diff --git a/Makefile b/Makefile
index 9a68fd52c..3528c703e 100644
--- a/Makefile
+++ b/Makefile
@@ -179,7 +179,7 @@ uninstall-server-package:
 	@$(PYTHON_PIP) uninstall elyra -y
 
 install-server-package: uninstall-server-package
-	$(PYTHON_PIP) install --upgrade --upgrade-strategy $(UPGRADE_STRATEGY) "$(shell find dist -name "elyra-*-py3-none-any.whl")[kfp-tekton]"
+	$(PYTHON_PIP) install --upgrade --upgrade-strategy $(UPGRADE_STRATEGY) "$(shell find dist -name "elyra-*-py3-none-any.whl")"
 
 install-server: build-dependencies lint-server build-server install-server-package ## Build and install backend
 
diff --git a/elyra/metadata/schemasproviders.py b/elyra/metadata/schemasproviders.py
index 03fdbb7e9..ab135d263 100644
--- a/elyra/metadata/schemasproviders.py
+++ b/elyra/metadata/schemasproviders.py
@@ -23,12 +23,6 @@
 import entrypoints
 from traitlets import log  # noqa H306
 
-try:
-    from kfp_tekton import TektonClient
-except ImportError:
-    # We may not have kfp-tekton available and that's okay!
-    TektonClient = None
-
 from elyra.metadata.schema import SchemasProvider
 from elyra.metadata.schemaspaces import CodeSnippets
 from elyra.metadata.schemaspaces import ComponentCatalogs
@@ -93,16 +87,12 @@ def get_schemas(self) -> List[Dict]:
                 )
 
         if kfp_schema_present:  # Update the kfp engine enum to reflect current packages...
-            # If TektonClient package is missing, navigate to the engine property
-            # and remove 'tekton' entry if present and return updated result.
-            if not TektonClient:
-                # locate the schema and update the enum
-                for schema in runtime_schemas:
-                    if schema["name"] == "kfp":
-                        engine_enum: list = schema["properties"]["metadata"]["properties"]["engine"]["enum"]
-                        if "Tekton" in engine_enum:
-                            engine_enum.remove("Tekton")
-                            schema["properties"]["metadata"]["properties"]["engine"]["enum"] = engine_enum
+            for schema in runtime_schemas:
+                if schema["name"] == "kfp":
+                    engine_enum: list = schema["properties"]["metadata"]["properties"]["engine"]["enum"]
+                    if "Tekton" in engine_enum:
+                        engine_enum.remove("Tekton")
+                        schema["properties"]["metadata"]["properties"]["engine"]["enum"] = engine_enum
 
