Format python files

.ci/validate-examples.py

@@ -51,7 +51,6 @@
 
 
 class ErrorCollection:
-
     _errors: List[str] = []
 
     def add(self, error: str):
@@ -253,7 +252,6 @@ def _lint_python_cell(file_name: str, code_lines: List[str]) -> List[str]:
 
 
 if __name__ == "__main__":
-
     ERRORS = ErrorCollection()
 
     res = requests.get(url=RECIPE_SCHEMA_URL)

examples/api/houseprice.py

@@ -21,7 +21,6 @@ async def predict(BedroomAbvGr: int = None, YearBuilt: int = None):
     a = pd.DataFrame([[BedroomAbvGr, YearBuilt]], columns=["BedroomAbvGr", "YearBuilt"])
     v = lr.predict(a)
     if not ((0 <= BedroomAbvGr <= 8) and (1872 <= YearBuilt <= 2100)):
-
         raise HTTPException(
             status_code=400,
             detail="Please enter BedroomAbvGr between 0 and 8. Enter YearBuilt between 1872 and 2100",

examples/rapids/01-rapids-single-gpu.ipynb

@@ -133,7 +133,6 @@
    "outputs": [],
    "source": [
     "def prep_df(df: cudf.DataFrame) -> cudf.DataFrame:\n",
-    "\n",
     "    df = df[df[\"fare_amount\"] > 0]  # to avoid a divide by zero error\n",
     "    df[\"tip_fraction\"] = df[\"tip_amount\"] / df[\"fare_amount\"]\n",
     "    df[\"target\"] = df[\"tip_fraction\"] > 0.2\n",

examples/rapids/02-rapids-gpu-cluster.ipynb

@@ -211,7 +211,6 @@
    "outputs": [],
    "source": [
     "def prep_df(df: dask_cudf.DataFrame) -> dask_cudf.DataFrame:\n",
-    "\n",
     "    df = df[df[\"fare_amount\"] > 0]  # to avoid a divide by zero error\n",
     "    df[\"tip_fraction\"] = df[\"tip_amount\"] / df[\"fare_amount\"]\n",
     "    df[\"target\"] = df[\"tip_fraction\"] > 0.2\n",

examples/registering-images/register.py

@@ -14,12 +14,12 @@
 with open("/home/jovyan/image_spec.json") as f:
     IMAGE_SPEC = json.load(f)
 
-    
+
 with open("/home/jovyan/base_image_spec.json") as f:
     BASE_IMAGE_SPEC = json.load(f)
-
 
-DRY_RUN = os.getenv('DRY_RUN', 'TRUE').lower() == 'true'
+
+DRY_RUN = os.getenv("DRY_RUN", "TRUE").lower() == "true"
 
 
 # this should be populated by Saturn.
@@ -35,17 +35,15 @@ def list_images(ecr_image_name: str) -> List[Dict[str, str]]:
     - image_uri
     - image_tag
     """
-    ecr = boto3.client('ecr')
+    ecr = boto3.client("ecr")
 
-    repository = ecr.describe_repositories(repositoryNames=[ecr_image_name])[
-        'repositories'
-    ][0]
-    repository_uri = repository['repositoryUri']
+    repository = ecr.describe_repositories(repositoryNames=[ecr_image_name])["repositories"][0]
+    repository_uri = repository["repositoryUri"]
 
     list_images = ecr.get_paginator("list_images")
     for page in list_images.paginate(repositoryName=ecr_image_name):
-        for image_id in page['imageIds']:
-            tag = image_id.get('imageTag', None)
+        for image_id in page["imageIds"]:
+            tag = image_id.get("imageTag", None)
             if tag:
                 yield dict(image_uri=f"{repository_uri}:{tag}", image_tag=tag)
 
@@ -57,7 +55,6 @@ def make_url(path: str, queries: Optional[Dict[str, str]] = None) -> str:
         return f"{BASE_URL}/{path}"
 
 
-
 def register(image_uri: str, version: str, saturn_image_name: str, dry_run: bool = False):
     """
     looks up Saturn image_id from saturn_image_name.
@@ -68,50 +65,50 @@ def register(image_uri: str, version: str, saturn_image_name: str, dry_run: bool
     q = f"owner:{SATURN_USERNAME} name:{saturn_image_name}"
     url = make_url("api/images", dict(q=q, page_size="-1"))
     images = requests.get(url, headers=saturn_headers).json()
-    images = [x for x in images['images'] if x['name'] == saturn_image_name]
+    images = [x for x in images["images"] if x["name"] == saturn_image_name]
     if not images:
-        raise ValueError(f'no image found for {q}')
+        raise ValueError(f"no image found for {q}")
     elif len(images) > 1:
-        raise ValueError(f'multiple images found for {q}')
+        raise ValueError(f"multiple images found for {q}")
     image = images[0]
-    image_id = image['id']
+    image_id = image["id"]
 
     q = f"version:{version}"
     url = make_url(f"api/images/{image_id}/tags", dict(q=q, page_size="-1"))
 
