# Resources

**Package:** `flyte`

Resources such as CPU, Memory, and GPU that can be allocated to a task.

Set via `TaskEnvironment(resources=...)` or `task.override(resources=...)`.

```python
# Simple: 1 CPU, 1 GiB memory, 1 T4 GPU
Resources(cpu=1, memory="1Gi", gpu="T4:1")

# Range: request 1 CPU (limit 2), 2 GiB memory, 8 A100 GPUs, 10 GiB disk
Resources(cpu=(1, 2), memory="2Gi", gpu="A100:8", disk="10Gi")

# Advanced GPU with partitioning
Resources(gpu=GPU(device="A100", quantity=1, partition="1g.5gb"))

# TPU
Resources(gpu=TPU(device="V5P", partition="2x2x1"))
```

## Parameters

```python
class Resources(
    cpu: typing.Union[int, float, str, typing.Tuple[int | float | str, int | float | str], NoneType],
    memory: typing.Union[str, typing.Tuple[str, str], NoneType],
    gpu: typing.Union[typing.Literal['A10:1', 'A10:2', 'A10:3', 'A10:4', 'A10:5', 'A10:6', 'A10:7', 'A10:8', 'A10G:1', 'A10G:2', 'A10G:3', 'A10G:4', 'A10G:5', 'A10G:6', 'A10G:7', 'A10G:8', 'A100:1', 'A100:2', 'A100:3', 'A100:4', 'A100:5', 'A100:6', 'A100:7', 'A100:8', 'A100 80G:1', 'A100 80G:2', 'A100 80G:3', 'A100 80G:4', 'A100 80G:5', 'A100 80G:6', 'A100 80G:7', 'A100 80G:8', 'B200:1', 'B200:2', 'B200:3', 'B200:4', 'B200:5', 'B200:6', 'B200:7', 'B200:8', 'H100:1', 'H100:2', 'H100:3', 'H100:4', 'H100:5', 'H100:6', 'H100:7', 'H100:8', 'H200:1', 'H200:2', 'H200:3', 'H200:4', 'H200:5', 'H200:6', 'H200:7', 'H200:8', 'L4:1', 'L4:2', 'L4:3', 'L4:4', 'L4:5', 'L4:6', 'L4:7', 'L4:8', 'L40s:1', 'L40s:2', 'L40s:3', 'L40s:4', 'L40s:5', 'L40s:6', 'L40s:7', 'L40s:8', 'V100:1', 'V100:2', 'V100:3', 'V100:4', 'V100:5', 'V100:6', 'V100:7', 'V100:8', 'RTX PRO 6000:1', 'GB10:1', 'T4:1', 'T4:2', 'T4:3', 'T4:4', 'T4:5', 'T4:6', 'T4:7', 'T4:8', 'Trn1:1', 'Trn1:4', 'Trn1:8', 'Trn1:16', 'Trn1n:1', 'Trn1n:4', 'Trn1n:8', 'Trn1n:16', 'Trn2:1', 'Trn2:4', 'Trn2:8', 'Trn2:16', 'Trn2u:1', 'Trn2u:4', 'Trn2u:8', 'Trn2u:16', 'Inf1:1', 'Inf1:2', 'Inf1:3', 'Inf1:4', 'Inf1:5', 'Inf1:6', 'Inf1:7', 'Inf1:8', 'Inf1:9', 'Inf1:10', 'Inf1:11', 'Inf1:12', 'Inf1:13', 'Inf1:14', 'Inf1:15', 'Inf1:16', 'Inf2:1', 'Inf2:2', 'Inf2:3', 'Inf2:4', 'Inf2:5', 'Inf2:6', 'Inf2:7', 'Inf2:8', 'Inf2:9', 'Inf2:10', 'Inf2:11', 'Inf2:12', 'MI100:1', 'MI210:1', 'MI250:1', 'MI250X:1', 'MI300A:1', 'MI300X:1', 'MI325X:1', 'MI350X:1', 'MI355X:1', 'Gaudi1:1'], int, flyte._resources.Device, NoneType],
    disk: typing.Optional[str],
    shm: typing.Union[str, typing.Literal['auto'], NoneType],
)
```
| Parameter | Type | Description |
|-|-|-|
| `cpu` | `typing.Union[int, float, str, typing.Tuple[int \| float \| str, int \| float \| str], NoneType]` | CPU allocation. Accepts several formats  - `int` or `float`: CPU cores (e.g., `1`, `0.5`) - `str`: Kubernetes-style (e.g., `"500m"` for 0.5 cores, `"2"` for 2 cores) - `tuple`: Request/limit range (e.g., `(1, 4)` requests 1 core, limits to 4) |
| `memory` | `typing.Union[str, typing.Tuple[str, str], NoneType]` | Memory allocation using Kubernetes unit conventions  - Binary units: `"512Mi"`, `"1Gi"`, `"4Gi"` - Decimal units: `"500M"`, `"1G"` - `tuple`: Request/limit range (e.g., `("1Gi", "4Gi")`) |
