The #1 2025 Guide: Run Cloud Python Scripts with uv & Coiled

Introduction: The Need for Speed in Cloud Python

In the world of data science, machine learning, and large-scale computing, time is more than just money—it's the currency of innovation. For years, Python developers have grappled with a persistent bottleneck: slow and cumbersome dependency management. Waiting for pip or conda to resolve and install complex environments, especially for cloud workloads, has been a universal source of frustration. This lost time adds up, delaying everything from interactive analysis to critical production deployments.

Enter 2025, and the landscape is undergoing a seismic shift. Two powerful tools, uv and Coiled, have emerged to tackle these challenges head-on. uv, a lightning-fast Python package installer from the creators of Ruff, promises to make dependency management an afterthought. Coiled provides an elegant, on-demand platform for scaling your Python scripts to the cloud without the headache of managing infrastructure.

This guide will demonstrate why the combination of uv and Coiled is not just an improvement—it's the new gold standard for efficient, reproducible, and massively scalable cloud Python development.

What is uv and Why Does It Matter?

uv is an extremely fast Python package installer and resolver, written in Rust. Developed by Astral, it's designed as a drop-in replacement for common pip and pip-tools workflows. Think of it as pip on steroids. Its primary mission is to eliminate the time you spend waiting for your tools and let you focus on your code.

Key Features of uv

• Blazing Speed: By leveraging Rust's performance and advanced parallel processing, uv is often 10-100x faster than pip. It utilizes a global package cache, avoiding redundant downloads and significantly speeding up repeated installations.
• Drop-in Replacement: You can use uv pip install and uv pip compile with the same arguments you're already used to. The transition is virtually seamless.
• Advanced Dependency Resolution: It features a high-performance dependency resolver that quickly finds a compatible set of packages, even in complex projects with conflicting requirements.
• Unified Toolchain: uv combines the functionality of pip, pip-tools, pip-sync, and virtual environments into a single, cohesive command-line tool.

Introducing Coiled: Your Python Supercomputer

While uv solves the local and build-time environment problem, Coiled solves the runtime scaling problem. Coiled is a managed service that makes it trivial to run your Python scripts on powerful cloud clusters. It abstracts away the complexity of provisioning and managing cloud resources on AWS, GCP, or Azure, letting you focus on your analysis rather than on infrastructure.

How Coiled Solves Cloud Scaling Challenges

• Effortless Cluster Management: Spin up and down Dask clusters with hundreds of machines using a simple Python API or decorator.
• Seamless Software Environments: Coiled automatically builds and caches your project's software environment in the cloud, ensuring every worker node has the exact same dependencies.
• Cost Optimization: Clusters automatically shut down when idle, and you can leverage spot instances to reduce costs by up to 80%.
• Enterprise-Grade Security: Coiled runs within your own cloud account, ensuring your data and code remain secure and compliant.

The Power Couple: Why uv + Coiled is a Game-Changer

When you combine the build-time speed of uv with the runtime scalability of Coiled, you unlock a workflow that is orders of magnitude more efficient than traditional methods.

Unprecedented Speed in Environment Creation

The slowest part of starting a new Coiled cluster is often the initial software environment build. Coiled needs to install all your specified packages (like pandas, scikit-learn, and pytorch) onto a base image. By integrating uv, Coiled can slash this environment creation time dramatically. A build that might take 10 minutes with pip could be completed in under a minute with uv. This means faster starts for your clusters, quicker iterations for interactive development, and more responsive CI/CD pipelines.

Rock-Solid, Reproducible Environments

The dreaded "it works on my machine" problem is a killer for productivity. uv helps eradicate this issue. Using uv pip compile, you can generate a fully-pinned requirements.txt lockfile. This file specifies the exact version of every single package and its dependencies.

When you provide this lockfile to Coiled, you guarantee that the environment built in the cloud is identical to the one you tested locally. This deterministic approach ensures that your code runs the same way, every time, whether on your laptop or on a 1000-core cluster.

