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Update version to 0.22.0
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README.md

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<!-- Delete on release branches -->
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<img src='https://s3-us-west-2.amazonaws.com/cortex-public/logo.png' height='42'>
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<br>
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<!-- Delete on release branches -->
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<!-- CORTEX_VERSION_README_MINOR -->
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[install](https://docs.cortex.dev/install)[documentation](https://docs.cortex.dev)[examples](https://github.com/cortexlabs/cortex/tree/0.21/examples)[support](https://gitter.im/cortexlabs/cortex)
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# Deploy machine learning models to production
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Cortex is an open source platform for deploying, managing, and scaling machine learning in production.

build/build-image.sh

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ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")"/.. >/dev/null && pwd)"
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CORTEX_VERSION=master
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CORTEX_VERSION=0.22.0
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image=$1
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dir="${ROOT}/images/${image/-slim}"

build/cli.sh

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ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")"/.. >/dev/null && pwd)"
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CORTEX_VERSION=master
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CORTEX_VERSION=0.22.0
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arg1=${1:-""}
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upload="false"

build/lint.sh

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! -path "**/.vscode/*" \
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! -path "**/__pycache__/*" \
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! -path "./docs/contributing/development.md" \
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! -path "./dev/config/*" \
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! -path "./bin/*" \
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! -path "./.git/*" \
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! -name ".*" \
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! -path "./vendor/*" \
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! -path "**/.vscode/*" \
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! -path "**/__pycache__/*" \
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! -path "./dev/config/*" \
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! -path "./bin/*" \
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! -path "./.git/*" \
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! -name ".*" \
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! -path "./vendor/*" \
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! -path "**/.vscode/*" \
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! -path "**/__pycache__/*" \
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! -path "./dev/config/*" \
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! -path "./bin/*" \
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! -path "./.git/*" \
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! -name ".*" \
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! -path "./vendor/*" \
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! -path "**/.vscode/*" \
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! -path "**/__pycache__/*" \
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! -path "./dev/config/*" \
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! -path "./bin/*" \
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! -path "./.git/*" \
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! -name ".*" \
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! -path "./vendor/*" \
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! -path "**/.vscode/*" \
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! -path "**/__pycache__/*" \
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! -path "./dev/config/*" \
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! -path "./bin/*" \
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! -path "./.git/*" \
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! -name ".*" \
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! -path "./vendor/*" \
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! -path "**/.vscode/*" \
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! -path "**/__pycache__/*" \
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! -path "./dev/config/*" \
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! -path "./bin/*" \
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! -path "./.git/*" \
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! -name ".*" \

build/push-image.sh

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set -euo pipefail
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CORTEX_VERSION=master
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CORTEX_VERSION=0.22.0
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image=$1
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cli/cluster/errors.go

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msg += "\nif you have a cluster running:\n"
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msg += fmt.Sprintf(" → run `cortex cluster info --configure-env %s` to update your environment (include `--config <cluster.yaml>` if you have a cluster configuration file)\n", envName)
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// CORTEX_VERSION_MINOR
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msg += " → if you set `operator_load_balancer_scheme: internal` in your cluster configuration file, your CLI must run from within a VPC that has access to your cluster's VPC (see https://docs.cortex.dev/v/master/guides/vpc-peering)\n"
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msg += " → if you set `operator_load_balancer_scheme: internal` in your cluster configuration file, your CLI must run from within a VPC that has access to your cluster's VPC (see https://docs.cortex.dev/v/0.22/guides/vpc-peering)\n"
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}
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return errors.WithStack(&errors.Error{

