To use MATLAB code in Hadoop's MapReduce framework, you need to follow these steps:

1. Write the MATLAB code that performs the required computation.
2. Convert the MATLAB code into a standalone executable using MATLAB Compiler.
3. Create a MapReduce job that runs the MATLAB executable as the mapper or reducer.
4. Package the MATLAB executable and any required dependencies into a JAR file.
5. Submit the JAR file to the Hadoop cluster and run the MapReduce job.

Here is an example of how to create a MapReduce job using MATLAB:

1. Write the MATLAB code that performs the computation you want to run. For example, suppose you have a MATLAB script my_script.m that takes an input file, performs some computation on it, and writes the results to an output file.

2. Convert the MATLAB script into a standalone executable using MATLAB Compiler. To do this, you can use the MATLAB Compiler Toolbox. For example, you can use the mcc command to compile my_script.m into an executable named my_script. 

mcc -m my_script.m

This will create an executable file named my_script and a set of supporting files in a directory named my_script_mcr.

3. Create a MapReduce job that runs the MATLAB executable as the mapper or reducer. You can use the Hadoop Streaming API to create a MapReduce job that runs the MATLAB executable. For example, suppose you have a Hadoop cluster with input data stored in HDFS, and you want to run my_script on each input file and output the results to a separate file. You can use the following command to create a MapReduce job that runs my_script as the mapper: 

hadoop jar hadoop-streaming.jar \
    -input input_dir \
    -output output_dir \
    -mapper "/path/to/my_script" \
    -file /path/to/my_script

This will run the my_script executable on each input file in input_dir and write the results to a separate file in output_dir.

4. Package the MATLAB executable and any required dependencies into a JAR file. You can use the jar command to create a JAR file that includes the MATLAB executable and any required dependencies. For example:

jar cf my_script.jar my_script my_script_mcr

This will create a JAR file named my_script.jar that includes the my_script executable and the my_script_mcr directory.

5. Submit the JAR file to the Hadoop cluster and run the MapReduce job. You can use the hadoop jar command to submit the JAR file to the Hadoop cluster and run the MapReduce job. For example:

hadoop jar my_script.jar \
    -input input_dir \
    -output output_dir \
    -mapper my_script \
    -file my_script

This will submit the my_script.jar file to the Hadoop cluster and run the MapReduce job. The -file option specifies that the my_script executable should be distributed to each node in the cluster, and the -mapper option specifies that the my_script executable should be used as the mapper. The -input and -output options specify the input and output directories for the job.