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Creating scalable and environment friendly machine studying (ML) pipelines is essential for streamlining the event, deployment, and administration of ML fashions. On this put up, we current a framework for automating the creation of a directed acyclic graph (DAG) for Amazon SageMaker Pipelines based mostly on easy configuration recordsdata. The framework code and examples introduced right here solely cowl mannequin coaching pipelines, however might be readily prolonged to batch inference pipelines as nicely.

This dynamic framework makes use of configuration recordsdata to orchestrate preprocessing, coaching, analysis, and registration steps for each single-model and multi-model use circumstances based mostly on user-defined Python scripts, infrastructure wants (together with Amazon Digital Personal Cloud (Amazon VPC) subnets and safety teams, AWS Id and Entry Administration (IAM) roles, AWS Key Administration Service (AWS KMS) keys, containers registry, and occasion sorts), enter and output Amazon Easy Storage Service (Amazon S3) paths, and useful resource tags. Configuration recordsdata (YAML and JSON) enable ML practitioners to specify undifferentiated code for orchestrating coaching pipelines utilizing declarative syntax. This permits knowledge scientists to rapidly construct and iterate on ML fashions, and empowers ML engineers to run via steady integration and steady supply (CI/CD) ML pipelines sooner, lowering time to manufacturing for fashions.

Answer overview

The proposed framework code begins by studying the configuration recordsdata. It then dynamically creates a SageMaker Pipelines DAG based mostly on the steps declared within the configuration recordsdata and the interactions and dependencies amongst steps. This orchestration framework caters to each single-model and multi-model use circumstances, and gives a easy movement of information and processes. The next are the important thing advantages of this answer:

  • Automation – The complete ML workflow, from knowledge preprocessing to mannequin registry, is orchestrated with no guide intervention. This reduces the effort and time required for mannequin experimentation and operationalization.
  • Reproducibility – With a predefined configuration file, knowledge scientists and ML engineers can reproduce your entire workflow, attaining constant outcomes throughout a number of runs and environments.
  • Scalability – Amazon SageMaker is used all through the pipeline, enabling ML practitioners to course of massive datasets and prepare advanced fashions with out infrastructure considerations.
  • Flexibility – The framework is versatile and might accommodate a variety of ML use circumstances, ML frameworks (resembling XGBoost and TensorFlow), multi-model coaching, and multi-step coaching. Each step of the coaching DAG might be custom-made through the configuration file.
  • Mannequin governance – The Amazon SageMaker Mannequin Registry integration permits for monitoring mannequin variations, and subsequently selling them to manufacturing with confidence.

The next structure diagram depicts how you should use the proposed framework throughout each experimentation and operationalization of ML fashions. Throughout experimentation, you’ll be able to clone the framework code repository offered on this put up and your project-specific supply code repositories into Amazon SageMaker Studio, and set your digital setting (detailed later on this put up). You possibly can then iterate on preprocessing, coaching, and analysis scripts, in addition to configuration selections. To create and run a SageMaker Pipelines coaching DAG, you’ll be able to name the framework’s entry level, which can learn all of the configuration recordsdata, create the mandatory steps, and orchestrate them based mostly on the required step ordering and dependencies.

Throughout operationalization, the CI pipeline clones the framework code repository and project-specific coaching repositories into an AWS CodeBuild job, the place the framework’s entry level script is named to create or replace the SageMaker Pipelines coaching DAG, after which run it.

