Optimize AI/ML workloads for sustainability: Part 2, model development

{"value":"More complexity often means using more energy, and machine learning (ML) models are becoming bigger and more complex. And though ML hardware is getting more efficient, the energy required to train these ML models is [increasing sharply](https://arxiv.org/pdf/1906.02243.pdf).\n\nIn this series, we’re following the phases of the [Well-Architected machine learning lifecycle](https://docs.aws.amazon.com/wellarchitected/latest/machine-learning-lens/well-architected-machine-learning-lifecycle.html) (Figure 1) to optimize your artificial intelligence (AI)/ML workloads. In Part 2, we examine the model development phase and show you how to train, tune, and evaluate your ML model to help you reduce your carbon footprint.\n\n*If you missed the first part of this series, we showed you how to examine your workload to help you 1) evaluate the impact of your workload, 2) identify alternatives to training your own model, and 3) optimize data processing.*\n\n\n\nFigure 1. ML lifecycle\n\n#### **Model building**\n\n##### ***Define acceptable performance criteria***\nWhen you build an ML model, you’ll likely need to make trade-offs between your model’s accuracy and its carbon footprint. When we focus only on the model’s accuracy, we “[ignore the economic, environmental, or social cost of reaching the reported accuracy](https://arxiv.org/abs/1907.10597).” Because the [relationship between model accuracy and complexity is at best logarithmic](https://arxiv.org/pdf/1611.10012.pdf), training a model longer or looking for better hyperparameters only leads to a [small increase in performance](https://arxiv.org/pdf/1611.10012.pdf).\n\nEstablish performance criteria that support your sustainability goals while meeting your business requirements, not exceeding them.\n\n##### *Select energy-efficient algorithms*\nBegin with a simple algorithm to establish a baseline. Then, [test different algorithms with increasing complexity](https://docs.aws.amazon.com/wellarchitected/latest/machine-learning-lens/mlper-07.html) to observe whether performance has improved. If so, compare the performance gain against the difference in resources required.\n\nTry to find simplified versions of algorithms. This will help you use less resources to achieve a similar outcome. For example, [DistilBERT](https://blog.tensorflow.org/2020/05/how-hugging-face-achieved-2x-performance-boost-question-answering.html), a distilled version of [BERT](https://en.wikipedia.org/wiki/BERT_(language_model)), has 40% fewer parameters, runs 60% faster, and preserves 97% of BERT’s performance.\n\n##### *Use pre-trained or partially pre-trained models*\nConsider techniques to avoid training a model from scratch:\n\n- [**Transfer Learning**](https://en.wikipedia.org/wiki/Transfer_learning): Use a pre-trained source model and reuse it as the starting point for a second task. For example, a model trained on [ImageNet](https://image-net.org/) (14 million images) can generalize with other datasets.\n- [**Incremental Training**](https://docs.aws.amazon.com/sagemaker/latest/dg/incremental-training.html): Use artifacts from an existing model on an expanded dataset to train a new model.\n\n#### **Optimize your deep learning models to accelerate training**\nCompile your DL models from their high-level language representation to hardware-optimized instructions to reduce training time. You can achieve this with open-source compilers or [Amazon SageMaker](https://aws.amazon.com/sagemaker/) [Training Compiler](https://docs.aws.amazon.com/sagemaker/latest/dg/training-compiler.html), which can speed up training of DL models by up to 50% by more efficiently using SageMaker GPU instances.\n\n#### **Start with small experiments, datasets, and compute resources**\nExperiment with smaller datasets in your development notebook. This allows you to iterate quickly with limited carbon emission.\n\n#### **Automate the ML environment**\nWhen building your model, use [Lifecycle Configuration Scripts](https://docs.aws.amazon.com/sagemaker/latest/dg/notebook-lifecycle-config.html) to [automatically stop](https://github.com/aws-samples/amazon-sagemaker-notebook-instance-lifecycle-config-samples/tree/master/scripts/auto-stop-idle) idle SageMaker Notebook instances. If you are using [SageMaker Studio](https://aws.amazon.com/sagemaker/studio/), install the [auto-shutdown Jupyter extension](https://aws.amazon.com/blogs/machine-learning/save-costs-by-automatically-shutting-down-idle-resources-within-amazon-sagemaker-studio/) to detect and stop idle resources.