https://loizenai.com Programming Tutorial
1619693852
How to use Spring Kafka JsonSerializer (JsonDeserializer) to produce/consume Java Object
How to use Spring Kafka JsonSerializer (JsonDeserializer) to produce/consume Java Object messages
In the previous post, we had setup a Spring Kafka Application succesfully by explicitly configuration Kafka Factories with SpringBoot. But the messages had been used have String type. While in the development, POJO (Plain Old Java Object) are often used to construct messages. So with the tutorial, JavaSampleApproach will show how to use Spring Kafka JsonSerializer (JsonDeserializer) to produce/consume Java Object messages.
Related Articles:
- How to start Spring Kafka Application with Spring Boot
- How to start Spring Apache Kafka Application with SpringBoot Auto-Configuration
I. Technologies
– Java 8 – Maven build – Spring Boot – Apache Kafka – Spring Tool Suite editorII. Overview
In the tutorial, we send and receive Java object messages to/from Apache Kafka, so ProducerFactory uses JsonSerializer.class and ConsumerFactory uses JsonDeserializer.class to serialize/deserialize Java objects to Json bytes.
- KafkaProducerConfig:
@Bean
public ProducerFactory producerFactory() {
...
configProps.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, JsonSerializer.class);
return new DefaultKafkaProducerFactory<>(configProps);
}
@Bean
public KafkaTemplate kafkaTemplate() {
return new KafkaTemplate<>(producerFactory());
}
- KafkaConsumerConfig:
More at:
How to use Spring Kafka JsonSerializer (JsonDeserializer) to produce/consume Java Object messages
#spring kafka #jsonserializer #jsondeserializer
What is GEEK
Buddha Community
1654075127
Enhance Amazon Aurora Read/Write Capability with ShardingSphere-JDBC
1. Introduction
Amazon Aurora is a relational database management system (RDBMS) developed by AWS(Amazon Web Services). Aurora gives you the performance and availability of commercial-grade databases with full MySQL and PostgreSQL compatibility. In terms of high performance, Aurora MySQL and Aurora PostgreSQL have shown an increase in throughput of up to 5X over stock MySQL and 3X over stock PostgreSQL respectively on similar hardware. In terms of scalability, Aurora achieves enhancements and innovations in storage and computing, horizontal and vertical functions.
Aurora supports up to 128TB of storage capacity and supports dynamic scaling of storage layer in units of 10GB. In terms of computing, Aurora supports scalable configurations for multiple read replicas. Each region can have an additional 15 Aurora replicas. In addition, Aurora provides multi-primary architecture to support four read/write nodes. Its Serverless architecture allows vertical scaling and reduces typical latency to under a second, while the Global Database enables a single database cluster to span multiple AWS Regions in low latency.
Aurora already provides great scalability with the growth of user data volume. Can it handle more data and support more concurrent access? You may consider using sharding to support the configuration of multiple underlying Aurora clusters. To this end, a series of blogs, including this one, provides you with a reference in choosing between Proxy and JDBC for sharding.
1.1 Why sharding is needed
AWS Aurora offers a single relational database. Primary-secondary, multi-primary, and global database, and other forms of hosting architecture can satisfy various architectural scenarios above. However, Aurora doesn’t provide direct support for sharding scenarios, and sharding has a variety of forms, such as vertical and horizontal forms. If we want to further increase data capacity, some problems have to be solved, such as cross-node database Join, associated query, distributed transactions, SQL sorting, page turning, function calculation, database global primary key, capacity planning, and secondary capacity expansion after sharding.
1.2 Sharding methods
It is generally accepted that when the capacity of a MySQL table is less than 10 million, the time spent on queries is optimal because at this time the height of its BTREE index is between 3 and 5. Data sharding can reduce the amount of data in a single table and distribute the read and write loads to different data nodes at the same time. Data sharding can be divided into vertical sharding and horizontal sharding.
1. Advantages of vertical sharding
- Address the coupling of business system and make clearer.
- Implement hierarchical management, maintenance, monitoring, and expansion to data of different businesses, like micro-service governance.
- In high concurrency scenarios, vertical sharding removes the bottleneck of IO, database connections, and hardware resources on a single machine to some extent.
2. Disadvantages of vertical sharding
- After splitting the library,
Joincan only be implemented by interface aggregation, which will increase the complexity of development. - After splitting the library, it is complex to process distributed transactions.
- There is a large amount of data on a single table and horizontal sharding is required.
3. Advantages of horizontal sharding
- There is no such performance bottleneck as a large amount of data on a single database and high concurrency, and it increases system stability and load capacity.
- The business modules do not need to be split due to minor modification on the application client.
4. Disadvantages of horizontal sharding
- Transaction consistency across shards is hard to be guaranteed;
- The performance of associated query in cross-library
Joinis poor. - It’s difficult to scale the data many times and maintenance is a big workload.
Based on the analysis above, and the available studis on popular sharding middleware, we selected ShardingSphere, an open source product, combined with Amazon Aurora to introduce how the combination of these two products meets various forms of sharding and how to solve the problems brought by sharding.
ShardingSphere is an open source ecosystem including a set of distributed database middleware solutions, including 3 independent products, Sharding-JDBC, Sharding-Proxy & Sharding-Sidecar.
2. ShardingSphere introduction:
The characteristics of Sharding-JDBC are:
- With the client end connecting directly to the database, it provides service in the form of jar and requires no extra deployment and dependence.
- It can be considered as an enhanced JDBC driver, which is fully compatible with JDBC and all kinds of ORM frameworks.
- Applicable in any ORM framework based on JDBC, such as JPA, Hibernate, Mybatis, Spring JDBC Template or direct use of JDBC.
- Support any third-party database connection pool, such as DBCP, C3P0, BoneCP, Druid, HikariCP;
- Support any kind of JDBC standard database: MySQL, Oracle, SQLServer, PostgreSQL and any databases accessible to JDBC.
- Sharding-JDBC adopts decentralized architecture, applicable to high-performance light-weight OLTP application developed with Java
Hybrid Structure Integrating Sharding-JDBC and Applications
Sharding-JDBC’s core concepts
Data node: The smallest unit of a data slice, consisting of a data source name and a data table, such as ds_0.product_order_0.
Actual table: The physical table that really exists in the horizontal sharding database, such as product order tables: product_order_0, product_order_1, and product_order_2.
Logic table: The logical name of the horizontal sharding databases (tables) with the same schema. For instance, the logic table of the order product_order_0, product_order_1, and product_order_2 is product_order.
Binding table: It refers to the primary table and the joiner table with the same sharding rules. For example, product_order table and product_order_item are sharded by order_id, so they are binding tables with each other. Cartesian product correlation will not appear in the multi-tables correlating query, so the query efficiency will increase greatly.
Broadcast table: It refers to tables that exist in all sharding database sources. The schema and data must consist in each database. It can be applied to the small data volume that needs to correlate with big data tables to query, dictionary table and configuration table for example.
3. Testing ShardingSphere-JDBC
3.1 Example project
Download the example project code locally. In order to ensure the stability of the test code, we choose shardingsphere-example-4.0.0 version.
git clone https://github.com/apache/shardingsphere-example.git
Project description：
shardingsphere-example
├── example-core
│ ├── config-utility
│ ├── example-api
│ ├── example-raw-jdbc
│ ├── example-spring-jpa #spring+jpa integration-based entity，repository
│ └── example-spring-mybatis
├── sharding-jdbc-example
│ ├── sharding-example
│ │ ├── sharding-raw-jdbc-example
│ │ ├── sharding-spring-boot-jpa-example #integration-based sharding-jdbc functions
│ │ ├── sharding-spring-boot-mybatis-example
│ │ ├── sharding-spring-namespace-jpa-example
│ │ └── sharding-spring-namespace-mybatis-example
│ ├── orchestration-example
│ │ ├── orchestration-raw-jdbc-example
│ │ ├── orchestration-spring-boot-example #integration-based sharding-jdbc governance function
│ │ └── orchestration-spring-namespace-example
│ ├── transaction-example
│ │ ├── transaction-2pc-xa-example #sharding-jdbc sample of two-phase commit for a distributed transaction
│ │ └──transaction-base-seata-example #sharding-jdbc distributed transaction seata sample
│ ├── other-feature-example
│ │ ├── hint-example
│ │ └── encrypt-example
├── sharding-proxy-example
│ └── sharding-proxy-boot-mybatis-example
└── src/resources
└── manual_schema.sql
Configuration file description:
application-master-slave.properties #read/write splitting profile
application-sharding-databases-tables.properties #sharding profile
application-sharding-databases.properties #library split profile only
application-sharding-master-slave.properties #sharding and read/write splitting profile
application-sharding-tables.properties #table split profile
application.properties #spring boot profile
Code logic description:
The following is the entry class of the Spring Boot application below. Execute it to run the project.
The execution logic of demo is as follows:
3.2 Verifying read/write splitting
As business grows, the write and read requests can be split to different database nodes to effectively promote the processing capability of the entire database cluster. Aurora uses a reader/writer endpoint to meet users' requirements to write and read with strong consistency, and a read-only endpoint to meet the requirements to read without strong consistency. Aurora's read and write latency is within single-digit milliseconds, much lower than MySQL's binlog-based logical replication, so there's a lot of loads that can be directed to a read-only endpoint.
Through the one primary and multiple secondary configuration, query requests can be evenly distributed to multiple data replicas, which further improves the processing capability of the system. Read/write splitting can improve the throughput and availability of system, but it can also lead to data inconsistency. Aurora provides a primary/secondary architecture in a fully managed form, but applications on the upper-layer still need to manage multiple data sources when interacting with Aurora, routing SQL requests to different nodes based on the read/write type of SQL statements and certain routing policies.
ShardingSphere-JDBC provides read/write splitting features and it is integrated with application programs so that the complex configuration between application programs and database clusters can be separated from application programs. Developers can manage the Shard through configuration files and combine it with ORM frameworks such as Spring JPA and Mybatis to completely separate the duplicated logic from the code, which greatly improves the ability to maintain code and reduces the coupling between code and database.
