Baselime

baselime

Authors:
(1) Zhaoqing Wang, The University of Sydney and AI2Robotics;
(2) Xiaobo Xia, The University of Sydney;
(3) Ziye Chen, The University of Melbourne;
(4) Xiao He, AI2Robotics;
(5) Yandong Guo, AI2Robotics;
(6) Mingming Gong, The University of Melbourne and Mohamed bin Zayed University of Artificial Intelligence;
(7) Tongliang Liu, The University of Sydney. Table of Links Abstract and 1. Introduction 2. Related works 3. Method and 3.1. Problem definition 3.2. Baseline and 3.3. Uni-OVSeg framework 4. Experiments 4.1. Implementation details 4.2. Main results 4.3. Ablation study 5. Conclusion 6. Broader impacts and References A. Framework details B. Promptable segmentation C. Visualisation 3.2. Baseline We introduce a straightforward baseline using the knowledge of image-text and image-mask pairs. Specifically, we employ a CLIP model as the visual and text encoder, which is trained on a large amount of image-text pairs. Afterward, we use the image-mask pairs to obtain a branch of mask generation, predicting a set of binary masks. To perform open-vocabulary segmentation, we crop and pool the CLIP image features based on these predicted masks, which are further classified by the CLIP text embeddings. Although this straightforward baseline enables open-vocabulary segmentation, it exhibits an obvious knowledge gap between the image-level and pixel-level tasks. 3.3. Uni-OVSeg framework Overview. An overview of our framework Uni-OVSeg, for weakly-supervised open-vocabulary segmentation is illustrated in Fig. 2. On a macro level, Uni-OVSeg contains a CLIP model to extract features of both images and text descriptions. With the image-mask pairs, a branch of mask generation, including a visual prompt encoder, a pixel decoder and a mask decoder, is employed to predict a set of binary masks of an input image. With the image-text pairs, mask-text bipartite matching is used to exploit confident pairs between predicted masks and entities in text descriptions. Afterward, we adopt a multi-scale feature adapter to enhance the mask-wise visual embeddings, which are further aligned with associated entity embeddings based on the confident pairs. Finally, we perform open-vocabulary segmentation with the above-mentioned parts. More details can be found in Appendix A. Mask-text alignment. To enable the model categorising the predicted masks from an open set of vocabulary, given the image-text pairs, we build the correspondence between objects in the image and entities in the text description. Once a set of binary masks m are generated from the input image, we obtain the region embeddings ri by employing a mask pooling layer P and the CLIP visual projector Fv, Open-vocabulary inference. During inference, given the test categories Ctest, we conduct prompt engineering [21] and use the CLIP text encoder to extract text embeddings 1https://openai.com/chatgpt for open-vocabulary segmentation. For each input image, we input an uniform point grid as the visual prompt to predict a set of binary masks, and compute the cosine similarity with the text embeddings to predict the category with the maximum similarity as the label of the corresponding mask. This paper is available on arxiv under CC BY 4.0 DEED license. [1] https://openai.com/chatgpt Authors: (1) Zhaoqing Wang, The University of Sydney and AI2Robotics; (2) Xiaobo Xia, The University of Sydney; (3) Ziye Chen, The University of Melbourne; (4) Xiao He, AI2Robotics; (5) Yandong Guo, AI2Robotics; (6) Mingming Gong, The University of Melbourne and Mohamed bin Zayed University of Artificial Intelligence; (7) Tongliang Liu, The University of Sydney. Authors: Authors: (1) Zhaoqing Wang, The University of Sydney and AI2Robotics; (2) Xiaobo Xia, The University of Sydney; (3) Ziye Chen, The University of Melbourne; (4) Xiao He, AI2Robotics; (5) Yandong Guo, AI2Robotics; (6) Mingming Gong, The University of Melbourne and Mohamed bin Zayed University of Artificial Intelligence; (7) Tongliang Liu, The University of Sydney. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2. Related works 2. Related works 3. Method and 3.1. Problem definition 3. Method and 3.1. Problem definition 3.2. Baseline and 3.3. Uni-OVSeg framework 3.2. Baseline and 3.3. Uni-OVSeg framework 4. Experiments 4.1. Implementation details 4.1. Implementation details 4.2. Main results 4.2. Main results 4.3. Ablation study 4.3. Ablation study 5. Conclusion 5. Conclusion 6. Broader impacts and References 6. Broader impacts and References A. Framework details A. Framework details B. Promptable segmentation B. Promptable segmentation C. Visualisation C. Visualisation 3.2. Baseline We introduce a straightforward baseline using the knowledge of image-text and image-mask pairs. Specifically, we employ a CLIP model as the visual and text encoder, which is trained on a large amount of image-text pairs. Afterward, we use the image-mask pairs to obtain a branch of mask generation, predicting a set of binary masks. To perform open-vocabulary segmentation, we crop and pool the CLIP image features based on these predicted masks, which are further classified by the CLIP text embeddings. Although this straightforward baseline enables open-vocabulary segmentation, it exhibits an obvious knowledge gap between the image-level and pixel-level tasks. 3.3. Uni-OVSeg framework Overview . An overview of our framework Uni-OVSeg, for weakly-supervised open-vocabulary segmentation is illustrated in Fig. 2. On a macro level, Uni-OVSeg contains a CLIP model to extract features of both images and text descriptions. With the image-mask pairs, a branch of mask generation, including a visual prompt encoder, a pixel decoder and a mask decoder, is employed to predict a set of binary masks of an input image. With the image-text pairs, mask-text bipartite matching is used to exploit confident pairs between predicted masks and entities in text descriptions. Afterward, we adopt a multi-scale feature adapter to enhance the mask-wise visual embeddings, which are further aligned with associated entity embeddings based on the confident pairs. Finally, we perform open-vocabulary segmentation with the above-mentioned parts. More details can be found in Appendix A. Overview Mask-text alignment. To enable the model categorising the predicted masks from an open set of vocabulary, given the image-text pairs, we build the correspondence between objects in the image and entities in the text description. Once a set of binary masks m are generated from the input image, we obtain the region embeddings ri by employing a mask pooling layer P and the CLIP visual projector Fv, Mask-text alignment. Open-vocabulary inference. During inference, given the test categories Ctest, we conduct prompt engineering [21] and use the CLIP text encoder to extract text embeddings 1https://openai.com/chatgpt for open-vocabulary segmentation. For each input image, we input an uniform point grid as the visual prompt to predict a set of binary masks, and compute the cosine similarity with the text embeddings to predict the category with the maximum similarity as the label of the corresponding mask. Open-vocabulary inference. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv [1] https://openai.com/chatgpt