\n\nUse the [fully managed training process](https://docs.aws.amazon.com/sagemaker/latest/dg/how-it-works-training.html) provided by SageMaker to automatically launch training instances and shut them down as soon as the training job is complete. This minimizes idle compute resources and thus limits the environmental impact of your training job.\n\nAdopt a serverless architecture for your [MLOps](https://aws.amazon.com/sagemaker/mlops/) pipelines. For example, orchestration tools like [Amazon Web Services Step Functions](https://aws.amazon.com/step-functions/) or [SageMaker Pipelines](https://aws.amazon.com/sagemaker/pipelines/) only provision resources when work needs to be done. This way, you’re not maintaining compute infrastructure 24/7.\n\n#### **Model training**\n##### **Select sustainable Amazon Web Services Regions**\nAs mentioned in [Part 1](https://aws.amazon.com/blogs/architecture/optimize-ai-ml-workloads-for-sustainability-part-1-identify-business-goals-validate-ml-use-and-process-data/), select an Amazon Web Services Region with sustainable energy sources. When regulations and legal aspects allow, choose Regions [near Amazon renewable energy projects](https://sustainability.aboutamazon.com/about/around-the-globe?energyType=true) and Regions where the grid has low published carbon intensity to train your model.\n\n##### **Use a debugger**\nA debugger like [SageMaker Debugger](https://docs.aws.amazon.com/sagemaker/latest/dg/train-debugger.html) can identify training problems like system bottlenecks, overfitting, saturated activation functions, and [under-utilization of system resources](https://aws.amazon.com/fr/blogs/machine-learning/identifying-training-bottlenecks-and-system-resource-under-utilization-with-amazon-sagemaker-debugger/). It also provides [built-in rules](https://docs.aws.amazon.com/sagemaker/latest/dg/debugger-built-in-rules.html) likelike ```LowGPUUtilization``` or ```Overfit```These rules monitor your workload and will automatically stop a training job as soon as it detects a bug (Figure 2), which helps you avoid unnecessary carbon emissions.\n\n\n\nFigure 2. Automatically stop buggy training jobs with SageMaker Debugger\n\n##### **Optimize the resources of your training environment**\nReference the recommended instance types for the algorithm you’ve selected in the SageMaker documentation. For example, [for DeepAR](https://docs.aws.amazon.com/sagemaker/latest/dg/deepar.html), you should start with a single CPU instance and only switch to GPU and multiple instances [when necessary](https://docs.aws.amazon.com/sagemaker/latest/dg/deepar.html#deepar-instances).\n\nRight size your training jobs with [Amazon CloudWatch](http://aws.amazon.com/cloudwatch) metrics that monitor the utilization of resources like CPU, GPU, memory, and disk utilization.\n\nConsider [Managed Spot Training](https://docs.aws.amazon.com/sagemaker/latest/dg/model-managed-spot-training.html), which takes advantage of unused [Amazon Elastic Compute Cloud (Amazon EC2)](http://aws.amazon.com/ec2) capacity and can save you up to 90% in cost compared to On-Demand instances. By shaping your demand for the existing supply of EC2 instance capacity, you will improve your overall resource efficiency and [reduce idle capacity of the overall Amazon Web Services Cloud](https://docs.aws.amazon.com/wellarchitected/latest/sustainability-pillar/use-instance-types-with-the-least-impact.html).\n\n##### **Use efficient silicon**\nUse [Amazon Web Services Trainium](https://aws.amazon.com/machine-learning/trainium/) for optimized for DL training workloads. It is expected to be [our most energy efficient processor for this purpose](https://youtu.be/9NEQbFLtDmg?t=4538).\n\n##### **Archive or delete unnecessary training artifacts**\nOrganize your ML experiments with [SageMaker Experiments](https://docs.aws.amazon.com/sagemaker/latest/dg/experiments.html) to [clean up training resources](https://docs.aws.amazon.com/sagemaker/latest/dg/experiments-cleanup.html) you no longer need.\n\nReduce the volume of logs you keep. By default, CloudWatch retains logs indefinitely. By [setting limited retention time](https://docs.aws.amazon.com/AmazonCloudWatch/latest/logs/Working-with-log-groups-and-streams.html#SettingLogRetention) for your notebooks and training logs, you’ll avoid the carbon footprint of unnecessary log storage.