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pipeline.yaml
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# PIPELINE DEFINITION
# Name: instructlab
# Description: InstructLab pipeline
# Inputs:
# final_eval_batch_size: str [Default: 'auto']
# final_eval_few_shots: int [Default: 5.0]
# final_eval_max_workers: str [Default: 'auto']
# final_eval_merge_system_user_message: bool [Default: False]
# k8s_storage_class_name: str [Default: 'standard']
# mt_bench_max_workers: str [Default: 'auto']
# mt_bench_merge_system_user_message: bool [Default: False]
# sdg_base_model: str [Default: 's3://<BUCKET>/<PATH_TO_MODEL>']
# sdg_max_batch_len: int [Default: 5000.0]
# sdg_pipeline: str [Default: 'full']
# sdg_repo_branch: str
# sdg_repo_pr: int
# sdg_repo_url: str [Default: 'https://github.com/instructlab/taxonomy.git']
# sdg_sample_size: float [Default: 1.0]
# sdg_scale_factor: int [Default: 30.0]
# train_effective_batch_size_phase_1: int [Default: 128.0]
# train_effective_batch_size_phase_2: int [Default: 3840.0]
# train_learning_rate_phase_1: float [Default: 2e-05]
# train_learning_rate_phase_2: float [Default: 6e-06]
# train_max_batch_len: int [Default: 5000.0]
# train_nnodes: int [Default: 2.0]
# train_nproc_per_node: int [Default: 2.0]
# train_num_epochs_phase_1: int [Default: 7.0]
# train_num_epochs_phase_2: int [Default: 10.0]
# train_num_warmup_steps_phase_1: int [Default: 1000.0]
# train_num_warmup_steps_phase_2: int [Default: 1000.0]
# train_save_samples: int [Default: 250000.0]
# train_seed: int [Default: 42.0]
components:
comp-createpvc:
executorLabel: exec-createpvc
inputDefinitions:
parameters:
access_modes:
description: 'AccessModes to request for the provisioned PVC. May
be one or more of ``''ReadWriteOnce''``, ``''ReadOnlyMany''``, ``''ReadWriteMany''``,
or
``''ReadWriteOncePod''``. Corresponds to `PersistentVolumeClaim.spec.accessModes
<https://kubernetes.io/docs/concepts/storage/persistent-volumes/#access-modes>`_.'
parameterType: LIST
annotations:
description: Annotations for the PVC's metadata. Corresponds to `PersistentVolumeClaim.metadata.annotations
<https://kubernetes.io/docs/reference/kubernetes-api/config-and-storage-resources/persistent-volume-claim-v1/#PersistentVolumeClaim>`_.
isOptional: true
parameterType: STRUCT
pvc_name:
description: 'Name of the PVC. Corresponds to `PersistentVolumeClaim.metadata.name
<https://kubernetes.io/docs/reference/kubernetes-api/config-and-storage-resources/persistent-volume-claim-v1/#PersistentVolumeClaim>`_.
Only one of ``pvc_name`` and ``pvc_name_suffix`` can
be provided.'
isOptional: true
parameterType: STRING
pvc_name_suffix:
description: 'Prefix to use for a dynamically generated name, which
will take the form ``<argo-workflow-name>-<pvc_name_suffix>``. Only one
of ``pvc_name`` and ``pvc_name_suffix`` can be provided.'
isOptional: true
parameterType: STRING
size:
description: The size of storage requested by the PVC that will be provisioned.
For example, ``'5Gi'``. Corresponds to `PersistentVolumeClaim.spec.resources.requests.storage
<https://kubernetes.io/docs/reference/kubernetes-api/config-and-storage-resources/persistent-volume-claim-v1/#PersistentVolumeClaimSpec>`_.
parameterType: STRING
storage_class_name:
defaultValue: ''
description: 'Name of StorageClass from which to provision the PV
to back the PVC. ``None`` indicates to use the cluster''s default
storage_class_name. Set to ``''''`` for a statically specified PVC.'
isOptional: true
parameterType: STRING
volume_name:
description: 'Pre-existing PersistentVolume that should back the
provisioned PersistentVolumeClaim. Used for statically
specified PV only. Corresponds to `PersistentVolumeClaim.spec.volumeName
<https://kubernetes.io/docs/reference/kubernetes-api/config-and-storage-resources/persistent-volume-claim-v1/#PersistentVolumeClaimSpec>`_.'
isOptional: true
parameterType: STRING
outputDefinitions:
parameters:
name:
parameterType: STRING
comp-createpvc-2:
executorLabel: exec-createpvc-2
inputDefinitions:
parameters:
access_modes:
description: 'AccessModes to request for the provisioned PVC. May
be one or more of ``''ReadWriteOnce''``, ``''ReadOnlyMany''``, ``''ReadWriteMany''``,
or
``''ReadWriteOncePod''``. Corresponds to `PersistentVolumeClaim.spec.accessModes
<https://kubernetes.io/docs/concepts/storage/persistent-volumes/#access-modes>`_.'
parameterType: LIST
annotations:
description: Annotations for the PVC's metadata. Corresponds to `PersistentVolumeClaim.metadata.annotations
<https://kubernetes.io/docs/reference/kubernetes-api/config-and-storage-resources/persistent-volume-claim-v1/#PersistentVolumeClaim>`_.
isOptional: true
parameterType: STRUCT
pvc_name:
description: 'Name of the PVC. Corresponds to `PersistentVolumeClaim.metadata.name
<https://kubernetes.io/docs/reference/kubernetes-api/config-and-storage-resources/persistent-volume-claim-v1/#PersistentVolumeClaim>`_.
