more URL updates

Author: t-kalinowski

R/cloudml-package.R

@@ -8,16 +8,16 @@
 #' @description
 #'
 #' The **cloudml** package provides an R interface to [Google Cloud Machine
-#' Learning Engine](https://cloud.google.com/ml-engine/), a managed service that
+#' Learning Engine](https://cloud.google.com/vertex-ai), a managed service that
 #' enables:
 #'
 #' * Scalable training of models built with the
-#' [keras](https://keras.rstudio.com/),
-#' [tfestimators](https://tensorflow.rstudio.com/tfestimators), and
+#' [keras](https://keras3.posit.co/),
+#' [tfestimators](https://github.com/rstudio/tfestimators), and
 #' [tensorflow](https://tensorflow.rstudio.com/) R packages.
 #'
 #' * On-demand access to training on GPUs, including the new [Tesla P100
-#' GPUs](http://www.nvidia.com/object/tesla-p100.html) from NVIDIA®.
+#' GPUs](https://www.nvidia.com/en-us/data-center/) from NVIDIA®.
 #'
 #' * Hyperparameter tuning to optimize key attributes of model architectures in
 #' order to maximize predictive accuracy.
@@ -29,13 +29,13 @@
 #'
 #' CloudML is a managed service where you pay only for the hardware resources
 #' that you use. Prices vary depending on configuration (e.g. CPU vs. GPU vs.
-#' multiple GPUs). See <https://cloud.google.com/ml-engine/pricing> for
+#' multiple GPUs). See <https://cloud.google.com/vertex-ai/pricing> for
 #' additional details.
 #'
 #' For documentation on using the R interface to CloudML see the package website
-#' at <https://tensorflow.rstudio.com/tools/cloudml/>
+#' at <https://github.com/rstudio/cloudml>
 #'
-#' @references <https://tensorflow.rstudio.com/tools/cloudml/>
+#' @references <https://github.com/rstudio/cloudml>
 #' @name cloudml-package
 #' @aliases cloudml
 #' @keywords internal

R/jobs.R

@@ -12,13 +12,13 @@
 #' @param master_type Training master node machine type. "standard" provides a
 #'   basic machine configuration suitable for training simple models with small
 #'   to moderate datasets. See the documentation at
-#'   <https://cloud.google.com/ml-engine/docs/tensorflow/machine-types#machine_type_table>
+#'   <https://cloud.google.com/vertex-ai/docs/reference/rest/v1/MachineSpec>
 #'    for details on available machine types.
 #'
 #' @param region The region to be used for training.
 #'
 #' @param config A list, `YAML` or `JSON` configuration file as described
-#'   <https://cloud.google.com/ml-engine/reference/rest/v1/projects.jobs>.
+#'   <https://cloud.google.com/vertex-ai>.
 #'
 #' @param collect Logical. If TRUE, collect job when training is completed
 #'   (blocks waiting for the job to complete). The default (`"ask"`) will

README.md

@@ -7,14 +7,14 @@ Status](https://ci.appveyor.com/api/projects/status/github/rstudio/cloudml?branc
 
 The **cloudml** package provides an R interface to [Google Cloud Machine Learning Engine](https://cloud.google.com/vertex-ai), a managed service that enables:
 
-* Scalable training of models built with the [keras](https://keras3.posit.co/), [tfestimators](https://tensorflow.rstudio.com/tfestimators), and [tensorflow](https://tensorflow.rstudio.com/) R packages.
+* Scalable training of models built with the [keras](https://keras3.posit.co/), [tfestimators](https://github.com/rstudio/tfestimators), and [tensorflow](https://tensorflow.rstudio.com/) R packages.
 
 * On-demand access to training on GPUs, including the new [Tesla P100 GPUs](https://www.nvidia.com/en-us/data-center/) from NVIDIA®.
 
 * Hyperparameter tuning to optimize key attributes of model architectures in order to maximize predictive accuracy.
 
 * Deployment of trained models to the Google global prediction platform that can support thousands of users and TBs of data.
 
