Graal Platform Documentation

Graal Platform Documentation

  • Docs
  • Help

›Tutorials

Overview

  • What is Graal Platform?
  • Why use our platform?
  • How Graal Platform works?
  • Concepts
  • Jobs & workflows
  • Security

Quickstart

  • Quickstart

Tutorials

  • Get started with Python
  • Get started with Dask
  • Get started with XGBoost
  • Get started with Apache Spark and Maven
  • Get started with Apache PySpark
  • Get started with Apache Beam and Gradle
  • Use the API
  • Using the command line tool (graalctl)
  • Using secrets
  • Migration from Databricks
  • Get started with Tensorflow
  • Get started with Pytorch
  • Get started with Mxnet
  • Setting up the Hadoop bridge
  • Get started with Apache Flink and Maven
  • Get started with Dbt
  • Get started with Pulsar
  • Get started with Apache Spark Streaming Pulsar
  • Get started with Debezium
  • Get started with the SDK

How-to guides

  • Using Graal Platform with Azure Data Factory
  • Publishing your artefacts with Azure DevOps
  • Using Graal Platform with Apache Airflow
  • Publishing your artefacts with Jenkins
  • Spark
  • Network, VPN, gateway and firewall
  • Logs
  • Pricing

Security

  • Overview
  • Comply with requirements
  • Infrastructures under Graal Systems
  • Responsibilities

Troubleshoot & debug

  • Troubleshooting
  • Common issues
  • Debug jobs

Get started with Tensorflow

Prerequisites

You need the following:

  • Git
  • Python >3.7
  • pip

Some libraries installed on Graal:

  • adlfs==2022.2.0
  • aiohttp==3.8.1
  • gcsfs==2022.2.0
  • prometheus-client==0.13.1
  • protobuf==3.19.4
  • pyarrow==7.0.0
  • python-socketio==5.4.1
  • s3fs==2022.2.0
  • h5py==3.6.0
  • pandas==1.4.2
  • tensorflow-datasets==4.5.2

Distributed feature

With the Tensorflow runtime, in addition to using Tensorflow in a classical way, it is possible to do distributed training. For the moment only the "MultiWorkerMirroredStrategy" strategy is compatible with Graal. For more information on how to configure your code: multi_worker_strategy_tensorflow Note that Graal takes care of creating and configuring the TF_CONFIG.

Example

Clone the example project and use pip to build it.

The example project named tensorflow_examples is composed of 2 modules. We can find an implementation of a classification model with Tensorflow and the same model with Tensorflow Distributed.

← Migration from DatabricksGet started with Pytorch →
Graal Platform Documentation
Overview
What is Graal Platform?
Quickstart
Apache SparkApache FlinkApache BeamPythonTensorflowDaskDistributed XGBoost
Links
HomeConsoleCopyrights
Copyright © 2023 Graal Systems