| `gpu` | `typing.Union[typing.Literal['A10:1', 'A10:2', 'A10:3', 'A10:4', 'A10:5', 'A10:6', 'A10:7', 'A10:8', 'A10G:1', 'A10G:2', 'A10G:3', 'A10G:4', 'A10G:5', 'A10G:6', 'A10G:7', 'A10G:8', 'A100:1', 'A100:2', 'A100:3', 'A100:4', 'A100:5', 'A100:6', 'A100:7', 'A100:8', 'A100 80G:1', 'A100 80G:2', 'A100 80G:3', 'A100 80G:4', 'A100 80G:5', 'A100 80G:6', 'A100 80G:7', 'A100 80G:8', 'B200:1', 'B200:2', 'B200:3', 'B200:4', 'B200:5', 'B200:6', 'B200:7', 'B200:8', 'H100:1', 'H100:2', 'H100:3', 'H100:4', 'H100:5', 'H100:6', 'H100:7', 'H100:8', 'H200:1', 'H200:2', 'H200:3', 'H200:4', 'H200:5', 'H200:6', 'H200:7', 'H200:8', 'L4:1', 'L4:2', 'L4:3', 'L4:4', 'L4:5', 'L4:6', 'L4:7', 'L4:8', 'L40s:1', 'L40s:2', 'L40s:3', 'L40s:4', 'L40s:5', 'L40s:6', 'L40s:7', 'L40s:8', 'V100:1', 'V100:2', 'V100:3', 'V100:4', 'V100:5', 'V100:6', 'V100:7', 'V100:8', 'RTX PRO 6000:1', 'GB10:1', 'T4:1', 'T4:2', 'T4:3', 'T4:4', 'T4:5', 'T4:6', 'T4:7', 'T4:8', 'Trn1:1', 'Trn1:4', 'Trn1:8', 'Trn1:16', 'Trn1n:1', 'Trn1n:4', 'Trn1n:8', 'Trn1n:16', 'Trn2:1', 'Trn2:4', 'Trn2:8', 'Trn2:16', 'Trn2u:1', 'Trn2u:4', 'Trn2u:8', 'Trn2u:16', 'Inf1:1', 'Inf1:2', 'Inf1:3', 'Inf1:4', 'Inf1:5', 'Inf1:6', 'Inf1:7', 'Inf1:8', 'Inf1:9', 'Inf1:10', 'Inf1:11', 'Inf1:12', 'Inf1:13', 'Inf1:14', 'Inf1:15', 'Inf1:16', 'Inf2:1', 'Inf2:2', 'Inf2:3', 'Inf2:4', 'Inf2:5', 'Inf2:6', 'Inf2:7', 'Inf2:8', 'Inf2:9', 'Inf2:10', 'Inf2:11', 'Inf2:12', 'MI100:1', 'MI210:1', 'MI250:1', 'MI250X:1', 'MI300A:1', 'MI300X:1', 'MI325X:1', 'MI350X:1', 'MI355X:1', 'Gaudi1:1'], int, flyte._resources.Device, NoneType]` | GPU, TPU, or other accelerator allocation. Accepts  - `int`: GPU count, any available type (e.g., `1`, `4`) - `str`: Type and quantity (e.g., `"T4:1"`, `"A100:2"`, `"H100:8"`) - `Device`: Advanced config via `GPU()`, `TPU()`, or `Device()` for partitioning and custom device types. See `GPU`, `TPU`, `Device` for details.  Supported GPU types include T4, L4, L40s, A10, A10G, A100, A100 80G, B200, H100, H200, V100. GPU partitioning (MIG) is available on A100, A100 80G, H100, and H200. |
| `disk` | `typing.Optional[str]` | Ephemeral disk storage as a string with Kubernetes units (e.g., `"10Gi"`, `"100Gi"`, `"1Ti"`). Automatically cleaned up when the task completes. |
| `shm` | `typing.Union[str, typing.Literal['auto'], NoneType]` | Shared memory (`/dev/shm`) allocation. Useful for ML data loading and inter-process communication:  - `str`: Size with units (e.g., `"1Gi"`, `"16Gi"`) - `"auto"`: Set to the maximum shared memory available on the node |

## Methods

| Method | Description |
|-|-|
| [`get_device()`](#get_device) | Get the accelerator string for the task. |
| [`get_shared_memory()`](#get_shared_memory) | Get the shared memory string for the task. |

### get_device()

```python
def get_device()
```
Get the accelerator string for the task.

         Default cloud provider labels typically use the following values: `1g.5gb`, `2g.10gb`, etc.

**Returns:** If GPUs are requested, return a tuple of the device name, and potentially a partition string.

### get_shared_memory()

```python
def get_shared_memory()
```
Get the shared memory string for the task.

**Returns:** The shared memory string.

---
**Source**: https://github.com/unionai/unionai-docs/blob/main/content/api-reference/flyte-sdk/packages/flyte/resources.md
**HTML**: https://www.union.ai/docs/v2/union/api-reference/flyte-sdk/packages/flyte/resources/