3.2.1 Setting up the database environment
Create a set of Aurora MySQL read/write splitting clusters. The model is db.r5.2xlarge. Each set of clusters has one write node and two read nodes.
3.2.2 Configuring Sharding-JDBC
application.properties spring boot Master profile description:
You need to replace the green ones with your own environment configuration.
# Jpa automatically creates and drops data tables based on entities
spring.jpa.properties.hibernate.hbm2ddl.auto=create-drop
spring.jpa.properties.hibernate.dialect=org.hibernate.dialect.MySQL5Dialect
spring.jpa.properties.hibernate.show_sql=true
#spring.profiles.active=sharding-databases
#spring.profiles.active=sharding-tables
#spring.profiles.active=sharding-databases-tables
#Activate master-slave configuration item so that sharding-jdbc can use master-slave profile
spring.profiles.active=master-slave
#spring.profiles.active=sharding-master-slave
application-master-slave.properties sharding-jdbc profile description:
spring.shardingsphere.datasource.names=ds_master,ds_slave_0,ds_slave_1
# data souce-master
spring.shardingsphere.datasource.ds_master.driver-class-name=com.mysql.jdbc.Driver
spring.shardingsphere.datasource.ds_master.password=Your master DB password
spring.shardingsphere.datasource.ds_master.type=com.zaxxer.hikari.HikariDataSource
spring.shardingsphere.datasource.ds_master.jdbc-url=Your primary DB data sourceurl spring.shardingsphere.datasource.ds_master.username=Your primary DB username
# data source-slave
spring.shardingsphere.datasource.ds_slave_0.driver-class-name=com.mysql.jdbc.Driver
spring.shardingsphere.datasource.ds_slave_0.password= Your slave DB password
spring.shardingsphere.datasource.ds_slave_0.type=com.zaxxer.hikari.HikariDataSource
spring.shardingsphere.datasource.ds_slave_0.jdbc-url=Your slave DB data source url
spring.shardingsphere.datasource.ds_slave_0.username= Your slave DB username
# data source-slave
spring.shardingsphere.datasource.ds_slave_1.driver-class-name=com.mysql.jdbc.Driver
spring.shardingsphere.datasource.ds_slave_1.password= Your slave DB password
spring.shardingsphere.datasource.ds_slave_1.type=com.zaxxer.hikari.HikariDataSource
spring.shardingsphere.datasource.ds_slave_1.jdbc-url= Your slave DB data source url
spring.shardingsphere.datasource.ds_slave_1.username= Your slave DB username
# Routing Policy Configuration
spring.shardingsphere.masterslave.load-balance-algorithm-type=round_robin
spring.shardingsphere.masterslave.name=ds_ms
spring.shardingsphere.masterslave.master-data-source-name=ds_master
spring.shardingsphere.masterslave.slave-data-source-names=ds_slave_0,ds_slave_1
# sharding-jdbc configures the information storage mode
spring.shardingsphere.mode.type=Memory
# start shardingsphere log，and you can see the conversion from logical SQL to actual SQL from the print
spring.shardingsphere.props.sql.show=true
3.2.3 Test and verification process description
- Test environment data initialization: Spring JPA initialization automatically creates tables for testing.
- Write data to the master instance
As shown in the ShardingSphere-SQL log figure below, the write SQL is executed on the ds_master data source.
- Data query operations are performed on the slave library.
As shown in the ShardingSphere-SQL log figure below, the read SQL is executed on the ds_slave data source in the form of polling.
[INFO ] 2022-04-02 19:43:39,376 --main-- [ShardingSphere-SQL] Rule Type: master-slave
[INFO ] 2022-04-02 19:43:39,376 --main-- [ShardingSphere-SQL] SQL: select orderentit0_.order_id as order_id1_1_, orderentit0_.address_id as address_2_1_,
orderentit0_.status as status3_1_, orderentit0_.user_id as user_id4_1_ from t_order orderentit0_ ::: DataSources: ds_slave_0
---------------------------- Print OrderItem Data -------------------
Hibernate: select orderiteme1_.order_item_id as order_it1_2_, orderiteme1_.order_id as order_id2_2_, orderiteme1_.status as status3_2_, orderiteme1_.user_id
as user_id4_2_ from t_order orderentit0_ cross join t_order_item orderiteme1_ where orderentit0_.order_id=orderiteme1_.order_id
[INFO ] 2022-04-02 19:43:40,898 --main-- [ShardingSphere-SQL] Rule Type: master-slave
[INFO ] 2022-04-02 19:43:40,898 --main-- [ShardingSphere-SQL] SQL: select orderiteme1_.order_item_id as order_it1_2_, orderiteme1_.order_id as order_id2_2_, orderiteme1_.status as status3_2_,
orderiteme1_.user_id as user_id4_2_ from t_order orderentit0_ cross join t_order_item orderiteme1_ where orderentit0_.order_id=orderiteme1_.order_id ::: DataSources: ds_slave_1
Note: As shown in the figure below, if there are both reads and writes in a transaction, Sharding-JDBC routes both read and write operations to the master library. If the read/write requests are not in the same transaction, the corresponding read requests are distributed to different read nodes according to the routing policy.
@Override
@Transactional // When a transaction is started, both read and write in the transaction go through the master library. When closed, read goes through the slave library and write goes through the master library
public void processSuccess() throws SQLException {
System.out.println("-------------- Process Success Begin ---------------");
List<Long> orderIds = insertData();
printData();
deleteData(orderIds);
printData();
System.out.println("-------------- Process Success Finish --------------");
}
3.2.4 Verifying Aurora failover scenario
The Aurora database environment adopts the configuration described in Section 2.2.1.
3.2.4.1 Verification process description
- Start the
Spring-Bootproject
2. Perform a failover on Aurora’s console
3. Execute the Rest API request
4. Repeatedly execute POST (http://localhost:8088/save-user) until the call to the API failed to write to Aurora and eventually recovered successfully.
5. The following figure shows the process of executing code failover. It takes about 37 seconds from the time when the latest SQL write is successfully performed to the time when the next SQL write is successfully performed. That is, the application can be automatically recovered from Aurora failover, and the recovery time is about 37 seconds.
3.3 Testing table sharding-only function
3.3.1 Configuring Sharding-JDBC
application.properties spring boot master profile description
# Jpa automatically creates and drops data tables based on entities
spring.jpa.properties.hibernate.hbm2ddl.auto=create-drop
spring.jpa.properties.hibernate.dialect=org.hibernate.dialect.MySQL5Dialect
spring.jpa.properties.hibernate.show_sql=true
#spring.profiles.active=sharding-databases
#Activate sharding-tables configuration items
#spring.profiles.active=sharding-tables
#spring.profiles.active=sharding-databases-tables
# spring.profiles.active=master-slave
#spring.profiles.active=sharding-master-slave
application-sharding-tables.properties sharding-jdbc profile description
## configure primary-key policy
spring.shardingsphere.sharding.tables.t_order.key-generator.column=order_id
spring.shardingsphere.sharding.tables.t_order.key-generator.type=SNOWFLAKE
spring.shardingsphere.sharding.tables.t_order.key-generator.props.worker.id=123
spring.shardingsphere.sharding.tables.t_order_item.actual-data-nodes=ds.t_order_item_$->{0..1}
spring.shardingsphere.sharding.tables.t_order_item.table-strategy.inline.sharding-column=order_id
spring.shardingsphere.sharding.tables.t_order_item.table-strategy.inline.algorithm-expression=t_order_item_$->{order_id % 2}
spring.shardingsphere.sharding.tables.t_order_item.key-generator.column=order_item_id
spring.shardingsphere.sharding.tables.t_order_item.key-generator.type=SNOWFLAKE
spring.shardingsphere.sharding.tables.t_order_item.key-generator.props.worker.id=123
# configure the binding relation of t_order and t_order_item
spring.shardingsphere.sharding.binding-tables[0]=t_order,t_order_item
# configure broadcast tables
spring.shardingsphere.sharding.broadcast-tables=t_address
# sharding-jdbc mode
spring.shardingsphere.mode.type=Memory
# start shardingsphere log
spring.shardingsphere.props.sql.show=true
3.3.2 Test and verification process description
1. DDL operation
JPA automatically creates tables for testing. When Sharding-JDBC routing rules are configured, the client executes DDL, and Sharding-JDBC automatically creates corresponding tables according to the table splitting rules. If t_address is a broadcast table, create a t_address because there is only one master instance. Two physical tables t_order_0 and t_order_1 will be created when creating t_order.
2. Write operation
As shown in the figure below, Logic SQL inserts a record into t_order. When Sharding-JDBC is executed, data will be distributed to t_order_0 and t_order_1 according to the table splitting rules.
When t_order and t_order_item are bound, the records associated with order_item and order are placed on the same physical table.
3. Read operation
As shown in the figure below, perform the join query operations to order and order_item under the binding table, and the physical shard is precisely located based on the binding relationship.
The join query operations on order and order_item under the unbound table will traverse all shards.
3.4 Testing database sharding-only function
3.4.1 Setting up the database environment
Create two instances on Aurora: ds_0 and ds_1
When the sharding-spring-boot-jpa-example project is started, tables t_order, t_order_item，t_address will be created on two Aurora instances.