\n\n#### **Model tuning and evaluation**\n\n##### **Use efficient cross-validation techniques for hyperparameter optimization**\n[Prefer Bayesian search over random search](https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-how-it-works.html) (and [avoid grid search](https://arxiv.org/pdf/1910.09700.pdf)). Bayesian search makes intelligent guesses about the next set of parameters to pick based on the prior set of trials. It typically requires [10 times fewer jobs](https://d1.awsstatic.com/events/reinvent/2019/NEW_LAUNCH_REPEAT_1_Optimizing_Your_Machine_Learning_Models_on_Amazon_SageMaker_AIM361-R1.pdf) than random search, and thus 10 times less compute resources, to find the best hyperparameters.\n\n[Limit the maximum number of concurrent training jobs](https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-considerations.html#automatic-model-tuning-parallelism). Running hyperparameter tuning jobs concurrently gets more work done quickly. However, a tuning job improves only through successive rounds of experiments. Typically, running one training job at a time achieves the best results with the least amount of compute resources.\n\n[Carefully choose the number of hyperparameters and their ranges](https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-considerations.html#automatic-model-tuning-choosing-ranges). You get better results and use less compute resources by limiting your search to a few parameters and small ranges of values. If you know that a hyperparameter is log-scaled, convert it to further improve the optimization.\n\n#### **Use warm-start hyperparameter tuning**\nUse [warm-start](https://aws.amazon.com/blogs/machine-learning/amazon-sagemaker-automatic-model-tuning-becomes-more-efficient-with-warm-start-of-hyperparameter-tuning-jobs/) to leverage the learning gathered in previous tuning jobs to inform which combinations of hyperparameters to search over in the new tuning job. This technique avoids restarting hyperparameter optimization jobs from scratch and thus reduces the compute resources needed.\n\n#### **Measure results and improve**\nTo monitor and quantify improvements of your training jobs, track the following metrics:\n- [Resources provisioned for your training jobs](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeTrainingJob.html#sagemaker-DescribeTrainingJob-response-ResourceConfig) (```InstanceCount```, ```InstanceType```, and ```VolumeSizeInGB```)\n- [Efficient use of these resources](https://docs.aws.amazon.com/sagemaker/latest/dg/monitoring-cloudwatch.html#cloudwatch-metrics-jobs)(```CPUUtilization```, ```GPUUtilization```, ```GPUMemoryUtilization```, ```MemoryUtilization```, and ```DiskUtilization```) in the [SageMaker Console](https://docs.aws.amazon.com/sagemaker/latest/dg/training-metrics.html#view-train-metrics-sm), the [CloudWatch Console](https://docs.aws.amazon.com/sagemaker/latest/dg/training-metrics.html#view-train-metrics-cw) or your [SageMaker Debugger Profiling Report](https://docs.aws.amazon.com/sagemaker/latest/dg/debugger-profiling-report.html#debugger-profiling-report-walkthrough-system-usage)\n\nFor storage:\n\n- The total size of your [Amazon Simple Storage Service (Amazon S3)](http://aws.amazon.com/s3) buckets and storage class distribution, using [Amazon S3](https://aws.amazon.com/s3/storage-analytics-insights/) Storage Lens\n- The [size of your CloudWatch log groups](https://docs.aws.amazon.com/AmazonCloudWatchLogs/latest/APIReference/API_LogGroup.html#CWL-Type-LogGroup-storedBytes)\n\n#### **Conclusion**\nIn this blog post, we discussed techniques and best practices to reduce the energy required to build, train, and evaluate your ML models.\n\nWe also provided recommendations for the tuning process as it makes up a large part of the carbon impact of building an ML model. During hyperparameter and neural design search, hundreds of versions of a given model are created, trained, and evaluated before identifying an optimal design.\n\nIn the next post, we’ll continue our sustainability journey through the ML lifecycle and discuss the best practices you can follow when deploying and monitoring your model in production.\n\n**Want to learn more?** Check out the [Architecting for sustainability session at re:Invent 2021](https://www.youtube.com/watch?v=3-Zq2W1-odU), and other blog posts on [architecting for sustainability](https://aws.amazon.com/blogs/architecture/tag/sustainability/). \n\nThese practices are part of the [Sustainability Pillar](https://docs.aws.amazon.com/wellarchitected/latest/sustainability-pillar/sustainability-pillar.html) of the [Amazon Web Services Well-Architected Framework](https://aws.amazon.com/architecture/well-architected/), which helps you build secure, high-performing, resilient, and efficient infrastructure for your applications and workloads. Use the [Amazon Web Services Well-Architected](https://aws.amazon.com/well-architected-tool/) Tool to address important design considerations and ensure that your workloads follow the best practices and guidance of the Well-Architected Framework. For follow-up questions or comments, join our growing community on [Amazon Web Services re:Post](https://www.repost.aws/topics/TA5g9gZfzuQoWLsZ3wxihrgw/well-architected-framework).