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To verify accelerator support, match four things as one configuration: the exact accelerator model and architecture, your operating system and release, the framework version, and the accelerator runtime or backend version. Check both the framework’s installation requirements and the hardware vendor’s release-specific compatibility matrix, install the documented combination, then confirm device detection and run a small representative workload.
A framework reporting a device proves that the local build can recognize it. It does not prove that every operator, model, numerical precision, or performance target is supported. Treat compatibility as a configuration-and-workload question, not a yes-or-no property of a framework brand.
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What “supported” should mean
Accelerator support has several layers. A framework may have code for a backend while your exact device, operating system, or software stack is outside the combinations that the maintainers test. Conversely, a device can appear in a compatibility table while a particular model uses unsupported operations or performs poorly.
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- Platform support: the framework publishes an installation path for your operating system and accelerator backend.
- Device support: the exact accelerator model or architecture appears in the vendor’s matrix for the relevant runtime release.
- Build support: the package, wheel, system image, or container was built with that backend enabled.
- Workload support: the operators, data types, model, and memory requirements used by your application are implemented.
- Performance suitability: the configuration meets your latency, throughput, memory, and reliability goals.
Only the first three are usually answered by an installation page and a compatibility matrix. The last two require a workload test.
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Record the complete configuration before checking
Write down the details that identify your proposed stack. “I have a GPU” or “this framework supports AMD” is not precise enough for a compatibility decision.
| Item | What to capture | Why it matters |
|---|---|---|
| Accelerator | Vendor, exact model, architecture or family, memory size | Support can differ between models that share a product name or generation. |
| Operating system | Distribution or edition and release; include WSL2 when applicable | Driver packages, kernels, and framework builds are platform-specific. |
| Framework | Name and exact version | Backend support can be added, removed, or constrained between releases. |
| Runtime/backend | For example, a vendor runtime, GPU API, or accelerator plugin and its version | The framework package must be built for the same backend family and supported runtime range. |
| Driver | Installed driver version, when the installation guide lists one | A correct framework package can still fail when the driver is too old or mismatched. |
| Language runtime | Python or other required language version | Prebuilt packages are often limited to specific interpreter versions. |
Keep this inventory with the date you checked it. Compatibility pages are release-specific and change as hardware, drivers, and framework builds evolve.
Use two complementary sources of evidence
Start with the framework’s official installation and hardware pages
Find the framework’s supported accelerator backends, operating systems, installation command or package, and minimum software requirements. Confirm that the page describes your platform rather than a different backend. A framework’s general availability, a code example written for another accelerator, or a package that installs successfully is not proof that your device is supported.
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Then check the accelerator vendor’s release-specific matrix
Open the matrix for the runtime release you intend to install. Match the exact accelerator model or architecture and operating system. Follow the row or linked support data to the framework version; a device listed for one runtime release does not automatically carry forward to another.
For example, AMD’s ROCm documentation states that every ROCm release has specific hardware and software configurations. Its current ROCm 7.2.2 framework installation guide, dated 2026-01-29, applies to Linux, lists PyTorch, TensorFlow, and JAX among supported frameworks, and directs readers to framework-specific installation instructions and prerequisites. The ROCm 7.0.1 compatibility matrix is a separate, versioned document: entries in it should not be treated as timeless support for ROCm 7.2.2 or any later release.
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Resolve disagreements instead of averaging them
If the framework page lists a backend but the vendor matrix omits your exact device, treat the configuration as unconfirmed. If the vendor matrix lists the device but the framework has no installation path for your operating system, it is not a supported setup on that platform. When pages name different versions, use the narrow intersection: the exact framework, runtime, driver, operating system, and device combination that both sets of requirements allow.
Install the documented combination
- Choose a supported release pair. Select the framework version and accelerator runtime version that appear together in the current documentation. Do not mix a package built for one runtime generation with a matrix entry from another.
- Prepare the host. Install the required operating-system packages, kernel components, driver, and language runtime. Reboot or restart services when the installation guide requires it.
