diff --git a/tensorflow_serving/g3doc/api_rest.md b/tensorflow_serving/g3doc/api_rest.md index 8edd1d3acbc..202b61ac3dd 100644 --- a/tensorflow_serving/g3doc/api_rest.md +++ b/tensorflow_serving/g3doc/api_rest.md @@ -323,7 +323,7 @@ data between systems. For supported types, the encodings are described on a type-by-type basis in the table below. Types not listed below are implied to be unsupported. -[TF Data Type](https://www.tensorflow.org/versions/r1.1/programmers_guide/dims_types#data_types) | [JSON Value](http://json.org/) | JSON example | Notes +[TF Data Type](https://www.tensorflow.org/api_docs/python/tf/dtypes/DType) | [JSON Value](http://json.org/) | JSON example | Notes ------------------------------------------------------------------------------------------------ | ------------------------------ | ---------------------------------- | ----- DT_BOOL | true, false | *true, false* | DT_STRING | string | *"Hello World!"* | If `DT_STRING` represents binary bytes (e.g. serialized image bytes or protobuf), encode these in Base64. See [Encoding binary values](#encoding-binary-values) for more info. diff --git a/tensorflow_serving/g3doc/docker.md b/tensorflow_serving/g3doc/docker.md index 97e2c12a0be..5a20b742244 100644 --- a/tensorflow_serving/g3doc/docker.md +++ b/tensorflow_serving/g3doc/docker.md @@ -33,7 +33,8 @@ links here: * [Docker for macOS](https://docs.docker.com/docker-for-mac/install/) * [Docker for Windows](https://docs.docker.com/docker-for-windows/install/) for Windows 10 Pro or later -* [Docker Toolbox](https://docs.docker.com/toolbox/) for much older versions +* [Docker Toolbox](https://docs.docker.com/retired/#docker-toolbox) + for much older versions of macOS, or versions of Windows before Windows 10 Pro ## Serving with Docker diff --git a/tensorflow_serving/g3doc/serving_advanced.md b/tensorflow_serving/g3doc/serving_advanced.md index 4ecb0e9fc57..b100093d7ca 100644 --- a/tensorflow_serving/g3doc/serving_advanced.md +++ b/tensorflow_serving/g3doc/serving_advanced.md @@ -9,7 +9,7 @@ server to serve your models, see This tutorial uses the simple Softmax Regression model introduced in the TensorFlow tutorial for handwritten image (MNIST data) classification. If you don't know what TensorFlow or MNIST is, see the -[MNIST For ML Beginners](http://www.tensorflow.org/tutorials/mnist/beginners/index.html#mnist-for-ml-beginners) +[TensorFlow 2 quickstart for beginners](https://www.tensorflow.org/tutorials/quickstart/beginner) tutorial. The code for this tutorial consists of two parts: diff --git a/tensorflow_serving/g3doc/serving_kubernetes.md b/tensorflow_serving/g3doc/serving_kubernetes.md index b0d5368b0be..205455ee611 100644 --- a/tensorflow_serving/g3doc/serving_kubernetes.md +++ b/tensorflow_serving/g3doc/serving_kubernetes.md @@ -203,7 +203,7 @@ resnet-serving-cluster us-central1-f 1.1.8 104.197.163.119 n1-stand ``` Set the default cluster for gcloud container command and pass cluster -credentials to [kubectl](http://kubernetes.io/docs/user-guide/kubectl-overview/). +credentials to [kubectl](https://kubernetes.io/docs/concepts/overview/kubectl/). ```shell gcloud config set container/cluster resnet-serving-cluster @@ -245,11 +245,11 @@ docker push gcr.io/tensorflow-serving/resnet ### Create Kubernetes Deployment and Service The deployment consists of 3 replicas of `resnet_inference` server controlled by -a [Kubernetes Deployment](http://kubernetes.io/docs/user-guide/deployments/). +a [Kubernetes Deployment](https://kubernetes.io/docs/concepts/workloads/controllers/deployment/). The replicas are exposed externally by a -[Kubernetes Service](http://kubernetes.io/docs/user-guide/services/) along with +[Kubernetes Service](https://kubernetes.io/docs/concepts/services-networking/service/) along with an -[External Load Balancer](http://kubernetes.io/docs/user-guide/load-balancer/). +[External Load Balancer](https://kubernetes.io/docs/concepts/services-networking/service/#loadbalancer). We create them using the example Kubernetes config [resnet_k8s.yaml](https://github.com/tensorflow/serving/tree/master/tensorflow_serving/example/resnet_k8s.yaml).