-CloudML is a managed service where you pay only for the hardware resources that you use. Prices vary depending on configuration (e.g. CPU vs. GPU vs. multiple GPUs). See <https://cloud.google.com/vertex-aipricing> for additional details.
+CloudML is a managed service where you pay only for the hardware resources that you use. Prices vary depending on configuration (e.g. CPU vs. GPU vs. multiple GPUs). See <https://cloud.google.com/vertex-ai/pricing> for additional details.
 
-For documentation on using the R interface to CloudML see the package website at <https://tensorflow.rstudio.com/tools/cloudml/>
+For documentation on using the R interface to CloudML see the package website at <https://github.com/rstudio/cloudml>

man/cloudml-package.Rd

@@ -32,7 +32,7 @@ Cloud ML Engine can host your models so that you can get predictions from them i
 ### Exporting a SavedModel
 
 The Cloud ML prediction service makes use of models exported through the
-`export_savedmodel()` function which is available for models created using the [tensorflow](https://tensorflow.rstudio.com/tensorflow/), [keras](https://keras3.posit.co/) and
+`export_savedmodel()` function which is available for models created using the [tensorflow](https://tensorflow.rstudio.com/), [keras](https://keras3.posit.co/) and
 [tfestimators](https://github.com/rstudio/tfestimators) packages or any other tool that support the [tf.train.Saver](https://www.tensorflow.org/api_docs/python/tf/compat/v1/train/Saver) interface.
 
 For instance, we can use `examples/keras/train.R` included in this package to define

man/cloudml_deploy.Rd

@@ -1,6 +1,6 @@
 ---
 title: "R Interface to Google CloudML"
-output: 
+output:
   rmarkdown::html_vignette: default
 vignette: >
   %\VignetteIndexEntry{Getting Started}
@@ -29,15 +29,15 @@ knitr::opts_chunk$set(echo = TRUE, eval=FALSE)
 
 The **cloudml** package provides an R interface to [Google Cloud Machine Learning Engine](https://cloud.google.com/vertex-ai), a managed service that enables:
 
-* Scalable training of models built with the [keras](https://keras3.posit.co/), [tfestimators](https://tensorflow.rstudio.com/tfestimators), and [tensorflow](https://tensorflow.rstudio.com/) R packages.
+* Scalable training of models built with the [keras](https://keras3.posit.co/), [tfestimators](https://github.com/rstudio/tfestimators), and [tensorflow](https://tensorflow.rstudio.com/) R packages.
 
-* On-demand access to training on GPUs, including the new [Tesla P100 GPUs](https://www.nvidia.com/en-us/data-center/) from NVIDIA®. 
+* On-demand access to training on GPUs, including the new [Tesla P100 GPUs](https://www.nvidia.com/en-us/data-center/) from NVIDIA®.
 
 * Hyperparameter tuning to optmize key attributes of model architectures in order to maximize predictive accuracy.
 
 * Deployment of trained models to the Google global prediction platform that can support thousands of users and TBs of data.
 
-CloudML is a managed service where you pay only for the hardware resources that you use. Prices vary depending on configuration (e.g. CPU vs. GPU vs. multiple GPUs). See <https://cloud.google.com/vertex-aipricing> for additional details. 
+CloudML is a managed service where you pay only for the hardware resources that you use. Prices vary depending on configuration (e.g. CPU vs. GPU vs. multiple GPUs). See <https://cloud.google.com/vertex-ai/pricing> for additional details.
 
 <div style="height: 25px;"></div>
 
@@ -58,7 +58,7 @@ Start by installing the cloudml R package from CRAN as follows:
 install.packages("cloudml")
 ```
 
-Then, install the *Google Cloud SDK*, a set of utilties that enable you to interact with your Google Cloud account from within R. You can install the SDK using the `gcloud_install()` function. 
+Then, install the *Google Cloud SDK*, a set of utilties that enable you to interact with your Google Cloud account from within R. You can install the SDK using the `gcloud_install()` function.
 
 ```{r}
 library(cloudml)
@@ -93,7 +93,7 @@ cloudml_train("train.R")
 All of the files within the current working directory will be bundled up and sent along with the script to CloudML.
 
