Related
In a 'new computer' with Ubuntu 20.04 (using docker and pulling ubuntu:20.04), if I install miniconda3 and just run:
conda install -c anaconda tensorflow-gpu
Everything is good to go to use GPU for machine learning, because I can run:
import tensorflow as tf
print('Num GPUs Available: ', len(tf.config.list_physical_devices('GPU')))
Num GPUs Available: 1
This is okay.
But the 'problem' is that anaconda installs a lot of packages when I run conda install -c anaconda tensorflow-gpu
Before run the command conda install -c anaconda tensorflow-gpu, if I run conda list I get:
# Name
Version
Build
Channel
_libgcc_mutex
0.1
main
_openmp_mutex
4.5
1_gnu
brotlipy
0.7.0
py39h27cfd23_1003
ca-certificates
2022.3.29
h06a4308_1
certifi
2021.10.8
py39h06a4308_2
cffi
1.15.0
py39hd667e15_1
charset-normalizer
2.0.4
pyhd3eb1b0_0
colorama
0.4.4
pyhd3eb1b0_0
conda
4.12.0
py39h06a4308_0
conda-content-trust
0.1.1
pyhd3eb1b0_0
conda-package-handling
1.8.1
py39h7f8727e_0
cryptography
36.0.0
py39h9ce1e76_0
idna
3.3
pyhd3eb1b0_0
ld_impl_linux-64
2.35.1
h7274673_9
libffi
3.3
he6710b0_2
libgcc-ng
9.3.0
h5101ec6_17
libgomp
9.3.0
h5101ec6_17
libstdcxx-ng
9.3.0
hd4cf53a_17
ncurses
6.3
h7f8727e_2
openssl
1.1.1n
h7f8727e_0
pip
21.2.4
py39h06a4308_0
pycosat
0.6.3
py39h27cfd23_0
pycparser
2.21
pyhd3eb1b0_0
pyopenssl
22.0.0
pyhd3eb1b0_0
pysocks
1.7.1
py39h06a4308_0
python
3.9.12
h12debd9_0
readline
8.1.2
h7f8727e_1
requests
2.27.1
pyhd3eb1b0_0
ruamel_yaml
0.15.100
py39h27cfd23_0
setuptools
61.2.0
py39h06a4308_0
six
1.16.0
pyhd3eb1b0_1
sqlite
3.38.2
hc218d9a_0
tk
8.6.11
h1ccaba5_0
tqdm
4.63.0
pyhd3eb1b0_0
tzdata
2022a
hda174b7_0
urllib3
1.26.8
pyhd3eb1b0_0
wheel
0.37.1
pyhd3eb1b0_0
xz
5.2.5
h7b6447c_0
yaml
0.2.5
h7b6447c_0
zlib
1.2.12
h7f8727e_1
After run the command conda install -c anaconda tensorflow-gpu, if I run conda list I get:
# Name
Version
Build
Channel
_libgcc_mutex
0.1
main
_openmp_mutex
4.5
1_gnu
_tflow_select
2.1.0
gpu
anaconda
absl-py
0.15.0
pyhd3eb1b0_0
anaconda
aiohttp
3.8.1
py39h7f8727e_1
anaconda
aiosignal
1.2.0
pyhd3eb1b0_0
anaconda
astor
0.8.1
py39h06a4308_0
anaconda
astunparse
1.6.3
py_0
anaconda
async-timeout
4.0.1
pyhd3eb1b0_0
anaconda
attrs
21.4.0
pyhd3eb1b0_0
anaconda
blas
1.0
mkl
anaconda
blinker
1.4
py39h06a4308_0
anaconda
brotlipy
0.7.0
py39h27cfd23_1003
c-ares
1.18.1
h7f8727e_0
anaconda
ca-certificates
2022.07.19
h06a4308_0
anaconda
cachetools
4.2.2
pyhd3eb1b0_0
anaconda
certifi
2022.6.15
py39h06a4308_0
anaconda
cffi
1.15.0
py39hd667e15_1
charset-normalizer
2.0.4
pyhd3eb1b0_0
click
8.0.4
py39h06a4308_0
anaconda
colorama
0.4.4
pyhd3eb1b0_0
conda
4.13.0
py39h06a4308_0
anaconda
conda-content-trust
0.1.1
pyhd3eb1b0_0
conda-package-handling
1.8.1
py39h7f8727e_0
cryptography
36.0.0
py39h9ce1e76_0
cudatoolkit
10.1.243
h6bb024c_0
anaconda
cudnn
7.6.5
cuda10.1_0
anaconda
cupti
10.1.168 0
anaconda
dataclasses
0.8
pyh6d0b6a4_7
anaconda
frozenlist
1.2.0
py39h7f8727e_0
anaconda
gast
0.4.0
pyhd3eb1b0_0
anaconda
google-auth
2.6.0
pyhd3eb1b0_0
anaconda
google-auth-oauthlib
0.4.4
pyhd3eb1b0_0
anaconda
google-pasta
0.2.0
pyhd3eb1b0_0
anaconda
grpcio
1.42.0
py39hce63b2e_0
anaconda
h5py
2.10.0
py39hec9cf62_0
anaconda
hdf5
1.10.6
hb1b8bf9_0
anaconda
idna
3.3
pyhd3eb1b0_0
importlib-metadata
4.11.3
py39h06a4308_0
anaconda
intel-openmp
2021.4.0
h06a4308_3561
anaconda
keras-preprocessing
1.1.2
pyhd3eb1b0_0
anaconda
ld_impl_linux-64
2.35.1
h7274673_9
libffi
3.3
he6710b0_2
libgcc-ng
9.3.0
h5101ec6_17
libgfortran-ng
7.5.0
ha8ba4b0_17
anaconda
libgfortran4
7.5.0
ha8ba4b0_17
anaconda
libgomp
9.3.0
h5101ec6_17
libprotobuf
3.20.1
h4ff587b_0
anaconda
libstdcxx-ng
9.3.0
hd4cf53a_17
markdown
3.3.4
py39h06a4308_0
anaconda
mkl
2021.4.0
h06a4308_640
anaconda
mkl-service
2.4.0
py39h7f8727e_0
anaconda
mkl_fft
1.3.1
py39hd3c417c_0
anaconda
mkl_random
1.2.2
py39h51133e4_0
anaconda
multidict
5.2.0
py39h7f8727e_2
anaconda
ncurses
6.3
h7f8727e_2
numpy
1.22.3
py39he7a7128_0
anaconda
numpy-base
1.22.3
py39hf524024_0
anaconda
oauthlib
3.1.0
py_0
anaconda
openssl
1.1.1q
h7f8727e_0
anaconda
opt_einsum
3.3.0
pyhd3eb1b0_1
anaconda
pip
21.2.4
py39h06a4308_0
protobuf
3.20.1
py39h295c915_0
anaconda
pyasn1
0.4.8
pyhd3eb1b0_0
anaconda
pyasn1-modules
0.2.8
py_0
anaconda
pycosat
0.6.3
py39h27cfd23_0
pycparser
2.21
pyhd3eb1b0_0
pyjwt
2.4.0
py39h06a4308_0
anaconda
pyopenssl
22.0.0
pyhd3eb1b0_0
pysocks
1.7.1
py39h06a4308_0
python
3.9.12
h12debd9_0
python-flatbuffers
2.0
pyhd3eb1b0_0
anaconda
readline
8.1.2
h7f8727e_1
requests
2.27.1
pyhd3eb1b0_0
requests-oauthlib
1.3.0
py_0
anaconda
rsa
4.7.2
pyhd3eb1b0_1
anaconda
ruamel_yaml
0.15.100
py39h27cfd23_0
scipy
1.7.3
py39hc147768_0
anaconda
setuptools
61.2.0
py39h06a4308_0
six
1.16.0
pyhd3eb1b0_1
sqlite
3.38.2
hc218d9a_0
tensorboard
2.8.0
py39h06a4308_0
anaconda
tensorboard-data-server
0.6.0
py39hca6d32c_0
anaconda
tensorboard-plugin-wit
1.8.1
py39h06a4308_0
anaconda
tensorflow
2.4.1
gpu_py39h8236f22_0
anaconda
tensorflow-base
2.4.1
gpu_py39h29c2da4_0
anaconda
tensorflow-estimator
2.6.0
pyh7b7c402_0
anaconda
tensorflow-gpu
2.4.1
h30adc30_0
anaconda
termcolor
1.1.0
py39h06a4308_1
anaconda
tk
8.6.11
h1ccaba5_0
tqdm
4.63.0
pyhd3eb1b0_0
typing-extensions
4.3.0
py39h06a4308_0
anaconda
typing_extensions
4.3.0
py39h06a4308_0
anaconda
tzdata
2022a
hda174b7_0
urllib3
1.26.8
pyhd3eb1b0_0
werkzeug
2.0.3
pyhd3eb1b0_0
anaconda
wheel
0.37.1
pyhd3eb1b0_0
wrapt
1.13.3
py39h7f8727e_2
anaconda
xz
5.2.5
h7b6447c_0
yaml
0.2.5
h7b6447c_0
yarl
1.6.3
py39h27cfd23_0
anaconda
zipp
3.8.0
py39h06a4308_0
anaconda
zlib
1.2.12
h7f8727e_1
I know that the following packages are needed:
cudnn
tensorflow-gpu
, so is anything else needed to run the commands?:
import tensorflow as tf
print('Num GPUs Available: ', len(tf.config.list_physical_devices('GPU')))
Num GPUs Available: 1
,or are all packages installed with conda install -c anaconda tensorflow-gpu necessary?