3.4.2 Configuring Sharding-JDBC
application.properties springboot master profile description
# Jpa automatically creates and drops data tables based on entities
spring.jpa.properties.hibernate.hbm2ddl.auto=create
spring.jpa.properties.hibernate.dialect=org.hibernate.dialect.MySQL5Dialect
spring.jpa.properties.hibernate.show_sql=true
# Activate sharding-databases configuration items
spring.profiles.active=sharding-databases
#spring.profiles.active=sharding-tables
#spring.profiles.active=sharding-databases-tables
#spring.profiles.active=master-slave
#spring.profiles.active=sharding-master-slave
application-sharding-databases.properties sharding-jdbc profile description
spring.shardingsphere.datasource.names=ds_0,ds_1
# ds_0
spring.shardingsphere.datasource.ds_0.type=com.zaxxer.hikari.HikariDataSource
spring.shardingsphere.datasource.ds_0.driver-class-name=com.mysql.jdbc.Driver
spring.shardingsphere.datasource.ds_0.jdbc-url= spring.shardingsphere.datasource.ds_0.username=
spring.shardingsphere.datasource.ds_0.password=
# ds_1
spring.shardingsphere.datasource.ds_1.type=com.zaxxer.hikari.HikariDataSource
spring.shardingsphere.datasource.ds_1.driver-class-name=com.mysql.jdbc.Driver
spring.shardingsphere.datasource.ds_1.jdbc-url=
spring.shardingsphere.datasource.ds_1.username=
spring.shardingsphere.datasource.ds_1.password=
spring.shardingsphere.sharding.default-database-strategy.inline.sharding-column=user_id
spring.shardingsphere.sharding.default-database-strategy.inline.algorithm-expression=ds_$->{user_id % 2}
spring.shardingsphere.sharding.binding-tables=t_order,t_order_item
spring.shardingsphere.sharding.broadcast-tables=t_address
spring.shardingsphere.sharding.default-data-source-name=ds_0
spring.shardingsphere.sharding.tables.t_order.actual-data-nodes=ds_$->{0..1}.t_order
spring.shardingsphere.sharding.tables.t_order.key-generator.column=order_id
spring.shardingsphere.sharding.tables.t_order.key-generator.type=SNOWFLAKE
spring.shardingsphere.sharding.tables.t_order.key-generator.props.worker.id=123
spring.shardingsphere.sharding.tables.t_order_item.actual-data-nodes=ds_$->{0..1}.t_order_item
spring.shardingsphere.sharding.tables.t_order_item.key-generator.column=order_item_id
spring.shardingsphere.sharding.tables.t_order_item.key-generator.type=SNOWFLAKE
spring.shardingsphere.sharding.tables.t_order_item.key-generator.props.worker.id=123
# sharding-jdbc mode
spring.shardingsphere.mode.type=Memory
# start shardingsphere log
spring.shardingsphere.props.sql.show=true
3.4.3 Test and verification process description
1. DDL operation
JPA automatically creates tables for testing. When Sharding-JDBC’s library splitting and routing rules are configured, the client executes DDL, and Sharding-JDBC will automatically create corresponding tables according to table splitting rules. If t_address is a broadcast table, physical tables will be created on ds_0 and ds_1. The three tables, t_address, t_order and t_order_item will be created on ds_0 and ds_1 respectively.
2. Write operation
For the broadcast table t_address, each record written will also be written to the t_address tables of ds_0 and ds_1.
The tables t_order and t_order_item of the slave library are written on the table in the corresponding instance according to the slave library field and routing policy.
3. Read operation
Query order is routed to the corresponding Aurora instance according to the routing rules of the slave library .
Query Address. Since address is a broadcast table, an instance of address will be randomly selected and queried from the nodes used.
As shown in the figure below, perform the join query operations to order and order_item under the binding table, and the physical shard is precisely located based on the binding relationship.
3.5 Verifying sharding function
3.5.1 Setting up the database environment
As shown in the figure below, create two instances on Aurora: ds_0 and ds_1
When the sharding-spring-boot-jpa-example project is started, physical tables t_order_01, t_order_02, t_order_item_01，and t_order_item_02 and global table t_address will be created on two Aurora instances.
3.5.2 Configuring Sharding-JDBC
application.properties springboot master profile description
# Jpa automatically creates and drops data tables based on entities
spring.jpa.properties.hibernate.hbm2ddl.auto=create
spring.jpa.properties.hibernate.dialect=org.hibernate.dialect.MySQL5Dialect
spring.jpa.properties.hibernate.show_sql=true
# Activate sharding-databases-tables configuration items
#spring.profiles.active=sharding-databases
#spring.profiles.active=sharding-tables
spring.profiles.active=sharding-databases-tables
#spring.profiles.active=master-slave
#spring.profiles.active=sharding-master-slave
application-sharding-databases.properties sharding-jdbc profile description
spring.shardingsphere.datasource.names=ds_0,ds_1
# ds_0
spring.shardingsphere.datasource.ds_0.type=com.zaxxer.hikari.HikariDataSource
spring.shardingsphere.datasource.ds_0.driver-class-name=com.mysql.jdbc.Driver
spring.shardingsphere.datasource.ds_0.jdbc-url= 306/dev?useSSL=false&characterEncoding=utf-8
spring.shardingsphere.datasource.ds_0.username=
spring.shardingsphere.datasource.ds_0.password=
spring.shardingsphere.datasource.ds_0.max-active=16
# ds_1
spring.shardingsphere.datasource.ds_1.type=com.zaxxer.hikari.HikariDataSource
spring.shardingsphere.datasource.ds_1.driver-class-name=com.mysql.jdbc.Driver
spring.shardingsphere.datasource.ds_1.jdbc-url=
spring.shardingsphere.datasource.ds_1.username=
spring.shardingsphere.datasource.ds_1.password=
spring.shardingsphere.datasource.ds_1.max-active=16
# default library splitting policy
spring.shardingsphere.sharding.default-database-strategy.inline.sharding-column=user_id
spring.shardingsphere.sharding.default-database-strategy.inline.algorithm-expression=ds_$->{user_id % 2}
spring.shardingsphere.sharding.binding-tables=t_order,t_order_item
spring.shardingsphere.sharding.broadcast-tables=t_address
# Tables that do not meet the library splitting policy are placed on ds_0
spring.shardingsphere.sharding.default-data-source-name=ds_0
# t_order table splitting policy
spring.shardingsphere.sharding.tables.t_order.actual-data-nodes=ds_$->{0..1}.t_order_$->{0..1}
spring.shardingsphere.sharding.tables.t_order.table-strategy.inline.sharding-column=order_id
spring.shardingsphere.sharding.tables.t_order.table-strategy.inline.algorithm-expression=t_order_$->{order_id % 2}
spring.shardingsphere.sharding.tables.t_order.key-generator.column=order_id
spring.shardingsphere.sharding.tables.t_order.key-generator.type=SNOWFLAKE
spring.shardingsphere.sharding.tables.t_order.key-generator.props.worker.id=123
# t_order_item table splitting policy
spring.shardingsphere.sharding.tables.t_order_item.actual-data-nodes=ds_$->{0..1}.t_order_item_$->{0..1}
spring.shardingsphere.sharding.tables.t_order_item.table-strategy.inline.sharding-column=order_id
spring.shardingsphere.sharding.tables.t_order_item.table-strategy.inline.algorithm-expression=t_order_item_$->{order_id % 2}
spring.shardingsphere.sharding.tables.t_order_item.key-generator.column=order_item_id
spring.shardingsphere.sharding.tables.t_order_item.key-generator.type=SNOWFLAKE
spring.shardingsphere.sharding.tables.t_order_item.key-generator.props.worker.id=123
# sharding-jdbc mdoe
spring.shardingsphere.mode.type=Memory
# start shardingsphere log
spring.shardingsphere.props.sql.show=true
3.5.3 Test and verification process description
1. DDL operation
JPA automatically creates tables for testing. When Sharding-JDBC’s sharding and routing rules are configured, the client executes DDL, and Sharding-JDBC will automatically create corresponding tables according to table splitting rules. If t_address is a broadcast table, t_address will be created on both ds_0 and ds_1. The three tables, t_address, t_order and t_order_item will be created on ds_0 and ds_1 respectively.
2. Write operation
For the broadcast table t_address, each record written will also be written to the t_address tables of ds_0 and ds_1.
The tables t_order and t_order_item of the sub-library are written to the table on the corresponding instance according to the slave library field and routing policy.
3. Read operation
The read operation is similar to the library split function verification described in section2.4.3.
3.6 Testing database sharding, table sharding and read/write splitting function
3.6.1 Setting up the database environment
The following figure shows the physical table of the created database instance.
3.6.2 Configuring Sharding-JDBC
application.properties spring boot master profile description
# Jpa automatically creates and drops data tables based on entities
spring.jpa.properties.hibernate.hbm2ddl.auto=create
spring.jpa.properties.hibernate.dialect=org.hibernate.dialect.MySQL5Dialect
spring.jpa.properties.hibernate.show_sql=true
# activate sharding-databases-tables configuration items
#spring.profiles.active=sharding-databases
#spring.profiles.active=sharding-tables
#spring.profiles.active=sharding-databases-tables
#spring.profiles.active=master-slave
spring.profiles.active=sharding-master-slave
application-sharding-master-slave.properties sharding-jdbc profile description
The url, name and password of the database need to be changed to your own database parameters.
