How should I fix this error?
[jalal#goku bin]$ source activate deep_emotion
(deep_emotion) [jalal#goku bin]$ python
Python 3.5.4 | packaged by conda-forge | (default, Nov 4 2017, 10:11:29)
[GCC 4.8.2 20140120 (Red Hat 4.8.2-15)] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import keras
Using Theano backend.
>>> quit()
(deep_emotion) [jalal#goku bin]$ export KERAS_BACKEND=tensorflow
(deep_emotion) [jalal#goku bin]$ python
Python 3.5.4 | packaged by conda-forge | (default, Nov 4 2017, 10:11:29)
[GCC 4.8.2 20140120 (Red Hat 4.8.2-15)] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import keras
Using TensorFlow backend.
2017-11-20 17:49:18.666294: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
2017-11-20 17:49:18.666337: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
2017-11-20 17:49:18.666347: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
2017-11-20 17:49:18.666354: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
2017-11-20 17:49:18.666363: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
2017-11-20 17:49:19.196610: I tensorflow/core/common_runtime/gpu/gpu_device.cc:955] Found device 0 with properties:
name: GeForce GTX 1080 Ti
major: 6 minor: 1 memoryClockRate (GHz) 1.6705
pciBusID 0000:05:00.0
Total memory: 10.91GiB
Free memory: 158.06MiB
2017-11-20 17:49:19.426132: W tensorflow/stream_executor/cuda/cuda_driver.cc:523] A non-primary context 0x42e9db0 exists before initializing the StreamExecutor. We haven't verified StreamExecutor works with that.
2017-11-20 17:49:19.426768: I tensorflow/core/common_runtime/gpu/gpu_device.cc:955] Found device 1 with properties:
name: GeForce GTX 1080 Ti
major: 6 minor: 1 memoryClockRate (GHz) 1.6705
pciBusID 0000:06:00.0
Total memory: 10.91GiB
Free memory: 398.44MiB
2017-11-20 17:49:19.427277: I tensorflow/core/common_runtime/gpu/gpu_device.cc:976] DMA: 0 1
2017-11-20 17:49:19.427309: I tensorflow/core/common_runtime/gpu/gpu_device.cc:986] 0: Y Y
2017-11-20 17:49:19.427323: I tensorflow/core/common_runtime/gpu/gpu_device.cc:986] 1: Y Y
2017-11-20 17:49:19.427347: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1045] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1080 Ti, pci bus id: 0000:05:00.0)
2017-11-20 17:49:19.427362: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1045] Creating TensorFlow device (/gpu:1) -> (device: 1, name: GeForce GTX 1080 Ti, pci bus id: 0000:06:00.0)
2017-11-20 17:49:19.429776: E tensorflow/stream_executor/cuda/cuda_driver.cc:924] failed to allocate 158.06M (165740544 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
>>> quit()
(deep_emotion) [jalal#goku bin]$ conda list | grep keras
keras 2.0.9 py35_0 conda-forge
(deep_emotion) [jalal#goku bin]$ conda list | grep tensorflow
tensorflow-gpu 1.3.0 0
tensorflow-gpu-base 1.3.0 py35cuda8.0cudnn6.0_1
tensorflow-tensorboard 0.1.5 py35_0
Sys info is as follows:
$ uname -a
Linux goku.bu.edu 3.10.0-693.5.2.el7.x86_64 #1 SMP Fri Oct 20 20:32:50 UTC 2017 x86_64 x86_64 x86_64 GNU/Linux
and
(deep_emotion) [jalal#goku bin]$ nvidia-smi
Mon Nov 20 17:51:50 2017
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 384.81 Driver Version: 384.81 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 GeForce GTX 108... Off | 00000000:05:00.0 On | N/A |
| 0% 25C P8 19W / 250W | 10862MiB / 11172MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 1 GeForce GTX 108... Off | 00000000:06:00.0 Off | N/A |
| 0% 36C P8 19W / 250W | 10622MiB / 11172MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 0 2062 G /usr/bin/X 183MiB |
| 0 2779 G /usr/bin/gnome-shell 176MiB |
| 0 3298 C /cs/software/anaconda3/bin/python 10341MiB |
| 0 4350 G ...-token=2BC290A510039A38C05EF3ECBAA5E5E5 78MiB |
| 0 5212 G /usr/lib64/firefox/plugin-container 5MiB |
| 0 32257 G /proc/self/exe 64MiB |
| 1 3298 C /cs/software/anaconda3/bin/python 10611MiB |
+-----------------------------------------------------------------------------+
Thanks to Robert Crovella for the suggestions. Restarting the machine solved the problem:
[jalal#goku ~]$ source activate deep_emotion
(deep_emotion) [jalal#goku ~]$ export KERAS_BACKEND=tensorflow
(deep_emotion) [jalal#goku ~]$ python
Python 3.5.4 | packaged by conda-forge | (default, Nov 4 2017, 10:11:29)
[GCC 4.8.2 20140120 (Red Hat 4.8.2-15)] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import keras
Using TensorFlow backend.
