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# How to run TensorFlow on Hadoop
This document describes how to run TensorFlow on Hadoop. It will be expanded to
describe running on various cluster managers, but only describes running on HDFS
at the moment.
## HDFS
We assume that you are familiar with [reading data](../reading_data/index.md).
To use HDFS with TensorFlow, change the file paths you use to read and write
data to an HDFS path. For example:
```python
filename_queue = tf.train.string_input_producer([
"hdfs://namenode:8020/path/to/file1.csv",
"hdfs://namenode:8020/path/to/file2.csv",
])
```
If you want to use the namenode specified in your HDFS configuration files, then
change the file prefix to `hdfs://default/`.
When launching your TensorFlow program, the following environment variables must
be set:
* **JAVA_HOME**: The location of your Java installation.
* **HADOOP_HDFS_HOME**: The location of your HDFS installation. You can also
set this environment variable by running:
```shell
source ${HADOOP_HOME}/libexec/hadoop-config.sh
```
* **LD_LIBRARY_PATH**: To include the path to libjvm.so. On Linux:
```shell
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:${JAVA_HOME}/jre/lib/amd64/server
```
* **CLASSPATH**: The Hadoop jars must be added prior to running your
TensorFlow program. The CLASSPATH set by
`${HADOOP_HOME}/libexec/hadoop-config.sh` is insufficient. Globs must be
expanded as described in the libhdfs documentation:
```shell
CLASSPATH=$($HADOOP_HDFS_HOME}/bin/hadoop classpath --glob) python your_script.py
```
If you are running [Distributed TensorFlow](../distributed/index.md), then all
workers must have the environment variables set and Hadoop installed.
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