he Baseline and Uni-OVSeg Framework for Open-Vocabulary Segmentation

Authors:
(1) Zhaoqing Wang, The University of Sydney and AI2Robotics;
(2) Xiaobo Xia, The University of Sydney;
(3) Ziye Chen, The University of Melbourne;
(4) Xiao He, AI2Robotics;
(5) Yandong Guo, AI2Robotics;
(6) Mingming Gong, The University of Melbourne and Mohamed bin Zayed University of Artificial Intelligence;
(7) Tongliang Liu, The University of Sydney. Table of Links Abstract and 1. Introduction 2. Related works 3. Method and 3.1. Problem definition 3.2. Baseline and 3.3. Uni-OVSeg framework 4. Experiments 4.1. Implementation details 4.2. Main results 4.3. Ablation study 5. Conclusion 6. Broader impacts and References A. Framework details B. Promptable segmentation C. Visualisation 3. Method In this section, we first define the problem of openvocabulary segmentation in Sec. 3.1. We then introduce a straightforward baseline in Sec. 3.2. Finally, we present our proposed Uni-OVSeg framework in Sec. 3.3, including an overview, mask generation, mask-text alignment, and open-vocabulary inference. 3.1. Problem definition This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Zhaoqing Wang, The University of Sydney and AI2Robotics; (2) Xiaobo Xia, The University of Sydney; (3) Ziye Chen, The University of Melbourne; (4) Xiao He, AI2Robotics; (5) Yandong Guo, AI2Robotics; (6) Mingming Gong, The University of Melbourne and Mohamed bin Zayed University of Artificial Intelligence; (7) Tongliang Liu, The University of Sydney. Authors: Authors: (1) Zhaoqing Wang, The University of Sydney and AI2Robotics; (2) Xiaobo Xia, The University of Sydney; (3) Ziye Chen, The University of Melbourne; (4) Xiao He, AI2Robotics; (5) Yandong Guo, AI2Robotics; (6) Mingming Gong, The University of Melbourne and Mohamed bin Zayed University of Artificial Intelligence; (7) Tongliang Liu, The University of Sydney. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2. Related works 2. Related works 3. Method and 3.1. Problem definition 3. Method and 3.1. Problem definition 3.2. Baseline and 3.3. Uni-OVSeg framework 3.2. Baseline and 3.3. Uni-OVSeg framework 4. Experiments 4.1. Implementation details 4.1. Implementation details 4.2. Main results 4.2. Main results 4.3. Ablation study 4.3. Ablation study 5. Conclusion 5. Conclusion 6. Broader impacts and References 6. Broader impacts and References A. Framework details A. Framework details B. Promptable segmentation B. Promptable segmentation C. Visualisation C. Visualisation 3. Method In this section, we first define the problem of openvocabulary segmentation in Sec. 3.1. We then introduce a straightforward baseline in Sec. 3.2. Finally, we present our proposed Uni-OVSeg framework in Sec. 3.3, including an overview, mask generation, mask-text alignment, and open-vocabulary inference. 3.1. Problem definition This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Defining Open-Vocabulary Segmentation: Problem Setup, Baseline, and the Uni-OVSeg Framework

Authors:
(1) Rafael Rafailo, Stanford University and Equal contribution; more junior authors listed earlier;
(2) Archit Sharma, Stanford University and Equal contribution; more junior authors listed earlier;
(3) Eric Mitchel, Stanford University and Equal contribution; more junior authors listed earlier;
(4) Stefano Ermon, CZ Biohub;
(5) Christopher D. Manning, Stanford University;
(6) Chelsea Finn, Stanford University. Table of Links Abstract and 1. Introduction 2 Related Work 3 Preliminaries 4 Direct Preference Optimization 5 Theoretical Analysis of DPO 6 Experiments 7 Discussion, Acknowledgements, and References Author Contributions A Mathematical Derivations A.1 Deriving the Optimum of the KL-Constrained Reward Maximization Objective A.2 Deriving the DPO Objective Under the Bradley-Terry Model A.3 Deriving the DPO Objective Under the Plackett-Luce Model A.4 Deriving the Gradient of the DPO Objective and A.5 Proof of Lemma 1 and 2 A.6 Proof of Theorem 1 B DPO Implementation Details and Hyperparameters C Further Details on the Experimental Set-Up and C.1 IMDb Sentiment Experiment and Baseline Details C.2 GPT-4 prompts for computing summarization and dialogue win rates C.3 Unlikelihood baseline D Additional Empirical Results D.1 Performance of Best of N baseline for Various N and D.2 Sample Responses and GPT-4 Judgments D.3 Human study details Additional Empirical Results D.1 Performance of Best of N baseline for Various N We find that the Best of N baseline is a strong (although computationally expensive, requiring sampling many times) baseline in our experiments. We include an evaluation of the Best of N baseline for various N for the Anthropic-HH dialogue and TL;DR summarization; the results are shown in Figure 4. D.2 Sample Responses and GPT-4 Judgments In this section, we present examples of comparisons between DPO and the baseline (PPO temp 0. for summarization, and the ground truth chosen response for dialogue). See Tables 4-6 for summarization examples, and Tables 7-10 for dialogue examples. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: (1) Rafael Rafailo, Stanford University and Equal contribution; more junior authors listed earlier; (2) Archit Sharma, Stanford University and Equal contribution; more junior authors listed earlier; (3) Eric Mitchel, Stanford University and Equal contribution; more junior authors listed earlier; (4) Stefano Ermon, CZ Biohub; (5) Christopher D. Manning, Stanford University; (6) Chelsea Finn, Stanford University. Authors: Authors: (1) Rafael Rafailo, Stanford University and Equal contribution; more junior authors listed earlier; (2) Archit Sharma, Stanford University and Equal contribution; more junior authors listed earlier; (3) Eric Mitchel, Stanford University and Equal contribution; more junior authors listed earlier; (4) Stefano Ermon, CZ Biohub; (5) Christopher D. Manning, Stanford University; (6) Chelsea Finn, Stanford University. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2 Related Work 2 Related Work 3 Preliminaries 3 Preliminaries 4 Direct Preference Optimization 4 Direct Preference Optimization 5 Theoretical Analysis of DPO 5 Theoretical Analysis of DPO 6 Experiments 6 Experiments 7 Discussion, Acknowledgements, and References 7 Discussion, Acknowledgements, and References Author Contributions Author Contributions A Mathematical Derivations A.1 Deriving the Optimum of the KL-Constrained Reward Maximization Objective A.1 Deriving the Optimum of the KL-Constrained Reward Maximization Objective A.2 Deriving the DPO Objective Under the Bradley-Terry Model A.2 Deriving the DPO Objective Under the Bradley-Terry Model A.3 Deriving the DPO Objective Under the Plackett-Luce Model A.3 Deriving the DPO Objective Under the Plackett-Luce Model A.4 Deriving the Gradient of the DPO Objective and A.5 Proof of Lemma 1 and 2 A.4 Deriving the Gradient of the DPO Objective and A.5 Proof of Lemma 1 and 2 A.6 Proof of Theorem 1 A.6 Proof of Theorem 1 B DPO Implementation Details and Hyperparameters B DPO Implementation Details and Hyperparameters C Further Details on the Experimental Set-Up and C.1 IMDb Sentiment Experiment and Baseline Details C Further Details on the Experimental Set-Up and C.1 IMDb Sentiment Experiment and Baseline Details C.2 GPT-4 prompts for computing summarization and dialogue win rates C.2 GPT-4 prompts for computing summarization and dialogue win rates C.3 Unlikelihood baseline C.3 Unlikelihood baseline D Additional Empirical Results D.1 Performance of Best of N baseline for Various N and D.2 Sample Responses and GPT-4 Judgments D.1 Performance of Best of N baseline for Various N and D.2 Sample Responses and GPT-4 Judgments D.3 Human study details D.3 Human study details Additional Empirical Results D.1 Performance of Best of N baseline for Various N We find that the Best of N baseline is a strong (although computationally expensive, requiring sampling many times) baseline in our experiments. We include an evaluation of the Best of N baseline for various N for the Anthropic-HH dialogue and TL;DR summarization; the results are shown in Figure 4. D.2 Sample Responses and GPT-4 Judgments In this section, we present examples of comparisons between DPO and the baseline (PPO temp 0. for summarization, and the ground truth chosen response for dialogue). See Tables 4-6 for summarization examples, and Tables 7-10 for dialogue examples. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv

Performance of Best of N Baseline for Various N and Sample Responses and GPT-4 Judgments

Authors:
(1) Rafael Rafailo, Stanford University and Equal contribution; more junior authors listed earlier;
(2) Archit Sharma, Stanford University and Equal contribution; more junior authors listed earlier;
(3) Eric Mitchel, Stanford University and Equal contribution; more junior authors listed earlier;
(4) Stefano Ermon, CZ Biohub;
(5) Christopher D. Manning, Stanford University;
(6) Chelsea Finn, Stanford University. Table of Links Abstract and 1. Introduction 2 Related Work 3 Preliminaries 4 Direct Preference Optimization 5 Theoretical Analysis of DPO 6 Experiments 7 Discussion, Acknowledgements, and References Author Contributions A Mathematical Derivations A.1 Deriving the Optimum of the KL-Constrained Reward Maximization Objective A.2 Deriving the DPO Objective Under the Bradley-Terry Model A.3 Deriving the DPO Objective Under the Plackett-Luce Model A.4 Deriving the Gradient of the DPO Objective and A.5 Proof of Lemma 1 and 2 A.6 Proof of Theorem 1 B DPO Implementation Details and Hyperparameters C Further Details on the Experimental Set-Up and C.1 IMDb Sentiment Experiment and Baseline Details C.2 GPT-4 prompts for computing summarization and dialogue win rates C.3 Unlikelihood baseline D Additional Empirical Results D.1 Performance of Best of N baseline for Various N and D.2 Sample Responses and GPT-4 Judgments D.3 Human study details C.3 Unlikelihood baseline This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: (1) Rafael Rafailo, Stanford University and Equal contribution; more junior authors listed earlier; (2) Archit Sharma, Stanford University and Equal contribution; more junior authors listed earlier; (3) Eric Mitchel, Stanford University and Equal contribution; more junior authors listed earlier; (4) Stefano Ermon, CZ Biohub; (5) Christopher D. Manning, Stanford University; (6) Chelsea Finn, Stanford University. Authors: Authors: (1) Rafael Rafailo, Stanford University and Equal contribution; more junior authors listed earlier; (2) Archit Sharma, Stanford University and Equal contribution; more junior authors listed earlier; (3) Eric Mitchel, Stanford University and Equal contribution; more junior authors listed earlier; (4) Stefano Ermon, CZ Biohub; (5) Christopher D. Manning, Stanford University; (6) Chelsea Finn, Stanford University. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2 Related Work 2 Related Work 3 Preliminaries 3 Preliminaries 4 Direct Preference Optimization 4 Direct Preference Optimization 5 Theoretical Analysis of DPO 5 Theoretical Analysis of DPO 6 Experiments 6 Experiments 7 Discussion, Acknowledgements, and References 7 Discussion, Acknowledgements, and References Author Contributions Author Contributions A Mathematical Derivations A.1 Deriving the Optimum of the KL-Constrained Reward Maximization Objective A.1 Deriving the Optimum of the KL-Constrained Reward Maximization Objective A.2 Deriving the DPO Objective Under the Bradley-Terry Model A.2 Deriving the DPO Objective Under the Bradley-Terry Model A.3 Deriving the DPO Objective Under the Plackett-Luce Model A.3 Deriving the DPO Objective Under the Plackett-Luce Model A.4 Deriving the Gradient of the DPO Objective and A.5 Proof of Lemma 1 and 2 A.4 Deriving the Gradient of the DPO Objective and A.5 Proof of Lemma 1 and 2 A.6 Proof of Theorem 1 A.6 Proof of Theorem 1 B DPO Implementation Details and Hyperparameters B DPO Implementation Details and Hyperparameters C Further Details on the Experimental Set-Up and C.1 IMDb Sentiment Experiment and Baseline Details C Further Details on the Experimental Set-Up and C.1 IMDb Sentiment Experiment and Baseline Details C.2 GPT-4 prompts for computing summarization and dialogue win rates C.2 GPT-4 prompts for computing summarization and dialogue win rates C.3 Unlikelihood baseline C.3 Unlikelihood baseline D Additional Empirical Results D.1 Performance of Best of N baseline for Various N and D.2 Sample Responses and GPT-4 Judgments D.1 Performance of Best of N baseline for Various N and D.2 Sample Responses and GPT-4 Judgments D.3 Human study details D.3 Human study details C.3 Unlikelihood baseline This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv

The Unlikelihood Baseline in Sentiment Experiments

Authors:
(1) Xiao-Yang Liu, Hongyang Yang, Columbia University (xl2427,hy2500@columbia.edu);
(2) Jiechao Gao, University of Virginia (jg5ycn@virginia.edu);
(3) Christina Dan Wang (Corresponding Author), New York University Shanghai (christina.wang@nyu.edu). Table of Links Abstract and 1 Introduction 2 Related Works and 2.1 Deep Reinforcement Learning Algorithms 2.2 Deep Reinforcement Learning Libraries and 2.3 Deep Reinforcement Learning in Finance 3 The Proposed FinRL Framework and 3.1 Overview of FinRL Framework 3.2 Application Layer 3.3 Agent Layer 3.4 Environment Layer 3.5 Training-Testing-Trading Pipeline 4 Hands-on Tutorials and Benchmark Performance and 4.1 Backtesting Module 4.2 Baseline Strategies and Trading Metrics 4.3 Hands-on Tutorials 4.4 Use Case I: Stock Trading 4.5 Use Case II: Portfolio Allocation and 4.6 Use Case III: Cryptocurrencies Trading 5 Ecosystem of FinRL and Conclusions, and References 4.2 Baseline Strategies and Trading Metrics Baseline trading strategies are provided to compare with DRL strategies. Investors usually have two mutually conflicting objectives: the highest possible profits and the lowest possible risks [43]. We include three conventional strategies as baselines. Passive trading strategy [31] is an easy and popular strategy that has the minimal trading activities. Investors simply buy and hold index ETFs [46] to replicate a broad market index or indices such as Dow Jones Industrial Average (DJIA) index and Standard & Poor’s 500 (S&P 500) index. Mean-variance and min-variance strategy [2] both aim to achieve an optimal balance between the risks and profits. It selects a diversified portfolio with risky assets, and the risk is diversified when traded together. Equally weighted strategy is a type of portfolio allocation method. It gives the same importance to each asset in a portfolio. FinRL includes common metrics to evaluate trading performance: Final portfolio value: the amount of money at the end of the trading period. Cumulative return: subtracting the initial value from the final portfolio value, then dividing by the initial value. Annualized return and standard deviation: geometric average return in a yearly sense, and the corresponding deviation. Maximum drawdown ratio: the maximum observed loss from a historical peak to a trough of a portfolio, before a new peak is achieved. Maximum drawdown is an indicator of downside risk over a time period. Sharpe ratio in (1) is the average return earned in excess of the risk-free rate per unit of volatility. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Xiao-Yang Liu, Hongyang Yang, Columbia University (xl2427,hy2500@columbia.edu); (2) Jiechao Gao, University of Virginia (jg5ycn@virginia.edu); (3) Christina Dan Wang (Corresponding Author), New York University Shanghai (christina.wang@nyu.edu). Authors: Authors: (1) Xiao-Yang Liu, Hongyang Yang, Columbia University (xl2427,hy2500@columbia.edu); (2) Jiechao Gao, University of Virginia (jg5ycn@virginia.edu); (3) Christina Dan Wang ( Corresponding Author ), New York University Shanghai (christina.wang@nyu.edu). Corresponding Author Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Related Works and 2.1 Deep Reinforcement Learning Algorithms 2 Related Works and 2.1 Deep Reinforcement Learning Algorithms 2.2 Deep Reinforcement Learning Libraries and 2.3 Deep Reinforcement Learning in Finance 2.2 Deep Reinforcement Learning Libraries and 2.3 Deep Reinforcement Learning in Finance 3 The Proposed FinRL Framework and 3.1 Overview of FinRL Framework 3 The Proposed FinRL Framework and 3.1 Overview of FinRL Framework 3.2 Application Layer 3.2 Application Layer 3.3 Agent Layer 3.3 Agent Layer 3.4 Environment Layer 3.4 Environment Layer 3.5 Training-Testing-Trading Pipeline 3.5 Training-Testing-Trading Pipeline 4 Hands-on Tutorials and Benchmark Performance and 4.1 Backtesting Module 4 Hands-on Tutorials and Benchmark Performance and 4.1 Backtesting Module 4.2 Baseline Strategies and Trading Metrics 4.2 Baseline Strategies and Trading Metrics 4.3 Hands-on Tutorials 4.3 Hands-on Tutorials 4.4 Use Case I: Stock Trading 4.4 Use Case I: Stock Trading 4.5 Use Case II: Portfolio Allocation and 4.6 Use Case III: Cryptocurrencies Trading 4.5 Use Case II: Portfolio Allocation and 4.6 Use Case III: Cryptocurrencies Trading 5 Ecosystem of FinRL and Conclusions, and References 5 Ecosystem of FinRL and Conclusions, and References 4.2 Baseline Strategies and Trading Metrics Baseline trading strategies are provided to compare with DRL strategies. Investors usually have two mutually conflicting objectives: the highest possible profits and the lowest possible risks [43]. We include three conventional strategies as baselines. Passive trading strategy [31] is an easy and popular strategy that has the minimal trading activities. Investors simply buy and hold index ETFs [46] to replicate a broad market index or indices such as Dow Jones Industrial Average (DJIA) index and Standard & Poor’s 500 (S&P 500) index. Passive trading strategy Mean-variance and min-variance strategy [2] both aim to achieve an optimal balance between the risks and profits. It selects a diversified portfolio with risky assets, and the risk is diversified when traded together. Mean-variance and min-variance strategy Equally weighted strategy is a type of portfolio allocation method. It gives the same importance to each asset in a portfolio. Equally weighted strategy FinRL includes common metrics to evaluate trading performance: Final portfolio value : the amount of money at the end of the trading period. Final portfolio value Cumulative return : subtracting the initial value from the final portfolio value, then dividing by the initial value. Cumulative return Annualized return and standard deviation : geometric average return in a yearly sense, and the corresponding deviation. Annualized return and standard deviation Maximum drawdown ratio : the maximum observed loss from a historical peak to a trough of a portfolio, before a new peak is achieved. Maximum drawdown is an indicator of downside risk over a time period. Maximum drawdown ratio Sharpe ratio in (1) is the average return earned in excess of the risk-free rate per unit of volatility. Sharpe ratio This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Unveiling FinRL's Baseline Strategies and Key Trading Metrics for Portfolio Evaluation