As the title says, I would like to know which are the strictly required (bare minimum)
libraries/packages to run this
Thanks in advance
Yes, it needs a lot more.
Typically, a large and complicated package like tensorflow has a whole tree of dependencies.
If I take your list of packages after the install and remove the packages before the install, the following results:
'_tflow_select', 'absl-py', 'aiohttp', 'aiosignal', 'astor',
'astunparse', 'async-timeout', 'attrs', 'blas', 'blinker',
'c-ares', 'cachetools', 'click', 'cudatoolkit', 'cudnn',
'cupti', 'dataclasses', 'frozenlist', 'gast', 'google-auth',
'google-auth-oauthlib', 'google-pasta', 'grpcio', 'h5py',
'hdf5', 'importlib-metadata', 'intel-openmp',
'keras-preprocessing', 'libgfortran-ng', 'libgfortran4',
'libprotobuf', 'markdown', 'mkl', 'mkl-service', 'mkl_fft',
'mkl_random', 'multidict', 'numpy', 'numpy-base', 'oauthlib',
'opt_einsum', 'protobuf', 'pyasn1', 'pyasn1-modules', 'pyjwt',
'python-flatbuffers', 'requests-oauthlib', 'rsa', 'scipy',
'tensorboard', 'tensorboard-data-server',
'tensorboard-plugin-wit', 'tensorflow', 'tensorflow-base',
'tensorflow-estimator', 'tensorflow-gpu', 'termcolor',
'typing-extensions', 'typing_extensions', 'werkzeug',
'wrapt', 'yarl', 'zipp'
Tensorflow depends both on a number of Python and C/C++ libraries. Each of those may have dependencies of their own. For example, tensorflow requires keras which requires hdf5. And tensorflow requires numpy which requires a BLAS library (in this case mkl) which requires the Fortran runtime.
Now, it may be that some of those dependencies are optional.
But at first glance I don't see any of those.
Trying to pare down the dependencies is a significant task; you would basically have to build the whole dependency tree from source, for every dependency checking which of its dependencies are optional and if you want to do without them.
Personally, I would not bother in this case.
I've seen multiple issue about the:
RuntimeError: NCCL error in: /opt/conda/conda-bld/pytorch_1614378083779/work/torch/lib/c10d/ProcessGroupNCCL.cpp:825, unhandled cuda error, NCCL version 2.7.8
ncclUnhandledCudaError: Call to CUDA function failed.
but none seem to fix it for me:
https://github.com/pytorch/pytorch/issues/54550
https://github.com/pytorch/pytorch/issues/47885
https://github.com/pytorch/pytorch/issues/50921
https://github.com/pytorch/pytorch/issues/54823
I've tried to do torch.cuda.set_device(device) manually at the beginning of every script. That didn't seem to work for me. I've tried different GPUS. I've tried downgrading pytorch version and cuda version. Different combinations of 1.6.0, 1.7.1, 1.8.0 and cuda 10.2, 11.0, 11.1. I am unsure what else to do. What did people do to solve this issue?
very related perhaps?
Pytorch "NCCL error": unhandled system error, NCCL version 2.4.8"
More complete error message:
('jobid', 4852)
('slurm_jobid', -1)
('slurm_array_task_id', -1)
('condor_jobid', 4852)
('current_time', 'Mar25_16-27-35')
('tb_dir', PosixPath('/home/miranda9/data/logs/logs_Mar25_16-27-35_jobid_4852/tb'))
('gpu_name', 'GeForce GTX TITAN X')
('PID', '30688')
torch.cuda.device_count()=2
opts.world_size=2
ABOUT TO SPAWN WORKERS
done setting sharing strategy...next mp.spawn
INFO:root:Added key: store_based_barrier_key:1 to store for rank: 1
INFO:root:Added key: store_based_barrier_key:1 to store for rank: 0
rank=0
mp.current_process()=<SpawnProcess name='SpawnProcess-1' parent=30688 started>
os.getpid()=30704
setting up rank=0 (with world_size=2)
MASTER_ADDR='127.0.0.1'
59264
backend='nccl'
--> done setting up rank=0
setup process done for rank=0
Traceback (most recent call last):
File "/home/miranda9/ML4Coq/ml4coq-proj/embeddings_zoo/tree_nns/main_brando.py", line 279, in <module>
main_distributed()
File "/home/miranda9/ML4Coq/ml4coq-proj/embeddings_zoo/tree_nns/main_brando.py", line 188, in main_distributed
spawn_return = mp.spawn(fn=train, args=(opts,), nprocs=opts.world_size)
File "/home/miranda9/miniconda3/envs/metalearning11.1/lib/python3.8/site-packages/torch/multiprocessing/spawn.py", line 230, in spawn
return start_processes(fn, args, nprocs, join, daemon, start_method='spawn')
File "/home/miranda9/miniconda3/envs/metalearning11.1/lib/python3.8/site-packages/torch/multiprocessing/spawn.py", line 188, in start_processes
while not context.join():
File "/home/miranda9/miniconda3/envs/metalearning11.1/lib/python3.8/site-packages/torch/multiprocessing/spawn.py", line 150, in join
raise ProcessRaisedException(msg, error_index, failed_process.pid)
torch.multiprocessing.spawn.ProcessRaisedException:
-- Process 0 terminated with the following error:
Traceback (most recent call last):
File "/home/miranda9/miniconda3/envs/metalearning11.1/lib/python3.8/site-packages/torch/multiprocessing/spawn.py", line 59, in _wrap
fn(i, *args)
File "/home/miranda9/ML4Coq/ml4coq-proj/embeddings_zoo/tree_nns/main_brando.py", line 212, in train
tactic_predictor = move_to_ddp(rank, opts, tactic_predictor)
File "/home/miranda9/ultimate-utils/ultimate-utils-project/uutils/torch/distributed.py", line 162, in move_to_ddp
model = DistributedDataParallel(model, find_unused_parameters=True, device_ids=[opts.gpu])
File "/home/miranda9/miniconda3/envs/metalearning11.1/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 446, in __init__
self._sync_params_and_buffers(authoritative_rank=0)
File "/home/miranda9/miniconda3/envs/metalearning11.1/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 457, in _sync_params_and_buffers
self._distributed_broadcast_coalesced(
File "/home/miranda9/miniconda3/envs/metalearning11.1/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1155, in _distributed_broadcast_coalesced
dist._broadcast_coalesced(
RuntimeError: NCCL error in: /opt/conda/conda-bld/pytorch_1616554793803/work/torch/lib/c10d/ProcessGroupNCCL.cpp:825, unhandled cuda error, NCCL version 2.7.8
ncclUnhandledCudaError: Call to CUDA function failed.