Only one of ``pvc_name`` and ``pvc_name_suffix`` can
be provided.'
isOptional: true
parameterType: STRING
pvc_name_suffix:
description: 'Prefix to use for a dynamically generated name, which
will take the form ``<argo-workflow-name>-<pvc_name_suffix>``. Only one
of ``pvc_name`` and ``pvc_name_suffix`` can be provided.'
isOptional: true
parameterType: STRING
size:
description: The size of storage requested by the PVC that will be provisioned.
For example, ``'5Gi'``. Corresponds to `PersistentVolumeClaim.spec.resources.requests.storage
<https://kubernetes.io/docs/reference/kubernetes-api/config-and-storage-resources/persistent-volume-claim-v1/#PersistentVolumeClaimSpec>`_.
parameterType: STRING
storage_class_name:
defaultValue: ''
description: 'Name of StorageClass from which to provision the PV
to back the PVC. ``None`` indicates to use the cluster''s default
storage_class_name. Set to ``''''`` for a statically specified PVC.'
isOptional: true
parameterType: STRING
volume_name:
description: 'Pre-existing PersistentVolume that should back the
provisioned PersistentVolumeClaim. Used for statically
specified PV only. Corresponds to `PersistentVolumeClaim.spec.volumeName
<https://kubernetes.io/docs/reference/kubernetes-api/config-and-storage-resources/persistent-volume-claim-v1/#PersistentVolumeClaimSpec>`_.'
isOptional: true
parameterType: STRING
outputDefinitions:
parameters:
name:
parameterType: STRING
comp-createpvc-3:
executorLabel: exec-createpvc-3
inputDefinitions:
parameters:
access_modes:
description: 'AccessModes to request for the provisioned PVC. May
be one or more of ``''ReadWriteOnce''``, ``''ReadOnlyMany''``, ``''ReadWriteMany''``,
or
``''ReadWriteOncePod''``. Corresponds to `PersistentVolumeClaim.spec.accessModes
<https://kubernetes.io/docs/concepts/storage/persistent-volumes/#access-modes>`_.'
parameterType: LIST
annotations:
description: Annotations for the PVC's metadata. Corresponds to `PersistentVolumeClaim.metadata.annotations
<https://kubernetes.io/docs/reference/kubernetes-api/config-and-storage-resources/persistent-volume-claim-v1/#PersistentVolumeClaim>`_.
isOptional: true
parameterType: STRUCT
pvc_name:
description: 'Name of the PVC. Corresponds to `PersistentVolumeClaim.metadata.name
<https://kubernetes.io/docs/reference/kubernetes-api/config-and-storage-resources/persistent-volume-claim-v1/#PersistentVolumeClaim>`_.
Only one of ``pvc_name`` and ``pvc_name_suffix`` can
be provided.'
isOptional: true
parameterType: STRING
pvc_name_suffix:
description: 'Prefix to use for a dynamically generated name, which
will take the form ``<argo-workflow-name>-<pvc_name_suffix>``. Only one
of ``pvc_name`` and ``pvc_name_suffix`` can be provided.'
isOptional: true
parameterType: STRING
size:
description: The size of storage requested by the PVC that will be provisioned.
For example, ``'5Gi'``. Corresponds to `PersistentVolumeClaim.spec.resources.requests.storage
<https://kubernetes.io/docs/reference/kubernetes-api/config-and-storage-resources/persistent-volume-claim-v1/#PersistentVolumeClaimSpec>`_.
parameterType: STRING
storage_class_name:
defaultValue: ''
description: 'Name of StorageClass from which to provision the PV
to back the PVC. ``None`` indicates to use the cluster''s default
storage_class_name. Set to ``''''`` for a statically specified PVC.'
isOptional: true
parameterType: STRING
volume_name:
description: 'Pre-existing PersistentVolume that should back the
provisioned PersistentVolumeClaim. Used for statically
specified PV only. Corresponds to `PersistentVolumeClaim.spec.volumeName
<https://kubernetes.io/docs/reference/kubernetes-api/config-and-storage-resources/persistent-volume-claim-v1/#PersistentVolumeClaimSpec>`_.'
isOptional: true
parameterType: STRING
outputDefinitions:
parameters:
name:
parameterType: STRING
comp-data-processing-op:
executorLabel: exec-data-processing-op
inputDefinitions:
parameters:
knowledge_path:
defaultValue: /data/knowledge
isOptional: true
parameterType: STRING
max_batch_len:
defaultValue: 20000.0
isOptional: true
parameterType: NUMBER_INTEGER
max_seq_len:
defaultValue: 4096.0
isOptional: true
parameterType: NUMBER_INTEGER
model_path:
defaultValue: /model
isOptional: true
parameterType: STRING
sdg_path:
defaultValue: /data/sdg
isOptional: true
parameterType: STRING
skills_path:
defaultValue: /data/skills
isOptional: true
parameterType: STRING
comp-deletepvc:
executorLabel: exec-deletepvc
inputDefinitions:
parameters:
pvc_name:
description: Name of the PVC to delete. Supports passing a runtime-generated
name, such as a name provided by ``kubernetes.CreatePvcOp().outputs['name']``.
parameterType: STRING
comp-deletepvc-2:
executorLabel: exec-deletepvc-2
inputDefinitions:
parameters:
pvc_name:
description: Name of the PVC to delete. Supports passing a runtime-generated
name, such as a name provided by ``kubernetes.CreatePvcOp().outputs['name']``.
parameterType: STRING
comp-deletepvc-3:
executorLabel: exec-deletepvc-3
inputDefinitions:
parameters:
pvc_name:
description: Name of the PVC to delete. Supports passing a runtime-generated
name, such as a name provided by ``kubernetes.CreatePvcOp().outputs['name']``.