Repository construction

The GitHub repository accommodates the next directories and recordsdata:

  • /framework/conf/ – This listing accommodates a configuration file that’s used to set frequent variables throughout all modeling items resembling subnets, safety teams, and IAM position on the runtime. A modeling unit is a sequence of as much as six steps for coaching an ML mannequin.
  • /framework/createmodel/ – This listing accommodates a Python script that creates a SageMaker model object based mostly on mannequin artifacts from a SageMaker Pipelines coaching step. The mannequin object is later utilized in a SageMaker batch rework job for evaluating mannequin efficiency on a check set.
  • /framework/modelmetrics/ – This listing accommodates a Python script that creates an Amazon SageMaker Processing job for producing a mannequin metrics JSON report for a educated mannequin based mostly on outcomes of a SageMaker batch rework job carried out on check knowledge.
  • /framework/pipeline/ – This listing accommodates Python scripts that use Python courses outlined in different framework directories to create or replace a SageMaker Pipelines DAG based mostly on the required configurations. The model_unit.py script is utilized by pipeline_service.py to create a number of modeling items. Every modeling unit is a sequence of as much as six steps for coaching an ML mannequin: course of, prepare, create mannequin, rework, metrics, and register mannequin. Configurations for every modeling unit ought to be specified within the mannequin’s respective repository. The pipeline_service.py additionally units dependencies amongst SageMaker Pipelines steps (how steps inside and throughout modeling items are sequenced or chained) based mostly on the sagemakerPipeline part, which ought to be outlined within the configuration file of one of many mannequin repositories (the anchor mannequin). This lets you override default dependencies inferred by SageMaker Pipelines. We talk about the configuration file construction later on this put up.
  • /framework/processing/ – This listing accommodates a Python script that creates a SageMaker Processing job based mostly on the required Docker picture and entry level script.
  • /framework/registermodel/ – This listing accommodates a Python script for registering a educated mannequin together with its calculated metrics in SageMaker Mannequin Registry.
  • /framework/coaching/ – This listing accommodates a Python script that creates a SageMaker coaching job.
  • /framework/rework/ – This listing accommodates a Python script that creates a SageMaker batch rework job. Within the context of mannequin coaching, that is used to calculate the efficiency metric of a educated mannequin on check knowledge.
  • /framework/utilities/ – This listing accommodates utility scripts for studying and becoming a member of configuration recordsdata, in addition to logging.
  • /framework_entrypoint.py – This file is the entry level of the framework code. It calls a operate outlined within the /framework/pipeline/ listing to create or replace a SageMaker Pipelines DAG and run it.
  • /examples/ – This listing accommodates a number of examples of how you should use this automation framework to create easy and sophisticated coaching DAGs.
  • /env.env – This file permits you to set frequent variables resembling subnets, safety teams, and IAM position as setting variables.
  • /necessities.txt – This file specifies Python libraries which are required for the framework code.

Stipulations

It’s best to have the next stipulations earlier than deploying this answer:

  • An AWS account
  • SageMaker Studio
  • A SageMaker position with Amazon S3 learn/write and AWS KMS encrypt/decrypt permissions
  • An S3 bucket for storing knowledge, scripts, and mannequin artifacts
  • Optionally, the AWS Command Line Interface (AWS CLI)
  • Python3 (Python 3.7 or better) and the next Python packages:
  • Further Python packages utilized in your customized scripts

Deploy the answer

Full the next steps to deploy the answer:

  1. Manage your mannequin coaching repository in accordance with the next construction:
    <MODEL-DIR-REPO>
     .
    ├── <MODEL-DIR>
    |    ├── conf
    |    |   └── conf.yaml
    |    └── scripts
    |        ├── preprocess.py
    |        ├── prepare.py
    |        ├── rework.py
    |        └── consider.py
    └── README.md
    

  2. Clone the framework code and your mannequin supply code from the Git repositories:
    • Clone dynamic-sagemaker-pipelines-framework repo right into a coaching listing. Within the following code, we assume the coaching listing is named aws-train:
      git clone https://github.com/aws-samples/dynamic-sagemaker-pipelines-framework.git aws-train

    • Clone the mannequin supply code below the identical listing. For multi-model coaching, repeat this step for as many fashions as it’s worthwhile to prepare.
      git clone https:<MODEL-DIR-REPO>.git aws-train

For single-model coaching, your listing ought to appear like the next:

<aws-train>  
.  
├── framework
└── <MODEL-DIR>

For multi-model coaching, your listing ought to appear like the next:

<aws-train>  
.  
├── framework
└── <MODEL-DIR-1>
└── <MODEL-DIR-2>
└── <MODEL-DIR-3>

  1. Arrange the next setting variables. Asterisks point out setting variables which are required; the remaining are optionally available.
Atmosphere Variable Description
SMP_ACCOUNTID* AWS account the place the SageMaker pipeline is run
SMP_REGION* AWS Area the place the SageMaker pipeline is run
SMP_S3BUCKETNAME* S3 bucket identify
SMP_ROLE* SageMaker position
SMP_MODEL_CONFIGPATH* Relative path of the of single-model or multi-model configuration recordsdata
SMP_SUBNETS Subnet IDs for SageMaker networking configuration
SMP_SECURITYGROUPS Safety group IDs for SageMaker networking configuration

For single-model use circumstances, SMP_MODEL_CONFIGPATH might be <MODEL-DIR>/conf/conf.yaml. For multi-model use circumstances, SMP_MODEL_CONFIGPATH might be */conf/conf.yaml, which lets you discover all conf.yaml recordsdata utilizing Python’s glob module and mix them to type a world configuration file. Throughout experimentation (native testing), you’ll be able to specify setting variables contained in the env.env file after which export them by working the next command in your terminal:

Word that the values of setting variables in env.env ought to be positioned inside citation marks (for instance, SMP_REGION="us-east-1"). Throughout operationalization, these setting variables ought to be set by the CI pipeline.

  1. Create and activate a digital setting by working the next instructions:
    python -m venv .venv
    
    supply .venv/bin/activate

  2. Set up the required Python packages by working the next command:
    pip set up -r necessities.txt

  3. Edit your mannequin coaching conf.yaml recordsdata. We talk about the configuration file construction within the subsequent part.
  4. From the terminal, name the framework’s entry level to create or replace and run the SageMaker Pipeline coaching DAG:
    python framework/framework_entrypoint.py

  5. View and debug the SageMaker Pipelines run on the Pipelines tab of the SageMaker Studio UI.

Configuration file construction

There are two forms of configuration recordsdata within the proposed answer: framework configuration and mannequin configuration. On this part, we describe every intimately.

Framework configuration

The /framework/conf/conf.yaml file units the variables which are frequent throughout all modeling items. This consists of SMP_S3BUCKETNAME, SMP_ROLE, SMP_MODEL_CONFIGPATH, SMP_SUBNETS, SMP_SECURITYGROUPS, and SMP_MODELNAME. Consult with Step 3 of deployment directions for descriptions of those variables and tips on how to set them through setting variables.

Mannequin configuration

For every mannequin within the challenge, we have to specify the next within the <MODEL-DIR>/conf/conf.yaml file (asterisks point out required sections; the remaining are optionally available):