             # For KFP schemas replace placeholders:
             # - properties.metadata.properties.auth_type.enum ({AUTH_PROVIDER_PLACEHOLDERS})
diff --git a/elyra/pipeline/kfp/PipelineConf.py b/elyra/pipeline/kfp/PipelineConf.py
new file mode 100644
index 000000000..1e4f1aae6
--- /dev/null
+++ b/elyra/pipeline/kfp/PipelineConf.py
@@ -0,0 +1,157 @@
+from typing import Union
+from kubernetes.client.models import V1PodDNSConfig
+
+class PipelineConf():
+    """PipelineConf contains pipeline level settings."""
+
+    def __init__(self):
+        self.image_pull_secrets = []
+        self.timeout = 0
+        self.ttl_seconds_after_finished = -1
+        self._pod_disruption_budget_min_available = None
+        self.op_transformers = []
+        self.default_pod_node_selector = {}
+        self.image_pull_policy = None
+        self.parallelism = None
+        self._data_passing_method = None
+        self.dns_config = None
+
+    def set_image_pull_secrets(self, image_pull_secrets):
+        """Configures the pipeline level imagepullsecret.
+
+        Args:
+          image_pull_secrets: a list of Kubernetes V1LocalObjectReference For
+            detailed description, check Kubernetes V1LocalObjectReference definition
+            https://github.com/kubernetes-client/python/blob/master/kubernetes/docs/V1LocalObjectReference.md
+        """
+        self.image_pull_secrets = image_pull_secrets
+        return self
+
+    def set_timeout(self, seconds: int):
+        """Configures the pipeline level timeout.
+
+        Args:
+          seconds: number of seconds for timeout
+        """
+        self.timeout = seconds
+        return self
+
+    def set_parallelism(self, max_num_pods: int):
+        """Configures the max number of total parallel pods that can execute at
+        the same time in a workflow.
+
+        Args:
+          max_num_pods: max number of total parallel pods.
+        """
+        if max_num_pods < 1:
+            raise ValueError(
+                'Pipeline max_num_pods set to < 1, allowed values are > 0')
+
+        self.parallelism = max_num_pods
+        return self
+
+    def set_ttl_seconds_after_finished(self, seconds: int):
+        """Configures the ttl after the pipeline has finished.
+
+        Args:
+          seconds: number of seconds for the workflow to be garbage collected after
+            it is finished.
+        """
+        self.ttl_seconds_after_finished = seconds
+        return self
+
+    def set_pod_disruption_budget(self, min_available: Union[int, str]):
+        """PodDisruptionBudget holds the number of concurrent disruptions that
+        you allow for pipeline Pods.
+
+        Args:
+          min_available (Union[int, str]):  An eviction is allowed if at least
+            "minAvailable" pods selected by "selector" will still be available after
+            the eviction, i.e. even in the absence of the evicted pod.  So for
+            example you can prevent all voluntary evictions by specifying "100%".
+            "minAvailable" can be either an absolute number or a percentage.
+        """
+        self._pod_disruption_budget_min_available = min_available
+        return self
+
+    def set_default_pod_node_selector(self, label_name: str, value: str):
+        """Add a constraint for nodeSelector for a pipeline.
+
+        Each constraint is a key-value pair label.
+
+        For the container to be eligible to run on a node, the node must have each
+        of the constraints appeared as labels.
+
+        Args:
+          label_name: The name of the constraint label.
+          value: The value of the constraint label.
+        """
+        self.default_pod_node_selector[label_name] = value
+        return self
+
+    def set_image_pull_policy(self, policy: str):
+        """Configures the default image pull policy.
+
+        Args:
+          policy: the pull policy, has to be one of: Always, Never, IfNotPresent.
+            For more info:
+            https://github.com/kubernetes-client/python/blob/10a7f95435c0b94a6d949ba98375f8cc85a70e5a/kubernetes/docs/V1Container.md
+        """
+        self.image_pull_policy = policy
+        return self
+
+    def add_op_transformer(self, transformer):
+        """Configures the op_transformers which will be applied to all ops in
+        the pipeline. The ops can be ResourceOp, VolumeOp, or ContainerOp.
+
+        Args:
+          transformer: A function that takes a kfp Op as input and returns a kfp Op
+        """
+        self.op_transformers.append(transformer)
+
+    def set_dns_config(self, dns_config: V1PodDNSConfig):
+        """Set the dnsConfig to be given to each pod.
+
+        Args:
+          dns_config: Kubernetes V1PodDNSConfig For detailed description, check
+            Kubernetes V1PodDNSConfig definition
+            https://github.com/kubernetes-client/python/blob/master/kubernetes/docs/V1PodDNSConfig.md
+
+        Example:
+          ::
+
+            import kfp
+            from kubernetes.client.models import V1PodDNSConfig, V1PodDNSConfigOption
+            pipeline_conf = kfp.dsl.PipelineConf()
+            pipeline_conf.set_dns_config(dns_config=V1PodDNSConfig(
+                nameservers=["1.2.3.4"],
+                options=[V1PodDNSConfigOption(name="ndots", value="2")],
+            ))
+        """
+        self.dns_config = dns_config
+
+    @property
+    def data_passing_method(self):
+        return self._data_passing_method
+
+    @data_passing_method.setter
+    def data_passing_method(self, value):
+        """Sets the object representing the method used for intermediate data
+        passing.
+
+        Example:
+          ::
+
+            from kfp.dsl import PipelineConf, data_passing_methods
+            from kubernetes.client.models import V1Volume, V1PersistentVolumeClaimVolumeSource
+            pipeline_conf = PipelineConf()
+            pipeline_conf.data_passing_method =
+            data_passing_methods.KubernetesVolume(
+                volume=V1Volume(
+                    name='data',
+                    persistent_volume_claim=V1PersistentVolumeClaimVolumeSource('data-volume'),
+                ),
+                path_prefix='artifact_data/',
+            )
+        """
+        self._data_passing_method = value
\ No newline at end of file
diff --git a/elyra/pipeline/kfp/kfp_authentication.py b/elyra/pipeline/kfp/kfp_authentication.py
index 3ca44519a..c369fc3eb 100644
--- a/elyra/pipeline/kfp/kfp_authentication.py
+++ b/elyra/pipeline/kfp/kfp_authentication.py
@@ -27,9 +27,9 @@
 from typing import Tuple
 from urllib.parse import urlsplit
 
