My 2025 Python Setup: 10x Faster on the Cloud with uv & Coiled

The Bottleneck in Modern Python Development

For years, the Python development cycle has been plagued by silent productivity killers. We accept them as the cost of doing business: the slow crawl of pip install on a large project, the arcane complexities of managing Conda environments, and the agonizing wait for cloud clusters to spin up and provision their software environments. In a world where iteration speed is paramount, these delays are more than just an annoyance—they're a significant drag on innovation.

Think about it. How many times have you been deep in a data analysis workflow, only to realize you need a new library? You run pip, wait a few minutes for it to resolve dependencies and install, and by the time it's done, your mental context has shifted. Now, multiply that friction by a team of developers and scale it to the cloud, where each new worker in your Dask or Spark cluster has to repeat a similar, slow setup process. The minutes add up to hours, and the hours add up to days of lost productivity.

This is the problem my 2025 Python setup is designed to solve. It's not about a fancy new editor or a niche library; it's about fundamentally re-architecting the most painful parts of the development loop to be an order of magnitude faster. The solution lies in two transformative tools: uv and Coiled.

The 2025 Python Stack: Speed and Simplicity

My modern setup is built on a simple premise: use the fastest, most efficient tools for the job. For local and cloud-based package management, that tool is uv. For scalable, on-demand cloud computing, it's Coiled. Together, they create a development experience that is seamless, reproducible, and incredibly fast.

• uv: An extremely fast Python package installer and resolver, written in Rust by the same team behind the linter Ruff. It's a drop-in replacement for `pip` and `pip-tools` that can be 10-100x faster.
• Coiled: A managed, serverless platform for scaling Python with Dask. It abstracts away the complexities of cloud infrastructure (VPCs, security groups, VMs) and, crucially, automates the creation of software environments on your cluster.

Let's dive into what makes each component so special and how they combine to deliver a 10x performance boost.

uv: The Blazing-Fast Python Packager

What is uv?

From the brilliant minds at Astral, `uv` is a complete rethink of Python packaging. Written in Rust, it leverages modern programming paradigms like parallelism and aggressive caching to obliterate the performance bottlenecks of traditional tools. It’s not just an installer; it's a unified toolchain that can create virtual environments, install packages, lock dependencies, and more, all from a single, lightning-fast binary.

Why uv is a Game-Changer

The difference is palpable. Running `uv pip install` feels instantaneous compared to `pip`. Here’s why:

• Parallel I/O: `uv` downloads and resolves packages in parallel, maximizing your network and CPU usage. No more waiting for packages to download one by one.
• Advanced Caching: It features a global cache that shares packages across all your projects. If you've installed `pandas==2.2.0` once, you'll never download it again, for any project.
• Rust-Powered Resolver: The dependency resolver is implemented in Rust, making it orders of magnitude faster than pip's Python-based resolver, especially for complex dependency trees.
• Unified Tooling: `uv` replaces `python -m venv`, `pip`, and `pip-tools` with a single, cohesive command-line interface. `uv venv` creates an environment, `uv pip install` installs packages, and `uv pip compile` locks dependencies. Simple.

Coiled: Scalable Python, On-Demand

What is Coiled?

Coiled is the missing link for data scientists and engineers who want to use tools like Dask without becoming cloud infrastructure experts. It provides a simple Python API to request a Dask cluster of any size, which is then automatically provisioned in your cloud account (AWS, GCP, or Azure) in about a minute.

The Coiled Advantage for Data Work

While you can set up Dask on your own, Coiled handles the tedious, error-prone parts of the process:

• Software Environment Management: Coiled automatically builds and mirrors your local Python environment on every worker in the cluster, ensuring perfect reproducibility.
• Cost Optimization: Clusters automatically shut down after a period of inactivity, so you never pay for idle resources.
• Simplified Infrastructure: No more wrestling with IAM roles, VPCs, or Kubernetes configurations. Coiled handles it all behind the scenes.
• Team Collaboration: Easily share configurations, environments, and cost monitoring across your entire team.

The Synergy: How uv & Coiled Create a 10x Experience

Here's where the magic happens. The biggest hidden cost of using a service like Coiled is the cluster startup time. A significant portion of that time is spent building the software environment on the cloud workers. If your environment has dozens of packages, a standard `pip` or `conda` install can take several minutes. This means every time you start a cluster, you're looking at a 5-10 minute wait.

But Coiled is smart. It allows you to specify a custom build command for your environment. By telling Coiled to use `uv`, we replace the slowest part of the process with the fastest tool available.

The result? Cloud software environments that build in seconds, not minutes. Cluster startup times are slashed, often from over 5 minutes to under 90 seconds. This transforms the experience from a batch-oriented, "go get a coffee" process to a truly interactive one. You can spin up a 100-machine cluster almost as fast as you can start a local Python session.

Performance Showdown: Old vs. New

Let's quantify the difference. The table below compares a typical workflow using traditional tools versus the modern `uv` and `Coiled` stack for creating a data science environment with libraries like `pandas`, `dask`, `scikit-learn`, and `pyarrow`.

*Times are illustrative and depend on network speed and project complexity, but the order-of-magnitude difference is real.

Your Step-by-Step Guide to the 2025 Setup

Ready to try it? Here’s how you can get up and running in minutes.

Step 1: Install uv

The recommended way to install `uv` is with `pipx` to keep it isolated from your project environments.

pipx install uv

Step 2: Create Your Project Environment

Navigate to your project folder and use `uv` to create and activate a virtual environment. Then, install your packages.

# Create a virtual environment
cd my-project
uv venv

# Activate the environment (macOS/Linux)
source .venv/bin/activate

# Install packages with uv
uv pip install pandas dask coiled scikit-learn

Notice how much faster that install is! Now, lock your dependencies for reproducibility:

uv pip compile requirements.in -o requirements.txt

Step 3: Set Up Coiled

If you haven't already, sign up for a Coiled account and configure it with your cloud credentials. Then, log in from your terminal:

coiled login

Step 4: Launch a Coiled Cluster with uv

This is the key step. In your Python script, when you create your Coiled cluster, you'll pass a `backend_options` dictionary telling it to use `uv` for the build. Coiled will automatically detect your `requirements.txt` file.

import coiled
import dask

# Define backend options to use uv for the build
backend_options = {
    "build_command": f"/usr/bin/python3.11 -m uv pip install -r requirements.txt"
}

# Spin up the cluster
cluster = coiled.Cluster(
    name="uv-powered-cluster",
    n_workers=20,
    backend_options=backend_options
)

# Connect a Dask client
client = dask.distributed.Client(cluster)

print(f"Client ready! Dashboard at: {client.dashboard_link}")
# Now you're ready to run your scalable computation!
# ... your Dask code here ...

And that's it! You've just launched a powerful Dask cluster with a software environment built at record speed.

Looking Ahead: The Future is Fast

The combination of `uv` and `Coiled` represents a paradigm shift in Python development, particularly for data-intensive applications. By targeting the two most significant time sinks—dependency management and cloud environment provisioning—we can reclaim countless hours of productivity. This isn't just an incremental improvement; it's a transformation that makes scalable Python development more interactive, less frustrating, and ultimately, more powerful.

If you're still waiting minutes for your packages to install or your clusters to start in 2025, you're leaving a massive amount of productivity on the table. Give `uv` and `Coiled` a try, and experience what a 10x faster workflow feels like.