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WIP: Remove hardcoded training image refernce #248

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WIP: Remove hardcoded training image refernce #248

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5 changes: 5 additions & 0 deletions pipeline.py
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Original file line number Diff line number Diff line change
Expand Up @@ -16,6 +16,9 @@
use_secret_as_volume,
)

from utils.consts import RHELAI_IMAGE


TEACHER_CONFIG_MAP = "teacher-server"
TEACHER_SECRET = "teacher-server"
JUDGE_CONFIG_MAP = "judge-server"
Expand Down Expand Up @@ -310,6 +313,7 @@ def pipeline(
save_samples=train_save_samples,
max_batch_len=train_max_batch_len,
seed=train_seed,
image=RHELAI_IMAGE,
)
training_phase_1.after(data_processing_task, model_to_pvc_task)
training_phase_1.set_caching_options(False)
Expand All @@ -330,6 +334,7 @@ def pipeline(
save_samples=train_save_samples,
max_batch_len=train_max_batch_len,
seed=train_seed,
image=RHELAI_IMAGE,
)

training_phase_2.set_caching_options(False)
Expand Down
106 changes: 58 additions & 48 deletions pipeline.yaml
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Original file line number Diff line number Diff line change
Expand Up @@ -351,6 +351,8 @@ components:
defaultValue: 3840.0
isOptional: true
parameterType: NUMBER_INTEGER
image:
parameterType: STRING
input_pvc_name:
parameterType: STRING
learning_rate:
Expand Down Expand Up @@ -401,6 +403,8 @@ components:
defaultValue: 3840.0
isOptional: true
parameterType: NUMBER_INTEGER
image:
parameterType: STRING
input_pvc_name:
parameterType: STRING
learning_rate:
Expand Down Expand Up @@ -731,32 +735,32 @@ deploymentSpec:
'
- "\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\
\ str,\n output_pvc_name: str,\n name_suffix: str,\n image: 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.stage.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}\"\
Unsupported value of {phase_num=}\")\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\
Expand Down Expand Up @@ -935,32 +939,32 @@ deploymentSpec:
'
- "\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\
\ str,\n output_pvc_name: str,\n name_suffix: str,\n image: 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.stage.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}\"\
Unsupported value of {phase_num=}\")\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\
Expand Down Expand Up @@ -1868,6 +1872,9 @@ root:
parameters:
effective_batch_size:
componentInputParameter: train_effective_batch_size_phase_1
image:
runtimeValue:
constant: registry.stage.redhat.io/rhelai1/instructlab-nvidia-rhel9:1.3.1
input_pvc_name:
taskOutputParameter:
outputParameterKey: name
Expand Down Expand Up @@ -1918,6 +1925,9 @@ root:
parameters:
effective_batch_size:
componentInputParameter: train_effective_batch_size_phase_2
image:
runtimeValue:
constant: registry.stage.redhat.io/rhelai1/instructlab-nvidia-rhel9:1.3.1
input_pvc_name:
taskOutputParameter:
outputParameterKey: name
Expand Down
3 changes: 1 addition & 2 deletions training/components.py
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Original file line number Diff line number Diff line change
Expand Up @@ -126,6 +126,7 @@ def pytorchjob_manifest_op(
input_pvc_name: str,
output_pvc_name: str,
name_suffix: str,
image: str,
# path_to_model: str,
phase_num: int,
nproc_per_node: int = 3,
Expand Down Expand Up @@ -167,8 +168,6 @@ def list_phase1_final_model():
else:
raise RuntimeError(f"Unsupported value of {phase_num=}")

image = "registry.stage.redhat.io/rhelai1/instructlab-nvidia-rhel9:1.3.1"

manifest = inspect.cleandoc(
f"""
apiVersion: kubeflow.org/v1
Expand Down
1 change: 1 addition & 0 deletions training/faked/components.py
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Original file line number Diff line number Diff line change
Expand Up @@ -14,6 +14,7 @@ def pytorchjob_manifest_op(
input_pvc_name: str,
output_pvc_name: str,
name_suffix: str,
image: str,
) -> NamedTuple("outputs", manifest=str, name=str):
Outputs = NamedTuple("outputs", manifest=str, name=str)
return Outputs("", "")
Expand Down
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