\n\n#### **Other posts in this series**\n- [Optimize AI/ML workloads for sustainability: Part 1, identify business goals, validate ML use, and process data](https://aws.amazon.com/blogs/architecture/optimize-ai-ml-workloads-for-sustainability-part-1-identify-business-goals-validate-ml-use-and-process-data/)\n- [Optimize AI/ML workloads for sustainability: Part 3, deployment and monitoring](https://aws.amazon.com/blogs/architecture/optimize-ai-ml-workloads-for-sustainability-part-3-deployment-and-monitoring/)\n#### **Looking for more architecture content?**\n[Amazon Web Services Architecture Center](https://aws.amazon.com/architecture/) provides reference architecture diagrams, vetted architecture solutions, Amazon Web Services [Well-Architected](https://aws.amazon.com/architecture/well-architected/) best practices, patterns, icons, and more!\n\n\n**Benoit de Chateauvieux**\nBenoit de Chateauvieux is a Startup Solutions Architect at Amazon Web Services, based in Montreal, Canada. As a former CTO, he enjoys helping startups build great and sustainable products using the cloud. Outside of work, you’ll find Benoit in canoe-camping expeditions, paddling across Canadian rivers.\n\n\n\n**Eddie Pick**\nEddie Pick is a Senior Startup Solutions Architect at Amazon Web Services based in Montréal, Canada. As an ex co-founder and former CTO, his goal is to help startups build great products faster on the Cloud, particularly using machine learning.\n\n\n\n**Dan Ferguson**\nDan Ferguson is a Solutions Architect at Amazon Web Services, based in New York, USA. As a machine learning services expert, Dan works to support customers on their journey to integrating ML workflows efficiently, effectively, and sustainably.\n\n\n\n**Brendan Sisson**\nBrendan Sisson is a Principal Sustainability Solutions Architect at Amazon Web Services based in London, UK. As a contributor to the Sustainability Pillar of the Amazon Web Services Well-Architected Framework, he supports customers on how they can optimize their workloads running in the Amazon Web Services Cloud and how they can use the Amazon Web Services Cloud to help solve their wider sustainability challenges.","render":"<p>More complexity often means using more energy, and machine learning (ML) models are becoming bigger and more complex. And though ML hardware is getting more efficient, the energy required to train these ML models is <a href=\"https://arxiv.org/pdf/1906.02243.pdf\" target=\"_blank\">increasing sharply</a>.</p>\n<p>In this series, we’re following the phases of the <a href=\"https://docs.aws.amazon.com/wellarchitected/latest/machine-learning-lens/well-architected-machine-learning-lifecycle.html\" target=\"_blank\">Well-Architected machine learning lifecycle</a> (Figure 1) to optimize your artificial intelligence (AI)/ML workloads. In Part 2, we examine the model development phase and show you how to train, tune, and evaluate your ML model to help you reduce your carbon footprint.</p>\n<p><em>If you missed the first part of this series, we showed you how to examine your workload to help you 1) evaluate the impact of your workload, 2) identify alternatives to training your own model, and 3) optimize data processing.</em></p>\n<p><img src=\"https://dev-media.amazoncloud.cn/0bebe2ac2b274b0fad2c065f74ffdfac_image.png\" alt=\"image.png\" /></p>\n<p>Figure 1. ML lifecycle</p>\n<h4><a id=\"Model_building_10\"></a><strong>Model building</strong></h4>\n<h5><a id=\"Define_acceptable_performance_criteria_12\"></a><em><strong>Define acceptable performance criteria</strong></em></h5>\n<p>When you build an ML model, you’ll likely need to make trade-offs between your model’s accuracy and its carbon footprint. When we focus only on the model’s accuracy, we “<a href=\"https://arxiv.org/abs/1907.10597\" target=\"_blank\">ignore the economic, environmental, or social cost of reaching the reported accuracy</a>.” Because the <a href=\"https://arxiv.org/pdf/1611.10012.pdf\" target=\"_blank\">relationship between model accuracy and complexity is at best logarithmic</a>, training a model longer or looking for better hyperparameters only leads to a <a href=\"https://arxiv.org/pdf/1611.10012.pdf\" target=\"_blank\">small increase in performance</a>.</p>\n<p>Establish performance criteria that support your sustainability goals while meeting your business requirements, not exceeding them.