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Hello team, I'm not able to run this example workload on Kubernetes.
Kubernetes version: v1.30.2 installed with kubeadm
Conainer Runtime: containerd
sudo containerd version
revision=83031836b2cf55637d7abf847b17134c51b38e53 version=v1.7.16
AMD GPU Plugin installed with Helm
helm list -A
NAME NAMESPACE REVISION UPDATED STATUS CHART APP VERSION
amd-gpu-plugin kube-system 3 2024-10-28 14:22:16.138491735 +0000 UTC deployed amd-gpu-0.14.0 1.31.0.0
calico kube-system 1 2024-10-24 13:06:16.670494216 +0000 UTC deployed calico-cni-3.28.1 v3.28.1
apiVersion: v1kind: Podmetadata:
name: alexnet-tf-gpu-podlabels:
purpose: demo-tf-amdgpuspec:
containers:
- name: alexnet-tf-gpu-containerimage: rocm/tensorflow:latestworkingDir: /rootenv:
- name: HIP_VISIBLE_DEVICESvalue: "0"# # 0,1,2,...,n for running on GPU and select the GPUs, -1 for running on CPUcommand: ["/bin/bash", "-c", "--"]args: ["python3 /benchmarks/scripts/tf_cnn_benchmarks/tf_cnn_benchmarks.py --model=alexnet; trap : TERM INT; sleep infinity & wait"]resources:
limits:
amd.com/gpu: 1# requesting a GPU
the pod starts but logs show errors:
kubectl logs alexnet-tf-gpu-pod
2024-11-04 15:49:31.826063: E external/local_xla/xla/stream_executor/plugin_registry.cc:91] Invalid plugin kind specified: FFT
2024-11-04 15:49:31.864338: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: SSE3 SSE4.1 SSE4.2 AVX AVX2 AVX512F AVX512_VNNI AVX512_BF16 AVX512_FP16 AVX_VNNI AMX_TILE AMX_INT8 AMX_BF16 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-11-04 15:49:32.195859: E external/local_xla/xla/stream_executor/plugin_registry.cc:91] Invalid plugin kind specified: DNN
/usr/lib/python3/dist-packages/requests/__init__.py:89: RequestsDependencyWarning: urllib3 (1.26.20) or chardet (3.0.4) doesn't match a supported version!
warnings.warn("urllib3 ({}) or chardet ({}) doesn't match a supported "
/usr/local/lib/python3.9/dist-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.26.4
warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}"
Traceback (most recent call last):
File "/benchmarks/scripts/tf_cnn_benchmarks/tf_cnn_benchmarks.py", line 25, in <module>
import benchmark_cnn
File "/benchmarks/scripts/tf_cnn_benchmarks/benchmark_cnn.py", line 44, in <module>
from models import model_config
File "/benchmarks/scripts/tf_cnn_benchmarks/models/model_config.py", line 31, in <module>
from models.experimental import deepspeech
File "/benchmarks/scripts/tf_cnn_benchmarks/models/experimental/deepspeech.py", line 121, in <module>
class DeepSpeech2Model(model_lib.Model):
File "/benchmarks/scripts/tf_cnn_benchmarks/models/experimental/deepspeech.py", line 126, in DeepSpeech2Model
'lstm': tf.nn.rnn_cell.BasicLSTMCell,
File "/usr/local/lib/python3.9/dist-packages/tensorflow/python/util/lazy_loader.py", line 207, in __getattr__
raise AttributeError(
AttributeError: `BasicLSTMCell` is not available with Keras 3.