  • /conf/fashions* – On this part, you’ll be able to configure a number of modeling items. When the framework code is run, it would robotically learn all configuration recordsdata throughout runtime and append them to the config tree. Theoretically, you’ll be able to specify all modeling items in the identical conf.yaml file, nevertheless it’s advisable to specify every modeling unit configuration in its respective listing or Git repository to attenuate errors. The items are as follows:
    • {model-name}* – The identify of the mannequin.
    • source_directory* – A typical source_dir path to make use of for all steps throughout the modeling unit.
    • preprocess – This part specifies preprocessing parameters.
    • prepare* – This part specifies coaching job parameters.
    • rework* – This part specifies SageMaker Remodel job parameters for making predictions on the check knowledge.
    • consider – This part specifies SageMaker Processing job parameters for producing a mannequin metrics JSON report for the educated mannequin.
    • registry* – This part specifies parameters for registering the educated mannequin in SageMaker Mannequin Registry.
  • /conf/sagemakerPipeline* – This part defines the SageMaker Pipelines movement, together with dependencies amongst steps. For single-model use circumstances, this part is outlined on the finish of the configuration file. For multi-model use circumstances, the sagemakerPipeline part solely must be outlined within the configuration file of one of many fashions (any of the fashions). We confer with this mannequin because the anchor mannequin. The parameters are as follows:
    • pipelineName* – Title of the SageMaker pipeline.
    • fashions* – Nested checklist of modeling items:
      • {model-name}* – Mannequin identifier, which ought to match a {model-name} identifier within the /conf/fashions part.
        • steps*
          • step_name* – Step identify to be displayed within the SageMaker Pipelines DAG.
          • step_class* – (Union[Processing, Training, CreateModel, Transform, Metrics, RegisterModel])
          • step_type* – This parameter is simply required for preprocessing steps, for which it ought to be set to preprocess. That is wanted to tell apart preprocess and consider steps, each of which have a step_class of Processing.
          • enable_cache – ([Union[True, False]]). This means whether or not to allow SageMaker Pipelines caching for this step.
          • chain_input_source_step – ([list[step_name]]). You need to use this to set the channel outputs of one other step as enter to this step.
          • chain_input_additional_prefix – That is solely allowed for steps of the Remodel step_class, and can be utilized along side chain_input_source_step parameter to pinpoint the file that ought to be used because the enter to the rework step.
    • dependencies – This part specifies the sequence through which the SageMaker Pipelines steps ought to be run. We’ve tailored the Apache Airflow notation for this part (for instance, {step_name} >> {step_name}). If this part is left clean, specific dependencies specified by the chain_input_source_step parameter or implicit dependencies outline the SageMaker Pipelines DAG movement.

Word that we suggest having one coaching step per modeling unit. If a number of coaching steps are outlined for a modeling unit, the following steps implicitly take the final coaching step to create the mannequin object, calculate metrics, and register the mannequin. If it’s worthwhile to prepare a number of fashions, it’s advisable to create a number of modeling items.

Examples

On this part, we show three examples of ML mannequin coaching DAGs created utilizing the introduced framework.

Single-model coaching: LightGBM

This can be a single-model instance for a classification use case the place we use LightGBM in script mode on SageMaker. The dataset consists of categorical and numerical variables to foretell the binary label Income (to foretell if the topic makes a purchase order or not). The preprocessing script is used to mannequin the info for coaching and testing after which stage it in an S3 bucket. The S3 paths are then offered to the training step within the configuration file.

When the coaching step runs, SageMaker hundreds the file on the container at /choose/ml/enter/knowledge/{channelName}/, accessible through the setting variable SM_CHANNEL_{channelName} on the container (channelName= ‘train’ or ‘test’).The training script does the next:

  1. Load the recordsdata domestically from native container paths utilizing the NumPy load module.
  2. Set hyperparameters for the coaching algorithm.
  3. Save the educated mannequin on the native container path /choose/ml/mannequin/.

SageMaker takes the content material below /choose/ml/mannequin/ to create a tarball that’s used to deploy the mannequin to SageMaker for internet hosting.

The rework step takes as enter the staged test file as input and the educated mannequin to make predictions on the educated mannequin. The output of the rework step is chained to the metrics step to judge the mannequin towards the ground truth, which is explicitly equipped to the metrics step. Lastly, the output of the metrics step is implicitly chained to the register step to register the mannequin in SageMaker Mannequin Registry with details about the mannequin’s efficiency produced within the metrics step. The next determine reveals a visible illustration of the coaching DAG. You possibly can confer with the scripts and configuration file for this instance within the GitHub repo.

Single-model coaching: LLM fine-tuning

That is one other single-model coaching instance, the place we orchestrate fine-tuning of a Falcon-40B massive language mannequin (LLM) from Hugging Face Hub for a textual content summarization use case. The preprocessing script hundreds the samsum dataset from Hugging Face, hundreds the tokenizer for the mannequin, and processes the prepare/check knowledge splits for fine-tuning the mannequin on this area knowledge within the falcon-text-summarization-preprocess step.

