Features — GradientPond

📊

Experiment Tracking

Log every metric, hyperparameter, and artifact automatically. Compare runs side-by-side with interactive visualizations. Never lose track of what worked — or why.

✓ Auto-log metrics from PyTorch, TensorFlow, JAX
✓ Interactive comparison dashboards
✓ Custom visualizations & charts
✓ Artifact versioning & lineage
✓ Real-time collaboration & annotations

Experiment Dashboard — Run Comparison ● Live

BEST LOSS

0.0234

ACCURACY

97.8%

TOTAL RUNS

1,247

GPU HOURS

8,432

run-001run-050run-100

distributed_train.py

$ gp launch --nodes 4 --gpus-per-node 8

✓ Cluster provisioned: 4 nodes × 8x A100
✓ NCCL initialized across 32 GPUs
✓ Data sharding: 32 partitions ready
✓ Mixed precision: BF16 enabled

▶ Training distributed across 32 GPUs

Node 0: ━━━━━━━━━━━━━━━━━━━━ 100% | 847 samples/s
Node 1: ━━━━━━━━━━━━━━━━━━━━ 100% | 832 samples/s
Node 2: ━━━━━━━━━━━━━━━━━━━━ 100% | 851 samples/s
Node 3: ━━━━━━━━━━━━━━━━━━━━ 100% | 839 samples/s

Total throughput: 3,369 samples/s
Scaling efficiency: 94.7%

🔀

Distributed Training

Scale from a single GPU to thousands with zero code changes. Built-in support for data parallelism, model parallelism, and pipeline parallelism across any cluster.

✓ Zero-code distributed scaling
✓ Data, model & pipeline parallelism
✓ Automatic gradient synchronization
✓ Fault-tolerant checkpointing
✓ Multi-cloud & on-premise support

🗂️

Dataset Versioning

Version your datasets like code. Track lineage, manage splits, and ensure reproducibility across your entire team with Git-like semantics for data.

✓ Git-like branching & merging for data
✓ Automatic deduplication & compression
✓ Data lineage & provenance tracking
✓ Lazy loading for petabyte-scale datasets
✓ Integration with S3, GCS, Azure Blob

dataset_ops.py

            import gradientpond as gp
            
            # Create a versioned dataset
            ds = gp.Dataset.create(
                name="imagenet-cleaned",
                source="s3://data/imagenet/"
            )
            
            # Branch for experiments
            ds.branch("augmented-v2")
            ds.add(new_samples)
            ds.commit("Add 50k augmented samples")
            
            # Compare versions
            diff = ds.diff("main", "augmented-v2")
          

Model Registry 12 models

gpt-finetune-v3

Production • 2.1B params

DEPLOYED

bert-classifier-v7

Staging • 340M params

STAGING

vision-transformer-v2

Archived • 632M params

ARCHIVED

whisper-finetune-v1

Development • 1.5B params

DEV

📦

Model Registry

Centralized model management with versioning, staging, and production promotion workflows. Deploy anywhere — Kubernetes, serverless, or edge devices.

✓ Semantic versioning for models
✓ Stage gates: Dev → Staging → Production
✓ One-click rollback & canary deploys
✓ Model cards & documentation
✓ CI/CD integration & webhooks

🎯

Hyperparameter Optimization

Automatically find the best hyperparameters with state-of-the-art optimization algorithms. Save weeks of manual tuning with intelligent search strategies.

✓ Bayesian optimization (TPE, GP)
✓ Population-based training (PBT)
✓ Early stopping with ASHA scheduler
✓ Multi-objective optimization
✓ Parallel trial execution at scale

sweep.py

            import gradientpond as gp
            
            # Define search space
            sweep = gp.Sweep(
                method="bayesian",
                metric={"name": "val_loss", "goal": "minimize"},
                parameters={
                    "lr": {"min": 1e-5, "max": 1e-2},
                    "batch_size": {"values": [16, 32, 64]},
                    "dropout": {"min": 0.1, "max": 0.5}
                }
            )
            
            # Launch 100 trials across cluster
            sweep.run(count=100, gpus=8)
          

Integrations 20+ frameworks

🔥

PyTorch

Native support

🧠

TensorFlow

Full integration

⚡

JAX

First-class

🤗

Hugging Face

Transformers

⚙️

Kubernetes

Orchestration

☁️

AWS / GCP / Azure

Multi-cloud

🔌

Integrations

Works with every framework and tool in your ML stack. Native integrations with PyTorch, TensorFlow, JAX, Hugging Face, and more — plus REST APIs for custom workflows.

✓ PyTorch Lightning & Fabric callbacks
✓ TensorFlow/Keras integration
✓ Hugging Face Trainer & Accelerate
✓ Jupyter Notebook widgets
✓ REST API & webhooks for CI/CD