Operating System
Ubuntu 22.04.5 LTS
CPU
Intel(R) Xeon(R) Platinum 8462Y+
GPU
AMD Instinct MI300X (gfx942)
ROCm Version
ROCm 6.2.0
ls -l /opt/rocm
rocm/ rocm-6.2.0/
ROCm Component
ROCm
Steps to Reproduce
See Problem Description
(Optional for Linux users) Output of /opt/rocm/bin/rocminfo --support
From the runing pod:
kubectl exec -it alexnet-tf-gpu-pod -- bash
tf-docker ~ > rocminfo --support
ROCk module version 6.8.5 is loaded
=====================
HSA System Attributes
=====================
Runtime Version: 1.14
Runtime Ext Version: 1.6
System Timestamp Freq.: 1000.000000MHz
Sig. Max Wait Duration: 18446744073709551615 (0xFFFFFFFFFFFFFFFF) (timestamp count)
Machine Model: LARGE
System Endianness: LITTLE
Mwaitx: DISABLED
DMAbuf Support: YES
==========
HSA Agents
==========
*******
Agent 1
*******
Name: Intel(R) Xeon(R) Platinum 8462Y+
Uuid: CPU-XX
Marketing Name: Intel(R) Xeon(R) Platinum 8462Y+
Vendor Name: CPU
Feature: None specified
Profile: FULL_PROFILE
Float Round Mode: NEAR
Max Queue Number: 0(0x0)
Queue Min Size: 0(0x0)
Queue Max Size: 0(0x0)
Queue Type: MULTI
Node: 0
Device Type: CPU
Cache Info:
L1: 49152(0xc000) KB
Chip ID: 0(0x0)
ASIC Revision: 0(0x0)
Cacheline Size: 64(0x40)
Max Clock Freq. (MHz): 4100
BDFID: 0
Internal Node ID: 0
Compute Unit: 64
SIMDs per CU: 0
Shader Engines: 0
Shader Arrs. per Eng.: 0
WatchPts on Addr. Ranges:1
Memory Properties:
Features: None
Pool Info:
Pool 1
Segment: GLOBAL; FLAGS: FINE GRAINED
Size: 1056378596(0x3ef70ee4) KB
Allocatable: TRUE
Alloc Granule: 4KB
Alloc Recommended Granule:4KB
Alloc Alignment: 4KB
Accessible by all: TRUE
Pool 2
Segment: GLOBAL; FLAGS: KERNARG, FINE GRAINED
Size: 1056378596(0x3ef70ee4) KB
Allocatable: TRUE
Alloc Granule: 4KB
Alloc Recommended Granule:4KB
Alloc Alignment: 4KB
Accessible by all: TRUE
Pool 3
Segment: GLOBAL; FLAGS: COARSE GRAINED
Size: 1056378596(0x3ef70ee4) KB
Allocatable: TRUE
Alloc Granule: 4KB
Alloc Recommended Granule:4KB
Alloc Alignment: 4KB
Accessible by all: TRUE
ISA Info:
*******
Agent 2
*******
Name: Intel(R) Xeon(R) Platinum 8462Y+
Uuid: CPU-XX
Marketing Name: Intel(R) Xeon(R) Platinum 8462Y+
Vendor Name: CPU
Feature: None specified
Profile: FULL_PROFILE
Float Round Mode: NEAR
Max Queue Number: 0(0x0)
Queue Min Size: 0(0x0)
Queue Max Size: 0(0x0)
Queue Type: MULTI
Node: 1
Device Type: CPU
Cache Info:
L1: 49152(0xc000) KB
Chip ID: 0(0x0)
ASIC Revision: 0(0x0)
Cacheline Size: 64(0x40)
Max Clock Freq. (MHz): 4100
BDFID: 0
Internal Node ID: 1
Compute Unit: 64
SIMDs per CU: 0
Shader Engines: 0
Shader Arrs. per Eng.: 0
WatchPts on Addr. Ranges:1
Memory Properties:
Features: None
Pool Info:
Pool 1
Segment: GLOBAL; FLAGS: FINE GRAINED
Size: 1056890548(0x3efedeb4) KB
Allocatable: TRUE
Alloc Granule: 4KB
Alloc Recommended Granule:4KB
Alloc Alignment: 4KB
Accessible by all: TRUE
Pool 2
Segment: GLOBAL; FLAGS: KERNARG, FINE GRAINED
Size: 1056890548(0x3efedeb4) KB
Allocatable: TRUE
Alloc Granule: 4KB
Alloc Recommended Granule:4KB
Alloc Alignment: 4KB
Accessible by all: TRUE
Pool 3
Segment: GLOBAL; FLAGS: COARSE GRAINED
Size: 1056890548(0x3efedeb4) KB
Allocatable: TRUE
Alloc Granule: 4KB
Alloc Recommended Granule:4KB
Alloc Alignment: 4KB
