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’m trying to implement in Kubernetes Dask distributed with one scheduler and three workers. I have one pod (frontend.yaml) for the scheduler and three other pods (replicas: 3 in worker.yaml) for the workers. The problem is that the workers are trying to connect to the scheduler and get a timeout as they don’t know the ..
I have in Azure a Kubernetes cluster that deploys three Docker containers: a Flask server, a Dask Scheduler and a Dask Worker. This works fine, now I need to deploy N workers instead of one. This is the yaml file: apiVersion: apps/v1 kind: Deployment metadata: name: img-python namespace: default spec: replicas: 1 selector: matchLabels: app: ..
I tried to run a scheduler and workers docker containers on Amazon’s ECS. I’m using this example: https://docs.dask.org/en/latest/setup/docker.html The scheduler works perfectly, I successfully connected to it from my local machine: distributed.scheduler – INFO – Remove client Client-0ae5b0fa distributed.scheduler – INFO – Close client connection: Client-0ae5b0fa distributed.scheduler – INFO – Remove client Client-0ae5b0fa distributed.core – ..
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 question regarding an implementation of a Dockerfile on dask-docker. FROM continuumio/miniconda3:4.8.2 RUN conda install –yes -c conda-forge python==3.8 […] && rm -rf /opt/conda/pkgs COPY prepare.sh /usr/bin/prepare.sh RUN mkdir /opt/app ENTRYPOINT ["tini", "-g", "–", "/usr/bin/prepare.sh"] prepare.sh is just facilitating installation of additional packages via conda, pip and apt. There are two things I ..