Saving deployment changes

app/docker-compose_template.yaml

@@ -0,0 +1,21 @@
+version: "3.7"
+
+services:
+
+  streamlit_app:
+    container_name: streamlit_container
+    build:
+     context: .
+     dockerfile: Dockerfile
+
+    networks:
+    - my_bridge_network
+  model_service:
+   container_name: NSL_model
+   build:
+    context: .
+    dockerfile: Dockerfile.df
+
+networks:
+  my_bridge_network:
+    driver: bridge

app/model_service/main.py

@@ -0,0 +1,18 @@
+"""doc
+"""
+
+from fastapi import FastAPI
+import uvicorn
+
+
+app = FastAPI()
+
+
+@app.post("/predict")
+def inference():
+    data = "Hello"
+    return data
+
+
+if __name__ == "__main__":
+    uvicorn.run("main:app", port=3030, reload=True)

app/model_service/model.py

@@ -0,0 +1,29 @@
+"""
+Ray serve for deployment
+"""
+
+from typing import Any
+import wandb
+from ray import serve
+
+
+@serve.deployment
+class SIGN2TEXT:
+    """_summary_"""
+
+    def __init__(self) -> None:
+        pass
+
+    def __call__(self, *args: Any, **kwds: Any) -> Any:
+        pass
+
+
+@serve.deployment
+class YB2AUDIO:
+    """_summary_"""
+
+    def __init__(self) -> None:
+        pass
+
+    def __call__(self, *args: Any, **kwds: Any) -> Any:
+        pass

signa2text/src/dev2.ipynb

@@ -0,0 +1,220 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Previous directory: /workspace/NSL_2_AUDIO/signa2text\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "\n",
+    "# Get the current working directory\n",
+    "current_dir = os.getcwd()\n",
+    "\n",
+    "# Go back to the previous directory\n",
+    "os.chdir('..')\n",
+    "\n",
+    "# Now, the current working directory is the previous directory\n",
+    "previous_dir = os.getcwd()\n",
+    "\n",
+    "print(\"Previous directory:\", previous_dir)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'/workspace/NSL_2_AUDIO/signa2text'"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pwd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from models.baseline_transformer import ASLTransformer, LandmarkEmbedding, TokenEmbedding\n",
+    "from dataset.dataset_loader import get_dataset, prepare_dataloader  # get_test_dataset\n",
+    "from dataset.dataset_paths import get_dataset_paths"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_data_paths, valid_data_paths = get_dataset_paths(dev_mode=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_dataset = get_dataset(train_data_paths)\n",
+    "train_dataset = prepare_dataloader(train_dataset,10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for source, target in train_dataset:\n",
+    "    break"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(torch.Size([10, 128, 345]), torch.Size([10, 64]))"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "source.shape , target.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "source_emb = LandmarkEmbedding(64)\n",
+    "target_emd = TokenEmbedding(num_vocab=62, embedding_dim= 64, maxlen=64)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(torch.Size([10, 64]), torch.Size([10, 128, 345]))"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "target.size(), source.size()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "source_emb_ans= source_emb(source)\n",
+    "target_emd_ans = target_emd(target)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([10, 64, 64])"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "target_emd_ans.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([10, 64])"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "source_emb_ans.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}