GPUJob

Params

name

A human readable description of the object.

server

An instance of GPUServer.

data_transferred

Sum of all data uploads and downloads for request manually defined gpu job from e-footprint hypothesis in kilobyte.

data_stored

Data stored by request manually defined gpu job from e-footprint hypothesis in kilobyte.

request_duration

Request duration of manually defined gpu job from e-footprint hypothesis in second.

compute_needed

Gpus needed on server on premise gpu server to process manually defined gpu job from e-footprint hypothesis in gpu.

ram_needed

Ram needed on server on premise gpu server to process manually defined gpu job from e-footprint hypothesis in megabyte_ram.

Backwards links

• UsageJourneyStep

Calculated attributes

hourly_occurrences_per_usage_pattern

Dictionary with UsagePattern as keys and Hourly manually defined gpu job occurrences in usage pattern as values, in occurrence.

Example value: {
35f108: 26298 values from 2024-12-31 23:00:00+00:00 to 2028-01-01 17:00:00+00:00 in occurrence:
first 10 vals [2.0, 6.0, 8.0, 1.0, 6.0, 5.0, 3.0, 5.0, 7.0, 5.0],
last 10 vals [2.0, 1.0, 6.0, 1.0, 9.0, 8.0, 3.0, 3.0, 1.0, 3.0],
}

Depends directly on:

• usage pattern UTC

through the following calculations:

You can also visit the link to Hourly Manually defined GPU job occurrences in usage pattern’s full calculation graph.

hourly_avg_occurrences_per_usage_pattern

Dictionary with UsagePattern as keys and Average hourly manually defined gpu job occurrences in usage pattern as values, in concurrent.

Example value: {
35f108: 26298 values from 2024-12-31 23:00:00+00:00 to 2028-01-01 17:00:00+00:00 in concurrent:
first 10 vals [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
last 10 vals [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
}

Depends directly on:

• Hourly Manually defined GPU job occurrences in usage pattern
• Request duration of Manually defined GPU job from e-footprint hypothesis

through the following calculations:

You can also visit the link to Average hourly Manually defined GPU job occurrences in usage pattern’s full calculation graph.

hourly_data_transferred_per_usage_pattern

Dictionary with UsagePattern as keys and Hourly data transferred for manually defined gpu job in usage pattern as values, in concurrent * hour * kilobyte / second.

Example value: {
35f108: 26298 values from 2024-12-31 23:00:00+00:00 to 2028-01-01 17:00:00+00:00 in concurrent * h * kB / s:
first 10 vals [0.08, 0.25, 0.33, 0.04, 0.25, 0.21, 0.12, 0.21, 0.29, 0.21],
last 10 vals [0.08, 0.04, 0.25, 0.04, 0.38, 0.33, 0.12, 0.12, 0.04, 0.12],
}

Depends directly on:

• Average hourly Manually defined GPU job occurrences in usage pattern
• Sum of all data uploads and downloads for request Manually defined GPU job from e-footprint hypothesis
• Request duration of Manually defined GPU job from e-footprint hypothesis

through the following calculations:

You can also visit the link to Hourly data transferred for Manually defined GPU job in usage pattern’s full calculation graph.

hourly_data_stored_per_usage_pattern

Dictionary with UsagePattern as keys and Hourly data stored for manually defined gpu job in usage pattern as values, in concurrent * hour * kilobyte / second.

Example value: {
35f108: 26298 values from 2024-12-31 23:00:00+00:00 to 2028-01-01 17:00:00+00:00 in concurrent * h * kB / s:
first 10 vals [0.06, 0.17, 0.22, 0.03, 0.17, 0.14, 0.08, 0.14, 0.19, 0.14],
last 10 vals [0.06, 0.03, 0.17, 0.03, 0.25, 0.22, 0.08, 0.08, 0.03, 0.08],
}

Depends directly on:

• Average hourly Manually defined GPU job occurrences in usage pattern
• Data stored by request Manually defined GPU job from e-footprint hypothesis
• Request duration of Manually defined GPU job from e-footprint hypothesis

through the following calculations:

You can also visit the link to Hourly data stored for Manually defined GPU job in usage pattern’s full calculation graph.

hourly_avg_occurrences_across_usage_patterns

Hourly manually defined gpu job average occurrences across usage patterns in concurrent.

Example value: 26298 values from 2024-12-31 23:00:00+00:00 to 2028-01-01 17:00:00+00:00 in concurrent:
first 10 vals [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
last 10 vals [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]

Depends directly on:

• Average hourly Manually defined GPU job occurrences in usage pattern

through the following calculations:

You can also visit the link to Hourly Manually defined GPU job average occurrences across usage patterns’s full calculation graph.

hourly_data_transferred_across_usage_patterns

Hourly manually defined gpu job data transferred across usage patterns in concurrent * hour * kilobyte / second.

Example value: 26298 values from 2024-12-31 23:00:00+00:00 to 2028-01-01 17:00:00+00:00 in concurrent * h * kB / s:
first 10 vals [0.08, 0.25, 0.33, 0.04, 0.25, 0.21, 0.12, 0.21, 0.29, 0.21],
last 10 vals [0.08, 0.04, 0.25, 0.04, 0.38, 0.33, 0.12, 0.12, 0.04, 0.12]

Depends directly on:

• Hourly data transferred for Manually defined GPU job in usage pattern

through the following calculations:

You can also visit the link to Hourly Manually defined GPU job data transferred across usage patterns’s full calculation graph.

hourly_data_stored_across_usage_patterns

Hourly manually defined gpu job data stored across usage patterns in concurrent * hour * kilobyte / second.

Example value: 26298 values from 2024-12-31 23:00:00+00:00 to 2028-01-01 17:00:00+00:00 in concurrent * h * kB / s:
first 10 vals [0.06, 0.17, 0.22, 0.03, 0.17, 0.14, 0.08, 0.14, 0.19, 0.14],
last 10 vals [0.06, 0.03, 0.17, 0.03, 0.25, 0.22, 0.08, 0.08, 0.03, 0.08]

Depends directly on:

• Hourly data stored for Manually defined GPU job in usage pattern

through the following calculations:

You can also visit the link to Hourly Manually defined GPU job data stored across usage patterns’s full calculation graph.