| Type: | Package |
| Title: | Deploy 'TensorFlow' Models |
| Version: | 0.6.1 |
| Maintainer: | Daniel Falbel <daniel@rstudio.com> |
| Description: | Tools to deploy 'TensorFlow' https://www.tensorflow.org/ models across multiple services. Currently, it provides a local server for testing 'cloudml' compatible services. |
| License: | Apache License 2.0 |
| Encoding: | UTF-8 |
| LazyData: | true |
| Imports: | httpuv, httr, jsonlite, magrittr, reticulate, swagger, tensorflow |
| Suggests: | cloudml, knitr, pixels, processx, testthat, yaml, stringr |
| RoxygenNote: | 6.1.1 |
| VignetteBuilder: | knitr |
| NeedsCompilation: | no |
| Packaged: | 2019-06-13 18:26:35 UTC; dfalbel |
| Author: | Javier Luraschi [aut, ctb], Daniel Falbel [cre, ctb], RStudio [cph] |
| Repository: | CRAN |
| Date/Publication: | 2019-06-14 16:30:03 UTC |
Load a SavedModel
Description
Loads a SavedModel using the given TensorFlow session and returns the model's graph.
Usage
load_savedmodel(sess = NULL, model_dir = NULL)
Arguments
sess |
The TensorFlow session. |
model_dir |
The path to the exported model, as a string. Defaults to a "savedmodel" path or the latest training run. |
Details
Loading a model improves performance over multiple predict_savedmodel()
calls.
See Also
export_savedmodel(), predict_savedmodel()
Examples
## Not run:
# start session
sess <- tensorflow::tf$Session()
# preload an existing model into a TensorFlow session
graph <- tfdeploy::load_savedmodel(
sess,
system.file("models/tensorflow-mnist", package = "tfdeploy")
)
# perform prediction based on a pre-loaded model
tfdeploy::predict_savedmodel(
list(rep(9, 784)),
graph
)
# close session
sess$close()
## End(Not run)
Predict using a SavedModel
Description
Runs a prediction over a saved model file, web API or graph object.
Usage
predict_savedmodel(instances, model, ...)
Arguments
instances |
A list of prediction instances to be passed as input tensors to the service. Even for single predictions, a list with one entry is expected. |
model |
The model as a local path, a REST url or graph object. A local path can be exported using A |
... |
See #' @section Implementations: |
See Also
export_savedmodel(), serve_savedmodel(), load_savedmodel()
Examples
## Not run:
# perform prediction based on an existing model
tfdeploy::predict_savedmodel(
list(rep(9, 784)),
system.file("models/tensorflow-mnist", package = "tfdeploy")
)
## End(Not run)
Predict using an Exported SavedModel
Description
Performs a prediction using a locally exported SavedModel.
Usage
## S3 method for class 'export_prediction'
predict_savedmodel(instances, model,
signature_name = "serving_default", ...)
Arguments
instances |
A list of prediction instances to be passed as input tensors to the service. Even for single predictions, a list with one entry is expected. |
model |
The model as a local path, a REST url or graph object. A local path can be exported using A |
signature_name |
The named entry point to use in the model for prediction. |
... |
See #' @section Implementations: |
Predict using a Loaded SavedModel
Description
Performs a prediction using a SavedModel model already loaded using
load_savedmodel().
Usage
## S3 method for class 'graph_prediction'
predict_savedmodel(instances, model, sess,
signature_name = "serving_default", ...)
Arguments
instances |
A list of prediction instances to be passed as input tensors to the service. Even for single predictions, a list with one entry is expected. |
model |
The model as a local path, a REST url or graph object. A local path can be exported using A |
sess |
The active TensorFlow session. |
signature_name |
The named entry point to use in the model for prediction. |
... |
See #' @section Implementations: |
Predict using a Web API
Description
Performs a prediction using a Web API providing a SavedModel.
Usage
## S3 method for class 'webapi_prediction'
predict_savedmodel(instances, model, ...)
Arguments
instances |
A list of prediction instances to be passed as input tensors to the service. Even for single predictions, a list with one entry is expected. |
model |
The model as a local path, a REST url or graph object. A local path can be exported using A |
... |
See #' @section Implementations: |
Objects exported from other packages
Description
These objects are imported from other packages. Follow the links below to see their documentation.
- magrittr
- tensorflow
Serve a SavedModel
Description
Serve a TensorFlow SavedModel as a local web api.
Usage
serve_savedmodel(model_dir, host = "127.0.0.1", port = 8089,
daemonized = FALSE, browse = !daemonized)
Arguments
model_dir |
The path to the exported model, as a string. |
host |
Address to use to serve model, as a string. |
port |
Port to use to serve model, as numeric. |
daemonized |
Makes 'httpuv' server daemonized so R interactive sessions are not blocked to handle requests. To terminate a daemonized server, call 'httpuv::stopDaemonizedServer()' with the handle returned from this call. |
browse |
Launch browser with serving landing page? |
See Also
Examples
## Not run:
# serve an existing model over a web interface
tfdeploy::serve_savedmodel(
system.file("models/tensorflow-mnist", package = "tfdeploy")
)
## End(Not run)