docs: Rename image for improved container ux

Signed-off-by: JiangJiaWei1103 <[email protected]>

docs/user_guide/customizing_dependencies/imagespec.md

@@ -17,7 +17,7 @@ For example, [flytekitplugins-envd](https://github.com/flyteorg/flytekit/blob/c0
 For every {py:class}`flytekit.PythonFunctionTask` task or a task decorated with the `@task` decorator,
 you can specify rules for binding container images. By default, flytekit binds a single container image, i.e.,
 the [default Docker image](https://ghcr.io/flyteorg/flytekit), to all tasks. To modify this behavior,
-use the `container_image` parameter available in the {py:func}`flytekit.task` decorator, and pass an
+use the `image` parameter available in the {py:func}`flytekit.task` decorator, and pass an
 `ImageSpec`.
 
 Before building the image, Flytekit checks the container registry to see if the image already exists.
@@ -100,12 +100,12 @@ tensorflow_image_spec = ImageSpec(
 if tensorflow_image_spec.is_container(): 
   import tensorflow as tf
 
-@task(container_image=pandas_image_spec)
+@task(image=pandas_image_spec)
 def task1() -> pd.DataFrame:
   return pd.DataFrame({"Name": ["Tom", "Joseph"], "Age": [1, 22]})
 
 
-@task(container_image=tensorflow_image_spec)
+@task(image=tensorflow_image_spec)
 def task2() -> int:
   num_gpus = len(tf.config.list_physical_devices('GPU'))
   print("Num GPUs Available: ", num_gpus)
@@ -189,7 +189,7 @@ image_spec = ImageSpec(
     copy=["files/input.txt"],
 )
 
-@task(container_image=image_spec)
+@task(image=image_spec)
 def my_task() -> str:
     with open("/root/files/input.txt", "r") as f:
         return f.read()

docs/user_guide/customizing_dependencies/multiple_images_in_a_workflow.md

@@ -8,7 +8,7 @@
 
 For every {py:class}`flytekit.PythonFunctionTask` task or a task decorated with the `@task` decorator, you can specify rules for binding container images.
 By default, flytekit binds a single container image, i.e., the [default Docker image](https://ghcr.io/flyteorg/flytekit), to all tasks.
-To modify this behavior, use the `container_image` parameter available in the {py:func}`flytekit.task` decorator.
+To modify this behavior, use the `image` parameter available in the {py:func}`flytekit.task` decorator.
 
 :::{note}
 If the Docker image is not available publicly, refer to {ref}`Pulling Private Images<private_images>`.
@@ -32,7 +32,7 @@ The following parameters can be used to configure images in the `@task` decorato
 
 1. `image` refers to the name of the image in the image configuration. The name `default` is a reserved keyword and will automatically apply to the default image name for this repository.
 2. `fqn` refers to the fully qualified name of the image. For example, it includes the repository and domain URL of the image. Example: docker.io/my_repo/xyz.
-3. `version` refers to the tag of the image. For example: latest, or python-3.9 etc. If `container_image` is not specified, then the default configured image for the project is used.
+3. `version` refers to the tag of the image. For example: latest, or python-3.9 etc. If `image` parameter of the {py:func}`flytekit.task` decorator is not specified, then the default configured image for the project is used.
 
 ## Sending images to `pyflyte` command
 

docs/user_guide/flyte_fundamentals/optimizing_tasks.md

@@ -214,15 +214,15 @@ import torch.nn as nn
 
 @task(
     requests=Resources(cpu="2", mem="16Gi"),
-    container_image="ghcr.io/flyteorg/flytekit:py3.9-latest",
+    image="ghcr.io/flyteorg/flytekit:py3.9-latest",
 )
 def get_data() -> Tuple[np.ndarray, np.ndarray]:
     ...  # get dataset as numpy ndarrays
 
 
 @task(
     requests=Resources(cpu="4", gpu="1", mem="16Gi"),
-    container_image="ghcr.io/flyteorg/flytecookbook:kfpytorch-latest",
+    image="ghcr.io/flyteorg/flytecookbook:kfpytorch-latest",
 )
 def train_model(features: np.ndarray, target: np.ndarray) -> nn.Module:
     ...  # train a model using gpus
@@ -257,7 +257,7 @@ from flytekitplugins.kfpytorch import PyTorch
     task_config=PyTorch(num_workers=2),
     requests=Resources(cpu="2", gpu="1", mem="8Gi"),
     limits=Resources(cpu="4", gpu="2", mem="16Gi"),
-    container_image="ghcr.io/flyteorg/flytecookbook:kfpytorch-latest",
+    image="ghcr.io/flyteorg/flytecookbook:kfpytorch-latest",
 )
 def train_model(features: np.ndarray, target: np.ndarray) -> nn.Module:
     ...  # train a model using gpus