# Using different Python versions with EMR Serverless
<a name="using-python"></a>

In addition to the use case in [Using Python libraries with EMR Serverless](using-python-libraries.md), you can also use Python virtual environments to work with different Python versions than the version packaged in the Amazon EMR release for your Amazon EMR Serverless application. To do this, build a Python virtual environment with the Python version you want to use.

**To submit a job from a Python virtual environment**

1. Build your virtual environment with the commands in the following example. This example installs Python 3.9.9 into a virtual environment package and copies the archive to an Amazon S3 location.
**Important**  
If you use Amazon EMR releases 7.0.0 and higher, run your commands in an Amazon Linux 2023 environment similar to the one you use for your EMR Serverless applications.   
 If you use release 6.15.0 or lower, run the following commands in a similar Amazon Linux 2 environment.

   ```
   # install Python 3.9.9 and activate the venv
   yum install -y gcc openssl-devel bzip2-devel libffi-devel tar gzip wget make
   wget https://www.python.org/ftp/python/3.9.9/Python-3.9.9.tgz && \
   tar xzf Python-3.9.9.tgz && cd Python-3.9.9 && \
   ./configure --enable-optimizations --enable-shared && \
   make altinstall
   
   # create python venv with Python 3.9.9
   python3.9 -m venv pyspark_venv_python_3.9.9 --copies 
   source pyspark_venv_python_3.9.9/bin/activate
   
   # copy system python3 libraries and shared libraries to venv
   cp -r /usr/local/lib/python3.9/* ./pyspark_venv_python_3.9.9/lib/python3.9/
   cp /usr/local/lib/libpython3.9* ./pyspark_venv_python_3.9.9/lib/
   
   # package venv to archive. 
   # **Note** that you have to supply --python-prefix option 
   # to make sure python starts with the path where your 
   # copied libraries are present.
   # Copying the python binary to the "environment" directory.
   pip3 install venv-pack
   venv-pack -f -o pyspark_venv_python_3.9.9.tar.gz --python-prefix /home/hadoop/environment
   
   # stage the archive in S3 
   aws s3 cp pyspark_venv_python_3.9.9.tar.gz s3://<path>
   
   # optionally, remove the virtual environment directory
   rm -fr pyspark_venv_python_3.9.9
   ```

1. Set your properties to use the Python virtual environment and submit the Spark job.

   ```
   # note that the archive suffix "environment" is the same as the directory where you copied the Python binary.
   --conf spark.archives=s3://amzn-s3-demo-bucket/EXAMPLE-PREFIX/pyspark_venv_python_3.9.9.tar.gz#environment 
   --conf spark.emr-serverless.driverEnv.PYSPARK_DRIVER_PYTHON=./environment/bin/python
   --conf spark.emr-serverless.driverEnv.PYSPARK_PYTHON=./environment/bin/python 
   --conf spark.executorEnv.PYSPARK_PYTHON=./environment/bin/python
   --conf spark.emr-serverless.driverEnv.LD_LIBRARY_PATH=./environment/lib
   --conf spark.executorEnv.LD_LIBRARY_PATH=./environment/lib
   ```

For more on how to use Python virtual environments for PySpark jobs, refer to [Using Virtualenv](https://spark.apache.org/docs/latest/api/python/tutorial/python_packaging.html#using-virtualenv). For more examples of how to submit Spark jobs, refer to [Using Spark configurations when you run EMR Serverless jobs](jobs-spark.md).