Bonus 1:
I still have errors:
ncclSystemError: System call (socket, malloc, munmap, etc) failed.
Traceback (most recent call last):
File "/home/miranda9/diversity-for-predictive-success-of-meta-learning/div_src/diversity_src/experiment_mains/main_dist_maml_l2l.py", line 1423, in <module>
main()
File "/home/miranda9/diversity-for-predictive-success-of-meta-learning/div_src/diversity_src/experiment_mains/main_dist_maml_l2l.py", line 1365, in main
train(args=args)
File "/home/miranda9/diversity-for-predictive-success-of-meta-learning/div_src/diversity_src/experiment_mains/main_dist_maml_l2l.py", line 1385, in train
args.opt = move_opt_to_cherry_opt_and_sync_params(args) if is_running_parallel(args.rank) else args.opt
File "/home/miranda9/ultimate-utils/ultimate-utils-proj-src/uutils/torch_uu/distributed.py", line 456, in move_opt_to_cherry_opt_and_sync_params
args.opt = cherry.optim.Distributed(args.model.parameters(), opt=args.opt, sync=syn)
File "/home/miranda9/miniconda3/envs/meta_learning_a100/lib/python3.9/site-packages/cherry/optim.py", line 62, in __init__
self.sync_parameters()
File "/home/miranda9/miniconda3/envs/meta_learning_a100/lib/python3.9/site-packages/cherry/optim.py", line 78, in sync_parameters
dist.broadcast(p.data, src=root)
File "/home/miranda9/miniconda3/envs/meta_learning_a100/lib/python3.9/site-packages/torch/distributed/distributed_c10d.py", line 1090, in broadcast
work = default_pg.broadcast([tensor], opts)
RuntimeError: NCCL error in: ../torch/lib/c10d/ProcessGroupNCCL.cpp:911, unhandled system error, NCCL version 2.7.8
one of the answers suggested to have nvcca & pytorch.version.cuda to match but they do not:
(meta_learning_a100) [miranda9#hal-dgx ~]$ python -c "import torch;print(torch.version.cuda)"
11.1
(meta_learning_a100) [miranda9#hal-dgx ~]$ nvcc -V
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2020 NVIDIA Corporation
Built on Wed_Jul_22_19:09:09_PDT_2020
Cuda compilation tools, release 11.0, V11.0.221
Build cuda_11.0_bu.TC445_37.28845127_0
How do I match them?
I had the right cuda installed meaning:
python -c "import torch;print(torch.version.cuda)"
#was equal to
nvcc -V
and
ldconfig -v | grep "libnccl.so" | tail -n1 | sed -r 's/^.*\.so\.//'
was giving out some version of nccl (e.g., 2.10.3 )
The fix was to remove nccl:
sudo apt remove libnccl2 libnccl-dev
then the libnccl version check was not giving any version, but ddp training was working fine!
This is not a very satisfactory answer but this seems to be what ended up working for me. I simply used pytorch 1.7.1 and it's cuda version 10.2. As long as cuda 11.0 is loaded it seems to be working. To install that version do:
conda install -y pytorch==1.7.1 torchvision torchaudio cudatoolkit=10.2 -c pytorch -c conda-forge
if your are in an HPC do module avail to make sure the right cuda version is loaded. Perhaps you need to source bash and other things for the submission job to work. My setup looks as follows:
#!/bin/bash
echo JOB STARTED
# a submission job is usually empty and has the root of the submission so you probably need your HOME env var
export HOME=/home/miranda9
# to have modules work and the conda command work
source /etc/bashrc
source /etc/profile
source /etc/profile.d/modules.sh
source ~/.bashrc
source ~/.bash_profile
conda activate metalearningpy1.7.1c10.2
#conda activate metalearning1.7.1c11.1
#conda activate metalearning11.1
#module load cuda-toolkit/10.2
module load cuda-toolkit/11.1
#nvidia-smi
nvcc --version
#conda list
hostname
echo $PATH
which python
# - run script
python -u ~/ML4Coq/ml4coq-proj/embeddings_zoo/tree_nns/main_brando.py
I also echo other useful things like the nvcc version to make sure load worked (note the top of nvidia-smi doesn't show the right cuda version).
Note I think this is probably just a bug since cuda 11.1 + pytorch 1.8.1 are new as of this writing. I did try
torch.cuda.set_device(opts.gpu) # https://github.com/pytorch/pytorch/issues/54550
but I can't say that it always works or why it doesn't. I do have it in my current code but I think I still get error with pytorch 1.8.x + cuda 11.x.
