Pipeline have their own set of log files that are retrieved and analyzed as needed with the either through:

• The Pipeline logs method (returns either a DataFrame or Apache Arrow).
• The Pipeline export_logs method (saves either a DataFrame file in JSON format, or an Apache Arrow file).

Get Pipeline Logs

Pipeline logs are retrieved through the Pipeline logs method. By default, logs are returned as a DataFrame in reverse chronological order of insertion, with the most recent files displayed first.

Pipeline logs are segmented by pipeline versions. For example, if a new model step is added to a pipeline, a model swapped out of a pipeline step, etc - this generated a new pipeline version. log method requests will return logs based on the parameter that match the pipeline version. To request logs of a specific pipeline version, specify the start_datetime and end_datetime parameters based on the pipeline version logs requested.

This command takes the following parameters.

All of the parameters can be used together, but start_datetime and end_datetime must be combined; if one is used, then so must the other. If start_datetime and end_datetime are used with any other parameter, then the log results are in chronological order of record insertion.

Log requests are limited to around 100k in size. For requests greater than 100k in size, use the Pipeline export_logs() method.

Logs include the following standard datasets:

In this example, the last 50 logs to the pipeline mainpipeline between two sample dates. In this case, all of the time column fields are the same since the inference request was sent as a batch.

logs = mainpipeline.logs(start_datetime=date_start, end_datetime=date_end)

display(len(logs))
display(logs)

538

538 rows × 4 columns

Metadata Requests Restrictions

The following restrictions are in place when requesting the following datasets:

• metadata
• metadata.elasped
• metadata.last_model
• metadata.pipeline_version

Standard Pipeline Steps Log Requests

Effected pipeline steps:

• add_model_step
• replace_with_model_step

For log file requests, the following metadata dataset requests for standard pipeline steps are available:

• metadata

These must be paired with specific columns. * is not available when paired with metadata.

• in: All input fields.
• out: All output fields.
• time: The DateTime the inference request was made.
• in.{input_fields}: Any input fields (tensor, etc.)
• out.{output_fields}: Any output fields (out.house_price, out.variable, etc.)
• anomaly.count: Any anomalies detected from validations.
• anomaly.{validation}: The validation that triggered the anomaly detection and whether it is True (indicating an anomaly was detected) or False. For more details, see Wallaroo SDK Essentials Guide: Anomaly Detection

For example, the following requests the metadata plus any output fields.

metadatalogs = mainpipeline.logs(dataset=["out","metadata"])
display(metadatalogs.loc[:, ['out.variable', 'metadata.last_model']])

Shadow Deploy Testing Pipeline Steps

Effected pipeline steps:

• add_shadow_deploy
• replace_with_shadow_deploy

For log file requests, the following metadata dataset requests for shadow deploy testing pipeline steps are available:

• metadata

These must be paired with specific columns. * is not available when paired with metadata. time must be included if dataset is used.

• in: All input fields.
• out: All output fields.
• time: The DateTime the inference request was made.
• in.{input_fields}: Any input fields (tensor, etc.).
• out.{output_fields}: Any output fields matching the specific output_field (out.house_price, out.variable, etc.).
• out_: All shadow deployed challenger steps Any output fields matching the specific output_field (out.house_price, out.variable, etc.).
• anomaly.count: Any anomalies detected from validations.
• anomaly.{validation}: The validation that triggered the anomaly detection and whether it is True (indicating an anomaly was detected) or False. For more details, see Wallaroo SDK Essentials Guide: Anomaly Detection

The following example retrieves the logs from a pipeline with shadow deployed models, and displays the specific shadow deployed model outputs and the metadata.elasped field.

# Display metadata

metadatalogs = mainpipeline.logs(dataset=["out_logcontrolchallenger01.variable", 
                                          "out_logcontrolchallenger02.variable", 
                                          "metadata"
                                          ]
                                )

display(metadatalogs.loc[:, ['out_logcontrolchallenger01.variable',	
                             'out_logcontrolchallenger02.variable', 
                             'metadata.elapsed'
                             ]
                        ])

A/B Deploy Testing Pipeline Steps

Effected pipeline steps:

• add_random_split
• replace_with_random_split

For log file requests, the following metadata dataset requests for A/B testing pipeline steps are available:

• metadata

These must be paired with specific columns. * is not available when paired with metadata. time must be included if dataset is used.