For years, web developers of all experience levels have relied on __caniuse.com __to understand the support of a given feature. Reviewing caniuse.com is helpful in determining backward compatibility or which of the modern browsers supports cutting-edge improvements to web specifications. In 2023, Google introduced Baseline to the web community. This tool is not just another addition to the developer's toolkit but a crucial aid in determining the safety of features and APIs for use in production-grade web applications. It’s a platform for interoperability, a much-needed resource to help engineers produce more consistent and efficient experiences, and a testament to Google's commitment to improving web best practices. Why Google Baseline? Browsers are constantly evolving, and vendors release updates monthly. With more individuals contributing and working on specifications than ever before and a far more transparent dialogue between the main browser vendors (Safari, Firefox, Chrome, Edge), it's become increasingly difficult to track which new features are stable across the landscape. However, with the introduction of Baseline, this struggle is significantly alleviated. Considering the above, as new features are released into browser engines, engineers often need more time to ramp up, refactor, or strategically plan the introduction of new features into their code bases. More importantly, as Google is often one of the platforms that push front-end technology forward, it has admittedly accepted that it is partly to blame here. It’s one of the reasons the Chrome team came up with web.dev and developer.chrome.com, as Chrome is often the first browser to land cutting-edge technologies. The introduction of Baseline has also led to further documentation and conferences focused on interoperability. New features mean documentation, and Google has made strides to ensure MDN has the relevant documentation for newer technologies as they land in Chrome. Closing the Vendor Gap From Google's perspective, engineers have one significant struggle: the behaviour and consistency of features across vendors and browser engines. It’s a pain point for all engineers in the field, requiring complex planning and solutions often to overcome a particular browser engine's lack of support. The solution here (for the last two years) was Interop 2022 and Interop 2023 - a conference and community that helps bridge the gap between browsers to provide a more consistent and performant web. What is Baseline? Baseline serves as the universal flag of acceptance across browser vendors. It helps engineers determine what is fully stable across the landscape and across browser vendors for the most recent two versions of each browser. This feature stability assurance is a game-changer in web development, providing a reliable benchmark for engineers to base their decisions on. How Does Baseline Work? You can access Google Baseline on MDN, web.dev and caniuse.com. Baseline also plans to provide a way to see what has become stable across all browser vendors through a year-on-year approach. For instance, you can read the following article to see what got added to Baseline in 2023: Baseline 2023 As engineering teams, we must keep up with these changes and avoid getting stuck in old solutions for common problems. Importantly, Baseline has two labels: Newly available: the feature is finally interoperable.
Widely available: 30 months have passed since the interoperable date. Baseline considers the major browser vendors to be: Safari (macOS and iOS)
Firefox (Desktop + Android)
Chrome (Desktop + Android)
Edge (Desktop) Google Baseline’s Effect on Engineering Teams One of the most significant benefits of Google Baseline for engineering teams is the removal of guesswork and research required to implement new features. For instance, a Frontend Engineer may want to leverage CSS Grid's new sub-grid feature. With Baseline, they can quickly and confidently determine its support across major browsers, saving time and effort. Sub-grid helps engineers define a grid within a grid to have more refined control over layouts. Heading to caniuse.com tells us there’s a global usage percentage of 87.42%—pretty decent, but we still need to ascertain which browser vendors support / don’t support / partially support the feature. CanIUse now displays a Baseline badge that tells us that this feature is “Newly available across major browsers.” Likewise, if you review the documentation for the sub-grid on MDN, you’ll see a similar banner: Finally, if you review the sub-grid article on web.dev, you’ll see this feature got added to Baseline in 2023: A Word on Evergreen Browsers In my opinion, we do not talk enough about this topic. For most modern users, the days of manually updating browsers are gone. Chrome, Firefox, and Edge auto-update automatically. Safari is an outlier in this case. Safari requires a manual update when a new release is sent out from Apple or when OS X is upgraded annually. Regardless, more users than ever use the latest version of browsers. Although we should ensure the features we build degrade gracefully, this is less of a fundamental consideration nowadays and more of a luxury assumption. Useful Links Google Baseline
Google Baseline Introduction
Baseline 2023
Interop 2022 / Interop 2023 For years, web developers of all experience levels have relied on __ caniuse.com __to understand the support of a given feature. Reviewing caniuse.com is helpful in determining backward compatibility or which of the modern browsers supports cutting-edge improvements to web specifications. caniuse.com In 2023, Google introduced Baseline to the web community. This tool is not just another addition to the developer's toolkit but a crucial aid in determining the safety of features and APIs for use in production-grade web applications. It’s a platform for interoperability, a much-needed resource to help engineers produce more consistent and efficient experiences, and a testament to Google's commitment to improving web best practices. Baseline Baseline Why Google Baseline? Browsers are constantly evolving, and vendors release updates monthly. With more individuals contributing and working on specifications than ever before and a far more transparent dialogue between the main browser vendors (Safari, Firefox, Chrome, Edge), it's become increasingly difficult to track which new features are stable across the landscape. However, with the introduction of Baseline, this struggle is significantly alleviated. Considering the above, as new features are released into browser engines, engineers often need more time to ramp up, refactor, or strategically plan the introduction of new features into their code bases. More importantly, as Google is often one of the platforms that push front-end technology forward, it has admittedly accepted that it is partly to blame here. It’s one of the reasons the Chrome team came up with web.dev and developer.chrome.com , as Chrome is often the first browser to land cutting-edge technologies. The introduction of Baseline has also led to further documentation and conferences focused on interoperability. web.dev web.dev developer.chrome.com developer.chrome.com New features mean documentation, and Google has made strides to ensure MDN has the relevant documentation for newer technologies as they land in Chrome. Closing the Vendor Gap From Google's perspective, engineers have one significant struggle: the behaviour and consistency of features across vendors and browser engines . It’s a pain point for all engineers in the field, requiring complex planning and solutions often to overcome a particular browser engine's lack of support. the behaviour and consistency of features across vendors and browser engines The solution here (for the last two years) was Interop 2022 and Interop 2023 - a conference and community that helps bridge the gap between browsers to provide a more consistent and performant web. Interop 2022 Interop 2022 Interop 2023 Interop 2023 What is Baseline? Baseline serves as the universal flag of acceptance across browser vendors. It helps engineers determine what is fully stable across the landscape and across browser vendors for the most recent two versions of each browser. This feature stability assurance is a game-changer in web development, providing a reliable benchmark for engineers to base their decisions on. How Does Baseline Work? You can access Google Baseline on MDN , web.dev and caniuse.com . MDN MDN web.dev web.dev caniuse.com caniuse.com Baseline also plans to provide a way to see what has become stable across all browser vendors through a year-on-year approach. For instance, you can read the following article to see what got added to Baseline in 2023: Baseline 2023 Baseline 2023 Baseline 2023 As engineering teams, we must keep up with these changes and avoid getting stuck in old solutions for common problems. Importantly, Baseline has two labels: Newly available: the feature is finally interoperable. Widely available: 30 months have passed since the interoperable date. Newly available: the feature is finally interoperable. Widely available: 30 months have passed since the interoperable date. Baseline considers the major browser vendors to be: Safari (macOS and iOS) Firefox (Desktop + Android) Chrome (Desktop + Android) Edge (Desktop) Safari (macOS and iOS) Safari Safari Firefox (Desktop + Android) Firefox Firefox Chrome (Desktop + Android) Chrome Chrome Edge (Desktop) Edge Edge Google Baseline’s Effect on Engineering Teams One of the most significant benefits of Google Baseline for engineering teams is the removal of guesswork and research required to implement new features. For instance, a Frontend Engineer may want to leverage CSS Grid's new sub-grid feature. With Baseline, they can quickly and confidently determine its support across major browsers, saving time and effort. sub-grid sub-grid Sub-grid helps engineers define a grid within a grid to have more refined control over layouts. Heading to caniuse.com tells us there’s a global usage percentage of 87.42%—pretty decent, but we still need to ascertain which browser vendors support / don’t support / partially support the feature. caniuse.com caniuse.com CanIUse now displays a Baseline badge that tells us that this feature is “ Newly available across major browsers. ” CanIUse CanIUse Newly available across major browsers. Likewise, if you review the documentation for the sub-grid on MDN, you’ll see a similar banner: Finally, if you review the sub-grid article on web.dev , you’ll see this feature got added to Baseline in 2023 : web.dev web.dev Baseline in 2023 Baseline in 2023 A Word on Evergreen Browsers In my opinion, we do not talk enough about this topic. For most modern users, the days of manually updating browsers are gone. Chrome, Firefox, and Edge auto-update automatically. Safari is an outlier in this case. Safari requires a manual update when a new release is sent out from Apple or when OS X is upgraded annually. Regardless, more users than ever use the latest version of browsers. Although we should ensure the features we build degrade gracefully, this is less of a fundamental consideration nowadays and more of a luxury assumption. Useful Links Google Baseline Google Baseline Introduction Baseline 2023 Interop 2022 / Interop 2023 Google Baseline Google Baseline Google Baseline Google Baseline Introduction Google Baseline Introduction Google Baseline Introduction Baseline 2023 Baseline 2023 Baseline 2023 Interop 2022 / Interop 2023 Interop 2022 Interop 2022 Interop 2023 Interop 2023