see my conda list in case it helps:
$ conda list
# packages in environment at /home/miranda9/miniconda3/envs/metalearningpy1.7.1c10.2:
#
# Name Version Build Channel
_libgcc_mutex 0.1 main
absl-py 0.12.0 py38h06a4308_0
aioconsole 0.3.1 pypi_0 pypi
aiohttp 3.7.4 py38h27cfd23_1
anatome 0.0.1 pypi_0 pypi
argcomplete 1.12.2 pypi_0 pypi
astunparse 1.6.3 pypi_0 pypi
async-timeout 3.0.1 py38h06a4308_0
attrs 20.3.0 pyhd3eb1b0_0
beautifulsoup4 4.9.3 pyha847dfd_0
blas 1.0 mkl
blinker 1.4 py38h06a4308_0
boto 2.49.0 pypi_0 pypi
brotlipy 0.7.0 py38h27cfd23_1003
bzip2 1.0.8 h7b6447c_0
c-ares 1.17.1 h27cfd23_0
ca-certificates 2021.1.19 h06a4308_1
cachetools 4.2.1 pyhd3eb1b0_0
cairo 1.14.12 h8948797_3
certifi 2020.12.5 py38h06a4308_0
cffi 1.14.0 py38h2e261b9_0
chardet 3.0.4 py38h06a4308_1003
click 7.1.2 pyhd3eb1b0_0
cloudpickle 1.6.0 pypi_0 pypi
conda 4.9.2 py38h06a4308_0
conda-build 3.21.4 py38h06a4308_0
conda-package-handling 1.7.2 py38h03888b9_0
coverage 5.5 py38h27cfd23_2
crcmod 1.7 pypi_0 pypi
cryptography 3.4.7 py38hd23ed53_0
cudatoolkit 10.2.89 hfd86e86_1
cycler 0.10.0 py38_0
cython 0.29.22 py38h2531618_0
dbus 1.13.18 hb2f20db_0
decorator 5.0.3 pyhd3eb1b0_0
dgl-cuda10.2 0.6.0post1 py38_0 dglteam
dill 0.3.3 pyhd3eb1b0_0
expat 2.3.0 h2531618_2
fasteners 0.16 pypi_0 pypi
filelock 3.0.12 pyhd3eb1b0_1
flatbuffers 1.12 pypi_0 pypi
fontconfig 2.13.1 h6c09931_0
freetype 2.10.4 h7ca028e_0 conda-forge
fribidi 1.0.10 h7b6447c_0
future 0.18.2 pypi_0 pypi
gast 0.3.3 pypi_0 pypi
gcs-oauth2-boto-plugin 2.7 pypi_0 pypi
glib 2.63.1 h5a9c865_0
glob2 0.7 pyhd3eb1b0_0
google-apitools 0.5.31 pypi_0 pypi
google-auth 1.28.0 pyhd3eb1b0_0
google-auth-oauthlib 0.4.3 pyhd3eb1b0_0
google-pasta 0.2.0 pypi_0 pypi
google-reauth 0.1.1 pypi_0 pypi
graphite2 1.3.14 h23475e2_0
graphviz 2.40.1 h21bd128_2
grpcio 1.32.0 pypi_0 pypi
gst-plugins-base 1.14.0 hbbd80ab_1
gstreamer 1.14.0 hb453b48_1
gsutil 4.60 pypi_0 pypi
gym 0.18.0 pypi_0 pypi
h5py 2.10.0 pypi_0 pypi
harfbuzz 1.8.8 hffaf4a1_0
higher 0.2.1 pypi_0 pypi
httplib2 0.19.0 pypi_0 pypi
icu 58.2 he6710b0_3
idna 2.10 pyhd3eb1b0_0
importlib-metadata 3.7.3 py38h06a4308_1
intel-openmp 2020.2 254
jinja2 2.11.3 pyhd3eb1b0_0
joblib 1.0.1 pyhd3eb1b0_0
jpeg 9b h024ee3a_2
keras-preprocessing 1.1.2 pypi_0 pypi
kiwisolver 1.3.1 py38h2531618_0
lark-parser 0.6.5 pypi_0 pypi
lcms2 2.11 h396b838_0
ld_impl_linux-64 2.33.1 h53a641e_7
learn2learn 0.1.5 pypi_0 pypi
libarchive 3.4.2 h62408e4_0
libffi 3.2.1 hf484d3e_1007
libgcc-ng 9.1.0 hdf63c60_0
libgfortran-ng 7.3.0 hdf63c60_0
liblief 0.10.1 he6710b0_0
libpng 1.6.37 h21135ba_2 conda-forge
libprotobuf 3.14.0 h8c45485_0
libstdcxx-ng 9.1.0 hdf63c60_0
libtiff 4.1.0 h2733197_1
libuuid 1.0.3 h1bed415_2
libuv 1.40.0 h7b6447c_0
libxcb 1.14 h7b6447c_0
libxml2 2.9.10 hb55368b_3
lmdb 0.94 pypi_0 pypi
lz4-c 1.9.2 he1b5a44_3 conda-forge
markdown 3.3.4 py38h06a4308_0
markupsafe 1.1.1 py38h7b6447c_0
matplotlib 3.3.4 py38h06a4308_0
matplotlib-base 3.3.4 py38h62a2d02_0
memory-profiler 0.58.0 pypi_0 pypi
mkl 2020.2 256
mkl-service 2.3.0 py38h1e0a361_2 conda-forge
mkl_fft 1.3.0 py38h54f3939_0
mkl_random 1.2.0 py38hc5bc63f_1 conda-forge
mock 2.0.0 pypi_0 pypi
monotonic 1.5 pypi_0 pypi
multidict 5.1.0 py38h27cfd23_2
ncurses 6.2 he6710b0_1
networkx 2.5 py_0
ninja 1.10.2 py38hff7bd54_0
numpy 1.19.2 py38h54aff64_0
numpy-base 1.19.2 py38hfa32c7d_0
oauth2client 4.1.3 pypi_0 pypi
oauthlib 3.1.0 py_0
olefile 0.46 pyh9f0ad1d_1 conda-forge
openssl 1.1.1k h27cfd23_0
opt-einsum 3.3.0 pypi_0 pypi
ordered-set 4.0.2 pypi_0 pypi
pandas 1.2.3 py38ha9443f7_0
pango 1.42.4 h049681c_0
patchelf 0.12 h2531618_1
pbr 5.5.1 pypi_0 pypi
pcre 8.44 he6710b0_0
pexpect 4.6.0 pypi_0 pypi
pillow 7.2.0 pypi_0 pypi
pip 21.0.1 py38h06a4308_0
pixman 0.40.0 h7b6447c_0
pkginfo 1.7.0 py38h06a4308_0
progressbar2 3.39.3 pypi_0 pypi
protobuf 3.14.0 py38h2531618_1
psutil 5.8.0 py38h27cfd23_1
ptyprocess 0.7.0 pypi_0 pypi
py-lief 0.10.1 py38h403a769_0
pyasn1 0.4.8 py_0
pyasn1-modules 0.2.8 py_0
pycapnp 1.0.0 pypi_0 pypi
pycosat 0.6.3 py38h7b6447c_1
pycparser 2.20 py_2
pyglet 1.5.0 pypi_0 pypi
pyjwt 1.7.1 py38_0
pyopenssl 20.0.1 pyhd3eb1b0_1
pyparsing 2.4.7 pyhd3eb1b0_0
pyqt 5.9.2 py38h05f1152_4
pysocks 1.7.1 py38h06a4308_0
python 3.8.2 hcf32534_0
python-dateutil 2.8.1 pyhd3eb1b0_0
python-libarchive-c 2.9 pyhd3eb1b0_0
python-utils 2.5.6 pypi_0 pypi
python_abi 3.8 1_cp38 conda-forge
pytorch 1.7.1 py3.8_cuda10.2.89_cudnn7.6.5_0 pytorch
pytz 2021.1 pyhd3eb1b0_0
pyu2f 0.1.5 pypi_0 pypi
pyyaml 5.4.1 py38h27cfd23_1
qt 5.9.7 h5867ecd_1
readline 8.1 h27cfd23_0
requests 2.25.1 pyhd3eb1b0_0
requests-oauthlib 1.3.0 py_0
retry-decorator 1.1.1 pypi_0 pypi
ripgrep 12.1.1 0
rsa 4.7.2 pyhd3eb1b0_1
ruamel_yaml 0.15.100 py38h27cfd23_0
scikit-learn 0.24.1 py38ha9443f7_0
scipy 1.6.2 py38h91f5cce_0
setuptools 52.0.0 py38h06a4308_0
sexpdata 0.0.3 pypi_0 pypi
sip 4.19.13 py38he6710b0_0
six 1.15.0 pyh9f0ad1d_0 conda-forge
soupsieve 2.2.1 pyhd3eb1b0_0
sqlite 3.35.2 hdfb4753_0
tensorboard 2.4.0 pyhc547734_0
tensorboard-plugin-wit 1.6.0 py_0
tensorflow 2.4.1 pypi_0 pypi
tensorflow-estimator 2.4.0 pypi_0 pypi
termcolor 1.1.0 pypi_0 pypi
threadpoolctl 2.1.0 pyh5ca1d4c_0
tk 8.6.10 hbc83047_0
torchaudio 0.7.2 py38 pytorch
torchmeta 1.7.0 pypi_0 pypi
torchtext 0.8.1 py38 pytorch
torchvision 0.8.2 py38_cu102 pytorch
tornado 6.1 py38h27cfd23_0
tqdm 4.56.0 pypi_0 pypi
typing-extensions 3.7.4.3 0
typing_extensions 3.7.4.3 py_0 conda-forge
urllib3 1.26.4 pyhd3eb1b0_0
werkzeug 1.0.1 pyhd3eb1b0_0
wheel 0.36.2 pyhd3eb1b0_0
wrapt 1.12.1 pypi_0 pypi
xz 5.2.5 h7b6447c_0
yaml 0.2.5 h7b6447c_0
yarl 1.6.3 py38h27cfd23_0
zipp 3.4.1 pyhd3eb1b0_0
zlib 1.2.11 h7b6447c_3
zstd 1.4.5 h9ceee32_0
For a100s this seemed to work at some point:
pip3 install torch==1.9.1+cu111 torchvision==0.10.1+cu111 torchaudio==0.9.1 -f https://download.pytorch.org/whl/torch_stable.html
You should get the answer at https://pytorch.org/get-started/locally/
For me it worked by setting this up:
pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu116
As discussed in the related question Pytorch "NCCL error": unhandled system error, NCCL version 2.4.8", unhandled cuda error, NCCL version ... means something is wrong on the NCCL side. You need to set an environment variable NCCL_DEBUG=INFO to ask NCCL to print out its log so you can figure out what is exactly the problem. (Tip: look for the first WARN line in NCCL log).