2017-11-20 18:43:28.424658: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
2017-11-20 18:43:28.424690: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
2017-11-20 18:43:28.424727: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
2017-11-20 18:43:28.424734: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
2017-11-20 18:43:28.424745: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
2017-11-20 18:43:28.951509: I tensorflow/core/common_runtime/gpu/gpu_device.cc:955] Found device 0 with properties:
name: GeForce GTX 1080 Ti
major: 6 minor: 1 memoryClockRate (GHz) 1.6705
pciBusID 0000:05:00.0
Total memory: 10.91GiB
Free memory: 10.44GiB
2017-11-20 18:43:29.172079: W tensorflow/stream_executor/cuda/cuda_driver.cc:523] A non-primary context 0x31d6630 exists before initializing the StreamExecutor. We haven't verified StreamExecutor works with that.
2017-11-20 18:43:29.172825: I tensorflow/core/common_runtime/gpu/gpu_device.cc:955] Found device 1 with properties:
name: GeForce GTX 1080 Ti
major: 6 minor: 1 memoryClockRate (GHz) 1.6705
pciBusID 0000:06:00.0
Total memory: 10.91GiB
Free memory: 10.75GiB
2017-11-20 18:43:29.173970: I tensorflow/core/common_runtime/gpu/gpu_device.cc:976] DMA: 0 1
2017-11-20 18:43:29.174019: I tensorflow/core/common_runtime/gpu/gpu_device.cc:986] 0: Y Y
2017-11-20 18:43:29.174034: I tensorflow/core/common_runtime/gpu/gpu_device.cc:986] 1: Y Y
2017-11-20 18:43:29.174055: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1045] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1080 Ti, pci bus id: 0000:05:00.0)
2017-11-20 18:43:29.174070: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1045] Creating TensorFlow device (/gpu:1) -> (device: 1, name: GeForce GTX 1080 Ti, pci bus id: 0000:06:00.0)
>>> import tensorflow
>>>
Related
Trying to change GPU Graphics and Memory Transfer Rate Clock Offset on my nVidia EVGA 1030 SC on bootup. I am a linux noob here using Rocky Linux 9.
Question:
How do I check my current set values for GPUGraphicsClockOffsetAllPerformanceLevels and GPUMemoryTransferRateOffsetAllPerformanceLevels?
Currently I am checking by starting up nVidia X Server then look for the values in Graphics Clock Offset and Memory Transfer Rate Offset under the PowerMizer tab. Is there a better way? I don't even really know if that is always up to date...
What did I do wrong and what do I need to change to fix my bootup script below to work?
I think my bootup script is NOT working because the PowerMizer in nVidia X Server does NOT show clock offset values of 50MHz and 200MHz for Graphics and Memory Transfer Rates, respectively, when I boot with my script. It only shows 0 and 0.
However, it does show 50MHz and 200MHz when I enter the following 3 commands line after line directly in bash terminal.
nvidia-smi -pm 1
nvidia-settings -a [gpu:0]/"GPUGraphicsClockOffsetAllPerformanceLevels=50"
nvidia-settings -a [gpu:0]/"GPUMemoryTransferRateOffsetAllPerformanceLevels=200"
Below is the bootup script...
i.