</p>\n<h5><a id=\"Select_energyefficient_algorithms_17\"></a><em>Select energy-efficient algorithms</em></h5>\n<p>Begin with a simple algorithm to establish a baseline. Then, <a href=\"https://docs.aws.amazon.com/wellarchitected/latest/machine-learning-lens/mlper-07.html\" target=\"_blank\">test different algorithms with increasing complexity</a> to observe whether performance has improved. If so, compare the performance gain against the difference in resources required.</p>\n<p>Try to find simplified versions of algorithms. This will help you use less resources to achieve a similar outcome. For example, <a href=\"https://blog.tensorflow.org/2020/05/how-hugging-face-achieved-2x-performance-boost-question-answering.html\" target=\"_blank\">DistilBERT</a>, a distilled version of <a href=\"https://en.wikipedia.org/wiki/BERT_(language_model)\" target=\"_blank\">BERT</a>, has 40% fewer parameters, runs 60% faster, and preserves 97% of BERT’s performance.</p>\n<h5><a id=\"Use_pretrained_or_partially_pretrained_models_22\"></a><em>Use pre-trained or partially pre-trained models</em></h5>\n<p>Consider techniques to avoid training a model from scratch:</p>\n<ul>\n<li><a href=\"https://en.wikipedia.org/wiki/Transfer_learning\" target=\"_blank\"><strong>Transfer Learning</strong></a>: Use a pre-trained source model and reuse it as the starting point for a second task. For example, a model trained on <a href=\"https://image-net.org/\" target=\"_blank\">ImageNet</a> (14 million images) can generalize with other datasets.</li>\n<li><a href=\"https://docs.aws.amazon.com/sagemaker/latest/dg/incremental-training.html\" target=\"_blank\"><strong>Incremental Training</strong></a>: Use artifacts from an existing model on an expanded dataset to train a new model.</li>\n</ul>\n<h4><a id=\"Optimize_your_deep_learning_models_to_accelerate_training_28\"></a><strong>Optimize your deep learning models to accelerate training</strong></h4>\n<p>Compile your DL models from their high-level language representation to hardware-optimized instructions to reduce training time. You can achieve this with open-source compilers or <a href=\"https://aws.amazon.com/sagemaker/\" target=\"_blank\">Amazon SageMaker</a> <a href=\"https://docs.aws.amazon.com/sagemaker/latest/dg/training-compiler.html\" target=\"_blank\">Training Compiler</a>, which can speed up training of DL models by up to 50% by more efficiently using SageMaker GPU instances.</p>\n<h4><a id=\"Start_with_small_experiments_datasets_and_compute_resources_31\"></a><strong>Start with small experiments, datasets, and compute resources</strong></h4>\n<p>Experiment with smaller datasets in your development notebook. This allows you to iterate quickly with limited carbon emission.</p>\n<h4><a id=\"Automate_the_ML_environment_34\"></a><strong>Automate the ML environment</strong></h4>\n<p>When building your model, use <a href=\"https://docs.aws.amazon.com/sagemaker/latest/dg/notebook-lifecycle-config.html\" target=\"_blank\">Lifecycle Configuration Scripts</a> to <a href=\"https://github.com/aws-samples/amazon-sagemaker-notebook-instance-lifecycle-config-samples/tree/master/scripts/auto-stop-idle\" target=\"_blank\">automatically stop</a> idle SageMaker Notebook instances. If you are using <a href=\"https://aws.amazon.com/sagemaker/studio/\" target=\"_blank\">SageMaker Studio</a>, install the <a href=\"https://aws.amazon.com/blogs/machine-learning/save-costs-by-automatically-shutting-down-idle-resources-within-amazon-sagemaker-studio/\" target=\"_blank\">auto-shutdown Jupyter extension</a> to detect and stop idle resources.</p>\n<p>Use the <a href=\"https://docs.aws.amazon.com/sagemaker/latest/dg/how-it-works-training.html\" target=\"_blank\">fully managed training process</a> provided by SageMaker to automatically launch training instances and shut them down as soon as the training job is complete. This minimizes idle compute resources and thus limits the environmental impact of your training job.</p>\n<p>Adopt a serverless architecture for your <a href=\"https://aws.amazon.com/sagemaker/mlops/\" target=\"_blank\">MLOps</a> pipelines. For example, orchestration tools like <a href=\"https://aws.amazon.com/step-functions/\" target=\"_blank\">Amazon Web Services Step Functions</a> or <a href=\"https://aws.amazon.com/sagemaker/pipelines/\" target=\"_blank\">SageMaker Pipelines</a> only provision resources when work needs to be done. This way, you’re not maintaining compute infrastructure 24/7.