arm64 osx platform not supported?
Opened this issue · 2 comments
erzakiev commented
When trying to install scArches as per instructions in https://docs.scarches.org/en/latest/installation.html, both listed installation options fail on arm64 osx platform.
- When installing via a manual route, i.e. by
conda create -n scArches # creating new environment
conda install python=3.8 # installing the recommended python version
conda install anaconda::jupyter
pip install -U scarches
the installation finishes correctly, but when importin scPoli in the following code snippet inside of a jupyter .ipynb file:
import numpy as np
import scanpy as sc
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.metrics import classification_report
from scarches.models.scpoli import scPoliit throws an error #211.
- When trying to clone the repo, it fails due to inavailability of pytorch-cuda:
git clone https://github.com/theislab/scarches
cd scarches
conda env create -f envs/scarches_linux.yaml
conda activate scarches
Cloning into 'scarches'...
remote: Enumerating objects: 7553, done.
remote: Counting objects: 100% (681/681), done.
remote: Compressing objects: 100% (256/256), done.
remote: Total 7553 (delta 454), reused 618 (delta 421), pack-reused 6872
Receiving objects: 100% (7553/7553), 803.94 MiB | 5.50 MiB/s, done.
Resolving deltas: 100% (5030/5030), done.
Channels:
- pytorch
- nvidia
- defaults
- anaconda
- conda-forge
- bioconda
Platform: osx-arm64
Collecting package metadata (repodata.json): done
Solving environment: failed
Channels:
- pytorch
- nvidia
- defaults
- anaconda
- conda-forge
- bioconda
Platform: osx-arm64
Collecting package metadata (repodata.json): done
Solving environment: failed
LibMambaUnsatisfiableError: Encountered problems while solving:
- nothing provides cuda 11.8.* needed by pytorch-cuda-11.8-h8dd9ede_2
Could not solve for environment specs
The following package could not be installed
└─ pytorch-cuda 11.8** is not installable because it requires
└─ cuda 11.8.* , which does not exist (perhaps a missing channel).
EnvironmentNameNotFound: Could not find conda environment: scarches
You can list all discoverable environments with `conda info --envs`.
erzakiev commented
it seems like (for now, at least), when taking the first route but using python v3.9, i.e.
conda create -n scArches # creating new environment
conda install python=3.9 # installing the recommended python version
conda install anaconda::jupyter
pip install -U scarches
it doesn't complain when importing scPoli anymore. But pip was complaining about incompatibility of phate and pandas was uninstalled, which is concerning:
pip install -U scarches
Collecting scarches
Using cached scArches-0.6.1-py3-none-any.whl.metadata (3.5 kB)
Collecting scanpy>=1.6.0 (from scarches)
Downloading scanpy-1.10.1-py3-none-any.whl.metadata (8.6 kB)
Collecting anndata>=0.7.4 (from scarches)
Downloading anndata-0.10.7-py3-none-any.whl.metadata (6.6 kB)
Collecting scHPL>=1.0.0 (from scarches)
Using cached scHPL-1.0.5-py3-none-any.whl.metadata (3.5 kB)
Collecting h5py>=2.10.0 (from scarches)
Downloading h5py-3.11.0-cp39-cp39-macosx_11_0_arm64.whl.metadata (2.5 kB)
Collecting torch>=1.8.0 (from scarches)
Downloading torch-2.3.0-cp39-none-macosx_11_0_arm64.whl.metadata (26 kB)
Collecting numpy>=1.19.2 (from scarches)
Downloading numpy-1.26.4-cp39-cp39-macosx_11_0_arm64.whl.metadata (61 kB)
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Collecting scipy>=1.5.2 (from scarches)
Downloading scipy-1.13.1-cp39-cp39-macosx_12_0_arm64.whl.metadata (60 kB)
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Collecting scikit-learn>=0.23.2 (from scarches)
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Collecting matplotlib>=3.3.1 (from scarches)
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Collecting get-annotations (from scanpy>=1.6.0->scarches)
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Collecting session-info (from scanpy>=1.6.0->scarches)
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Preparing metadata (setup.py) ... done
Collecting statsmodels>=0.13 (from scanpy>=1.6.0->scarches)
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Collecting pandas>=1.1.2 (from scarches)
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Collecting newick~=1.0.0 (from scHPL>=1.0.0->scarches)
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Building wheels for collected packages: docrep, ml-collections, session-info
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Installing collected packages: texttable, pyro-api, newick, multipledispatch, mpmath, array-api-compat, tqdm, toolz, threadpoolctl, sympy, stdlib_list, pyparsing, protobuf, pillow, numpy, networkx, natsort, multidict, msgpack, mdurl, llvmlite, lightning-utilities, legacy-api-wrap, kiwisolver, joblib, importlib-resources, igraph, get-annotations, fsspec, frozenlist, fonttools, filelock, etils, docrep, cycler, contextlib2, async-timeout, absl-py, yarl, torch, session-info, scipy, patsy, pandas, opt-einsum, numba, ml-dtypes, ml-collections, markdown-it-py, leidenalg, h5py, contourpy, aiosignal, torchmetrics, tensorstore, statsmodels, scikit-learn, rich, pyro-ppl, matplotlib, jaxlib, jax, gdown, anndata, aiohttp, seaborn, pynndescent, orbax-checkpoint, numpyro, mudata, chex, umap-learn, scHPL, pytorch-lightning, optax, scanpy, lightning, flax, scvi-tools, muon, scarches
Attempting uninstall: pandas
Found existing installation: pandas 2.0.3
Uninstalling pandas-2.0.3:
Successfully uninstalled pandas-2.0.3
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
phate 1.0.11 requires Deprecated, which is not installed.
