Tags
- scipy 85
- Python 83
- Programming 85
- dask 67
- pangeo 1
- HPC 3
- distributed 4
- jobqueue 1
- GPU 8
- array 2
- cupy 1
- Pandas 1
- dataframe 7
- release 3
- MPI 1
- RAPIDS 3
- Dask 1
- Dask-GLM 1
- CuPy 2
- Sparse 1
- numba 1
- python 3
- scikit-image 1
- dask-image 1
- IO 3
- User Survey 4
- imaging 5
- machine-learning 2
- dask-ml 2
- SciPy 1
- Community 3
- Talk 1
- config 1
- ray 1
- Tutorials 1
- Helm 2
- Dask Gateway 1
- Deployment 1
- memory 1
- profiling 1
- ram 1
- deep learning 1
- PyTorch 1
- life science 5
- skan 1
- skeleton analysis 1
- Dask Summit 2
- Distributed 1
- Tools 1
- Organisations 1
- Australia 1
- geoscience 1
- performance 1
- image analysis 1
- Kubernetes 2
- deployment 2
- kubernetes 1
- dask-kubernetes 1
- clusters 1
- Flyte 1
scipy
- Load Large Image Data with Dask Array
- Python and GPUs: A Status Update
- Dask on HPC
- Experiments in High Performance Networking with UCX and DGX
- Dask Version 1.0
- Refactor Documentation
- Dask Development Log
- Dask Release 0.19.0
- High level performance of Pandas, Dask, Spark, and Arrow
- Building SAGA optimization for Dask arrays
- Dask Development Log
- Pickle isn't slow, it's a protocol
- Dask Development Log, Scipy 2018
- Who uses Dask?
- Dask Development Log
- Dask Scaling Limits
- Dask Release 0.18.0
- Beyond Numpy Arrays in Python
- Dask Release 0.17.2
- Craft Minimal Bug Reports
- Dask Release 0.17.0
- Pangeo: JupyterHub, Dask, and XArray on the Cloud
- Dask Development Log
- Dask Release 0.16.0
- Optimizing Data Structure Access in Python
- Streaming Dataframes
- Notes on Kafka in Python
- Dask Release 0.15.3
- Fast GeoSpatial Analysis in Python
- Dask on HPC - Initial Work
- Dask Release 0.15.2
- Scikit-Image and Dask Performance
- Dask Benchmarks
- Use Apache Parquet
- Dask Release 0.15.0
- Dask Release 0.14.3
- Dask Development Log
- Asynchronous Optimization Algorithms with Dask
- Dask and Pandas and XGBoost
- Dask Release 0.14.1
- Developing Convex Optimization Algorithms in Dask
- Dask Release 0.14.0
- Dask Development Log
- Experiment with Dask and TensorFlow
- Two Easy Ways to Use Scikit Learn and Dask
- Dask Development Log
- Custom Parallel Algorithms on a Cluster with Dask
- Dask Development Log
- Distributed NumPy on a Cluster with Dask Arrays
- Distributed Pandas on a Cluster with Dask DataFrames
- Dask Release 0.13.0
- Dask Development Log
- Dask Development Log
- Dask Development Log
- Dask Development Log
- Dask Cluster Deployments
- Dask and Celery
- Dask Distributed Release 1.13.0
- Dask for Institutions
- Ad Hoc Distributed Random Forests
- Fast Message Serialization
- Distributed Dask Arrays
- Pandas on HDFS with Dask Dataframes
- Introducing Dask distributed
- Distributed Prototype
- Caching
- Custom Parallel Workflows
- Write Complex Parallel Algorithms
- Distributed Scheduling
- State of Dask
- Towards Out-of-core DataFrames
- Towards Out-of-core ND-Arrays -- Dask + Toolz = Bag
- Towards Out-of-core ND-Arrays -- Slicing and Stacking
- Towards Out-of-core ND-Arrays -- Spilling to Disk
- Towards Out-of-core ND-Arrays -- Benchmark MatMul
- Towards Out-of-core ND-Arrays -- Multi-core Scheduling
- Towards Out-of-core ND-Arrays -- Frontend
- Towards Out-of-core ND-Arrays
Python
- Managing dask workloads with Flyte
- Dask on HPC
- Dask Version 1.0
- Refactor Documentation
- Dask Development Log
- Dask Release 0.19.0
- High level performance of Pandas, Dask, Spark, and Arrow
- Building SAGA optimization for Dask arrays
- Dask Development Log
- Pickle isn't slow, it's a protocol
- Dask Development Log, Scipy 2018
- Who uses Dask?
