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1619693852
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:
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.
@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());
}
More at:
How to use Spring Kafka JsonSerializer (JsonDeserializer) to produce/consume Java Object messages
#spring kafka #jsonserializer #jsondeserializer
1654075127
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.
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.
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
2. Disadvantages of vertical sharding
Join
can only be implemented by interface aggregation, which will increase the complexity of development.3. Advantages of horizontal sharding
4. Disadvantages of horizontal sharding
Join
is poor.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.
The characteristics of Sharding-JDBC are:
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.
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:
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.
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.
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
As shown in the ShardingSphere-SQL log
figure below, the write SQL is executed on the ds_master
data source.
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 --------------");
}
The Aurora database environment adopts the configuration described in Section 2.2.1.
3.2.4.1 Verification process description
Spring-Boot
project2. 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.
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
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.
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.
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
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.
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.
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
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.
The following figure shows the physical table of the created database instance.
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
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
Port of deeplearning4j to clojure
Contact info
If you have any questions,
NOT YET RELEASED TO CLOJARS
If using Maven add the following repository definition to your pom.xml:
<repository>
<id>clojars.org</id>
<url>http://clojars.org/repo</url>
</repository>
With Leiningen:
n/a
With Maven:
n/a
<dependency>
<groupId>_</groupId>
<artifactId>_</artifactId>
<version>_</version>
</dependency>
All functions for creating dl4j objects return code by default
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
(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))"]
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
(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))
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 {}}})
Multi Layer models
(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
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))
(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
dl4j Spark usage
How it is done in dl4clj
(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))
(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))
Coming soon
Implement ComputationGraphs and the classes which use them
NLP
Parallelism
TSNE
UI
Author: yetanalytics
Source Code: https://github.com/yetanalytics/dl4clj
License: BSD-2-Clause License
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