</p>\n<h4><a id=\"Model_training_41\"></a><strong>Model training</strong></h4>\n<h5><a id=\"Select_sustainable_Amazon_Web_Services_Regions_42\"></a><strong>Select sustainable Amazon Web Services Regions</strong></h5>\n<p>As mentioned in <a href=\"https://aws.amazon.com/blogs/architecture/optimize-ai-ml-workloads-for-sustainability-part-1-identify-business-goals-validate-ml-use-and-process-data/\" target=\"_blank\">Part 1</a>, select an Amazon Web Services Region with sustainable energy sources. When regulations and legal aspects allow, choose Regions <a href=\"https://sustainability.aboutamazon.com/about/around-the-globe?energyType=true\" target=\"_blank\">near Amazon renewable energy projects</a> and Regions where the grid has low published carbon intensity to train your model.</p>\n<h5><a id=\"Use_a_debugger_45\"></a><strong>Use a debugger</strong></h5>\n<p>A debugger like <a href=\"https://docs.aws.amazon.com/sagemaker/latest/dg/train-debugger.html\" target=\"_blank\">SageMaker Debugger</a> can identify training problems like system bottlenecks, overfitting, saturated activation functions, and <a href=\"https://aws.amazon.com/fr/blogs/machine-learning/identifying-training-bottlenecks-and-system-resource-under-utilization-with-amazon-sagemaker-debugger/\" target=\"_blank\">under-utilization of system resources</a>. It also provides <a href=\"https://docs.aws.amazon.com/sagemaker/latest/dg/debugger-built-in-rules.html\" target=\"_blank\">built-in rules</a> likelike <code>LowGPUUtilization</code> or <code>Overfit</code>These rules monitor your workload and will automatically stop a training job as soon as it detects a bug (Figure 2), which helps you avoid unnecessary carbon emissions.</p>\n<p><img src=\"https://dev-media.amazoncloud.cn/68f605df14ad4176beb9615a6c39fc10_image.png\" alt=\"image.png\" /></p>\n<p>Figure 2. Automatically stop buggy training jobs with SageMaker Debugger</p>\n<h5><a id=\"Optimize_the_resources_of_your_training_environment_52\"></a><strong>Optimize the resources of your training environment</strong></h5>\n<p>Reference the recommended instance types for the algorithm you’ve selected in the SageMaker documentation. For example, <a href=\"https://docs.aws.amazon.com/sagemaker/latest/dg/deepar.html\" target=\"_blank\">for DeepAR</a>, you should start with a single CPU instance and only switch to GPU and multiple instances <a href=\"https://docs.aws.amazon.com/sagemaker/latest/dg/deepar.html#deepar-instances\" target=\"_blank\">when necessary</a>.</p>\n<p>Right size your training jobs with <a href=\"http://aws.amazon.com/cloudwatch\" target=\"_blank\">Amazon CloudWatch</a> metrics that monitor the utilization of resources like CPU, GPU, memory, and disk utilization.</p>\n<p>Consider <a href=\"https://docs.aws.amazon.com/sagemaker/latest/dg/model-managed-spot-training.html\" target=\"_blank\">Managed Spot Training</a>, which takes advantage of unused <a href=\"http://aws.amazon.com/ec2\" target=\"_blank\">Amazon Elastic Compute Cloud (Amazon EC2)</a> capacity and can save you up to 90% in cost compared to On-Demand instances. By shaping your demand for the existing supply of EC2 instance capacity, you will improve your overall resource efficiency and <a href=\"https://docs.aws.amazon.com/wellarchitected/latest/sustainability-pillar/use-instance-types-with-the-least-impact.html\" target=\"_blank\">reduce idle capacity of the overall Amazon Web Services Cloud</a>.</p>\n<h5><a id=\"Use_efficient_silicon_59\"></a><strong>Use efficient silicon</strong></h5>\n<p>Use <a href=\"https://aws.amazon.com/machine-learning/trainium/\" target=\"_blank\">Amazon Web Services Trainium</a> for optimized for DL training workloads. It is expected to be <a href=\"https://youtu.be/9NEQbFLtDmg?t=4538\" target=\"_blank\">our most energy efficient processor for this purpose</a>.</p>\n<h5><a id=\"Archive_or_delete_unnecessary_training_artifacts_62\"></a><strong>Archive or delete unnecessary training artifacts</strong></h5>\n<p>Organize your ML experiments with <a href=\"https://docs.aws.amazon.com/sagemaker/latest/dg/experiments.html\" target=\"_blank\">SageMaker Experiments</a> to <a href=\"https://docs.aws.amazon.com/sagemaker/latest/dg/experiments-cleanup.html\" target=\"_blank\">clean up training resources</a> you no longer need.</p>\n<p>Reduce the volume of logs you keep. By default, CloudWatch retains logs indefinitely. By <a href=\"https://docs.aws.amazon.com/AmazonCloudWatch/latest/logs/Working-with-log-groups-and-streams.html#SettingLogRetention\" target=\"_blank\">setting limited retention time</a> for your notebooks and training logs, you’ll avoid the carbon footprint of unnecessary log storage.