15 Questions
What is the main purpose of deep learning?
To learn complex patterns
True or false: Model parallelism is the most common type of distributed training.
False
True or false: Data parallelism does not require communication between the nodes during the processing of a mini batch.
True
What is the main difference between model parallelism and data parallelism?
Model parallelism requires communication between hosts while data parallelism does not
True or false: XPU is more efficient than CPU when it comes to model parallelism.
False
What is the main drawback of model parallelism?
The bottleneck shifts from compute to communication
True or false: Model parallelism is when a single device is used to train a large neural network.
False
What type of distributed training is the most common?
Data parallelism
True or false: Task parallelism divides the task of training a model into multiple subtasks that can be executed on a single worker.
False
What type of distributed training divides the task of training a model into multiple subtasks?
Task parallelism
What is the main benefit of using data parallelism?
No communication between nodes during processing
What type of computer resources are important to consider when using deep learning?
All of the above
What type of distributed training uses multiple workers to train the same model simultaneously?
Model parallelism
What type of nodes are more sensitive to communication overhead?
GPU nodes
What type of nodes are more efficient?
CPU nodes
Study Notes
- Deep learning is a technique that uses large neural networks to learn complex patterns.
- A neural network is a model that contains many parameters called weights.
- The training process involves many iterations where the weight becomes slightly modified by adding delta W.
- In distributed training, we need to take communication overhead into consideration.
- The fully connected topology has the lowest latency.
- There are two types of distributed training: model parallelism and data parallelism.
- Model parallelism happens when we train a very large neural network that cannot fit into a single device.
- In model parallelism, the communication between the hosts is intensive. PCIe transaction overhead can be high, and GPU cards must compete for PCIe bus bandwidth.
- This is the drawback, and, in some cases, may be a waste of computer resources of the GPU, because the bottleneck shifts from compute to communication.
- In data parallelism, there is no communication between the nodes during the processing of a mini batch. These nodes only communicate when a mini batch is processed, and the model update, delta W, is broadcasted.
- CPU, GPU, and XPU are compared on model parallelism.
- GPU nodes are more sensitive to communication overhead, and CPU nodes are more efficient.
- Deep learning requires a lot of computation and communication between computer resources.
- There are three main types of distributed training models: data parallelism, model parallelism, and task parallelism.
- Data parallelism is the most common type of distributed training, where the training data is split between multiple workers.
- Model parallelism uses multiple workers to train the same model simultaneously, while task parallelism divides the task of training a model into multiple subtasks, which can be executed on multiple workers.
- The communication overhead required for deep learning is high, which is why it is important to consider not only the compute power of the machines, but also the computer, memory, and communication resources.
Test your knowledge of distributed training in deep learning, including model parallelism, data parallelism, and communication overhead. Explore the challenges and considerations when using CPU, GPU, and XPU nodes for distributed training.
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