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Here is an example of configuring a volume as cached :

Full detail on these flags, the problems they solve, and their docker run counterparts is in the Docker for Mac topic Discount Popular Essential Top City4Lights Vcut by VIDA VIDA Discount Release Dates t8Im3zutIV

no is the default restart policy, and it does not restart a container under any circumstance. When always is specified, the container always restarts. The on-failure policy restarts a container if the exit code indicates an on-failure error.

Note : This option is ignored when deploying a stack in swarm mode with a (version 3) Compose file. Use restart_policy instead.

Each of these is a single value, analogous to its Buy Cheap Online Sleeveless Top HUMMINGBIRD VII by VIDA VIDA Looking For Cheap Online Buy Cheap Order Buy Cheap With Credit Card Cheap For Sale jCnM3b

Some configuration options, such as the interval and timeout sub-options for loose track trousers Black Frankie Morello Clearance Online Fake 72rOnu24
, accept a duration as a string in a format that looks like this:

The supported units are us , ms , s , m and h .

Some configuration options, such as the shm_size sub-option for Sophia camisole Pink amp; Purple Gilda amp; Pearl Cheap High Quality Cheap View The Cheapest Sale Online Clearance Visa Payment Cheap Authentic BVxNPze
, accept a byte value as a string in a format that looks like this:

The supported units are b , k , m and g , and their alternative notation kb , mb and gb . Decimal values are not supported at this time.

While it is possible to declare Amazon Online Inexpensive Ago trousers Black Max Mara Recommend Cheap Online Outlet Finishline ziT9RURRmX
on the file as part of the service declaration, this section allows you to create named volumes (without relying on volumes_from ) that can be reused across multiple services, and are easily retrieved and inspected using the docker command line or API. See the DRESSES 3/4 length dresses Derek Lam Buy Cheap Collections Outlet Deals Cheap Discounts 6nNTYqWJ
subcommand documentation for more information.

See Use volumes and Angora long cardigan Lanvin Sale Manchester UX7z0
for general information on volumes.

Here’s an example of a two-service setup where a database’s data directory is shared with another service as a volume so that it can be periodically backed up:

An entry under the top-level volumes key can be empty, in which case it uses the default driver configured by the Engine (in most cases, this is the local driver). Optionally, you can configure it with the following keys:

Specify which volume driver should be used for this volume. Defaults to whatever driver the Docker Engine has been configured to use, which in most cases is local . If the driver is not available, the Engine returns an error when docker-compose up tries to create the volume.

80.8Use Two EntityManagers

Even if the default EntityManagerFactory works fine, you need to define a new one. Otherwise, the presence of the second bean of that type switches off the default. To make it easy to do, you can use the convenient EntityManagerBuilder provided by Spring Boot. Alternatively, you can just the LocalContainerEntityManagerFactoryBean directly from Spring ORM, as shown in the following example:

The configuration above almost works on its own. To complete the picture, you need to configure TransactionManagers for the two EntityManagers as well. If you mark one of them as @Primary , it could be picked up by the default JpaTransactionManager in Spring Boot. The other would have to be explicitly injected into a new instance. Alternatively, you might be able to use a JTA transaction manager that spans both.

If you use Spring Data, you need to configure @EnableJpaRepositories accordingly, as shown in the following example:

80.9Use a Traditional File

Spring Boot will not search for or use a META-INF/persistence.xml by default. If you prefer to use a traditional persistence.xml , you need to define your own @Bean of type LocalEntityManagerFactoryBean (with an ID of ‘entityManagerFactory’) and set the persistence unit name there.

See JpaBaseConfiguration for the default settings.

80.10Use Spring Data JPA and Mongo Repositories

Spring Data JPA and Spring Data Mongo can both automatically create Repository implementations for you. If they are both present on the classpath, you might have to do some extra configuration to tell Spring Boot which repositories to create. The most explicit way to do that is to use the standard Spring Data @EnableJpaRepositories and @EnableMongoRepositories annotations and provide the location of your Repository interfaces.

There are also flags (*.repositories.enabled and*.repositories.type ) that you can use to switch the auto-configured repositories on and off in external configuration. Doing so is useful, for instance, in case you want to switch off the Mongo repositories and still use the auto-configured MongoTemplate .

The same obstacle and the same features exist for other auto-configured Spring Data repository types (Elasticsearch, Solr, and others). To work with them, change the names of the annotations and flags accordingly.

80.11Expose Spring Data Repositories as REST Endpoint

Spring Data REST can expose the Repository implementations as REST endpoints for you, provided Spring MVC has been enabled for the application.

Spring Boot exposes a set of useful properties (from the namespace) that customize the RepositoryRestConfiguration . If you need to provide additional customization, you should use a RepositoryRestConfigurer bean.

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In this paper we present for the first time a relocalisation method for keyframe-based SLAM that can deal with severe viewpoint change, at frame-rate, in maps containing thousands of keyframes. As this method relies on local features, it permits the interoperability between cameras, allowing a camera to relocalise in a map built by a different camera. We also perform loop closing (detection + correction), at keyframerate, in loops containing hundreds of keyframes. For both relocalisation and loop closing, we propose a bag of words place recognizer with ORB features, which is able to recognize places spending less than 39 ms, including feature extraction, in databases containing 10K images (without geometrical verification). We evaluate the performance of this recognizer in four different datasets, achieving high recall and no false matches, and getting better results than the state-of-art in place recognition, being one order of magnitude faster.
Published in: 2014 IEEE International Conference on Robotics and Automation (ICRA)
Date of Conference: 31 May-7 June 2014
Date Added to IEEE : 29 September 2014
ISBN Information:
Electronic ISBN: 978-1-4799-3685-4
ISSN Information:
Print ISSN: 1050-4729
INSPEC Accession Number: 14616536
DOI: 10.1109/ICRA.2014.6906953
Publisher: IEEE
Conference Location: Hong Kong, China

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