</p>\n<h4><a id=\"Model_tuning_and_evaluation_67\"></a><strong>Model tuning and evaluation</strong></h4>\n<h5><a id=\"Use_efficient_crossvalidation_techniques_for_hyperparameter_optimization_69\"></a><strong>Use efficient cross-validation techniques for hyperparameter optimization</strong></h5>\n<p><a href=\"https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-how-it-works.html\" target=\"_blank\">Prefer Bayesian search over random search</a> (and <a href=\"https://arxiv.org/pdf/1910.09700.pdf\" target=\"_blank\">avoid grid search</a>). Bayesian search makes intelligent guesses about the next set of parameters to pick based on the prior set of trials. It typically requires <a href=\"https://d1.awsstatic.com/events/reinvent/2019/NEW_LAUNCH_REPEAT_1_Optimizing_Your_Machine_Learning_Models_on_Amazon_SageMaker_AIM361-R1.pdf\" target=\"_blank\">10 times fewer jobs</a> than random search, and thus 10 times less compute resources, to find the best hyperparameters.</p>\n<p><a href=\"https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-considerations.html#automatic-model-tuning-parallelism\" target=\"_blank\">Limit the maximum number of concurrent training jobs</a>. Running hyperparameter tuning jobs concurrently gets more work done quickly. However, a tuning job improves only through successive rounds of experiments. Typically, running one training job at a time achieves the best results with the least amount of compute resources.</p>\n<p><a href=\"https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-considerations.html#automatic-model-tuning-choosing-ranges\" target=\"_blank\">Carefully choose the number of hyperparameters and their ranges</a>. You get better results and use less compute resources by limiting your search to a few parameters and small ranges of values. If you know that a hyperparameter is log-scaled, convert it to further improve the optimization.</p>\n<h4><a id=\"Use_warmstart_hyperparameter_tuning_76\"></a><strong>Use warm-start hyperparameter tuning</strong></h4>\n<p>Use <a href=\"https://aws.amazon.com/blogs/machine-learning/amazon-sagemaker-automatic-model-tuning-becomes-more-efficient-with-warm-start-of-hyperparameter-tuning-jobs/\" target=\"_blank\">warm-start</a> to leverage the learning gathered in previous tuning jobs to inform which combinations of hyperparameters to search over in the new tuning job. This technique avoids restarting hyperparameter optimization jobs from scratch and thus reduces the compute resources needed.</p>\n<h4><a id=\"Measure_results_and_improve_79\"></a><strong>Measure results and improve</strong></h4>\n<p>To monitor and quantify improvements of your training jobs, track the following metrics:</p>\n<ul>\n<li><a href=\"https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeTrainingJob.html#sagemaker-DescribeTrainingJob-response-ResourceConfig\" target=\"_blank\">Resources provisioned for your training jobs</a> (<code>InstanceCount</code>, <code>InstanceType</code>, and <code>VolumeSizeInGB</code>)</li>\n<li><a href=\"https://docs.aws.amazon.com/sagemaker/latest/dg/monitoring-cloudwatch.html#cloudwatch-metrics-jobs\" target=\"_blank\">Efficient use of these resources</a>(<code>CPUUtilization</code>, <code>GPUUtilization</code>, <code>GPUMemoryUtilization</code>, <code>MemoryUtilization</code>, and <code>DiskUtilization</code>) in the <a href=\"https://docs.aws.amazon.com/sagemaker/latest/dg/training-metrics.html#view-train-metrics-sm\" target=\"_blank\">SageMaker Console</a>, the <a href=\"https://docs.aws.amazon.com/sagemaker/latest/dg/training-metrics.html#view-train-metrics-cw\" target=\"_blank\">CloudWatch Console</a> or your <a href=\"https://docs.aws.amazon.com/sagemaker/latest/dg/debugger-profiling-report.html#debugger-profiling-report-walkthrough-system-usage\" target=\"_blank\">SageMaker Debugger Profiling Report</a></li>\n</ul>\n<p>For storage:</p>\n<ul>\n<li>The total size of your <a href=\"http://aws.amazon.com/s3\" target=\"_blank\">Amazon Simple Storage Service (Amazon S3)</a> buckets and storage class distribution, using <a href=\"https://aws.amazon.com/s3/storage-analytics-insights/\" target=\"_blank\">Amazon S3</a> Storage Lens</li>\n<li>The <a href=\"https://docs.aws.amazon.com/AmazonCloudWatchLogs/latest/APIReference/API_LogGroup.html#CWL-Type-LogGroup-storedBytes\" target=\"_blank\">size of your CloudWatch log groups</a></li>\n</ul>\n<h4><a id=\"Conclusion_89\"></a><strong>Conclusion</strong></h4>\n<p>In this blog post, we discussed techniques and best practices to reduce the energy required to build, train, and evaluate your ML models.</p>\n<p>We also provided recommendations for the tuning process as it makes up a large part of the carbon impact of building an ML model. During hyperparameter and neural design search, hundreds of versions of a given model are created, trained, and evaluated before identifying an optimal design.