spring.shardingsphere.datasource.names=ds_master_0,ds_master_1,ds_master_0_slave_0,ds_master_0_slave_1,ds_master_1_slave_0,ds_master_1_slave_1
spring.shardingsphere.datasource.ds_master_0.type=com.zaxxer.hikari.HikariDataSource
spring.shardingsphere.datasource.ds_master_0.driver-class-name=com.mysql.jdbc.Driver
spring.shardingsphere.datasource.ds_master_0.jdbc-url= spring.shardingsphere.datasource.ds_master_0.username=
spring.shardingsphere.datasource.ds_master_0.password=
spring.shardingsphere.datasource.ds_master_0.max-active=16
spring.shardingsphere.datasource.ds_master_0_slave_0.type=com.zaxxer.hikari.HikariDataSource
spring.shardingsphere.datasource.ds_master_0_slave_0.driver-class-name=com.mysql.jdbc.Driver
spring.shardingsphere.datasource.ds_master_0_slave_0.jdbc-url= spring.shardingsphere.datasource.ds_master_0_slave_0.username=
spring.shardingsphere.datasource.ds_master_0_slave_0.password=
spring.shardingsphere.datasource.ds_master_0_slave_0.max-active=16
spring.shardingsphere.datasource.ds_master_0_slave_1.type=com.zaxxer.hikari.HikariDataSource
spring.shardingsphere.datasource.ds_master_0_slave_1.driver-class-name=com.mysql.jdbc.Driver
spring.shardingsphere.datasource.ds_master_0_slave_1.jdbc-url= spring.shardingsphere.datasource.ds_master_0_slave_1.username=
spring.shardingsphere.datasource.ds_master_0_slave_1.password=
spring.shardingsphere.datasource.ds_master_0_slave_1.max-active=16
spring.shardingsphere.datasource.ds_master_1.type=com.zaxxer.hikari.HikariDataSource
spring.shardingsphere.datasource.ds_master_1.driver-class-name=com.mysql.jdbc.Driver
spring.shardingsphere.datasource.ds_master_1.jdbc-url=
spring.shardingsphere.datasource.ds_master_1.username=
spring.shardingsphere.datasource.ds_master_1.password=
spring.shardingsphere.datasource.ds_master_1.max-active=16
spring.shardingsphere.datasource.ds_master_1_slave_0.type=com.zaxxer.hikari.HikariDataSource
spring.shardingsphere.datasource.ds_master_1_slave_0.driver-class-name=com.mysql.jdbc.Driver
spring.shardingsphere.datasource.ds_master_1_slave_0.jdbc-url=
spring.shardingsphere.datasource.ds_master_1_slave_0.username=
spring.shardingsphere.datasource.ds_master_1_slave_0.password=
spring.shardingsphere.datasource.ds_master_1_slave_0.max-active=16
spring.shardingsphere.datasource.ds_master_1_slave_1.type=com.zaxxer.hikari.HikariDataSource
spring.shardingsphere.datasource.ds_master_1_slave_1.driver-class-name=com.mysql.jdbc.Driver
spring.shardingsphere.datasource.ds_master_1_slave_1.jdbc-url= spring.shardingsphere.datasource.ds_master_1_slave_1.username=admin
spring.shardingsphere.datasource.ds_master_1_slave_1.password=
spring.shardingsphere.datasource.ds_master_1_slave_1.max-active=16
spring.shardingsphere.sharding.default-database-strategy.inline.sharding-column=user_id
spring.shardingsphere.sharding.default-database-strategy.inline.algorithm-expression=ds_$->{user_id % 2}
spring.shardingsphere.sharding.binding-tables=t_order,t_order_item
spring.shardingsphere.sharding.broadcast-tables=t_address
spring.shardingsphere.sharding.default-data-source-name=ds_master_0
spring.shardingsphere.sharding.tables.t_order.actual-data-nodes=ds_$->{0..1}.t_order_$->{0..1}
spring.shardingsphere.sharding.tables.t_order.table-strategy.inline.sharding-column=order_id
spring.shardingsphere.sharding.tables.t_order.table-strategy.inline.algorithm-expression=t_order_$->{order_id % 2}
spring.shardingsphere.sharding.tables.t_order.key-generator.column=order_id
spring.shardingsphere.sharding.tables.t_order.key-generator.type=SNOWFLAKE
spring.shardingsphere.sharding.tables.t_order.key-generator.props.worker.id=123
spring.shardingsphere.sharding.tables.t_order_item.actual-data-nodes=ds_$->{0..1}.t_order_item_$->{0..1}
spring.shardingsphere.sharding.tables.t_order_item.table-strategy.inline.sharding-column=order_id
spring.shardingsphere.sharding.tables.t_order_item.table-strategy.inline.algorithm-expression=t_order_item_$->{order_id % 2}
spring.shardingsphere.sharding.tables.t_order_item.key-generator.column=order_item_id
spring.shardingsphere.sharding.tables.t_order_item.key-generator.type=SNOWFLAKE
spring.shardingsphere.sharding.tables.t_order_item.key-generator.props.worker.id=123
# master/slave data source and slave data source configuration
spring.shardingsphere.sharding.master-slave-rules.ds_0.master-data-source-name=ds_master_0
spring.shardingsphere.sharding.master-slave-rules.ds_0.slave-data-source-names=ds_master_0_slave_0, ds_master_0_slave_1
spring.shardingsphere.sharding.master-slave-rules.ds_1.master-data-source-name=ds_master_1
spring.shardingsphere.sharding.master-slave-rules.ds_1.slave-data-source-names=ds_master_1_slave_0, ds_master_1_slave_1
# sharding-jdbc mode
spring.shardingsphere.mode.type=Memory
# start shardingsphere log
spring.shardingsphere.props.sql.show=true
3.6.3 Test and verification process description
1. DDL operation
JPA automatically creates tables for testing. When Sharding-JDBC’s library splitting and routing rules are configured, the client executes DDL, and Sharding-JDBC will automatically create corresponding tables according to table splitting rules. If t_address is a broadcast table, t_address will be created on both ds_0 and ds_1. The three tables, t_address, t_order and t_order_item will be created on ds_0 and ds_1 respectively.
2. Write operation
For the broadcast table t_address, each record written will also be written to the t_address tables of ds_0 and ds_1.
The tables t_order and t_order_item of the slave library are written to the table on the corresponding instance according to the slave library field and routing policy.
3. Read operation
The join query operations on order and order_item under the binding table are shown below.
3. Conclusion
As an open source product focusing on database enhancement, ShardingSphere is pretty good in terms of its community activitiy, product maturity and documentation richness.
Among its products, ShardingSphere-JDBC is a sharding solution based on the client-side, which supports all sharding scenarios. And there’s no need to introduce an intermediate layer like Proxy, so the complexity of operation and maintenance is reduced. Its latency is theoretically lower than Proxy due to the lack of intermediate layer. In addition, ShardingSphere-JDBC can support a variety of relational databases based on SQL standards such as MySQL/PostgreSQL/Oracle/SQL Server, etc.
However, due to the integration of Sharding-JDBC with the application program, it only supports Java language for now, and is strongly dependent on the application programs. Nevertheless, Sharding-JDBC separates all sharding configuration from the application program, which brings relatively small changes when switching to other middleware.
In conclusion, Sharding-JDBC is a good choice if you use a Java-based system and have to to interconnect with different relational databases — and don’t want to bother with introducing an intermediate layer.
Author
Sun Jinhua
A senior solution architect at AWS, Sun is responsible for the design and consult on cloud architecture. for providing customers with cloud-related design and consulting services. Before joining AWS, he ran his own business, specializing in building e-commerce platforms and designing the overall architecture for e-commerce platforms of automotive companies. He worked in a global leading communication equipment company as a senior engineer, responsible for the development and architecture design of multiple subsystems of LTE equipment system. He has rich experience in architecture design with high concurrency and high availability system, microservice architecture design, database, middleware, IOT etc.
1650636000
Dl4clj: Clojure Wrapper for Deeplearning4j.
dl4clj
Port of deeplearning4j to clojure
Contact info
If you have any questions,
- my email is will@yetanalytics.com
- I'm will_hoyt in the clojurians slack
- twitter is @FeLungz (don't check very often)
TODO
- update examples dir
- finish README
- add in examples using Transfer Learning
- finish tests
- eval is missing regression tests, roc tests
- nn-test is missing regression tests
- spark tests need to be redone
- need dl4clj.core tests
- revist spark for updates
- write specs for user facing functions
- this is very important, match isnt strict for maps
- provides 100% certianty of the input -> output flow
- check the args as they come in, dispatch once I know its safe, test the pure output
- collapse overlapping api namespaces
- add to core use case flows
Features
Stable Features with tests
- Neural Networks DSL
- Early Stopping Training
- Transfer Learning
- Evaluation
- Data import
Features being worked on for 0.1.0
- Clustering (testing in progress)
- Spark (currently being refactored)
- Front End (maybe current release, maybe future release. Not sure yet)
- Version of dl4j is 0.0.8 in this project. Current dl4j version is 0.0.9
- Parallelism
- Kafka support
- Other items mentioned in TODO
Features being worked on for future releases
- NLP
- Computational Graphs
- Reinforement Learning
- Arbiter
Artifacts
NOT YET RELEASED TO CLOJARS
- fork or clone to try it out
If using Maven add the following repository definition to your pom.xml:
<repository>
<id>clojars.org</id>
<url>http://clojars.org/repo</url>
</repository>
Latest release
With Leiningen:
n/a
With Maven:
n/a
<dependency>
<groupId>_</groupId>
<artifactId>_</artifactId>
<version>_</version>
</dependency>
Usage
Things you need to know
All functions for creating dl4j objects return code by default
- All of these functions have an option to return the dl4j object
- :as-code? = false
- This because all builders require the code representation of dl4j objects
- this requirement is not going to change
- INDarray creation fns default to objects, this is for convenience
- :as-code? is still respected
API functions return code when all args are provided as code
API functions return the value of calling the wrapped method when args are provided as a mixture of objects and code or just objects
The tests are there to help clarify behavior, if you are unsure of how to use a fn, search the tests
- for questions about spark, refer to the spark section bellow
Example of obj/code duality
(ns my.ns
(:require [dl4clj.nn.conf.builders.layers :as l]))
;; as code (the default)
(l/dense-layer-builder
:activation-fn :relu
:learning-rate 0.006
:weight-init :xavier
:layer-name "example layer"
:n-in 10
:n-out 1)
;; =>
(doto
(org.deeplearning4j.nn.conf.layers.DenseLayer$Builder.)
