Pool best practices | Databricks on AWS
Pool best practices | Databricks on AWS
Dec 18, 2024
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Understand compute targets - Azure Machine Learning
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You’re estimating training compute for an LLM. You calculate 2 × num_params × num_tokens, thinking backward pass mirrors forward pass. Wrong. You just underbudgeted by 300%. Most engineers assume: > Training cost = Forward pass Backward pass = 2N FLOPs That assumption instantly signals lack of production experience. Because backpropagation is not symmetric. ✅ The Solution (The Backprop Asymmetry Explained) Forward pass - One job: compute activations - Cost ≈ 2 FLOPs per parameter Backward pass B
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