Wrote a shell script file named nVidiaStartUp.sh and placed it in: /etc/rc.d/init.d
nVidiaStartUp.sh contains
#!/bin/bash
nvidia-smi -pm 1
nvidia-settings -a [gpu:0]/"GPUGraphicsClockOffsetAllPerformanceLevels=50"
nvidia-settings -a [gpu:0]/"GPUMemoryTransferRateOffsetAllPerformanceLevels=200"
ii.
In Terminal, executed chmod +x /etc/rc.d/init.d/nVidiaStartUp.sh
iii.
Added a script named nVidiaStartUp.service in /etc/systemd/system with contents below
[Unit]
Description=nVidia Startup Script Call with Undervolt
After=getty.target
[Service]
Type=simple
ExecStart=/etc/rc.d/init.d/nVidiaStartUp.sh
TimeoutStartSec=0
#RemainAfterExit=yes
[Install]
WantedBy=default.target
#WantedBy=graphical.target
#WantedBy=multi-user.target
iv.
Ran in terminal
systemctl enable nVidiaStartUp.service
v.
Reboot and then I check my clockoffset under PowerMizer in nVidia X Server. I don't see 50MHz and 200MHz. I only see 0 and 0. That seems to imply my bootup script isn't working? Please help!
======================================================================================================================================================
Additional background info:
I have installed nVidia driver and it loads with proper GPU information. Here is what it shows after running nvidia-smi
Fri Nov 4 11:39:36 2022
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 520.61.05 Driver Version: 520.61.05 CUDA Version: 11.8 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 NVIDIA GeForce ... On | 00000000:0B:00.0 On | N/A |
| 48% 50C P0 N/A / 30W | 373MiB / 2048MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| 0 N/A N/A 2309 G /usr/libexec/Xorg 100MiB |
| 0 N/A N/A 2439 G /usr/bin/gnome-shell 139MiB |
| 0 N/A N/A 3270 G ...470561649073451082,131072 130MiB |
+-----------------------------------------------------------------------------+
My relevant sections for Cool-bits (I am using 8 for cool-bits as this card is passively cooled) of my /etc/X11/xorg.conf shows
Section "Device"
Identifier "Device0"
Driver "nvidia"
VendorName "NVIDIA Corporation"
BoardName "NVIDIA GeForce GT 1030"
EndSection
Section "Screen"
Identifier "Screen0"
Device "Device0"
Monitor "Monitor0"
DefaultDepth 24
Option "Coolbits" "8"
SubSection "Display"
Depth 24
EndSubSection
EndSection
I can't run use mongodb on my arch linux.
package installed: mongodb-bin
error when use mongo: illegal hardware instruction (core dumped) mongo
My processor: AMD Athlon(tm) II X2 270
From the
release notes:
MongoDB 5.0 requires AMD Bulldozer or later.
How to check:
cat /proc/cpuinfo | grep -i avx
(Advanced Vector Extention(avx) needed for 5.0)
I have setup a kubernetes node with a nvidia tesla k80 and followed this tutorial to try to run a pytorch docker image with nvidia drivers and cuda drivers working.
My nvidia drivers and cuda drivers are all accessible inside my pod at /usr/local:
$> ls /usr/local
bin cuda cuda-10.0 etc games include lib man nvidia sbin share src
And my GPU is also recongnized by my image nvidia/cuda:10.0-runtime-ubuntu18.04:
$> /usr/local/nvidia/bin/nvidia-smi
Fri Nov 8 16:24:35 2019
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 410.79 Driver Version: 410.79 CUDA Version: 10.0 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 Tesla K80 Off | 00000000:00:04.0 Off | 0 |
| N/A 73C P8 35W / 149W | 0MiB / 11441MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| No running processes found |
+-----------------------------------------------------------------------------+
But after installing pytorch 1.3.0 i'm not able to make pytorch recognize my cuda installation even with LD_LIBRARY_PATH set to /usr/local/nvidia/lib64:/usr/local/cuda/lib64:
$> python3 -c "import torch; print(torch.cuda.is_available())"
False
$> python3
Python 3.6.8 (default, Oct 7 2019, 12:59:55)
[GCC 8.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import torch
>>> print ('\t\ttorch.cuda.current_device() =', torch.cuda.current_device())
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python3.6/dist-packages/torch/cuda/__init__.py", line 386, in current_device
_lazy_init()
File "/usr/local/lib/python3.6/dist-packages/torch/cuda/__init__.py", line 192, in _lazy_init
_check_driver()
File "/usr/local/lib/python3.6/dist-packages/torch/cuda/__init__.py", line 111, in _check_driver
of the CUDA driver.""".format(str(torch._C._cuda_getDriverVersion())))
AssertionError:
The NVIDIA driver on your system is too old (found version 10000).