As for OP's problem, it's likely caused by some mismatch between driver version / cuda version / cuda version pytorch is compiled with. In that case, if you check the NCCL log, it's going to show something like:
[5] transport/p2p.cc:238 NCCL WARN failed to open CUDA IPC handle : 36 API call is not supported in the installed CUDA driver
which clearly tells the problem. That's why we need to use NCCL_DEBUG=INFO when debugging unhandled cuda error.
Update:
Q: How to set NCCL_DEBUG=INFO?
A: Option 1: prepend NCCL_DEBUG=INFO to the commandline. For example NCCL_DEBUG=INFO python yourscript.py.
Option 2: Set it in Python script. For example,
import os
os.environ["NCCL_DEBUG"] = "INFO"
Option 3: Set it in your shell. For example, export NCCL_DEBUG=INFO
Q: How to match the version of CUDA and Pytorch?
A: OP seems to be using CUDA 11.0. That's a bit tricky because Pytorch no longer offers prebuilt package with CUDA 11.0. So you need to either use an old Pytorch prebuilt package (I think the last version with CUDA 11.0 is Pytorch 1.7.1) or update your system CUDA version. Or you can try to build Pytorch from the source.
If you are OK with an old Pytorch.
conda create --name=tmp pytorch=1.7.1 cudatoolkit=11.0 -c pytorch -c nvidia
Having trouble with CUDA + Pytorch this is the error. I reinstalled CUDA and cudnn multiple times.
Conda env is detecting GPU but its giving errors with pytorch and certain cuda libraries. I tried with Cuda 10.1 and 10.0, and cudnn version 8 and 7.6.5, Added cuda to path and everything.
However anaconda is showing cuda tool kit 9.0 is installed, whilst I clearly installed 10.0, so I am not entirely sure what's the deal with that.
=> loading model from models/pytorch/pose_coco/pose_hrnet_w32_256x192.pth
Traceback (most recent call last):
File "hydroman2.py", line 580, in <module>
pose_model.load_state_dict(torch.load(cfg.TEST.MODEL_FILE), strict=False)
File "C:\Users\Fardin\anaconda3\envs\myenv\lib\site-packages\torch\serialization.py", line 593, in load
return _legacy_load(opened_file, map_location, pickle_module, **pickle_load_args)
File "C:\Users\Fardin\anaconda3\envs\myenv\lib\site-packages\torch\serialization.py", line 773, in _legacy_load
result = unpickler.load()
File "C:\Users\Fardin\anaconda3\envs\myenv\lib\site-packages\torch\serialization.py", line 729, in persistent_load
deserialized_objects[root_key] = restore_location(obj, location)
File "C:\Users\Fardin\anaconda3\envs\myenv\lib\site-packages\torch\serialization.py", line 178, in default_restore_location
result = fn(storage, location)
File "C:\Users\Fardin\anaconda3\envs\myenv\lib\site-packages\torch\serialization.py", line 154, in _cuda_deserialize
device = validate_cuda_device(location)
File "C:\Users\Fardin\anaconda3\envs\myenv\lib\site-packages\torch\serialization.py", line 138, in validate_cuda_device
raise RuntimeError('Attempting to deserialize object on a CUDA '
RuntimeError: Attempting to deserialize object on a CUDA device but torch.cuda.is_available() is False. If you are running on a CPU-only machine, please use torch.load with map_location=torch.device('cpu') to map your storages to the CPU.
System info
System info:
--------------------------------------------------------------------------------
__Time Stamp__
Report started (local time) : 2021-03-19 19:59:06.957967
UTC start time : 2021-03-19 15:59:06.957967
Running time (s) : 4.003899
__Hardware Information__
Machine : AMD64
CPU Name : znver1
CPU Count : 12
Number of accessible CPUs : 12
List of accessible CPUs cores : 0 1 2 3 4 5 6 7 8 9 10 11
CFS Restrictions (CPUs worth of runtime) : None
CPU Features : 64bit adx aes avx avx2 bmi bmi2
clflushopt clzero cmov cx16 cx8
f16c fma fsgsbase fxsr lzcnt mmx
movbe mwaitx pclmul popcnt prfchw
rdrnd rdseed sahf sha sse sse2
sse3 sse4.1 sse4.2 sse4a ssse3
xsave xsavec xsaveopt xsaves
Memory Total (MB) : 16334
Memory Available (MB) : 8787
__OS Information__
Platform Name : Windows-10-10.0.19041-SP0
Platform Release : 10
OS Name : Windows
OS Version : 10.0.19041
OS Specific Version : 10 10.0.19041 SP0 Multiprocessor Free
Libc Version : ?
__Python Information__
Python Compiler : MSC v.1916 64 bit (AMD64)
Python Implementation : CPython
Python Version : 3.8.5
Python Locale : en_US.cp1252
__LLVM Information__
LLVM Version : 10.0.1
__CUDA Information__
CUDA Device Initialized : True
CUDA Driver Version : 11020
CUDA Detect Output:
Found 1 CUDA devices
id 0 b'GeForce GTX 1070' [SUPPORTED]
compute capability: 6.1
pci device id: 0
pci bus id: 6
Summary:
1/1 devices are supported
CUDA Librairies Test Output:
Finding cublas from <unknown>
named cublas.dll
trying to open library... ERROR: failed to open cublas:
Could not find module 'cublas.dll' (or one of its dependencies). Try using the full path with constructor syntax.
Finding cusparse from <unknown>
named cusparse.dll
trying to open library... ERROR: failed to open cusparse:
Could not find module 'cusparse.dll' (or one of its dependencies). Try using the full path with constructor syntax.
Finding cufft from <unknown>
named cufft.dll
trying to open library... ERROR: failed to open cufft:
Could not find module 'cufft.dll' (or one of its dependencies). Try using the full path with constructor syntax.
Finding curand from <unknown>
named curand.dll
trying to open library... ERROR: failed to open curand:
Could not find module 'curand.dll' (or one of its dependencies). Try using the full path with constructor syntax.
Finding nvvm from <unknown>
named nvvm.dll
trying to open library... ERROR: failed to open nvvm:
Could not find module 'nvvm.dll' (or one of its dependencies). Try using the full path with constructor syntax.