parameterType: STRING
comp-git-clone-op:
executorLabel: exec-git-clone-op
inputDefinitions:
parameters:
repo_branch:
parameterType: STRING
repo_pr:
parameterType: NUMBER_INTEGER
repo_url:
parameterType: STRING
taxonomy_path:
defaultValue: /data/taxonomy
isOptional: true
parameterType: STRING
comp-importer:
executorLabel: exec-importer
inputDefinitions:
parameters:
uri:
parameterType: STRING
outputDefinitions:
artifacts:
artifact:
artifactType:
schemaTitle: system.Model
schemaVersion: 0.0.1
comp-knowledge-processed-data-to-artifact-op:
executorLabel: exec-knowledge-processed-data-to-artifact-op
inputDefinitions:
parameters:
pvc_path:
defaultValue: /data/knowledge
isOptional: true
parameterType: STRING
outputDefinitions:
artifacts:
knowledge_processed_data:
artifactType:
schemaTitle: system.Dataset
schemaVersion: 0.0.1
comp-model-to-pvc-op:
executorLabel: exec-model-to-pvc-op
inputDefinitions:
artifacts:
model:
artifactType:
schemaTitle: system.Model
schemaVersion: 0.0.1
parameters:
pvc_path:
defaultValue: /model
isOptional: true
parameterType: STRING
comp-pvc-to-model-op:
executorLabel: exec-pvc-to-model-op
inputDefinitions:
parameters:
pvc_path:
parameterType: STRING
outputDefinitions:
artifacts:
model:
artifactType:
schemaTitle: system.Model
schemaVersion: 0.0.1
comp-pvc-to-mt-bench-op:
executorLabel: exec-pvc-to-mt-bench-op
inputDefinitions:
parameters:
pvc_path:
parameterType: STRING
outputDefinitions:
artifacts:
mt_bench_output:
artifactType:
schemaTitle: system.Artifact
schemaVersion: 0.0.1
comp-pytorchjob-manifest-op:
executorLabel: exec-pytorchjob-manifest-op
inputDefinitions:
parameters:
effective_batch_size:
defaultValue: 3840.0
isOptional: true
parameterType: NUMBER_INTEGER
input_pvc_name:
parameterType: STRING
learning_rate:
defaultValue: 0.0001
isOptional: true
parameterType: NUMBER_DOUBLE
max_batch_len:
defaultValue: 20000.0
isOptional: true
parameterType: NUMBER_INTEGER
model_pvc_name:
parameterType: STRING
name_suffix:
parameterType: STRING
nnodes:
defaultValue: 2.0
isOptional: true
parameterType: NUMBER_INTEGER
nproc_per_node:
defaultValue: 3.0
isOptional: true
parameterType: NUMBER_INTEGER
num_epochs:
defaultValue: 2.0
isOptional: true
parameterType: NUMBER_INTEGER
num_warmup_steps:
defaultValue: 800.0
isOptional: true
parameterType: NUMBER_INTEGER
output_pvc_name:
parameterType: STRING
phase_num:
parameterType: NUMBER_INTEGER
save_samples:
defaultValue: 0.0
isOptional: true
parameterType: NUMBER_INTEGER
seed:
defaultValue: 42.0
isOptional: true
parameterType: NUMBER_INTEGER
comp-pytorchjob-manifest-op-2:
executorLabel: exec-pytorchjob-manifest-op-2
inputDefinitions:
parameters:
effective_batch_size:
defaultValue: 3840.0
isOptional: true
parameterType: NUMBER_INTEGER
input_pvc_name:
parameterType: STRING
learning_rate:
defaultValue: 0.0001
isOptional: true
parameterType: NUMBER_DOUBLE
max_batch_len:
defaultValue: 20000.0
isOptional: true
parameterType: NUMBER_INTEGER
model_pvc_name:
parameterType: STRING
name_suffix:
parameterType: STRING
nnodes:
defaultValue: 2.0
isOptional: true
parameterType: NUMBER_INTEGER
nproc_per_node:
defaultValue: 3.0
isOptional: true
parameterType: NUMBER_INTEGER
num_epochs:
defaultValue: 2.0
isOptional: true
parameterType: NUMBER_INTEGER
num_warmup_steps:
defaultValue: 800.0
isOptional: true
parameterType: NUMBER_INTEGER
output_pvc_name:
parameterType: STRING
phase_num:
parameterType: NUMBER_INTEGER
save_samples:
defaultValue: 0.0
isOptional: true
parameterType: NUMBER_INTEGER
seed:
defaultValue: 42.0
isOptional: true
parameterType: NUMBER_INTEGER
comp-run-final-eval-op:
executorLabel: exec-run-final-eval-op
inputDefinitions:
parameters:
base_branch:
parameterType: STRING
base_model_dir:
parameterType: STRING
batch_size:
parameterType: STRING
candidate_branch:
parameterType: STRING
candidate_model:
isOptional: true
parameterType: STRING
few_shots:
parameterType: NUMBER_INTEGER
max_workers:
parameterType: STRING
merge_system_user_message:
parameterType: BOOLEAN
sdg_path:
defaultValue: /input/sdg
isOptional: true
parameterType: STRING
taxonomy_path:
defaultValue: /input/taxonomy
isOptional: true
parameterType: STRING
outputDefinitions:
artifacts:
mmlu_branch_output:
artifactType:
schemaTitle: system.Artifact