(.nOut 1)
(.activation (dl4clj.constants/value-of {:activation-fn :relu}))
(.weightInit (dl4clj.constants/value-of {:weight-init :xavier}))
(.nIn 10)
(.name "example layer")
(.learningRate 0.006))
;; as an object
(l/dense-layer-builder
:activation-fn :relu
:learning-rate 0.006
:weight-init :xavier
:layer-name "example layer"
:n-in 10
:n-out 1
:as-code? false)
;; =>
#object[org.deeplearning4j.nn.conf.layers.DenseLayer 0x69d7d160 "DenseLayer(super=FeedForwardLayer(super=Layer(layerName=example layer, activationFn=relu, weightInit=XAVIER, biasInit=NaN, dist=null, learningRate=0.006, biasLearningRate=NaN, learningRateSchedule=null, momentum=NaN, momentumSchedule=null, l1=NaN, l2=NaN, l1Bias=NaN, l2Bias=NaN, dropOut=NaN, updater=null, rho=NaN, epsilon=NaN, rmsDecay=NaN, adamMeanDecay=NaN, adamVarDecay=NaN, gradientNormalization=null, gradientNormalizationThreshold=NaN), nIn=10, nOut=1))"]
General usage examples
Importing data
Loading data from a file (here its a csv)
(ns my.ns
(:require [dl4clj.datasets.input-splits :as s]
[dl4clj.datasets.record-readers :as rr]
[dl4clj.datasets.api.record-readers :refer :all]
[dl4clj.datasets.iterators :as ds-iter]
[dl4clj.datasets.api.iterators :refer :all]
[dl4clj.helpers :refer [data-from-iter]]))
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; file splits (convert the data to records)
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
(def poker-path "resources/poker-hand-training.csv")
;; this is not a complete dataset, it is just here to sever as an example
(def file-split (s/new-filesplit :path poker-path))
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; record readers, (read the records created by the file split)
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
(def csv-rr (initialize-rr! :rr (rr/new-csv-record-reader :skip-n-lines 0 :delimiter ",")
:input-split file-split))
;; lets look at some data
(println (next-record! :rr csv-rr :as-code? false))
;; => #object[java.util.ArrayList 0x2473e02d [1, 10, 1, 11, 1, 13, 1, 12, 1, 1, 9]]
;; this is our first line from the csv
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; record readers dataset iterators (turn our writables into a dataset)
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
(def rr-ds-iter (ds-iter/new-record-reader-dataset-iterator
:record-reader csv-rr
:batch-size 1
:label-idx 10
:n-possible-labels 10))
;; we use our record reader created above
;; we want to see one example per dataset obj returned (:batch-size = 1)
;; we know our label is at the last index, so :label-idx = 10
;; there are 10 possible types of poker hands so :n-possible-labels = 10
;; you can also set :label-idx to -1 to use the last index no matter the size of the seq
(def other-rr-ds-iter (ds-iter/new-record-reader-dataset-iterator
:record-reader csv-rr
:batch-size 1
:label-idx -1
:n-possible-labels 10))
(str (next-example! :iter rr-ds-iter :as-code? false))
;; =>
;;===========INPUT===================
;;[1.00, 10.00, 1.00, 11.00, 1.00, 13.00, 1.00, 12.00, 1.00, 1.00]
;;=================OUTPUT==================
;;[0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 1.00]
;; and to show that :label-idx = -1 gives us the same output
(= (next-example! :iter rr-ds-iter :as-code? false)
(next-example! :iter other-rr-ds-iter :as-code? false)) ;; => true
INDArrays and Datasets from clojure data structures
(ns my.ns
(:require [nd4clj.linalg.factory.nd4j :refer [vec->indarray matrix->indarray
indarray-of-zeros indarray-of-ones
indarray-of-rand vec-or-matrix->indarray]]
[dl4clj.datasets.new-datasets :refer [new-ds]]
[dl4clj.datasets.api.datasets :refer [as-list]]
[dl4clj.datasets.iterators :refer [new-existing-dataset-iterator]]
[dl4clj.datasets.api.iterators :refer :all]
[dl4clj.datasets.pre-processors :as ds-pp]
[dl4clj.datasets.api.pre-processors :refer :all]
[dl4clj.core :as c]))
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; INDArray creation
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;;TODO: consider defaulting to code
;; can create from a vector
(vec->indarray [1 2 3 4])
;; => #object[org.nd4j.linalg.cpu.nativecpu.NDArray 0x269df212 [1.00, 2.00, 3.00, 4.00]]
;; or from a matrix
(matrix->indarray [[1 2 3 4] [2 4 6 8]])
;; => #object[org.nd4j.linalg.cpu.nativecpu.NDArray 0x20aa7fe1
;; [[1.00, 2.00, 3.00, 4.00], [2.00, 4.00, 6.00, 8.00]]]
;; will fill in spareness with zeros
(matrix->indarray [[1 2 3 4] [2 4 6 8] [10 12]])
;; => #object[org.nd4j.linalg.cpu.nativecpu.NDArray 0x8b7796c
;;[[1.00, 2.00, 3.00, 4.00],
;; [2.00, 4.00, 6.00, 8.00],
;; [10.00, 12.00, 0.00, 0.00]]]
;; can create an indarray of all zeros with specified shape
;; defaults to :rows = 1 :columns = 1
(indarray-of-zeros :rows 3 :columns 2)
;; => #object[org.nd4j.linalg.cpu.nativecpu.NDArray 0x6f586a7e
;;[[0.00, 0.00],
;; [0.00, 0.00],
;; [0.00, 0.00]]]
(indarray-of-zeros) ;; => #object[org.nd4j.linalg.cpu.nativecpu.NDArray 0xe59ffec 0.00]
;; and if only one is supplied, will get a vector of specified length
(indarray-of-zeros :rows 2)
;; => #object[org.nd4j.linalg.cpu.nativecpu.NDArray 0x2899d974 [0.00, 0.00]]
(indarray-of-zeros :columns 2)
;; => #object[org.nd4j.linalg.cpu.nativecpu.NDArray 0xa5b9782 [0.00, 0.00]]
;; same considerations/defaults for indarray-of-ones and indarray-of-rand
(indarray-of-ones :rows 2 :columns 3)
;; => #object[org.nd4j.linalg.cpu.nativecpu.NDArray 0x54f08662 [[1.00, 1.00, 1.00], [1.00, 1.00, 1.00]]]
(indarray-of-rand :rows 2 :columns 3)
;; all values are greater than 0 but less than 1
;; => #object[org.nd4j.linalg.cpu.nativecpu.NDArray 0x2f20293b [[0.85, 0.86, 0.13], [0.94, 0.04, 0.36]]]
;; vec-or-matrix->indarray is built into all functions which require INDArrays
;; so that you can use clojure data structures
;; but you still have the option of passing existing INDArrays
(def example-array (vec-or-matrix->indarray [1 2 3 4]))
;; => #object[org.nd4j.linalg.cpu.nativecpu.NDArray 0x5c44c71f [1.00, 2.00, 3.00, 4.00]]
(vec-or-matrix->indarray example-array)
;; => #object[org.nd4j.linalg.cpu.nativecpu.NDArray 0x607b03b0 [1.00, 2.00, 3.00, 4.00]]
(vec-or-matrix->indarray (indarray-of-rand :rows 2))
;; => #object[org.nd4j.linalg.cpu.nativecpu.NDArray 0x49143b08 [0.76, 0.92]]
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; data-set creation
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
(def ds-with-single-example (new-ds :input [1 2 3 4]
:output [0.0 1.0 0.0]))
(as-list :ds ds-with-single-example :as-code? false)
;; =>
;; #object[java.util.ArrayList 0x5d703d12
;;[===========INPUT===================
;;[1.00, 2.00, 3.00, 4.00]
;;=================OUTPUT==================
;;[0.00, 1.00, 0.00]]]
(def ds-with-multiple-examples (new-ds
:input [[1 2 3 4] [2 4 6 8]]
:output [[0.0 1.0 0.0] [0.0 0.0 1.0]]))
(as-list :ds ds-with-multiple-examples :as-code? false)
;; =>
;;#object[java.util.ArrayList 0x29c7a9e2
;;[===========INPUT===================
;;[1.00, 2.00, 3.00, 4.00]
;;=================OUTPUT==================
;;[0.00, 1.00, 0.00],
;;===========INPUT===================
;;[2.00, 4.00, 6.00, 8.00]
;;=================OUTPUT==================
;;[0.00, 0.00, 1.00]]]
;; we can create a dataset iterator from the code which creates datasets
;; and set the labels for our outputs (optional)
(def ds-with-multiple-examples
(new-ds
:input [[1 2 3 4] [2 4 6 8]]
:output [[0.0 1.0 0.0] [0.0 0.0 1.0]]))
;; iterator
(def training-rr-ds-iter
(new-existing-dataset-iterator
:dataset ds-with-multiple-examples
:labels ["foo" "baz" "foobaz"]))
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; data-set normalization
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; this gathers statistics on the dataset and normalizes the data
;; and applies the transformation to all dataset objects in the iterator
(def train-iter-normalized
(c/normalize-iter! :iter training-rr-ds-iter
:normalizer (ds-pp/new-standardize-normalization-ds-preprocessor)
:as-code? false))
;; above returns the normalized iterator
;; to get fit normalizer
(def the-normalizer
(get-pre-processor train-iter-normalized))
Model configuration
Creating a neural network configuration with singe and multiple layers
(ns my.ns
(:require [dl4clj.nn.conf.builders.layers :as l]
[dl4clj.nn.conf.builders.nn :as nn]
[dl4clj.nn.conf.distributions :as dist]
[dl4clj.nn.conf.input-pre-processor :as pp]
[dl4clj.nn.conf.step-fns :as s-fn]))
;; nn/builder has 3 types of args
;; 1) args which set network configuration params
;; 2) args which set default values for layers
;; 3) args which set multi layer network configuration params
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; single layer nn configuration
;; here we are setting network configuration
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
(nn/builder :optimization-algo :stochastic-gradient-descent
:seed 123
:iterations 1
:minimize? true
:use-drop-connect? false
:lr-score-based-decay-rate 0.002
:regularization? false
:step-fn :default-step-fn
:layers {:dense-layer {:activation-fn :relu
:updater :adam
:adam-mean-decay 0.2
:adam-var-decay 0.1
:learning-rate 0.006
:weight-init :xavier
:layer-name "single layer model example"
:n-in 10
:n-out 20}})
;; there are several options within a nn-conf map which can be configuration maps
;; or calls to fns
;; It doesn't matter which option you choose and you don't have to stay consistent
;; the list of params which can be passed as config maps or fn calls will
;; be enumerated at a later date
(nn/builder :optimization-algo :stochastic-gradient-descent
:seed 123
:iterations 1
:minimize? true
:use-drop-connect? false
:lr-score-based-decay-rate 0.002
:regularization? false
:step-fn (s-fn/new-default-step-fn)
:build? true
;; dont need to specify layer order, theres only one
:layers (l/dense-layer-builder
:activation-fn :relu
:updater :adam
:adam-mean-decay 0.2
:adam-var-decay 0.1
:dist (dist/new-normal-distribution :mean 0 :std 1)
:learning-rate 0.006
:weight-init :xavier
:layer-name "single layer model example"
:n-in 10
:n-out 20))
;; these configurations are the same
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; multi-layer configuration
;; here we are also setting layer defaults
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; defaults will apply to layers which do not specify those value in their config
(nn/builder
:optimization-algo :stochastic-gradient-descent
:seed 123
:iterations 1
:minimize? true
:use-drop-connect? false
:lr-score-based-decay-rate 0.002
:regularization? false
:default-activation-fn :sigmoid
:default-weight-init :uniform
;; we need to specify the layer order
:layers {0 (l/activation-layer-builder
:activation-fn :relu
:updater :adam
:adam-mean-decay 0.2
:adam-var-decay 0.1
:learning-rate 0.006
:weight-init :xavier
:layer-name "example first layer"
:n-in 10
:n-out 20)
1 {:output-layer {:n-in 20
:n-out 2
:loss-fn :mse
:layer-name "example output layer"}}})
;; specifying multi-layer config params
(nn/builder
;; network args
:optimization-algo :stochastic-gradient-descent
:seed 123
:iterations 1
:minimize? true
:use-drop-connect? false
:lr-score-based-decay-rate 0.002
:regularization? false
;; layer defaults
:default-activation-fn :sigmoid
:default-weight-init :uniform
;; the layers
:layers {0 (l/activation-layer-builder
:activation-fn :relu
:updater :adam
:adam-mean-decay 0.2
:adam-var-decay 0.1
:learning-rate 0.006
:weight-init :xavier
:layer-name "example first layer"
:n-in 10
:n-out 20)
1 {:output-layer {:n-in 20
:n-out 2
:loss-fn :mse
:layer-name "example output layer"}}}
;; multi layer network args
:backprop? true
:backprop-type :standard
:pretrain? false
:input-pre-processors {0 (pp/new-zero-mean-pre-pre-processor)
1 {:unit-variance-processor {}}})
Configuration to Trained models
Multi Layer models
- an implementation of the dl4j mnist classification example
(ns my.ns
(:require [dl4clj.datasets.iterators :as iter]
[dl4clj.datasets.input-splits :as split]
[dl4clj.datasets.record-readers :as rr]
[dl4clj.optimize.listeners :as listener]
[dl4clj.nn.conf.builders.nn :as nn]
[dl4clj.nn.multilayer.multi-layer-network :as mln]
[dl4clj.nn.api.model :refer [init! set-listeners!]]