-from kfp.auth import KF_PIPELINES_SA_TOKEN_ENV
-from kfp.auth import KF_PIPELINES_SA_TOKEN_PATH
-from kfp.auth import ServiceAccountTokenVolumeCredentials
+from kfp.client import KF_PIPELINES_SA_TOKEN_ENV
+from kfp.client import KF_PIPELINES_SA_TOKEN_PATH
+from kfp.client import ServiceAccountTokenVolumeCredentials
 import requests
 
 
diff --git a/elyra/pipeline/kfp/processor_kfp.py b/elyra/pipeline/kfp/processor_kfp.py
index 4e7f9be57..d259b9186 100644
--- a/elyra/pipeline/kfp/processor_kfp.py
+++ b/elyra/pipeline/kfp/processor_kfp.py
@@ -39,19 +39,11 @@
 from kfp import Client as ArgoClient
 from kfp import compiler as kfp_argo_compiler
 from kfp import components as components
-from kfp.dsl import PipelineConf
-from kfp.dsl import RUN_ID_PLACEHOLDER
 from kubernetes import client as k8s_client
 from traitlets import default
 from traitlets import Unicode
 
-try:
-    from kfp_tekton import compiler as kfp_tekton_compiler
-    from kfp_tekton import TektonClient
-except ImportError:
-    # We may not have kfp-tekton available and that's okay!
-    kfp_tekton_compiler = None
-    TektonClient = None
+RUN_ID_PLACEHOLDER = "random-placeholder"
 
 from elyra._version import __version__
 from elyra.metadata.schemaspaces import RuntimeImages
@@ -81,6 +73,8 @@
 from elyra.util.kubernetes import sanitize_label_value
 from elyra.util.path import get_absolute_path
 
+from elyra.pipeline.kfp.PipelineConf import PipelineConf
+
 
 @unique
 class WorkflowEngineType(Enum):
@@ -113,8 +107,6 @@ def get_instance_by_value(value: str) -> "WorkflowEngineType":
 CRIO_VOL_MOUNT_PATH = "/opt/app-root/src"
 CRIO_VOL_WORKDIR_PATH = f"{CRIO_VOL_MOUNT_PATH}/jupyter-work-dir"
 CRIO_VOL_PYTHON_PATH = f"{CRIO_VOL_WORKDIR_PATH}/python3"
-
-
 class KfpPipelineProcessor(RuntimePipelineProcessor):
     _type = RuntimeProcessorType.KUBEFLOW_PIPELINES
     _name = "kfp"
@@ -173,11 +165,6 @@ def process(self, pipeline):
         api_password = runtime_configuration.metadata.get("api_password")
         user_namespace = runtime_configuration.metadata.get("user_namespace")
         workflow_engine = WorkflowEngineType.get_instance_by_value(runtime_configuration.metadata.get("engine", "argo"))
-        if workflow_engine == WorkflowEngineType.TEKTON and not TektonClient:
-            raise ValueError(
-                "Python package `kfp-tekton` is not installed. "
-                "Please install using `elyra[kfp-tekton]` to use Tekton engine."
-            )
 