schemaVersion: 0.0.1
mt_bench_branch_output:
artifactType:
schemaTitle: system.Artifact
schemaVersion: 0.0.1
comp-run-mt-bench-op:
executorLabel: exec-run-mt-bench-op
inputDefinitions:
parameters:
best_score_file:
isOptional: true
parameterType: STRING
max_workers:
parameterType: STRING
merge_system_user_message:
parameterType: BOOLEAN
models_folder:
parameterType: STRING
output_path:
defaultValue: /output/mt_bench_data.json
isOptional: true
parameterType: STRING
outputDefinitions:
parameters:
best_model:
parameterType: STRING
best_score:
parameterType: NUMBER_DOUBLE
comp-sdg-op:
executorLabel: exec-sdg-op
inputDefinitions:
parameters:
num_instructions_to_generate:
parameterType: NUMBER_INTEGER
pipeline:
parameterType: STRING
repo_branch:
parameterType: STRING
repo_pr:
parameterType: NUMBER_INTEGER
sdg_path:
defaultValue: /data/sdg
isOptional: true
parameterType: STRING
sdg_sampling_size:
defaultValue: 1.0
isOptional: true
parameterType: NUMBER_DOUBLE
taxonomy_path:
defaultValue: /data/taxonomy
isOptional: true
parameterType: STRING
comp-sdg-to-artifact-op:
executorLabel: exec-sdg-to-artifact-op
inputDefinitions:
parameters:
pvc_path:
defaultValue: /data/sdg
isOptional: true
parameterType: STRING
outputDefinitions:
artifacts:
sdg:
artifactType:
schemaTitle: system.Dataset
schemaVersion: 0.0.1
comp-skills-processed-data-to-artifact-op:
executorLabel: exec-skills-processed-data-to-artifact-op
inputDefinitions:
parameters:
pvc_path:
defaultValue: /data/skills
isOptional: true
parameterType: STRING
outputDefinitions:
artifacts:
skills_processed_data:
artifactType:
schemaTitle: system.Dataset
schemaVersion: 0.0.1
comp-taxonomy-to-artifact-op:
executorLabel: exec-taxonomy-to-artifact-op
inputDefinitions:
parameters:
pvc_path:
defaultValue: /data/taxonomy
isOptional: true
parameterType: STRING
outputDefinitions:
artifacts:
taxonomy:
artifactType:
schemaTitle: system.Dataset
schemaVersion: 0.0.1
deploymentSpec:
executors:
exec-createpvc:
container:
image: argostub/createpvc
exec-createpvc-2:
container:
image: argostub/createpvc
exec-createpvc-3:
container:
image: argostub/createpvc
exec-data-processing-op:
container:
args:
- --executor_input
- '{{$}}'
- --function_to_execute
- data_processing_op
command:
- sh
- -ec
- 'program_path=$(mktemp -d)
printf "%s" "$0" > "$program_path/ephemeral_component.py"
_KFP_RUNTIME=true python3 -m kfp.dsl.executor_main --component_module_path "$program_path/ephemeral_component.py" "$@"
'
- "\nimport kfp\nfrom kfp import dsl\nfrom kfp.dsl import *\nfrom typing import\
\ *\n\ndef data_processing_op(\n model_path: str = \"/model\",\n sdg_path:\
\ str = \"/data/sdg\",\n skills_path: str = \"/data/skills\",\n knowledge_path:\
\ str = \"/data/knowledge\",\n max_seq_len: Optional[int] = 4096,\n \
\ max_batch_len: Optional[int] = 20000,\n):\n import os\n\n import\
\ instructlab.training.data_process as dp\n from instructlab.training\
\ import (\n DataProcessArgs,\n TrainingArgs,\n )\n\n \
\ # define training-specific arguments\n skill_training_args = TrainingArgs(\n\
\ # define data-specific arguments\n model_path=model_path,\n\
\ data_path=f\"{sdg_path}/skills_train_msgs*.jsonl\",\n data_output_dir=skills_path,\n\
\ # define model-trianing parameters\n max_seq_len=max_seq_len,\n\
\ max_batch_len=max_batch_len,\n # XXX(shanand): We don't\
\ need the following arguments\n # for data processing. Added them\
\ for now to avoid\n # Pydantic validation errors for TrainingArgs\n\
\ ckpt_output_dir=\"data/saved_checkpoints\",\n num_epochs=2,\n\
\ effective_batch_size=3840,\n save_samples=0,\n learning_rate=2e-6,\n\
\ warmup_steps=800,\n is_padding_free=True,\n )\n\n \
\ knowledge_training_args = TrainingArgs(\n # define data-specific\
\ arguments\n model_path=model_path,\n data_path=f\"{sdg_path}/knowledge_train_msgs*.jsonl\"\
,\n data_output_dir=knowledge_path,\n # define model-trianing\
\ parameters\n max_seq_len=max_seq_len,\n max_batch_len=max_batch_len,\n\
\ # XXX(shanand): We don't need the following arguments\n \
\ # for data processing. Added them for now to avoid\n # Pydantic\
\ validation errors for TrainingArgs\n ckpt_output_dir=\"data/saved_checkpoints\"\
,\n num_epochs=2,\n effective_batch_size=3840,\n save_samples=0,\n\