The output is chained to the falcon-text-summarization-tuning step, the place the training script hundreds the Falcon-40B LLM from Hugging Face Hub and begins accelerated fine-tuning utilizing LoRA on the prepare break up. The mannequin is evaluated in the identical step after fine-tuning, which gatekeeps the analysis loss to fail the falcon-text-summarization-tuning step, which causes the SageMaker pipeline to cease earlier than it is ready to register the fine-tuned mannequin. In any other case, the falcon-text-summarization-tuning step runs efficiently and the mannequin is registered in SageMaker Mannequin Registry. The next determine reveals a visible illustration of the LLM fine-tuning DAG. The scripts and configuration file for this instance can be found within the GitHub repo.

Multi-model coaching

This can be a multi-model coaching instance the place a principal part evaluation (PCA) mannequin is educated for dimensionality discount, and a TensorFlow Multilayer Perceptron mannequin is educated for California Housing Price prediction. The TensorFlow mannequin’s preprocessing step makes use of a educated PCA mannequin to cut back dimensionality of its coaching knowledge. We add a dependency within the configuration to make sure the TensorFlow mannequin is registered after PCA mannequin registration. The next determine reveals a visible illustration of the multi-model coaching DAG instance. The scripts and configuration recordsdata for this instance can be found within the GitHub repo.

Clear up

Full the next steps to scrub up your assets:

  1. Use the AWS CLI to checklist and take away any remaining pipelines which are created by the Python scripts.
  2. Optionally, delete different AWS assets such because the S3 bucket or IAM position created outdoors SageMaker Pipelines.

Conclusion

On this put up, we introduced a framework for automating SageMaker Pipelines DAG creation based mostly on configuration recordsdata. The proposed framework presents a forward-looking answer to the problem of orchestrating advanced ML workloads. Through the use of a configuration file, SageMaker Pipelines gives the flexibleness to construct orchestration with minimal code, so you’ll be able to streamline the method of making and managing each single-model and multi-model pipelines. This strategy not solely saves time and assets, but additionally promotes MLOps finest practices, contributing to the general success of ML initiatives. For extra details about implementation particulars, assessment the GitHub repo.


Concerning the Authors

Luis Felipe Yepez Barrios, is a Machine Studying Engineer with AWS Skilled Companies, targeted on scalable distributed programs and automation tooling to expedite scientific innovation within the discipline of Machine Studying (ML). Moreover, he assists enterprise shoppers in optimizing their machine studying options via AWS providers.

Jinzhao Feng, is a Machine Studying Engineer at AWS Skilled Companies. He focuses on architecting and implementing massive scale Generative AI and classical ML pipeline options. He’s specialised in FMOps, LLMOps and distributed coaching.

Harsh Asnani, is a Machine Studying Engineer at AWS. His Background is in Utilized Information Science with a deal with operationalizing Machine Studying workloads within the cloud at scale.

Hasan Shojaei, is a Sr. Information Scientist with AWS Skilled Companies, the place he helps clients throughout totally different industries resolve their enterprise challenges via the usage of large knowledge, machine studying, and cloud applied sciences. Previous to this position, Hasan led a number of initiatives to develop novel physics-based and data-driven modeling strategies for prime vitality corporations. Exterior of labor, Hasan is obsessed with books, mountain climbing, pictures, and historical past.

Alec Jenab, is a Machine Studying Engineer who focuses on growing and operationalizing machine studying options at scale for enterprise clients. Alec is obsessed with bringing modern options to market, particularly in areas the place machine studying can meaningfully enhance finish person expertise. Exterior of labor, he enjoys enjoying basketball, snowboarding, and discovering hidden gems in San Francisco.

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