this using the earlier example for choosing quantization and/or clustering techniques for model optimization. We have a search space which has two boolean valued parameters: quantization and clustering hyperparameters. Some of the commonly tuned hyperparameters are the learning rate and the momentum of the optimization algorithm and the training batch size. Other aspects of the training pipeline like data augmentation quality and footprint) that each string produces. Unlike the guitar which has a few knobs, the hyperparameter search space can be quite large. Moreover, unlike a guitar knob which is associated with a single

001之间，就会有更多的搜索资源可用，还有在0.001到0.01之间等等。 20 超参数调整的方法 Hyperparameter 1 Hyperparameter 2 Hyperparameter 1 Hyperparameter 2 21 由粗到细调整超参数 Hyperparameter 1 Hyperparameter 2 22 熊猫方式与鱼子酱方式 由计算资源决定 23 Batch Norm

rvice/ Pipeline Images: https://github.com/pachyderm/pachyderm/tree/master/doc/examples/ml/hyperparameter 谢谢！

最终，我们真正关心的是生成一个模型，它能够在从未见过的数据上表现良好。但“训练”模型只能将模型 与我们实际能看到的数据相拟合。因此，我们可以将拟合模型的任务分解为两个关键问题： • 优化（optimization）：用模型拟合观测数据的过程； 39 https://discuss.d2l.ai/t/1751 2.4. 微积分 63 • 泛化（generalization）：数学原理和实践者的 size）。η表示学习率（learning rate）。批量 大小和学习率的值通常是手动预先指定，而不是通过模型训练得到的。这些可以调整但不在训练过程中更新 的参数称为超参数（hyperparameter）。调参（hyperparameter tuning）是选择超参数的过程。超参数通常 88 3. 线性神经网络 是我们根据训练迭代结果来调整的，而训练迭代结果是在独立的验证数据集（validation dataset）上评估得 ∇2f）。这相当于根据半正定矩阵的定义，H ⪰ 0。 11.2.3 约束 凸优化的一个很好的特性是能够让我们有效地处理约束（constraints）。即它使我们能够解决以下形式的约 束优化（constrained optimization）问题： minimize x f(x) subject to ci(x) ≤ 0 for all i ∈ {1, . . . , N}. (11.2.16) 这里f是目标函数，ci是约束函数。例如第一个约束c1(x)

+ ? ≥ 1 Negative class: ?!? + ? ≤ −1 ? = 1 ? Figure 2: Hard-margin SVM. aim of the above optimization problem is to find a hyperplane (parameterized by ω and b) with margin γ = 1/∥ω∥ maximized, while training set. 2.2 Preliminary Knowledge of Convex Optimization 2.2.1 Optimization Problems and Lagrangian Duality We now consider the following optimization problem min ω f(ω) (9) s.t. gi(ω) ≤ 0, i = 1 gk(ω) and the equality constraints h1(ω), · · · , hl(ω). We construct the Lagrangian of the above optimization problem as L(ω, α, β ) = f(ω) + k � i=1 αigi(ω) + l � j=1 β jhj(ω) (12) In fact, L(ω, α

Outline 1 SVM: A Primal Form 2 Convex Optimization Review 3 The Lagrange Dual Problem of SVM 4 SVM with Kernels 5 Soft-Margin SVM 6 Sequential Minimal Optimization (SMO) Algorithm Feng Li (SDU) SVM December 28, 2021 15 / 82 Convex Optimization Review Optimization Problem Lagrangian Duality KKT Conditions Convex Optimization S. Boyd and L. Vandenberghe, 2004. Convex Optimization. Cambridge university press press. Feng Li (SDU) SVM December 28, 2021 16 / 82 Optimization Problems Considering the following optimization problem min ω f (ω) s.t. gi(ω) ≤ 0, i = 1, · · · , k hj(ω) = 0, j = 1, · · · , l with

in a Knative based platform ● Performance bottleneck analysis and tuning ○ Istio scalability optimization during Knative Service provisioning ○ Unleash maximum scalability by fully leveraging Istio features MEM Knative Version Knative 0.16, 0.17, 0.18 Istio Version 1.5, 1.6, 1.7 Istio scalability optimization during Knative Service provisioning • Benchmark: Kperf (https://github.com/knative-sandbox/kperf) resolved this issue. o Istiod MEM bumped with large numbers of Knative Services (#25532) Mem usage optimization of pilot resolved this issue. • Tune CPU/MEM to ensure enough capacity Leveraged Metrics to

translation alternatives • Runtime optimizations • load management, scheduling, state management • Optimization semantics, correctness, profitability Topics covered in this lecture ??? Vasiliki Kalavri | different strategies? • before execution or during runtime Query optimization (I) ??? Vasiliki Kalavri | Boston University 2020 10 Optimization strategies • enumerate equivalent execution plans • minimize minimize monetary costs (if running in the cloud) Query optimization (II) ??? Vasiliki Kalavri | Boston University 2020 Cost-based optimization 11 Parsed program representation Optimizer statistics

Kyuubi and nothing more. The Kyuubi server-side or the corresponding engines could do most of the optimization. On the other hand, we don’t wholly restrict end-users to special handling of specific cases to Execution (AQE) in Kyuubi The Basics of AQE Spark Adaptive Query Execution (AQE) is a query re-optimization that occurs during query execution. In terms of technical architecture, the AQE is a framework storage, actually without performing the shuffle across the network. The local shuffle reader optimization consists of avoiding shuffle when the SortMerge Join transforms to BroadcastHash Join after applying

Kyuubi and nothing more. The Kyuubi server-side or the corresponding engines could do most of the optimization. On the other hand, we don’t wholly restrict end-users to special handling of specific cases to Execution (AQE) in Kyuubi The Basics of AQE Spark Adaptive Query Execution (AQE) is a query re-optimization that occurs during query execution. In terms of technical architecture, the AQE is a framework storage, actually without performing the shuffle across the network. The local shuffle reader optimization consists of avoiding shuffle when the SortMerge Join transforms to BroadcastHash Join after applying