Creating scalable and environment friendly machine studying (ML) pipelines is essential for streamlining the event, deployment, and administration of ML fashions. On this submit, 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 offered right here solely cowl mannequin coaching pipelines, however could be readily prolonged to batch inference pipelines as properly.
This dynamic framework makes use of configuration recordsdata to orchestrate preprocessing, coaching, analysis, and registration steps for each single-model and multi-model use instances 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) permit ML practitioners to specify undifferentiated code for orchestrating coaching pipelines utilizing declarative syntax. This allows knowledge scientists to rapidly construct and iterate on ML fashions, and empowers ML engineers to run by way of steady integration and steady supply (CI/CD) ML pipelines sooner, reducing 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 instances, and gives a easy move of knowledge and processes. The next are the important thing advantages of this resolution:
- Automation – Your 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 the whole 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 giant datasets and practice advanced fashions with out infrastructure considerations.
- Flexibility – The framework is versatile and may accommodate a variety of ML use instances, ML frameworks (similar to XGBoost and TensorFlow), multi-model coaching, and multi-step coaching. Each step of the coaching DAG could be personalized by way of 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 supplied on this submit and your project-specific supply code repositories into Amazon SageMaker Studio, and set your digital surroundings (detailed later on this submit). You possibly can then iterate on preprocessing, coaching, and analysis scripts, in addition to configuration decisions. To create and run a SageMaker Pipelines coaching DAG, you’ll be able to name the framework’s entry level, which is able to 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 comprises the next directories and recordsdata:
- /framework/conf/ – This listing comprises a configuration file that’s used to set frequent variables throughout all modeling items similar to 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 comprises a Python script that creates a SageMaker mannequin 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 take a look at set.
- /framework/modelmetrics/ – This listing comprises 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 take a look at knowledge.
- /framework/pipeline/ – This listing comprises 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, practice, create mannequin, rework, metrics, and register mannequin. Configurations for every modeling unit needs 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 needs 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 focus on the configuration file construction later on this submit.
- /framework/processing/ – This listing comprises a Python script that creates a SageMaker Processing job based mostly on the required Docker picture and entry level script.
- /framework/registermodel/ – This listing comprises a Python script for registering a educated mannequin together with its calculated metrics in SageMaker Mannequin Registry.
- /framework/coaching/ – This listing comprises a Python script that creates a SageMaker coaching job.
- /framework/rework/ – This listing comprises 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 take a look at knowledge.
- /framework/utilities/ – This listing comprises 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 perform outlined within the /framework/pipeline/ listing to create or replace a SageMaker Pipelines DAG and run it.
- /examples/ – This listing comprises a number of examples of how you should use this automation framework to create easy and sophisticated coaching DAGs.
- /env.env – This file lets you set frequent variables similar to subnets, safety teams, and IAM position as surroundings variables.
- /necessities.txt – This file specifies Python libraries which might be required for the framework code.
Stipulations
It is best to have the next conditions earlier than deploying this resolution:
- 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 higher) and the next Python packages:
- Further Python packages utilized in your customized scripts
Deploy the answer
Full the next steps to deploy the answer:
- Manage your mannequin coaching repository in keeping with the next construction:
- 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 namedaws-train
: - Clone the mannequin supply code underneath the identical listing. For multi-model coaching, repeat this step for as many fashions as that you must practice.
- Clone
For single-model coaching, your listing ought to seem like the next:
For multi-model coaching, your listing ought to seem like the next:
- Arrange the next surroundings variables. Asterisks point out surroundings variables which might be required; the remainder are non-obligatory.
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 title |
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 instances, SMP_MODEL_CONFIGPATH
shall be <MODEL-DIR>/conf/conf.yaml
. For multi-model use instances, SMP_MODEL_CONFIGPATH
shall 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 surroundings variables contained in the env.env file after which export them by working the next command in your terminal:
Word that the values of surroundings variables in env.env
needs to be positioned inside citation marks (for instance, SMP_REGION="us-east-1"
). Throughout operationalization, these surroundings variables needs to be set by the CI pipeline.
- Create and activate a digital surroundings by working the next instructions:
- Set up the required Python packages by working the next command:
- Edit your mannequin coaching
conf.yaml
recordsdata. We focus on the configuration file construction within the subsequent part. - From the terminal, name the framework’s entry level to create or replace and run the SageMaker Pipeline coaching DAG:
- View and debug the SageMaker Pipelines run on the Pipelines tab of the SageMaker Studio UI.
Configuration file construction
There are two varieties of configuration recordsdata within the proposed resolution: framework configuration and mannequin configuration. On this part, we describe every intimately.
Framework configuration
The /framework/conf/conf.yaml
file units the variables which might be frequent throughout all modeling items. This contains SMP_S3BUCKETNAME
, SMP_ROLE
, SMP_MODEL_CONFIGPATH
, SMP_SUBNETS
, SMP_SECURITYGROUPS
, and SMP_MODELNAME
. Confer with Step 3 of deployment directions for descriptions of those variables and find out how to set them by way of surroundings variables.
