standardized deployment with Docker

.gitignore

@@ -12,4 +12,4 @@ data/.DS_Store
 *.gz
 .spyproject/
 .vscode/*
-model.npz
+chatbot.npz

Dockerfile

@@ -0,0 +1,37 @@
+FROM tensorflow/tensorflow:latest-py3
+
+ARG TL_VERSION
+
+RUN echo "Container Tag: ${TF_CONTAINER_VERSION}" \
+    && apt-get update \
+    && case $TF_CONTAINER_VERSION in \
+            latest-py3 | latest-gpu-py3) apt-get install -y python3-tk  ;; \
+            *)                           apt-get install -y python-tk ;; \
+        esac \
+    && if [ -z "$TL_VERSION" ]; then \
+        echo "Building a Nightly Release" \
+        && apt-get install -y git \
+        && mkdir /dist/ && cd /dist/ \
+        && git clone https://github.com/tensorlayer/tensorlayer.git \
+        && cd tensorlayer \
+        && pip install --disable-pip-version-check --no-cache-dir --upgrade -e .[all]; \
+    else \
+        echo "Building Tag Release: $TL_VERSION" \
+        && pip install  --disable-pip-version-check --no-cache-dir --upgrade tensorlayer[all]=="$TL_VERSION"; \
+    fi \
+    && apt-get autoremove -y \
+    && rm -rf /var/lib/apt/lists/*
+
+#Environment Variables for Click
+ENV LC_ALL=C.UTF-8
+ENV LANG=C.UTF-8
+
+COPY requirements.txt /tmp/
+RUN pip3 install --requirement /tmp/requirements.txt
+
+COPY . /chatbot
+
+WORKDIR /chatbot
+RUN python3 main.py --batch-size 32 --num-epochs 1 -lr 0.001
+
+CMD python3 main.py --inference-mode

data/twitter/data.py

@@ -202,11 +202,9 @@ def process_data():
 
 def load_data(PATH=''):
     # read data control dictionaries
-    try:
-        with open(PATH + 'metadata.pkl', 'rb') as f:
-            metadata = pickle.load(f)
-    except:
-        metadata = None
+    with open(PATH + 'metadata.pkl', 'rb') as f:
+        metadata = pickle.load(f)
+
     # read numpy arrays
     idx_q = np.load(PATH + 'idx_q.npy')
     idx_a = np.load(PATH + 'idx_a.npy')

docker-compose.yml

@@ -0,0 +1,7 @@
+version: '2.1'
+
+services:
+  chatbot:
+    build: .
+    volumes:
+      - ./models:/chatbot/models/

main.py

@@ -84,7 +84,7 @@ def train(data_corpus, batch_size, num_epochs, learning_rate, inference_mode):
     encode_seqs = tf.placeholder(dtype=tf.int64, shape=[batch_size, None], name="encode_seqs")
     decode_seqs = tf.placeholder(dtype=tf.int64, shape=[batch_size, None], name="decode_seqs")
     target_seqs = tf.placeholder(dtype=tf.int64, shape=[batch_size, None], name="target_seqs")
-    target_mask = tf.placeholder(dtype=tf.int64, shape=[batch_size, None], name="target_mask") 
+    target_mask = tf.placeholder(dtype=tf.int64, shape=[batch_size, None], name="target_mask")
 
     net_out, _ = create_model(encode_seqs, decode_seqs, src_vocab_size, emb_dim, is_train=True, reuse=False)
     net_out.print_params(False)
@@ -97,7 +97,7 @@ def train(data_corpus, batch_size, num_epochs, learning_rate, inference_mode):
     y = tf.nn.softmax(net.outputs)
 
     # Loss Function
-    loss = tl.cost.cross_entropy_seq_with_mask(logits=net_out.outputs, target_seqs=target_seqs, 
+    loss = tl.cost.cross_entropy_seq_with_mask(logits=net_out.outputs, target_seqs=target_seqs,
                                                 input_mask=target_mask, return_details=False, name='cost')
 
     # Optimizer
@@ -107,14 +107,14 @@ def train(data_corpus, batch_size, num_epochs, learning_rate, inference_mode):
     sess.run(tf.global_variables_initializer())
 
     # Load Model
-    tl.files.load_and_assign_npz(sess=sess, name='model.npz', network=net)
+    tl.files.load_and_assign_npz(sess=sess, name='/chatbot/models/chatbot.npz', network=net)
 
     """
     Inference using pre-trained model
     """
     def inference(seed):
         seed_id = [word2idx.get(w, unk_id) for w in seed.split(" ")]
-        
+
         # Encode and get state
         state = sess.run(net_rnn.final_state_encode,
                         {encode_seqs2: [seed_id]})
@@ -150,7 +150,7 @@ def inference(seed):
         for epoch in range(num_epochs):
             trainX, trainY = shuffle(trainX, trainY, random_state=0)
             total_loss, n_iter = 0, 0
-            for X, Y in tqdm(tl.iterate.minibatches(inputs=trainX, targets=trainY, batch_size=batch_size, shuffle=False), 
+            for X, Y in tqdm(tl.iterate.minibatches(inputs=trainX, targets=trainY, batch_size=batch_size, shuffle=False),
                             total=n_step, desc='Epoch[{}/{}]'.format(epoch + 1, num_epochs), leave=False):
 
                 X = tl.prepro.pad_sequences(X)
@@ -174,17 +174,17 @@ def inference(seed):
 
             # printing average loss after every epoch
             print('Epoch [{}/{}]: loss {:.4f}'.format(epoch + 1, num_epochs, total_loss / n_iter))
-            
+
             # inference after every epoch
             for seed in seeds:
                 print("Query >", seed)
                 for _ in range(5):
                     sentence = inference(seed)
                     print(" >", ' '.join(sentence))
-            
+
             # saving the model
-            tl.files.save_npz(net.all_params, name='model.npz', sess=sess)
-    
+            tl.files.save_npz(net.all_params, name='/chatbot/models/chatbot.npz', sess=sess)
+
     # session cleanup
     sess.close()
 
@@ -207,7 +207,7 @@ def create_model(encode_seqs, decode_seqs, src_vocab_size, emb_dim, is_train=Tru
                 vocabulary_size = src_vocab_size,
                 embedding_size = emb_dim,
                 name = 'seq_embedding')
-            
+
         net_rnn = Seq2Seq(net_encode, net_decode,
                 cell_fn = tf.nn.rnn_cell.LSTMCell,
                 n_hidden = emb_dim,
@@ -227,7 +227,8 @@ def create_model(encode_seqs, decode_seqs, src_vocab_size, emb_dim, is_train=Tru
 Initial Setup
 """
 def initial_setup(data_corpus):
-    metadata, idx_q, idx_a = data.load_data(PATH='data/{}/'.format(data_corpus))
+    #metadata, idx_q, idx_a = data.load_data(PATH='data/{}/'.format(data_corpus))
+    metadata, idx_q, idx_a = data.load_data(PATH='./data/twitter/')
     (trainX, trainY), (testX, testY), (validX, validY) = data.split_dataset(idx_q, idx_a)
     trainX = tl.prepro.remove_pad_sequences(trainX.tolist())
     trainY = tl.prepro.remove_pad_sequences(trainY.tolist())

models/.gitignore

@@ -0,0 +1,4 @@
+# Ignore everything in this directory
+*
+# Except this file
+!.gitignore