 <div class="bs-callout bs-callout-warning">
-Note that the very first time you submit a job to CloudML the various packages required to run your script will be compiled from source. This will make the execution time of the job considerably longer that you might expect. It's only the first job that incurs this overhead though (since the package installations are cached), and subsequent jobs will run more quickly. 
+Note that the very first time you submit a job to CloudML the various packages required to run your script will be compiled from source. This will make the execution time of the job considerably longer that you might expect. It's only the first job that incurs this overhead though (since the package installations are cached), and subsequent jobs will run more quickly.
 </div>
 
 If you are using [RStudio v1.1](https://posit.co/download/rstudio-desktop/) or higher, then the CloudML training job is monitored (and it's results collected) using a background terminal:
@@ -112,7 +112,7 @@ You can list all previous runs as a data frame using the `ls_runs()` function:
 ls_runs()
 ```
 ```
-Data frame: 6 x 37 
+Data frame: 6 x 37
                             run_dir eval_loss eval_acc metric_loss metric_acc metric_val_loss metric_val_acc
 6 runs/cloudml_2018_01_26_135812740    0.1049   0.9789      0.0852     0.9760          0.1093         0.9770
 2 runs/cloudml_2018_01_26_140015601    0.1402   0.9664      0.1708     0.9517          0.1379         0.9687
@@ -141,7 +141,7 @@ There are many tools available to list, filter, and compare training runs. For a
 
 ## Training with a GPU
 
-By default, CloudML utilizes "standard" CPU-based instances suitable for training simple models with small to moderate datasets. You can request the use of other machine types, including ones with GPUs, using the `master_type` parameter of `cloudml_train()`. 
+By default, CloudML utilizes "standard" CPU-based instances suitable for training simple models with small to moderate datasets. You can request the use of other machine types, including ones with GPUs, using the `master_type` parameter of `cloudml_train()`.
 
 For example, the following would train the same model as above but with a [Tesla K80 GPU](http://www.nvidia.com/object/tesla-k80.html):
 
@@ -174,9 +174,3 @@ To learn more about using CloudML with R, see the following articles:
 * [Google Cloud Storage](storage.html) provides information on copying data between your local machine and Google Storage and also describes how to use data within Google Storage during training.
 
 * [Deploying Models](deployment.html) describes how to deploy trained models and generate predictions from them.
-
-
-
-
-
-

man/cloudml_train.Rd

@@ -60,7 +60,7 @@ Note that to use these functions you need to import the cloudml package with `li
 
 There are two distinct ways to read data from Google Storage. Which you use will depend on whether the TensorFlow API you are using supports direct references to `gs://` bucket URLs. 
 
-If you are using the [TensorFlow Datasets](https://tensorflow.rstudio.com/tools/tfdatasets/articles/introduction.html) API, then you can use `gs://` bucket URLs directly. In this case you'll want to use the `gs://` URL when running on CloudML, and a synchonized copy of the bucket when running locally. You can use the `gs_data_dir()` function to accomplish this. For example:
+If you are using the [TensorFlow Datasets](https://tensorflow.rstudio.com/guides/tfdatasets/) API, then you can use `gs://` bucket URLs directly. In this case you'll want to use the `gs://` URL when running on CloudML, and a synchonized copy of the bucket when running locally. You can use the `gs_data_dir()` function to accomplish this. For example:
 
 ```{r}
 library(tfdatasets)

vignettes/deployment.Rmd

@@ -43,7 +43,7 @@ Working on a CloudML project always begins with developing a training script tha
 
 - [keras](https://keras3.posit.co/) --- A high-level interface for neural networks, with a focus on enabling fast experimentation.
 
-- [tfestimators](https://tensorflow.rstudio.com/tfestimators) --- High-level implementations of common model types such as regressors and classifiers.
+- [tfestimators](https://github.com/rstudio/tfestimators) --- High-level implementations of common model types such as regressors and classifiers.
 
 - [tensorflow](https://tensorflow.rstudio.com/) --- Lower-level interface that provides full access to the TensorFlow computational graph.