[dl4clj.nn.api.multi-layer-network :refer [evaluate-classification]]
[dl4clj.datasets.api.record-readers :refer [initialize-rr!]]
[dl4clj.eval.api.eval :refer [get-stats get-accuracy]]
[dl4clj.core :as c]))
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; nn-conf -> multi-layer-network
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
(def nn-conf
(nn/builder
;; network args
:optimization-algo :stochastic-gradient-descent
:seed 123 :iterations 1 :regularization? true
;; setting layer defaults
:default-activation-fn :relu :default-l2 7.5e-6
:default-weight-init :xavier :default-learning-rate 0.0015
:default-updater :nesterovs :default-momentum 0.98
;; setting layer configuration
:layers {0 {:dense-layer
{:layer-name "example first layer"
:n-in 784 :n-out 500}}
1 {:dense-layer
{:layer-name "example second layer"
:n-in 500 :n-out 100}}
2 {:output-layer
{:n-in 100 :n-out 10
;; layer specific params
:loss-fn :negativeloglikelihood
:activation-fn :softmax
:layer-name "example output layer"}}}
;; multi layer args
:backprop? true
:pretrain? false))
(def multi-layer-network (c/model-from-conf nn-conf))
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; local cpu training with dl4j pre-built iterators
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; lets use the pre-built Mnist data set iterator
(def train-mnist-iter
(iter/new-mnist-data-set-iterator
:batch-size 64
:train? true
:seed 123))
(def test-mnist-iter
(iter/new-mnist-data-set-iterator
:batch-size 64
:train? false
:seed 123))
;; and lets set a listener so we can know how training is going
(def score-listener (listener/new-score-iteration-listener :print-every-n 5))
;; and attach it to our model
;; TODO: listeners are broken, look into log4j warnning
(def mln-with-listener (set-listeners! :model multi-layer-network
:listeners [score-listener]))
(def trained-mln (mln/train-mln-with-ds-iter! :mln mln-with-listener
:iter train-mnist-iter
:n-epochs 15
:as-code? false))
;; training happens because :as-code? = false
;; if it was true, we would still just have a data structure
;; we now have a trained model that has seen the training dataset 15 times
;; time to evaluate our model
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;;Create an evaluation object
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
(def eval-obj (evaluate-classification :mln trained-mln
:iter test-mnist-iter))
;; always remember that these objects are stateful, dont use the same eval-obj
;; to eval two different networks
;; we trained the model on a training dataset. We evaluate on a test set
(println (get-stats :evaler eval-obj))
;; this will print the stats to standard out for each feature/label pair
;;Examples labeled as 0 classified by model as 0: 968 times
;;Examples labeled as 0 classified by model as 1: 1 times
;;Examples labeled as 0 classified by model as 2: 1 times
;;Examples labeled as 0 classified by model as 3: 1 times
;;Examples labeled as 0 classified by model as 5: 1 times
;;Examples labeled as 0 classified by model as 6: 3 times
;;Examples labeled as 0 classified by model as 7: 1 times
;;Examples labeled as 0 classified by model as 8: 2 times
;;Examples labeled as 0 classified by model as 9: 2 times
;;Examples labeled as 1 classified by model as 1: 1126 times
;;Examples labeled as 1 classified by model as 2: 2 times
;;Examples labeled as 1 classified by model as 3: 1 times
;;Examples labeled as 1 classified by model as 5: 1 times
;;Examples labeled as 1 classified by model as 6: 2 times
;;Examples labeled as 1 classified by model as 7: 1 times
;;Examples labeled as 1 classified by model as 8: 2 times
;;Examples labeled as 2 classified by model as 0: 3 times
;;Examples labeled as 2 classified by model as 1: 2 times
;;Examples labeled as 2 classified by model as 2: 1006 times
;;Examples labeled as 2 classified by model as 3: 2 times
;;Examples labeled as 2 classified by model as 4: 3 times
;;Examples labeled as 2 classified by model as 6: 3 times
;;Examples labeled as 2 classified by model as 7: 7 times
;;Examples labeled as 2 classified by model as 8: 6 times
;;Examples labeled as 3 classified by model as 2: 4 times
;;Examples labeled as 3 classified by model as 3: 990 times
;;Examples labeled as 3 classified by model as 5: 3 times
;;Examples labeled as 3 classified by model as 7: 3 times
;;Examples labeled as 3 classified by model as 8: 3 times
;;Examples labeled as 3 classified by model as 9: 7 times
;;Examples labeled as 4 classified by model as 2: 2 times
;;Examples labeled as 4 classified by model as 3: 1 times
;;Examples labeled as 4 classified by model as 4: 967 times
;;Examples labeled as 4 classified by model as 6: 4 times
;;Examples labeled as 4 classified by model as 7: 1 times
;;Examples labeled as 4 classified by model as 9: 7 times
;;Examples labeled as 5 classified by model as 0: 2 times
;;Examples labeled as 5 classified by model as 3: 6 times
;;Examples labeled as 5 classified by model as 4: 1 times
;;Examples labeled as 5 classified by model as 5: 874 times
;;Examples labeled as 5 classified by model as 6: 3 times
;;Examples labeled as 5 classified by model as 7: 1 times
;;Examples labeled as 5 classified by model as 8: 3 times
;;Examples labeled as 5 classified by model as 9: 2 times
;;Examples labeled as 6 classified by model as 0: 4 times
;;Examples labeled as 6 classified by model as 1: 3 times
;;Examples labeled as 6 classified by model as 3: 2 times
;;Examples labeled as 6 classified by model as 4: 4 times
;;Examples labeled as 6 classified by model as 5: 4 times
;;Examples labeled as 6 classified by model as 6: 939 times
;;Examples labeled as 6 classified by model as 7: 1 times
;;Examples labeled as 6 classified by model as 8: 1 times
;;Examples labeled as 7 classified by model as 1: 7 times
;;Examples labeled as 7 classified by model as 2: 4 times
;;Examples labeled as 7 classified by model as 3: 3 times
;;Examples labeled as 7 classified by model as 7: 1005 times
;;Examples labeled as 7 classified by model as 8: 2 times
;;Examples labeled as 7 classified by model as 9: 7 times
;;Examples labeled as 8 classified by model as 0: 3 times
;;Examples labeled as 8 classified by model as 2: 3 times
;;Examples labeled as 8 classified by model as 3: 2 times
;;Examples labeled as 8 classified by model as 4: 4 times
;;Examples labeled as 8 classified by model as 5: 3 times
;;Examples labeled as 8 classified by model as 6: 2 times
;;Examples labeled as 8 classified by model as 7: 4 times
;;Examples labeled as 8 classified by model as 8: 947 times
;;Examples labeled as 8 classified by model as 9: 6 times
;;Examples labeled as 9 classified by model as 0: 2 times
;;Examples labeled as 9 classified by model as 1: 2 times
;;Examples labeled as 9 classified by model as 3: 4 times
;;Examples labeled as 9 classified by model as 4: 8 times
;;Examples labeled as 9 classified by model as 6: 1 times
;;Examples labeled as 9 classified by model as 7: 4 times
;;Examples labeled as 9 classified by model as 8: 2 times
;;Examples labeled as 9 classified by model as 9: 986 times
;;==========================Scores========================================
;; Accuracy: 0.9808
;; Precision: 0.9808
;; Recall: 0.9807
;; F1 Score: 0.9807
;;========================================================================
;; can get the stats that are printed via fns in the evaluation namespace
;; after running eval-model-whole-ds
(get-accuracy :evaler evaler-with-stats) ;; => 0.9808
Model Tuning
Early Stopping (controlling training)
it is recommened you start here when designing models
using dl4clj.core
(ns my.ns
(:require [dl4clj.earlystopping.termination-conditions :refer :all]
[dl4clj.earlystopping.model-saver :refer [new-in-memory-saver]]
[dl4clj.nn.api.multi-layer-network :refer [evaluate-classification]]
[dl4clj.eval.api.eval :refer [get-stats]]
[dl4clj.nn.conf.builders.nn :as nn]
[dl4clj.datasets.iterators :as iter]
[dl4clj.core :as c]))
(def nn-conf
(nn/builder
;; network args
:optimization-algo :stochastic-gradient-descent
:seed 123
:iterations 1
:regularization? true
;; setting layer defaults
:default-activation-fn :relu
:default-l2 7.5e-6
:default-weight-init :xavier
:default-learning-rate 0.0015
:default-updater :nesterovs
:default-momentum 0.98
;; setting layer configuration
:layers {0 {:dense-layer
{:layer-name "example first layer"
:n-in 784 :n-out 500}}
1 {:dense-layer
{:layer-name "example second layer"
:n-in 500 :n-out 100}}
2 {:output-layer
{:n-in 100 :n-out 10
;; layer specific params
:loss-fn :negativeloglikelihood
:activation-fn :softmax
:layer-name "example output layer"}}}
;; multi layer args
:backprop? true
:pretrain? false))
(def train-iter
(iter/new-mnist-data-set-iterator
:batch-size 64
:train? true
:seed 123))
(def test-iter
(iter/new-mnist-data-set-iterator
:batch-size 64
:train? false
:seed 123))
(def invalid-score-condition (new-invalid-score-iteration-termination-condition))
(def max-score-condition (new-max-score-iteration-termination-condition
:max-score 20.0))
(def max-time-condition (new-max-time-iteration-termination-condition
:max-time-val 10
:max-time-unit :minutes))
(def score-doesnt-improve-condition (new-score-improvement-epoch-termination-condition
:max-n-epoch-no-improve 5))
(def target-score-condition (new-best-score-epoch-termination-condition
:best-expected-score 0.009))
(def max-number-epochs-condition (new-max-epochs-termination-condition :max-n 20))
(def in-mem-saver (new-in-memory-saver))
(def trained-mln
;; defaults to returning the model
(c/train-with-early-stopping
:nn-conf nn-conf
:training-iter train-mnist-iter
:testing-iter test-mnist-iter
:eval-every-n-epochs 1
:iteration-termination-conditions [invalid-score-condition
max-score-condition
max-time-condition]