\ learning_rate=2e-6,\n warmup_steps=800,\n is_padding_free=True,\n\
\ )\n\n def data_processing(train_args: TrainingArgs) -> None:\n \
\ # early validation logic here\n if train_args.max_batch_len\
\ < train_args.max_seq_len:\n raise ValueError(\n \
\ f\"the 'max_batch_len' cannot be less than 'max_seq_len': {train_args.max_batch_len=}\
\ < {train_args.max_seq_len=}\"\n )\n\n # process\
\ the training data\n if not os.path.exists(train_args.data_output_dir):\n\
\ os.makedirs(train_args.data_output_dir, exist_ok=True)\n \
\ dp.main(\n DataProcessArgs(\n # XXX(osilkin):\
\ make a decision here, either:\n # 1. the CLI is fully\
\ responsible for managing where the data is written\n #\
\ 2. we never cache it and simply write it to a tmp file every time.\n\
\ #\n # An important reason for why #1 would\
\ be preferable is in the case of OpenShift/SELinux\n # where\
\ the user has a defined place for new temporary data to be written.\n \
\ data_output_path=train_args.data_output_dir,\n \
\ model_path=train_args.model_path,\n data_path=train_args.data_path,\n\
\ max_seq_len=train_args.max_seq_len,\n chat_tmpl_path=train_args.chat_tmpl_path,\n\
\ )\n )\n\n data_processing(train_args=skill_training_args)\n\
\ data_processing(train_args=knowledge_training_args)\n\n"
env:
- name: XDG_CACHE_HOME
value: /tmp
image: registry.redhat.io/rhelai1/instructlab-nvidia-rhel9@sha256:b3dc9af0244aa6b84e6c3ef53e714a316daaefaae67e28de397cd71ee4b2ac7e
exec-deletepvc:
container:
image: argostub/deletepvc
exec-deletepvc-2:
container:
image: argostub/deletepvc
exec-deletepvc-3:
container:
image: argostub/deletepvc
exec-git-clone-op:
container:
args:
- 'git clone {{$.inputs.parameters[''repo_url'']}} {{$.inputs.parameters[''taxonomy_path'']}}
&& cd {{$.inputs.parameters[''taxonomy_path'']}} && if [ -n "{{$.inputs.parameters[''repo_branch'']}}"
]; then git fetch origin {{$.inputs.parameters[''repo_branch'']}} && git
checkout {{$.inputs.parameters[''repo_branch'']}}; elif [ -n "{{$.inputs.parameters[''repo_pr'']}}"
] && [ {{$.inputs.parameters[''repo_pr'']}} -gt 0 ]; then git fetch origin
pull/{{$.inputs.parameters[''repo_pr'']}}/head:{{$.inputs.parameters[''repo_pr'']}}
&& git checkout {{$.inputs.parameters[''repo_pr'']}}; fi '
command:
- /bin/sh
- -c
image: registry.redhat.io/ubi9/toolbox@sha256:da31dee8904a535d12689346e65e5b00d11a6179abf1fa69b548dbd755fa2770
exec-importer:
importer:
artifactUri:
runtimeParameter: uri
typeSchema:
schemaTitle: system.Model
schemaVersion: 0.0.1
exec-knowledge-processed-data-to-artifact-op:
container:
args:
- cp -r {{$.inputs.parameters['pvc_path']}} {{$.outputs.artifacts['knowledge_processed_data'].path}}
command:
- /bin/sh
- -c
image: registry.redhat.io/ubi9/toolbox@sha256:da31dee8904a535d12689346e65e5b00d11a6179abf1fa69b548dbd755fa2770
exec-model-to-pvc-op:
container:
args:
- cp -r {{$.inputs.artifacts['model'].path}}/* {{$.inputs.parameters['pvc_path']}}
command:
- /bin/sh
- -c
image: registry.redhat.io/ubi9/toolbox@sha256:da31dee8904a535d12689346e65e5b00d11a6179abf1fa69b548dbd755fa2770
exec-pvc-to-model-op:
container:
args:
- cp -r {{$.inputs.parameters['pvc_path']}} {{$.outputs.artifacts['model'].path}}
command:
- /bin/sh
- -c
image: registry.redhat.io/ubi9/toolbox@sha256:da31dee8904a535d12689346e65e5b00d11a6179abf1fa69b548dbd755fa2770
exec-pvc-to-mt-bench-op:
container:
args:
- cp -r {{$.inputs.parameters['pvc_path']}} {{$.outputs.artifacts['mt_bench_output'].path}}
command:
- /bin/sh
- -c
image: registry.redhat.io/ubi9/toolbox@sha256:da31dee8904a535d12689346e65e5b00d11a6179abf1fa69b548dbd755fa2770
exec-pytorchjob-manifest-op:
container:
args:
- --executor_input
- '{{$}}'
- --function_to_execute
- pytorchjob_manifest_op
command:
- sh
- -ec
- 'program_path=$(mktemp -d)
printf "%s" "$0" > "$program_path/ephemeral_component.py"
_KFP_RUNTIME=true python3 -m kfp.dsl.executor_main --component_module_path "$program_path/ephemeral_component.py" "$@"
'
- "\nimport kfp\nfrom kfp import dsl\nfrom kfp.dsl import *\nfrom typing import\
\ *\n\ndef pytorchjob_manifest_op(\n model_pvc_name: str,\n input_pvc_name:\
\ str,\n output_pvc_name: str,\n name_suffix: str,\n # path_to_model:\
\ str,\n phase_num: int,\n nproc_per_node: int = 3,\n nnodes: int\
