Enum TrainingInputMode

#[non_exhaustive]
pub enum TrainingInputMode {
    Fastfile,
    File,
    Pipe,
    Unknown(UnknownVariantValue),
}

When writing a match expression against TrainingInputMode, it is important to ensure your code is forward-compatible. That is, if a match arm handles a case for a feature that is supported by the service but has not been represented as an enum variant in a current version of SDK, your code should continue to work when you upgrade SDK to a future version in which the enum does include a variant for that feature.

Here is an example of how you can make a match expression forward-compatible:

# let traininginputmode = unimplemented!();
match traininginputmode {
    TrainingInputMode::Fastfile => { /* ... */ },
    TrainingInputMode::File => { /* ... */ },
    TrainingInputMode::Pipe => { /* ... */ },
    other @ _ if other.as_str() == "NewFeature" => { /* handles a case for `NewFeature` */ },
    _ => { /* ... */ },
}

The above code demonstrates that when traininginputmode represents NewFeature, the execution path will lead to the second last match arm, even though the enum does not contain a variant TrainingInputMode::NewFeature in the current version of SDK. The reason is that the variable other, created by the @ operator, is bound to TrainingInputMode::Unknown(UnknownVariantValue("NewFeature".to_owned())) and calling as_str on it yields "NewFeature". This match expression is forward-compatible when executed with a newer version of SDK where the variant TrainingInputMode::NewFeature is defined. Specifically, when traininginputmode represents NewFeature, the execution path will hit the second last match arm as before by virtue of calling as_str on TrainingInputMode::NewFeature also yielding "NewFeature".

Explicitly matching on the Unknown variant should be avoided for two reasons:

• The inner data UnknownVariantValue is opaque, and no further information can be extracted.
• It might inadvertently shadow other intended match arms.

The training input mode that the algorithm supports. For more information about input modes, see Algorithms.

Pipe mode

If an algorithm supports Pipe mode, Amazon SageMaker streams data directly from Amazon S3 to the container.

File mode

If an algorithm supports File mode, SageMaker downloads the training data from S3 to the provisioned ML storage volume, and mounts the directory to the Docker volume for the training container.

You must provision the ML storage volume with sufficient capacity to accommodate the data downloaded from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container uses the ML storage volume to also store intermediate information, if any.

For distributed algorithms, training data is distributed uniformly. Your training duration is predictable if the input data objects sizes are approximately the same. SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed when one host in a training cluster is overloaded, thus becoming a bottleneck in training.

FastFile mode

If an algorithm supports FastFile mode, SageMaker streams data directly from S3 to the container with no code changes, and provides file system access to the data. Users can author their training script to interact with these files as if they were stored on disk.

FastFile mode works best when the data is read sequentially. Augmented manifest files aren't supported. The startup time is lower when there are fewer files in the S3 bucket provided.

Variants (Non-exhaustive)

Non-exhaustive enums could have additional variants added in future. Therefore, when matching against variants of non-exhaustive enums, an extra wildcard arm must be added to account for any future variants.

Fastfile

File

Pipe

Unknown(UnknownVariantValue)

👎Deprecated: Don’t directly match on Unknown. See the docs on this enum for the correct way to handle unknown variants.

Unknown contains new variants that have been added since this code was generated.

Implementations

impl TrainingInputMode

pub fn as_str(&self) -> &str

Returns the &str value of the enum member.

pub const fn values() -> &'static [&'static str]

Returns all the &str representations of the enum members.

impl TrainingInputMode

pub fn try_parse(value: &str) -> Result<Self, UnknownVariantError>

Parses the enum value while disallowing unknown variants.

Unknown variants will result in an error.

Trait Implementations

impl AsRef<str> for TrainingInputMode

fn as_ref(&self) -> &str

Converts this type into a shared reference of the (usually inferred) input type.

impl Clone for TrainingInputMode

fn clone(&self) -> TrainingInputMode

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for TrainingInputMode

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Display for TrainingInputMode

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl From<&str> for TrainingInputMode

fn from(s: &str) -> Self

Converts to this type from the input type.

impl FromStr for TrainingInputMode

type Err = Infallible

The associated error which can be returned from parsing.

fn from_str(s: &str) -> Result<Self, <Self as FromStr>::Err>

Parses a string s to return a value of this type.

impl Hash for TrainingInputMode

fn hash<__H: Hasher>(&self, state: &mut __H)

Feeds this value into the given Hasher.

fn hash_slice<H>(data: &[Self], state: &mut H)

where H: Hasher, Self: Sized,

Feeds a slice of this type into the given Hasher.

impl Ord for TrainingInputMode

fn cmp(&self, other: &TrainingInputMode) -> Ordering

This method returns an Ordering between self and other.

fn max(self, other: Self) -> Self

where Self: Sized,

Compares and returns the maximum of two values.

fn min(self, other: Self) -> Self

where Self: Sized,

Compares and returns the minimum of two values.

fn clamp(self, min: Self, max: Self) -> Self

where Self: Sized,

Restrict a value to a certain interval.

impl PartialEq for TrainingInputMode

fn eq(&self, other: &TrainingInputMode) -> bool

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl PartialOrd for TrainingInputMode

fn partial_cmp(&self, other: &TrainingInputMode) -> Option<Ordering>

This method returns an ordering between self and other values if one exists.

fn lt(&self, other: &Rhs) -> bool

Tests less than (for self and other) and is used by the < operator.

fn le(&self, other: &Rhs) -> bool

Tests less than or equal to (for self and other) and is used by the <= operator.

fn gt(&self, other: &Rhs) -> bool

Tests greater than (for self and other) and is used by the > operator.

fn ge(&self, other: &Rhs) -> bool

Tests greater than or equal to (for self and other) and is used by the >= operator.

impl Eq for TrainingInputMode

impl StructuralPartialEq for TrainingInputMode