phate 1.0.11 requires future, which is not installed.
phate 1.0.11 requires graphtools>=1.5.3, which is not installed.
phate 1.0.11 requires s_gd2>=1.8.1, which is not installed.
phate 1.0.11 requires scprep>=0.11.1, which is not installed.
phate 1.0.11 requires tasklogger>=1.2, which is not installed.
Successfully installed absl-py-2.1.0 aiohttp-3.9.5 aiosignal-1.3.1 anndata-0.10.7 array-api-compat-1.7.1 async-timeout-4.0.3 chex-0.1.86 contextlib2-21.6.0 contourpy-1.2.1 cycler-0.12.1 docrep-0.3.2 etils-1.5.2 filelock-3.14.0 flax-0.8.4 fonttools-4.53.0 frozenlist-1.4.1 fsspec-2024.6.0 gdown-5.2.0 get-annotations-0.1.2 h5py-3.11.0 igraph-0.11.5 importlib-resources-6.4.0 jax-0.4.28 jaxlib-0.4.28 joblib-1.4.2 kiwisolver-1.4.5 legacy-api-wrap-1.4 leidenalg-0.10.2 lightning-2.1.4 lightning-utilities-0.11.2 llvmlite-0.42.0 markdown-it-py-3.0.0 matplotlib-3.9.0 mdurl-0.1.2 ml-collections-0.1.1 ml-dtypes-0.4.0 mpmath-1.3.0 msgpack-1.0.8 mudata-0.2.3 multidict-6.0.5 multipledispatch-1.0.0 muon-0.1.6 natsort-8.4.0 networkx-3.2.1 newick-1.0.0 numba-0.59.1 numpy-1.26.4 numpyro-0.15.0 opt-einsum-3.3.0 optax-0.2.2 orbax-checkpoint-0.5.15 pandas-1.5.3 patsy-0.5.6 pillow-10.3.0 protobuf-5.27.0 pynndescent-0.5.12 pyparsing-3.1.2 pyro-api-0.1.2 pyro-ppl-1.9.1 pytorch-lightning-2.2.5 rich-13.7.1 scHPL-1.0.5 scanpy-1.10.1 scarches-0.6.1 scikit-learn-1.5.0 scipy-1.13.1 scvi-tools-1.1.2 seaborn-0.13.2 session-info-1.0.0 statsmodels-0.14.2 stdlib_list-0.10.0 sympy-1.12.1 tensorstore-0.1.60 texttable-1.7.0 threadpoolctl-3.5.0 toolz-0.12.1 torch-2.3.0 torchmetrics-1.4.0.post0 tqdm-4.66.4 umap-learn-0.5.6 yarl-1.9.4
erzakiev commented
So I was able to run the code in what seems to be - judging by the speed of training - a cpu mode. But when trying train, for instance, an scGen model - it takes ages. I suspect that it's because on macos there's no pytorch-cuda, but we do have mps.
When running, for instance, a simple training from the tutorial,
network = sca.models.scgen(adata = source_adata,
hidden_layer_sizes=[256,128])
network.train(n_epochs=epoch, early_stopping_kwargs = early_stopping_kwargs)
|██------------------| 14.0% - epoch_loss: 0.0647465525 - val_loss: 0.0618337077
the GPU usage is minimal, yet CPU usage is high, so it's def not using the mps:
CPU Power: 30096 mW
GPU Power: 66 mW
ANE Power: 0 mW
Combined Power (CPU + GPU + ANE): 30162 mW
Are scgen and other tools from the scArches toolkit limited to cuda for GPU calculations? Just to know, if it's even worth trying to make it work or if I just need to never ever run it on a mac and always go for linux if I need to train a model