Please update your GPU driver by downloading and installing a new
version from the URL: http://www.nvidia.com/Download/index.aspx
Alternatively, go to: https://pytorch.org to install
a PyTorch version that has been compiled with your version
of the CUDA driver.
The error above is strange because my cuda version for my image is 10.0 and Google GKE mentions that:
The latest supported CUDA version is 10.0
Also, it's GKE's daemonsets that automatically installs NVIDIA drivers
After adding GPU nodes to your cluster, you need to install NVIDIA's device drivers to the nodes.
Google provides a DaemonSet that automatically installs the drivers for you.
Refer to the section below for installation instructions for Container-Optimized OS (COS) and Ubuntu nodes.
To deploy the installation DaemonSet, run the following command:
kubectl apply -f https://raw.githubusercontent.com/GoogleCloudPlatform/container-engine-accelerators/master/nvidia-driver-installer/cos/daemonset-preloaded.yaml
I have tried everything i could think of, without success...
I have resolved my problem by downgrading my pytorch version by buildling my docker images from pytorch/pytorch:1.2-cuda10.0-cudnn7-devel.
I still don't really know why before it was not working as it should otherwise then by guessing that pytorch 1.3.0 is not compatible with cuda 10.0.
I just installed DrRacket, and tried out the language "How To Design Programs - Beginning Student".
Racket - A programmable programming language
Racket - Getting Started
I run (+ 1 1), and it takes over ten seconds for this to show up:
Welcome to DrRacket, version 6.5 [3m].
Language: Beginning Student; memory limit: 128 MB.
2
>
As far as I can tell, my installation is pretty much "out of the box".
What I'm wondering is if my experience is unusual,
and if there's any obvious way to troubleshoot it if so
(I've looked around the settings and didn't find anything obvious to tweak),
or if maybe the whole HTDP language was quietly abandoned or something...?
EDIT 1
I have these files:
/usr/share/racket $
find -iname "*htdp*.zo"
./pkgs/htdp-lib/lang/private/compiled/create-htdp-executable_rkt.zo
./pkgs/htdp-lib/lang/compiled/htdp-reader_rkt.zo
./pkgs/htdp-lib/lang/compiled/htdp-beginner-abbr-reader_rkt.zo
./pkgs/htdp-lib/lang/compiled/htdp-langs-save-file-prefix_rkt.zo
./pkgs/htdp-lib/lang/compiled/htdp-advanced-reader_rkt.zo
./pkgs/htdp-lib/lang/compiled/htdp-intermediate-lambda-reader_rkt.zo
./pkgs/htdp-lib/lang/compiled/htdp-advanced_rkt.zo
./pkgs/htdp-lib/lang/compiled/htdp-beginner-abbr_rkt.zo
./pkgs/htdp-lib/lang/compiled/htdp-intermediate_rkt.zo
./pkgs/htdp-lib/lang/compiled/htdp-intermediate-reader_rkt.zo
./pkgs/htdp-lib/lang/compiled/htdp-langs_rkt.zo
./pkgs/htdp-lib/lang/compiled/htdp-beginner_rkt.zo
./pkgs/htdp-lib/lang/compiled/htdp-intermediate-lambda_rkt.zo
./pkgs/htdp-lib/lang/compiled/htdp-beginner-reader_rkt.zo
./pkgs/htdp-doc/scribblings/htdp-langs/compiled/htdp-langs_scrbl.zo
./pkgs/htdp-doc/scribblings/htdp-langs/compiled/htdp-ptr_scrbl.zo
./pkgs/htdp-doc/htdp/compiled/htdp_scrbl.zo
./pkgs/htdp-doc/htdp/compiled/htdp-lib_scrbl.zo
./pkgs/htdp-doc/teachpack/htdp/scribblings/compiled/htdp_scrbl.zo