- Dask Development Log
- Dask Scaling Limits
- Dask Release 0.18.0
- Beyond Numpy Arrays in Python
- Dask Release 0.17.2
- Craft Minimal Bug Reports
- Dask Release 0.17.0
- Credit Modeling with Dask
- Pangeo: JupyterHub, Dask, and XArray on the Cloud
- Dask Development Log
- Dask Release 0.16.0
- Optimizing Data Structure Access in Python
- Streaming Dataframes
- Notes on Kafka in Python
- Dask Release 0.15.3
- Fast GeoSpatial Analysis in Python
- Dask on HPC - Initial Work
- Dask Release 0.15.2
- Scikit-Image and Dask Performance
- Dask Benchmarks
- Use Apache Parquet
- Dask Release 0.15.0
- Dask Release 0.14.3
- Dask Development Log
- Asynchronous Optimization Algorithms with Dask
- Dask and Pandas and XGBoost
- Dask Release 0.14.1
- Developing Convex Optimization Algorithms in Dask
- Dask Release 0.14.0
- Dask Development Log
- Experiment with Dask and TensorFlow
- Two Easy Ways to Use Scikit Learn and Dask
- Dask Development Log
- Custom Parallel Algorithms on a Cluster with Dask
- Dask Development Log
- Distributed NumPy on a Cluster with Dask Arrays
- Distributed Pandas on a Cluster with Dask DataFrames
- Dask Release 0.13.0
- Dask Development Log
- Dask Development Log
- Dask Development Log
- Dask Development Log
- Dask Cluster Deployments
- Dask and Celery
- Dask Distributed Release 1.13.0
- Dask for Institutions
- Ad Hoc Distributed Random Forests
- Fast Message Serialization
- Distributed Dask Arrays
- Pandas on HDFS with Dask Dataframes
- Introducing Dask distributed
- Distributed Prototype
- Caching
- Custom Parallel Workflows
- Write Complex Parallel Algorithms
- Distributed Scheduling
- Towards Out-of-core DataFrames
- Towards Out-of-core ND-Arrays -- Dask + Toolz = Bag
- Towards Out-of-core ND-Arrays -- Slicing and Stacking
- Towards Out-of-core ND-Arrays -- Spilling to Disk
- Towards Out-of-core ND-Arrays -- Benchmark MatMul
- Towards Out-of-core ND-Arrays -- Multi-core Scheduling
- Towards Out-of-core ND-Arrays -- Frontend
- Towards Out-of-core ND-Arrays
Programming
- Dask on HPC
- Dask Version 1.0
- Refactor Documentation
- Dask Development Log
- Dask Release 0.19.0
- High level performance of Pandas, Dask, Spark, and Arrow
- Building SAGA optimization for Dask arrays
- Dask Development Log
- Pickle isn't slow, it's a protocol
- Dask Development Log, Scipy 2018
- Who uses Dask?
- Dask Development Log
- Dask Scaling Limits
- Dask Release 0.18.0
- Beyond Numpy Arrays in Python
- Dask Release 0.17.2
- Craft Minimal Bug Reports
- Dask Release 0.17.0
- Credit Modeling with Dask
- Pangeo: JupyterHub, Dask, and XArray on the Cloud
- Dask Development Log
- Dask Release 0.16.0
- Optimizing Data Structure Access in Python
- Streaming Dataframes
- Notes on Kafka in Python
- Dask Release 0.15.3
- Fast GeoSpatial Analysis in Python
- Dask on HPC - Initial Work
- Dask Release 0.15.2
- Scikit-Image and Dask Performance
- Dask Benchmarks
- Use Apache Parquet
- Dask Release 0.15.0
- Dask Release 0.14.3
- Dask Development Log
- Asynchronous Optimization Algorithms with Dask
- Dask and Pandas and XGBoost
- Dask Release 0.14.1
- Developing Convex Optimization Algorithms in Dask
- Dask Release 0.14.0
- Dask Development Log
- Experiment with Dask and TensorFlow
- Two Easy Ways to Use Scikit Learn and Dask
- Dask Development Log
- Custom Parallel Algorithms on a Cluster with Dask
- Dask Development Log
- Distributed NumPy on a Cluster with Dask Arrays
- Distributed Pandas on a Cluster with Dask DataFrames
- Dask Release 0.13.0
- Dask Development Log
- Dask Development Log
- Dask Development Log
- Dask Development Log
- Dask Cluster Deployments
- Dask and Celery
- Dask Distributed Release 1.13.0
- Dask for Institutions
- Dask and Scikit-Learn -- Model Parallelism
- Ad Hoc Distributed Random Forests
- Fast Message Serialization
- Distributed Dask Arrays
- Pandas on HDFS with Dask Dataframes
- Introducing Dask distributed
- Dask is one year old
- Distributed Prototype
- Caching
- Custom Parallel Workflows
- Write Complex Parallel Algorithms
- Distributed Scheduling
- State of Dask
- Towards Out-of-core DataFrames
- Towards Out-of-core ND-Arrays -- Dask + Toolz = Bag
- Towards Out-of-core ND-Arrays -- Slicing and Stacking
- Towards Out-of-core ND-Arrays -- Spilling to Disk
- Towards Out-of-core ND-Arrays -- Benchmark MatMul
- Towards Out-of-core ND-Arrays -- Multi-core Scheduling
- Towards Out-of-core ND-Arrays -- Frontend
- Towards Out-of-core ND-Arrays
dask
- Managing dask workloads with Flyte
- Measuring Dask memory usage with dask-memusage
- Comparing Dask-ML and Ray Tune's Model Selection Algorithms
- DataFrame Groupby Aggregations
- Dask on HPC
- Composing Dask Array with Numba Stencils
- cuML and Dask hyperparameter optimization
- Extension Arrays in Dask DataFrame
- Dask Version 1.0
- Refactor Documentation
- Dask Development Log
- Dask Release 0.19.0
- High level performance of Pandas, Dask, Spark, and Arrow
- Building SAGA optimization for Dask arrays
- Dask Development Log
- Pickle isn't slow, it's a protocol
- Dask Development Log, Scipy 2018
- Who uses Dask?