- Prefer an official package or container. Use the framework’s documented wheel, package, or image. AMD recommends official prebuilt ROCm Docker images as an easier way to obtain a matching environment; the host still needs the supported driver and device access.
- Check the active environment. Activate the same virtual environment or container in which you will run the application. Multiple Python installations and stale libraries are common causes of false failures.
- Record versions after installation. Save the framework, runtime, driver, and operating-system versions so a later failure can be compared with the documented combination.
Installation success only shows that the package resolver and basic files are in place. Continue with device enumeration and a workload test.
Verify that the framework sees the accelerator
TensorFlow check
TensorFlow’s official guide provides this Python command for GPU enumeration:
python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"A non-empty list means the active TensorFlow process can see at least one GPU through the installed backend. An empty list means the framework did not expose a usable device in that environment; it does not identify the precise cause.
Other frameworks
Use the device-query function shown in your framework’s current installation guide. The check should run inside the same environment as the application and should report the device name or architecture, not merely import the framework successfully. Save the command and its output with your configuration record.
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Do not infer the backend from familiar API names. PyTorch’s HIP/ROCm documentation explains why ROCm builds can retain CUDA-named APIs for compatibility. Seeing a method or namespace with “CUDA” in its name does not prove that the machine is using an NVIDIA or CUDA backend; the installed PyTorch build and the reported device backend determine that.
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Interpret the result carefully
- Device listed: the local build recognizes the accelerator. Proceed to an operation test.
- No device listed: verify the driver and runtime, the framework build, environment activation, permissions, and platform requirements before changing hardware.
- Import or library error: inspect version alignment and duplicate libraries. A conflicting installation of another runtime can prevent initialization.
- Device listed with warnings: read the warning before testing; unsupported precision, missing kernels, or reduced-feature modes may affect the workload.
Run a small workload, not just an enumeration command
Enumeration is a gate, not a certification. Create a short test that exercises the operations and data type your application needs: for example, a few training or inference steps with representative tensor shapes and the intended precision. Confirm that tensors or model parameters are actually placed on the accelerator and that the operation completes without silently falling back to the CPU.
Watch for out-of-memory errors, unsupported-operator messages, compilation failures, unexpectedly long initialization, and numerical differences. A tiny matrix operation can pass while a transformer, convolution, custom kernel, or mixed-precision path fails. Include the largest realistic batch or sequence size if memory capacity is part of the decision.
When the stack passes the functional test, benchmark the real workload. AMD recommends running system-health benchmarks after installing ROCm and the desired libraries and before AI workloads. A benchmark should use a fixed model, input shape, precision, batch size, warm-up procedure, and measurement interval so that results can be compared with another configuration.
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ROCm and AMD accelerators
ROCm support is tied to a release’s hardware and software configuration. The ROCm 7.2.2 framework guide dated 2026-01-29 is Linux-focused and points to requirements, prerequisites, framework installation pages, and prebuilt ROCm containers. Check that your exact GPU family is in the matrix for the ROCm release you plan to use; do not substitute a similar Radeon model or copy an entry from ROCm 7.0.1.
After installation, verify device visibility through the framework, then test the libraries and model you need. A GPU can be recognized while a particular operator or precision mode remains unavailable.
TensorFlow on Linux, Windows, WSL2, and macOS
TensorFlow’s official pip guidance documents GPU installation for supported Linux/CUDA and WSL2 setups. It currently states that there is no official GPU support for macOS. It also states that TensorFlow 2.10 was the last release with native-Windows GPU support; newer Windows GPU use is directed to WSL2 or an optional DirectML plugin route. These statements are release-sensitive, so confirm the current TensorFlow guide before installing.
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If TensorFlow returns no GPU, its guide notes that undetected components or conflicts with an existing CUDA installation can be responsible. Check the active driver and runtime, remove or isolate conflicting libraries, and verify that the installed TensorFlow build matches the documented platform.
