openrunner-sdk 0.1.1

OpenRunner SDK - W&B-compatible ML experiment tracking client

OpenRunner SDK

Open-source, self-hosted ML experiment tracking — a drop-in replacement for Weights & Biases.

Install

pip install openrunner-sdk

Setup

export OPENRUNNER_API_KEY="or_your_key"
export OPENRUNNER_BASE_URL="https://your-server.com"

Or use the CLI:

openrunner login

Quick Start

import openrunner

# Start a run
openrunner.init(project="my-project", config={"lr": 0.001, "epochs": 10})

# Log metrics in your training loop
for epoch in range(10):
    loss = train(epoch)
    acc = evaluate()
    openrunner.log({"loss": loss, "accuracy": acc, "epoch": epoch})

# End the run
openrunner.finish()

API Reference

Core Functions

openrunner.init()

Initialize a new experiment run.

run = openrunner.init(
    project="my-project",          # Project name (auto-created if missing)
    name="experiment-1",           # Optional display name
    config={"lr": 0.001},          # Hyperparameters
    tags=["baseline", "v2"],       # Optional tags
    notes="Testing new arch",      # Optional notes
    group="sweep-1",               # Optional group name
    job_type="train",              # Optional job type
    resume=True,                   # Resume a previous run by ID
)

openrunner.log()

Log metrics. Non-blocking — never slows down training.

# Basic logging
openrunner.log({"loss": 0.5, "accuracy": 0.85})

# With explicit step
openrunner.log({"loss": 0.3}, step=100)

# Log images
openrunner.log({"predictions": openrunner.Image(img_array, caption="epoch 5")})

# Log tables
table = openrunner.Table(
    columns=["input", "predicted", "actual"],
    data=[["img_01", 7, 7], ["img_02", 3, 5]],
)
openrunner.log({"eval_results": table})

openrunner.finish()

End the current run. Flushes all buffered metrics.

openrunner.finish()
openrunner.finish(exit_code=0)    # With exit code
openrunner.finish(quiet=True)     # Suppress output

Config

Dict-like object with dot notation. Set at init(), accessible throughout the run.

openrunner.init(config={"optimizer": {"lr": 0.001, "weight_decay": 1e-5}})

# Access
print(openrunner.config["optimizer.lr"])    # 0.001 (flattened keys)
print(openrunner.config.optimizer.lr)       # 0.001 (dot notation)

# Update after init
openrunner.config.update({"batch_size": 64})
openrunner.config["new_param"] = "value"

Summary

Auto-updated with the last logged value for each key. Can also be set explicitly.

# Auto-populated from log()
openrunner.log({"loss": 0.5})
openrunner.log({"loss": 0.3})
print(openrunner.summary["loss"])  # 0.3 (last value)

# Explicit set
openrunner.summary["best_accuracy"] = 0.95
openrunner.summary["final_loss"] = 0.1

Artifacts

Version datasets, models, and checkpoints with content-hash deduplication.

# Log a model artifact
artifact = openrunner.Artifact(name="my-model", type="model")
artifact.add_file("model.pth")
artifact.add_file("config.json")
run.log_artifact(artifact)

# Use an artifact from a previous run
artifact = run.use_artifact("my-model:v2")
artifact.download("/path/to/dir")

Media Types

Images

import numpy as np

# From numpy array
img = openrunner.Image(np.random.rand(28, 28, 3), caption="sample")

# From PIL Image
from PIL import Image as PILImage
pil_img = PILImage.open("photo.png")
img = openrunner.Image(pil_img, caption="photo")

# From file path
img = openrunner.Image("output.png", caption="result")

openrunner.log({"examples": img})

Tables

table = openrunner.Table(
    columns=["epoch", "loss", "accuracy"],
    data=[
        [1, 0.9, 0.65],
        [2, 0.5, 0.82],
        [3, 0.3, 0.91],
    ],
)
openrunner.log({"metrics_table": table})

Run Properties

run = openrunner.init(project="test")

print(run.id)          # "a1b2c3d4" (8-char ID)
print(run.name)        # Display name
print(run.project)     # Project name
print(run.config)      # Config object
print(run.summary)     # Summary object

Migrating from W&B

Change one import — everything else stays the same:

# Before
import wandb
wandb.init(project="my-project")
wandb.log({"loss": 0.5})
wandb.finish()

# After
import openrunner as wandb
wandb.init(project="my-project")
wandb.log({"loss": 0.5})
wandb.finish()

Framework Integrations

PyTorch

from openrunner.integration.pytorch import log_gradients

openrunner.init(project="pytorch-example")

for batch in dataloader:
    loss = model(batch)
    loss.backward()
    log_gradients(model)  # Logs gradient norms
    optimizer.step()

openrunner.finish()

HuggingFace Transformers

from openrunner.integration.huggingface import OpenRunnerCallback

openrunner.init(project="hf-example")

trainer = Trainer(
    model=model,
    args=training_args,
    callbacks=[OpenRunnerCallback()],
)
trainer.train()

openrunner.finish()

PyTorch Lightning

from openrunner.integration.lightning import OpenRunnerLogger

logger = OpenRunnerLogger(project="lightning-example")

trainer = pl.Trainer(logger=logger)
trainer.fit(model)

Offline Mode

Train without connectivity, sync later:

export OPENRUNNER_MODE=offline
python train.py

# When back online
openrunner sync

Offline runs are stored as JSONL files (human-readable, crash-safe). Sync is additive and idempotent — interrupted syncs resume without data loss.

CLI

# Authenticate
openrunner login

# Sync offline runs
openrunner sync

# List projects and runs
openrunner ls

System Metrics

Automatically collected during training (enabled by default):

• CPU utilization (%)
• System memory usage (%)
• GPU utilization (%) — requires pip install openrunner-sdk[gpu]
• GPU memory usage (%)

Disable with:

export OPENRUNNER_SYSTEM_METRICS=false

Environment Variables

Variable	Description	Default
OPENRUNNER_API_KEY	API key for authentication	(required)
OPENRUNNER_BASE_URL	Server URL	http://localhost:8000
OPENRUNNER_PROJECT	Default project name	(none)
OPENRUNNER_MODE	online or offline	online
OPENRUNNER_SYSTEM_METRICS	Enable system metrics	true
OPENRUNNER_OFFLINE_DIR	Offline storage directory	~/.openrunner/offline

W&B env vars (WANDB_API_KEY, WANDB_BASE_URL) are also supported as fallback for migration.

Self-Hosting

OpenRunner is designed to be self-hosted. See the main repo for server setup with Docker Compose.

License

MIT