Differences

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--- eman2:install:condainstall [2025/06/19 21:41] – steveludtke
+++ eman2:install:condainstall [2025/08/03 17:50] (current) – steveludtke
@@ Line 7: / Line 7: @@
 . **Install Anaconda or [[https://github.com/conda-forge/miniforge/releases|MiniForge]]** (for your platform)
+    * If you already have Anaconda installed in your account, you should generally skip this step and move on to step 2
     * On that page, scroll down to the Latest Release, then download the appropriate file, for example: Miniforge3-Linux-x86_64.sh
     * Run the installer, for example: bash Miniforge3-Linux-x86_64.sh
-    * //On M1/M2 Macs:// make sure you have the ARM64 !MiniForge, not Intel.
+    * //On M1/M2 Macs:// make sure you have the ARM64 MiniForge, not Intel.
     * //On Windows 11:// the native Windows !MiniForge will not work, you must use the Linux !MiniForge within a [[https://learn.microsoft.com/en-us/windows/wsl/install|WSL2]] environment!
     * We suggest !MiniForge to avoid licensing issues with Anaconda, but if your institution subscribes to Anaconda, that's fine too. If you use Anaconda, you may need to install Mamba manually below.
@@ Line 54: / Line 55: @@
 </code>
-<<Anchor(GPU)>>
 ===== GPU Aceleration & Deep Learning =====
 Older EMAN2 programs make use of Tensorflow for Deep Learning/GPU support, but we have been gradually migrating to the new JAX library, so having both installed is strongly suggested.
@@ Line 60: / Line 60: @@
   * Tensorflow
     * Should be installed automatically as part of the EMAN2 environment setup above
-    * You can check to see what version of Tensorflow and whether it is GPU accelerated with: "conda list|grep tensorflow". On a machine with an NVIDIA GPU and properly configured drivers, you should see a tensorflow version like "2.17.0    cuda120py312h02ad488_203". This says that you are using the CUDA 12.0, Python 3.12 version of tensorflow 2.17.0. If you see "cpu" instead of "cuda" then you do not have GPU acceleration, and you will need to sort that out.
+    * You can check to see what version of Tensorflow and whether it is GPU accelerated with: //conda list|grep tensorflow//. On a machine with an NVIDIA GPU and properly configured drivers, you should see a tensorflow version like **2.17.0    cuda120py312h02ad488_203**. This says that you are using the CUDA 12.0, Python 3.12 version of tensorflow 2.17.0. If you see "cpu" instead of "cuda" then you do not have GPU acceleration, and you will need to sort that out.
     * You can test tensorflow with: //$CONDA_PREFIX/examples/test_tensorflow_basic.py//
@@ Line 66: / Line 66: @@
     * While it is possible to install JAX using conda/mamba, it can be difficult to find a version of GPU JAX and a version of GPU Tensorflow which will install properly together this way.
     * Instead, we strongly suggest just installing JAX using pip:
-      * conda activate eman2
+      <code>
-      * Linux (NVIDIA): //pip install jax[cuda12] optax//
+conda activate eman2
-      * Other (cpu): //pip install jax optax//
+# Linux (NVIDIA):
+pip install jax[cuda12] optax
+#Other (cpu):
+pip install jax optax
+      </code>
     * see [[https://pypi.org/project/jax/]] for up to date JAX instructions, this may change more frequently than this page is updated
     * You can test after installation with: //$CONDA_PREFIX/examples/test_jax_basic.py//