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schema

zeus.optimizer.batch_size.server.database.schema

Database schema.

Base

Bases: DeclarativeBase

Base class for schemas.

Source code in zeus/optimizer/batch_size/server/database/schema.py 
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class Base(DeclarativeBase):
    """Base class for schemas."""

    pass

JobTable

Bases: Base

Job table schema.

Refer to JobState for attributes.

Source code in zeus/optimizer/batch_size/server/database/schema.py 
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class JobTable(Base):
    """Job table schema.

    Refer to [`JobState`][zeus.optimizer.batch_size.server.job.models.JobState] for attributes.
    """

    __tablename__ = "Job"

    job_id: Mapped[str] = mapped_column(VARCHAR(400), primary_key=True)
    job_id_prefix: Mapped[str] = mapped_column(VARCHAR(300), nullable=False)
    default_batch_size: Mapped[int] = mapped_column(Integer, nullable=False)
    higher_is_better_metric: Mapped[bool] = mapped_column(Boolean, default=True)
    eta_knob: Mapped[float] = mapped_column(Float, default=0.5)
    beta_knob: Mapped[Optional[float]] = mapped_column(Float, nullable=True)
    target_metric: Mapped[float] = mapped_column(Float, default=0.5)
    max_epochs: Mapped[int] = mapped_column(Integer, default=100)
    num_pruning_rounds: Mapped[int] = mapped_column(Integer, default=2)
    window_size: Mapped[int] = mapped_column(Integer, default=10)

    max_power: Mapped[float] = mapped_column(Float, nullable=False)
    number_of_gpus: Mapped[int] = mapped_column(Integer, nullable=False)
    gpu_model: Mapped[str] = mapped_column(VARCHAR(length=30), nullable=False)

    mab_prior_mean: Mapped[float] = mapped_column(Float, default=0.0)
    mab_prior_precision: Mapped[float] = mapped_column(Float, default=0.0)
    mab_num_explorations: Mapped[int] = mapped_column(Integer, default=2)
    mab_seed: Mapped[Optional[int]] = mapped_column(Integer, nullable=True)

    mab_random_generator_state: Mapped[Optional[str]] = mapped_column(
        VARCHAR(length=10000), nullable=True
    )
    exp_default_batch_size: Mapped[int] = mapped_column(Integer, nullable=False)

    stage: Mapped[Stage] = mapped_column(Enum(Stage), default=Stage.Pruning)
    min_cost: Mapped[Optional[float]] = mapped_column(Float, nullable=True)
    min_cost_batch_size: Mapped[int] = mapped_column(Integer, nullable=False)

    batch_sizes: Mapped[list["BatchSizeTable"]] = relationship(
        order_by="BatchSizeTable.batch_size.asc()",
        back_populates="job",
        # always fetch batch size(int) whenever we fetch the job.
        # https://docs.sqlalchemy.org/en/14/orm/loading_relationships.html#relationship-loading-techniques
        lazy="joined",
        # Delete all children if the job gets deleted.
        # https://docs.sqlalchemy.org/en/20/orm/cascades.html
        cascade="all, delete-orphan",
    )

BatchSizeTable

Bases: Base

Batch size states table schema. Represents one batch size of a job.

(job_id, batch_size) as a pk, and have three states(exploration, measurement, GaussianTs arm state) as fk. For explorations and measurements, one-to-many relationship. For arm_state, one-to-(zero or one) relationship.

Source code in zeus/optimizer/batch_size/server/database/schema.py 
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class BatchSizeTable(Base):
    """Batch size states table schema. Represents one batch size of a job.

    (job_id, batch_size) as a pk, and have three states(exploration, measurement, GaussianTs arm state) as fk.
    For explorations and measurements, one-to-many relationship. For arm_state, one-to-(zero or one) relationship.
    """

    __tablename__ = "BatchSize"

    job_id: Mapped[str] = mapped_column(
        ForeignKey(
            "Job.job_id",
            ondelete="CASCADE",
        ),
        primary_key=True,
    )
    batch_size: Mapped[int] = mapped_column(Integer, primary_key=True)

    trials: Mapped[list["TrialTable"]] = relationship(
        back_populates="batch_size_state", cascade="all, delete-orphan"
    )

    arm_state: Mapped[Optional["GaussianTsArmStateTable"]] = relationship(
        back_populates="batch_size_state",  # populates GaussianTsArmState->BatchSize
        # https://stackoverflow.com/questions/39869793/when-do-i-need-to-use-sqlalchemy-back-populates
        cascade="all, delete-orphan",
    )

    job: Mapped["JobTable"] = relationship(back_populates="batch_sizes")

GaussianTsArmStateTable

Bases: Base

Gaussian arm state schema. Represents a gaussian thompson arm states of a batch size.