Workflow Comparison: Traditional vs. uv + Coiled

Step-by-Step Guide: Running a Script with uv and Coiled

Let's walk through a practical example of using uv and Coiled together to run a simple data processing script in the cloud.

Step 1: Install uv and Coiled

First, get the necessary tools. Installing uv is a one-liner. We'll use pip to install coiled.

# Install uv
curl -LsSf https://astral.sh/uv/install.sh | sh

# Install Coiled and Dask
pip install coiled dask

Step 2: Define Your Dependencies

Create a requirements.in file to list your top-level project dependencies. This keeps your primary requirements clean and easy to manage.

# requirements.in
dask[complete]
pandas==2.2.0
s3fs

Step 3: Generate a Lockfile with uv

Now, use uv to compile your input file into a fully-pinned requirements.txt lockfile. This is the key to reproducibility.

uv pip compile requirements.in -o requirements.txt

Your requirements.txt will now contain the exact versions of all packages, like this (output will vary):

#
# This file was autogenerated by uv via the following command:
#    uv pip compile requirements.in -o requirements.txt
#
dask==2024.1.0
...
pandas==2.2.0
...
s3fs==2023.12.2
...

Step 4: Configure a Coiled Software Environment

You can tell Coiled to use your new lockfile. While Coiled offers many ways to define an environment, pointing to your requirements.txt is the most robust method. You can specify this directly in your script or create a reusable named environment.

Step 5: Run Your Python Script on a Coiled Cluster

Finally, write your Python script. We'll use the @coiled.function decorator to tell Coiled to run this specific function in the cloud, using an environment defined by our lockfile.

# process_data.py
import coiled
import dask
import dask.dataframe as dd

@coiled.function(
    software="./requirements.txt",
    vm_type="m6i.large",
    n_workers=20
)
def process_large_dataset(s3_path):
    # This code now runs on a 20-machine Coiled cluster
    # The environment was built using your uv-generated lockfile
    df = dd.read_parquet(s3_path)
    result = df.groupby("user_id").value.mean().compute()
    return result

if __name__ == "__main__":
    # Replace with your S3 path
    s3_bucket_path = "s3://coiled-datasets/timeseries/20-years/parquet/"
    
    # This call triggers the cloud execution
    future = process_large_dataset.submit(s3_bucket_path)
    
    print("Job submitted to Coiled. Waiting for result...")
    final_result = future.result()
    print("Result received:")
    print(final_result.head())

When you run python process_data.py, Coiled packages your function, provisions a 20-worker Dask cluster, rapidly builds the software environment using your `requirements.txt`, executes your code in parallel, and streams the result back to your local machine.

Real-World Use Cases

Large-Scale Data Processing

Data engineering teams can define their ETL/ELT jobs and dependencies in a Git repository. A CI/CD pipeline can use uv to lock dependencies and then trigger Coiled jobs, ensuring that daily data processing pipelines are both fast and reliable.

Machine Learning Model Training

Data scientists can iterate faster by quickly spinning up GPU-enabled Coiled clusters to train models. With uv, tweaking a dependency (e.g., trying a new version of PyTorch) and rebuilding the environment takes minutes, not hours, dramatically shortening the experimentation cycle.

High-Throughput CI/CD Pipelines

For organizations with many Python projects, CI/CD costs and runtimes can spiral. Using uv's caching and speed within CI runners, combined with Coiled for scalable integration tests, can drastically reduce pipeline duration and compute costs.

Conclusion: The Future is Fast and Scalable

The days of tolerating slow, brittle Python dependency management for cloud workflows are over. The combination of uv's phenomenal speed and Coiled's effortless scalability creates a developer experience that was previously unimaginable. You get the best of both worlds: rapid local development and testing, backed by a robust, reproducible, and infinitely scalable cloud backend.

By adopting the uv + Coiled workflow, you are not just saving time—you are investing in a more agile, reliable, and powerful way to bring your Python projects to life. This is the definitive standard for modern cloud Python in 2025 and beyond.