I’m trying to deploy Dask distributed on Kubernetes using Helm. It works fine, but I need to customize the deployment as described here. What I need is to have the workers access a mounted volume to read/write files. All the workers would have access to the same volume. The example says that the values below ..
I followed these instructions to deploy a Dask cluster on Kubernetes/Minikube with Helm. I installed and the deployed with the following command: helm install dask-chart dask/dask Running kubectl get services I see the scheduler, however the EXTERNAL-IP is none and I cannot connect to the scheduler: NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE dask-chart-scheduler ClusterIP 10.107.222.251 ..
I have the Dask code below that submits N workers, where each worker is implemented in a Docker container: client.upload_file(‘/code/app/worker.py’) default_sums = client.map(process_asset_defaults, build_worker_args(req, numWorkers)) future_total_sum = client.submit(sum, default_sums) total_defaults_sum = future_total_sum.result() The problem is that in a development environment when I change the worker’s code I need to restart all the containers manually for ..
I am trying to run a distributed computation using Dask on a AWS Fargate cluster (using dask.cloudprovider API) and I am running into the exact same issue as this question. Based on the partial answers to the linked question, and on things like this, I heavily suspect it is due to the pandas version in ..
I have a Dask application that works fine in my laptop, and I need to deploy it on Azure. The image is pushed to the Azure registry, and using the Azure context I’m trying to run docker compose: docker compose up –scale worker=2 Problem is that the command waits 900 seconds and then fails: C:daskdiogo>docker ..
I need to run a scikit-learn RandomForestClassifier with multiple processes in parallel. For that, I’m looking into implementing a Dask scheduler with N workers, where the scheduler and each worker run in a separate Docker container. The client application, that also runs in a separate Docker container, will first connect to the scheduler and initiate ..
I have a dask setup on AWS as follows: Dask Scheduler on an EC2 Instance. Dask worker inside a docker container within another EC2 Instance. Both of the EC2 instances are on the same VPC. The problem is just spawning the dask-worker on the container with just the ip of the scheduler doesn’t work as, ..
I’m attempting to move a sklearn fit from a local Loky parallelism to Dask distributed, however seem to be hitting issues whereby distributed is taking much longer to run (a 1min task locally will take 20mins+ distributed). This seems larger than one would expect from just the distribution overhead. As such, I’m attempting to diagnose ..
I have a number of image files that I’m running a face recognition model on, in order to generate a Dask Dataframe of facial encodings, the file paths for the images that contain each face, and the coordinates in the image of each face. Because I have a huge number of photos, I’m using Dask ..
I’m currently using Docker Swarm to deploy/manage multiple Dask Workers across a cluster. For easier debugging I’d like to be able to name the workers based on what node in the Swarm it is running on. The dask-worker command has a –name parameter, however, Docker’s templating doesn’t seem to work in the entrypoint or cmd ..