Finding cudart from <unknown>
named cudart.dll
trying to open library... ERROR: failed to open cudart:
Could not find module 'cudart.dll' (or one of its dependencies). Try using the full path with constructor syntax.
Finding libdevice from <unknown>
searching for compute_20... ERROR: can't open libdevice for compute_20
searching for compute_30... ERROR: can't open libdevice for compute_30
searching for compute_35... ERROR: can't open libdevice for compute_35
searching for compute_50... ERROR: can't open libdevice for compute_50
__ROC information__
ROC Available : False
ROC Toolchains : None
HSA Agents Count : 0
HSA Agents:
None
HSA Discrete GPUs Count : 0
HSA Discrete GPUs : None
__SVML Information__
SVML State, config.USING_SVML : True
SVML Library Loaded : True
llvmlite Using SVML Patched LLVM : True
SVML Operational : True
__Threading Layer Information__
TBB Threading Layer Available : False
+--> Disabled due to Unknown import problem.
OpenMP Threading Layer Available : True
+-->Vendor: MS
Workqueue Threading Layer Available : True
+-->Workqueue imported successfully.
__Numba Environment Variable Information__
None found.
__Conda Information__
Conda Build : 3.20.5
Conda Env : 4.9.2
Conda Platform : win-64
Conda Python Version : 3.8.5.final.0
Conda Root Writable : True
__Installed Packages__
_pytorch_select 1.1.0 cpu anaconda
_tflow_select 2.3.0 mkl anaconda
absl-py 0.12.0 pypi_0 pypi
alabaster 0.7.12 pypi_0 pypi
appdirs 1.4.3 py36h28b3542_0 anaconda
argparse 1.4.0 pypi_0 pypi
asn1crypto 1.3.0 py36_0 anaconda
astor 0.8.1 pyh9f0ad1d_0 conda-forge
astunparse 1.6.3 pypi_0 pypi
atomicwrites 1.4.0 py_0 anaconda
attrs 19.3.0 py_0 anaconda
babel 2.9.0 pypi_0 pypi
backcall 0.2.0 py_0 anaconda
backports 1.0 py_2 anaconda
backports.weakref 1.0.post1 py36h9f0ad1d_1001 conda-forge
blas 1.0 mkl anaconda
bleach 1.5.0 py36_0 conda-forge
blinker 1.4 py_1 conda-forge
brotlipy 0.7.0 py36he774522_1000 anaconda
bzip2 1.0.8 he774522_0 anaconda
ca-certificates 2020.10.14 0 anaconda
cachetools 4.1.1 py_0 anaconda
certifi 2020.6.20 py36_0 anaconda
cffi 1.14.0 py36h7a1dbc1_0 anaconda
chardet 3.0.4 py36_1003 anaconda
click 7.1.2 pyh9f0ad1d_0 conda-forge
cloudpickle 1.4.1 py_0 anaconda
colorama 0.4.3 py_0 anaconda
contextlib2 0.6.0.post1 py_0 anaconda
cpuonly 1.0 0 pytorch
cryptography 2.9.2 py36h7a1dbc1_0 anaconda
cudatoolkit 9.0 1 anaconda
cudnn 7.6.5 cuda9.0_0 anaconda
curl 7.71.0 h2a8f88b_0 anaconda
cycler 0.10.0 py36h009560c_0 anaconda
cython 0.29.22 pypi_0 pypi
cytoolz 0.10.1 py36he774522_0 anaconda
dask-core 2.19.0 py_0 anaconda
decorator 4.4.2 py_0 anaconda
defusedxml 0.6.0 py_0 anaconda
dlib 19.20 py36h5653133_1 conda-forge
docker-py 4.2.1 py36h9f0ad1d_0 conda-forge
docker-pycreds 0.4.0 py_0 anaconda
docutils 0.16 pypi_0 pypi
easydict 1.7 pypi_0 pypi
entrypoints 0.3 py36_0 anaconda
ffmpeg 2.7.0 0 menpo
flake8 3.8.3 py_0 anaconda
flake8-polyfill 1.0.2 py36_0 anaconda
flake8-quotes 3.0.0 pyh9f0ad1d_0 conda-forge
flatbuffers 1.12 pypi_0 pypi
freetype 2.10.2 hd328e21_0 anaconda
gast 0.2.2 pypi_0 pypi
geos 3.8.1 h33f27b4_0 anaconda
gettext 0.19.8.1 hb01d8f6_1002 conda-forge
git 2.23.0 h6bb4b03_0 anaconda
glib 2.58.3 py36h04c7ab9_1004 conda-forge
google-auth 1.28.0 pypi_0 pypi
google-auth-oauthlib 0.4.3 pypi_0 pypi
google-pasta 0.2.0 pyh8c360ce_0 conda-forge
grpcio 1.32.0 pypi_0 pypi
h5py 2.10.0 py36h5e291fa_0 anaconda
hdf5 1.10.4 h7ebc959_0 anaconda
html5lib 0.9999999 py36_0 conda-forge
icc_rt 2019.0.0 h0cc432a_1 anaconda
icu 58.2 ha925a31_3 anaconda
idna 2.10 py_0 anaconda
imageio 2.8.0 py_0 anaconda
imageio-ffmpeg 0.4.2 py_0 conda-forge
imagesize 1.2.0 pypi_0 pypi
imgaug 0.4.0 pypi_0 pypi
importlib-metadata 1.7.0 py36_0 anaconda
importlib_metadata 1.7.0 0 anaconda
intel-openmp 2019.4 245 anaconda
ipykernel 5.3.0 py36h5ca1d4c_0 anaconda
ipyparallel 6.3.0 pypi_0 pypi
ipython 7.16.1 py36h5ca1d4c_0 anaconda
ipython_genutils 0.2.0 py36_0 anaconda
ipywidgets 7.5.1 py_0 anaconda
jedi 0.17.1 py36_0 anaconda
jinja2 2.11.2 py_0 anaconda
joblib 0.15.1 py_0 anaconda
jpeg 9d he774522_0 conda-forge
json-tricks 3.15.5 pypi_0 pypi
jsonschema 3.2.0 py36_0 anaconda
jupyter 1.0.0 py36_7 anaconda
jupyter_client 6.1.3 py_0 anaconda
jupyter_console 6.1.0 py_0 anaconda
jupyter_core 4.6.3 py36_0 anaconda
keras-applications 1.0.8 py_1 anaconda
keras-preprocessing 1.1.2 pypi_0 pypi
kiwisolver 1.2.0 py36h74a9793_0 anaconda
krb5 1.18.2 hc04afaa_0 anaconda
leptonica 1.78.0 h919f142_2 conda-forge
libarchive 3.3.3 h0643e63_5 anaconda
libcurl 7.71.0 h2a8f88b_0 anaconda
libffi 3.2.1 h6538335_1007 conda-forge
libgpuarray 0.7.6 hfa6e2cd_1003 conda-forge
libiconv 1.15 vc14h29686d3_5 [vc14] anaconda
libmklml 2019.0.5 0 anaconda
libpng 1.6.37 h2a8f88b_0 anaconda
libprotobuf 3.12.3 h7bd577a_0 anaconda
libsodium 1.0.18 h62dcd97_0 anaconda
libssh2 1.9.0 h7a1dbc1_1 anaconda
libtiff 4.1.0 h56a325e_0 anaconda
libwebp 1.0.2 hfa6e2cd_5 conda-forge
libxml2 2.9.10 h464c3ec_1 anaconda
libxslt 1.1.34 he774522_0 anaconda
lxml 4.5.0 py36h1350720_0 anaconda
lz4-c 1.8.1.2 h2fa13f4_0 anaconda
lzo 2.10 he774522_2 anaconda
m2w64-gcc-libgfortran 5.3.0 6 conda-forge
