This document outlines the end-to-end standard workflow for utilizing an AI compute cluster, from initial data ingestion to model deployment and ongoing management. This workflow is designed to ensure efficiency, scalability, and reproducibility for all AI and machine learning projects.
The workflow is cyclical and can be broken down into five primary phases:
- Data Management & Preparation
- Model Development & Experimentation
- Large-Scale Training & Optimization
- Deployment & Inference
- Monitoring & Model Lifecycle Management
Phase 1: Data Management & Preparation
This phase is the foundation of any AI project. The goal is to ingest, process, and version-control data to make it readily available for model training.
- 1.1 Data Ingestion:
- Process: Raw data (e.g., images, text, logs, structured data) is ingested from various sources into a centralized storage repository accessible by the cluster.
- Tools: Apache Kafka, Spark, NiFi; Cloud storage solutions (S3, GCS, Azure Blob).
- Cluster Role: High-throughput nodes are used to handle initial data transfer and storage operations.
- 1.2 Data Preprocessing & Transformation (ETL):
- Process: Raw data is cleaned, normalized, augmented, and transformed into a feature-rich format suitable for training. This is a computationally intensive step.
- Tools: Apache Spark, Dask, NVIDIA RAPIDS.
- Cluster Role: The cluster’s CPU and GPU resources are used in parallel to process massive datasets quickly. Distributed frameworks like Spark are essential here.
- 1.3 Data Versioning & Storage:
- Process: The processed data is stored in a high-performance file system and versioned to ensure reproducibility. Every dataset used for an experiment must be traceable.
- Tools: DVC (Data Version Control), Pachyderm; Network-attached storage (NAS) or parallel file systems (e.g., Lustre, GPFS).
- Cluster Role: The cluster requires high-speed access to this storage to prevent data I/O from becoming a bottleneck during training.
Phase 2: Model Development & Experimentation
This phase involves interactive development and small-scale experiments to validate hypotheses and select candidate models.
- 2.1 Environment Setup:
- Process: Developers access a containerized environment with all necessary libraries and dependencies (e.g., TensorFlow, PyTorch, CUDA).
- Tools: Docker, Singularity, NVIDIA NGC Containers.
- Cluster Role: The cluster provides sandboxed environments, often granting a developer access to a single GPU or a fraction of a node for interactive sessions via tools like JupyterHub.
- 2.2 Model Prototyping & Debugging:
- Process: Data scientists and ML engineers write and debug model code, typically working with a small subset of the data to iterate quickly.
- Tools: Jupyter Notebooks, VS Code (with remote access), PyCharm.
- Cluster Role: The cluster provides on-demand access to powerful nodes, allowing developers to test code on actual hardware without tying up large resources.
- 2.3 Experiment Tracking:
- Process: Every experiment (code, data version, hyperparameters, and resulting metrics) is meticulously logged.
- Tools: MLflow, Weights & Biases (W&B), Comet.ml.
- Cluster Role: The cluster nodes are configured to automatically report metrics and parameters to the tracking server.
Phase 3: Large-Scale Training & Optimization
Once a promising model is identified, it is trained on the full dataset, often across multiple nodes.
- 3.1 Job Scheduling & Submission:
- Process: The developer submits a training script as a “job” to the cluster’s workload manager, specifying resource requirements (e.g., number of GPUs, memory).
- Tools: Slurm, Kubernetes (with Kubeflow or Volcano), LSF.
- Cluster Role: The workload manager queues the job and allocates the requested compute nodes as they become available, ensuring fair and efficient use of the shared resources.
- 3.2 Distributed Training:
- Process: The training job is scaled across multiple GPUs or nodes to accelerate the process. This can be done via data parallelism (distributing data) or model parallelism (distributing the model itself).
- Tools: PyTorch DistributedDataParallel (DDP), TensorFlow MirroredStrategy, Horovod, NVIDIA Megatron-LM.
- Cluster Role: This is the core function of the AI cluster. High-speed interconnects (e.g., NVLink, InfiniBand) between nodes are critical for efficient communication during distributed training.
- 3.3 Hyperparameter Optimization (HPO):
- Process: Automated tools are used to run hundreds of training jobs in parallel, each with different hyperparameters, to find the optimal model configuration.
- Tools: Ray Tune, Optuna, Hyperopt.
- Cluster Role: The cluster’s ability to run a massive number of jobs in parallel is essential for completing HPO in a reasonable timeframe.
Phase 4: Deployment & Inference
After a model is trained and validated, it is deployed to serve predictions on new data.
- 4.1 Model Conversion & Optimization:
- Process: The trained model is converted to a lightweight, high-performance format optimized for inference. This may involve quantization or graph compilation.
- Tools: NVIDIA TensorRT, ONNX Runtime, Intel OpenVINO.
- Cluster Role: GPU nodes are used to run the optimization and conversion tools, which can be computationally intensive.
- 4.2 Model Serving:
- Process: The optimized model is deployed behind an API endpoint. This can be for real-time (synchronous) or batch (asynchronous) inference.
- Tools: NVIDIA Triton Inference Server, TensorFlow Serving, TorchServe, KServe.
- Cluster Role: A subset of the cluster’s nodes (often with specialized inference GPUs) is dedicated to running the serving engine, which needs to handle incoming requests with low latency and high throughput.
Phase 5: Monitoring & Model Lifecycle Management
Deployed models must be monitored to ensure their performance does not degrade over time.
- 5.1 Performance Monitoring:
- Process: Key metrics like inference latency, throughput, and resource utilization (GPU/CPU usage) are continuously monitored.
- Tools: Prometheus, Grafana, NVIDIA DCGM.
- Cluster Role: Monitoring agents run on the inference nodes, collecting and exporting metrics to a central dashboard.
- 5.2 Concept Drift Detection:
- Process: The statistical properties of the live data are monitored to detect if they have shifted significantly from the training data (a phenomenon known as “concept drift”). Model prediction accuracy is also tracked.
- Tools: Custom statistical analysis scripts; Libraries like Evidently AI, NannyML.
- Cluster Role: The cluster can run periodic analysis jobs to compare incoming data distributions with the training data.
- 5.3 Retraining & Automation (MLOps):
- Process: If model performance degrades, an automated workflow (CI/CD pipeline for ML) is triggered. This pipeline automatically pulls the latest data, retrains the model, and redeploys it.
- Tools: Jenkins, GitLab CI, Argo Workflows, Kubeflow Pipelines.
- Cluster Role: The entire workflow loops back to Phase 1, using the cluster’s resources to automatically execute the data preparation, training, and deployment steps in a continuous cycle.