./pkgs/htdp-doc/teachpack/2htdp/scribblings/compiled/2htdp_scrbl.zo
EDIT 2 - cpu and harddrive specs
CPU
$ cat /proc/cpuinfo
processor : 0
vendor_id : GenuineIntel
cpu family : 6
model : 28
model name : Intel(R) Atom(TM) CPU N450 # 1.66GHz
stepping : 10
microcode : 0x107
cpu MHz : 1000.000
cache size : 512 KB
physical id : 0
siblings : 2
core id : 0
cpu cores : 1
apicid : 0
initial apicid : 0
fpu : yes
fpu_exception : yes
cpuid level : 10
wp : yes
flags : fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx lm constant_tsc arch_perfmon pebs bts nopl aperfmperf pni dtes64 monitor ds_cpl est tm2 ssse3 cx16 xtpr pdcm movbe lahf_lm dtherm
bugs :
bogomips : 3325.00
clflush size : 64
cache_alignment : 64
address sizes : 32 bits physical, 48 bits virtual
power management:
processor : 1
vendor_id : GenuineIntel
cpu family : 6
model : 28
model name : Intel(R) Atom(TM) CPU N450 # 1.66GHz
stepping : 10
microcode : 0x107
cpu MHz : 1000.000
cache size : 512 KB
physical id : 0
siblings : 2
core id : 0
cpu cores : 1
apicid : 1
initial apicid : 1
fpu : yes
fpu_exception : yes
cpuid level : 10
wp : yes
flags : fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx lm constant_tsc arch_perfmon pebs bts nopl aperfmperf pni dtes64 monitor ds_cpl est tm2 ssse3 cx16 xtpr pdcm movbe lahf_lm dtherm
bugs :
bogomips : 3325.00
clflush size : 64
cache_alignment : 64
address sizes : 32 bits physical, 48 bits virtual
power management:
HD
$ sudo hdparm -I /dev/sda
/dev/sda:
ATA device, with non-removable media
Model Number: Hitachi HTS545016B9A300
Serial Number: 100324PBPB06ECC0K6XL
Firmware Revision: PBBOC60F
Transport: Serial, ATA8-AST, SATA 1.0a, SATA II Extensions, SATA Rev 2.5, SATA Rev 2.6; Revision: ATA8-AST T13 Project D1697 Revision 0b
Standards:
Used: unknown (minor revision code 0x0028)
Supported: 8 7 6 5
Likely used: 8
Configuration:
Logical max current
cylinders 16383 16383
heads 16 16
sectors/track 63 63
--
CHS current addressable sectors: 16514064
LBA user addressable sectors: 268435455
LBA48 user addressable sectors: 312581808
Logical/Physical Sector size: 512 bytes
device size with M = 1024*1024: 152627 MBytes
device size with M = 1000*1000: 160041 MBytes (160 GB)
cache/buffer size = 7208 KBytes (type=DualPortCache)
Form Factor: 2.5 inch
Nominal Media Rotation Rate: 5400
Capabilities:
LBA, IORDY(can be disabled)
Queue depth: 32
Standby timer values: spec'd by Vendor, no device specific minimum
R/W multiple sector transfer: Max = 16 Current = 16
Advanced power management level: 254
Recommended acoustic management value: 128, current value: 254
DMA: mdma0 mdma1 mdma2 udma0 udma1 udma2 udma3 udma4 udma5 *udma6
Cycle time: min=120ns recommended=120ns
PIO: pio0 pio1 pio2 pio3 pio4
Cycle time: no flow control=120ns IORDY flow control=120ns
Commands/features:
Enabled Supported:
* SMART feature set
Security Mode feature set
* Power Management feature set
* Write cache
* Look-ahead
* Host Protected Area feature set
* WRITE_BUFFER command
* READ_BUFFER command
* NOP cmd
* DOWNLOAD_MICROCODE
* Advanced Power Management feature set
Power-Up In Standby feature set
* SET_FEATURES required to spinup after power up
SET_MAX security extension