\ = 2,\n num_epochs: int = 2,\n effective_batch_size: int = 3840,\n\
\ learning_rate: float = 1e-4,\n num_warmup_steps: int = 800,\n \
\ save_samples: int = 0,\n max_batch_len: int = 20000,\n seed: int\
\ = 42,\n):\n import inspect\n import os\n import time\n\n import\
\ kubernetes\n import urllib3\n import yaml\n\n def list_phase1_final_model():\n\
\ model_dir = \"/output/phase_1/model/hf_format\"\n models\
\ = os.listdir(model_dir)\n newest_idx = max(\n (os.path.getmtime(f\"\
{model_dir}/{model}\"), i)\n for i, model in enumerate(models)\n\
\ )[-1]\n newest_model = models[newest_idx]\n return\
\ f\"{model_dir}/{newest_model}\"\n\n name = f\"train-phase-{phase_num}-{name_suffix.rstrip('-sdg')}\"\
\n\n if phase_num == 1:\n path_to_model = \"/input_model\"\n \
\ path_to_data = \"/input_data/knowledge/data.jsonl\"\n elif phase_num\
\ == 2:\n path_to_model = list_phase1_final_model()\n path_to_data\
\ = \"/input_data/skills/data.jsonl\"\n else:\n raise RuntimeError(f\"\
Unsupported value of {phase_num=}\")\n\n image = \"registry.redhat.io/rhelai1/instructlab-nvidia-rhel9:1.3.1\"\
\n\n manifest = inspect.cleandoc(\n f\"\"\"\n apiVersion:\
\ kubeflow.org/v1\n kind: PyTorchJob\n metadata:\n \
\ name: {name}\n spec:\n nprocPerNode: \\\"{nproc_per_node}\\\
\"\n pytorchReplicaSpecs:\n Master:\n replicas:\
\ 1\n restartPolicy: OnFailure\n template:\n \
\ metadata:\n annotations:\n \
\ sidecar.istio.io/inject: 'false'\n spec:\n \
\ containers:\n - args:\n \
\ - |\n echo \"Running phase {phase_num}\"\
\n echo \"Using {path_to_model} model for training\"\
\n echo \"Using {path_to_data} data for training\"\
\n mkdir -p /output/phase_{phase_num}/model;\n\
\ mkdir -p /output/data;\n \
\ torchrun --nnodes {nnodes} \\\n --nproc_per_node\
\ {nproc_per_node} \\\n --node_rank \\$(RANK)\
\ \\\n --rdzv_endpoint \\$(MASTER_ADDR):\\\
$(MASTER_PORT) \\\n -m instructlab.training.main_ds\
\ \\\n --model_name_or_path={path_to_model}\
\ \\\n --data_path={path_to_data} \\\n \
\ --output_dir=/output/phase_{phase_num}/model\
\ \\\n --num_epochs={num_epochs} \\\n \
\ --effective_batch_size={effective_batch_size}\
\ \\\n --learning_rate={learning_rate} \\\n\
\ --num_warmup_steps={num_warmup_steps} \\\n\
\ --save_samples={save_samples} \\\n \
\ --log_level=INFO \\\n \
\ --max_batch_len={max_batch_len} \\\n \
\ --seed={seed} \\\n --cpu_offload_optimizer\
\ \\\n --cpu_offload_params_fsdp \\\n \
\ --distributed_training_framework fsdp \\\n \
\ --checkpoint_at_epoch\n \
\ command:\n - /bin/bash\n \
\ - '-c'\n - '--'\n image:\
\ {image}\n name: pytorch\n volumeMounts:\n\
\ - mountPath: /input_data\n \
\ name: input-data\n readOnly: true\n \
\ - mountPath: /input_model\n \
\ name: model\n readOnly: true\n \
\ - mountPath: /output\n name: output\n\
\ env:\n - name: NNODES\n \
\ value: \\\"{nnodes}\\\"\n \
\ - name: NPROC_PER_NODE\n value: \\\"{nproc_per_node}\\\
\"\n - name: XDG_CACHE_HOME\n \
\ value: /tmp\n - name: TRITON_CACHE_DIR\n\
\ value: /tmp\n - name:\
\ HF_HOME\n value: /tmp\n \
\ - name: TRANSFORMERS_CACHE\n value: /tmp\n\
\ resources:\n requests:\n \
\ \"nvidia.com/gpu\": {nproc_per_node}\n \
\ limits:\n \"nvidia.com/gpu\"\
: {nproc_per_node}\n volumes:\n - name:\
\ input-data\n persistentVolumeClaim:\n \
\ claimName: {input_pvc_name}\n - name: model\n\
\ persistentVolumeClaim:\n claimName:\
\ {model_pvc_name}\n - name: output\n \
\ persistentVolumeClaim:\n claimName: {output_pvc_name}\n\
\ Worker:\n replicas: {nnodes-1}\n \
\ restartPolicy: OnFailure\n template:\n metadata:\n\
\ annotations:\n sidecar.istio.io/inject:\
\ 'false'\n spec:\n containers:\n \
\ - args:\n - |\n \
\ echo \"Running phase {phase_num}\"\n echo\
\ \"Using {path_to_model} model for training\"\n \
\ echo \"Using {path_to_data} data for training\"\n \
\ mkdir -p /tmp/model;\n torchrun --nnodes\
\ {nnodes} \\\n --nproc_per_node {nproc_per_node}\
\ \\\n --node_rank \\$(RANK) \\\n \
\ --rdzv_endpoint \\$(MASTER_ADDR):\\$(MASTER_PORT) \\\n\
\ -m instructlab.training.main_ds \\\n \
\ --model_name_or_path={path_to_model} \\\n \
\ --data_path={path_to_data} \\\n \
\ --output_dir=/tmp/model \\\n --num_epochs={num_epochs}\
\ \\\n --effective_batch_size={effective_batch_size}\