• in: All input fields.
• out: All output fields.
• time: The DateTime the inference request was made. Must be requested in all dataset requests.
• in.{input_fields}: Any input fields (tensor, etc.).
• out.{output_fields}: Any output fields matching the specific output_field (out.house_price, out.variable, etc.).
• anomaly.count: Any anomalies detected from validations.
• anomaly.{validation}: The validation that triggered the anomaly detection and whether it is True (indicating an anomaly was detected) or False. For more details, see Wallaroo SDK Essentials Guide: Anomaly Detection

The following example retrieves the logs from a pipeline with A/B deployed models, and displays the output and the specific metadata.last_model field.

metadatalogs = mainpipeline.logs(dataset=["time",
                                          "out", 
                                          "metadata"
                                          ]
                                )

display(metadatalogs.loc[:, ['out.variable', 'metadata.last_model']])

Export Pipeline Logs as File

The Pipeline method export_logs returns the Pipeline records as either by default pandas records in Newline Delimited JSON (NDJSON) format, or an Apache Arrow table files.

The output files are by default stores in the current working directory ./logs with the default prefix as the {pipeline name}-1, {pipeline name}-2, etc.

The suffix by default will be json for pandas records in Newline Delimited JSON (NDJSON) format files. Logs are segmented by pipeline version across the limit, data_size_limit, or start_datetime and end_datetime parameters.

By default, logs are returned as a pandas record in NDJSON in reverse chronological order of insertion, with the most recent log insertions displayed first.

Pipeline logs are segmented by pipeline versions. For example, if a new model step is added to a pipeline, a model swapped out of a pipeline step, etc - this generated a new pipeline version.

This command takes the following parameters.

All of the parameters can be used together, but start_datetime and end_datetime must be combined; if one is used, then so must the other. If start_datetime and end_datetime are used with any other parameter, then the log results are in chronological order of record insertion.

File sizes are limited to around 10 MB in size. If the requested log file is greater than 10 MB, a Warning will be displayed indicating the end date of the log file downloaded so the request can be adjusted to capture the requested log files.

data_df = pd.read_json("mainpipeline_logs.df.json", lines=True)
display(data_df)

In this example, the log files are saved as both Pandas DataFrame and Apache Arrow.

# Save the DataFrame version of the log file

mainpipeline.export_logs()
display(os.listdir('./logs'))

mainpipeline.export_logs(arrow=True)
display(os.listdir('./logs'))

    Warning: There are more logs available. Please set a larger limit to export more data.
    

    ['pipeline-logs-1.json']

    Warning: There are more logs available. Please set a larger limit to export more data.
    

    ['pipeline-logs-1.arrow', 'pipeline-logs-1.json']

Pipeline Log Storage

Pipeline logs have a set allocation of storage space and data requirements.

Pipeline Log Storage Warnings

To prevent storage and performance issues, inference result data may be dropped from pipeline logs by the following standards:

• Columns are progressively removed from the row starting with the largest input data size and working to the smallest, then the same for outputs.

For example, Computer Vision ML Models typically have large inputs and output values - a single pandas DataFrame inference request may be over 13 MB in size, and the inference results nearly as large. To prevent pipeline log storage issues, the input may be dropped from the pipeline logs, and if additional space is needed, the inference outputs would follow. The time column is preserved.

If a pipeline has dropped columns for space purposes, this will be displayed when a log request is made with the following warning, with {columns} replaced with the dropped columns.

The inference log is above the allowable limit and the following columns may have been suppressed for various rows in the logs: {columns}. To review the dropped columns for an individual inference’s suppressed data, include dataset=["metadata"] in the log request.

Review Dropped Columns

To review what columns are dropped from pipeline logs for storage reasons, include the dataset metadata in the request to view the column metadata.dropped. This metadata field displays a List of any columns dropped from the pipeline logs.

For example:

metadatalogs = mainpipeline.logs(dataset=["time", "metadata"])

Suppressed Data Elements

Data elements that do not fit the supported data types below, such as None or Null values, are not supported in pipeline logs. When present, undefined data will be written in the place of the null value, typically zeroes. Any null list values will present an empty list.