Automatic Acoustic Management feature set
* 48-bit Address feature set
* Device Configuration Overlay feature set
* Mandatory FLUSH_CACHE
* FLUSH_CACHE_EXT
* SMART error logging
* SMART self-test
* General Purpose Logging feature set
* WRITE_{DMA|MULTIPLE}_FUA_EXT
* 64-bit World wide name
* IDLE_IMMEDIATE with UNLOAD
* WRITE_UNCORRECTABLE_EXT command
* {READ,WRITE}_DMA_EXT_GPL commands
* Segmented DOWNLOAD_MICROCODE
* Gen1 signaling speed (1.5Gb/s)
* Gen2 signaling speed (3.0Gb/s)
* Native Command Queueing (NCQ)
* Host-initiated interface power management
* Phy event counters
* NCQ priority information
Non-Zero buffer offsets in DMA Setup FIS
* DMA Setup Auto-Activate optimization
Device-initiated interface power management
In-order data delivery
* Software settings preservation
* SMART Command Transport (SCT) feature set
* SCT Write Same (AC2)
* SCT Error Recovery Control (AC3)
* SCT Features Control (AC4)
* SCT Data Tables (AC5)
Security:
Master password revision code = 65534
supported
not enabled
not locked
frozen
not expired: security count
supported: enhanced erase
64min for SECURITY ERASE UNIT. 66min for ENHANCED SECURITY ERASE UNIT.
Logical Unit WWN Device Identifier: 5000cca5ffc040a7
NAA : 5
IEEE OUI : 000cca
Unique ID : 5ffc040a7
Checksum: correct
EDIT 3 command-line times
ran (+ 1 1) in HTDP-beginner 3 times -- Over 5 seconds.
$ time racket -t racket_HTDP_beginner.rkt
2
5.60user 1.04system 0:08.46elapsed 78%CPU (0avgtext+0avgdata 127968maxresident)k
5496inputs+0outputs (46major+40955minor)pagefaults 0swaps
$ time racket -t racket_HTDP_beginner.rkt
2
5.51user 0.67system 0:06.71elapsed 92%CPU (0avgtext+0avgdata 128124maxresident)k
24inputs+0outputs (0major+41790minor)pagefaults 0swaps
$ time racket -t racket_HTDP_beginner.rkt
2
5.41user 0.67system 0:06.55elapsed 92%CPU (0avgtext+0avgdata 128180maxresident)k
0inputs+0outputs (0major+36683minor)pagefaults 0swaps
ran (+ 1 1) in #lang racket 3 times -- A bit over 2 seconds.
$ time racket -t racket_lang_racket.rkt
2
2.13user 0.25system 0:02.71elapsed 87%CPU (0avgtext+0avgdata 64996maxresident)k
0inputs+0outputs (0major+12437minor)pagefaults 0swaps
$ time racket -t racket_lang_racket.rkt
2
2.15user 0.25system 0:02.63elapsed 91%CPU (0avgtext+0avgdata 61700maxresident)k
0inputs+0outputs (0major+15853minor)pagefaults 0swaps
$ time racket -t racket_lang_racket.rkt
2
2.28user 0.29system 0:02.89elapsed 89%CPU (0avgtext+0avgdata 61500maxresident)k
0inputs+0outputs (0major+15015minor)pagefaults 0swaps
EDIT 4
Running free -h every second while DrRacket is running (+ 1 1) in lang HTDP-beginner
(not running any other applications besides DrRacket and my basic system (ie window manager etc))
(this is fish shell, by the way):
http://pastebin.com/2RdZAuXj
At that point I had to kill DrRacket cuz everything was freezing up.
Anyway, yeah, it's leaking, obviously.
Everytime I re-ran the code in DrRacket, memory usage went up and stayed up.
I had only run it about twenty...two-ish (?) more times
(so maybe thirty-ish in total?)
by the point where it started getting near the limit
and I killed it to unfreeze the system.
I guess I should try this with normal #lang racket and see what happens...