         # unpack Cloud Object Storage configs
         cos_endpoint = runtime_configuration.metadata["cos_endpoint"]
@@ -206,22 +193,13 @@ def process(self, pipeline):
         # Create Kubeflow Client
         #############
         try:
-            if workflow_engine == WorkflowEngineType.TEKTON:
-                client = TektonClient(
-                    host=api_endpoint,
-                    cookies=auth_info.get("cookies", None),
-                    credentials=auth_info.get("credentials", None),
-                    existing_token=auth_info.get("existing_token", None),
-                    namespace=user_namespace,
-                )
-            else:
-                client = ArgoClient(
-                    host=api_endpoint,
-                    cookies=auth_info.get("cookies", None),
-                    credentials=auth_info.get("credentials", None),
-                    existing_token=auth_info.get("existing_token", None),
-                    namespace=user_namespace,
-                )
+            client = ArgoClient(
+                host=api_endpoint,
+                cookies=auth_info.get("cookies", None),
+                credentials=auth_info.get("credentials", None),
+                existing_token=auth_info.get("existing_token", None),
+                namespace=user_namespace,
+            )
         except Exception as ex:
             # a common cause of these errors is forgetting to include `/pipeline` or including it with an 's'
             api_endpoint_obj = urlsplit(api_endpoint)
@@ -275,7 +253,7 @@ def process(self, pipeline):
 
         with tempfile.TemporaryDirectory() as temp_dir:
             self.log.debug(f"Created temporary directory at: {temp_dir}")
-            pipeline_path = os.path.join(temp_dir, f"{pipeline_name}.tar.gz")
+            pipeline_path = os.path.join(temp_dir, f"{pipeline_name}.yaml")
 
             #############
             # Get Pipeline ID
@@ -351,11 +329,15 @@ def process(self, pipeline):
                     )
 
                     # extract the ID of the pipeline we created
-                    pipeline_id = kfp_pipeline.id
+                    pipeline_id = kfp_pipeline.pipeline_id
 
                     # the initial "pipeline version" has the same id as the pipeline itself
-                    version_id = pipeline_id
-
+                    version_details = client.list_pipeline_versions(pipeline_id=pipeline_id)
+                    version_list = version_details.pipeline_versions
+                    if isinstance(version_list, list):
+                        version_id = version_list[0].pipeline_version_id
+                    else:
+                        version_id = None
                 # CASE 2: pipeline already exists
                 else:
                     # upload the "pipeline version"
@@ -366,7 +348,7 @@ def process(self, pipeline):
                     )
 
                     # extract the id of the "pipeline version" that was created
-                    version_id = kfp_pipeline.id
+                    version_id = kfp_pipeline.pipeline_version_id
 
             except Exception as ex:
                 # a common cause of these errors is forgetting to include `/pipeline` or including it with an 's'
@@ -416,7 +398,7 @@ def process(self, pipeline):
 
                 # create pipeline run (or specified pipeline version)
                 run = client.run_pipeline(
-                    experiment_id=experiment.id, job_name=job_name, pipeline_id=pipeline_id, version_id=version_id
+                    experiment_id=experiment.experiment_id, job_name=job_name, pipeline_id=pipeline_id, version_id=version_id
                 )
 
             except Exception as ex:
@@ -435,7 +417,7 @@ def process(self, pipeline):
 
             self.log_pipeline_info(
                 pipeline_name,
-                f"pipeline submitted: {public_api_endpoint}/#/runs/details/{run.id}",
+                f"pipeline submitted: {public_api_endpoint}/#/runs/details/{run.run_id}",
                 duration=time.time() - t0,
             )
 
