| Apache Ignite TensorFlow Integration Module |
| ------------------------ |
| |
| Apache Ignite TensorFlow Integration Module allowed using TensorFlow with Apache Ignite. In this scenario Apache Ignite |
| will be a datasource for any TensorFlow model training. |
| |
| Import Apache Ignite TensorFlow Integration Module In Maven Project |
| ------------------------------------- |
| |
| If you are using Maven to manage dependencies of your project, you can add TensorFlow module |
| dependency like this (replace '${ignite.version}' with actual Ignite version you are |
| interested in): |
| |
| <project xmlns="http://maven.apache.org/POM/4.0.0" |
| xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" |
| xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 |
| http://maven.apache.org/xsd/maven-4.0.0.xsd"> |
| ... |
| <dependencies> |
| ... |
| <dependency> |
| <groupId>org.apache.ignite</groupId> |
| <artifactId>ignite-tensorflow</artifactId> |
| <version>${ignite.version}</version> |
| </dependency> |
| ... |
| </dependencies> |
| ... |
| </project> |
| ------------------------------------- |
| |
| TensorFlow integration module provides command line tool that allows to start, maintain and stop distributed deep |
| learning utilizing Apache Ignite infrastructure and data. This tool provides several commands that are shown here: |
| |
| Usage: ignite-tf [-hV] [-c=<cfg>] [COMMAND] |
| Apache Ignite and TensorFlow integration command line utility that allows to |
| start, maintain and stop distributed deep learning utilizing Apache Ignite |
| infrastructure and data. |
| -c, --config=<cfg> Apache Ignite client configuration. |
| -h, --help Show this help message and exit. |
| -V, --version Print version information and exit. |
| Commands: |
| start Starts a new TensorFlow cluster and attaches to user script process. |
| stop Stops a running TensorFlow cluster. |
| attach Attaches to running TensorFlow cluster (user script process). |
| ps Prints identifiers of all running TensorFlow clusters. |
| |
| To start TensorFlow cluster you need to specify upstream cache that will be used as data source for training, folder |
| that contains code that actually performs training and command that should be called on this code to start training |
| correctly. Command "start" have the following help output: |
| |
| Usage: ignite-tf start [-hV] [-c=<cfg>] CACHE_NAME JOB_DIR JOB_CMD [JOB_ARGS...] |
| Starts a new TensorFlow cluster and attaches to user script process. |
| CACHE_NAME Upstream cache name. |
| JOB_DIR Job folder (or zip archive). |
| JOB_CMD Job command. |
| [JOB_ARGS...] Job arguments. |
| -c, --config=<cfg> Apache Ignite client configuration. |
| -h, --help Show this help message and exit. |
| -V, --version Print version information and exit. |
| |
| To attach to running TensorFlow cluster or stop it you can use commands "attach" and "stop" correspondingly. These |
| commands accepts cluster identifier as a parameter: |
| |
| Usage: ignite-tf attach [-hV] [-c=<cfg>] CLUSTER_ID |
| Attaches to running TensorFlow cluster (user script process). |
| CLUSTER_ID Cluster identifier. |
| -c, --config=<cfg> Apache Ignite client configuration. |
| -h, --help Show this help message and exit. |
| -V, --version Print version information and exit. |
| |
| Usage: ignite-tf stop [-hV] [-c=<cfg>] CLUSTER_ID |
| Stops a running TensorFlow cluster. |
| CLUSTER_ID Cluster identifier. |
| -c, --config=<cfg> Apache Ignite client configuration. |
| -h, --help Show this help message and exit. |
| -V, --version Print version information and exit. |
| |
| To find out what TensorFlow clusters are currently running on top of Apache Ignite you can use "ps" command that doesn't |
| require arguments. |