\ \\\n --learning_rate={learning_rate} \\\n \
\ --num_warmup_steps={num_warmup_steps} \\\n \
\ --save_samples={save_samples} \\\n \
\ --log_level=INFO \\\n --max_batch_len={max_batch_len}\
\ \\\n --seed={seed} \\\n \
\ --cpu_offload_optimizer \\\n --cpu_offload_params_fsdp\
\ \\\n --distributed_training_framework fsdp\
\ \\\n --checkpoint_at_epoch\n \
\ command:\n - /bin/bash\n \
\ - '-c'\n - '--'\n \
\ image: {image}\n name: pytorch\n \
\ volumeMounts:\n - mountPath: /input_data\n\
\ name: input-data\n readOnly:\
\ true\n - mountPath: /input_model\n \
\ name: model\n readOnly: true\n \
\ - mountPath: /output\n \
\ name: output\n readOnly: true\n \
\ env:\n - name: NNODES\n \
\ value: \\\"{nnodes}\\\"\n - name: NPROC_PER_NODE\n\
\ value: \\\"{nproc_per_node}\\\"\n \
\ - name: XDG_CACHE_HOME\n value: /tmp\n\
\ - name: TRITON_CACHE_DIR\n \
\ value: /tmp\n - name: HF_HOME\n \
\ value: /tmp\n - name: TRANSFORMERS_CACHE\n\
\ value: /tmp\n resources:\n\
\ requests:\n \"nvidia.com/gpu\"\
: {nproc_per_node}\n limits:\n \
\ \"nvidia.com/gpu\": {nproc_per_node}\n volumes:\n\
\ - name: input-data\n persistentVolumeClaim:\n\
\ claimName: {input_pvc_name}\n \
\ - name: model\n persistentVolumeClaim:\n \
\ claimName: {model_pvc_name}\n - name:\
\ output\n persistentVolumeClaim:\n \
\ claimName: {output_pvc_name}\n \"\"\"\n )\n\n try:\n\
\ manifest_yaml = yaml.safe_load(manifest)\n except yaml.YAMLError\
\ as exc:\n raise RuntimeError(f\"Error parsing manifest: {exc}\"\
) from exc\n\n # Discover the namespace in which the pod is running\n\
\ with open(\n \"/var/run/secrets/kubernetes.io/serviceaccount/namespace\"\
, \"r\", encoding=\"utf-8\"\n ) as f:\n namespace = f.read().strip()\n\
\ print(f\"The pod is running in the namespace: {namespace}\")\n\n\
\ try:\n kubernetes.config.load_kube_config()\n print(\"\
Loaded kube config\")\n except kubernetes.config.ConfigException:\n \
\ print(\"Failed to load kube config. Trying in-cluster config\")\n\
\ kubernetes.config.load_incluster_config()\n\n api = kubernetes.client.CustomObjectsApi()\n\
\ try:\n api.create_namespaced_custom_object(\n group=\"\
kubeflow.org\",\n version=\"v1\",\n namespace=namespace,\n\
\ plural=\"pytorchjobs\",\n body=manifest_yaml,\n\
\ )\n except kubernetes.client.rest.ApiException as exc:\n \
\ if exc.status == 409:\n print(\n \"{} '{}/{}'\
\ already exists.\".format(\n manifest_yaml[\"kind\"\
],\n namespace,\n manifest_yaml[\"\
metadata\"][\"name\"],\n )\n )\n else:\n\
\ raise\n\n # Get the CR status and wait for it to be completed\n\
\ w = kubernetes.watch.Watch()\n exit_flag = False\n start_time\
\ = time.time()\n timeout_seconds = 24 * 60 * 60 # 24 hours\n\n while\
\ not exit_flag: # Keep the watch active\n if time.time() - start_time\
\ > timeout_seconds:\n raise RuntimeError(\n \"\
Timeout (24h) reached waiting for the PytorchJob to complete.\"\n \
\ )\n\n try:\n print(\"Watching for PytorchJob\"\
)\n for event in w.stream(\n api.list_namespaced_custom_object,\n\
\ group=\"kubeflow.org\",\n version=\"v1\"\
,\n namespace=namespace,\n plural=\"pytorchjobs\"\
,\n timeout_seconds=60, # Timeout after 1 minute\n \
\ ):\n pytorchjob_event = event[\"object\"]\n \
\ if (\n pytorchjob_event[\"metadata\"][\"\
name\"]\n != manifest_yaml[\"metadata\"][\"name\"]\n\
\ ):\n continue\n pytorchjob_name\
\ = pytorchjob_event[\"metadata\"][\"name\"]\n\n if (\n \
\ \"status\" not in pytorchjob_event\n \
\ or \"conditions\" not in pytorchjob_event[\"status\"]\n \
\ ):\n continue\n print(\n \
\ f\"PytorchJob: {pytorchjob_name} - {pytorchjob_event['status'].get('conditions',\
\ 'No conditions yet')}\"\n )\n for job_condition\
\ in reversed(pytorchjob_event[\"status\"][\"conditions\"]):\n \
\ if job_condition[\"type\"] == \"Succeeded\":\n \
\ print(\n f\"PytorchJob '{pytorchjob_name}'\
\ completed successfully: {job_condition['reason']}\"\n \
\ )\n print(f\"Training phase {phase_num}\
\ completed.\")\n w.stop()\n \
\ exit_flag = True\n # Break here to avoid going\
\ into other conditions, we are done\n break\n \
\ elif job_condition[\"type\"] == \"Failed\":\n \
\ print(\n f\"PytorchJob '{pytorchjob_name}'\
\ failed: {job_condition['reason']}\"\n )\n \
\ w.stop()\n raise RuntimeError(\"\
Job failed.\")\n except kubernetes.client.exceptions.ApiException\