-    tags = requests.get(url, headers=saturn_headers).json()['image_tags']
-    if image_uri in [x['image_uri'] for x in tags]:
-        print(f'found {image_uri}')
+    tags = requests.get(url, headers=saturn_headers).json()["image_tags"]
+    if image_uri in [x["image_uri"] for x in tags]:
+        print(f"found {image_uri}")
         return
 
     print(f"REGISTER {image_uri} {image}")
     if not dry_run:
         url = make_url(f"api/images/{image_id}/tags")
-        requests.post(url, json={'image_uri': image_uri}, headers=saturn_headers)
+        requests.post(url, json={"image_uri": image_uri}, headers=saturn_headers)
+
 
-
 def get_all_tags(saturn_image_id: str) -> List[Dict]:
     url = make_url(f"api/images/{saturn_image_id}/tags", dict(page_size="-1"))
-    tags = requests.get(url, headers=saturn_headers).json()['image_tags']
+    tags = requests.get(url, headers=saturn_headers).json()["image_tags"]
     return tags
 
 
-def delete_all_tags(saturn_image_id: str, tags: List[Dict], dry_run: bool=False):
+def delete_all_tags(saturn_image_id: str, tags: List[Dict], dry_run: bool = False):
     tags = get_all_tags(saturn_image_id)
     for t in tags:
         url = make_url(f"api/images/{saturn_image_id}/tags/{t['id']}")
-        print('delete', url)
+        print("delete", url)
         if not dry_run:
-            resp = requests.delete(url, headers=saturn_headers)
-            
-            
+            requests.delete(url, headers=saturn_headers)
+
+
 def register_by_id(image_uri: str, version: str, saturn_image_id: str, dry_run: bool = False):
     """
     Create a new ImageTag object with image_uri and version under saturn_image_name
     """
     if not dry_run:
         url = make_url(f"api/images/{saturn_image_id}/tags")
-        requests.post(url, json={'image_uri': image_uri}, headers=saturn_headers)
+        requests.post(url, json={"image_uri": image_uri}, headers=saturn_headers)
 
 
 def register_base_image(ecr_image_name: str, saturn_image_id: str):
@@ -124,40 +121,40 @@ def register_base_image(ecr_image_name: str, saturn_image_id: str):
     3. Register a new image tag under saturn_image_id
     """
     ecr_images = list(list_images(ecr_image_name))
-    ecr_image = sorted(ecr_images, key=lambda x: x['image_tag'])[-1]
-    image_uri = ecr_image['image_uri']
-    image_tag = ecr_image['image_tag']
+    ecr_image = sorted(ecr_images, key=lambda x: x["image_tag"])[-1]
+    image_uri = ecr_image["image_uri"]
+    image_tag = ecr_image["image_tag"]
     tags = get_all_tags(saturn_image_id)
-    if image_uri in [x['image_uri'] for x in tags]:
-        print(f'found {image_uri}')
+    if image_uri in [x["image_uri"] for x in tags]:
+        print(f"found {image_uri}")
         return
     delete_all_tags(saturn_image_id, tags, dry_run=DRY_RUN)
     register_by_id(image_uri, image_tag, saturn_image_id, dry_run=DRY_RUN)
 
-    
+
 def register_all(ecr_image_name: str, saturn_image_name: str):
     """
     for a given ecr image name, retrieve all image_uris/tags from ECR.
     attempt to register all in Saturn
     """
     ecr_images = list_images(ecr_image_name)
     for image in ecr_images:
-        image_uri = image['image_uri']
-        image_tag = image['image_tag']
+        image_uri = image["image_uri"]
+        image_tag = image["image_tag"]
         register(image_uri, image_tag, saturn_image_name, dry_run=DRY_RUN)
 
-        
+
 def sync_base():
     for image_spec in BASE_IMAGE_SPEC:
-        ecr_image_name = image_spec['ecr_image_name']
-        saturn_image_id = image_spec['saturn_image_id']
+        ecr_image_name = image_spec["ecr_image_name"]
+        saturn_image_id = image_spec["saturn_image_id"]
         register_base_image(ecr_image_name, saturn_image_id)
 
-        
+
 def sync():
     for image_spec in IMAGE_SPEC:
-        ecr_image_name = image_spec['ecr_image_name']
-        saturn_image_name = image_spec['saturn_image_name']
+        ecr_image_name = image_spec["ecr_image_name"]
+        saturn_image_name = image_spec["saturn_image_name"]
         register_all(ecr_image_name, saturn_image_name)
 
