design strategies, such as few-shot prompting. Sometimesthe pretrained models don't perform tasks as well as you'd like them to. Thismight be because the tasks you want the model to perform are specialized tasksthat are difficult to teach a model with only prompt design. In those cases, youcan use model tuning to improve the performance of a model on specific tasks.Model tuning can also help it adhere to specific output requirements wheninstructions aren't sufficient. This page provides an overview of model tuning,describes the tuning options available on Vertex AI, and helps youdetermine when each tuning option should be used.

Model tuning works by providing a model with a training dataset that containsmany examples of a unique task. For unique, niche tasks, you can get significantimprovements in model performance by tuning the model on a modest number ofexamples. After you tune a model, fewer examples are required in its prompts.

Supervised tuning - Supervised tuning of a text model isa good option when the output of your model isn't complex and is relativelyeasy to define. Supervised tuning is recommended for classification, sentimentanalysis, entity extraction, summarization of content that's not complex, andwriting domain-specific queries. For code models, supervised tuning is the only option.

Reinforcement learning from human feedback (RLHF) tuning -RLHF tuning is a good option when the output of your model is complex. RLHFworks well on models with two objectives that aren't easily differentiatedwith supervised tuning. RLHF tuning is recommended for question answering,summarization of complex content, and content creation, such as a rewrite.RLHF tuning isn't supported by code models.

Supervised tuning improves the performance of a model by teaching it a newskill. Data that contains hundreds of labeled examples is used to teach themodel to mimic a desired behavior or task. Each labeled example demonstrateswhat you want the model to output during inference.

When you run a supervised tuning job, the model learns additional parametersthat help it encode the necessary information to perform the desired task orlearn the desired behavior. These parameters are used during inference. Theoutput of the tuning job is a new model that combines the newly learnedparameters with the original model.

After model tuning completes, the tuned model is deployed to a Vertex AIendpoint. The name of the endpoint is the same as the name of the tuned model.Tuned models are available to select in Generative AI Studio when you want tocreate a new prompt. be457b7860