This is the Linux app named Horovod whose latest release can be downloaded as CustomdataloadersinSparkTorchEstimator,moremodelparallelisminKeras,improvedallgatherperformance,fixesforlatestPyTorchandTensorFlowversions.zip. It can be run online in the free hosting provider OnWorks for workstations.
Download and run online this app named Horovod with OnWorks for free.
Follow these instructions in order to run this app:
- 1. Downloaded this application in your PC.
- 2. Enter in our file manager https://www.onworks.net/myfiles.php?username=XXXXX with the username that you want.
- 3. Upload this application in such filemanager.
- 4. Start the OnWorks Linux online or Windows online emulator or MACOS online emulator from this website.
- 5. From the OnWorks Linux OS you have just started, goto our file manager https://www.onworks.net/myfiles.php?username=XXXXX with the username that you want.
- 6. Download the application, install it and run it.
SCREENSHOTS:
Horovod
DESCRIPTION:
Horovod was originally developed by Uber to make distributed deep learning fast and easy to use, bringing model training time down from days and weeks to hours and minutes. With Horovod, an existing training script can be scaled up to run on hundreds of GPUs in just a few lines of Python code. Horovod can be installed on-premise or run out-of-the-box in cloud platforms, including AWS, Azure, and Databricks. Horovod can additionally run on top of Apache Spark, making it possible to unify data processing and model training into a single pipeline. Once Horovod has been configured, the same infrastructure can be used to train models with any framework, making it easy to switch between TensorFlow, PyTorch, MXNet, and future frameworks as machine learning tech stacks continue to evolve. Start scaling your model training with just a few lines of Python code. Scale up to hundreds of GPUs with upwards of 90% scaling efficiency.
Features
- Distributed deep learning training framework
- For TensorFlow, Keras, PyTorch, and Apache MXNet
- Scale up to hundreds of GPUs with upwards of 90% scaling efficiency
- Start scaling your model training with just a few lines of Python code
- Runs the same for TensorFlow, Keras, PyTorch, and MXNet
- On premise, in the cloud, and on Apache Spark
Programming Language
Python
Categories
This is an application that can also be fetched from https://sourceforge.net/projects/horovod.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.