js client

Author: hhsecond

js_client/tensorflow_imagenet.js

@@ -19,8 +19,8 @@ async function load_model() {
   const script = fs.readFileSync(script_path, {'flag': 'r'})
 
   redis.call('AI.MODELSET', 'imagenet_model', 'TF', 'CPU', 
-             'INPUTS', 'images', 'OUTPUTS', 'output', model)
-  redis.call('AI.SCRIPTSET', 'imagenet_script', 'CPU', script)
+             'INPUTS', 'images', 'OUTPUTS', 'output', "BLOB", model)
+  redis.call('AI.SCRIPTSET', 'imagenet_script', 'CPU', "SOURCE", script)
 }
 
 async function run(filename) {

js_client/tensorflow_mobilenet.js

@@ -42,7 +42,7 @@ function run(filenames) {
           * https://github.com/luin/ioredis#pipelining
           */
          redis.pipeline()
-              .call('AI.MODELSET', 'mobilenet', 'TF', 'CPU', 'INPUTS', config.inputNode, 'OUTPUTS', config.outputNode, model)
+              .call('AI.MODELSET', 'mobilenet', 'TF', 'CPU', 'INPUTS', config.inputNode, 'OUTPUTS', "BLOB", config.outputNode, model)
               .call('AI.TENSORSET', 'input_' + key, 'FLOAT', 1, config.imageWidth, config.imageHeight, 3, 'BLOB', buffer)
               /**
                * Important note: we're using the same input/output keys here...why? Well, in this example

js_client/torch_charrnn.js

@@ -20,7 +20,7 @@ const batch_size = 1
 
 async function load_model() {
   const model = fs.readFileSync(model_path, {'flag': 'r'})
-  redis.call('AI.MODELSET', 'char_rnn', 'TORCH', 'CPU', model)
+  redis.call('AI.MODELSET', 'char_rnn', 'TORCH', 'CPU', "BLOB", model)
 }
 
 async function run(prime) {

js_client/torch_imagenet.js

@@ -18,8 +18,8 @@ async function load_model() {
   const model = fs.readFileSync(model_path, {'flag': 'r'})
   const script = fs.readFileSync(script_path, {'flag': 'r'})
 
-  redis.call('AI.MODELSET', 'imagenet_model', 'TORCH', 'CPU', model)
-  redis.call('AI.SCRIPTSET', 'imagenet_script', 'CPU', script)
+  redis.call('AI.MODELSET', 'imagenet_model', 'TORCH', 'CPU', "BLOB", model)
+  redis.call('AI.SCRIPTSET', 'imagenet_script', 'CPU', "SOURCE", script)
 }
 
 async function run(filename) {