\ as e:\n print(f\"API exception occurred: {str(e)}\")\n \
\ time.sleep(5) # Backoff before retrying\n # Catches the\
\ following error:\n # urllib3.exceptions.ProtocolError: (\"Connection\
\ broken: InvalidChunkLength\n except urllib3.exceptions.ProtocolError\
\ as e:\n print(f\"Connection broken reconnecting the watcher\
\ {str(e)}\")\n time.sleep(5) # Backoff before retrying\n \
\ finally:\n w.stop()\n\n"
image: quay.io/modh/odh-generic-data-science-notebook@sha256:0efbb3ad6f8f342360cf1f002d40716a39d4c58f69163e053d5bd19b4fe732d4
exec-pytorchjob-manifest-op-2:
container:
args:
- --executor_input
- '{{$}}'
- --function_to_execute
- pytorchjob_manifest_op
command:
- sh
- -ec
- 'program_path=$(mktemp -d)
printf "%s" "$0" > "$program_path/ephemeral_component.py"
_KFP_RUNTIME=true python3 -m kfp.dsl.executor_main --component_module_path "$program_path/ephemeral_component.py" "$@"
'
- "\nimport kfp\nfrom kfp import dsl\nfrom kfp.dsl import *\nfrom typing import\
\ *\n\ndef pytorchjob_manifest_op(\n model_pvc_name: str,\n input_pvc_name:\
\ str,\n output_pvc_name: str,\n name_suffix: str,\n # path_to_model:\
\ str,\n phase_num: int,\n nproc_per_node: int = 3,\n nnodes: int\
\ = 2,\n num_epochs: int = 2,\n effective_batch_size: int = 3840,\n\
\ learning_rate: float = 1e-4,\n num_warmup_steps: int = 800,\n \
\ save_samples: int = 0,\n max_batch_len: int = 20000,\n seed: int\
\ = 42,\n):\n import inspect\n import os\n import time\n\n import\
\ kubernetes\n import urllib3\n import yaml\n\n def list_phase1_final_model():\n\
\ model_dir = \"/output/phase_1/model/hf_format\"\n models\
\ = os.listdir(model_dir)\n newest_idx = max(\n (os.path.getmtime(f\"\
{model_dir}/{model}\"), i)\n for i, model in enumerate(models)\n\
\ )[-1]\n newest_model = models[newest_idx]\n return\
\ f\"{model_dir}/{newest_model}\"\n\n name = f\"train-phase-{phase_num}-{name_suffix.rstrip('-sdg')}\"\
\n\n if phase_num == 1:\n path_to_model = \"/input_model\"\n \
\ path_to_data = \"/input_data/knowledge/data.jsonl\"\n elif phase_num\
\ == 2:\n path_to_model = list_phase1_final_model()\n path_to_data\
\ = \"/input_data/skills/data.jsonl\"\n else:\n raise RuntimeError(f\"\
Unsupported value of {phase_num=}\")\n\n image = \"registry.redhat.io/rhelai1/instructlab-nvidia-rhel9:1.3.1\"\
\n\n manifest = inspect.cleandoc(\n f\"\"\"\n apiVersion:\
\ kubeflow.org/v1\n kind: PyTorchJob\n metadata:\n \
\ name: {name}\n spec:\n nprocPerNode: \\\"{nproc_per_node}\\\
\"\n pytorchReplicaSpecs:\n Master:\n replicas:\
\ 1\n restartPolicy: OnFailure\n template:\n \
\ metadata:\n annotations:\n \
\ sidecar.istio.io/inject: 'false'\n spec:\n \
\ containers:\n - args:\n \
\ - |\n echo \"Running phase {phase_num}\"\
\n echo \"Using {path_to_model} model for training\"\
\n echo \"Using {path_to_data} data for training\"\
\n mkdir -p /output/phase_{phase_num}/model;\n\
\ mkdir -p /output/data;\n \
\ torchrun --nnodes {nnodes} \\\n --nproc_per_node\
\ {nproc_per_node} \\\n --node_rank \\$(RANK)\
\ \\\n --rdzv_endpoint \\$(MASTER_ADDR):\\\
$(MASTER_PORT) \\\n -m instructlab.training.main_ds\
\ \\\n --model_name_or_path={path_to_model}\
\ \\\n --data_path={path_to_data} \\\n \
\ --output_dir=/output/phase_{phase_num}/model\
\ \\\n --num_epochs={num_epochs} \\\n \
\ --effective_batch_size={effective_batch_size}\
\ \\\n --learning_rate={learning_rate} \\\n\
\ --num_warmup_steps={num_warmup_steps} \\\n\
\ --save_samples={save_samples} \\\n \
\ --log_level=INFO \\\n \
\ --max_batch_len={max_batch_len} \\\n \
\ --seed={seed} \\\n --cpu_offload_optimizer\
\ \\\n --cpu_offload_params_fsdp \\\n \
\ --distributed_training_framework fsdp \\\n \
\ --checkpoint_at_epoch\n \
\ command:\n - /bin/bash\n \
\ - '-c'\n - '--'\n image:\
\ {image}\n name: pytorch\n volumeMounts:\n\
\ - mountPath: /input_data\n \
\ name: input-data\n readOnly: true\n \
\ - mountPath: /input_model\n \
\ name: model\n readOnly: true\n \
\ - mountPath: /output\n name: output\n\
\ env:\n - name: NNODES\n \
\ value: \\\"{nnodes}\\\"\n \
\ - name: NPROC_PER_NODE\n value: \\\"{nproc_per_node}\\\
\"\n - name: XDG_CACHE_HOME\n \
\ value: /tmp\n - name: TRITON_CACHE_DIR\n\
\ value: /tmp\n - name:\
\ HF_HOME\n value: /tmp\n \