- Dask Development Log
- Dask Scaling Limits
- Dask Release 0.18.0
- Beyond Numpy Arrays in Python
- Dask Release 0.17.2
- Dask Release 0.17.0
- Pangeo: JupyterHub, Dask, and XArray on the Cloud
- Dask Development Log
- Dask Release 0.16.0
- Optimizing Data Structure Access in Python
- Streaming Dataframes
- Notes on Kafka in Python
- Dask Release 0.15.3
- Fast GeoSpatial Analysis in Python
- Dask on HPC - Initial Work
- Dask Release 0.15.2
- Scikit-Image and Dask Performance
- Dask Benchmarks
- Use Apache Parquet
- Dask Release 0.15.0
- Dask Release 0.14.3
- Dask Release 0.14.1
- Dask Distributed Release 1.13.0
- Dask for Institutions
- Dask and Scikit-Learn -- Model Parallelism
- Ad Hoc Distributed Random Forests
- Fast Message Serialization
- Distributed Dask Arrays
- Pandas on HDFS with Dask Dataframes
- Introducing Dask distributed
- Dask is one year old
- Distributed Prototype
- Caching
- Custom Parallel Workflows
- Write Complex Parallel Algorithms
- Distributed Scheduling
- State of Dask
- Towards Out-of-core DataFrames
- Towards Out-of-core ND-Arrays -- Dask + Toolz = Bag
- Towards Out-of-core ND-Arrays -- Slicing and Stacking
- Towards Out-of-core ND-Arrays -- Spilling to Disk
- Towards Out-of-core ND-Arrays -- Benchmark MatMul
- Towards Out-of-core ND-Arrays -- Multi-core Scheduling
- Towards Out-of-core ND-Arrays -- Frontend
- Towards Out-of-core ND-Arrays
pangeo
HPC
distributed
- Data Proximate Computation on a Dask Cluster Distributed Between Data Centres
- Measuring Dask memory usage with dask-memusage
- Configuring a Distributed Dask Cluster
- Dask-jobqueue
jobqueue
GPU
- Easy CPU/GPU Arrays and Dataframes
- Large SVDs
- cuML and Dask hyperparameter optimization
- Building GPU Groupby-Aggregations for Dask
- Single-Node Multi-GPU Dataframe Joins
- Dask, Pandas, and GPUs: first steps
- GPU Dask Arrays, first steps
array
cupy
Pandas
dataframe
- Do you need consistent environments between the client, scheduler and workers?
- Deep Dive into creating a Dask DataFrame Collection with from_map
- Understanding Dask’s meta keyword argument
- DataFrame Groupby Aggregations
- Building GPU Groupby-Aggregations for Dask
- Single-Node Multi-GPU Dataframe Joins
- Extension Arrays in Dask DataFrame
release
MPI
RAPIDS
Dask
Dask-GLM
CuPy
Sparse
numba
python
- Load Large Image Data with Dask Array
- Python and GPUs: A Status Update
- Experiments in High Performance Networking with UCX and DGX
scikit-image
dask-image
IO
- Do you need consistent environments between the client, scheduler and workers?
- Deep Dive into creating a Dask DataFrame Collection with from_map
- Extracting fsspec from Dask
User Survey
- 2021 Dask User Survey
- The 2021 Dask User Survey is out now
- 2020 Dask User Survey
- 2019 Dask User Survey
imaging
- Skeleton analysis
- Dask with PyTorch for large scale image analysis
- Image segmentation with Dask
- Getting to know the life science community
- Dask and ITK for large scale image analysis
machine-learning
- Comparing Dask-ML and Ray Tune's Model Selection Algorithms
- Better and faster hyperparameter optimization with Dask
dask-ml
- Comparing Dask-ML and Ray Tune's Model Selection Algorithms
- Better and faster hyperparameter optimization with Dask
SciPy
Community
Talk
config
ray
Tutorials
Helm
Dask Gateway
Deployment
memory
profiling
ram
deep learning
PyTorch
life science
- Reflections on one year as the Dask life science fellow
- Mosaic Image Fusion
- CZI EOSS Update
- Life sciences at the 2021 Dask Summit
- Skeleton analysis
skan
skeleton analysis
Dask Summit
Distributed
Tools
Organisations
Australia
geoscience
performance
image analysis
Kubernetes
deployment
- Dask Kubernetes Operator
- Data Proximate Computation on a Dask Cluster Distributed Between Data Centres