adding ci pipeline and make sure quality and tests work

.github/workflows/quality.yml

@@ -0,0 +1,17 @@
+name: Quality Check
+
+on: [pull_request]
+
+jobs:
+  quality:
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v2
+    - name: Set up Python 3.6
+      uses: actions/setup-python@v2
+      with:
+        python-version: 3.6
+    - name: Install Python dependencies
+      run: pip install -e .[quality]
+    - name: Run Quality check
+      run: make quality

.github/workflows/test.yml

@@ -0,0 +1,19 @@
+name: Run Tests
+
+on: [pull_request]
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v2
+    - name: Set up Python 3.6
+      uses: actions/setup-python@v2
+      with:
+        python-version: 3.6
+    - name: Install Python dependencies
+      run: pip install -e .[test,dev]
+    - name: Run Unit Tests
+      run: make unit-test
+    # - name: Run Integration Tests
+    #   run: make integ-test

makefile

@@ -1,6 +1,6 @@
 .PHONY: quality style unit-test integ-test
 
-check_dirs := src deployment docker tests
+check_dirs := src tests
 
 # run tests
 

setup.py

@@ -45,9 +45,8 @@
 extras["transformers"] = ["transformers[sklearn,sentencepiece]>=4.5.1"]
 
 # framework specific dependencies
-extras["torch"] = ["torch>=1.4.0"]
-extras["tensorflow-cpu"] = ["tensorflow-cpu>=2.3"]
-extras["tensorflow"] = ["tensorflow>=2.3"]
+extras["torch"] = ["torch>=1.8.0"]
+extras["tensorflow"] = ["tensorflow>=2.4.0"]
 
 # MMS Server dependencies
 extras["mms"] = ["multi-model-server>=1.1.4", "retrying"]
@@ -74,7 +73,7 @@
     "flake8>=3.8.3",
 ]
 
-extras["all"] = extras["test"] + extras["quality"] + extras["benchmark"] + extras["transformers"] + extras["mms"]
+extras["dev"] = extras["transformers"] + extras["mms"] + extras["torch"] + extras["tensorflow"]
 
 setup(
     name="sagemaker-huggingface-inference-toolkit",

src/sagemaker_huggingface_inference_toolkit/decoder_encoder.py

@@ -40,7 +40,7 @@ def decode_csv(string_like):  # type: (str) -> np.array
     # detects if the incoming csv has headers
     if not any(header in string_like.splitlines()[0].lower() for header in ["question", "context", "inputs"]):
         raise PredictionException(
-            f"You need to provide the correct CSV with Header columns to use it with the inference toolkit default handler.",
+            "You need to provide the correct CSV with Header columns to use it with the inference toolkit default handler.",
             400,
         )
     # reads csv as io

src/sagemaker_huggingface_inference_toolkit/handler_service.py

@@ -22,7 +22,6 @@
 from sagemaker_inference import content_types, environment, utils
 from transformers.pipelines import SUPPORTED_TASKS
 
-from mms import metrics
 from mms.service import PredictionException
 from sagemaker_huggingface_inference_toolkit import decoder_encoder
 from sagemaker_huggingface_inference_toolkit.transformers_utils import (

src/sagemaker_huggingface_inference_toolkit/transformers_utils.py

@@ -20,7 +20,8 @@
 from huggingface_hub.file_download import cached_download, hf_hub_url
 from transformers import pipeline
 from transformers.file_utils import is_tf_available, is_torch_available
-from transformers.pipelines import Pipeline, Conversation
+from transformers.pipelines import Conversation, Pipeline
+
 
 if is_tf_available():
     import tensorflow as tf

tests/unit/test_decoder_encoder.py

@@ -45,7 +45,7 @@ def test_decode_csv():
         ]
     }
     text_classification_input = "inputs\r\nI love you\r\nI like you"
-    decoded_data = decoder_encoder.decode_csv(DECODE_CSV_INPUT)
+    decoded_data = decoder_encoder.decode_csv(text_classification_input)
     assert decoded_data == {"inputs": ["I love you", "I like you"]}
 
 

tests/unit/test_handler_service.py

@@ -13,7 +13,6 @@
 # limitations under the License.
 import json
 import os
-import sys
 import tempfile
 
 import pytest

tests/unit/test_transformers_utils.py

@@ -14,8 +14,8 @@
 import os
 import tempfile
 
-from transformers.file_utils import is_torch_available
 from transformers import pipeline
+from transformers.file_utils import is_torch_available
 from transformers.testing_utils import require_tf, require_torch, slow
 
 from sagemaker_huggingface_inference_toolkit.transformers_utils import (