docs/cluster-management/config.md

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# whether the subnets used for EC2 instances should be public or private (default: "public")
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# if "public", instances will be assigned public IP addresses; if "private", instances won't have public IPs and a NAT gateway will be created to allow outgoing network requests
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# see https://docs.cortex.dev/v/master/miscellaneous/security#private-cluster for more information
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# see https://docs.cortex.dev/v/0.22/miscellaneous/security#private-cluster for more information
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subnet_visibility: public # must be "public" or "private"
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# whether to include a NAT gateway with the cluster (a NAT gateway is necessary when using private subnets)
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# whether the API load balancer should be internet-facing or internal (default: "internet-facing")
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# note: if using "internal", APIs will still be accessible via the public API Gateway endpoint unless you also disable API Gateway in your API's configuration (if you do that, you must configure VPC Peering to connect to your APIs)
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# see https://docs.cortex.dev/v/master/miscellaneous/security#private-cluster for more information
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# see https://docs.cortex.dev/v/0.22/miscellaneous/security#private-cluster for more information
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api_load_balancer_scheme: internet-facing # must be "internet-facing" or "internal"
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# whether the operator load balancer should be internet-facing or internal (default: "internet-facing")
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# note: if using "internal", you must configure VPC Peering to connect your CLI to your cluster operator (https://docs.cortex.dev/v/master/guides/vpc-peering)
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# see https://docs.cortex.dev/v/master/miscellaneous/security#private-operator for more information
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# note: if using "internal", you must configure VPC Peering to connect your CLI to your cluster operator (https://docs.cortex.dev/v/0.22/guides/vpc-peering)
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# see https://docs.cortex.dev/v/0.22/miscellaneous/security#private-operator for more information
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operator_load_balancer_scheme: internet-facing # must be "internet-facing" or "internal"
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# whether to disable API gateway cluster-wide
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tags: # <string>: <string> map of key/value pairs
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# whether to use spot instances in the cluster (default: false)
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# see https://docs.cortex.dev/v/master/cluster-management/spot-instances for additional details on spot configuration
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# see https://docs.cortex.dev/v/0.22/cluster-management/spot-instances for additional details on spot configuration
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spot: false
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# see https://docs.cortex.dev/v/master/guides/custom-domain for instructions on how to set up a custom domain
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# see https://docs.cortex.dev/v/0.22/guides/custom-domain for instructions on how to set up a custom domain
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ssl_certificate_arn:
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# primary CIDR block for the cluster's VPC (default: 192.168.0.0/16)
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<!-- CORTEX_VERSION_BRANCH_STABLE -->
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```yaml
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# docker image paths
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image_operator: cortexlabs/operator:master
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image_manager: cortexlabs/manager:master
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image_downloader: cortexlabs/downloader:master
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image_request_monitor: cortexlabs/request-monitor:master
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image_cluster_autoscaler: cortexlabs/cluster-autoscaler:master
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image_metrics_server: cortexlabs/metrics-server:master
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image_inferentia: cortexlabs/inferentia:master
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image_neuron_rtd: cortexlabs/neuron-rtd:master
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image_nvidia: cortexlabs/nvidia:master
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image_fluentd: cortexlabs/fluentd:master
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image_statsd: cortexlabs/statsd:master
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image_istio_proxy: cortexlabs/istio-proxy:master
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image_istio_pilot: cortexlabs/istio-pilot:master
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image_operator: cortexlabs/operator:0.22.0
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image_manager: cortexlabs/manager:0.22.0
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image_downloader: cortexlabs/downloader:0.22.0
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image_request_monitor: cortexlabs/request-monitor:0.22.0
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image_cluster_autoscaler: cortexlabs/cluster-autoscaler:0.22.0
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image_metrics_server: cortexlabs/metrics-server:0.22.0
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image_inferentia: cortexlabs/inferentia:0.22.0
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image_neuron_rtd: cortexlabs/neuron-rtd:0.22.0
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image_nvidia: cortexlabs/nvidia:0.22.0
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image_fluentd: cortexlabs/fluentd:0.22.0
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image_statsd: cortexlabs/statsd:0.22.0
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image_istio_proxy: cortexlabs/istio-proxy:0.22.0
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image_istio_pilot: cortexlabs/istio-pilot:0.22.0
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```

docs/cluster-management/install.md

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## Install the CLI
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<!-- CORTEX_VERSION_MINOR -->
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```bash
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# clone the Cortex repository
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* Try the [tutorial](../../examples/pytorch/text-generator/README.md) to learn more about how to use Cortex.
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* Deploy one of our [examples](https://github.com/cortexlabs/cortex/tree/master/examples).
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* Deploy one of our [examples](https://github.com/cortexlabs/cortex/tree/0.22/examples).
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* See our [exporting guide](../guides/exporting.md) for how to export your model to use in an API.
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* View documentation for [realtime APIs](../deployments/realtime-api.md) or [batch APIs](../deployments/batch-api.md).
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* See [uninstall](uninstall.md) if you'd like to spin down your cluster.

docs/cluster-management/update.md

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cortex cluster down
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# update your CLI
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bash -c "$(curl -sS https://raw.githubusercontent.com/cortexlabs/cortex/master/get-cli.sh)"
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# confirm version
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cortex version

docs/deployments/batch-api/deployment.md

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<!-- CORTEX_VERSION_MINOR -->
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* [Tutorial](../../../examples/batch/image-classifier/README.md) provides a step-by-step walkthrough of deploying an image classification batch API
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* [CLI documentation](../../miscellaneous/cli.md) lists all CLI commands
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* [Examples](https://github.com/cortexlabs/cortex/tree/master/examples/batch) demonstrate how to deploy models from common ML libraries
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* [Examples](https://github.com/cortexlabs/cortex/tree/0.22/examples/batch) demonstrate how to deploy models from common ML libraries

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