Choosing a sandbox runtime for long-lived coding agents
How we evaluated sandbox providers for coding agents, what we chose for now, and the trade-offs behind that decision.
Start locally without complicated setup hassle
- ZenML is available as a simple pip package that lets you run and track pipelines locally.
- ZenML integrates with your orchestration layer of choice, avoiding having to learn different paradigms for dev, staging, and prod.
- ZenML integrates with your orchestration layer of choice or can be extended with your own orchestration service.
Abstract away infrastructure complexity
- Most orchestrators assume some form of infrastructure knowledge to use them maximally — ZenML abstracts that complexity away.
- ZenML separates infrastructure setup like Docker building from the application logic, and automates the tedious parts.
- ZenML focuses on the handovers between MLOps Engineers, ML Engineers, and Data Scientists.
Switch between orchestrators depending on your context
- You can switch between different orchestration services with a single click — from dev to staging to production.
- The more engineering-minded in the team still retain control over their productionalization because the framework is extensible.
- ZenML handles the pain of packaging your code into Docker to be deployed to your orchestration service of choice.
Explore in Detail What Makes ZenML Unique
| Feature | ZenML | Dagster |
|---|---|---|
| ML Workflow Orchestration | Yes Specialized for machine learning pipelines | Not supported General-purpose data pipeline orchestration not purpose-built for MLOps |
| ML Framework Integration | Yes Built-in integrations with popular ML frameworks (scikit-learn, TensorFlow, PyTorch) | Not supported Requires custom integration with ML frameworks |
| Experiment Tracking | Yes Built-in experiment tracking and comparison | Not supported Relies on external tools for experiment tracking |
| Model Registry | Yes Integrated model registry for versioning and deployment | Not supported No built-in model registry |
| Data Processing | Yes Supports data processing tasks within ML pipelines | Yes Robust support for data processing and ETL workflows |
| Pipeline Definition | Yes Clean and intuitive pipeline definition using Python decorators | Yes Flexible pipeline definition using Python or YAML |
| Cloud Integration | Yes Built-in support for popular cloud platforms (AWS, GCP, Azure) | Yes Integrates with various cloud platforms and data stores |
| Scalability | Yes Scales ML workloads across different compute backends | Yes Scales data pipelines through various execution engines |
| Workflow Scheduling | Yes Supports scheduled execution of ML pipelines | Yes Robust scheduling and triggering of data pipelines |
| Community and Ecosystem | Yes Growing community focused on ML workflows | Yes Large and active community around data engineering and ETL |
Code comparison
ZenML and Dagster side by side
from zenml import pipeline, step
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error
@step
def ingest_data():
return pd.read_csv("data/dataset.csv")
@step
def train_model(df):
X, y = df.drop("target", axis=1), df["target"]
model = RandomForestRegressor(n_estimators=100)
model.fit(X, y)
return model
@step
def evaluate_model(model, df):
X, y = df.drop("target", axis=1), df["target"]
rmse = mean_squared_error(y, model.predict(X)) ** 0.5
print(f"RMSE: {rmse}")
@pipeline
def ml_pipeline():
df = ingest_data()
model = train_model(df)
evaluate_model(model, df)
ml_pipeline()from dagster import pipeline, solid
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error
@solid
def ingest_data(_):
return pd.read_csv("data/dataset.csv")
@solid
def train_model(_, df):
X, y = df.drop("target", axis=1), df["target"]
model = RandomForestRegressor(n_estimators=100)
model.fit(X, y)
return model
@solid
def evaluate_model(_, model, df):
X, y = df.drop("target", axis=1), df["target"]
rmse = mean_squared_error(y, model.predict(X)) ** 0.5
print(f"RMSE: {rmse}")
@pipeline
def ml_pipeline():
df = ingest_data()
model = train_model(df)
evaluate_model(model, df)
ml_pipeline.execute_in_process()01.
ML-Centric Design
ZenML is purpose-built for machine learning workflows, providing a more specialized and optimized experience compared to Dagster's general-purpose pipeline orchestration.
02.
Seamless Integration with ML Frameworks
ZenML offers built-in integrations with popular ML frameworks, such as scikit-learn, TensorFlow, and PyTorch, making it easier to incorporate ML tasks into your pipelines.
03.
Built-in Experiment Tracking and Model Registry
With ZenML, you can leverage built-in experiment tracking and model registry capabilities, streamlining the management of your ML experiments and model versions.
04.
Intuitive Pipeline Definition
ZenML provides a clean and intuitive way to define ML pipelines using Python decorators, allowing you to focus on the core logic of your workflows.
05.
Strong Focus on MLOps and Reproducibility
ZenML places a strong emphasis on MLOps best practices, ensuring your ML workflows are reproducible, traceable, and compliant with governance requirements.
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Explore the Advantages of ZenML Over Other Orchestrator Tools
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