What Is Google Baseline?: A Quick Look at Its Impact on Browser Compatibility and Interoperability

Starting Simple: The Strategic Advantage of Baseline Models in Machine Learning

Begin eenvoudig: die strategiese voordeel van basislynmodelle in masjienleer

ቀላል ጅምር፡ በማሽን መማሪያ ውስጥ የመነሻ ሞዴሎች ስልታዊ ጥቅም

البدء ببساطة: الميزة الاستراتيجية للنماذج الأساسية في التعلم الآلي

Sapuru qalltaña: Ventaja Estratégica de Modelos de Línea Base en Aprendizaje Máquina

Sadə Başlanğıc: Maşın Öyrənməsində Baza Modellərinin Strateji Üstünlüyü

Пачаць з простага: стратэгічная перавага базавых мадэляў у машынным навучанні

Да започнем лесно: Стратегическото предимство на базовите модели в машинното обучение

Počevši od jednostavnog: Strateška prednost osnovnih modela u mašinskom učenju

Començant senzill: l'avantatge estratègic dels models de referència en l'aprenentatge automàtic

Začít jednoduše: Strategická výhoda základních modelů ve strojovém učení

Begyndende enkelt: Den strategiske fordel ved basismodeller i maskinlæring

Ξεκινώντας απλά: Το στρατηγικό πλεονέκτημα των βασικών μοντέλων στη μηχανική μάθηση

Hasi sinplea: oinarrizko ereduen abantaila estrategikoa ikaskuntza automatikoan

شروع ساده: مزیت استراتژیک مدل های پایه در یادگیری ماشین

Aloitetaan yksinkertaisesta: perusmallien strateginen etu koneoppimisessa

Comezando sinxelo: a vantaxe estratéxica dos modelos de referencia na aprendizaxe automática

התחלה פשוטה: היתרון האסטרטגי של מודלים בסיסיים בלמידת מכונה

Početi jednostavno: strateška prednost osnovnih modela u strojnom učenju

Kòmanse Senp: Avantaj Estratejik nan Modèl debaz nan aprantisaj machin

Kezdve egyszerűen: Az alapmodellek stratégiai előnyei a gépi tanulásban

Սկսած պարզ. Հիմնական մոդելների ռազմավարական առավելությունը մեքենայական ուսուցման մեջ

Memulai dengan Sederhana: Keunggulan Strategis Model Dasar dalam Pembelajaran Mesin

Iniziare in modo semplice: il vantaggio strategico dei modelli di base nell'apprendimento automatico

საწყისი მარტივია: საბაზისო მოდელების სტრატეგიული უპირატესობა მანქანათმცოდნეობაში

Қарапайымнан бастау: машиналық оқытудағы базалық үлгілердің стратегиялық артықшылығы

ការចាប់ផ្តើមសាមញ្ញ៖ អត្ថប្រយោជន៍ជាយុទ្ធសាស្រ្តនៃគំរូមូលដ្ឋានក្នុងការរៀនម៉ាស៊ីន

Жөнөкөйдөн баштоо: Машиналарды үйрөнүүдө базалык моделдердин стратегиялык артыкчылыгы

Kobanda na pete: Avantage stratégique ya ba modèles ya base na apprentissage automatique

ເລີ່ມຕົ້ນງ່າຍດາຍ: ຄວາມໄດ້ປຽບຍຸດທະສາດຂອງຕົວແບບພື້ນຖານໃນການຮຽນຮູ້ເຄື່ອງຈັກ

Pradedant paprastai: strateginis bazinių modelių pranašumas mašininio mokymosi srityje

Sākot ar vienkāršu: bāzes modeļu stratēģiskās priekšrocības mašīnmācībā

Manomboka tsotra: Ny tombony stratejika amin'ny maodely fototra amin'ny fianarana milina