Refer to GaussianTsArmState for attributes.

Source code in zeus/optimizer/batch_size/server/database/schema.py 
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class GaussianTsArmStateTable(Base):
    """Gaussian arm state schema. Represents a gaussian thompson arm states of a batch size.

    Refer to [`GaussianTsArmState`][zeus.optimizer.batch_size.server.batch_size_state.models.GaussianTsArmState] for attributes.
    """

    __tablename__ = "GaussianTsArmState"

    job_id: Mapped[str] = mapped_column(VARCHAR(300), primary_key=True)
    batch_size: Mapped[int] = mapped_column(Integer, primary_key=True)  # arm

    param_mean: Mapped[float] = mapped_column(Float, default=0.0)
    param_precision: Mapped[float] = mapped_column(Float, default=0.0)
    reward_precision: Mapped[float] = mapped_column(Float, default=0.0)
    num_observations: Mapped[int] = mapped_column(Integer, default=0)

    batch_size_state: Mapped["BatchSizeTable"] = relationship(
        back_populates="arm_state"
    )

    __table_args__ = (
        ForeignKeyConstraint(
            [job_id, batch_size],
            [BatchSizeTable.job_id, BatchSizeTable.batch_size],
            ondelete="CASCADE",
        ),
    )

TrialType

Bases: Enum

Type of trial.

Exploration is a trial done during Pruning stage. Concurrent is a trial done as a concurrent job submission. MAB is a trial done during the MAB stage.

Source code in zeus/optimizer/batch_size/server/database/schema.py 
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class TrialType(enum.Enum):
    """Type of trial.

    Exploration is a trial done during Pruning stage.
    Concurrent is a trial done as a concurrent job submission.
    MAB is a trial done during the MAB stage.
    """

    Exploration = "Exploration"
    Concurrent = "Concurrent"
    MAB = "MAB"

TrialStatus

Bases: Enum

Status of trial.

Dispatched means this trial is issued. Succeded means trial ended without error. Failed means trial ended with error.

Source code in zeus/optimizer/batch_size/server/database/schema.py 
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class TrialStatus(enum.Enum):
    """Status of trial.

    Dispatched means this trial is issued.
    Succeded means trial ended without error.
    Failed means trial ended with error.
    """

    Dispatched = "Dispatched"
    Succeeded = "Succeeded"
    Failed = "Failed"

TrialTable

Bases: Base

Represents each trial of training.

Refer to Trial for attributes.

Source code in zeus/optimizer/batch_size/server/database/schema.py 
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class TrialTable(Base):
    """Represents each trial of training.

    Refer to [`Trial`][zeus.optimizer.batch_size.server.batch_size_state.models.Trial] for attributes.
    """

    __tablename__ = "Trial"

    job_id: Mapped[str] = mapped_column(VARCHAR(300), primary_key=True, nullable=False)
    batch_size: Mapped[int] = mapped_column(Integer, nullable=False)
    trial_number: Mapped[int] = mapped_column(Integer, primary_key=True, nullable=False)
    start_timestamp: Mapped[datetime] = mapped_column(DateTime, nullable=False)
    type: Mapped[TrialType] = mapped_column(Enum(TrialType), nullable=False)
    status: Mapped[TrialStatus] = mapped_column(Enum(TrialStatus), nullable=False)

    end_timestamp: Mapped[Optional[datetime]] = mapped_column(DateTime, nullable=True)
    time: Mapped[Optional[float]] = mapped_column(Float, nullable=True)
    energy: Mapped[Optional[float]] = mapped_column(Float, nullable=True)
    converged: Mapped[Optional[bool]] = mapped_column(Boolean, nullable=True)

    batch_size_state: Mapped["BatchSizeTable"] = relationship(back_populates="trials")

    __table_args__ = (
        ForeignKeyConstraint(
            [job_id, batch_size],
            [BatchSizeTable.job_id, BatchSizeTable.batch_size],
            ondelete="CASCADE",
        ),
    )