Mannequin configuration
For every mannequin within the undertaking, we have to specify the next within the <MODEL-DIR>/conf/conf.yaml
file (asterisks point out required sections; the remainder are non-obligatory):
- /conf/fashions* – On this part, you’ll be able to configure a number of modeling items. When the framework code is run, it’s going to 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, however it’s really useful to specify every modeling unit configuration in its respective listing or Git repository to attenuate errors. The items are as follows:- {model-name}* – The title 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.
- practice* – This part specifies coaching job parameters.
- rework* – This part specifies SageMaker Remodel job parameters for making predictions on the take a look at 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 move, together with dependencies amongst steps. For single-model use instances, this part is outlined on the finish of the configuration file. For multi-model use instances, 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* – Identify of the SageMaker pipeline.
- fashions* – Nested record of modeling items:
- {model-name}* – Mannequin identifier, which ought to match a {model-name} identifier within the /conf/fashions part.
- steps* –
- step_name* – Step title to be displayed within the SageMaker Pipelines DAG.
- step_class* – (Union[Processing, Training, CreateModel, Transform, Metrics, RegisterModel])
- step_type* – This parameter is barely required for preprocessing steps, for which it needs 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 withchain_input_source_step
parameter to pinpoint the file that needs to be used because the enter to the rework step.
- steps* –
- {model-name}* – Mannequin identifier, which ought to match a {model-name} identifier within the /conf/fashions part.
- dependencies – This part specifies the sequence through which the SageMaker Pipelines steps needs to be run. Now we have tailored the Apache Airflow notation for this part (for instance,
{step_name} >> {step_name}
). If this part is left clean, specific dependencies specified by thechain_input_source_step
parameter or implicit dependencies outline the SageMaker Pipelines DAG move.
Word that we suggest having one coaching step per modeling unit. If a number of coaching steps are outlined for a modeling unit, the next steps implicitly take the final coaching step to create the mannequin object, calculate metrics, and register the mannequin. If that you must practice a number of fashions, it’s really useful to create a number of modeling items.
Examples
On this part, we exhibit three examples of ML mannequin coaching DAGs created utilizing the offered 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 supplied to the coaching step within the configuration file.
When the coaching step runs, SageMaker hundreds the file on the container at /choose/ml/enter/knowledge/{channelName}/
, accessible by way of the surroundings variable SM_CHANNEL_{channelName}
on the container (channelName= ‘practice’ or ‘take a look at’).The coaching script does the next:
- Load the recordsdata domestically from native container paths utilizing the NumPy load module.
- Set hyperparameters for the coaching algorithm.
- Save the educated mannequin on the native container path
/choose/ml/mannequin/
.
SageMaker takes the content material underneath /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 take a look at file as enter and the educated mannequin to make predictions on the educated mannequin. The output of the rework step is chained to the metrics step to guage the mannequin in opposition to the floor fact, which is explicitly provided 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 giant 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 practice/take a look at 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 coaching script hundreds the Falcon-40B LLM from Hugging Face Hub and begins accelerated fine-tuning utilizing LoRA on the practice 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 element evaluation (PCA) mannequin is educated for dimensionality discount, and a TensorFlow Multilayer Perceptron mannequin is educated for California Housing Value prediction. The TensorFlow mannequin’s preprocessing step makes use of a educated PCA mannequin to scale 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 sources:
- Use the AWS CLI to record and take away any remaining pipelines which might be created by the Python scripts.
- Optionally, delete different AWS sources such because the S3 bucket or IAM position created outdoors SageMaker Pipelines.
Conclusion
On this submit, we offered a framework for automating SageMaker Pipelines DAG creation based mostly on configuration recordsdata. The proposed framework provides a forward-looking resolution to the problem of orchestrating advanced ML workloads. By utilizing 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 sources, but in addition promotes MLOps greatest practices, contributing to the general success of ML initiatives. For extra details about implementation particulars, evaluate the GitHub repo.
Concerning the Authors
Luis Felipe Yepez Barrios, is a Machine Studying Engineer with AWS Skilled Companies, targeted on scalable distributed techniques and automation tooling to expedite scientific innovation within the area of Machine Studying (ML). Moreover, he assists enterprise purchasers in optimizing their machine studying options by way of AWS companies.
Jinzhao Feng, is a Machine Studying Engineer at AWS Skilled Companies. He focuses on architecting and implementing giant 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 Knowledge Science with a deal with operationalizing Machine Studying workloads within the cloud at scale.
Hasan Shojaei, is a Sr. Knowledge Scientist with AWS Skilled Companies, the place he helps clients throughout completely different industries resolve their enterprise challenges by way of using huge 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 methods for prime vitality corporations. Outdoors of labor, Hasan is keen about books, mountaineering, 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 keen about bringing revolutionary options to market, particularly in areas the place machine studying can meaningfully enhance finish person expertise. Outdoors of labor, he enjoys taking part in basketball, snowboarding, and discovering hidden gems in San Francisco.