Accessible by all: TRUE
ISA Info:
*******
Agent 3
*******
Name: gfx942
Uuid: GPU-e438db49e30f0da9
Marketing Name: AMD Instinct MI300X
Vendor Name: AMD
Feature: KERNEL_DISPATCH
Profile: BASE_PROFILE
Float Round Mode: NEAR
Max Queue Number: 128(0x80)
Queue Min Size: 64(0x40)
Queue Max Size: 131072(0x20000)
Queue Type: MULTI
Node: 2
Device Type: GPU
Cache Info:
L1: 32(0x20) KB
L2: 4096(0x1000) KB
L3: 262144(0x40000) KB
Chip ID: 29857(0x74a1)
ASIC Revision: 1(0x1)
Cacheline Size: 64(0x40)
Max Clock Freq. (MHz): 2100
BDFID: 40448
Internal Node ID: 2
Compute Unit: 304
SIMDs per CU: 4
Shader Engines: 32
Shader Arrs. per Eng.: 1
WatchPts on Addr. Ranges:4
Coherent Host Access: FALSE
Memory Properties:
Features: KERNEL_DISPATCH
Fast F16 Operation: TRUE
Wavefront Size: 64(0x40)
Workgroup Max Size: 1024(0x400)
Workgroup Max Size per Dimension:
x 1024(0x400)
y 1024(0x400)
z 1024(0x400)
Max Waves Per CU: 32(0x20)
Max Work-item Per CU: 2048(0x800)
Grid Max Size: 4294967295(0xffffffff)
Grid Max Size per Dimension:
x 4294967295(0xffffffff)
y 4294967295(0xffffffff)
z 4294967295(0xffffffff)
Max fbarriers/Workgrp: 32
Packet Processor uCode:: 150
SDMA engine uCode:: 19
IOMMU Support:: None
Pool Info:
Pool 1
Segment: GLOBAL; FLAGS: COARSE GRAINED
Size: 201310208(0xbffc000) KB
Allocatable: TRUE
Alloc Granule: 4KB
Alloc Recommended Granule:2048KB
Alloc Alignment: 4KB
Accessible by all: FALSE
Pool 2
Segment: GLOBAL; FLAGS: EXTENDED FINE GRAINED
Size: 201310208(0xbffc000) KB
Allocatable: TRUE
Alloc Granule: 4KB
Alloc Recommended Granule:2048KB
Alloc Alignment: 4KB
Accessible by all: FALSE
Pool 3
Segment: GLOBAL; FLAGS: FINE GRAINED
Size: 201310208(0xbffc000) KB
Allocatable: TRUE
Alloc Granule: 4KB
Alloc Recommended Granule:2048KB
Alloc Alignment: 4KB
Accessible by all: FALSE
Pool 4
Segment: GROUP
Size: 64(0x40) KB
Allocatable: FALSE
Alloc Granule: 0KB
Alloc Recommended Granule:0KB
Alloc Alignment: 0KB
Accessible by all: FALSE
ISA Info:
ISA 1
Name: amdgcn-amd-amdhsa--gfx942:sramecc+:xnack-
Machine Models: HSA_MACHINE_MODEL_LARGE
Profiles: HSA_PROFILE_BASE
Default Rounding Mode: NEAR
Default Rounding Mode: NEAR
Fast f16: TRUE
Workgroup Max Size: 1024(0x400)
Workgroup Max Size per Dimension:
x 1024(0x400)
y 1024(0x400)
z 1024(0x400)
Grid Max Size: 4294967295(0xffffffff)
Grid Max Size per Dimension:
x 4294967295(0xffffffff)
y 4294967295(0xffffffff)
z 4294967295(0xffffffff)
FBarrier Max Size: 32
*** Done ***
tf-docker ~ >
Problem Description
Hello team, I'm not able to run this example workload on Kubernetes.
Kubernetes version:
v1.30.2
installed withkubeadm
Conainer Runtime:
containerd
AMD GPU Plugin installed with Helm
Used these values for Helm chart:
Apply this manifest:
the pod starts but logs show errors:
Operating System
Ubuntu 22.04.5 LTS
CPU
Intel(R) Xeon(R) Platinum 8462Y+
GPU
AMD Instinct MI300X (gfx942)
ROCm Version
ROCm 6.2.0
ROCm Component
ROCm
Steps to Reproduce
See Problem Description
(Optional for Linux users) Output of /opt/rocm/bin/rocminfo --support
From the runing pod:
kubectl exec -it alexnet-tf-gpu-pod -- bash
Additional Information
On the host machine:
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