Едноставно започнување: Стратешката предност на основните модели во машинското учење

Энгийн эхлэл: Машины сургалтын үндсэн загваруудын стратегийн давуу тал

Bermula Mudah: Kelebihan Strategik Model Baseline dalam Pembelajaran Mesin

सुरु गर्ने सरल: मेसिन लर्निङमा आधारभूत मोडेलहरूको रणनीतिक लाभ

Eenvoudig beginnen: het strategische voordeel van basismodellen in machinaal leren

Go Thoma ka Bonolo: Mohola wa Togamaano wa Mehlala ya Motheo go Thutong ya Motšhene

Zaczynając od prostoty: strategiczna przewaga modeli bazowych w uczeniu maszynowym

ساده پیل کول: د ماشین زده کړې کې د اساسی ماډلونو ستراتیژیک ګټه

Simple qallariy: Ventaja Estratégica nisqa Modelos de Base nisqamanta Maquina Aprendizaje nisqapi

Începe simplu: avantajul strategic al modelelor de bază în învățarea automată

Gutangira Byoroshye: Ibyiza Byingirakamaro Byibanze Byibanze mu Kwiga Imashini

සරලව ආරම්භ කිරීම: යන්ත්‍ර ඉගෙනීමේ මූලික මාදිලිවල උපාය මාර්ගික වාසිය

Začať jednoducho: Strategická výhoda základných modelov v strojovom učení

Kutanga Nyore: Iyo Strategic Advantage yeBaseline Models muMuchina Kudzidza

Bilaabida Fudud: Faa'iidada Istaraatiijiyadeed ee Moodooyinka Saldhigga ee Barashada Mashiinka

Fillimi i thjeshtë: Avantazhi Strategjik i Modeleve Bazë në Mësimin e Makinerisë

Почевши од једноставног: стратешка предност основних модела у машинском учењу

Att börja enkelt: Den strategiska fördelen med basmodeller i maskininlärning

Kuanza Rahisi: Manufaa ya Kimkakati ya Miundo ya Msingi katika Kujifunza kwa Mashine

எளிமையாக தொடங்குதல்: இயந்திர கற்றலில் அடிப்படை மாதிரிகளின் மூலோபாய நன்மை

Оғози оддӣ: Бартарии стратегии моделҳои ибтидоӣ дар омӯзиши мошин

เริ่มต้นง่ายๆ: ข้อได้เปรียบเชิงกลยุทธ์ของโมเดลพื้นฐานในการเรียนรู้ของเครื่องจักร

ቀሊል ምጅማር፡ ስትራተጂካዊ ብልጫ መሰረታዊ ሞዴላት ኣብ ማሽን ትምህርቲ

Pleönekeýje başlamak: Maşyn öwrenmekde esasy modelleriň strategiki artykmaçlygy

Simpleng Simula: Ang Madiskarteng Advantage ng Baseline Models sa Machine Learning

Ku Sungula hi ku Olova: Vuyelo bya Maqhinga ya Timodelo ta Masungulo eka Dyondzo ya Muchini

Почати з простого: стратегічна перевага базових моделей у машинному навчанні

شروع کرنا آسان: مشین لرننگ میں بیس لائن ماڈلز کا اسٹریٹجک فائدہ

Oddiy boshlash: Mashinani o'rganishda asosiy modellarning strategik ustunligi

Ukuqala Okulula: UbuNcedo obuCwangcisiweyo beMifuziselo esisiseko kwiSifundo soomatshini

Ukuqala Okulula: Inzuzo Yamasu Yamamodeli Asesisekelo Ekufundeni Ngomshini

Authors: (1) Muzhaffar Hazman, University of Galway, Ireland; (2) Susan McKeever, Technological University Dublin, Ireland; (3) Josephine Griffith, University of Galway, Ireland. Table of Links Abstract and Introduction Related Works Methodology Results Limitations and Future Works Conclusion, Acknowledgments, and References A Hyperparameters and Settings B Metric: Weighted F1-Score C Architectural Details D Performance Benchmarking E Contingency Table: Baseline vs. Text-STILT E Contingency Table: Baseline vs. Text-STILT Table 8 shows the contingency table – as one would prepare for a McNemar’s Test between two classifiers (McNemar, 1947) – between the model trained with Text-STILT on 60% Memes and Baseline trained on 100% Memes available which had the most similar performance. While the two models performed similarly in terms of Weighted F1- scores, Text-STILT correctly classified a notable number of memes that Baseline did not and vice versa. Examples of such memes are discussed in Section 4.1. Furthermore, approximately 40% of memes in the testing set were incorrectly classified by both models. This suggests that these memes convey sentiment in a way that cannot be reliably predicted by either approach. This paper is under CC 4.0 license. available on arxiv