@@ -450,8 +432,8 @@ def process(self, pipeline):
             object_storage_path = None
 
         return KfpPipelineProcessorResponse(
-            run_id=run.id,
-            run_url=f"{public_api_endpoint}/#/runs/details/{run.id}",
+            run_id=run.run_id,
+            run_url=f"{public_api_endpoint}/#/runs/details/{run.run_id}",
             object_storage_url=object_storage_url,
             object_storage_path=object_storage_path,
         )
@@ -494,8 +476,6 @@ def export(
         )
 
         workflow_engine = WorkflowEngineType.get_instance_by_value(runtime_configuration.metadata.get("engine", "argo"))
-        if workflow_engine == WorkflowEngineType.TEKTON and not TektonClient:
-            raise ValueError("kfp-tekton not installed. Please install using elyra[kfp-tekton] to use Tekton engine.")
 
         if Path(absolute_pipeline_export_path).exists() and not overwrite:
             raise ValueError("File " + absolute_pipeline_export_path + " already exists.")
@@ -565,7 +545,7 @@ def _generate_pipeline_dsl(
             code_generation_options = {}
 
         # Load Kubeflow Pipelines Python DSL template
-        loader = PackageLoader("elyra", "templates/kubeflow/v1")
+        loader = PackageLoader("elyra", "templates/kubeflow/v2")
         template_env = Environment(loader=loader)
         # Add filter that produces a Python-safe variable name
         template_env.filters["python_safe"] = lambda x: re.sub(r"[" + re.escape(string.punctuation) + "\\s]", "_", x)
@@ -668,12 +648,7 @@ def _compile_pipeline_dsl(
                 # in the generated Python DSL "generated_pipeline"
                 pipeline_function = getattr(mod, "generated_pipeline")
                 # compile the DSL
-                if workflow_engine == WorkflowEngineType.TEKTON:
-                    kfp_tekton_compiler.TektonCompiler().compile(
-                        pipeline_function, output_file, pipeline_conf=pipeline_conf
-                    )
-                else:
-                    kfp_argo_compiler.Compiler().compile(pipeline_function, output_file, pipeline_conf=pipeline_conf)
+                kfp_argo_compiler.Compiler().compile(pipeline_function, output_file)
             except Exception as ex:
                 raise RuntimeError(
                     f"Failed to compile pipeline with workflow_engine '{workflow_engine.value}' to '{output_file}'"
@@ -729,7 +704,7 @@ def _generate_workflow_tasks(
                 pipeline.pipeline_properties.get(pipeline_constants.COS_OBJECT_PREFIX), pipeline_instance_id
             )
             # - load the generic component definition template
-            template_env = Environment(loader=PackageLoader("elyra", "templates/kubeflow/v1"))
+            template_env = Environment(loader=PackageLoader("elyra", "templates/kubeflow/v2"))
             generic_component_template = template_env.get_template("generic_component_definition_template.jinja2")
             # Add filter that escapes the " character in strings
             template_env.filters["string_delimiter_safe"] = lambda string: re.sub('"', '\\\\\\\\"', string)
diff --git a/elyra/templates/kubeflow/v2/generic_component_definition_template.jinja2 b/elyra/templates/kubeflow/v2/generic_component_definition_template.jinja2
new file mode 100644
index 000000000..85c8eb086
--- /dev/null
+++ b/elyra/templates/kubeflow/v2/generic_component_definition_template.jinja2
@@ -0,0 +1,24 @@
+name: Run a file
+description: Run a Jupyter notebook or Python/R script
+{% if task_parameters %}
+inputs:
+{%- for parameter in task_parameters %}
+- {name: {{ parameter.name }}, type: {{ parameter.input_type.component_input_type }}{% if parameter.description %}, description: "{{ parameter.description | string_delimiter_safe}}"{% endif %}{% if parameter.default_value is not none %}, default: {% if parameter.selected_type == 'String' %} "{{ parameter.default_value|string_delimiter_safe }}"{% else %}{{ parameter.default_value }}{% endif %}{% endif %}, optional: {{ (not parameter.required)|tojson }}}
+{%- endfor %}
+{% endif %}
+implementation:
+  container:
+    image: {{ container_image }}
+    command: [sh, -c]
+    args:
+    - |
+      {%- for parameter in task_parameters %}
+      {{ parameter.name }}="${{ loop.index0 }}"
+      {%- endfor %}
+      {%- for command in command_args %}
+      sh -c "{{command}}"
+      {%- endfor %}
+
+    {%- for parameter in task_parameters %}
+    - {inputValue: {{ parameter.name }}}
+    {%- endfor %}
diff --git a/elyra/templates/kubeflow/v2/python_dsl_template.jinja2 b/elyra/templates/kubeflow/v2/python_dsl_template.jinja2
new file mode 100644
index 000000000..f01fb2961
--- /dev/null
+++ b/elyra/templates/kubeflow/v2/python_dsl_template.jinja2
@@ -0,0 +1,164 @@
+#
+# Generated by Elyra {{ elyra_version }}
+#
+import kfp
+from kubernetes.client import *
+from kubernetes.client.models import *
+from kfp.kubernetes import secret, volume
+
+from typing import Optional
+
+# ------------------------------------------------------------------
+# kfp-kubernetes 1.1.0 is misisng these function, explicity using them,
+# TODO: remove these function once a new release of kfp-kubernetes is made.
+# ------------------------------------------------------------------
+
+from google.protobuf import json_format
+from kfp.dsl import PipelineTask
+from kfp.kubernetes import common
+from kfp.kubernetes import kubernetes_executor_config_pb2 as pb
+
+try:
+    from typing import Literal
+except ImportError:
+    from typing_extensions import Literal
+
+
+def add_pod_label(
+    task: PipelineTask,
+    label_key: str,
+    label_value: str,
+) -> PipelineTask:
+
+    msg = common.get_existing_kubernetes_config_as_message(task)
+    msg.pod_metadata.labels.update({label_key: label_value})
+    task.platform_config['kubernetes'] = json_format.MessageToDict(msg)
+
+    return task
+
+
+def add_pod_annotation(
+    task: PipelineTask,
+    annotation_key: str,
+    annotation_value: str,
+) -> PipelineTask:
+
+    msg = common.get_existing_kubernetes_config_as_message(task)
+    msg.pod_metadata.annotations.update({annotation_key: annotation_value})
+    task.platform_config['kubernetes'] = json_format.MessageToDict(msg)
+
+    return task
+
+# ------------------------------------------------------------------
+# end of missing functions
+# ------------------------------------------------------------------
+
+{# Load statements for custom components                            -#}
+{# component_hash = """<TEXT>"""                                  -#}
+{# factory_hash = kfp.components.load_component_from_text(component_hash) -#}
+{% for hash, component_definition in component_definitions.items() %}
+component_def_{{ hash | python_safe }} = """
+{{ component_definition }}
+"""
+
+factory_{{ hash | python_safe }} = kfp.components.load_component_from_text(component_def_{{ hash | python_safe }})
+{% endfor %}
+
+{#  Define pipeline  -#}
+{% if pipeline_description %}
+@kfp.dsl.pipeline(name="{{ pipeline_name }}", description="{{ pipeline_description | string_delimiter_safe }}")
+{% else %}