m2w64-gcc-libs 5.3.0 7 conda-forge
m2w64-gcc-libs-core 5.3.0 7 conda-forge
m2w64-gmp 6.1.0 2 conda-forge
m2w64-libwinpthread-git 5.0.0.4634.697f757 2 conda-forge
mako 1.1.0 py_0 anaconda
markdown 3.3.4 pypi_0 pypi
markupsafe 1.1.1 py36he774522_0 anaconda
matplotlib 3.1.3 py36_0 anaconda
matplotlib-base 3.1.3 py36h64f37c6_0 anaconda
mccabe 0.6.1 py36_1 anaconda
mistune 0.8.4 py36he774522_0 anaconda
mkl 2018.0.3 1 anaconda
mkl_fft 1.0.6 py36hdbbee80_0 anaconda
mkl_random 1.0.1 py36h77b88f5_1 anaconda
mock 4.0.3 pypi_0 pypi
more-itertools 8.4.0 py_0 anaconda
moviepy 1.0.1 py_0 conda-forge
msys2-conda-epoch 20160418 1 conda-forge
nbconvert 5.6.1 py36_0 anaconda
nbformat 5.0.7 py_0 anaconda
networkx 2.4 py_0 anaconda
ninja 1.9.0 py36h74a9793_0 anaconda
nose 1.3.7 pypi_0 pypi
notebook 6.0.3 py36_0 anaconda
numpy 1.19.5 pypi_0 pypi
oauthlib 3.1.0 py_0 anaconda
olefile 0.46 py36_0 anaconda
opencv-python 3.4.1.15 pypi_0 pypi
openjpeg 2.3.1 h57dd2e7_3 conda-forge
openssl 1.1.1h he774522_0 anaconda
opt-einsum 3.3.0 pypi_0 pypi
packaging 20.4 py_0 anaconda
pandas 1.0.3 py36h47e9c7a_0 anaconda
pandoc 2.9.2.1 0 anaconda
pandocfilters 1.4.2 py36_1 anaconda
parso 0.7.0 py_0 anaconda
pcre 8.44 ha925a31_0 anaconda
pep8-naming 0.8.2 py36_0 anaconda
pickleshare 0.7.5 py36_0 anaconda
pillow 7.1.2 py36hcc1f983_0 anaconda
pip 20.2.4 py36_0 anaconda
pluggy 0.13.1 py36_0 anaconda
poppler 0.87.0 hdbe765f_0 conda-forge
poppler-data 0.4.9 1 conda-forge
proglog 0.1.9 py_0 conda-forge
prometheus_client 0.8.0 py_0 anaconda
prompt-toolkit 3.0.5 py_0 anaconda
prompt_toolkit 3.0.5 0 anaconda
protobuf 3.12.3 py36h33f27b4_0 anaconda
psutil 5.8.0 pypi_0 pypi
py 1.9.0 py_0 anaconda
pyasn1 0.4.8 py_0 anaconda
pyasn1-modules 0.2.8 pypi_0 pypi
pycocotools 2.0 pypi_0 pypi
pycodestyle 2.6.0 py_0 anaconda
pycparser 2.20 py_0 anaconda
pyflakes 2.2.0 py_0 anaconda
pygments 2.6.1 py_0 anaconda
pygpu 0.7.6 py36h7725771_1001 conda-forge
pyjwt 1.7.1 py_0 conda-forge
pyopenssl 19.1.0 py36_0 anaconda
pyparsing 2.4.7 py_0 anaconda
pyqt 5.9.2 py36h6538335_2 anaconda
pyreadline 2.1 py36_1001 conda-forge
pyrsistent 0.16.0 py36he774522_0 anaconda
pysocks 1.7.1 py36_0 anaconda
pytesseract 0.3.3 pyh8c360ce_0 conda-forge
pytest 5.4.3 py36_0 anaconda
python 3.6.10 h9f7ef89_1 anaconda
python-dateutil 2.8.1 py_0 anaconda
python_abi 3.6 1_cp36m conda-forge
pytorch 1.5.1 py3.6_cpu_0 [cpuonly] pytorch
pytz 2020.1 py_0 anaconda
pywavelets 1.1.1 py36he774522_0 anaconda
pywin32 223 py36hfa6e2cd_1 anaconda
pywinpty 0.5.7 py36_0 anaconda
pyyaml 5.3.1 py36he774522_0 anaconda
pyzmq 19.0.1 py36ha925a31_1 anaconda
qt 5.9.7 vc14h73c81de_0 [vc14] anaconda
qtconsole 4.7.5 py_0 anaconda
qtpy 1.9.0 py_0 anaconda
requests 2.24.0 py_0 anaconda
requests-oauthlib 1.3.0 pyh9f0ad1d_0 conda-forge
rsa 4.6 pyh9f0ad1d_0 conda-forge
scikit-image 0.16.2 py36h47e9c7a_0 anaconda
scikit-learn 0.20.1 py36hb854c30_0 anaconda
scipy 1.4.1 pypi_0 pypi
send2trash 1.5.0 py36_0 anaconda
setuptools 50.3.0 py36h9490d1a_1 anaconda
shapely 1.6.4 pypi_0 pypi
simplejson 3.17.0 py36he774522_0 anaconda
sip 4.19.8 py36h6538335_0 anaconda
six 1.15.0 py_0 anaconda
sklearn 0.0 pypi_0 pypi
slidingwindow 0.0.14 pypi_0 pypi
snowballstemmer 2.1.0 pypi_0 pypi
sphinx 3.5.2 pypi_0 pypi
sphinxcontrib-applehelp 1.0.2 pypi_0 pypi
sphinxcontrib-devhelp 1.0.2 pypi_0 pypi
sphinxcontrib-htmlhelp 1.0.3 pypi_0 pypi
sphinxcontrib-jsmath 1.0.1 pypi_0 pypi
sphinxcontrib-qthelp 1.0.3 pypi_0 pypi
sphinxcontrib-serializinghtml 1.1.4 pypi_0 pypi
sqlite 3.32.3 h2a8f88b_0 anaconda
swig 3.0.12 h047fa9f_3 anaconda
tbb 2020.0 h74a9793_0 anaconda
tbb4py 2020.0 py36h74a9793_0 anaconda
tensorboard 1.13.1 pypi_0 pypi
tensorboard-plugin-wit 1.8.0 pypi_0 pypi
tensorboardx 1.6 py_0 conda-forge
tensorflow 2.4.1 pypi_0 pypi
tensorflow-estimator 1.13.0 pypi_0 pypi
tensorflow-gpu 1.13.1 pypi_0 pypi
tensorflow-gpu-estimator 2.1.0 pypi_0 pypi
termcolor 1.1.0 pypi_0 pypi
terminado 0.8.3 py36_0 anaconda
testpath 0.4.4 py_0 anaconda
theano 1.0.4 py36h003fed8_1002 conda-forge
threadpoolctl 2.1.0 pyh5ca1d4c_0 anaconda
tk 8.6.10 he774522_0 anaconda
toolz 0.10.0 py_0 anaconda
torchfile 0.1.0 py_0 conda-forge
torchvision 0.6.1 py36_cpu [cpuonly] pytorch
tornado 6.0.4 py36he774522_1 anaconda
tqdm 4.47.0 py_0 anaconda
traitlets 4.3.3 py36_0 anaconda
typing-extensions 3.7.4.3 pypi_0 pypi
urllib3 1.25.11 py_0 anaconda
vc 14.1 h0510ff6_4 anaconda
visdom 0.1.8.9 0 conda-forge
vs2015_runtime 14.16.27012 hf0eaf9b_3 anaconda
vs2017_win-64 19.16.27038 h2e3bad8_2 conda-forge
vswhere 2.7.1 h21ff451_0 anaconda
wcwidth 0.2.5 py_0 anaconda
webencodings 0.5.1 py36_1 anaconda
websocket-client 0.57.0 py36_1 anaconda
werkzeug 1.0.1 pyh9f0ad1d_0 conda-forge
wget 1.16.3 0 menpo
wheel 0.35.1 py_0 anaconda
widgetsnbextension 3.5.1 py36_0 anaconda
win_inet_pton 1.1.0 py36_0 anaconda
wincertstore 0.2 py36h7fe50ca_0 anaconda
winpty 0.4.3 4 anaconda
wrapt 1.12.1 py36h68a101e_1 conda-forge
xz 5.2.5 h62dcd97_0 anaconda
yacs 0.1.8 pypi_0 pypi
yaml 0.1.7 hc54c509_2 anaconda
zeromq 4.3.2 ha925a31_2 anaconda
zipp 3.3.1 py_0 anaconda
zlib 1.2.11 h62dcd97_4 anaconda
zstd 1.3.7 h508b16e_0 anaconda
No errors reported.
