- Python 3 (Conda is Preferred)
- (Windows Only) Install PowerShell
pip uninstall cli-pipeline
pip install cli-pipeline==1.5.311 --default-timeout=120 --ignore-installed --no-cache --upgrade
Notes:
- You may need to uninstall any existing
cli-pipeline
usingpip uninstall cli-pipeline
- You may also use
--user
if you're still having issues. - This command line interface requires Python 2 or 3 and Docker as detailed above in the Pre-Requisites section.
- If you're having trouble, use one of our support channels HERE to let us know!
- Followed these steps described here: https://apple.stackexchange.com/questions/254380/macos-sierra-invalid-active-developer-path: 1)
sudo xcode-select --reset
(didn't work for me, but including this because it worked for others) or 2)xcode-select --install
- If you have any issues, you may want to create a separate virtualenv or conda environment to isolate the environments.
- You may need to increase
--default-timeout=120
to avoidReadTimeoutError: HTTPSConnectionPool(host='files.pythonhosted.org', port=443): Read timed out.
- You may need to run
pip uninstall -y python-dateutil
if you see an issue related topip._vendor.pkg_resources.ContextualVersionConflict
- Ignore anything along these lines:
urllib3 (1.23) or chardet (3.0.4) doesn't match a supported version! RequestsDependencyWarning
pipeline version
### EXPECTED OUTPUT ###
cli_version: 1.5.x <-- MAKE SURE THIS MATCHES THE VERSION YOU INSTALLED ABOVE
default train base image: docker.io/pipelineai/train-cpu:1.5.0
default predict base image: docker.io/pipelineai/predict-cpu:1.5.0
Train and Deploy your ML and AI Models in the Following Environments:
pipeline
### EXPECTED OUTPUT ###
...
env-kube-activate <-- Switch Kubernetes Clusters
env-conda-activate <-- Switch Conda Environments
env-registry-sync <-- Sync with the latest Docker images
help <-- This List of CLI Commands
model-archive-tar <-- Create Tar Archive for Model Server
model-archive-untar <-- Untar Model Server Archive
predict-http-test <-- Test Model Cluster (Http-based)
predict-kube-autoscale <-- Configure AutoScaling for Model Cluster
predict-kube-connect <-- Create Secure Tunnel to Model Cluster
predict-kube-describe <-- Describe Model Cluster (Raw)
predict-kube-endpoint <-- Retrieve Model Cluster Endpoint
predict-kube-endpoints <-- Retrieve All Model Cluster Endpoints
predict-kube-logs <-- View Model Cluster Logs
predict-kube-route <-- Route Live Traffic
predict-kube-routes <-- Describe Routes
predict-kube-scale <-- Scale Model Cluster
predict-kube-shell <-- Shell into Model Cluster
predict-kube-start <-- Start Model Cluster from Docker Registry
predict-kube-stop <-- Stop Model Cluster
predict-kube-test <-- Test Model Cluster
predict-sage-describe <-- Describe of SageMaker Model Predict Cluster
predict-sage-route <-- Route Live Traffic in SageMaker
predict-sage-start <-- Start Model Cluster in SageMaker
predict-sage-stop <-- Stop Model Cluster in SageMaker
predict-sage-test <-- Test Model Cluster in SageMaker
predict-server-build <-- Build Model Server
predict-server-describe <-- Describe Model Server
predict-server-logs <-- View Model Server Logs
predict-server-pull <-- Pull Model Server from Docker Registry
predict-server-register <-- Register Model Server with Docker Registry
predict-server-shell <-- Shell into Model Server (Debugging)
predict-server-start <-- Start Model Server
predict-server-stop <-- Stop Model Server
predict-server-tar <-- Tar Model Server
predict-server-test <-- Test Model Server (Http-based)
predict-server-untar <-- Untar Model Server Tar File
predict-stream-test <-- Test Stream-based Model Server
resource-upload <-- Add Model to PipelineAI Cluster
resource-optimize-and-deploy <-- Optimize and Deploy Model to PipelineAI Cluster
resource-routes-get <-- Retrieve Current Model Server Routes
resource-routes-set <-- Set Model Server Routes
stream-http-consume <-- Consume Stream Messages (REST API)
stream-kube-consume <-- Consume Messages from Stream
stream-kube-produce <-- Produce Messages to Stream
train-kube-connect <-- Create Secure Tunnel to Training Cluster
train-kube-describe <-- Describe Training Cluster
train-kube-logs <-- View Training Cluster Logs
train-kube-scale <-- Scale Training Cluster
train-kube-shell <-- Shell into Training Cluster
train-kube-start <-- Start Training Cluster from Docker Registry
train-kube-stop <-- Stop Training Cluster
train-server-build <-- Build Training Server
train-server-logs <-- View Training Server Logs
train-server-pull <-- Pull Training Server from Docker Registry
train-server-register <-- Register Training Server with Docker Registry
train-server-shell <-- Shell into Training Server (Debugging)
train-server-start <-- Start Training Server
train-server-stop <-- Stop Training Server
version <-- View This CLI Version
- Slack: https://joinslack.pipeline.ai
- Email: help@pipeline.ai
- Web: https://support.pipeline.ai
- YouTube: https://youtube.pipeline.ai
- Slideshare: https://slideshare.pipeline.ai
- Troubleshooting Guide