TensorFlow 2.0 Tutorial 05: Distributed Training across Multiple Nodes
Distributed training allows scaling up deep learning task so bigger models can be learned or training can be conducted at a faster pace. In a previous ...
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Introducing NVIDIA SHARP on Lambda 1CC: Next-Gen Performance for Distributed AI Workloads
Lambda’s 1-Click Clusters(1CC) provide AI teams with streamlined access to scalable, multi-node GPU clusters, cutting through the complexity of distributed infrastructure. Now, we're pushing the envelope further by integrating NVIDIA's Scalable Hierarchical Aggregation and Reduction Protocol (SHARP) into our multi-tenant 1CC environments. This technology reduces communication latency and improves bandwidth efficiency, directly accelerating training speed of distributed AI workloads.
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TensorFlow 2.0 Tutorial 04: Early Stopping
During training, weights in the neural networks are updated so that the model performs better on the training data. For a while, improvements on the training ...
TensorFlow 2.0 Tutorial 03: Saving Checkpoints
This tutorial combines two items from previous tutorials: saving models and callbacks. Checkpoints are saved model states that occur during training. With ...
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