I’m using mambaforge on WSL2 Ubuntu 22.04 with systemd enabled. I’m trying to install TensorFlow 2.10 with CUDA enabled, by using the command:
mamba install tensorflow
And the command nvidia-smi -q from WSL2 gives:
==============NVSMI LOG==============
Timestamp : Sat Dec 17 23:22:43 2022
Driver Version : 527.56
CUDA Version : 12.0
Attached GPUs : 1
GPU 00000000:01:00.0
Product Name : NVIDIA GeForce RTX 3070 Laptop GPU
Product Brand : GeForce
Product Architecture : Ampere
Display Mode : Disabled
Display Active : Disabled
Persistence Mode : Enabled
MIG Mode
Current : N/A
Pending : N/A
Accounting Mode : Disabled
Accounting Mode Buffer Size : 4000
Driver Model
Current : WDDM
Pending : WDDM
Serial Number : N/A
GPU UUID : GPU-f03a575d-7930-47f3-4965-290b89514ae7
Minor Number : N/A
VBIOS Version : 94.04.3f.00.d7
MultiGPU Board : No
Board ID : 0x100
Board Part Number : N/A
GPU Part Number : 249D-750-A1
Module ID : 1
Inforom Version
Image Version : G001.0000.03.03
OEM Object : 2.0
ECC Object : N/A
Power Management Object : N/A
GPU Operation Mode
Current : N/A
Pending : N/A
GSP Firmware Version : N/A
GPU Virtualization Mode
Virtualization Mode : None
Host VGPU Mode : N/A
IBMNPU
Relaxed Ordering Mode : N/A
PCI
Bus : 0x01
Device : 0x00
Domain : 0x0000
Device Id : 0x249D10DE
Bus Id : 00000000:01:00.0
Sub System Id : 0x118C1043
GPU Link Info
PCIe Generation
Max : 3
Current : 3
Device Current : 3
Device Max : 4
Host Max : 3
Link Width
Max : 16x
Current : 8x
Bridge Chip
Type : N/A
Firmware : N/A
Replays Since Reset : 0
Replay Number Rollovers : 0
Tx Throughput : 0 KB/s
Rx Throughput : 0 KB/s
Atomic Caps Inbound : N/A
Atomic Caps Outbound : N/A
Fan Speed : N/A
Performance State : P8
Clocks Throttle Reasons
Idle : Active
Applications Clocks Setting : Not Active
SW Power Cap : Not Active
HW Slowdown : Not Active
HW Thermal Slowdown : Not Active
HW Power Brake Slowdown : Not Active
Sync Boost : Not Active
SW Thermal Slowdown : Not Active
Display Clock Setting : Not Active
FB Memory Usage
Total : 8192 MiB
Reserved : 159 MiB
Used : 12 MiB
Free : 8020 MiB
BAR1 Memory Usage
Total : 8192 MiB
Used : 1 MiB
Free : 8191 MiB
Compute Mode : Default
Utilization
Gpu : 0 %
Memory : 0 %
Encoder : 0 %
Decoder : 0 %
Encoder Stats
Active Sessions : 0
Average FPS : 0
Average Latency : 0
FBC Stats
Active Sessions : 0
Average FPS : 0
Average Latency : 0
Ecc Mode
Current : N/A
Pending : N/A
ECC Errors
Volatile
SRAM Correctable : N/A
SRAM Uncorrectable : N/A
DRAM Correctable : N/A
DRAM Uncorrectable : N/A
Aggregate
SRAM Correctable : N/A
SRAM Uncorrectable : N/A
DRAM Correctable : N/A
DRAM Uncorrectable : N/A
Retired Pages
Single Bit ECC : N/A
Double Bit ECC : N/A
Pending Page Blacklist : N/A
Remapped Rows : N/A
Temperature
GPU Current Temp : 46 C
GPU Shutdown Temp : 101 C
GPU Slowdown Temp : 98 C
GPU Max Operating Temp : 87 C
GPU Target Temperature : N/A
Memory Current Temp : N/A
Memory Max Operating Temp : N/A
Power Readings
Power Management : Supported
Power Draw : 12.08 W
Power Limit : 4294967.50 W
Default Power Limit : 80.00 W
Enforced Power Limit : 100.00 W
Min Power Limit : 1.00 W
Max Power Limit : 100.00 W
Clocks
Graphics : 210 MHz
SM : 210 MHz
Memory : 405 MHz
Video : 555 MHz
Applications Clocks
Graphics : N/A
Memory : N/A
Default Applications Clocks
Graphics : N/A
Memory : N/A
Deferred Clocks
Memory : N/A
Max Clocks
Graphics : 2100 MHz
SM : 2100 MHz
Memory : 6001 MHz
Video : 1950 MHz
Max Customer Boost Clocks
Graphics : N/A
Clock Policy
Auto Boost : N/A
Auto Boost Default : N/A
Voltage
Graphics : 637.500 mV
Fabric
State : N/A
Status : N/A
Processes
GPU instance ID : N/A
Compute instance ID : N/A
Process ID : 24
Type : G
Name : /Xwayland
Used GPU Memory : Not available in WDDM driver model
And my other enviroment works as expected:
⬢ [Systemd] ❯ mamba activate tf ~ via 🅒 tf via 🐏 774MiB/19GiB | 0B/5GiB ⬢ [Systemd] ❯ python Python 3.9.15 | packaged by conda-forge | (main, Nov 22 2022, 08:45:29) [GCC 10.4.0] on linux Type "help", "copyright", "credits" or "license" for more information. >>> import tensorflow as tf 2022-12-17 23:25:13.867166: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: SSE4.1 SSE4.2 AVX AVX2 FMA To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
Then, it tries to install package version cuda112py39h9333c2f_1, winch uses Python 3.9, but I want Python 3.10. Whenever I try to install the version for 3.10, it shows the error:
Could not solve for environment specs Encountered problems while solving: - nothing provides __cuda needed by tensorflow-2.10.0-cuda112py310he87a039_0 The environment can't be solved, aborting the operation
Why is this error occurring and how can I solve it?
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Answer
I ran into this today and found a solution that works (after also seeing your GitHub post). Long story short, you need to use CONDA_CUDA_OVERRIDE to make this work as described in this conda-forge blog post.
For example, with CUDA 11.8 and mamba, use:
CONDA_CUDA_OVERRIDE="11.8" mamba install tensorflow -c conda-forge
For CUDA 11.8 and conda, it would be:
CONDA_CUDA_OVERRIDE="11.8" conda install tensorflow -c conda-forge
Depending on your setup, you may also want to install cudatoolkit as well, e.g.,
CONDA_CUDA_OVERRIDE="11.8" mamba install tensorflow cudatoolkit -c conda-forge