GradientPond integrates seamlessly with the frameworks, cloud providers, and tools your team already uses.
Native callback integration. Auto-log gradients, losses, and model graphs.
Keras callbacks and TF2 integration. TensorBoard-compatible logging.
First-class JAX support with Flax and Haiku integration. XLA-aware logging.
Transformers Trainer integration. Auto-log with one line of code.
SageMaker, S3, EC2, and EKS integration. IAM role-based authentication.
Vertex AI, GCS, GKE, and TPU support. Service account authentication.
Azure ML, Blob Storage, and AKS integration. Azure AD SSO support.
Helm charts, operators, and native K8s job scheduling. Multi-cluster support.
Pre-built images, custom Dockerfiles, and container registry integration.
GitHub Actions, PR comments with metrics, and code-to-experiment linking.
Native notebook support. Magic commands and inline visualizations.
Extension for experiment browsing, run comparison, and inline metrics.
Direct data pipeline from Snowflake tables to training datasets.
Delta Lake integration, Spark ML pipelines, and Unity Catalog support.
Direct database connections for feature stores and metadata queries.
Distributed data processing with automatic experiment tracking.
We're constantly adding new integrations. Request one or build your own with our open API.