Contingency Table: Baseline vs. Text-STILT

Authors: (1) Savvas Petridis, Google Research, New York, New York, USA; (2) Ben Wedin, Google Research, Cambridge, Massachusetts, USA; (3) James Wexler, Google Research, Cambridge, Massachusetts, USA; (4) Aaron Donsbach, Google Research, Seattle, Washington, USA; (5) Mahima Pushkarna, Google Research, Cambridge, Massachusetts, USA; (6) Nitesh Goyal, Google Research, New York, New York, USA; (7) Carrie J. Cai, Google Research, Mountain View, California, USA; (8) Michael Terry, Google Research, Cambridge, Massachusetts, USA. Table Of Links Abstract & Introduction Related Work Formative Study Constitution Maker Implementation User Study Findings Discussion Conclusion and References 6 USER STUDY To understand (1) if the principle elicitation features help users write principles to steer LLM outputs and (2) what other kinds of feedback they wanted to give, we conducted a 14-participant within-subjects user study, comparing ConstitutionMaker to an ablated version without the principle elicitation features. This ablated version still offered users the ability to rewind parts of the conversation, but participants could only see one chatbot output at a time and had to write their own principles. 6.1 Procedure The overall outline of this study is as follows: (1) Participants spent 40 minutes writing principles for two separate chatbots, one with ConstitutionMaker (20 minutes) and the other with the baseline version (20 minutes), while thinking aloud. Condition order was counterbalanced, with chatbot assignment per condition also balanced. (2) After writing principles for both chatbots, participants completed a post-study questionnaire, which compared the process of writing principles with each tool. (4) Finally, in a semi-structured interview, participants described the positives and negatives of each tool and their workflow. The total time commitment of the study was 1 hour. The two chatbots participants wrote principles for were VacationBot, an assistant that helps users plan and explore different vacation options, and FoodBot, an assistant that helps you plan your meals and figure out what to eat. These two chatbots were chosen because they support tasks that most people are experienced with, so that participants could have opinions on their outputs and write principles. For both chatbots, participants were given the name and capabilities (Figure 1A), so as to focus predominantly on principle writing. Also, we balanced which chatbot went with each condition, so half of the participants used ConstitutionMaker to write principles for VacationBot, and the other half used the baseline for VacationBot. Finally, prior to using each version, participants watched a short video showing that respective tool’s features. To situate the task, participants were asked to pretend to be a chatbot designer and that they were writing principles to dictate the chatbot’s behavior so that it performs better for users. We wanted to observe their process for writing principles and see if the tools impacted how many principles they could write, so we encouraged participants to write at least 10 principles for each chatbot, to give them a concrete goal and to motivate them to write more principles. However, we emphasized that this was only to encourage them to write principles and that they should only write a principle if they thought it would be useful to future users. 6.2 Measurements and Analysis 6.2.1 Questionnaire. elicitation features help users write principles. Our questionnaire (Table 1) probes a few aspects of principle writing including participants’ perception of (1) how well the output principles effectively guide the bot, the diversity of the output principles, how easy it was to convert their feedback into principles, the efficiency of their principle writing process, and the requisite mental demand [9] for writing principles with each tool. To compare the two conditions, we conducted paired sample Wilcoxon tests with full Bonferroni correction, since the study was within-subjects and the questionnaire data was ordinal. We wanted to understand if and how well ConstitutionMaker’s principle To shed further light on which tool helped participants write principles more, we recorded the number of principles written for each condition. Moreover, to understand which of the principles elicitation features was most helpful, we recorded how often each was used during the experimental (full ConstitutionMaker) condition. To compare the average number of principles collected across the two conditions, we conducted a paired t-test. 6.2.2 Feature Usage Metrics. 6.3 Participants We recruited 14 industry professionals at a large technology company (average age = 32, 6 female and 8 male) via an email call for participation and word of mouth. These industry professionals included UX designers, software engineers, data scientists, and UX researchers. Eligible participants were those that had at least written a few LLM prompts in the past. The interviews were conducted remotely. Participants received a $25 gift card for their time. This paper is under CC 4.0 license. available on arxiv

Comparing ConstitutionMaker to Baseline: User Study Unveils Insights into Chatbot Principle Writing

Larger GPU Instances don’t always mean faster Processing The artificial intelligence, machine learning, and generative AI application's growth have swelled the demand for high-performance GPU workloads. To fulfill these needs, cloud services have introduced a broad range of instances to fulfill diverse needs. For many, has been a quick fix to increase computational power by replacing a smaller instance with a more powerful one. However, this methodology might be less effective or even pointless for GPU-intensive workloads. vertical scaling Understanding GPU Workloads GPUs are designed for high-speed mathematical calculations. These specialized computational capabilities make them a good choice or, for some applications, the only option for handling large dataset mathematical operations in machine learning (ML) and video editing. CPUs’ primary aim is to perform sequential task execution, whereas GPUs excel as workhorses when it comes to deep learning, 3D rendering, and scientific computations. The Basics of GPU Scaling Instances can be scaled vertically, horizontally, or through a combination of both as explained . The Vertical scaling strategy means upgrading to a larger server with more computational power, such as CPU, memory, and GPU. here The Horizontal scaling strategy involves adding more instances instead of upgrading the current one, hence increasing resources in parallel, very much like buying additional properties in the neighborhood to accommodate your growing family. Limitations of Vertical Scaling for GPUs Vertical scaling's effectiveness is evident when we need more CPU or memory resources, but it hits a roadblock with GPU workloads. This is because upgrading to a larger instance does not necessarily increase the GPU's computational power. For example, the instances from AWS both house the Nvidia Tesla T4 and T4G respectively. g5g.large and g4dn.large Despite any vertical scaling between these instance types, the GPU power remains unchanged, as they contain the same number of GPU cores as shown in the following image. Hence, vertical scaling will not address the actual bottleneck in GPU-intensive tasks. Practical Implications The implication is evident: when your application demands more GPU power, vertically scaling your instance will not deliver the required performance improvements. This inefficiency can lead to not just stagnant performance, but also increased costs, as you are paying for additional CPU and memory resources that your workload may never utilize. An Alternative to Vertical Scaling When the bottleneck is GPU computational capabilities, horizontal scaling becomes a more viable solution. By adding more instances to your cloud infrastructure, you can achieve a near-linear increase in GPU capabilities. Horizontal scaling with the right kind of servers will improve your infrastructure's resource utilization, thus offering a more cost-effective solution. Training an ML model with a vertically scaled server will add more CPU and memory but will not improve GPU power. Therefore, there will not be any improvement in training time. Shifting to horizontal scaling, with multiple GPU instances working in tandem, can significantly reduce the training time, showcasing the right approach to scaling for such tasks. Conclusion While vertical scaling offers a quick solution for prompt performance boost in certain scenarios, it falls short with the GPU instances option available. I highly recommend checking out for a deeper understanding of when to employ each strategy. horizontal and vertical scaling A nuanced grasp of these scaling methods is crucial for optimizing performance and effective cost management in various computational scenarios. For tasks that are GPU-bound, adding more GPU power through horizontal scaling or re-architecting the solution is often the more effective path. Even for horizontal scaling, opt for the least powered instance, such as or , to avoid paying for extra unused CPU power that is not required. Adopting the right scaling strategy, you can ensure that your infrastructure is not just robust but also cost-effective and performance-optimized for your specific computational needs. G5g.xlarge G4dn.xlarge More From the Author Learn About the Issues and Solution at Is AWS Deep Learning AMIs saving you time? A counterproductive approach hindering your progress! How Your AWS Deep Learning AMI is Holding You Back Read the full article at: A Cost-Effective Creating Deep Learning AMIs deployment with Spot Instances: Navigate parallel frontier! Deep Learning on AWS Graviton2 powered by Nvidia Tensor T4g A comprehensive guide is available at . Fine-Tune FFmpeg for Optimal Performance with detailed compilation guide: Optimize your multimedia backend for optimal performance! Tailor FFmpeg for your needs

Choosing Baseline GPU Instances: What Are the Advantages?

Stories, News & Mentions about BASELIME

Baselime’s Stories on HackerNoon