Solved.
Pytorch was installing CPU only version for some reason, reinstalling pytorch didn't help.
Uninstalling pytorch: conda uninstall pytorch
Followed by uninstalling cpu only: conda uninstall cpuonly
Then installing pytorch again solved it.
From the list of libraries, it looks like you've installed CPU only version of the Pytorch.
pytorch 1.5.1 py3.6_cpu_0 [cpuonly] pytorch
You can see the available conda packages here for different CUDA + Python versions: https://anaconda.org/pytorch/pytorch/files . When you install the pytorch version, make sure it also matches with the CUDA version of your computer.
I created an environment with conda and I want to install pytorch in it, but it doesn't work. After I get inside my environment with source activate env_name I tried this: conda install pytorch torchvision -c pytorch (I also tried it like this: conda install -c pytorch pytorch torchvision) but I am getting this error:
Using Anaconda Cloud api site https://api.anaconda.org
Fetching package metadata: ......
Solving package specifications: ......
Error: Could not find some dependencies for pytorch: mkl >=2018, cudatoolkit >=9.0,<9.1, blas * mkl, cudatoolkit >=10.0,<10.1, cudatoolkit >=9.2,<9.3, blas * openblas, cudnn 7.0.*, cudatoolkit 9.*
Did you mean one of these?
pytorch, pytorch-gpu, pytorch-cpu
Did you mean one of these?
cudatoolkit
You can search for this package on anaconda.org with
anaconda search -t conda cudatoolkit 9.*
(and similarly for the other packages)
Here are my installed packages:
backports 1.0 py34_0
backports.shutil-get-terminal-size 1.0.0 <pip>
decorator 4.0.11 py34_0
get_terminal_size 1.0.0 py34_0
ipython 4.2.0 py34_0
ipython-genutils 0.1.0 <pip>
ipython_genutils 0.1.0 py34_0
libgfortran 1.0 0
numpy 1.9.2 py34_0
openssl 1.0.2l 0
path.py 10.0 py34_0
pexpect 4.2.1 py34_0
pickleshare 0.7.4 py34_0
pip 9.0.1 py34_1
ptyprocess 0.5.1 py34_0
python 3.4.5 0
readline 6.2 2
scipy 0.16.0 np19py34_0
setuptools 27.2.0 py34_0
simplegeneric 0.8.1 py34_1
six 1.10.0 py34_0
sqlite 3.13.0 0
tk 8.5.18 0
traitlets 4.3.1 py34_0
wheel 0.29.0 py34_0
xz 5.2.3 0
zlib 1.2.11 0
What should I do? Thank you!
Pytorch's vision package (aka torchvision) was developed post-Python 3.4, and so only has versions supporting Python 2.7, 3.5-7. Please create a new environment with a later Python version. Note it is always better to include the packages you care about in the creation of the environment, e.g.,
conda create -n env_name -c pytorch torchvision
and Conda will figure the rest out. If you need to have a specific version of Python, you can include that as well (e.g., python=3.6).
Please try the following steps.It worked fine for me.
source activate env_name
conda install -c pytorch pytorch
open python shell
import torch
I can't give you a definite answer cause you didn't provided the info about the Python version, platform you're using.
Go to the official website for Pytorch, choose a installation method according to your platform, Python version and whether you need CUDA.
I am trying to install & use Pandas-Datareader, but when after I have installed it, I receive a ModuleNotFoundError when I try and import it.
I am using Jupyter Notebook installed using Anaconda - so use the conda installer to install new packages.
After typing source activate ipykernel_py3 to activate the Python3 kernel environment, I have used conda install -c anaconda pandas-datareader=0.4.0 to install Pandas-Datareader.
If I try conda list, then I get the output below - which shows Pandas_Datareader installed.
But if I try the command ```import pandas_datareader as pdr`` (as found in documentation here), then I get an error message
ModuleNotFoundError: No module named 'pandas_datareader'
(This happens in both the Jupyter notebook and in the Python3 interpreter running in this environment.
Can anybody help?
Many thanks
** Conda List output:**
# packages in environment at /Users/Chris/anaconda3/envs/ipykernel_py3:
#
appnope 0.1.0 py36_0
beautifulsoup4 4.5.3 py36_0
cycler 0.10.0 py36_0
decorator 4.0.11 py36_0
freetype 2.5.5 2
icu 54.1 0
ipykernel 4.6.1 py36_0
ipython 6.0.0 py36_0
ipython_genutils 0.2.0 py36_0
jupyter_client 5.0.1 py36_0
jupyter_core 4.3.0 py36_0
libpng 1.6.27 0
matplotlib 2.0.2 np112py36_0
mkl 2017.0.1 0
numpy 1.12.1 py36_0
openssl 1.0.2k 1
pandas 0.20.1 np112py36_0
pandas-datareader 0.4.0 py36_0 anaconda
path.py 10.3.1 py36_0
pexpect 4.2.1 py36_0
pickleshare 0.7.4 py36_0
pip 9.0.1 py36_1
prompt_toolkit 1.0.14 py36_0
ptyprocess 0.5.1 py36_0
pygments 2.2.0 py36_0
pyparsing 2.1.4 py36_0
pyqt 5.6.0 py36_2
python 3.6.1 0
python-dateutil 2.6.0 py36_0
pytz 2017.2 py36_0
pyzmq 16.0.2 py36_0
qt 5.6.2 2
readline 6.2 2
requests 2.14.2 py36_0 anaconda
requests-file 1.4.1 py36_0 anaconda
requests-ftp 0.3.1 py36_0 anaconda
scipy 0.19.0 np112py36_0
seaborn 0.7.1 py36_0
setuptools 27.2.0 py36_0
simplegeneric 0.8.1 py36_1
sip 4.18 py36_0
six 1.10.0 py36_0
sqlite 3.13.0 0
tk 8.5.18 0
tornado 4.5.1 py36_0
traitlets 4.3.2 py36_0
wcwidth 0.1.7 py36_0
wheel 0.29.0 py36_0
xz 5.2.2 1
zlib 1.2.8 3
Are you running the notebook in the ipykernel_py3 environment?
source activate ipykernel_py3
ipython notebook
Just type
pip install pandas_datareader in your anaconda prompt
and try running it again in jupyter.