:epoch-termination-conditions [score-doesnt-improve-condition
target-score-condition
max-number-epochs-condition]
:save-last-model? true
:model-saver in-mem-saver
:as-code? false))
(def model-evaler
(evaluate-classification :mln trained-mln :iter test-mnist-iter))
(println (get-stats :evaler model-evaler))
- explicit, step by step way of doing this
(ns my.ns
(:require [dl4clj.earlystopping.early-stopping-config :refer [new-early-stopping-config]]
[dl4clj.earlystopping.termination-conditions :refer :all]
[dl4clj.earlystopping.model-saver :refer [new-in-memory-saver new-local-file-model-saver]]
[dl4clj.earlystopping.score-calc :refer [new-ds-loss-calculator]]
[dl4clj.earlystopping.early-stopping-trainer :refer [new-early-stopping-trainer]]
[dl4clj.earlystopping.api.early-stopping-trainer :refer [fit-trainer!]]
[dl4clj.nn.conf.builders.nn :as nn]
[dl4clj.nn.multilayer.multi-layer-network :as mln]
[dl4clj.utils :refer [load-model!]]
[dl4clj.datasets.iterators :as iter]
[dl4clj.core :as c]))
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; start with our network config
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
(def nn-conf
(nn/builder
;; network args
:optimization-algo :stochastic-gradient-descent
:seed 123 :iterations 1 :regularization? true
;; setting layer defaults
:default-activation-fn :relu :default-l2 7.5e-6
:default-weight-init :xavier :default-learning-rate 0.0015
:default-updater :nesterovs :default-momentum 0.98
;; setting layer configuration
:layers {0 {:dense-layer
{:layer-name "example first layer"
:n-in 784 :n-out 500}}
1 {:dense-layer
{:layer-name "example second layer"
:n-in 500 :n-out 100}}
2 {:output-layer
{:n-in 100 :n-out 10
;; layer specific params
:loss-fn :negativeloglikelihood
:activation-fn :softmax
:layer-name "example output layer"}}}
;; multi layer args
:backprop? true
:pretrain? false))
(def mln (c/model-from-conf nn-conf))
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; the training/testing data
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
(def train-iter
(iter/new-mnist-data-set-iterator
:batch-size 64
:train? true
:seed 123))
(def test-iter
(iter/new-mnist-data-set-iterator
:batch-size 64
:train? false
:seed 123))
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; we are going to need termination conditions
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; these allow us to control when we exit training
;; this can be based off of iterations or epochs
;; iteration termination conditions
(def invalid-score-condition (new-invalid-score-iteration-termination-condition))
(def max-score-condition (new-max-score-iteration-termination-condition
:max-score 20.0))
(def max-time-condition (new-max-time-iteration-termination-condition
:max-time-val 10
:max-time-unit :minutes))
;; epoch termination conditions
(def score-doesnt-improve-condition (new-score-improvement-epoch-termination-condition
:max-n-epoch-no-improve 5))
(def target-score-condition (new-best-score-epoch-termination-condition :best-expected-score 0.009))
(def max-number-epochs-condition (new-max-epochs-termination-condition :max-n 20))
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; we also need a way to save our model
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; can be in memory or to a local directory
(def in-mem-saver (new-in-memory-saver))
(def local-file-saver (new-local-file-model-saver :directory "resources/tmp/readme/"))
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; set up your score calculator
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
(def score-calcer (new-ds-loss-calculator :iter test-iter
:average? true))
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; create an early stopping configuration
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; termination conditions
;; a way to save our model
;; a way to calculate the score of our model on the dataset
(def early-stopping-conf
(new-early-stopping-config
:epoch-termination-conditions [score-doesnt-improve-condition
target-score-condition
max-number-epochs-condition]
:iteration-termination-conditions [invalid-score-condition
max-score-condition
max-time-condition]
:eval-every-n-epochs 5
:model-saver local-file-saver
:save-last-model? true
:score-calculator score-calcer))
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; create an early stopping trainer from our data, model and early stopping conf
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
(def es-trainer (new-early-stopping-trainer :early-stopping-conf early-stopping-conf
:mln mln
:iter train-iter))
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; fit and use our early stopping trainer
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
(def es-trainer-fitted (fit-trainer! es-trainer :as-code? false))
;; when the trainer terminates, you will see something like this
;;[nREPL-worker-24] BaseEarlyStoppingTrainer INFO Completed training epoch 14
;;[nREPL-worker-24] BaseEarlyStoppingTrainer INFO New best model: score = 0.005225599372851298,
;; epoch = 14 (previous: score = 0.018243224899038346, epoch = 7)
;;[nREPL-worker-24] BaseEarlyStoppingTrainer INFO Hit epoch termination condition at epoch 14.
;; Details: BestScoreEpochTerminationCondition(0.009)
;; and if we look at the es-trainer-fitted object we see
;;#object[org.deeplearning4j.earlystopping.EarlyStoppingResult 0x5ab74f27 EarlyStoppingResult
;;(terminationReason=EpochTerminationCondition,details=BestScoreEpochTerminationCondition(0.009),
;; bestModelEpoch=14,bestModelScore=0.005225599372851298,totalEpochs=15)]
;; and our model has been saved to /resources/tmp/readme/bestModel.bin
;; there we have our model config, model params and our updater state
;; we can then load this model to use it or continue refining it
(def loaded-model (load-model! :path "resources/tmp/readme/bestModel.bin"
:load-updater? true))
Transfer Learning (freezing layers)
;; TODO: need to write up examples
Spark Training
dl4j Spark usage
How it is done in dl4clj
- Uses dl4clj.core
- This example uses a fn which takes care of most steps for you
- allows you to pass args as code bc the fn accounts for the multiple spark contexts issue encountered when everything is just a data structure
- This example uses a fn which takes care of most steps for you
(ns my.ns
(:require [dl4clj.nn.conf.builders.layers :as l]
[dl4clj.nn.conf.builders.nn :as nn]
[dl4clj.datasets.iterators :refer [new-iris-data-set-iterator]]
[dl4clj.eval.api.eval :refer [get-stats]]
[dl4clj.spark.masters.param-avg :as master]
[dl4clj.spark.data.java-rdd :refer [new-java-spark-context
java-rdd-from-iter]]
[dl4clj.spark.api.dl4j-multi-layer :refer [eval-classification-spark-mln
get-spark-context]]
[dl4clj.core :as c]))
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; Step 1, create your model config
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
(def mln-conf
(nn/builder
:optimization-algo :stochastic-gradient-descent
:default-learning-rate 0.006
:layers {0 (l/dense-layer-builder :n-in 4 :n-out 2 :activation-fn :relu)
1 {:output-layer
{:loss-fn :negativeloglikelihood
:n-in 2 :n-out 3
:activation-fn :soft-max
:weight-init :xavier}}}
:backprop? true
:backprop-type :standard))
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; Step 2, training master
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
(def training-master
(master/new-parameter-averaging-training-master
:build? true
:rdd-n-examples 10
:n-workers 4
:averaging-freq 10
:batch-size-per-worker 2
:export-dir "resources/spark/master/"
:rdd-training-approach :direct
:repartition-data :always
:repartition-strategy :balanced
:seed 1234
:save-updater? true
:storage-level :none))
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; Step 3, spark context
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
(def your-spark-context
(new-java-spark-context :app-name "example app"))
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; Step 4, training data
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
(def iris-iter
(new-iris-data-set-iterator
:batch-size 1
:n-examples 5))
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; Step 5, spark mln
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
(def fitted-spark-mln
(c/train-with-spark :spark-context your-spark-context
:mln-conf mln-conf
:training-master training-master
:iter iris-iter
:n-epochs 1
:as-code? false))
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; Step 5, use spark context from spark-mln to create rdd
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; TODO: eliminate this step
(def our-rdd
(let [sc (get-spark-context fitted-spark-mln :as-code? false)]
(java-rdd-from-iter :spark-context sc
:iter iris-iter)))
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; Step 6, evaluation model and print stats (poor performance of model expected)
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
(def eval-obj
(eval-classification-spark-mln
:spark-mln fitted-spark-mln
:rdd our-rdd))
(println (get-stats :evaler eval-obj))
- this example demonstrates the dl4j workflow
- NOTE: unlike the previous example, this one requires dl4j objects to be used
- this is becaues spark only wants you to have one spark context at a time
- NOTE: unlike the previous example, this one requires dl4j objects to be used
(ns my.ns
(:require [dl4clj.nn.conf.builders.layers :as l]
[dl4clj.nn.conf.builders.nn :as nn]
[dl4clj.datasets.iterators :refer [new-iris-data-set-iterator]]
[dl4clj.eval.api.eval :refer [get-stats]]
[dl4clj.spark.masters.param-avg :as master]
[dl4clj.spark.data.java-rdd :refer [new-java-spark-context java-rdd-from-iter]]
[dl4clj.spark.dl4j-multi-layer :as spark-mln]
[dl4clj.spark.api.dl4j-multi-layer :refer [fit-spark-mln!