+@kfp.dsl.pipeline(name="{{ pipeline_name }}")
+{% endif %}
+def generated_pipeline(
+{% if pipeline_parameters %}
+{% for parameter in pipeline_parameters %}
+    {{ parameter.name }}{% if parameter.input_type.type_hint %}: {{ parameter.input_type.type_hint }}{% endif %} = {{ parameter|param_val_to_python_var }},
+{% endfor %}
+{% endif %}
+):
+{% for workflow_task in workflow_tasks.values() %}
+    {% set task_name = "task_" + workflow_task.escaped_task_id %}
+    # Task for node '{{ workflow_task.name }}'
+    {{ task_name }} = factory_{{ workflow_task.component_definition_hash | python_safe }}(
+{%  for task_input_name, task_input_spec in workflow_task.task_inputs.items() %}
+{%     if task_input_spec.task_output_reference %}
+        {{ task_input_name }}=task_{{ task_input_spec.task_output_reference.task_id }}.outputs["{{ task_input_spec.task_output_reference.output_id }}"],
+{%     elif task_input_spec.pipeline_parameter_reference %}
+        {{ task_input_name }}={{ task_input_spec.pipeline_parameter_reference }},
+{%     elif task_input_spec.requires_quoted_rendering %}
+        {{ task_input_name }}="""{{ task_input_spec.value | string_delimiter_safe }}""",
+{%     else %}
+        {{ task_input_name }}={{ task_input_spec.value }},
+{%     endif %}
+{%  endfor %}
+    )
+{%  if workflow_task.task_modifiers.object_storage_secret %}
+    secret.use_secret_as_env({{ task_name }}, "{{ workflow_task.task_modifiers.object_storage_secret }}", { "AWS_ACCESS_KEY_ID": "AWS_ACCESS_KEY_ID", "AWS_SECRET_ACCESS_KEY": "AWS_SECRET_ACCESS_KEY" })
+{%  endif %}
+    {{ task_name }}.set_display_name("{{ workflow_task.name | string_delimiter_safe }}")
+{%  if workflow_task.doc %}
+    add_pod_annotation({{ task_name }}, "elyra/node-user-doc","""{{ workflow_task.doc| string_delimiter_safe }}""")
+{%  endif %}
+{%  if workflow_task.task_modifiers.cpu_request %}
+    {{ task_name }}.set_cpu_request(cpu="{{ workflow_task.task_modifiers.cpu_request }}")
+{%  endif %}
+{%  if workflow_task.task_modifiers.mem_request and workflow_task.task_modifiers.mem_request.size %}
+    {{ task_name }}.set_memory_request(memory="{{ workflow_task.task_modifiers.mem_request.size }}{{ workflow_task.task_modifiers.mem_request.units }}")
+{%  endif %}
+{%  if workflow_task.task_modifiers.cpu_limit %}
+    {{ task_name }}.set_cpu_limit(cpu="{{ workflow_task.task_modifiers.cpu_limit }}")
+{%  endif %}
+{%  if workflow_task.task_modifiers.memory_limit and workflow_task.task_modifiers.memory_limit.size %}
+    {{ task_name }}.set_memory_limit(memory="{{ workflow_task.task_modifiers.memory_limit.size }}{{ workflow_task.task_modifiers.memory_limit.units }}")
+{%  endif %}
+{%  if workflow_task.task_modifiers.gpu_limit and workflow_task.task_modifiers.gpu_limit.size %}
+    {{ task_name }}.set_gpu_limit("{{ workflow_task.task_modifiers.gpu_limit.size }}")
+{%  endif %}
+{%  if workflow_task.task_modifiers.env_variables %}
+{%    for env_var_name, env_var_value in workflow_task.task_modifiers.env_variables.items() %}
+    {{ task_name }}.set_env_variable(name="{{ env_var_name }}", value="{{ env_var_value | string_delimiter_safe }}")
+{%    endfor %}
+{%  endif %}
+{%  if workflow_task.task_modifiers.set_run_name %}
+    {{ task_name }}.set_env_variable(name="ELYRA_RUN_NAME", value="{{ workflow_task.task_modifiers.set_run_name }}")
+{%  endif %}
+{%  if workflow_task.task_modifiers.disable_node_caching %}
+    {{ task_name }}.execution_options.caching_strategy.max_cache_staleness = "P0D"
+{%  endif %}
