Tensorflow serving No versions of servable <MODEL> found under base path

For new version of tf serving, as you know, it no longer supports the model format used to be exported by SessionBundle but now SavedModelBuilder.

I suppose it's better to restore a session from your older model format and then export it by SavedModelBuilder. You can indicate the version of your model with it.

    def export_saved_model(version, path, sess=None):
        tf.app.flags.DEFINE_integer('version', version, 'version number of the model.')
        tf.app.flags.DEFINE_string('work_dir', path, 'your older model  directory.')
        tf.app.flags.DEFINE_string('model_dir', '/tmp/model_name', 'saved model directory')
        FLAGS = tf.app.flags.FLAGS

        # you can give the session and export your model immediately after training 
        if not sess: 
            saver = tf.train.import_meta_graph(os.path.join(path, 'xxx.ckpt.meta'))
            saver.restore(sess, tf.train.latest_checkpoint(path))

        export_path = os.path.join(
            tf.compat.as_bytes(FLAGS.model_dir),
            tf.compat.as_bytes(str(FLAGS.version)))
        builder = tf.saved_model.builder.SavedModelBuilder(export_path)

        # define the signature def map here
        # ...

        legacy_init_op = tf.group(tf.tables_initializer(), name='legacy_init_op')
        builder.add_meta_graph_and_variables(
            sess, [tf.saved_model.tag_constants.SERVING],
            signature_def_map={
                'predict_xxx':
                    prediction_signature
            },
            legacy_init_op=legacy_init_op
        )

        builder.save()
        print('Export SavedModel!')

you could find main part of the code above in tf serving example. Finally it will generate the SavedModel in a format that can be served.

Create a version folder under like - serving/model_name/0000123/saved_model.pb

Answer's above already explained why it is important to keep a version number inside the model folder. Follow below link , here they have different sets of built models , you can take it as a reference.

https://github.com/tensorflow/serving/tree/master/tensorflow_serving/servables/tensorflow/testdata

I had same problem, the reason is because object detection api does not assign version of your model when exporting your detection model. However, tensorflow serving requires you to assign a version number of your detection model, so that you could choose different versions of your models to serve. In your case, you should put your detection model(.pb file and variables folder) under folder: /serving/ssd_frozen/1/. In this way, you will assign your model to version 1, and tensorflow serving will automatically load this version since you only have one version. By default tensorflow serving will automatically serve the latest version(ie, the largest number of versions).

Note, after you created 1/ folder, the model_base_path is still need to be set to --model_base_path=/serving/ssd_frozen/.