 

examples/registering-users/register.py

@@ -14,12 +14,13 @@
 SATURN_TOKEN = os.getenv("SATURN_TOKEN")
 saturn_headers = {"Authorization": f"token {SATURN_TOKEN}"}
 
+
 def check_for_account_by_email(email: str) -> bool:
     url = f"{BASE_URL}/api/users"
     query_string = urlencode(dict(q=f"email:{email}", page=1, size=1))
     url = url + "?" + query_string
     response = requests.get(url, headers=saturn_headers)
-    results = response.json()['users']
+    results = response.json()["users"]
     if results:
         return True
     return False
@@ -30,15 +31,15 @@ def check_for_account_by_username(username: str) -> bool:
     query_string = urlencode(dict(q=f"username:{username}", page=1, size=1))
     url = url + "?" + query_string
     response = requests.get(url, headers=saturn_headers)
-    results = response.json()['users']
+    results = response.json()["users"]
 
     if results:
         return True
     return False
 
 
 def make_unique_username(email: str) -> str:
-    candidate_username = email.split('@')[0]
+    candidate_username = email.split("@")[0]
     candidate_username = "".join(c for c in candidate_username if c.isalnum())
 
     # we'll try 100 integers until we get a unique name
@@ -48,7 +49,7 @@ def make_unique_username(email: str) -> str:
             to_try = candidate_username + str(c)
         if not check_for_account_by_username(to_try):
             return to_try
-    raise ValueError(f'unable to find username for {candidate_username}')
+    raise ValueError(f"unable to find username for {candidate_username}")
 
 
 def make_account(username: str, email: str):
@@ -73,7 +74,7 @@ def ensure_account_exists(email: str) -> None:
 
 
 def run():
-    for email in EMAILS_FOR_ACCOUNTS.split('\n'):
+    for email in EMAILS_FOR_ACCOUNTS.split("\n"):
         if email:
             ensure_account_exists(email)
 

examples/snowflake-ml/training.py

@@ -19,7 +19,6 @@
 
 
 def simple_train_single(batch_size, downsample_to, n_epochs, base_lr, conn_kwargs):
-
     # --------- Format params --------- #
     device = torch.device("cuda")
     net = models.resnet50(pretrained=False)  # True means we start with the imagenet version
@@ -111,7 +110,6 @@ def simple_train_single(batch_size, downsample_to, n_epochs, base_lr, conn_kwarg
 
 
 if __name__ == "__main__":
-
     conn_kwargs = dict(
         user=os.environ["SNOWFLAKE_USER"],
         password=os.environ["SNOWFLAKE_PASSWORD"],

examples/tensorflow-comparison/comparison-training-multigpu.ipynb

@@ -124,7 +124,6 @@
     "def train_multigpu(\n",
     "    n_epochs, classes, base_lr, batchsize, wbargs, scale_batch=False, scale_lr=False\n",
     "):\n",
-    "\n",
     "    wbargs = {**wbargs, \"scale_batch\": scale_batch, \"scale_lr\": scale_lr}\n",
     "\n",
     "    # --------- Start wandb --------- #\n",

examples/tensorflow-comparison/comparison-training-singlenode.ipynb

@@ -126,7 +126,6 @@
    "outputs": [],
    "source": [
     "def train_model_fit(n_epochs, base_lr, batchsize, classes):\n",
-    "\n",
     "    model = tf.keras.applications.ResNet50(include_top=True, weights=None, classes=classes)\n",
     "\n",
     "    # --------- Start wandb --------- #\n",

examples/tensorflow-multigpu/multi-gpu-tensorflow.ipynb

@@ -87,7 +87,6 @@
    "outputs": [],
    "source": [
     "def train_multigpu(n_epochs, classes, base_lr, batchsize, scale_batch=False, scale_lr=False):\n",
-    "\n",
     "    strategy = tf.distribute.MirroredStrategy()\n",
     "    print(\"Number of devices: %d\" % strategy.num_replicas_in_sync)\n",
     "\n",

examples/tensorflow/single-gpu-tensorflow.ipynb

@@ -89,7 +89,6 @@
    "outputs": [],
    "source": [
     "def train_model_fit(n_epochs, base_lr, batchsize, classes):\n",
-    "\n",
     "    model = tf.keras.applications.ResNet50(include_top=True, weights=None, classes=classes)\n",
     "\n",
     "    # Data\n",

examples/wandb/wandb.ipynb

@@ -164,7 +164,6 @@
     "def simple_train_single(\n",
     "    bucket, prefix, batch_size, downsample_to, n_epochs, base_lr, pretrained_classes\n",
     "):\n",
-    "\n",
     "    # --------- Format params --------- #\n",
     "    device = torch.device(\"cuda\")\n",
     "    net = models.resnet50(pretrained=True)  # True means we start with the imagenet version\n",