+{%  if workflow_task.task_modifiers.pod_labels %}
+{%    for pod_label_key, pod_label_value in workflow_task.task_modifiers.pod_labels.items() %}
+    add_pod_label({{ task_name }}, "{{ pod_label_key }}", "{{ pod_label_value }}")
+{%    endfor %}
+{%  endif %}
+{%  if workflow_task.task_modifiers.pod_annotations %}
+{%    for pod_annotation_key, pod_annotation_value in workflow_task.task_modifiers.pod_annotations.items() %}
+    add_pod_annotation({{ task_name }}, "{{ pod_annotation_key }}" , """{{ pod_annotation_value | string_delimiter_safe }}""")
+{%    endfor %}
+{%  endif %}
+{%  if workflow_task.task_modifiers.kubernetes_secrets %}
+{%    for env_var, secret_dict in workflow_task.task_modifiers.kubernetes_secrets.items() %}
+    secret.use_secret_as_env({{ task_name }}, "{{ secret_dict.name }}", { "{{ secret_dict.key }}" : "{{ env_var }}" })
+{%    endfor %}
+{%  endif %}
+{%  if workflow_task.task_modifiers.kubernetes_volumes %}
+{%    for volume_path, volume_dict in workflow_task.task_modifiers.kubernetes_volumes.items() %}
+    volume.mount_pvc({{ task_name }}, "{{ volume_dict.pvc_name }}", "{{ volume_path }}")
+{%    endfor %}
+{%  endif %}
+{%  if workflow_task.task_modifiers.kubernetes_tolerations %}
+{%  endif %}
+{#  declare upstream dependencies -#}
+{%  if workflow_task.upstream_workflow_task_ids %}
+{%    for upstream_workflow_task_id in workflow_task.upstream_workflow_task_ids %}
+    {{ task_name }}.after(task_{{ upstream_workflow_task_id | python_safe }})
+{%    endfor %}
+{%  endif %}
+{% endfor %}
+
+if __name__ == "__main__":
+    from pathlib import Path
+    kfp.compiler.Compiler().compile(
+        pipeline_func=generated_pipeline,
+        package_path=Path(__file__).with_suffix(".yaml").name,
+    )
\ No newline at end of file
diff --git a/pyproject.toml b/pyproject.toml
index f8445c26d..8efc51be9 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -26,14 +26,14 @@ dependencies = [
     "jupyter-packaging>=0.10",
     "jupyter_server>=1.7.0",
     "jupyterlab>=3.4.6,<4.0",  # comment out to use local jupyterlab
-    "jupyterlab-lsp>=3.8.0",  # comment out to use local jupyterlab
-    "jupyterlab-git~=0.32",  # Avoid breaking 1.x changes
+    "jupyterlab-lsp<=4.2.0",  # comment out to use local jupyterlab
+    "jupyterlab-git<=0.44.0",  # Avoid breaking 1.x changes
     "jupyter-resource-usage>=0.5.1,<1",
     "MarkupSafe>=2.1",
     "minio>=7.0.0,!=7.2.1",
     "nbclient>=0.5.1",
     "nbconvert>=6.5.1",
-    "nbdime~=3.1",  # Cap from jupyterlab-git
+    "nbdime~=3.2.1",  # Cap from jupyterlab-git
     "nbformat>=5.1.2",
     "networkx>=2.5.1",
     "papermill>=2.3.4",
@@ -50,7 +50,8 @@ dependencies = [
     "yaspin",
     # see: https://stackoverflow.com/questions/76175487/sudden-importerror-cannot-import-name-appengine-from-requests-packages-urlli
     "appengine-python-standard",
-    "kfp>=1.7.0,<2.0,!=1.7.2",  # We cap the SDK to <2.0 due to possible breaking changes
+    "kfp>=2.0.0", # Cap kfp for protobuff compatibility
+    "kfp-kubernetes>=1.0.0",
     "pygithub",
     "black>=22.8.0",
 ]
@@ -85,10 +86,6 @@ test = [
     "pytest_virtualenv",
     "requests-mock",
     "requests-unixsocket",
-    "kfp-tekton"
-]
-kfp-tekton = [
-    "kfp-tekton>=1.5.2"  # requires kfp >= 1.8.19, which contains fix for Jupyterlab
 ]
 kfp-examples = [
     "elyra-examples-kfp-catalog"
@@ -98,7 +95,6 @@ gitlab = [
 ]
 # The following is a collection of "non-test" extra dependencies from above.
 all = [
-    "kfp-tekton>=1.5.2",
     "elyra-examples-kfp-catalog",
     "python-gitlab",
 ]