PyTorch and CUDA-named interfaces
PyTorch’s HIP/ROCm note describes how ROCm builds use interfaces that may retain CUDA-oriented names. Code written with those names is not enough to identify the hardware path. Confirm the installed build, backend, and device report, then execute a representative operation. This prevents a misleading “CUDA” label in code from being mistaken for proof of an NVIDIA/CUDA installation.
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When you can choose between two accelerator or software stacks, compare the complete configurations rather than only peak hardware specifications.
| Decision axis | Questions to answer | Evidence to record |
|---|---|---|
| Exact model and architecture | Is this device explicitly listed, or only a related family? | Model and architecture row in the vendor matrix. |
| Operating system | Does the framework support this edition and release, including WSL2 if used? | Framework platform requirements and vendor qualification. |
| Framework version | Is the intended version paired with the runtime release? | Version intersection in the installation page and matrix. |
| Runtime and driver | Are the driver minimums and runtime APIs aligned? | Required and installed versions. |
| Prebuilt environment | Is there an official wheel, package, or container? | Documented package name or image tag; otherwise, not stated. |
| Workload validation | Have required operators, precision, memory size, and model shape run successfully? | Functional test output and controlled benchmark. |
| Operational risk | Will updates, drivers, and support channels remain manageable? | Release cadence, known limitations, and rollback plan. |
A configuration with an official package may be easier to maintain than one that requires compiling from source, even when both are technically possible. A listed combination can still be unsuitable if the workload depends on an unsupported operation or misses its performance target.
Troubleshoot a missing or unusable accelerator
1. Confirm the physical and host-level view
Make sure the operating system and driver can see the accelerator before debugging the framework. Check power, device assignment in a virtual machine or container, permissions, and whether another process has exclusive access. If the host cannot see the device, framework changes will not fix the problem.
2. Verify the active software environment
Print the framework and language versions from the shell that launches the application. Confirm that the command resolves to the intended interpreter, virtual environment, or container. A system-wide package can hide the version you installed for the project.
3. Check runtime and driver alignment
Compare installed versions with the framework installation page and the accelerator matrix for the same release. Remove duplicate runtime libraries from search paths where the guide warns about conflicts. TensorFlow specifically identifies undetected components and conflicts with an existing CUDA installation as possible reasons for a missing GPU.
4. Recheck platform restrictions
Look for OS-specific limits such as TensorFlow’s native-Windows version boundary, WSL2 requirements, or the absence of official macOS GPU support. A configuration can be valid on Linux and unsupported on another operating system.
5. Separate detection from operator failure
If enumeration succeeds but the model fails, capture the failing operator, data type, tensor shape, and error message. Test a smaller reproducer, try a supported precision, or identify a framework implementation that has a CPU fallback. Do not downgrade or change hardware until you know whether the issue is an unsupported operation, memory limit, or installation problem.
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Run the same device check and workload after changing one variable. Keep the output and versions for the successful configuration so that an update can be rolled back if support regresses.
When a compatibility claim is strong enough to act on
You can treat a setup as documented and locally verified when the exact device, OS, framework, runtime, and driver fall within both official requirement sets, the documented package installs, the framework enumerates the accelerator, and a representative workload completes.
Call it workload-qualified only after the model’s operators, precision, memory use, and performance target have been tested. If any of those checks is missing, describe the setup as unconfirmed rather than promising compatibility.
Pre-install checklist
- Exact accelerator model and architecture recorded.
- Operating-system edition and release recorded.
- Framework and language versions selected.
- Runtime/backend and driver versions matched to that framework release.
- Framework installation page checked for the platform.
- Hardware vendor matrix checked for the exact model and runtime release.
- Official package or container selected where available.
- Device-enumeration command run in the application environment.
- Representative model operation completed on the accelerator.
- Health check and controlled benchmark completed when performance matters.
- Known OS, operator, precision, and version limitations documented.
Keep the result current
Framework builds, operating-system support, drivers, and hardware matrices change. Recheck the current official framework and vendor pages before installation, after a major upgrade, and whenever you change the accelerator or operating system. Preserve the versioned matrix entry and test output that justified your decision; that record is more useful than a generic claim that a framework “supports GPUs.”
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