eval-classification-spark-mln]]))
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; Step 1, create your model
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
(def mln-conf
(nn/builder
:optimization-algo :stochastic-gradient-descent
:default-learning-rate 0.006
:layers {0 (l/dense-layer-builder :n-in 4 :n-out 2 :activation-fn :relu)
1 {:output-layer
{:loss-fn :negativeloglikelihood
:n-in 2 :n-out 3
:activation-fn :soft-max
:weight-init :xavier}}}
:backprop? true
:as-code? false
:backprop-type :standard))
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; Step 2, create a training master
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; not all options specified, but most are
(def training-master
(master/new-parameter-averaging-training-master
:build? true
:rdd-n-examples 10
:n-workers 4
:averaging-freq 10
:batch-size-per-worker 2
:export-dir "resources/spark/master/"
:rdd-training-approach :direct
:repartition-data :always
:repartition-strategy :balanced
:seed 1234
:as-code? false
:save-updater? true
:storage-level :none))
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; Step 3, create a Spark Multi Layer Network
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
(def your-spark-context
(new-java-spark-context :app-name "example app" :as-code? false))
;; new-java-spark-context will turn an existing spark-configuration into a java spark context
;; or create a new java spark context with master set to "local[*]" and the app name
;; set to :app-name
(def spark-mln
(spark-mln/new-spark-multi-layer-network
:spark-context your-spark-context
:mln mln-conf
:training-master training-master
:as-code? false))
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; Step 4, load your data
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; one way is via a dataset-iterator
;; can make one directly from a dataset (iterator data-set)
;; see: nd4clj.linalg.dataset.api.data-set and nd4clj.linalg.dataset.data-set
;; we are going to use a pre-built one
(def iris-iter
(new-iris-data-set-iterator
:batch-size 1
:n-examples 5
:as-code? false))
;; now lets convert the data into a javaRDD
(def our-rdd
(java-rdd-from-iter :spark-context your-spark-context
:iter iris-iter))
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; Step 5, fit and evaluate the model
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
(def fitted-spark-mln
(fit-spark-mln!
:spark-mln spark-mln
:rdd our-rdd
:n-epochs 1))
;; this fn also has the option to supply :path-to-data instead of :rdd
;; that path should point to a directory containing a number of dataset objects
(def eval-obj
(eval-classification-spark-mln
:spark-mln fitted-spark-mln
:rdd our-rdd))
;; we would want to have different testing and training rdd's but here we are using
;; the data we trained on
;; lets get the stats for how our model performed
(println (get-stats :evaler eval-obj))
Terminology
Coming soon
Packages to come back to:
Implement ComputationGraphs and the classes which use them
- https://deeplearning4j.org/doc/org/deeplearning4j/nn/graph/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/nn/conf/ComputationGraphConfiguration.GraphBuilder.html
- https://deeplearning4j.org/doc/org/deeplearning4j/nn/conf/ComputationGraphConfiguration.html
- https://deeplearning4j.org/doc/org/deeplearning4j/nn/conf/graph/package-frame.html
- https://deeplearning4j.org/doc/org/deeplearning4j/nn/conf/graph/rnn/package-frame.html
NLP
- https://deeplearning4j.org/doc/org/deeplearning4j/bagofwords/vectorizer/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/datasets/vectorizer/Vectorizer.html
- https://deeplearning4j.org/doc/org/deeplearning4j/iterator/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/iterator/provider/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/models/embeddings/WeightLookupTable.html
- https://deeplearning4j.org/doc/org/deeplearning4j/models/embeddings/inmemory/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/models/embeddings/learning/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/models/embeddings/learning/impl/elements/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/models/embeddings/learning/impl/sequence/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/models/embeddings/loader/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/models/embeddings/reader/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/models/embeddings/reader/impl/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/models/embeddings/wordvectors/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/models/glove/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/models/glove/count/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/models/node2vec/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/models/paragraphvectors/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/models/sequencevectors/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/models/sequencevectors/enums/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/models/sequencevectors/interfaces/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/models/sequencevectors/iterators/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/models/sequencevectors/listeners/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/models/sequencevectors/sequence/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/models/sequencevectors/serialization/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/models/sequencevectors/transformers/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/models/sequencevectors/transformers/impl/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/models/sequencevectors/transformers/impl/iterables/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/models/word2vec/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/models/word2vec/iterator/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/models/word2vec/wordstore/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/models/word2vec/wordstore/inmemory/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/text/annotator/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/text/corpora/sentiwordnet/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/text/corpora/treeparser/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/text/corpora/treeparser/transformer/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/text/documentiterator/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/text/documentiterator/interoperability/DocumentIteratorConverter.html
- https://deeplearning4j.org/doc/org/deeplearning4j/text/inputsanitation/InputHomogenization.html
- https://deeplearning4j.org/doc/org/deeplearning4j/text/invertedindex/InvertedIndex.html
- https://deeplearning4j.org/doc/org/deeplearning4j/text/labels/LabelsProvider.html
- https://deeplearning4j.org/doc/org/deeplearning4j/text/movingwindow/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/text/sentenceiterator/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/text/sentenceiterator/interoperability/SentenceIteratorConverter.html
- https://deeplearning4j.org/doc/org/deeplearning4j/text/sentenceiterator/labelaware/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/text/stopwords/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/text/tokenization/tokenizer/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/text/tokenization/tokenizer/preprocessor/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/text/tokenization/tokenizer/tokenprepreprocessor/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/text/tokenization/tokenizerfactory/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/text/uima/UimaResource.html
Parallelism
- https://deeplearning4j.org/doc/org/deeplearning4j/parallelism/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/parallelism/factory/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/parallelism/main/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/parallelism/parameterserver/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/parallelism/trainer/package-summary.html
TSNE
UI
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/activation/PathUpdate.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/api/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/components/chart/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/components/chart/style/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/components/component/ComponentDiv.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/components/component/style/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/components/decorator/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/components/decorator/style/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/components/table/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/components/table/style/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/components/text/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/components/text/style/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/flow/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/flow/beans/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/flow/data/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/i18n/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/module/convolutional/ConvolutionalListenerModule.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/module/defaultModule/DefaultModule.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/module/flow/FlowListenerModule.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/module/histogram/HistogramModule.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/module/remote/RemoteReceiverModule.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/module/train/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/module/tsne/TsneModule.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/nearestneighbors/word2vec/NearestNeighborsQuery.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/play/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/play/misc/FunctionUtil.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/play/staticroutes/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/providers/ObjectMapperProvider.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/renders/PathUpdate.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/standalone/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/stats/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/stats/api/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/stats/impl/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/stats/impl/java/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/stats/sbe/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/storage/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/storage/impl/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/storage/mapdb/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/storage/sqlite/J7FileStatsStorage.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/weights/package-summary.html
- https://deeplearning4j.org/doc/org/deeplearning4j/ui/weights/beans/CompactModelAndGradient.html
Author: yetanalytics
Source Code: https://github.com/yetanalytics/dl4clj
License: BSD-2-Clause License
1591611780
How to Find Ulimit For user on Linux
How can I find the correct ulimit values for a user account or process on Linux systems?
For proper operation, we must ensure that the correct ulimit values set after installing various software. The Linux system provides means of restricting the number of resources that can be used. Limits set for each Linux user account. However, system limits are applied separately to each process that is running for that user too. For example, if certain thresholds are too low, the system might not be able to server web pages using Nginx/Apache or PHP/Python app. System resource limits viewed or set with the NA command. Let us see how to use the ulimit that provides control over the resources available to the shell and processes.
#[object object] #[object object] #[object object] #[object object] #[object object] #[object object] #[object object] #[object object] #[object object] #[object object]
1591993440
MEAN Stack Tutorial MongoDB ExpressJS AngularJS NodeJS
We are going to build a full stack Todo App using the MEAN (MongoDB, ExpressJS, AngularJS and NodeJS). This is the last part of three-post series tutorial.
MEAN Stack tutorial series:
AngularJS tutorial for beginners (Part I)
Creating RESTful APIs with NodeJS and MongoDB Tutorial (Part II)
MEAN Stack Tutorial: MongoDB, ExpressJS, AngularJS and NodeJS (Part III) 👈 you are here
Before completing the app, let’s cover some background about the this stack. If you rather jump to the hands-on part click here to get started.
#[object object] #[object object] #[object object] #[object object] #[object object] #[object object] #[object object] #[object object]
1591989720
Creating RESTful APIs with NodeJS and MongoDB Tutorial
Welcome to this tutorial about RESTful API using Node.js (Express.js) and MongoDB (mongoose)! We are going to learn how to install and use each component individually and then proceed to create a RESTful API.
MEAN Stack tutorial series:
AngularJS tutorial for beginners (Part I)
Creating RESTful APIs with NodeJS and MongoDB Tutorial (Part II) 👈 you are here
MEAN Stack Tutorial: MongoDB, ExpressJS, AngularJS and NodeJS (Part III)
#[object object] #[object object] #[object object] #[object object] #[object object] #[object object] #[object object]