Posts tagged coiled
Coiled observability wins: Chunksize
- 19 September 2023
Distributed computing is hard, distributed debugging is even harder. Dask tries to simplify this process as much as possible. Coiled adds additional observability features for your Dask clusters and processes them to help users understand their workflows better.
Parallel Serverless Functions at Scale
- 07 September 2023
The cloud offers amazing scale, but it can be difficult for Python data developers to use. This post walks through how to use Coiled Functions to run your existing code in parallel on the cloud with minimal code changes.
Reduce training time for CPU intensive models with scikit-learn and Coiled Functions
- 01 September 2023
Patrick Hoefler
Fine Performance Metrics and Spans
- 23 August 2023
While it’s trivial to measure the end-to-end runtime of a Dask workload, the next logical step - breaking down this time to understand if it could be faster - has historically been a much more arduous task that required a lot of intuition and legwork, for novice and expert users alike. We wanted to change that.
Data-proximate Computing with Coiled Functions
- 10 August 2023
Coiled Functions make it easy to improve performance and reduce costs by moving your computations next to your cloud data.
How to Train a Neural Network on a GPU in the Cloud with coiled functions
- 24 July 2023
Patrick Hoefler
Dask performance benchmarking put to the test: Fixing a pandas bottleneck
- 23 June 2023
Patrick Hoefler, Hendrik Makait
Coiled notebooks
- 14 June 2023
We recently pushed out a new, experimental notebooks feature for easily launching Jupyter servers in the cloud from your local machine. We’re excited about Coiled notebooks because they:
Performance testing at Coiled
- 05 May 2023
At Coiled we develop Dask and automatically deploy it to large clusters of cloud workers (sometimes 1000+ EC2 instances at once!). In order to avoid surprises when we publish a new release, Dask needs to be covered by a comprehensive battery of tests — both for functionality and performance.