EDIT 5
Yup, it leaks the same way:
http://pastebin.com/373PNnY7
I am 90% sure that the reason you had to wait was that your installation of Racket wasn't done properly.
During installation the program setup-plt needs to be run. It precompiles all racket files (.rkt) into so-called zo-files. If this step is omitted, then
DrRacket do the compilation for you the first time a file is needed.
In your case (I am guessing) it it your first time using the Beginner language, so all files relating to it needs to be compiled. And that takes a while.
The best solution is to use the official installers from http://download.racket-lang.org/ they all include precompiled zo-files.
If you happen to have used such an installer, then try again - and if the problem persist - do file a bug report (use the bug report in the Help menu in DrRacket).
On my pi after start there is no free memory, but i can not found, waht uses it:
pi#node1 ~ $ cat /proc/cpuinfo
processor : 0
model name : ARMv6-compatible processor rev 7 (v6l)
BogoMIPS : 2.00
Features : half thumb fastmult vfp edsp java tls
CPU implementer : 0x41
CPU architecture: 7
CPU variant : 0x0
CPU part : 0xb76
CPU revision : 7
Hardware : BCM2708
Revision : 0013
Serial : 00000000bf2e5e5c
pi#node1 ~ $ uname -a
Linux node1 4.0.7+ #801 PREEMPT Tue Jun 30 18:15:24 BST 2015 armv6l GNU/Linux
pi#node1 ~ $ head -n1 /etc/issue
Raspbian GNU/Linux 7 \n \l
pi#node1 ~ $ grep MemTotal /proc/meminfo
MemTotal: 493868 kB
pi#node1 ~ $ grep "model name" /proc/cpuinfo
model name : ARMv6-compatible processor rev 7 (v6l)
pi#node1 ~ $ ps -eo pmem,pcpu,vsize,pid,cmd | sort -k 1 -nr | head -5
0.6 0.2 6244 2377 -bash
0.3 0.0 6748 2458 sort -k 1 -nr
0.3 0.0 4140 2457 ps -eo pmem,pcpu,vsize,pid,cmd
0.2 0.1 9484 2376 sshd: pi#pts/0
0.2 0.1 5600 2236 /usr/sbin/ntpd -p /var/run/ntpd.pid -g -u 104:107
pi#node1 ~ $ free
total used free shared buffers cached
Mem: 493868 478364 15504 0 500 4956
-/+ buffers/cache: 472908 20960
Swap: 102396 116 102280
I am not a linux expert, but if I understand it right, there is just 15Mb free memory, but no task uses more than 0.6%. Than why is not there more free?
Memory is not exclusively allocated by Processes.
The bootloader and the init ram filesystem is stored in memory.
The kernel (could be very big) is loaded into memory.
The kernel reserve memory for it's processes. ps shows 0.0% for these system processes.
Driver allocate buffer memory
The graphics card needs memory
If you have not configured your swap space on a harddrive or SD card, it uses memory.
The network system allocates memory for unix sockets and shared memory.
100 processes with 0.1 % are 10%.
And, if you start a process and stop it not all of it memory will be released.
Try it. Show the memory usage with free. Start a process that need some memory. Stop the process and use free again. I would bet that there is more memory usage than before.
Edit
Here is an example of a pi with less memory usage. I have no problems running java on it. I have a WLAN Dongle and a original NOIR CAM installed.
I installed Raspbian Wheezy. I used a kernel that I compiled from sources:
> uname -a
Linux raspberrypi 3.18.14+ #2 PREEMPT Sun May 31 20:19:04 UTC 2015 armv6l GNU/Linux
> head -n1 /etc/issue
Raspbian GNU/Linux 7 \n \l
On this pi I can run java -version in an acceptable period of time.
time java -version
java version "1.8.0"
Java(TM) SE Runtime Environment (build 1.8.0-b132)
Java HotSpot(TM) Client VM (build 25.0-b70, mixed mode)
real 0m1.012s
user 0m0.800s
sys 0m0.190s
Here is my memory footprint
> free
total used free shared buffers cached
Mem: 380816 138304 242512 0 8916 96728
-/+ buffers/cache: 32660 348156
Swap: 102396 0 102396