</p>\n<p>In the next post, we’ll continue our sustainability journey through the ML lifecycle and discuss the best practices you can follow when deploying and monitoring your model in production.</p>\n<p><strong>Want to learn more?</strong> Check out the <a href=\"https://www.youtube.com/watch?v=3-Zq2W1-odU\" target=\"_blank\">Architecting for sustainability session at re:Invent 2021</a>, and other blog posts on <a href=\"https://aws.amazon.com/blogs/architecture/tag/sustainability/\" target=\"_blank\">architecting for sustainability</a>.</p>\n<p>These practices are part of the <a href=\"https://docs.aws.amazon.com/wellarchitected/latest/sustainability-pillar/sustainability-pillar.html\" target=\"_blank\">Sustainability Pillar</a> of the <a href=\"https://aws.amazon.com/architecture/well-architected/\" target=\"_blank\">Amazon Web Services Well-Architected Framework</a>, which helps you build secure, high-performing, resilient, and efficient infrastructure for your applications and workloads. Use the <a href=\"https://aws.amazon.com/well-architected-tool/\" target=\"_blank\">Amazon Web Services Well-Architected</a> Tool to address important design considerations and ensure that your workloads follow the best practices and guidance of the Well-Architected Framework. For follow-up questions or comments, join our growing community on <a href=\"https://www.repost.aws/topics/TA5g9gZfzuQoWLsZ3wxihrgw/well-architected-framework\" target=\"_blank\">Amazon Web Services re:Post</a>.</p>\n<h4><a id=\"Other_posts_in_this_series_100\"></a><strong>Other posts in this series</strong></h4>\n<ul>\n<li><a href=\"https://aws.amazon.com/blogs/architecture/optimize-ai-ml-workloads-for-sustainability-part-1-identify-business-goals-validate-ml-use-and-process-data/\" target=\"_blank\">Optimize AI/ML workloads for sustainability: Part 1, identify business goals, validate ML use, and process data</a></li>\n<li><a href=\"https://aws.amazon.com/blogs/architecture/optimize-ai-ml-workloads-for-sustainability-part-3-deployment-and-monitoring/\" target=\"_blank\">Optimize AI/ML workloads for sustainability: Part 3, deployment and monitoring</a></li>\n</ul>\n<h4><a id=\"Looking_for_more_architecture_content_103\"></a><strong>Looking for more architecture content?</strong></h4>\n<p><a href=\"https://aws.amazon.com/architecture/\" target=\"_blank\">Amazon Web Services Architecture Center</a> provides reference architecture diagrams, vetted architecture solutions, Amazon Web Services <a href=\"https://aws.amazon.com/architecture/well-architected/\" target=\"_blank\">Well-Architected</a> best practices, patterns, icons, and more!</p>\n<p><img src=\"data:image/png;base64,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\" alt=\"4f20927287be412c8a9722543b8adc7c_image1.png\" rel=\"1\" /><br />\n<strong>Benoit de Chateauvieux</strong><br />\nBenoit de Chateauvieux is a Startup Solutions Architect at Amazon Web Services, based in Montreal, Canada. As a former CTO, he enjoys helping startups build great and sustainable products using the cloud. Outside of work, you’ll find Benoit in canoe-camping expeditions, paddling across Canadian rivers.</p>\n<p><img src=\"https://dev-media.amazoncloud.cn/15fba8aab9ca4e0abf75dfee7bc5ae68_image.png\" alt=\"image.png\" /></p>\n<p><strong>Eddie Pick</strong><br />\nEddie Pick is a Senior Startup Solutions Architect at Amazon Web Services based in Montréal, Canada. As an ex co-founder and former CTO, his goal is to help startups build great products faster on the Cloud, particularly using machine learning.</p>\n<p><img src=\"https://dev-media.amazoncloud.cn/6189c27b00f74b2faada1192f4ff3790_image.png\" alt=\"image.png\" /></p>\n<p><strong>Dan Ferguson</strong><br />\nDan Ferguson is a Solutions Architect at Amazon Web Services, based in New York, USA. As a machine learning services expert, Dan works to support customers on their journey to integrating ML workflows efficiently, effectively, and sustainably.</p>\n<p><img src=\"https://dev-media.amazoncloud.cn/71f82755027d49f09f582017b552b1e7_image.png\" alt=\"image.png\" /></p>\n<p><strong>Brendan Sisson</strong><br />\nBrendan Sisson is a Principal Sustainability Solutions Architect at Amazon Web Services based in London, UK. As a contributor to the Sustainability Pillar of the Amazon Web Services Well-Architected Framework, he supports customers on how they can optimize their workloads running in the Amazon Web Services Cloud and how they can use the Amazon Web Services Cloud to help solve their wider sustainability challenges.</p>\n"}