Tensorflow record

1. Download Tensorflow

2. Create a simple model

3. Training
The primary use case is to automatically save checkpoints during and at the end of training. This way you can use a trained model without having to retrain it, or pick-up training where you left of—in case the training process was interrupted.
tf.keras.callbacks.ModelCheckpoint is a callback that performs this task. The callback takes a couple of arguments to configure checkpointing.

A. Deploy using Docker
1. install tensorflow serving with docker

2.run docker image

B. Deploy with Kubernetes
1.Export the Inception model

Build TensorFlow Serving Inception model exporter

/tensorflow/serving/.cache/_bazel_jfan/7a4a59242df6fd82e0e4108ffd6fce39/external/org_tensorflow/tensorflow/core/BUILD:2101:1: no such package '@zlib_archive//': java.io.IOException: Error downloading [https://mirror.bazel.build/zlib.net/zlib-1.2.11.tar.gz, https://zlib.net/zlib-1.2.11.tar.gz] to /tensorflow/serving/.cache/_bazel_jfan/7a4a59242df6fd82e0e4108ffd6fce39/external/zlib_archive/zlib-1.2.11.tar.gz: All mirrors are down: [sun.security.validator.ValidatorException: PKIX path building failed: sun.security.provider.certpath.SunCertPathBuilderException: unable to find valid certification path to requested target] and referenced by '@org_tensorflow//tensorflow/core:lib_internal_impl'

One thought

  1. Pingback: clindamicina 600

Leave a Reply